id	sid	tid	token	lemma	pos
ajst-23562	1	1	academic	academic	ADJ
ajst-23562	1	2	journal	journal	NOUN
ajst-23562	1	3	of	of	ADP
ajst-23562	1	4	science	science	NOUN
ajst-23562	1	5	and	and	CCONJ
ajst-23562	1	6	technology	technology	NOUN
ajst-23562	1	7	issn	issn	NOUN
ajst-23562	1	8	:	:	PUNCT
ajst-23562	1	9	2771	2771	NUM
ajst-23562	1	10	-	-	SYM
ajst-23562	1	11	3032	3032	NUM
ajst-23562	1	12	|	|	NOUN
ajst-23562	1	13	vol	vol	NOUN
ajst-23562	1	14	.	.	PROPN
ajst-23562	2	1	11	11	NUM
ajst-23562	2	2	,	,	PUNCT
ajst-23562	2	3	no	no	INTJ
ajst-23562	2	4	.	.	NOUN
ajst-23562	2	5	3	3	NUM
ajst-23562	2	6	,	,	PUNCT
ajst-23562	2	7	2024	2024	NUM
ajst-23562	2	8	97	97	NUM
ajst-23562	2	9	research	research	NOUN
ajst-23562	2	10	on	on	ADP
ajst-23562	2	11	indoor	indoor	ADJ
ajst-23562	2	12	localization	localization	NOUN
ajst-23562	2	13	method	method	NOUN
ajst-23562	2	14	based	base	VERB
ajst-23562	2	15	on	on	ADP
ajst-23562	2	16	deep	deep	ADJ
ajst-23562	2	17	learning	learning	NOUN
ajst-23562	2	18	and	and	CCONJ
ajst-23562	2	19	wireless	wireless	ADJ
ajst-23562	2	20	sensing	sense	VERB
ajst-23562	2	21	yong	yong	PROPN
ajst-23562	2	22	deng	deng	PROPN
ajst-23562	2	23	,	,	PUNCT
ajst-23562	3	1	chuanhao	chuanhao	PROPN
ajst-23562	3	2	li	li	PROPN
ajst-23562	3	3	school	school	PROPN
ajst-23562	3	4	of	of	ADP
ajst-23562	3	5	mechatronic	mechatronic	ADJ
ajst-23562	3	6	engineering	engineering	NOUN
ajst-23562	3	7	,	,	PUNCT
ajst-23562	3	8	southwest	southwest	ADJ
ajst-23562	3	9	petroleum	petroleum	PROPN
ajst-23562	3	10	university	university	PROPN
ajst-23562	3	11	,	,	PUNCT
ajst-23562	3	12	chengdu	chengdu	PROPN
ajst-23562	3	13	610500	610500	NUM
ajst-23562	3	14	,	,	PUNCT
ajst-23562	3	15	china	china	PROPN
ajst-23562	3	16	abstract	abstract	NOUN
ajst-23562	3	17	:	:	PUNCT
ajst-23562	3	18	aiming	aim	VERB
ajst-23562	3	19	at	at	ADP
ajst-23562	3	20	the	the	DET
ajst-23562	3	21	problems	problem	NOUN
ajst-23562	3	22	of	of	ADP
ajst-23562	3	23	indoor	indoor	ADJ
ajst-23562	3	24	localization	localization	NOUN
ajst-23562	3	25	accuracy	accuracy	NOUN
ajst-23562	3	26	due	due	ADP
ajst-23562	3	27	to	to	ADP
ajst-23562	3	28	scale	scale	NOUN
ajst-23562	3	29	difference	difference	NOUN
ajst-23562	3	30	and	and	CCONJ
ajst-23562	3	31	noise	noise	NOUN
ajst-23562	3	32	interference	interference	NOUN
ajst-23562	3	33	in	in	ADP
ajst-23562	3	34	wireless	wireless	ADJ
ajst-23562	3	35	sensing	sensing	NOUN
ajst-23562	3	36	,	,	PUNCT
ajst-23562	3	37	a	a	DET
ajst-23562	3	38	deep	deep	ADJ
ajst-23562	3	39	learning	learn	VERB
ajst-23562	3	40	indoor	indoor	ADJ
ajst-23562	3	41	localization	localization	NOUN
ajst-23562	3	42	architecture	architecture	NOUN
ajst-23562	3	43	for	for	ADP
ajst-23562	3	44	wireless	wireless	ADJ
ajst-23562	3	45	sensing	sensing	NOUN
ajst-23562	3	46	is	be	AUX
ajst-23562	3	47	proposed	propose	VERB
ajst-23562	3	48	.	.	PUNCT
ajst-23562	4	1	firstly	firstly	ADV
ajst-23562	4	2	,	,	PUNCT
ajst-23562	4	3	ibeacon	ibeacon	NOUN
ajst-23562	4	4	devices	device	NOUN
ajst-23562	4	5	are	be	AUX
ajst-23562	4	6	arranged	arrange	VERB
ajst-23562	4	7	in	in	ADP
ajst-23562	4	8	the	the	DET
ajst-23562	4	9	localization	localization	NOUN
ajst-23562	4	10	area	area	NOUN
ajst-23562	4	11	to	to	PART
ajst-23562	4	12	collect	collect	VERB
ajst-23562	4	13	location	location	NOUN
ajst-23562	4	14	fingerprints	fingerprint	NOUN
ajst-23562	4	15	,	,	PUNCT
ajst-23562	4	16	and	and	CCONJ
ajst-23562	4	17	then	then	ADV
ajst-23562	4	18	stacked	stack	VERB
ajst-23562	4	19	autoencoder	autoencoder	NOUN
ajst-23562	4	20	(	(	PUNCT
ajst-23562	4	21	sae	sae	PROPN
ajst-23562	4	22	)	)	PUNCT
ajst-23562	4	23	is	be	AUX
ajst-23562	4	24	introduced	introduce	VERB
ajst-23562	4	25	to	to	PART
ajst-23562	4	26	effectively	effectively	ADV
ajst-23562	4	27	capture	capture	VERB
ajst-23562	4	28	the	the	DET
ajst-23562	4	29	depth	depth	NOUN
ajst-23562	4	30	features	feature	NOUN
ajst-23562	4	31	of	of	ADP
ajst-23562	4	32	the	the	DET
ajst-23562	4	33	data	datum	NOUN
ajst-23562	4	34	to	to	PART
ajst-23562	4	35	improve	improve	VERB
ajst-23562	4	36	the	the	DET
ajst-23562	4	37	robustness	robustness	NOUN
ajst-23562	4	38	of	of	ADP
ajst-23562	4	39	the	the	DET
ajst-23562	4	40	subsequent	subsequent	ADJ
ajst-23562	4	41	localization	localization	NOUN
ajst-23562	4	42	model	model	NOUN
ajst-23562	4	43	.	.	PUNCT
ajst-23562	5	1	to	to	PART
ajst-23562	5	2	further	far	ADV
ajst-23562	5	3	enhance	enhance	VERB
ajst-23562	5	4	the	the	DET
ajst-23562	5	5	global	global	ADJ
ajst-23562	5	6	temporal	temporal	ADJ
ajst-23562	5	7	sensing	sense	VERB
ajst-23562	5	8	capability	capability	NOUN
ajst-23562	5	9	of	of	ADP
ajst-23562	5	10	the	the	DET
ajst-23562	5	11	model	model	NOUN
ajst-23562	5	12	,	,	PUNCT
ajst-23562	5	13	a	a	DET
ajst-23562	5	14	bidirectional	bidirectional	ADJ
ajst-23562	5	15	long	long	ADJ
ajst-23562	5	16	short	short	ADJ
ajst-23562	5	17	-	-	PUNCT
ajst-23562	5	18	term	term	NOUN
ajst-23562	5	19	memory	memory	NOUN
ajst-23562	5	20	network	network	NOUN
ajst-23562	5	21	(	(	PUNCT
ajst-23562	5	22	bilstm	bilstm	NOUN
ajst-23562	5	23	)	)	PUNCT
ajst-23562	5	24	is	be	AUX
ajst-23562	5	25	introduced	introduce	VERB
ajst-23562	5	26	for	for	ADP
ajst-23562	5	27	location	location	NOUN
ajst-23562	5	28	prediction	prediction	NOUN
ajst-23562	5	29	.	.	PUNCT
ajst-23562	6	1	the	the	DET
ajst-23562	6	2	experimental	experimental	ADJ
ajst-23562	6	3	results	result	NOUN
ajst-23562	6	4	show	show	VERB
ajst-23562	6	5	that	that	SCONJ
ajst-23562	6	6	the	the	DET
ajst-23562	6	7	algorithm	algorithm	NOUN
ajst-23562	6	8	achieves	achieve	VERB
ajst-23562	6	9	higher	high	ADJ
ajst-23562	6	10	positioning	positioning	NOUN
ajst-23562	6	11	accuracy	accuracy	NOUN
ajst-23562	6	12	and	and	CCONJ
ajst-23562	6	13	better	well	ADJ
ajst-23562	6	14	generalization	generalization	NOUN
ajst-23562	6	15	performance	performance	NOUN
ajst-23562	6	16	than	than	ADP
ajst-23562	6	17	the	the	DET
ajst-23562	6	18	traditional	traditional	ADJ
ajst-23562	6	19	indoor	indoor	ADJ
ajst-23562	6	20	localization	localization	NOUN
ajst-23562	6	21	methods	method	NOUN
ajst-23562	6	22	.	.	PUNCT
ajst-23562	7	1	keywords	keyword	NOUN
ajst-23562	7	2	:	:	PUNCT
ajst-23562	7	3	indoor	indoor	ADJ
ajst-23562	7	4	localization	localization	NOUN
ajst-23562	7	5	;	;	PUNCT
ajst-23562	7	6	wireless	wireless	ADJ
ajst-23562	7	7	sensing	sensing	NOUN
ajst-23562	7	8	;	;	PUNCT
ajst-23562	7	9	sae	sae	PROPN
ajst-23562	7	10	;	;	PUNCT
ajst-23562	7	11	bilstm	bilstm	NOUN
ajst-23562	7	12	.	.	PUNCT
ajst-23562	8	1	1	1	X
ajst-23562	8	2	.	.	X
ajst-23562	8	3	introduction	introduction	NOUN
ajst-23562	8	4	positioning	positioning	NOUN
ajst-23562	8	5	service	service	NOUN
ajst-23562	8	6	is	be	AUX
ajst-23562	8	7	a	a	DET
ajst-23562	8	8	technology	technology	NOUN
ajst-23562	8	9	that	that	PRON
ajst-23562	8	10	provides	provide	VERB
ajst-23562	8	11	users	user	NOUN
ajst-23562	8	12	with	with	ADP
ajst-23562	8	13	personalized	personalized	ADJ
ajst-23562	8	14	services	service	NOUN
ajst-23562	8	15	and	and	CCONJ
ajst-23562	8	16	real	real	ADJ
ajst-23562	8	17	-	-	PUNCT
ajst-23562	8	18	time	time	NOUN
ajst-23562	8	19	information	information	NOUN
ajst-23562	8	20	,	,	PUNCT
ajst-23562	8	21	and	and	CCONJ
ajst-23562	8	22	the	the	DET
ajst-23562	8	23	demand	demand	NOUN
ajst-23562	8	24	for	for	ADP
ajst-23562	8	25	indoor	indoor	ADJ
ajst-23562	8	26	positioning	positioning	NOUN
ajst-23562	8	27	such	such	ADJ
ajst-23562	8	28	as	as	ADP
ajst-23562	8	29	location	location	NOUN
ajst-23562	8	30	-	-	PUNCT
ajst-23562	8	31	based	base	VERB
ajst-23562	8	32	wireless	wireless	ADJ
ajst-23562	8	33	advertisement	advertisement	NOUN
ajst-23562	8	34	push	push	NOUN
ajst-23562	8	35	,	,	PUNCT
ajst-23562	8	36	information	information	NOUN
ajst-23562	8	37	retrieval	retrieval	NOUN
ajst-23562	8	38	,	,	PUNCT
ajst-23562	8	39	and	and	CCONJ
ajst-23562	8	40	pedestrian	pedestrian	NOUN
ajst-23562	8	41	navigation	navigation	NOUN
ajst-23562	8	42	is	be	AUX
ajst-23562	8	43	growing	grow	VERB
ajst-23562	8	44	rapidly	rapidly	ADV
ajst-23562	8	45	[	[	X
ajst-23562	8	46	1	1	NUM
ajst-23562	8	47	]	]	PUNCT
ajst-23562	8	48	.	.	PUNCT
ajst-23562	9	1	in	in	ADP
ajst-23562	9	2	outdoor	outdoor	ADJ
ajst-23562	9	3	environments	environment	NOUN
ajst-23562	9	4	,	,	PUNCT
ajst-23562	9	5	global	global	ADJ
ajst-23562	9	6	navigation	navigation	NOUN
ajst-23562	9	7	satellite	satellite	NOUN
ajst-23562	9	8	system	system	NOUN
ajst-23562	9	9	(	(	PUNCT
ajst-23562	9	10	gnss	gnss	NOUN
ajst-23562	9	11	)	)	PUNCT
ajst-23562	9	12	services	service	NOUN
ajst-23562	9	13	can	can	AUX
ajst-23562	9	14	provide	provide	VERB
ajst-23562	9	15	users	user	NOUN
ajst-23562	9	16	with	with	ADP
ajst-23562	9	17	highly	highly	ADV
ajst-23562	9	18	accurate	accurate	ADJ
ajst-23562	9	19	global	global	ADJ
ajst-23562	9	20	position	position	NOUN
ajst-23562	9	21	estimation	estimation	NOUN
ajst-23562	9	22	and	and	CCONJ
ajst-23562	9	23	convenient	convenient	ADJ
ajst-23562	9	24	positioning	positioning	NOUN
ajst-23562	9	25	services	service	NOUN
ajst-23562	9	26	[	[	X
ajst-23562	9	27	2	2	NUM
ajst-23562	9	28	]	]	PUNCT
ajst-23562	9	29	.	.	PUNCT
ajst-23562	10	1	however	however	ADV
ajst-23562	10	2	,	,	PUNCT
ajst-23562	10	3	gnss	gnss	NOUN
ajst-23562	10	4	performs	perform	VERB
ajst-23562	10	5	poorly	poorly	ADV
ajst-23562	10	6	in	in	ADP
ajst-23562	10	7	indoor	indoor	ADJ
ajst-23562	10	8	positioning	positioning	NOUN
ajst-23562	10	9	because	because	SCONJ
ajst-23562	10	10	satellite	satellite	NOUN
ajst-23562	10	11	signals	signal	NOUN
ajst-23562	10	12	are	be	AUX
ajst-23562	10	13	easily	easily	ADV
ajst-23562	10	14	blocked	block	VERB
ajst-23562	10	15	by	by	ADP
ajst-23562	10	16	buildings	building	NOUN
ajst-23562	10	17	and	and	CCONJ
ajst-23562	10	18	the	the	DET
ajst-23562	10	19	complex	complex	ADJ
ajst-23562	10	20	indoor	indoor	ADJ
ajst-23562	10	21	environment	environment	NOUN
ajst-23562	10	22	increases	increase	VERB
ajst-23562	10	23	signal	signal	ADJ
ajst-23562	10	24	interference	interference	NOUN
ajst-23562	10	25	.	.	PUNCT
ajst-23562	11	1	with	with	ADP
ajst-23562	11	2	the	the	DET
ajst-23562	11	3	development	development	NOUN
ajst-23562	11	4	of	of	ADP
ajst-23562	11	5	ibeacon	ibeacon	NOUN
ajst-23562	11	6	,	,	PUNCT
ajst-23562	11	7	a	a	DET
ajst-23562	11	8	low	low	ADJ
ajst-23562	11	9	-	-	PUNCT
ajst-23562	11	10	power	power	NOUN
ajst-23562	11	11	bluetooth	bluetooth	NOUN
ajst-23562	11	12	device	device	NOUN
ajst-23562	11	13	,	,	PUNCT
ajst-23562	11	14	and	and	CCONJ
ajst-23562	11	15	the	the	DET
ajst-23562	11	16	fact	fact	NOUN
ajst-23562	11	17	that	that	PRON
ajst-23562	11	18	received	receive	VERB
ajst-23562	11	19	signal	signal	NOUN
ajst-23562	11	20	strength	strength	NOUN
ajst-23562	11	21	index	index	NOUN
ajst-23562	11	22	(	(	PUNCT
ajst-23562	11	23	rssi	rssi	NOUN
ajst-23562	11	24	)	)	PUNCT
ajst-23562	11	25	is	be	AUX
ajst-23562	11	26	the	the	DET
ajst-23562	11	27	most	most	ADV
ajst-23562	11	28	commonly	commonly	ADV
ajst-23562	11	29	used	use	VERB
ajst-23562	11	30	source	source	NOUN
ajst-23562	11	31	of	of	ADP
ajst-23562	11	32	information	information	NOUN
ajst-23562	11	33	in	in	ADP
ajst-23562	11	34	sensing	sense	VERB
ajst-23562	11	35	strategies	strategy	NOUN
ajst-23562	11	36	,	,	PUNCT
ajst-23562	11	37	rssi	rssi	NOUN
ajst-23562	11	38	has	have	AUX
ajst-23562	11	39	become	become	VERB
ajst-23562	11	40	the	the	DET
ajst-23562	11	41	main	main	ADJ
ajst-23562	11	42	way	way	NOUN
ajst-23562	11	43	of	of	ADP
ajst-23562	11	44	location	location	NOUN
ajst-23562	11	45	prediction	prediction	NOUN
ajst-23562	11	46	[	[	X
ajst-23562	11	47	3	3	NUM
ajst-23562	11	48	]	]	PUNCT
ajst-23562	11	49	.	.	PUNCT
ajst-23562	12	1	a	a	DET
ajst-23562	12	2	common	common	ADJ
ajst-23562	12	3	approach	approach	NOUN
ajst-23562	12	4	to	to	PART
ajst-23562	12	5	address	address	VERB
ajst-23562	12	6	the	the	DET
ajst-23562	12	7	irregularity	irregularity	NOUN
ajst-23562	12	8	of	of	ADP
ajst-23562	12	9	signal	signal	ADJ
ajst-23562	12	10	power	power	NOUN
ajst-23562	12	11	attenuation	attenuation	NOUN
ajst-23562	12	12	with	with	ADP
ajst-23562	12	13	respect	respect	NOUN
ajst-23562	12	14	to	to	ADP
ajst-23562	12	15	location	location	NOUN
ajst-23562	12	16	is	be	AUX
ajst-23562	12	17	to	to	PART
ajst-23562	12	18	form	form	VERB
ajst-23562	12	19	a	a	DET
ajst-23562	12	20	location	location	NOUN
ajst-23562	12	21	database	database	NOUN
ajst-23562	12	22	by	by	ADP
ajst-23562	12	23	obtaining	obtain	VERB
ajst-23562	12	24	rssis	rssis	NOUN
ajst-23562	12	25	of	of	ADP
ajst-23562	12	26	multiple	multiple	ADJ
ajst-23562	12	27	aps	ap	NOUN
ajst-23562	12	28	from	from	ADP
ajst-23562	12	29	a	a	DET
ajst-23562	12	30	grid	grid	NOUN
ajst-23562	12	31	of	of	ADP
ajst-23562	12	32	reference	reference	NOUN
ajst-23562	12	33	points	point	NOUN
ajst-23562	12	34	,	,	PUNCT
ajst-23562	12	35	and	and	CCONJ
ajst-23562	12	36	location	location	NOUN
ajst-23562	12	37	derivation	derivation	NOUN
ajst-23562	12	38	is	be	AUX
ajst-23562	12	39	accomplished	accomplish	VERB
ajst-23562	12	40	by	by	ADP
ajst-23562	12	41	comparing	compare	VERB
ajst-23562	12	42	the	the	DET
ajst-23562	12	43	similarity	similarity	NOUN
ajst-23562	12	44	or	or	CCONJ
ajst-23562	12	45	distance	distance	NOUN
ajst-23562	12	46	between	between	ADP
ajst-23562	12	47	the	the	DET
ajst-23562	12	48	new	new	ADJ
ajst-23562	12	49	signals	signal	NOUN
ajst-23562	12	50	and	and	CCONJ
ajst-23562	12	51	the	the	DET
ajst-23562	12	52	previous	previous	ADJ
ajst-23562	12	53	reference	reference	NOUN
ajst-23562	12	54	points	point	NOUN
ajst-23562	12	55	.	.	PUNCT
ajst-23562	13	1	however	however	ADV
ajst-23562	13	2	,	,	PUNCT
ajst-23562	13	3	the	the	DET
ajst-23562	13	4	variation	variation	NOUN
ajst-23562	13	5	of	of	ADP
ajst-23562	13	6	rssi	rssi	NOUN
ajst-23562	13	7	due	due	ADP
ajst-23562	13	8	to	to	ADP
ajst-23562	13	9	the	the	DET
ajst-23562	13	10	fluctuating	fluctuate	VERB
ajst-23562	13	11	nature	nature	NOUN
ajst-23562	13	12	of	of	ADP
ajst-23562	13	13	wireless	wireless	ADJ
ajst-23562	13	14	signals	signal	NOUN
ajst-23562	13	15	is	be	AUX
ajst-23562	13	16	a	a	DET
ajst-23562	13	17	major	major	ADJ
ajst-23562	13	18	problem	problem	NOUN
ajst-23562	13	19	that	that	PRON
ajst-23562	13	20	affects	affect	VERB
ajst-23562	13	21	precise	precise	ADJ
ajst-23562	13	22	location	location	NOUN
ajst-23562	13	23	.	.	PUNCT
ajst-23562	14	1	therefore	therefore	ADV
ajst-23562	14	2	,	,	PUNCT
ajst-23562	14	3	extracting	extract	VERB
ajst-23562	14	4	reliable	reliable	ADJ
ajst-23562	14	5	features	feature	NOUN
ajst-23562	14	6	from	from	ADP
ajst-23562	14	7	largescale	largescale	ADJ
ajst-23562	14	8	reference	reference	NOUN
ajst-23562	14	9	points	point	NOUN
ajst-23562	14	10	and	and	CCONJ
ajst-23562	14	11	finding	find	VERB
ajst-23562	14	12	effective	effective	ADJ
ajst-23562	14	13	mapping	mapping	NOUN
ajst-23562	14	14	functions	function	NOUN
ajst-23562	14	15	become	become	VERB
ajst-23562	14	16	the	the	DET
ajst-23562	14	17	key	key	NOUN
ajst-23562	14	18	to	to	ADP
ajst-23562	14	19	wireless	wireless	ADJ
ajst-23562	14	20	localization	localization	NOUN
ajst-23562	14	21	.	.	PUNCT
ajst-23562	15	1	traditional	traditional	ADJ
ajst-23562	15	2	machine	machine	NOUN
ajst-23562	15	3	learning	learning	NOUN
ajst-23562	15	4	methods	method	NOUN
ajst-23562	15	5	are	be	AUX
ajst-23562	15	6	essentially	essentially	ADV
ajst-23562	15	7	shallow	shallow	ADJ
ajst-23562	15	8	learning	learn	VERB
ajst-23562	15	9	architectures	architecture	NOUN
ajst-23562	15	10	[	[	X
ajst-23562	15	11	4	4	NUM
ajst-23562	15	12	]	]	PUNCT
ajst-23562	15	13	,	,	PUNCT
ajst-23562	15	14	which	which	PRON
ajst-23562	15	15	have	have	AUX
ajst-23562	15	16	limited	limit	VERB
ajst-23562	15	17	modeling	modeling	NOUN
ajst-23562	15	18	and	and	CCONJ
ajst-23562	15	19	representation	representation	NOUN
ajst-23562	15	20	capabilities	capability	NOUN
ajst-23562	15	21	when	when	SCONJ
ajst-23562	15	22	dealing	deal	VERB
ajst-23562	15	23	with	with	ADP
ajst-23562	15	24	such	such	ADJ
ajst-23562	15	25	large	large	ADJ
ajst-23562	15	26	and	and	CCONJ
ajst-23562	15	27	noisy	noisy	ADJ
ajst-23562	15	28	data	datum	NOUN
ajst-23562	15	29	,	,	PUNCT
ajst-23562	15	30	and	and	CCONJ
ajst-23562	15	31	in	in	ADP
ajst-23562	15	32	order	order	NOUN
ajst-23562	15	33	to	to	PART
ajst-23562	15	34	extract	extract	VERB
ajst-23562	15	35	and	and	CCONJ
ajst-23562	15	36	construct	construct	VERB
ajst-23562	15	37	potential	potential	ADJ
ajst-23562	15	38	representations	representation	NOUN
ajst-23562	15	39	from	from	ADP
ajst-23562	15	40	rich	rich	ADJ
ajst-23562	15	41	data	datum	NOUN
ajst-23562	15	42	,	,	PUNCT
ajst-23562	15	43	deep	deep	ADJ
ajst-23562	15	44	learning	learning	NOUN
ajst-23562	15	45	architectures	architecture	NOUN
ajst-23562	15	46	with	with	ADP
ajst-23562	15	47	multiple	multiple	ADJ
ajst-23562	15	48	layers	layer	NOUN
ajst-23562	15	49	of	of	ADP
ajst-23562	15	50	nonlinear	nonlinear	ADJ
ajst-23562	15	51	processing	processing	NOUN
ajst-23562	15	52	capabilities	capability	NOUN
ajst-23562	15	53	are	be	AUX
ajst-23562	15	54	required	require	VERB
ajst-23562	15	55	.	.	PUNCT
ajst-23562	16	1	the	the	DET
ajst-23562	16	2	main	main	ADJ
ajst-23562	16	3	contributions	contribution	NOUN
ajst-23562	16	4	of	of	ADP
ajst-23562	16	5	this	this	DET
ajst-23562	16	6	paper	paper	NOUN
ajst-23562	16	7	are	be	AUX
ajst-23562	16	8	as	as	SCONJ
ajst-23562	16	9	follows	follow	VERB
ajst-23562	16	10	:	:	PUNCT
ajst-23562	16	11	(	(	PUNCT
ajst-23562	16	12	1	1	X
ajst-23562	16	13	)	)	PUNCT
ajst-23562	16	14	better	well	ADJ
ajst-23562	16	15	indoor	indoor	ADJ
ajst-23562	16	16	localization	localization	NOUN
ajst-23562	16	17	performance	performance	NOUN
ajst-23562	16	18	:	:	PUNCT
ajst-23562	16	19	the	the	DET
ajst-23562	16	20	method	method	NOUN
ajst-23562	16	21	proposed	propose	VERB
ajst-23562	16	22	in	in	ADP
ajst-23562	16	23	this	this	DET
ajst-23562	16	24	paper	paper	NOUN
ajst-23562	16	25	makes	make	VERB
ajst-23562	16	26	full	full	ADJ
ajst-23562	16	27	use	use	NOUN
ajst-23562	16	28	of	of	ADP
ajst-23562	16	29	rssi	rssi	ADJ
ajst-23562	16	30	temporal	temporal	ADJ
ajst-23562	16	31	information	information	NOUN
ajst-23562	16	32	.	.	PUNCT
ajst-23562	17	1	considering	consider	VERB
ajst-23562	17	2	the	the	DET
ajst-23562	17	3	spatio	spatio	PROPN
ajst-23562	17	4	-	-	PUNCT
ajst-23562	17	5	temporal	temporal	ADJ
ajst-23562	17	6	correlation	correlation	NOUN
ajst-23562	17	7	of	of	ADP
ajst-23562	17	8	rssi	rssi	NOUN
ajst-23562	17	9	,	,	PUNCT
ajst-23562	17	10	it	it	PRON
ajst-23562	17	11	achieves	achieve	VERB
ajst-23562	17	12	better	well	ADJ
ajst-23562	17	13	performance	performance	NOUN
ajst-23562	17	14	than	than	ADP
ajst-23562	17	15	existing	exist	VERB
ajst-23562	17	16	methods	method	NOUN
ajst-23562	17	17	.	.	PUNCT
ajst-23562	18	1	(	(	PUNCT
ajst-23562	18	2	2	2	X
ajst-23562	18	3	)	)	PUNCT
ajst-23562	18	4	reducing	reduce	VERB
ajst-23562	18	5	data	datum	NOUN
ajst-23562	18	6	noise	noise	NOUN
ajst-23562	18	7	:	:	PUNCT
ajst-23562	18	8	the	the	DET
ajst-23562	18	9	method	method	NOUN
ajst-23562	18	10	in	in	ADP
ajst-23562	18	11	this	this	DET
ajst-23562	18	12	paper	paper	NOUN
ajst-23562	18	13	is	be	AUX
ajst-23562	18	14	relatively	relatively	ADV
ajst-23562	18	15	less	less	ADV
ajst-23562	18	16	dependent	dependent	ADJ
ajst-23562	18	17	on	on	ADP
ajst-23562	18	18	the	the	DET
ajst-23562	18	19	amount	amount	NOUN
ajst-23562	18	20	of	of	ADP
ajst-23562	18	21	data	datum	NOUN
ajst-23562	18	22	,	,	PUNCT
ajst-23562	18	23	which	which	PRON
ajst-23562	18	24	reduces	reduce	VERB
ajst-23562	18	25	the	the	DET
ajst-23562	18	26	impact	impact	NOUN
ajst-23562	18	27	of	of	ADP
ajst-23562	18	28	noise	noise	NOUN
ajst-23562	18	29	on	on	ADP
ajst-23562	18	30	localization	localization	NOUN
ajst-23562	18	31	.	.	PUNCT
ajst-23562	19	1	(	(	PUNCT
ajst-23562	19	2	3	3	X
ajst-23562	19	3	)	)	PUNCT
ajst-23562	19	4	estimation	estimation	NOUN
ajst-23562	19	5	and	and	CCONJ
ajst-23562	19	6	validation	validation	NOUN
ajst-23562	19	7	:	:	PUNCT
ajst-23562	19	8	in	in	ADP
ajst-23562	19	9	order	order	NOUN
ajst-23562	19	10	to	to	PART
ajst-23562	19	11	estimate	estimate	VERB
ajst-23562	19	12	and	and	CCONJ
ajst-23562	19	13	validate	validate	VERB
ajst-23562	19	14	the	the	DET
ajst-23562	19	15	performance	performance	NOUN
ajst-23562	19	16	and	and	CCONJ
ajst-23562	19	17	effectiveness	effectiveness	NOUN
ajst-23562	19	18	of	of	ADP
ajst-23562	19	19	the	the	DET
ajst-23562	19	20	method	method	NOUN
ajst-23562	19	21	proposed	propose	VERB
ajst-23562	19	22	in	in	ADP
ajst-23562	19	23	this	this	DET
ajst-23562	19	24	paper	paper	NOUN
ajst-23562	19	25	,	,	PUNCT
ajst-23562	19	26	we	we	PRON
ajst-23562	19	27	have	have	AUX
ajst-23562	19	28	conducted	conduct	VERB
ajst-23562	19	29	a	a	DET
ajst-23562	19	30	large	large	ADJ
ajst-23562	19	31	number	number	NOUN
ajst-23562	19	32	of	of	ADP
ajst-23562	19	33	experiments	experiment	NOUN
ajst-23562	19	34	.	.	PUNCT
ajst-23562	20	1	2	2	X
ajst-23562	20	2	.	.	NUM
ajst-23562	20	3	related	relate	VERB
ajst-23562	20	4	work	work	NOUN
ajst-23562	20	5	methods	method	NOUN
ajst-23562	20	6	based	base	VERB
ajst-23562	20	7	on	on	ADP
ajst-23562	20	8	rssi	rssi	ADJ
ajst-23562	20	9	location	location	NOUN
ajst-23562	20	10	fingerprint	fingerprint	NOUN
ajst-23562	20	11	localization	localization	NOUN
ajst-23562	20	12	are	be	AUX
ajst-23562	20	13	summarized	summarize	VERB
ajst-23562	20	14	in	in	ADP
ajst-23562	20	15	the	the	DET
ajst-23562	20	16	following	follow	VERB
ajst-23562	20	17	three	three	NUM
ajst-23562	20	18	categories	category	NOUN
ajst-23562	20	19	:	:	PUNCT
ajst-23562	20	20	probabilistic	probabilistic	ADJ
ajst-23562	20	21	methods	method	NOUN
ajst-23562	20	22	,	,	PUNCT
ajst-23562	20	23	deterministic	deterministic	ADJ
ajst-23562	20	24	methods	method	NOUN
ajst-23562	20	25	and	and	CCONJ
ajst-23562	20	26	neural	neural	ADJ
ajst-23562	20	27	network	network	NOUN
ajst-23562	20	28	methods	method	NOUN
ajst-23562	20	29	.	.	PUNCT
ajst-23562	21	1	in	in	ADP
ajst-23562	21	2	probabilistic	probabilistic	ADJ
ajst-23562	21	3	methods	method	NOUN
ajst-23562	21	4	,	,	PUNCT
ajst-23562	21	5	it	it	PRON
ajst-23562	21	6	is	be	AUX
ajst-23562	21	7	assumed	assume	VERB
ajst-23562	21	8	that	that	SCONJ
ajst-23562	21	9	the	the	DET
ajst-23562	21	10	probability	probability	NOUN
ajst-23562	21	11	density	density	NOUN
ajst-23562	21	12	function	function	NOUN
ajst-23562	21	13	of	of	ADP
ajst-23562	21	14	rssi	rssi	NOUN
ajst-23562	21	15	has	have	VERB
ajst-23562	21	16	a	a	DET
ajst-23562	21	17	certain	certain	ADJ
ajst-23562	21	18	distribution	distribution	NOUN
ajst-23562	21	19	of	of	ADP
ajst-23562	21	20	empirical	empirical	ADJ
ajst-23562	21	21	parameters	parameter	NOUN
ajst-23562	21	22	,	,	PUNCT
ajst-23562	21	23	such	such	ADJ
ajst-23562	21	24	as	as	ADP
ajst-23562	21	25	the	the	DET
ajst-23562	21	26	gaussian	gaussian	ADJ
ajst-23562	21	27	distribution	distribution	NOUN
ajst-23562	21	28	,	,	PUNCT
ajst-23562	21	29	for	for	ADP
ajst-23562	21	30	now	now	ADV
ajst-23562	21	31	its	its	PRON
ajst-23562	21	32	matching	matching	NOUN
ajst-23562	21	33	with	with	ADP
ajst-23562	21	34	the	the	DET
ajst-23562	21	35	target	target	NOUN
ajst-23562	21	36	signal	signal	NOUN
ajst-23562	21	37	measurements	measurement	NOUN
ajst-23562	21	38	for	for	ADP
ajst-23562	21	39	localization	localization	NOUN
ajst-23562	21	40	.	.	PUNCT
ajst-23562	22	1	in	in	ADP
ajst-23562	22	2	deterministic	deterministic	ADJ
ajst-23562	22	3	methods	method	NOUN
ajst-23562	22	4	,	,	PUNCT
ajst-23562	22	5	rssi	rssi	NOUN
ajst-23562	22	6	is	be	AUX
ajst-23562	22	7	usually	usually	ADV
ajst-23562	22	8	used	use	VERB
ajst-23562	22	9	as	as	ADP
ajst-23562	22	10	a	a	DET
ajst-23562	22	11	feature	feature	NOUN
ajst-23562	22	12	parameter	parameter	NOUN
ajst-23562	22	13	combined	combine	VERB
ajst-23562	22	14	with	with	ADP
ajst-23562	22	15	a	a	DET
ajst-23562	22	16	deterministic	deterministic	ADJ
ajst-23562	22	17	matching	matching	NOUN
ajst-23562	22	18	algorithm	algorithm	NOUN
ajst-23562	22	19	for	for	ADP
ajst-23562	22	20	location	location	NOUN
ajst-23562	22	21	estimation	estimation	NOUN
ajst-23562	22	22	.	.	PUNCT
ajst-23562	23	1	compared	compare	VERB
ajst-23562	23	2	to	to	ADP
ajst-23562	23	3	these	these	DET
ajst-23562	23	4	algorithms	algorithm	NOUN
ajst-23562	23	5	,	,	PUNCT
ajst-23562	23	6	deep	deep	ADJ
ajst-23562	23	7	learning	learning	NOUN
ajst-23562	23	8	methods	method	NOUN
ajst-23562	23	9	provide	provide	VERB
ajst-23562	23	10	more	more	ADV
ajst-23562	23	11	stable	stable	ADJ
ajst-23562	23	12	and	and	CCONJ
ajst-23562	23	13	accurate	accurate	ADJ
ajst-23562	23	14	localization	localization	NOUN
ajst-23562	23	15	[	[	X
ajst-23562	23	16	5	5	NUM
ajst-23562	23	17	]	]	PUNCT
ajst-23562	23	18	.	.	PUNCT
ajst-23562	24	1	literature	literature	NOUN
ajst-23562	25	1	[	[	X
ajst-23562	25	2	6	6	NUM
ajst-23562	25	3	]	]	PUNCT
ajst-23562	25	4	introduced	introduce	VERB
ajst-23562	25	5	the	the	DET
ajst-23562	25	6	extracted	extract	VERB
ajst-23562	25	7	information	information	NOUN
ajst-23562	25	8	into	into	ADP
ajst-23562	25	9	a	a	DET
ajst-23562	25	10	shallow	shallow	ADJ
ajst-23562	25	11	neural	neural	ADJ
ajst-23562	25	12	network	network	NOUN
ajst-23562	25	13	for	for	ADP
ajst-23562	25	14	nonlinear	nonlinear	ADJ
ajst-23562	25	15	estimation	estimation	NOUN
ajst-23562	25	16	of	of	ADP
ajst-23562	25	17	node	node	ADJ
ajst-23562	25	18	location	location	NOUN
ajst-23562	25	19	coordinates	coordinate	NOUN
ajst-23562	25	20	.	.	PUNCT
ajst-23562	26	1	literature	literature	NOUN
ajst-23562	27	1	[	[	X
ajst-23562	27	2	7	7	X
ajst-23562	27	3	]	]	PUNCT
ajst-23562	27	4	calibrates	calibrate	VERB
ajst-23562	27	5	the	the	DET
ajst-23562	27	6	localization	localization	NOUN
ajst-23562	27	7	results	result	NOUN
ajst-23562	27	8	by	by	ADP
ajst-23562	27	9	adjusting	adjust	VERB
ajst-23562	27	10	the	the	DET
ajst-23562	27	11	loss	loss	NOUN
ajst-23562	27	12	function	function	NOUN
ajst-23562	27	13	and	and	CCONJ
ajst-23562	27	14	weights	weight	NOUN
ajst-23562	27	15	in	in	ADP
ajst-23562	27	16	the	the	DET
ajst-23562	27	17	cnn	cnn	PROPN
ajst-23562	27	18	model	model	NOUN
ajst-23562	27	19	.	.	PUNCT
ajst-23562	28	1	the	the	DET
ajst-23562	28	2	authors	author	NOUN
ajst-23562	28	3	proposed	propose	VERB
ajst-23562	28	4	a	a	DET
ajst-23562	28	5	wifi	wifi	NOUN
ajst-23562	28	6	fingerprint	fingerprint	NOUN
ajst-23562	28	7	localization	localization	NOUN
ajst-23562	28	8	method	method	NOUN
ajst-23562	28	9	based	base	VERB
ajst-23562	28	10	on	on	ADP
ajst-23562	28	11	traditional	traditional	ADJ
ajst-23562	28	12	dnn	dnn	PROPN
ajst-23562	28	13	and	and	CCONJ
ajst-23562	28	14	experimentally	experimentally	ADV
ajst-23562	28	15	demonstrated	demonstrate	VERB
ajst-23562	28	16	that	that	SCONJ
ajst-23562	28	17	the	the	DET
ajst-23562	28	18	proposed	propose	VERB
ajst-23562	28	19	4	4	NUM
ajst-23562	28	20	-	-	PUNCT
ajst-23562	28	21	layer	layer	NOUN
ajst-23562	28	22	network	network	NOUN
ajst-23562	28	23	containing	contain	VERB
ajst-23562	28	24	a	a	DET
ajst-23562	28	25	hidden	hide	VERB
ajst-23562	28	26	markov	markov	NOUN
ajst-23562	28	27	model	model	NOUN
ajst-23562	28	28	can	can	AUX
ajst-23562	28	29	effectively	effectively	ADV
ajst-23562	28	30	extract	extract	VERB
ajst-23562	28	31	rssi	rssi	ADJ
ajst-23562	28	32	signal	signal	NOUN
ajst-23562	28	33	features	feature	NOUN
ajst-23562	28	34	and	and	CCONJ
ajst-23562	28	35	generate	generate	VERB
ajst-23562	28	36	initial	initial	ADJ
ajst-23562	28	37	localization	localization	NOUN
ajst-23562	28	38	estimates	estimate	NOUN
ajst-23562	28	39	.	.	PUNCT
ajst-23562	29	1	in	in	ADP
ajst-23562	29	2	order	order	NOUN
ajst-23562	29	3	to	to	PART
ajst-23562	29	4	solve	solve	VERB
ajst-23562	29	5	the	the	DET
ajst-23562	29	6	existing	exist	VERB
ajst-23562	29	7	problems	problem	NOUN
ajst-23562	29	8	in	in	ADP
ajst-23562	29	9	the	the	DET
ajst-23562	29	10	current	current	ADJ
ajst-23562	29	11	indoor	indoor	ADJ
ajst-23562	29	12	localization	localization	NOUN
ajst-23562	29	13	methods	method	NOUN
ajst-23562	29	14	,	,	PUNCT
ajst-23562	29	15	this	this	DET
ajst-23562	29	16	paper	paper	NOUN
ajst-23562	29	17	proposes	propose	VERB
ajst-23562	29	18	an	an	DET
ajst-23562	29	19	indoor	indoor	ADJ
ajst-23562	29	20	localization	localization	NOUN
ajst-23562	29	21	method	method	NOUN
ajst-23562	29	22	based	base	VERB
ajst-23562	29	23	on	on	ADP
ajst-23562	29	24	sae	sae	PROPN
ajst-23562	29	25	-	-	ADJ
ajst-23562	29	26	bilstm	bilstm	ADJ
ajst-23562	29	27	utilizing	utilizing	NOUN
ajst-23562	29	28	time	time	NOUN
ajst-23562	29	29	series	series	PROPN
ajst-23562	29	30	rssi	rssi	NOUN
ajst-23562	29	31	by	by	ADP
ajst-23562	29	32	doing	do	VERB
ajst-23562	29	33	the	the	DET
ajst-23562	29	34	following	following	NOUN
ajst-23562	29	35	:	:	PUNCT
ajst-23562	29	36	(	(	PUNCT
ajst-23562	29	37	1	1	X
ajst-23562	29	38	)	)	PUNCT
ajst-23562	29	39	collect	collect	VERB
ajst-23562	29	40	and	and	CCONJ
ajst-23562	29	41	establish	establish	VERB
ajst-23562	29	42	a	a	DET
ajst-23562	29	43	time	time	NOUN
ajst-23562	29	44	series	series	NOUN
ajst-23562	29	45	dataset	dataset	NOUN
ajst-23562	29	46	.	.	PUNCT
ajst-23562	30	1	(	(	PUNCT
ajst-23562	30	2	2	2	X
ajst-23562	30	3	)	)	PUNCT
ajst-23562	30	4	considering	consider	VERB
ajst-23562	30	5	the	the	DET
ajst-23562	30	6	time	time	NOUN
ajst-23562	30	7	-	-	PUNCT
ajst-23562	30	8	series	series	NOUN
ajst-23562	30	9	information	information	NOUN
ajst-23562	30	10	of	of	ADP
ajst-23562	30	11	rssi	rssi	NOUN
ajst-23562	30	12	,	,	PUNCT
ajst-23562	30	13	the	the	DET
ajst-23562	30	14	effect	effect	NOUN
ajst-23562	30	15	of	of	ADP
ajst-23562	30	16	time	time	NOUN
ajst-23562	30	17	-	-	PUNCT
ajst-23562	30	18	series	series	NOUN
ajst-23562	30	19	length	length	NOUN
ajst-23562	30	20	on	on	ADP
ajst-23562	30	21	the	the	DET
ajst-23562	30	22	localization	localization	NOUN
ajst-23562	30	23	performance	performance	NOUN
ajst-23562	30	24	is	be	AUX
ajst-23562	30	25	investigated	investigate	VERB
ajst-23562	30	26	.	.	PUNCT
ajst-23562	31	1	(	(	PUNCT
ajst-23562	31	2	3	3	X
ajst-23562	31	3	)	)	PUNCT
ajst-23562	31	4	apply	apply	VERB
ajst-23562	31	5	the	the	DET
ajst-23562	31	6	model	model	NOUN
ajst-23562	31	7	to	to	ADP
ajst-23562	31	8	the	the	DET
ajst-23562	31	9	time	time	NOUN
ajst-23562	31	10	series	series	PROPN
ajst-23562	31	11	task	task	PROPN
ajst-23562	31	12	domain	domain	NOUN
ajst-23562	31	13	for	for	ADP
ajst-23562	31	14	indoor	indoor	ADJ
ajst-23562	31	15	localization	localization	NOUN
ajst-23562	31	16	.	.	PUNCT
ajst-23562	32	1	98	98	NUM
ajst-23562	32	2	3	3	NUM
ajst-23562	32	3	.	.	PUNCT
ajst-23562	32	4	methodological	methodological	ADJ
ajst-23562	32	5	overview	overview	NOUN
ajst-23562	32	6	3.1	3.1	NUM
ajst-23562	32	7	.	.	PUNCT
ajst-23562	32	8	sae	sae	PROPN
ajst-23562	32	9	feature	feature	NOUN
ajst-23562	32	10	extraction	extraction	NOUN
ajst-23562	32	11	method	method	NOUN
ajst-23562	32	12	in	in	ADP
ajst-23562	32	13	this	this	DET
ajst-23562	32	14	paper	paper	NOUN
ajst-23562	32	15	,	,	PUNCT
ajst-23562	32	16	we	we	PRON
ajst-23562	32	17	propose	propose	VERB
ajst-23562	32	18	a	a	DET
ajst-23562	32	19	time	time	NOUN
ajst-23562	32	20	-	-	PUNCT
ajst-23562	32	21	series	series	NOUN
ajst-23562	32	22	rssi	rssi	NOUN
ajst-23562	32	23	-	-	PUNCT
ajst-23562	32	24	based	base	VERB
ajst-23562	32	25	indoor	indoor	ADJ
ajst-23562	32	26	localization	localization	NOUN
ajst-23562	32	27	method	method	NOUN
ajst-23562	32	28	sae	sae	PROPN
ajst-23562	32	29	-	-	NOUN
ajst-23562	32	30	bilstm	bilstm	NOUN
ajst-23562	32	31	,	,	PUNCT
ajst-23562	32	32	which	which	PRON
ajst-23562	32	33	has	have	VERB
ajst-23562	32	34	a	a	DET
ajst-23562	32	35	smaller	small	ADJ
ajst-23562	32	36	average	average	ADJ
ajst-23562	32	37	euclidean	euclidean	ADJ
ajst-23562	32	38	distance	distance	NOUN
ajst-23562	32	39	and	and	CCONJ
ajst-23562	32	40	more	more	ADV
ajst-23562	32	41	stable	stable	ADJ
ajst-23562	32	42	performance	performance	NOUN
ajst-23562	32	43	.	.	PUNCT
ajst-23562	33	1	modeling	modeling	NOUN
ajst-23562	33	2	and	and	CCONJ
ajst-23562	33	3	analysis	analysis	NOUN
ajst-23562	33	4	using	use	VERB
ajst-23562	33	5	logarithmic	logarithmic	ADJ
ajst-23562	33	6	distance	distance	NOUN
ajst-23562	33	7	path	path	NOUN
ajst-23562	33	8	loss	loss	NOUN
ajst-23562	33	9	propagation	propagation	NOUN
ajst-23562	33	10	modeling	modeling	NOUN
ajst-23562	33	11	.	.	PUNCT
ajst-23562	34	1			NOUN
ajst-23562	34	2			PUNCT
ajst-23562	34	3			NOUN
ajst-23562	34	4	0	0	NOUN
ajst-23562	34	5	0	0	NUM
ajst-23562	34	6	10	10	NUM
ajst-23562	34	7	lgl	lgl	PROPN
ajst-23562	34	8	l	l	NOUN
ajst-23562	35	1	d	d	X
ajst-23562	35	2	p	p	X
ajst-23562	35	3	d	d	X
ajst-23562	35	4	p	p	PROPN
ajst-23562	35	5	d	d	PROPN
ajst-23562	35	6	n	n	PROPN
ajst-23562	35	7	d	d	PROPN
ajst-23562	35	8			PROPN
ajst-23562	35	9			NOUN
ajst-23562	35	10			NOUN
ajst-23562	35	11			PROPN
ajst-23562	35	12			PROPN
ajst-23562	35	13			ADV
ajst-23562	35	14			ADJ
ajst-23562	35	15			PROPN
ajst-23562	35	16			ADJ
ajst-23562	35	17			NOUN
ajst-23562	35	18	(	(	PUNCT
ajst-23562	35	19	1	1	NUM
ajst-23562	35	20	)	)	PUNCT
ajst-23562	35	21	where	where	SCONJ
ajst-23562	35	22			NOUN
ajst-23562	35	23	lp	lp	PUNCT
ajst-23562	36	1	d	d	NOUN
ajst-23562	36	2	is	be	AUX
ajst-23562	36	3	the	the	DET
ajst-23562	36	4	rssi	rssi	ADJ
ajst-23562	36	5	value	value	NOUN
ajst-23562	36	6	received	receive	VERB
ajst-23562	36	7	when	when	SCONJ
ajst-23562	36	8	the	the	DET
ajst-23562	36	9	distance	distance	NOUN
ajst-23562	36	10	from	from	ADP
ajst-23562	36	11	the	the	DET
ajst-23562	36	12	positioning	positioning	NOUN
ajst-23562	36	13	terminal	terminal	NOUN
ajst-23562	36	14	to	to	ADP
ajst-23562	36	15	the	the	DET
ajst-23562	36	16	beacon	beacon	NOUN
ajst-23562	36	17	base	base	NOUN
ajst-23562	36	18	station	station	NOUN
ajst-23562	36	19	is	be	AUX
ajst-23562	36	20	d	d	NOUN
ajst-23562	36	21	,	,	PUNCT
ajst-23562	36	22	and	and	CCONJ
ajst-23562	36	23			PROPN
ajst-23562	36	24	0lp	0lp	PUNCT
ajst-23562	37	1	d	d	PRON
ajst-23562	37	2	is	be	AUX
ajst-23562	37	3	the	the	DET
ajst-23562	37	4	rssi	rssi	ADJ
ajst-23562	37	5	value	value	NOUN
ajst-23562	37	6	when	when	SCONJ
ajst-23562	37	7	the	the	DET
ajst-23562	37	8	positioning	positioning	NOUN
ajst-23562	37	9	terminal	terminal	NOUN
ajst-23562	37	10	is	be	AUX
ajst-23562	37	11	0d	0d	NUM
ajst-23562	37	12	from	from	ADP
ajst-23562	37	13	the	the	DET
ajst-23562	37	14	beacon	beacon	NOUN
ajst-23562	37	15	base	base	NOUN
ajst-23562	37	16	station	station	NOUN
ajst-23562	37	17	.	.	PUNCT
ajst-23562	38	1	n	n	PRON
ajst-23562	38	2	is	be	AUX
ajst-23562	38	3	the	the	DET
ajst-23562	38	4	environmental	environmental	ADJ
ajst-23562	38	5	attenuation	attenuation	NOUN
ajst-23562	38	6	factor	factor	NOUN
ajst-23562	38	7	and	and	CCONJ
ajst-23562	38	8	x	x	PROPN
ajst-23562	38	9	is	be	AUX
ajst-23562	38	10	the	the	DET
ajst-23562	38	11	gaussian	gaussian	ADJ
ajst-23562	38	12	random	random	ADJ
ajst-23562	38	13	variable	variable	NOUN
ajst-23562	38	14	.	.	PUNCT
ajst-23562	39	1	in	in	ADP
ajst-23562	39	2	practical	practical	ADJ
ajst-23562	39	3	applications	application	NOUN
ajst-23562	39	4	of	of	ADP
ajst-23562	39	5	indoor	indoor	ADJ
ajst-23562	39	6	localization	localization	NOUN
ajst-23562	39	7	,	,	PUNCT
ajst-23562	39	8	it	it	PRON
ajst-23562	39	9	is	be	AUX
ajst-23562	39	10	common	common	ADJ
ajst-23562	39	11	to	to	PART
ajst-23562	39	12	set	set	VERB
ajst-23562	39	13			NOUN
ajst-23562	39	14	lp	lp	PUNCT
ajst-23562	40	1	d	d	NOUN
ajst-23562	40	2	rssi	rssi	NOUN
ajst-23562	40	3	,	,	PUNCT
ajst-23562	40	4			PROPN
ajst-23562	40	5	0	0	NOUN
ajst-23562	40	6	0lp	0lp	NOUN
ajst-23562	41	1	d	d	X
ajst-23562	41	2	p	p	NOUN
ajst-23562	41	3	,	,	PUNCT
ajst-23562	41	4	equation	equation	NOUN
ajst-23562	41	5	(	(	PUNCT
ajst-23562	41	6	1	1	X
ajst-23562	41	7	)	)	PUNCT
ajst-23562	41	8	can	can	AUX
ajst-23562	41	9	be	be	AUX
ajst-23562	41	10	simplified	simplify	VERB
ajst-23562	41	11	as	as	ADP
ajst-23562	41	12	:	:	PUNCT
ajst-23562	41	13			PROPN
ajst-23562	41	14	10	10	PROPN
ajst-23562	41	15	lgrssi	lgrssi	VERB
ajst-23562	41	16	a	a	DET
ajst-23562	41	17	n	n	CCONJ
ajst-23562	41	18	d	d	PROPN
ajst-23562	41	19			PROPN
ajst-23562	41	20	(	(	PUNCT
ajst-23562	41	21	2	2	NUM
ajst-23562	41	22	)	)	PUNCT
ajst-23562	41	23	where	where	SCONJ
ajst-23562	41	24	0a	0a	PROPN
ajst-23562	41	25	p	p	PROPN
ajst-23562	41	26	x	x	PROPN
ajst-23562	41	27			PROPN
ajst-23562	41	28	.	.	PUNCT
ajst-23562	42	1	different	different	ADJ
ajst-23562	42	2	indoor	indoor	ADJ
ajst-23562	42	3	environments	environment	NOUN
ajst-23562	42	4	and	and	CCONJ
ajst-23562	42	5	the	the	DET
ajst-23562	42	6	values	value	NOUN
ajst-23562	42	7	of	of	ADP
ajst-23562	42	8	and	and	CCONJ
ajst-23562	42	9	are	be	AUX
ajst-23562	42	10	different	different	ADJ
ajst-23562	42	11	,	,	PUNCT
ajst-23562	42	12	in	in	ADP
ajst-23562	42	13	order	order	NOUN
ajst-23562	42	14	to	to	PART
ajst-23562	42	15	obtain	obtain	VERB
ajst-23562	42	16	better	well	ADJ
ajst-23562	42	17	indoor	indoor	ADJ
ajst-23562	42	18	localization	localization	NOUN
ajst-23562	42	19	effect	effect	NOUN
ajst-23562	42	20	,	,	PUNCT
ajst-23562	42	21	this	this	DET
ajst-23562	42	22	paper	paper	NOUN
ajst-23562	42	23	selects	select	VERB
ajst-23562	42	24	an	an	DET
ajst-23562	42	25	indoor	indoor	ADJ
ajst-23562	42	26	space	space	NOUN
ajst-23562	42	27	of	of	ADP
ajst-23562	42	28	8m×8	8m×8	NUM
ajst-23562	42	29	m	m	NOUN
ajst-23562	42	30	to	to	PART
ajst-23562	42	31	arrange	arrange	VERB
ajst-23562	42	32	the	the	DET
ajst-23562	42	33	ibeacon	ibeacon	ADJ
ajst-23562	42	34	beacon	beacon	NOUN
ajst-23562	42	35	device	device	NOUN
ajst-23562	42	36	in	in	ADP
ajst-23562	42	37	a	a	DET
ajst-23562	42	38	symmetric	symmetric	ADJ
ajst-23562	42	39	way	way	NOUN
ajst-23562	42	40	and	and	CCONJ
ajst-23562	42	41	set	set	VERB
ajst-23562	42	42	the	the	DET
ajst-23562	42	43	rp	rp	NOUN
ajst-23562	42	44	to	to	PART
ajst-23562	42	45	collect	collect	VERB
ajst-23562	42	46	rssi	rssi	NOUN
ajst-23562	42	47	to	to	PART
ajst-23562	42	48	establish	establish	VERB
ajst-23562	42	49	the	the	DET
ajst-23562	42	50	location	location	NOUN
ajst-23562	42	51	fingerprint	fingerprint	NOUN
ajst-23562	42	52	library	library	NOUN
ajst-23562	42	53	.	.	PUNCT
ajst-23562	43	1	the	the	DET
ajst-23562	43	2	smartphone	smartphone	NOUN
ajst-23562	43	3	records	record	VERB
ajst-23562	43	4	the	the	DET
ajst-23562	43	5	rssi	rssi	ADJ
ajst-23562	43	6	value	value	NOUN
ajst-23562	43	7	from	from	ADP
ajst-23562	43	8	the	the	DET
ajst-23562	43	9	first	first	ADJ
ajst-23562	43	10	ap	ap	PROPN
ajst-23562	43	11	collected	collect	VERB
ajst-23562	43	12	at	at	ADP
ajst-23562	43	13	the	the	DET
ajst-23562	43	14	first	first	ADJ
ajst-23562	43	15	reference	reference	NOUN
ajst-23562	43	16	point	point	NOUN
ajst-23562	43	17	,	,	PUNCT
ajst-23562	43	18	and	and	CCONJ
ajst-23562	43	19	constructs	construct	VERB
ajst-23562	43	20	the	the	DET
ajst-23562	43	21	fingerprint	fingerprint	NOUN
ajst-23562	43	22	sequence	sequence	NOUN
ajst-23562	43	23	as	as	SCONJ
ajst-23562	43	24	shown	show	VERB
ajst-23562	43	25	in	in	ADP
ajst-23562	43	26	equation	equation	NOUN
ajst-23562	43	27	(	(	PUNCT
ajst-23562	43	28	3	3	NUM
ajst-23562	43	29	)	)	PUNCT
ajst-23562	43	30	.	.	PUNCT
ajst-23562	44	1	,	,	PUNCT
ajst-23562	44	2	1	1	NUM
ajst-23562	44	3	,	,	PUNCT
ajst-23562	44	4	2	2	NUM
ajst-23562	44	5	,	,	PUNCT
ajst-23562	44	6	,	,	PUNCT
ajst-23562	44	7	,	,	PUNCT
ajst-23562	44	8	,	,	PUNCT
ajst-23562	44	9	i	i	PRON
ajst-23562	45	1	i	i	PRON
ajst-23562	45	2	i	i	PRON
ajst-23562	46	1	i	i	PRON
ajst-23562	46	2	jrssi	jrssi	VERB
ajst-23562	46	3	rssi	rssi	ADJ
ajst-23562	46	4	rssi	rssi	NOUN
ajst-23562	46	5	rssi	rssi	NOUN
ajst-23562	46	6			PROPN
ajst-23562	46	7			NOUN
ajst-23562	46	8			PROPN
ajst-23562	46	9	(	(	PUNCT
ajst-23562	46	10	3	3	NUM
ajst-23562	46	11	)	)	PUNCT
ajst-23562	46	12	due	due	ADP
ajst-23562	46	13	to	to	ADP
ajst-23562	46	14	the	the	DET
ajst-23562	46	15	tedious	tedious	ADJ
ajst-23562	46	16	and	and	CCONJ
ajst-23562	46	17	time	time	NOUN
ajst-23562	46	18	-	-	PUNCT
ajst-23562	46	19	consuming	consume	VERB
ajst-23562	46	20	construction	construction	NOUN
ajst-23562	46	21	of	of	ADP
ajst-23562	46	22	offline	offline	ADJ
ajst-23562	46	23	location	location	NOUN
ajst-23562	46	24	fingerprint	fingerprint	NOUN
ajst-23562	46	25	library	library	NOUN
ajst-23562	46	26	,	,	PUNCT
ajst-23562	46	27	this	this	DET
ajst-23562	46	28	paper	paper	NOUN
ajst-23562	46	29	divides	divide	VERB
ajst-23562	46	30	the	the	DET
ajst-23562	46	31	localization	localization	NOUN
ajst-23562	46	32	area	area	NOUN
ajst-23562	46	33	into	into	ADP
ajst-23562	46	34	a	a	DET
ajst-23562	46	35	0.8m×0.8	0.8m×0.8	PROPN
ajst-23562	46	36	m	m	NOUN
ajst-23562	46	37	grid	grid	NOUN
ajst-23562	46	38	.	.	PUNCT
ajst-23562	47	1	the	the	DET
ajst-23562	47	2	localization	localization	NOUN
ajst-23562	47	3	area	area	NOUN
ajst-23562	47	4	is	be	AUX
ajst-23562	47	5	divided	divide	VERB
ajst-23562	47	6	into	into	ADP
ajst-23562	47	7	a	a	DET
ajst-23562	47	8	grid	grid	NOUN
ajst-23562	47	9	of	of	ADP
ajst-23562	47	10	0.8m×0.8	0.8m×0.8	PROPN
ajst-23562	47	11	m	m	NOUN
ajst-23562	47	12	,	,	PUNCT
ajst-23562	47	13	and	and	CCONJ
ajst-23562	47	14	9	9	NUM
ajst-23562	47	15	ibeacon	ibeacon	NOUN
ajst-23562	47	16	devices	device	NOUN
ajst-23562	47	17	are	be	AUX
ajst-23562	47	18	set	set	VERB
ajst-23562	47	19	as	as	ADP
ajst-23562	47	20	aps	ap	NOUN
ajst-23562	47	21	and	and	CCONJ
ajst-23562	47	22	15	15	NUM
ajst-23562	47	23	reference	reference	NOUN
ajst-23562	47	24	points	point	NOUN
ajst-23562	47	25	.	.	PUNCT
ajst-23562	48	1	the	the	DET
ajst-23562	48	2	mobile	mobile	ADJ
ajst-23562	48	3	device	device	NOUN
ajst-23562	48	4	is	be	AUX
ajst-23562	48	5	a	a	DET
ajst-23562	48	6	smartphone	smartphone	NOUN
ajst-23562	48	7	,	,	PUNCT
ajst-23562	48	8	and	and	CCONJ
ajst-23562	48	9	the	the	DET
ajst-23562	48	10	collection	collection	NOUN
ajst-23562	48	11	client	client	NOUN
ajst-23562	48	12	is	be	AUX
ajst-23562	48	13	realized	realize	VERB
ajst-23562	48	14	by	by	ADP
ajst-23562	48	15	using	use	VERB
ajst-23562	48	16	wechat	wechat	PROPN
ajst-23562	48	17	applet	applet	NOUN
ajst-23562	48	18	,	,	PUNCT
ajst-23562	48	19	because	because	SCONJ
ajst-23562	48	20	wechat	wechat	PROPN
ajst-23562	48	21	applet	applet	NOUN
ajst-23562	48	22	is	be	AUX
ajst-23562	48	23	a	a	DET
ajst-23562	48	24	lightweight	lightweight	ADJ
ajst-23562	48	25	application	application	NOUN
ajst-23562	48	26	across	across	ADP
ajst-23562	48	27	devices	device	NOUN
ajst-23562	48	28	and	and	CCONJ
ajst-23562	48	29	operating	operating	NOUN
ajst-23562	48	30	systems	system	NOUN
ajst-23562	48	31	.	.	PUNCT
ajst-23562	49	1	localization	localization	NOUN
ajst-23562	49	2	fingerprints	fingerprint	NOUN
ajst-23562	49	3	are	be	AUX
ajst-23562	49	4	collected	collect	VERB
ajst-23562	49	5	through	through	ADP
ajst-23562	49	6	the	the	DET
ajst-23562	49	7	static	static	ADJ
ajst-23562	49	8	collection	collection	NOUN
ajst-23562	49	9	method	method	NOUN
ajst-23562	49	10	of	of	ADP
ajst-23562	49	11	the	the	DET
ajst-23562	49	12	wechat	wechat	PROPN
ajst-23562	49	13	applet	applet	NOUN
ajst-23562	49	14	client	client	NOUN
ajst-23562	49	15	by	by	ADP
ajst-23562	49	16	using	use	VERB
ajst-23562	49	17	the	the	DET
ajst-23562	49	18	mobile	mobile	ADJ
ajst-23562	49	19	device	device	NOUN
ajst-23562	49	20	to	to	PART
ajst-23562	49	21	collect	collect	VERB
ajst-23562	49	22	1000	1000	NUM
ajst-23562	49	23	sets	set	NOUN
ajst-23562	49	24	of	of	ADP
ajst-23562	49	25	data	datum	NOUN
ajst-23562	49	26	as	as	ADP
ajst-23562	49	27	training	training	NOUN
ajst-23562	49	28	samples	sample	NOUN
ajst-23562	49	29	at	at	ADP
ajst-23562	49	30	each	each	DET
ajst-23562	49	31	reference	reference	NOUN
ajst-23562	49	32	point	point	NOUN
ajst-23562	49	33	and	and	CCONJ
ajst-23562	49	34	500	500	NUM
ajst-23562	49	35	sets	set	NOUN
ajst-23562	49	36	of	of	ADP
ajst-23562	49	37	data	datum	NOUN
ajst-23562	49	38	as	as	ADP
ajst-23562	49	39	verification	verification	NOUN
ajst-23562	49	40	samples	sample	NOUN
ajst-23562	49	41	at	at	ADP
ajst-23562	49	42	another	another	DET
ajst-23562	49	43	10	10	NUM
ajst-23562	49	44	random	random	ADJ
ajst-23562	49	45	locations	location	NOUN
ajst-23562	49	46	.	.	PUNCT
ajst-23562	50	1	due	due	ADP
ajst-23562	50	2	to	to	ADP
ajst-23562	50	3	electromagnetic	electromagnetic	ADJ
ajst-23562	50	4	interference	interference	NOUN
ajst-23562	50	5	,	,	PUNCT
ajst-23562	50	6	multipath	multipath	NOUN
ajst-23562	50	7	effect	effect	NOUN
ajst-23562	50	8	,	,	PUNCT
ajst-23562	50	9	etc	etc	X
ajst-23562	50	10	.	.	X
ajst-23562	50	11	,	,	PUNCT
ajst-23562	50	12	the	the	DET
ajst-23562	50	13	ap	ap	PROPN
ajst-23562	50	14	nodes	node	NOUN
ajst-23562	50	15	scanned	scan	VERB
ajst-23562	50	16	at	at	ADP
ajst-23562	50	17	different	different	ADJ
ajst-23562	50	18	reference	reference	NOUN
ajst-23562	50	19	points	point	NOUN
ajst-23562	50	20	will	will	AUX
ajst-23562	50	21	have	have	AUX
ajst-23562	50	22	missing	miss	VERB
ajst-23562	50	23	rssi	rssi	ADJ
ajst-23562	50	24	values	value	NOUN
ajst-23562	50	25	,	,	PUNCT
ajst-23562	50	26	and	and	CCONJ
ajst-23562	50	27	it	it	PRON
ajst-23562	50	28	is	be	AUX
ajst-23562	50	29	necessary	necessary	ADJ
ajst-23562	50	30	to	to	PART
ajst-23562	50	31	set	set	VERB
ajst-23562	50	32	the	the	DET
ajst-23562	50	33	missing	miss	VERB
ajst-23562	50	34	value	value	NOUN
ajst-23562	50	35	to	to	ADP
ajst-23562	50	36	110dbm	110dbm	NUM
ajst-23562	50	37	to	to	PART
ajst-23562	50	38	indicate	indicate	VERB
ajst-23562	50	39	the	the	DET
ajst-23562	50	40	unavailable	unavailable	ADJ
ajst-23562	50	41	aps	ap	NOUN
ajst-23562	50	42	when	when	SCONJ
ajst-23562	50	43	constructing	construct	VERB
ajst-23562	50	44	the	the	DET
ajst-23562	50	45	location	location	NOUN
ajst-23562	50	46	fingerprint	fingerprint	NOUN
ajst-23562	50	47	library	library	NOUN
ajst-23562	50	48	to	to	PART
ajst-23562	50	49	ensure	ensure	VERB
ajst-23562	50	50	the	the	DET
ajst-23562	50	51	distribution	distribution	NOUN
ajst-23562	50	52	range	range	NOUN
ajst-23562	50	53	of	of	ADP
ajst-23562	50	54	rssi	rssi	ADJ
ajst-23562	50	55	values	value	NOUN
ajst-23562	50	56	.	.	PUNCT
ajst-23562	51	1	after	after	ADP
ajst-23562	51	2	completing	complete	VERB
ajst-23562	51	3	the	the	DET
ajst-23562	51	4	acquisition	acquisition	NOUN
ajst-23562	51	5	and	and	CCONJ
ajst-23562	51	6	filtering	filtering	NOUN
ajst-23562	51	7	of	of	ADP
ajst-23562	51	8	all	all	DET
ajst-23562	51	9	reference	reference	NOUN
ajst-23562	51	10	points	point	NOUN
ajst-23562	51	11	,	,	PUNCT
ajst-23562	51	12	the	the	DET
ajst-23562	51	13	offline	offline	ADJ
ajst-23562	51	14	location	location	NOUN
ajst-23562	51	15	fingerprint	fingerprint	NOUN
ajst-23562	51	16	library	library	NOUN
ajst-23562	51	17	is	be	AUX
ajst-23562	51	18	constructed	construct	VERB
ajst-23562	51	19	as	as	SCONJ
ajst-23562	51	20	shown	show	VERB
ajst-23562	51	21	in	in	ADP
ajst-23562	51	22	fig	fig	NOUN
ajst-23562	51	23	.	.	PUNCT
ajst-23562	52	1	as	as	SCONJ
ajst-23562	52	2	shown	show	VERB
ajst-23562	52	3	in	in	ADP
ajst-23562	52	4	eq	eq	ADP
ajst-23562	52	5	.	.	PUNCT
ajst-23562	53	1	(	(	PUNCT
ajst-23562	53	2	4	4	NUM
ajst-23562	53	3	)	)	PUNCT
ajst-23562	53	4	.	.	PUNCT
ajst-23562	54	1			ADJ
ajst-23562	54	2			ADP
ajst-23562	54	3	1,1	1,1	NUM
ajst-23562	54	4	1,2	1,2	NUM
ajst-23562	54	5	1	1	NUM
ajst-23562	54	6	,	,	PUNCT
ajst-23562	54	7	1	1	NUM
ajst-23562	54	8	1	1	NUM
ajst-23562	54	9	1	1	NUM
ajst-23562	54	10	2,1	2,1	NUM
ajst-23562	54	11	2,2	2,2	NUM
ajst-23562	54	12	2	2	NUM
ajst-23562	54	13	,	,	PUNCT
ajst-23562	54	14	2	2	NUM
ajst-23562	54	15	2	2	NUM
ajst-23562	54	16	2	2	NUM
ajst-23562	54	17	1	1	NUM
ajst-23562	54	18	2	2	NUM
ajst-23562	54	19	,	,	PUNCT
ajst-23562	54	20	1	1	NUM
ajst-23562	54	21	,	,	PUNCT
ajst-23562	54	22	2	2	NUM
ajst-23562	54	23	,	,	PUNCT
ajst-23562	54	24	,	,	PUNCT
ajst-23562	54	25	,	,	PUNCT
ajst-23562	54	26	,	,	PUNCT
ajst-23562	54	27	j	j	PROPN
ajst-23562	54	28	t	t	PROPN
ajst-23562	55	1	j	j	PROPN
ajst-23562	56	1	i	i	PRON
ajst-23562	56	2	i	i	PRON
ajst-23562	57	1	i	i	PRON
ajst-23562	58	1	i	i	PRON
ajst-23562	58	2	j	j	VERB
ajst-23562	59	1	i	i	PRON
ajst-23562	59	2	i	i	PRON
ajst-23562	60	1	i	i	VERB
ajst-23562	60	2	rssi	rssi	ADJ
ajst-23562	60	3	rssi	rssi	ADJ
ajst-23562	60	4	rssi	rssi	NOUN
ajst-23562	60	5	x	x	PUNCT
ajst-23562	60	6	y	y	PROPN
ajst-23562	60	7	timestamp	timestamp	VERB
ajst-23562	60	8	rssi	rssi	ADJ
ajst-23562	60	9	rssi	rssi	ADJ
ajst-23562	60	10	rssi	rssi	NOUN
ajst-23562	60	11	x	x	PUNCT
ajst-23562	60	12	y	y	PROPN
ajst-23562	60	13	timestamp	timestamp	VERB
ajst-23562	60	14	f	f	PROPN
ajst-23562	60	15	rssi	rssi	ADJ
ajst-23562	60	16	rssi	rssi	ADJ
ajst-23562	60	17	rssi	rssi	ADJ
ajst-23562	60	18	rssi	rssi	ADJ
ajst-23562	60	19	rssi	rssi	ADJ
ajst-23562	60	20	rssi	rssi	NOUN
ajst-23562	60	21	x	x	PUNCT
ajst-23562	60	22	y	y	PROPN
ajst-23562	60	23	timestamp	timestamp	NOUN
ajst-23562	60	24			PROPN
ajst-23562	60	25			ADJ
ajst-23562	60	26			NOUN
ajst-23562	60	27			NOUN
ajst-23562	60	28			NOUN
ajst-23562	61	1			NOUN
ajst-23562	61	2			NUM
ajst-23562	61	3			NOUN
ajst-23562	61	4			NOUN
ajst-23562	61	5			NOUN
ajst-23562	61	6			NOUN
ajst-23562	61	7			VERB
ajst-23562	61	8			PROPN
ajst-23562	61	9			PROPN
ajst-23562	61	10			NOUN
ajst-23562	61	11			NOUN
ajst-23562	61	12			NOUN
ajst-23562	61	13			NUM
ajst-23562	61	14			NUM
ajst-23562	61	15			PROPN
ajst-23562	61	16			PROPN
ajst-23562	61	17			PROPN
ajst-23562	61	18			ADJ
ajst-23562	61	19			PROPN
ajst-23562	61	20			NOUN
ajst-23562	61	21	(	(	PUNCT
ajst-23562	61	22	4	4	NUM
ajst-23562	61	23	)	)	PUNCT
ajst-23562	61	24	fig	fig	NOUN
ajst-23562	61	25	1	1	NUM
ajst-23562	61	26	.	.	PUNCT
ajst-23562	61	27	rssi	rssi	ADJ
ajst-23562	61	28	fingerprint	fingerprint	NOUN
ajst-23562	61	29	localization	localization	NOUN
ajst-23562	61	30	auto	auto	NOUN
ajst-23562	61	31	encoder	encoder	NOUN
ajst-23562	61	32	(	(	PUNCT
ajst-23562	61	33	ae	ae	PROPN
ajst-23562	61	34	)	)	PUNCT
ajst-23562	61	35	is	be	AUX
ajst-23562	61	36	an	an	DET
ajst-23562	61	37	unsupervised	unsupervised	ADJ
ajst-23562	61	38	learning	learning	NOUN
ajst-23562	61	39	method	method	NOUN
ajst-23562	61	40	,	,	PUNCT
ajst-23562	61	41	which	which	PRON
ajst-23562	61	42	is	be	AUX
ajst-23562	61	43	designed	design	VERB
ajst-23562	61	44	to	to	PART
ajst-23562	61	45	be	be	AUX
ajst-23562	61	46	able	able	ADJ
ajst-23562	61	47	to	to	PART
ajst-23562	61	48	automatically	automatically	ADV
ajst-23562	61	49	learn	learn	VERB
ajst-23562	61	50	from	from	ADP
ajst-23562	61	51	a	a	DET
ajst-23562	61	52	large	large	ADJ
ajst-23562	61	53	amount	amount	NOUN
ajst-23562	61	54	of	of	ADP
ajst-23562	61	55	unlabeled	unlabeled	ADJ
ajst-23562	61	56	data	datum	NOUN
ajst-23562	61	57	to	to	PART
ajst-23562	61	58	capture	capture	VERB
ajst-23562	61	59	valid	valid	ADJ
ajst-23562	61	60	features	feature	NOUN
ajst-23562	61	61	in	in	ADP
ajst-23562	61	62	the	the	DET
ajst-23562	61	63	data	datum	NOUN
ajst-23562	61	64	,	,	PUNCT
ajst-23562	61	65	and	and	CCONJ
ajst-23562	61	66	the	the	DET
ajst-23562	61	67	output	output	NOUN
ajst-23562	61	68	target	target	NOUN
ajst-23562	61	69	is	be	AUX
ajst-23562	61	70	restored	restore	VERB
ajst-23562	61	71	to	to	ADP
ajst-23562	61	72	the	the	DET
ajst-23562	61	73	input	input	NOUN
ajst-23562	61	74	as	as	ADV
ajst-23562	61	75	much	much	ADV
ajst-23562	61	76	as	as	ADP
ajst-23562	61	77	possible	possible	ADJ
ajst-23562	61	78	.	.	PUNCT
ajst-23562	62	1	ae	ae	PROPN
ajst-23562	62	2	is	be	AUX
ajst-23562	62	3	mainly	mainly	ADV
ajst-23562	62	4	composed	compose	VERB
ajst-23562	62	5	of	of	ADP
ajst-23562	62	6	encoder	encoder	NOUN
ajst-23562	62	7	and	and	CCONJ
ajst-23562	62	8	decoder	decoder	NOUN
ajst-23562	62	9	,	,	PUNCT
ajst-23562	62	10	the	the	DET
ajst-23562	62	11	encoding	encoding	NOUN
ajst-23562	62	12	process	process	NOUN
ajst-23562	62	13	is	be	AUX
ajst-23562	62	14	a	a	DET
ajst-23562	62	15	high	high	ADV
ajst-23562	62	16	-	-	PUNCT
ajst-23562	62	17	dimensional	dimensional	ADJ
ajst-23562	62	18	output	output	NOUN
ajst-23562	62	19	data	datum	NOUN
ajst-23562	62	20	through	through	ADP
ajst-23562	62	21	the	the	DET
ajst-23562	62	22	process	process	NOUN
ajst-23562	62	23	of	of	ADP
ajst-23562	62	24	encoding	encode	VERB
ajst-23562	62	25	into	into	ADP
ajst-23562	62	26	a	a	DET
ajst-23562	62	27	higher	high	ADJ
ajst-23562	62	28	level	level	NOUN
ajst-23562	62	29	of	of	ADP
ajst-23562	62	30	lowdimensional	lowdimensional	ADJ
ajst-23562	62	31	representation	representation	NOUN
ajst-23562	62	32	;	;	PUNCT
ajst-23562	62	33	decoder	decoder	NOUN
ajst-23562	62	34	will	will	AUX
ajst-23562	62	35	be	be	AUX
ajst-23562	62	36	encoded	encode	VERB
ajst-23562	62	37	data	datum	NOUN
ajst-23562	62	38	decoding	decode	VERB
ajst-23562	62	39	and	and	CCONJ
ajst-23562	62	40	input	input	NOUN
ajst-23562	62	41	data	datum	NOUN
ajst-23562	62	42	dimensionality	dimensionality	NOUN
ajst-23562	62	43	of	of	ADP
ajst-23562	62	44	the	the	DET
ajst-23562	62	45	same	same	ADJ
ajst-23562	62	46	data	datum	NOUN
ajst-23562	62	47	,	,	PUNCT
ajst-23562	62	48	and	and	CCONJ
ajst-23562	62	49	compared	compare	VERB
ajst-23562	62	50	with	with	ADP
ajst-23562	62	51	the	the	DET
ajst-23562	62	52	input	input	NOUN
ajst-23562	62	53	data	datum	NOUN
ajst-23562	62	54	to	to	PART
ajst-23562	62	55	get	get	VERB
ajst-23562	62	56	the	the	DET
ajst-23562	62	57	reconstruction	reconstruction	NOUN
ajst-23562	62	58	error	error	NOUN
ajst-23562	62	59	,	,	PUNCT
ajst-23562	62	60	will	will	AUX
ajst-23562	62	61	be	be	AUX
ajst-23562	62	62	reconstructed	reconstruct	VERB
ajst-23562	62	63	to	to	ADP
ajst-23562	62	64	the	the	DET
ajst-23562	62	65	back	back	ADJ
ajst-23562	62	66	propagation	propagation	NOUN
ajst-23562	62	67	of	of	ADP
ajst-23562	62	68	the	the	DET
ajst-23562	62	69	reconstruction	reconstruction	NOUN
ajst-23562	62	70	error	error	NOUN
ajst-23562	62	71	to	to	ADP
ajst-23562	62	72	the	the	DET
ajst-23562	62	73	entire	entire	ADJ
ajst-23562	62	74	process	process	NOUN
ajst-23562	62	75	of	of	ADP
ajst-23562	62	76	adjusting	adjust	VERB
ajst-23562	62	77	the	the	DET
ajst-23562	62	78	parameters	parameter	NOUN
ajst-23562	62	79	,	,	PUNCT
ajst-23562	62	80	through	through	ADP
ajst-23562	62	81	the	the	DET
ajst-23562	62	82	continuous	continuous	ADJ
ajst-23562	62	83	iterative	iterative	NOUN
ajst-23562	62	84	tuning	tuning	NOUN
ajst-23562	62	85	parameters	parameter	NOUN
ajst-23562	62	86	.	.	PUNCT
ajst-23562	63	1	through	through	ADP
ajst-23562	63	2	continuous	continuous	ADJ
ajst-23562	63	3	iteration	iteration	NOUN
ajst-23562	63	4	of	of	ADP
ajst-23562	63	5	the	the	DET
ajst-23562	63	6	parameter	parameter	NOUN
ajst-23562	63	7	adjustment	adjustment	NOUN
ajst-23562	63	8	process	process	NOUN
ajst-23562	63	9	to	to	PART
ajst-23562	63	10	finally	finally	ADV
ajst-23562	63	11	get	get	VERB
ajst-23562	63	12	the	the	DET
ajst-23562	63	13	loss	loss	NOUN
ajst-23562	63	14	of	of	ADP
ajst-23562	63	15	information	information	NOUN
ajst-23562	63	16	within	within	ADP
ajst-23562	63	17	the	the	DET
ajst-23562	63	18	control	control	NOUN
ajst-23562	63	19	range	range	NOUN
ajst-23562	63	20	of	of	ADP
ajst-23562	63	21	the	the	DET
ajst-23562	63	22	original	original	ADJ
ajst-23562	63	23	high	high	ADJ
ajst-23562	63	24	-	-	PUNCT
ajst-23562	63	25	dimensional	dimensional	ADJ
ajst-23562	63	26	data	datum	NOUN
ajst-23562	63	27	99	99	NUM
ajst-23562	63	28	of	of	ADP
ajst-23562	63	29	the	the	DET
ajst-23562	63	30	low	low	ADJ
ajst-23562	63	31	-	-	PUNCT
ajst-23562	63	32	dimensional	dimensional	ADJ
ajst-23562	63	33	representation	representation	NOUN
ajst-23562	63	34	.	.	PUNCT
ajst-23562	64	1	the	the	DET
ajst-23562	64	2	training	training	NOUN
ajst-23562	64	3	process	process	NOUN
ajst-23562	64	4	is	be	AUX
ajst-23562	64	5	represented	represent	VERB
ajst-23562	64	6	by	by	ADP
ajst-23562	64	7	the	the	DET
ajst-23562	64	8	formula	formula	NOUN
ajst-23562	64	9	equation	equation	NOUN
ajst-23562	64	10	(	(	PUNCT
ajst-23562	64	11	5	5	NUM
ajst-23562	64	12	)	)	PUNCT
ajst-23562	64	13	.	.	PUNCT
ajst-23562	65	1	to	to	ADP
ajst-23562	65	2	equation	equation	NOUN
ajst-23562	65	3	(	(	PUNCT
ajst-23562	65	4	7	7	NUM
ajst-23562	65	5	)	)	PUNCT
ajst-23562	65	6	.	.	PUNCT
ajst-23562	66	1			NOUN
ajst-23562	66	2			PUNCT
ajst-23562	66	3			PROPN
ajst-23562	67	1	1	1	ADJ
ajst-23562	67	2	1y	1y	NUM
ajst-23562	67	3	x	x	SYM
ajst-23562	67	4	f	f	PROPN
ajst-23562	67	5	w	w	PROPN
ajst-23562	67	6	x	x	PROPN
ajst-23562	67	7	b	b	PROPN
ajst-23562	67	8			X
ajst-23562	67	9	(	(	PUNCT
ajst-23562	67	10	5	5	NUM
ajst-23562	67	11	)	)	PUNCT
ajst-23562	67	12			NOUN
ajst-23562	67	13			SYM
ajst-23562	67	14			NOUN
ajst-23562	68	1	2	2	ADP
ajst-23562	68	2	2x	2x	NUM
ajst-23562	68	3	y	y	PROPN
ajst-23562	68	4	g	g	PROPN
ajst-23562	68	5	w	w	PROPN
ajst-23562	68	6	y	y	PROPN
ajst-23562	68	7	b	b	PROPN
ajst-23562	68	8			PROPN
ajst-23562	68	9			PUNCT
ajst-23562	68	10	(	(	PUNCT
ajst-23562	68	11	6	6	NUM
ajst-23562	68	12	)	)	PUNCT
ajst-23562	68	13			NOUN
ajst-23562	68	14			PUNCT
ajst-23562	68	15			NOUN
ajst-23562	68	16			NOUN
ajst-23562	68	17			PROPN
ajst-23562	68	18			PROPN
ajst-23562	68	19	,	,	PUNCT
ajst-23562	68	20	,	,	PUNCT
ajst-23562	68	21	aej	aej	PROPN
ajst-23562	68	22	l	l	NOUN
ajst-23562	68	23	x	x	PUNCT
ajst-23562	68	24	x	x	PUNCT
ajst-23562	68	25	l	l	NOUN
ajst-23562	68	26	x	x	PUNCT
ajst-23562	68	27	g	g	NOUN
ajst-23562	68	28	f	f	PROPN
ajst-23562	69	1	x	x	PROPN
ajst-23562	69	2			PRON
ajst-23562	69	3	(	(	PUNCT
ajst-23562	69	4	7	7	NUM
ajst-23562	69	5	)	)	PUNCT
ajst-23562	69	6	sae	sae	PROPN
ajst-23562	69	7	is	be	AUX
ajst-23562	69	8	a	a	DET
ajst-23562	69	9	deep	deep	ADJ
ajst-23562	69	10	neural	neural	ADJ
ajst-23562	69	11	network	network	NOUN
ajst-23562	69	12	structure	structure	NOUN
ajst-23562	69	13	containing	contain	VERB
ajst-23562	69	14	multiple	multiple	ADJ
ajst-23562	69	15	hidden	hide	VERB
ajst-23562	69	16	layers	layer	NOUN
ajst-23562	69	17	deeply	deeply	ADV
ajst-23562	69	18	extended	extend	VERB
ajst-23562	69	19	by	by	ADP
ajst-23562	69	20	multiple	multiple	ADJ
ajst-23562	69	21	aes	aes	PROPN
ajst-23562	69	22	,	,	PUNCT
ajst-23562	69	23	which	which	PRON
ajst-23562	69	24	has	have	VERB
ajst-23562	69	25	more	more	ADV
ajst-23562	69	26	powerful	powerful	ADJ
ajst-23562	69	27	feature	feature	NOUN
ajst-23562	69	28	expression	expression	NOUN
ajst-23562	69	29	ability	ability	NOUN
ajst-23562	69	30	,	,	PUNCT
ajst-23562	69	31	and	and	CCONJ
ajst-23562	69	32	makes	make	VERB
ajst-23562	69	33	the	the	DET
ajst-23562	69	34	training	training	NOUN
ajst-23562	69	35	of	of	ADP
ajst-23562	69	36	the	the	DET
ajst-23562	69	37	network	network	NOUN
ajst-23562	69	38	more	more	ADV
ajst-23562	69	39	stable	stable	ADJ
ajst-23562	69	40	by	by	ADP
ajst-23562	69	41	layer	layer	NOUN
ajst-23562	69	42	-	-	PUNCT
ajst-23562	69	43	by	by	ADP
ajst-23562	69	44	-	-	PUNCT
ajst-23562	69	45	layer	layer	NOUN
ajst-23562	69	46	training	training	NOUN
ajst-23562	69	47	,	,	PUNCT
ajst-23562	69	48	avoiding	avoid	VERB
ajst-23562	69	49	problems	problem	NOUN
ajst-23562	69	50	such	such	ADJ
ajst-23562	69	51	as	as	ADP
ajst-23562	69	52	gradient	gradient	ADJ
ajst-23562	69	53	vanishing	vanishing	NOUN
ajst-23562	69	54	and	and	CCONJ
ajst-23562	69	55	gradient	gradient	NOUN
ajst-23562	69	56	explosion	explosion	NOUN
ajst-23562	69	57	.	.	PUNCT
ajst-23562	70	1	the	the	DET
ajst-23562	70	2	original	original	ADJ
ajst-23562	70	3	positional	positional	ADJ
ajst-23562	70	4	fingerprint	fingerprint	NOUN
ajst-23562	70	5	data	datum	NOUN
ajst-23562	70	6	are	be	AUX
ajst-23562	70	7	normalized	normalize	VERB
ajst-23562	70	8	using	use	VERB
ajst-23562	70	9	equation	equation	NOUN
ajst-23562	70	10	(	(	PUNCT
ajst-23562	70	11	8)	8)	NUM
ajst-23562	70	12	prior	prior	ADV
ajst-23562	70	13	to	to	ADP
ajst-23562	70	14	sae	sae	PROPN
ajst-23562	70	15	training	training	NOUN
ajst-23562	70	16	to	to	PART
ajst-23562	70	17	obtain	obtain	VERB
ajst-23562	70	18	normalized	normalized	ADJ
ajst-23562	70	19	data	datum	NOUN
ajst-23562	70	20	with	with	ADP
ajst-23562	70	21	unbiased	unbiased	ADJ
ajst-23562	70	22	low	low	ADJ
ajst-23562	70	23	variance	variance	NOUN
ajst-23562	70	24	of	of	ADP
ajst-23562	70	25	the	the	DET
ajst-23562	70	26	distribution	distribution	NOUN
ajst-23562	70	27	,	,	PUNCT
ajst-23562	70	28	and	and	CCONJ
ajst-23562	70	29	the	the	DET
ajst-23562	70	30	direct	direct	ADJ
ajst-23562	70	31	use	use	NOUN
ajst-23562	70	32	of	of	ADP
ajst-23562	70	33	the	the	DET
ajst-23562	70	34	original	original	ADJ
ajst-23562	70	35	data	datum	NOUN
ajst-23562	70	36	may	may	AUX
ajst-23562	70	37	lead	lead	VERB
ajst-23562	70	38	to	to	ADP
ajst-23562	70	39	difficulty	difficulty	NOUN
ajst-23562	70	40	in	in	ADP
ajst-23562	70	41	convergence	convergence	NOUN
ajst-23562	70	42	of	of	ADP
ajst-23562	70	43	the	the	DET
ajst-23562	70	44	model	model	NOUN
ajst-23562	70	45	during	during	ADP
ajst-23562	70	46	the	the	DET
ajst-23562	70	47	training	training	NOUN
ajst-23562	70	48	process	process	NOUN
ajst-23562	70	49	.	.	PUNCT
ajst-23562	71	1	,	,	PUNCT
ajst-23562	71	2	min	min	PROPN
ajst-23562	71	3	,	,	PUNCT
ajst-23562	71	4	max	max	PROPN
ajst-23562	71	5	min	min	PROPN
ajst-23562	72	1	i	i	PRON
ajst-23562	72	2	j	j	PROPN
ajst-23562	73	1	i	i	PRON
ajst-23562	73	2	j	j	PROPN
ajst-23562	74	1	rssi	rssi	ADJ
ajst-23562	74	2	rssi	rssi	ADJ
ajst-23562	74	3	rssi	rssi	ADJ
ajst-23562	74	4	rssi	rssi	ADJ
ajst-23562	74	5	rssi	rssi	ADJ
ajst-23562	74	6			PROPN
ajst-23562	74	7			PROPN
ajst-23562	74	8			PROPN
ajst-23562	74	9	(	(	PUNCT
ajst-23562	74	10	8)	8)	NUM
ajst-23562	74	11	the	the	DET
ajst-23562	74	12	sae	sae	PROPN
ajst-23562	74	13	feature	feature	NOUN
ajst-23562	74	14	learning	learn	VERB
ajst-23562	74	15	model	model	NOUN
ajst-23562	74	16	in	in	ADP
ajst-23562	74	17	this	this	DET
ajst-23562	74	18	paper	paper	NOUN
ajst-23562	74	19	consists	consist	VERB
ajst-23562	74	20	of	of	ADP
ajst-23562	74	21	three	three	NUM
ajst-23562	74	22	aes	aes	NOUN
ajst-23562	74	23	stacked	stack	VERB
ajst-23562	74	24	together	together	ADV
ajst-23562	74	25	,	,	PUNCT
ajst-23562	74	26	and	and	CCONJ
ajst-23562	74	27	the	the	DET
ajst-23562	74	28	encoder	encoder	NOUN
ajst-23562	74	29	and	and	CCONJ
ajst-23562	74	30	decoder	decoder	NOUN
ajst-23562	74	31	are	be	AUX
ajst-23562	74	32	symmetric	symmetric	ADJ
ajst-23562	74	33	structures	structure	NOUN
ajst-23562	74	34	.	.	PUNCT
ajst-23562	75	1	the	the	DET
ajst-23562	75	2	number	number	NOUN
ajst-23562	75	3	of	of	ADP
ajst-23562	75	4	neurons	neuron	NOUN
ajst-23562	75	5	in	in	ADP
ajst-23562	75	6	the	the	DET
ajst-23562	75	7	output	output	NOUN
ajst-23562	75	8	layer	layer	NOUN
ajst-23562	75	9	of	of	ADP
ajst-23562	75	10	the	the	DET
ajst-23562	75	11	encoder	encoder	NOUN
ajst-23562	75	12	with	with	ADP
ajst-23562	75	13	the	the	DET
ajst-23562	75	14	best	good	ADJ
ajst-23562	75	15	localization	localization	NOUN
ajst-23562	75	16	effect	effect	NOUN
ajst-23562	75	17	is	be	AUX
ajst-23562	75	18	256	256	NUM
ajst-23562	75	19	,	,	PUNCT
ajst-23562	75	20	128	128	NUM
ajst-23562	75	21	and	and	CCONJ
ajst-23562	75	22	64	64	NUM
ajst-23562	75	23	respectively	respectively	ADV
ajst-23562	75	24	,	,	PUNCT
ajst-23562	75	25	the	the	DET
ajst-23562	75	26	training	training	NOUN
ajst-23562	75	27	period	period	NOUN
ajst-23562	75	28	is	be	AUX
ajst-23562	75	29	150	150	NUM
ajst-23562	75	30	,	,	PUNCT
ajst-23562	75	31	the	the	DET
ajst-23562	75	32	batch	batch	NOUN
ajst-23562	75	33	size	size	NOUN
ajst-23562	75	34	is	be	AUX
ajst-23562	75	35	32	32	NUM
ajst-23562	75	36	,	,	PUNCT
ajst-23562	75	37	the	the	DET
ajst-23562	75	38	activation	activation	NOUN
ajst-23562	75	39	function	function	NOUN
ajst-23562	75	40	adopts	adopt	VERB
ajst-23562	75	41	sigmoid	sigmoid	NOUN
ajst-23562	75	42	,	,	PUNCT
ajst-23562	75	43	the	the	DET
ajst-23562	75	44	loss	loss	NOUN
ajst-23562	75	45	function	function	NOUN
ajst-23562	75	46	is	be	AUX
ajst-23562	75	47	mse	mse	NOUN
ajst-23562	75	48	,	,	PUNCT
ajst-23562	75	49	and	and	CCONJ
ajst-23562	75	50	the	the	DET
ajst-23562	75	51	optimizer	optimizer	NOUN
ajst-23562	75	52	chooses	choose	VERB
ajst-23562	75	53	adam	adam	PROPN
ajst-23562	75	54	.	.	PUNCT
ajst-23562	76	1	the	the	DET
ajst-23562	76	2	sae	sae	PROPN
ajst-23562	76	3	model	model	NOUN
ajst-23562	76	4	in	in	ADP
ajst-23562	76	5	this	this	DET
ajst-23562	76	6	paper	paper	NOUN
ajst-23562	76	7	adopts	adopt	VERB
ajst-23562	76	8	the	the	DET
ajst-23562	76	9	layer	layer	NOUN
ajst-23562	76	10	-	-	PUNCT
ajst-23562	76	11	by	by	ADP
ajst-23562	76	12	-	-	PUNCT
ajst-23562	76	13	layer	layer	NOUN
ajst-23562	76	14	greedy	greedy	ADJ
ajst-23562	76	15	training	training	NOUN
ajst-23562	76	16	strategy	strategy	NOUN
ajst-23562	76	17	,	,	PUNCT
ajst-23562	76	18	and	and	CCONJ
ajst-23562	76	19	after	after	ADP
ajst-23562	76	20	the	the	DET
ajst-23562	76	21	training	training	NOUN
ajst-23562	76	22	,	,	PUNCT
ajst-23562	76	23	the	the	DET
ajst-23562	76	24	sae	sae	PROPN
ajst-23562	76	25	model	model	NOUN
ajst-23562	76	26	can	can	AUX
ajst-23562	76	27	finally	finally	ADV
ajst-23562	76	28	extract	extract	VERB
ajst-23562	76	29	64	64	NUM
ajst-23562	76	30	robustness	robustness	NOUN
ajst-23562	76	31	features	feature	NOUN
ajst-23562	76	32	,	,	PUNCT
ajst-23562	76	33	which	which	PRON
ajst-23562	76	34	will	will	AUX
ajst-23562	76	35	be	be	AUX
ajst-23562	76	36	transmitted	transmit	VERB
ajst-23562	76	37	to	to	ADP
ajst-23562	76	38	the	the	DET
ajst-23562	76	39	location	location	NOUN
ajst-23562	76	40	prediction	prediction	NOUN
ajst-23562	76	41	model	model	NOUN
ajst-23562	76	42	for	for	ADP
ajst-23562	76	43	location	location	NOUN
ajst-23562	76	44	prediction	prediction	NOUN
ajst-23562	76	45	.	.	PUNCT
ajst-23562	77	1	fig	fig	NOUN
ajst-23562	77	2	2	2	NUM
ajst-23562	77	3	.	.	PUNCT
ajst-23562	77	4	sae	sae	PROPN
ajst-23562	77	5	feature	feature	PROPN
ajst-23562	77	6	extraction	extraction	NOUN
ajst-23562	77	7	model	model	NOUN
ajst-23562	77	8	recurrent	recurrent	NOUN
ajst-23562	77	9	neural	neural	ADJ
ajst-23562	77	10	network	network	NOUN
ajst-23562	77	11	(	(	PUNCT
ajst-23562	77	12	rnn	rnn	PROPN
ajst-23562	77	13	)	)	PUNCT
ajst-23562	77	14	is	be	AUX
ajst-23562	77	15	a	a	DET
ajst-23562	77	16	neural	neural	ADJ
ajst-23562	77	17	network	network	NOUN
ajst-23562	77	18	structure	structure	NOUN
ajst-23562	77	19	specialized	specialize	VERB
ajst-23562	77	20	in	in	ADP
ajst-23562	77	21	processing	process	VERB
ajst-23562	77	22	sequential	sequential	ADJ
ajst-23562	77	23	data	datum	NOUN
ajst-23562	77	24	[	[	X
ajst-23562	77	25	8	8	NUM
ajst-23562	77	26	]	]	PUNCT
ajst-23562	77	27	.	.	PUNCT
ajst-23562	78	1	the	the	DET
ajst-23562	78	2	chain	chain	NOUN
ajst-23562	78	3	-	-	PUNCT
ajst-23562	78	4	connected	connect	VERB
ajst-23562	78	5	memory	memory	NOUN
ajst-23562	78	6	units	unit	NOUN
ajst-23562	78	7	of	of	ADP
ajst-23562	78	8	an	an	DET
ajst-23562	78	9	rnn	rnn	NOUN
ajst-23562	78	10	enable	enable	VERB
ajst-23562	78	11	it	it	PRON
ajst-23562	78	12	to	to	PART
ajst-23562	78	13	recursively	recursively	ADV
ajst-23562	78	14	pass	pass	VERB
ajst-23562	78	15	historical	historical	ADJ
ajst-23562	78	16	information	information	NOUN
ajst-23562	78	17	in	in	ADP
ajst-23562	78	18	the	the	DET
ajst-23562	78	19	evolutionary	evolutionary	ADJ
ajst-23562	78	20	direction	direction	NOUN
ajst-23562	78	21	of	of	ADP
ajst-23562	78	22	the	the	DET
ajst-23562	78	23	sequence	sequence	NOUN
ajst-23562	78	24	in	in	ADP
ajst-23562	78	25	an	an	DET
ajst-23562	78	26	internal	internal	ADJ
ajst-23562	78	27	loop	loop	NOUN
ajst-23562	78	28	.	.	PUNCT
ajst-23562	79	1	although	although	SCONJ
ajst-23562	79	2	rnns	rnn	NOUN
ajst-23562	79	3	have	have	VERB
ajst-23562	79	4	certain	certain	ADJ
ajst-23562	79	5	historical	historical	ADJ
ajst-23562	79	6	information	information	NOUN
ajst-23562	79	7	storage	storage	NOUN
ajst-23562	79	8	and	and	CCONJ
ajst-23562	79	9	prediction	prediction	NOUN
ajst-23562	79	10	capabilities	capability	NOUN
ajst-23562	79	11	,	,	PUNCT
ajst-23562	79	12	they	they	PRON
ajst-23562	79	13	often	often	ADV
ajst-23562	79	14	cause	cause	VERB
ajst-23562	79	15	gradient	gradient	ADJ
ajst-23562	79	16	vanishing	vanishing	NOUN
ajst-23562	79	17	and	and	CCONJ
ajst-23562	79	18	gradient	gradient	ADJ
ajst-23562	79	19	explosion	explosion	NOUN
ajst-23562	79	20	problems	problem	NOUN
ajst-23562	79	21	for	for	ADP
ajst-23562	79	22	processing	process	VERB
ajst-23562	79	23	longer	long	ADJ
ajst-23562	79	24	sequences	sequence	NOUN
ajst-23562	79	25	,	,	PUNCT
ajst-23562	79	26	which	which	PRON
ajst-23562	79	27	greatly	greatly	ADV
ajst-23562	79	28	limits	limit	VERB
ajst-23562	79	29	their	their	PRON
ajst-23562	79	30	ability	ability	NOUN
ajst-23562	79	31	to	to	PART
ajst-23562	79	32	learn	learn	VERB
ajst-23562	79	33	long	long	ADJ
ajst-23562	79	34	-	-	PUNCT
ajst-23562	79	35	term	term	NOUN
ajst-23562	79	36	temporal	temporal	ADJ
ajst-23562	79	37	correlations	correlation	NOUN
ajst-23562	79	38	.	.	PUNCT
ajst-23562	80	1	long	long	ADJ
ajst-23562	80	2	short	short	ADJ
ajst-23562	80	3	-	-	PUNCT
ajst-23562	80	4	term	term	NOUN
ajst-23562	80	5	memory	memory	NOUN
ajst-23562	80	6	(	(	PUNCT
ajst-23562	80	7	lstm	lstm	NOUN
ajst-23562	80	8	)	)	PUNCT
ajst-23562	80	9	,	,	PUNCT
ajst-23562	80	10	as	as	ADP
ajst-23562	80	11	an	an	DET
ajst-23562	80	12	improvement	improvement	NOUN
ajst-23562	80	13	of	of	ADP
ajst-23562	80	14	rnn	rnn	NOUN
ajst-23562	80	15	network	network	NOUN
ajst-23562	80	16	,	,	PUNCT
ajst-23562	80	17	effectively	effectively	ADV
ajst-23562	80	18	overcomes	overcome	VERB
ajst-23562	80	19	the	the	DET
ajst-23562	80	20	problem	problem	NOUN
ajst-23562	80	21	of	of	ADP
ajst-23562	80	22	dealing	deal	VERB
ajst-23562	80	23	with	with	ADP
ajst-23562	80	24	long	long	ADJ
ajst-23562	80	25	sequence	sequence	NOUN
ajst-23562	80	26	data	datum	NOUN
ajst-23562	80	27	[	[	X
ajst-23562	80	28	9	9	NUM
ajst-23562	80	29	]	]	PUNCT
ajst-23562	80	30	.	.	PUNCT
ajst-23562	81	1	although	although	SCONJ
ajst-23562	81	2	lstm	lstm	NOUN
ajst-23562	81	3	can	can	AUX
ajst-23562	81	4	solve	solve	VERB
ajst-23562	81	5	the	the	DET
ajst-23562	81	6	problem	problem	NOUN
ajst-23562	81	7	of	of	ADP
ajst-23562	81	8	long	long	ADJ
ajst-23562	81	9	sequence	sequence	NOUN
ajst-23562	81	10	prediction	prediction	NOUN
ajst-23562	81	11	,	,	PUNCT
ajst-23562	81	12	it	it	PRON
ajst-23562	81	13	can	can	AUX
ajst-23562	81	14	only	only	ADV
ajst-23562	81	15	learn	learn	VERB
ajst-23562	81	16	the	the	DET
ajst-23562	81	17	forward	forward	ADJ
ajst-23562	81	18	information	information	NOUN
ajst-23562	81	19	of	of	ADP
ajst-23562	81	20	the	the	DET
ajst-23562	81	21	sequence	sequence	NOUN
ajst-23562	81	22	and	and	CCONJ
ajst-23562	81	23	ignore	ignore	VERB
ajst-23562	81	24	the	the	DET
ajst-23562	81	25	backward	backward	ADJ
ajst-23562	81	26	information	information	NOUN
ajst-23562	81	27	of	of	ADP
ajst-23562	81	28	the	the	DET
ajst-23562	81	29	sequence	sequence	NOUN
ajst-23562	81	30	.	.	PUNCT
ajst-23562	82	1	bidirectional	bidirectional	ADJ
ajst-23562	82	2	long	long	ADJ
ajst-23562	82	3	short	short	ADJ
ajst-23562	82	4	-	-	PUNCT
ajst-23562	82	5	term	term	NOUN
ajst-23562	82	6	memory	memory	NOUN
ajst-23562	82	7	(	(	PUNCT
ajst-23562	82	8	bilstm	bilstm	NOUN
ajst-23562	82	9	)	)	PUNCT
ajst-23562	82	10	is	be	AUX
ajst-23562	82	11	able	able	ADJ
ajst-23562	82	12	to	to	PART
ajst-23562	82	13	learn	learn	VERB
ajst-23562	82	14	both	both	PRON
ajst-23562	82	15	forward	forward	ADJ
ajst-23562	82	16	and	and	CCONJ
ajst-23562	82	17	backward	backward	ADJ
ajst-23562	82	18	information	information	NOUN
ajst-23562	82	19	of	of	ADP
ajst-23562	82	20	sequences	sequence	NOUN
ajst-23562	82	21	,	,	PUNCT
ajst-23562	82	22	and	and	CCONJ
ajst-23562	82	23	the	the	DET
ajst-23562	82	24	hidden	hidden	ADJ
ajst-23562	82	25	layers	layer	NOUN
ajst-23562	82	26	used	use	VERB
ajst-23562	82	27	for	for	ADP
ajst-23562	82	28	forward	forward	ADV
ajst-23562	82	29	and	and	CCONJ
ajst-23562	82	30	backward	backward	ADJ
ajst-23562	82	31	are	be	AUX
ajst-23562	82	32	independent	independent	ADJ
ajst-23562	82	33	of	of	ADP
ajst-23562	82	34	each	each	DET
ajst-23562	82	35	other	other	ADJ
ajst-23562	82	36	,	,	PUNCT
ajst-23562	82	37	which	which	PRON
ajst-23562	82	38	makes	make	VERB
ajst-23562	82	39	bilstm	bilstm	NOUN
ajst-23562	82	40	better	well	ADV
ajst-23562	82	41	able	able	ADJ
ajst-23562	82	42	to	to	PART
ajst-23562	82	43	mine	mine	VERB
ajst-23562	82	44	the	the	DET
ajst-23562	82	45	temporal	temporal	ADJ
ajst-23562	82	46	characteristics	characteristic	NOUN
ajst-23562	82	47	of	of	ADP
ajst-23562	82	48	data	datum	NOUN
ajst-23562	82	49	.	.	PUNCT
ajst-23562	83	1	3.2	3.2	NUM
ajst-23562	83	2	.	.	PUNCT
ajst-23562	83	3	bilstm	bilstm	NOUN
ajst-23562	83	4	location	location	NOUN
ajst-23562	83	5	prediction	prediction	NOUN
ajst-23562	83	6	model	model	NOUN
ajst-23562	83	7	fig	fig	NOUN
ajst-23562	83	8	3	3	X
ajst-23562	83	9	.	.	PUNCT
ajst-23562	84	1	lstm	lstm	PROPN
ajst-23562	84	2	model	model	NOUN
ajst-23562	84	3	bilstm	bilstm	NOUN
ajst-23562	84	4	consists	consist	VERB
ajst-23562	84	5	of	of	ADP
ajst-23562	84	6	two	two	NUM
ajst-23562	84	7	lstms	lstms	ADJ
ajst-23562	84	8	dealing	deal	VERB
ajst-23562	84	9	with	with	ADP
ajst-23562	84	10	forward	forward	ADJ
ajst-23562	84	11	and	and	CCONJ
ajst-23562	84	12	backward	backward	ADJ
ajst-23562	84	13	sequences	sequence	NOUN
ajst-23562	84	14	.	.	PUNCT
ajst-23562	85	1	in	in	ADP
ajst-23562	85	2	forward	forward	ADV
ajst-23562	85	3	lstm	lstm	PROPN
ajst-23562	85	4	,	,	PUNCT
ajst-23562	85	5	the	the	DET
ajst-23562	85	6	input	input	NOUN
ajst-23562	85	7	data	data	NOUN
ajst-23562	85	8	enters	enter	VERB
ajst-23562	85	9	in	in	ADP
ajst-23562	85	10	chronological	chronological	ADJ
ajst-23562	85	11	order	order	NOUN
ajst-23562	85	12	and	and	CCONJ
ajst-23562	85	13	the	the	DET
ajst-23562	85	14	output	output	NOUN
ajst-23562	85	15	of	of	ADP
ajst-23562	85	16	each	each	DET
ajst-23562	85	17	time	time	NOUN
ajst-23562	85	18	step	step	NOUN
ajst-23562	85	19	is	be	AUX
ajst-23562	85	20	used	use	VERB
ajst-23562	85	21	as	as	ADP
ajst-23562	85	22	input	input	NOUN
ajst-23562	85	23	for	for	ADP
ajst-23562	85	24	the	the	DET
ajst-23562	85	25	next	next	ADJ
ajst-23562	85	26	time	time	NOUN
ajst-23562	85	27	step	step	NOUN
ajst-23562	85	28	.	.	PUNCT
ajst-23562	86	1	in	in	ADP
ajst-23562	86	2	backward	backward	ADJ
ajst-23562	86	3	lstm	lstm	PROPN
ajst-23562	86	4	,	,	PUNCT
ajst-23562	86	5	the	the	DET
ajst-23562	86	6	input	input	NOUN
ajst-23562	86	7	data	data	NOUN
ajst-23562	86	8	enters	enter	VERB
ajst-23562	86	9	in	in	ADP
ajst-23562	86	10	the	the	DET
ajst-23562	86	11	reverse	reverse	ADJ
ajst-23562	86	12	order	order	NOUN
ajst-23562	86	13	of	of	ADP
ajst-23562	86	14	the	the	DET
ajst-23562	86	15	time	time	NOUN
ajst-23562	86	16	sequence	sequence	NOUN
ajst-23562	86	17	and	and	CCONJ
ajst-23562	86	18	the	the	DET
ajst-23562	86	19	output	output	NOUN
ajst-23562	86	20	of	of	ADP
ajst-23562	86	21	each	each	DET
ajst-23562	86	22	time	time	NOUN
ajst-23562	86	23	step	step	NOUN
ajst-23562	86	24	is	be	AUX
ajst-23562	86	25	similarly	similarly	ADV
ajst-23562	86	26	used	use	VERB
ajst-23562	86	27	as	as	ADP
ajst-23562	86	28	the	the	DET
ajst-23562	86	29	input	input	NOUN
ajst-23562	86	30	of	of	ADP
ajst-23562	86	31	the	the	DET
ajst-23562	86	32	next	next	ADJ
ajst-23562	86	33	time	time	NOUN
ajst-23562	86	34	step	step	NOUN
ajst-23562	86	35	.	.	PUNCT
ajst-23562	87	1	there	there	PRON
ajst-23562	87	2	is	be	VERB
ajst-23562	87	3	no	no	DET
ajst-23562	87	4	interaction	interaction	NOUN
ajst-23562	87	5	between	between	ADP
ajst-23562	87	6	the	the	DET
ajst-23562	87	7	forwardpropagating	forwardpropagate	VERB
ajst-23562	87	8	hidden	hide	VERB
ajst-23562	87	9	layer	layer	NOUN
ajst-23562	87	10	and	and	CCONJ
ajst-23562	87	11	the	the	DET
ajst-23562	87	12	backward	backward	ADV
ajst-23562	87	13	-	-	PUNCT
ajst-23562	87	14	propagating	propagate	VERB
ajst-23562	87	15	hidden	hide	VERB
ajst-23562	87	16	layer	layer	NOUN
ajst-23562	87	17	,	,	PUNCT
ajst-23562	87	18	forming	form	VERB
ajst-23562	87	19	two	two	NUM
ajst-23562	87	20	networks	network	NOUN
ajst-23562	87	21	that	that	PRON
ajst-23562	87	22	are	be	AUX
ajst-23562	87	23	independent	independent	ADJ
ajst-23562	87	24	of	of	ADP
ajst-23562	87	25	each	each	DET
ajst-23562	87	26	other	other	ADJ
ajst-23562	87	27	and	and	CCONJ
ajst-23562	87	28	have	have	VERB
ajst-23562	87	29	opposite	opposite	ADJ
ajst-23562	87	30	data	datum	NOUN
ajst-23562	87	31	flow	flow	NOUN
ajst-23562	87	32	directions	direction	NOUN
ajst-23562	87	33	,	,	PUNCT
ajst-23562	87	34	enabling	enable	VERB
ajst-23562	87	35	the	the	DET
ajst-23562	87	36	model	model	NOUN
ajst-23562	87	37	to	to	PART
ajst-23562	87	38	integrate	integrate	VERB
ajst-23562	87	39	forward	forward	ADV
ajst-23562	87	40	and	and	CCONJ
ajst-23562	87	41	backward	backward	ADJ
ajst-23562	87	42	information	information	NOUN
ajst-23562	87	43	and	and	CCONJ
ajst-23562	87	44	improve	improve	VERB
ajst-23562	87	45	its	its	PRON
ajst-23562	87	46	ability	ability	NOUN
ajst-23562	87	47	to	to	PART
ajst-23562	87	48	learn	learn	VERB
ajst-23562	87	49	the	the	DET
ajst-23562	87	50	hidden	hidden	ADJ
ajst-23562	87	51	information	information	NOUN
ajst-23562	87	52	of	of	ADP
ajst-23562	87	53	sequence	sequence	NOUN
ajst-23562	87	54	data	datum	NOUN
ajst-23562	87	55	.	.	PUNCT
ajst-23562	88	1	fig	fig	NOUN
ajst-23562	88	2	4	4	NUM
ajst-23562	88	3	.	.	PUNCT
ajst-23562	88	4	bilstm	bilstm	NOUN
ajst-23562	88	5	position	position	NOUN
ajst-23562	88	6	prediction	prediction	NOUN
ajst-23562	88	7	model	model	NOUN
ajst-23562	88	8	the	the	DET
ajst-23562	88	9	location	location	NOUN
ajst-23562	88	10	prediction	prediction	NOUN
ajst-23562	88	11	model	model	NOUN
ajst-23562	88	12	model	model	NOUN
ajst-23562	88	13	contains	contain	VERB
ajst-23562	88	14	two	two	NUM
ajst-23562	88	15	bidirectional	bidirectional	ADJ
ajst-23562	88	16	lstm	lstm	ADJ
ajst-23562	88	17	layers	layer	NOUN
ajst-23562	88	18	,	,	PUNCT
ajst-23562	88	19	each	each	DET
ajst-23562	88	20	bi	bi	ADJ
ajst-23562	88	21	-	-	ADJ
ajst-23562	88	22	directional	directional	ADJ
ajst-23562	88	23	lstm	lstm	NOUN
ajst-23562	88	24	layer	layer	NOUN
ajst-23562	88	25	contains	contain	VERB
ajst-23562	88	26	100	100	NUM
ajst-23562	88	27	neurons	neuron	NOUN
ajst-23562	88	28	,	,	PUNCT
ajst-23562	88	29	relu	relu	NOUN
ajst-23562	88	30	is	be	AUX
ajst-23562	88	31	used	use	VERB
ajst-23562	88	32	as	as	ADP
ajst-23562	88	33	the	the	DET
ajst-23562	88	34	activation	activation	NOUN
ajst-23562	88	35	function	function	NOUN
ajst-23562	88	36	,	,	PUNCT
ajst-23562	88	37	mse	mse	PROPN
ajst-23562	88	38	is	be	AUX
ajst-23562	88	39	used	use	VERB
ajst-23562	88	40	as	as	ADP
ajst-23562	88	41	the	the	DET
ajst-23562	88	42	loss	loss	NOUN
ajst-23562	88	43	function	function	NOUN
ajst-23562	88	44	,	,	PUNCT
ajst-23562	88	45	and	and	CCONJ
ajst-23562	88	46	the	the	DET
ajst-23562	88	47	output	output	NOUN
ajst-23562	88	48	is	be	AUX
ajst-23562	88	49	the	the	DET
ajst-23562	88	50	location	location	NOUN
ajst-23562	88	51	coordinates	coordinate	NOUN
ajst-23562	88	52	.	.	PUNCT
ajst-23562	89	1	in	in	ADP
ajst-23562	89	2	order	order	NOUN
ajst-23562	89	3	to	to	PART
ajst-23562	89	4	improve	improve	VERB
ajst-23562	89	5	the	the	DET
ajst-23562	89	6	model	model	NOUN
ajst-23562	89	7	training	training	NOUN
ajst-23562	89	8	speed	speed	NOUN
ajst-23562	89	9	while	while	SCONJ
ajst-23562	89	10	ensuring	ensure	VERB
ajst-23562	89	11	the	the	DET
ajst-23562	89	12	model	model	NOUN
ajst-23562	89	13	performance	performance	NOUN
ajst-23562	89	14	,	,	PUNCT
ajst-23562	89	15	the	the	DET
ajst-23562	89	16	adam	adam	PROPN
ajst-23562	89	17	optimization	optimization	NOUN
ajst-23562	89	18	algorithm	algorithm	NOUN
ajst-23562	89	19	is	be	AUX
ajst-23562	89	20	used	use	VERB
ajst-23562	89	21	in	in	ADP
ajst-23562	89	22	the	the	DET
ajst-23562	89	23	network	network	NOUN
ajst-23562	89	24	training	training	NOUN
ajst-23562	89	25	process	process	NOUN
ajst-23562	89	26	.	.	PUNCT
ajst-23562	90	1	this	this	DET
ajst-23562	90	2	algorithm	algorithm	NOUN
ajst-23562	90	3	is	be	AUX
ajst-23562	90	4	an	an	DET
ajst-23562	90	5	improved	improved	ADJ
ajst-23562	90	6	version	version	NOUN
ajst-23562	90	7	of	of	ADP
ajst-23562	90	8	stochastic	stochastic	ADJ
ajst-23562	90	9	gradient	gradient	ADJ
ajst-23562	90	10	descent	descent	NOUN
ajst-23562	90	11	(	(	PUNCT
ajst-23562	90	12	sgd	sgd	PROPN
ajst-23562	90	13	)	)	PUNCT
ajst-23562	90	14	,	,	PUNCT
ajst-23562	90	15	which	which	PRON
ajst-23562	90	16	uses	use	VERB
ajst-23562	90	17	only	only	ADV
ajst-23562	90	18	one	one	NUM
ajst-23562	90	19	value	value	NOUN
ajst-23562	90	20	of	of	ADP
ajst-23562	90	21	the	the	DET
ajst-23562	90	22	learning	learning	NOUN
ajst-23562	90	23	rate	rate	NOUN
ajst-23562	90	24	for	for	ADP
ajst-23562	90	25	updating	update	VERB
ajst-23562	90	26	the	the	DET
ajst-23562	90	27	network	network	NOUN
ajst-23562	90	28	parameters	parameter	NOUN
ajst-23562	90	29	during	during	ADP
ajst-23562	90	30	the	the	DET
ajst-23562	90	31	training	training	NOUN
ajst-23562	90	32	of	of	ADP
ajst-23562	90	33	the	the	DET
ajst-23562	90	34	model	model	NOUN
ajst-23562	90	35	and	and	CCONJ
ajst-23562	90	36	keeps	keep	VERB
ajst-23562	90	37	the	the	DET
ajst-23562	90	38	value	value	NOUN
ajst-23562	90	39	of	of	ADP
ajst-23562	90	40	the	the	DET
ajst-23562	90	41	learning	learning	NOUN
ajst-23562	90	42	rate	rate	NOUN
ajst-23562	90	43	unchanged	unchanged	ADJ
ajst-23562	90	44	until	until	ADP
ajst-23562	90	45	the	the	DET
ajst-23562	90	46	completion	completion	NOUN
ajst-23562	90	47	of	of	ADP
ajst-23562	90	48	the	the	DET
ajst-23562	90	49	training	training	NOUN
ajst-23562	90	50	,	,	PUNCT
ajst-23562	90	51	which	which	PRON
ajst-23562	90	52	tends	tend	VERB
ajst-23562	90	53	to	to	PART
ajst-23562	90	54	cause	cause	VERB
ajst-23562	90	55	the	the	DET
ajst-23562	90	56	network	network	NOUN
ajst-23562	90	57	to	to	PART
ajst-23562	90	58	fall	fall	VERB
ajst-23562	90	59	into	into	ADP
ajst-23562	90	60	the	the	DET
ajst-23562	90	61	local	local	ADJ
ajst-23562	90	62	optimum	optimum	NOUN
ajst-23562	90	63	during	during	ADP
ajst-23562	90	64	the	the	DET
ajst-23562	90	65	training	training	NOUN
ajst-23562	90	66	process	process	NOUN
ajst-23562	90	67	,	,	PUNCT
ajst-23562	90	68	and	and	CCONJ
ajst-23562	90	69	at	at	ADP
ajst-23562	90	70	the	the	DET
ajst-23562	90	71	same	same	ADJ
ajst-23562	90	72	time	time	NOUN
ajst-23562	90	73	slows	slow	VERB
ajst-23562	90	74	down	down	ADP
ajst-23562	90	75	the	the	DET
ajst-23562	90	76	training	training	NOUN
ajst-23562	90	77	speed	speed	NOUN
ajst-23562	90	78	of	of	ADP
ajst-23562	90	79	the	the	DET
ajst-23562	90	80	network[10	network[10	NOUN
ajst-23562	90	81	]	]	PUNCT
ajst-23562	90	82	.	.	PUNCT
ajst-23562	91	1	this	this	PRON
ajst-23562	91	2	tends	tend	VERB
ajst-23562	91	3	to	to	PART
ajst-23562	91	4	cause	cause	VERB
ajst-23562	91	5	the	the	DET
ajst-23562	91	6	network	network	NOUN
ajst-23562	91	7	to	to	PART
ajst-23562	91	8	fall	fall	VERB
ajst-23562	91	9	into	into	ADP
ajst-23562	91	10	100	100	NUM
ajst-23562	91	11	local	local	ADJ
ajst-23562	91	12	optima	optima	NOUN
ajst-23562	91	13	during	during	ADP
ajst-23562	91	14	the	the	DET
ajst-23562	91	15	training	training	NOUN
ajst-23562	91	16	process	process	NOUN
ajst-23562	91	17	,	,	PUNCT
ajst-23562	91	18	and	and	CCONJ
ajst-23562	91	19	also	also	ADV
ajst-23562	91	20	slows	slow	VERB
ajst-23562	91	21	down	down	ADP
ajst-23562	91	22	the	the	DET
ajst-23562	91	23	network	network	NOUN
ajst-23562	91	24	training	training	NOUN
ajst-23562	91	25	speed	speed	NOUN
ajst-23562	91	26	.	.	PUNCT
ajst-23562	92	1	however	however	ADV
ajst-23562	92	2	,	,	PUNCT
ajst-23562	92	3	the	the	DET
ajst-23562	92	4	adam	adam	PROPN
ajst-23562	92	5	optimization	optimization	NOUN
ajst-23562	92	6	algorithm	algorithm	NOUN
ajst-23562	92	7	can	can	AUX
ajst-23562	92	8	dynamically	dynamically	ADV
ajst-23562	92	9	adjust	adjust	VERB
ajst-23562	92	10	the	the	DET
ajst-23562	92	11	learning	learning	NOUN
ajst-23562	92	12	rate	rate	NOUN
ajst-23562	92	13	of	of	ADP
ajst-23562	92	14	different	different	ADJ
ajst-23562	92	15	parameters	parameter	NOUN
ajst-23562	92	16	according	accord	VERB
ajst-23562	92	17	to	to	ADP
ajst-23562	92	18	the	the	DET
ajst-23562	92	19	first	first	ADJ
ajst-23562	92	20	-	-	PUNCT
ajst-23562	92	21	order	order	NOUN
ajst-23562	92	22	moment	moment	NOUN
ajst-23562	92	23	estimation	estimation	NOUN
ajst-23562	92	24	and	and	CCONJ
ajst-23562	92	25	second	second	ADJ
ajst-23562	92	26	-	-	PUNCT
ajst-23562	92	27	order	order	NOUN
ajst-23562	92	28	moment	moment	NOUN
ajst-23562	92	29	estimation	estimation	NOUN
ajst-23562	92	30	of	of	ADP
ajst-23562	92	31	the	the	DET
ajst-23562	92	32	gradient	gradient	NOUN
ajst-23562	92	33	,	,	PUNCT
ajst-23562	92	34	which	which	PRON
ajst-23562	92	35	makes	make	VERB
ajst-23562	92	36	the	the	DET
ajst-23562	92	37	parameter	parameter	NOUN
ajst-23562	92	38	updating	update	VERB
ajst-23562	92	39	smoother	smoother	ADV
ajst-23562	92	40	and	and	CCONJ
ajst-23562	92	41	improves	improve	VERB
ajst-23562	92	42	the	the	DET
ajst-23562	92	43	generalization	generalization	NOUN
ajst-23562	92	44	ability	ability	NOUN
ajst-23562	92	45	and	and	CCONJ
ajst-23562	92	46	robustness	robustness	NOUN
ajst-23562	92	47	of	of	ADP
ajst-23562	92	48	the	the	DET
ajst-23562	92	49	network	network	NOUN
ajst-23562	92	50	.	.	PUNCT
ajst-23562	93	1	3.3	3.3	NUM
ajst-23562	93	2	.	.	PUNCT
ajst-23562	93	3	experimentation	experimentation	NOUN
ajst-23562	93	4	and	and	CCONJ
ajst-23562	93	5	analysis	analysis	NOUN
ajst-23562	93	6	in	in	ADP
ajst-23562	93	7	order	order	NOUN
ajst-23562	93	8	to	to	PART
ajst-23562	93	9	investigate	investigate	VERB
ajst-23562	93	10	the	the	DET
ajst-23562	93	11	effect	effect	NOUN
ajst-23562	93	12	of	of	ADP
ajst-23562	93	13	different	different	ADJ
ajst-23562	93	14	sae	sae	PROPN
ajst-23562	93	15	structures	structure	NOUN
ajst-23562	93	16	as	as	ADV
ajst-23562	93	17	well	well	ADV
ajst-23562	93	18	as	as	ADP
ajst-23562	93	19	time	time	NOUN
ajst-23562	93	20	series	series	NOUN
ajst-23562	93	21	lengths	length	NOUN
ajst-23562	93	22	on	on	ADP
ajst-23562	93	23	localization	localization	NOUN
ajst-23562	93	24	performance	performance	NOUN
ajst-23562	93	25	,	,	PUNCT
ajst-23562	93	26	different	different	ADJ
ajst-23562	93	27	combinations	combination	NOUN
ajst-23562	93	28	of	of	ADP
ajst-23562	93	29	sae	sae	PROPN
ajst-23562	93	30	structures	structure	NOUN
ajst-23562	93	31	as	as	ADV
ajst-23562	93	32	well	well	ADV
ajst-23562	93	33	as	as	ADP
ajst-23562	93	34	time	time	NOUN
ajst-23562	93	35	series	series	NOUN
ajst-23562	93	36	lengths	length	NOUN
ajst-23562	93	37	were	be	AUX
ajst-23562	93	38	performed	perform	VERB
ajst-23562	93	39	,	,	PUNCT
ajst-23562	93	40	with	with	ADP
ajst-23562	93	41	sae	sae	PROPN
ajst-23562	93	42	structures	structure	NOUN
ajst-23562	93	43	including	include	VERB
ajst-23562	93	44	two	two	NUM
ajst-23562	93	45	or	or	CCONJ
ajst-23562	93	46	three	three	NUM
ajst-23562	93	47	layers	layer	NOUN
ajst-23562	93	48	and	and	CCONJ
ajst-23562	93	49	different	different	ADJ
ajst-23562	93	50	numbers	number	NOUN
ajst-23562	93	51	of	of	ADP
ajst-23562	93	52	neurons	neuron	NOUN
ajst-23562	93	53	,	,	PUNCT
ajst-23562	93	54	and	and	CCONJ
ajst-23562	93	55	time	time	NOUN
ajst-23562	93	56	series	series	NOUN
ajst-23562	93	57	lengths	length	NOUN
ajst-23562	93	58	incremented	incremente	VERB
ajst-23562	93	59	from	from	ADP
ajst-23562	93	60	3	3	NUM
ajst-23562	93	61	to	to	ADP
ajst-23562	93	62	15	15	NUM
ajst-23562	93	63	.	.	PUNCT
ajst-23562	94	1	training	training	NOUN
ajst-23562	94	2	and	and	CCONJ
ajst-23562	94	3	validation	validation	NOUN
ajst-23562	94	4	are	be	AUX
ajst-23562	94	5	performed	perform	VERB
ajst-23562	94	6	using	use	VERB
ajst-23562	94	7	the	the	DET
ajst-23562	94	8	above	above	ADV
ajst-23562	94	9	normalized	normalize	VERB
ajst-23562	94	10	rssi	rssi	ADJ
ajst-23562	94	11	with	with	ADP
ajst-23562	94	12	evaluation	evaluation	NOUN
ajst-23562	94	13	metrics	metric	NOUN
ajst-23562	94	14	of	of	ADP
ajst-23562	94	15	mean	mean	ADJ
ajst-23562	94	16	euclidean	euclidean	ADJ
ajst-23562	94	17	distance	distance	NOUN
ajst-23562	94	18	,	,	PUNCT
ajst-23562	94	19	rmse	rmse	NOUN
ajst-23562	94	20	,	,	PUNCT
ajst-23562	94	21	mae	mae	PROPN
ajst-23562	94	22	,	,	PUNCT
ajst-23562	94	23	and	and	CCONJ
ajst-23562	94	24	r2	r2	PROPN
ajst-23562	94	25	score	score	NOUN
ajst-23562	94	26	.	.	PUNCT
ajst-23562	95	1	the	the	DET
ajst-23562	95	2	experimental	experimental	ADJ
ajst-23562	95	3	results	result	NOUN
ajst-23562	95	4	show	show	VERB
ajst-23562	95	5	that	that	SCONJ
ajst-23562	95	6	the	the	DET
ajst-23562	95	7	localization	localization	NOUN
ajst-23562	95	8	performance	performance	NOUN
ajst-23562	95	9	is	be	AUX
ajst-23562	95	10	better	well	ADJ
ajst-23562	95	11	under	under	ADP
ajst-23562	95	12	most	most	ADJ
ajst-23562	95	13	of	of	ADP
ajst-23562	95	14	the	the	DET
ajst-23562	95	15	sae	sae	PROPN
ajst-23562	95	16	structures	structure	NOUN
ajst-23562	95	17	with	with	ADP
ajst-23562	95	18	a	a	DET
ajst-23562	95	19	time	time	NOUN
ajst-23562	95	20	series	series	NOUN
ajst-23562	95	21	length	length	NOUN
ajst-23562	95	22	of	of	ADP
ajst-23562	95	23	13	13	NUM
ajst-23562	95	24	under	under	ADP
ajst-23562	95	25	this	this	DET
ajst-23562	95	26	structure	structure	NOUN
ajst-23562	95	27	.	.	PUNCT
ajst-23562	96	1	it	it	PRON
ajst-23562	96	2	is	be	AUX
ajst-23562	96	3	experimentally	experimentally	ADV
ajst-23562	96	4	proved	prove	VERB
ajst-23562	96	5	that	that	SCONJ
ajst-23562	96	6	the	the	DET
ajst-23562	96	7	localization	localization	NOUN
ajst-23562	96	8	performance	performance	NOUN
ajst-23562	96	9	is	be	AUX
ajst-23562	96	10	optimal	optimal	ADJ
ajst-23562	96	11	when	when	SCONJ
ajst-23562	96	12	the	the	DET
ajst-23562	96	13	sae	sae	PROPN
ajst-23562	96	14	structure	structure	NOUN
ajst-23562	96	15	is	be	AUX
ajst-23562	96	16	256	256	NUM
ajst-23562	96	17	-	-	SYM
ajst-23562	96	18	128	128	NUM
ajst-23562	96	19	-	-	PUNCT
ajst-23562	96	20	64	64	NUM
ajst-23562	96	21	and	and	CCONJ
ajst-23562	96	22	the	the	DET
ajst-23562	96	23	time	time	NOUN
ajst-23562	96	24	series	series	PROPN
ajst-23562	96	25	length	length	PROPN
ajst-23562	96	26	is	be	AUX
ajst-23562	96	27	13	13	NUM
ajst-23562	96	28	with	with	ADP
ajst-23562	96	29	an	an	DET
ajst-23562	96	30	average	average	ADJ
ajst-23562	96	31	euclidean	euclidean	ADJ
ajst-23562	96	32	distance	distance	NOUN
ajst-23562	96	33	of	of	ADP
ajst-23562	96	34	0.77	0.77	NUM
ajst-23562	96	35	m.	m.	NOUN
ajst-23562	96	36	fig	fig	NOUN
ajst-23562	96	37	5	5	NUM
ajst-23562	96	38	.	.	PUNCT
ajst-23562	96	39	performance	performance	NOUN
ajst-23562	96	40	metrics	metric	NOUN
ajst-23562	96	41	for	for	ADP
ajst-23562	96	42	optimal	optimal	ADJ
ajst-23562	96	43	models	model	NOUN
ajst-23562	96	44	in	in	ADP
ajst-23562	96	45	addition	addition	NOUN
ajst-23562	96	46	to	to	ADP
ajst-23562	96	47	this	this	PRON
ajst-23562	96	48	,	,	PUNCT
ajst-23562	96	49	we	we	PRON
ajst-23562	96	50	also	also	ADV
ajst-23562	96	51	compare	compare	VERB
ajst-23562	96	52	the	the	DET
ajst-23562	96	53	method	method	NOUN
ajst-23562	96	54	proposed	propose	VERB
ajst-23562	96	55	in	in	ADP
ajst-23562	96	56	this	this	DET
ajst-23562	96	57	paper	paper	NOUN
ajst-23562	96	58	with	with	ADP
ajst-23562	96	59	other	other	ADJ
ajst-23562	96	60	methods	method	NOUN
ajst-23562	96	61	,	,	PUNCT
ajst-23562	96	62	including	include	VERB
ajst-23562	96	63	some	some	DET
ajst-23562	96	64	machine	machine	NOUN
ajst-23562	96	65	learning	learn	VERB
ajst-23562	96	66	architectures	architecture	NOUN
ajst-23562	96	67	and	and	CCONJ
ajst-23562	96	68	deep	deep	ADJ
ajst-23562	96	69	learning	learning	NOUN
ajst-23562	96	70	architectures	architecture	NOUN
ajst-23562	96	71	,	,	PUNCT
ajst-23562	96	72	and	and	CCONJ
ajst-23562	96	73	compute	compute	VERB
ajst-23562	96	74	the	the	DET
ajst-23562	96	75	respective	respective	ADJ
ajst-23562	96	76	error	error	NOUN
ajst-23562	96	77	cumulative	cumulative	ADJ
ajst-23562	96	78	distribution	distribution	NOUN
ajst-23562	96	79	functions	function	NOUN
ajst-23562	96	80	and	and	CCONJ
ajst-23562	96	81	visualize	visualize	VERB
ajst-23562	96	82	the	the	DET
ajst-23562	96	83	comparisons	comparison	NOUN
ajst-23562	96	84	.	.	PUNCT
ajst-23562	97	1	table	table	NOUN
ajst-23562	97	2	1	1	NUM
ajst-23562	97	3	shows	show	VERB
ajst-23562	97	4	the	the	DET
ajst-23562	97	5	performance	performance	NOUN
ajst-23562	97	6	comparison	comparison	NOUN
ajst-23562	97	7	of	of	ADP
ajst-23562	97	8	different	different	ADJ
ajst-23562	97	9	methods	method	NOUN
ajst-23562	97	10	.	.	PUNCT
ajst-23562	98	1	table	table	NOUN
ajst-23562	98	2	1	1	NUM
ajst-23562	98	3	.	.	PUNCT
ajst-23562	99	1	performance	performance	NOUN
ajst-23562	99	2	comparison	comparison	NOUN
ajst-23562	99	3	of	of	ADP
ajst-23562	99	4	different	different	ADJ
ajst-23562	99	5	method	method	NOUN
ajst-23562	99	6	method	method	NOUN
ajst-23562	99	7	mean	mean	VERB
ajst-23562	99	8	euclidean	euclidean	ADJ
ajst-23562	99	9	distance	distance	NOUN
ajst-23562	99	10	/	/	SYM
ajst-23562	99	11	m	m	VERB
ajst-23562	99	12	random	random	ADJ
ajst-23562	99	13	forest	forest	NOUN
ajst-23562	99	14	1.63	1.63	NUM
ajst-23562	99	15	wknn	wknn	NOUN
ajst-23562	99	16	1.23	1.23	NUM
ajst-23562	99	17	lstm	lstm	NOUN
ajst-23562	99	18	1.61	1.61	NUM
ajst-23562	99	19	bilstm	bilstm	NOUN
ajst-23562	99	20	1.10	1.10	NUM
ajst-23562	99	21	dnn	dnn	PROPN
ajst-23562	99	22	1.26	1.26	NUM
ajst-23562	99	23	cnn	cnn	PROPN
ajst-23562	99	24	2.08	2.08	NUM
ajst-23562	99	25	sae	sae	PROPN
ajst-23562	99	26	-	-	PROPN
ajst-23562	99	27	cnn	cnn	PROPN
ajst-23562	99	28	0.83	0.83	NUM
ajst-23562	99	29	sae	sae	PROPN
ajst-23562	99	30	-	-	NOUN
ajst-23562	99	31	bilstm	bilstm	ADJ
ajst-23562	99	32	0.77	0.77	NUM
ajst-23562	99	33	fig	fig	NOUN
ajst-23562	99	34	6	6	NUM
ajst-23562	99	35	.	.	PUNCT
ajst-23562	100	1	mean	mean	ADJ
ajst-23562	100	2	euclidean	euclidean	ADJ
ajst-23562	100	3	distance	distance	NOUN
ajst-23562	100	4	,	,	PUNCT
ajst-23562	100	5	rmse	rmse	NOUN
ajst-23562	100	6	,	,	PUNCT
ajst-23562	100	7	mae	mae	PROPN
ajst-23562	100	8	,	,	PUNCT
ajst-23562	100	9	r2	r2	PROPN
ajst-23562	100	10	scores	score	NOUN
ajst-23562	100	11	for	for	ADP
ajst-23562	100	12	different	different	ADJ
ajst-23562	100	13	models	model	NOUN
ajst-23562	100	14	the	the	DET
ajst-23562	100	15	experimental	experimental	ADJ
ajst-23562	100	16	results	result	NOUN
ajst-23562	100	17	show	show	VERB
ajst-23562	100	18	that	that	SCONJ
ajst-23562	100	19	machine	machine	NOUN
ajst-23562	100	20	learning	learning	NOUN
ajst-23562	100	21	methods	method	NOUN
ajst-23562	100	22	such	such	ADJ
ajst-23562	100	23	as	as	ADP
ajst-23562	100	24	random	random	ADJ
ajst-23562	100	25	forest	forest	NOUN
ajst-23562	100	26	(	(	PUNCT
ajst-23562	100	27	rf	rf	NOUN
ajst-23562	100	28	)	)	PUNCT
ajst-23562	100	29	and	and	CCONJ
ajst-23562	100	30	wknn	wknn	NOUN
ajst-23562	100	31	perform	perform	VERB
ajst-23562	100	32	poorly	poorly	ADV
ajst-23562	100	33	in	in	ADP
ajst-23562	100	34	the	the	DET
ajst-23562	100	35	classification	classification	NOUN
ajst-23562	100	36	of	of	ADP
ajst-23562	100	37	buildings	building	NOUN
ajst-23562	100	38	and	and	CCONJ
ajst-23562	100	39	floors	floor	NOUN
ajst-23562	100	40	compared	compare	VERB
ajst-23562	100	41	to	to	ADP
ajst-23562	100	42	neural	neural	ADJ
ajst-23562	100	43	network	network	NOUN
ajst-23562	100	44	methods	method	NOUN
ajst-23562	100	45	.	.	PUNCT
ajst-23562	101	1	this	this	DET
ajst-23562	101	2	gap	gap	NOUN
ajst-23562	101	3	mainly	mainly	ADV
ajst-23562	101	4	comes	come	VERB
ajst-23562	101	5	from	from	ADP
ajst-23562	101	6	the	the	DET
ajst-23562	101	7	fact	fact	NOUN
ajst-23562	101	8	that	that	SCONJ
ajst-23562	101	9	traditional	traditional	ADJ
ajst-23562	101	10	methods	method	NOUN
ajst-23562	101	11	fail	fail	VERB
ajst-23562	101	12	to	to	PART
ajst-23562	101	13	obtain	obtain	VERB
ajst-23562	101	14	high	high	ADJ
ajst-23562	101	15	-	-	PUNCT
ajst-23562	101	16	level	level	NOUN
ajst-23562	101	17	feature	feature	NOUN
ajst-23562	101	18	representations	representation	NOUN
ajst-23562	101	19	when	when	SCONJ
ajst-23562	101	20	dealing	deal	VERB
ajst-23562	101	21	with	with	ADP
ajst-23562	101	22	nonlinear	nonlinear	ADJ
ajst-23562	101	23	relationships	relationship	NOUN
ajst-23562	101	24	and	and	CCONJ
ajst-23562	101	25	complex	complex	ADJ
ajst-23562	101	26	spatial	spatial	ADJ
ajst-23562	101	27	structures	structure	NOUN
ajst-23562	101	28	,	,	PUNCT
ajst-23562	101	29	and	and	CCONJ
ajst-23562	101	30	deep	deep	ADJ
ajst-23562	101	31	neural	neural	ADJ
ajst-23562	101	32	network	network	NOUN
ajst-23562	101	33	-	-	PUNCT
ajst-23562	101	34	based	base	VERB
ajst-23562	101	35	localization	localization	NOUN
ajst-23562	101	36	methods	method	NOUN
ajst-23562	101	37	achieve	achieve	VERB
ajst-23562	101	38	better	well	ADJ
ajst-23562	101	39	results	result	NOUN
ajst-23562	101	40	than	than	ADP
ajst-23562	101	41	machine	machine	NOUN
ajst-23562	101	42	learning	learning	NOUN
ajst-23562	101	43	methods	method	NOUN
ajst-23562	101	44	in	in	ADP
ajst-23562	101	45	indoor	indoor	ADJ
ajst-23562	101	46	localization	localization	NOUN
ajst-23562	101	47	.	.	PUNCT
ajst-23562	102	1	note	note	VERB
ajst-23562	102	2	that	that	SCONJ
ajst-23562	102	3	the	the	DET
ajst-23562	102	4	models	model	NOUN
ajst-23562	102	5	after	after	ADP
ajst-23562	102	6	adding	add	VERB
ajst-23562	102	7	the	the	DET
ajst-23562	102	8	sae	sae	PROPN
ajst-23562	102	9	feature	feature	NOUN
ajst-23562	102	10	learning	learn	VERB
ajst-23562	102	11	network	network	NOUN
ajst-23562	102	12	structure	structure	NOUN
ajst-23562	102	13	all	all	PRON
ajst-23562	102	14	achieve	achieve	VERB
ajst-23562	102	15	better	well	ADJ
ajst-23562	102	16	results	result	NOUN
ajst-23562	102	17	than	than	ADP
ajst-23562	102	18	before	before	ADV
ajst-23562	102	19	,	,	PUNCT
ajst-23562	102	20	as	as	SCONJ
ajst-23562	102	21	evidenced	evidence	VERB
ajst-23562	102	22	by	by	ADP
ajst-23562	102	23	experimental	experimental	ADJ
ajst-23562	102	24	results	result	NOUN
ajst-23562	102	25	for	for	ADP
ajst-23562	102	26	both	both	DET
ajst-23562	102	27	cnn	cnn	PROPN
ajst-23562	102	28	and	and	CCONJ
ajst-23562	102	29	bilstm	bilstm	NOUN
ajst-23562	102	30	.	.	PUNCT
ajst-23562	103	1	fig	fig	PROPN
ajst-23562	103	2	7	7	NUM
ajst-23562	103	3	.	.	PUNCT
ajst-23562	104	1	heat	heat	NOUN
ajst-23562	104	2	maps	map	NOUN
ajst-23562	104	3	of	of	ADP
ajst-23562	104	4	indicators	indicator	NOUN
ajst-23562	104	5	for	for	ADP
ajst-23562	104	6	different	different	ADJ
ajst-23562	104	7	models	model	NOUN
ajst-23562	104	8	0.6	0.6	NUM
ajst-23562	104	9	0.8	0.8	NUM
ajst-23562	104	10	1	1	NUM
ajst-23562	104	11	1.2	1.2	NUM
ajst-23562	104	12	1.4	1.4	NUM
ajst-23562	104	13	1.6	1.6	NUM
ajst-23562	104	14	1.8	1.8	NUM
ajst-23562	104	15	effect	effect	NOUN
ajst-23562	104	16	of	of	ADP
ajst-23562	104	17	sae	sae	PROPN
ajst-23562	104	18	structure	structure	NOUN
ajst-23562	104	19	and	and	CCONJ
ajst-23562	104	20	timestep	timestep	NOUN
ajst-23562	104	21	on	on	ADP
ajst-23562	104	22	mean	mean	ADJ
ajst-23562	104	23	euclidean	euclidean	ADJ
ajst-23562	104	24	distance	distance	NOUN
ajst-23562	104	25	(	(	PUNCT
ajst-23562	104	26	256	256	NUM
ajst-23562	104	27	-	-	SYM
ajst-23562	104	28	128	128	NUM
ajst-23562	104	29	-	-	PUNCT
ajst-23562	104	30	64	64	NUM
ajst-23562	104	31	,	,	PUNCT
ajst-23562	104	32	ts=13	ts=13	NOUN
ajst-23562	104	33	)	)	PUNCT
ajst-23562	104	34	0.5	0.5	NUM
ajst-23562	104	35	0.55	0.55	NUM
ajst-23562	104	36	0.6	0.6	NUM
ajst-23562	104	37	0.65	0.65	NUM
ajst-23562	104	38	0.7	0.7	NUM
ajst-23562	104	39	0.75	0.75	NUM
ajst-23562	104	40	0.8	0.8	NUM
ajst-23562	104	41	0.85	0.85	NUM
ajst-23562	104	42	0.9	0.9	NUM
ajst-23562	104	43	0.95	0.95	NUM
ajst-23562	104	44	effects	effect	NOUN
ajst-23562	104	45	of	of	ADP
ajst-23562	104	46	sae	sae	PROPN
ajst-23562	104	47	structure	structure	NOUN
ajst-23562	104	48	and	and	CCONJ
ajst-23562	104	49	timestep	timestep	NOUN
ajst-23562	104	50	on	on	ADP
ajst-23562	104	51	r2	r2	PROPN
ajst-23562	104	52	scores	score	NOUN
ajst-23562	104	53	(	(	PUNCT
ajst-23562	104	54	256	256	NUM
ajst-23562	104	55	-	-	SYM
ajst-23562	104	56	128	128	NUM
ajst-23562	104	57	-	-	PUNCT
ajst-23562	104	58	64	64	NUM
ajst-23562	104	59	,	,	PUNCT
ajst-23562	104	60	ts=13	ts=13	NOUN
ajst-23562	104	61	)	)	PUNCT
ajst-23562	104	62	0.6	0.6	NUM
ajst-23562	104	63	0.8	0.8	NUM
ajst-23562	104	64	1	1	NUM
ajst-23562	104	65	1.2	1.2	NUM
ajst-23562	104	66	1.4	1.4	NUM
ajst-23562	104	67	1.6	1.6	NUM
ajst-23562	104	68	the	the	DET
ajst-23562	104	69	influence	influence	NOUN
ajst-23562	104	70	of	of	ADP
ajst-23562	104	71	sae	sae	PROPN
ajst-23562	104	72	structure	structure	NOUN
ajst-23562	104	73	and	and	CCONJ
ajst-23562	104	74	timestep	timestep	NOUN
ajst-23562	104	75	on	on	ADP
ajst-23562	104	76	rmse	rmse	NOUN
ajst-23562	104	77	(	(	PUNCT
ajst-23562	104	78	256	256	NUM
ajst-23562	104	79	-	-	SYM
ajst-23562	104	80	128	128	NUM
ajst-23562	104	81	-	-	PUNCT
ajst-23562	104	82	64	64	NUM
ajst-23562	104	83	,	,	PUNCT
ajst-23562	104	84	ts=13	ts=13	NOUN
ajst-23562	104	85	)	)	PUNCT
ajst-23562	104	86	0.4	0.4	NUM
ajst-23562	104	87	0.5	0.5	NUM
ajst-23562	104	88	0.6	0.6	NUM
ajst-23562	104	89	0.7	0.7	NUM
ajst-23562	104	90	0.8	0.8	NUM
ajst-23562	104	91	0.9	0.9	NUM
ajst-23562	104	92	1	1	NUM
ajst-23562	104	93	1.1	1.1	NUM
ajst-23562	104	94	1.2	1.2	NUM
ajst-23562	104	95	effect	effect	NOUN
ajst-23562	104	96	of	of	ADP
ajst-23562	104	97	sae	sae	PROPN
ajst-23562	104	98	structure	structure	NOUN
ajst-23562	104	99	and	and	CCONJ
ajst-23562	104	100	timestep	timestep	NOUN
ajst-23562	104	101	on	on	ADP
ajst-23562	104	102	mae	mae	PROPN
ajst-23562	104	103	(	(	PUNCT
ajst-23562	104	104	256	256	NUM
ajst-23562	104	105	-	-	SYM
ajst-23562	104	106	128	128	NUM
ajst-23562	104	107	-	-	PUNCT
ajst-23562	104	108	64	64	NUM
ajst-23562	104	109	,	,	PUNCT
ajst-23562	104	110	ts=13	ts=13	NOUN
ajst-23562	104	111	)	)	PUNCT
ajst-23562	104	112	101	101	NUM
ajst-23562	104	113	fig	fig	NOUN
ajst-23562	104	114	8	8	NUM
ajst-23562	104	115	.	.	PUNCT
ajst-23562	105	1	cdf	cdf	NOUN
ajst-23562	105	2	for	for	ADP
ajst-23562	105	3	different	different	ADJ
ajst-23562	105	4	models	model	NOUN
ajst-23562	105	5	from	from	ADP
ajst-23562	105	6	fig	fig	NOUN
ajst-23562	105	7	.	.	PUNCT
ajst-23562	106	1	8	8	NUM
ajst-23562	106	2	,	,	PUNCT
ajst-23562	106	3	it	it	PRON
ajst-23562	106	4	can	can	AUX
ajst-23562	106	5	be	be	AUX
ajst-23562	106	6	seen	see	VERB
ajst-23562	106	7	that	that	SCONJ
ajst-23562	106	8	the	the	DET
ajst-23562	106	9	error	error	NOUN
ajst-23562	106	10	distributions	distribution	NOUN
ajst-23562	106	11	of	of	ADP
ajst-23562	106	12	the	the	DET
ajst-23562	106	13	sae	sae	PROPN
ajst-23562	106	14	-	-	ADJ
ajst-23562	106	15	bilstm	bilstm	ADJ
ajst-23562	106	16	model	model	NOUN
ajst-23562	106	17	are	be	AUX
ajst-23562	106	18	all	all	ADV
ajst-23562	106	19	concentrated	concentrate	VERB
ajst-23562	106	20	in	in	ADP
ajst-23562	106	21	a	a	DET
ajst-23562	106	22	small	small	ADJ
ajst-23562	106	23	range	range	NOUN
ajst-23562	106	24	,	,	PUNCT
ajst-23562	106	25	and	and	CCONJ
ajst-23562	106	26	the	the	DET
ajst-23562	106	27	slope	slope	NOUN
ajst-23562	106	28	of	of	ADP
ajst-23562	106	29	the	the	DET
ajst-23562	106	30	curve	curve	NOUN
ajst-23562	106	31	is	be	AUX
ajst-23562	106	32	relatively	relatively	ADV
ajst-23562	106	33	large	large	ADJ
ajst-23562	106	34	,	,	PUNCT
ajst-23562	106	35	indicating	indicate	VERB
ajst-23562	106	36	that	that	SCONJ
ajst-23562	106	37	the	the	DET
ajst-23562	106	38	range	range	NOUN
ajst-23562	106	39	of	of	ADP
ajst-23562	106	40	error	error	NOUN
ajst-23562	106	41	distribution	distribution	NOUN
ajst-23562	106	42	is	be	AUX
ajst-23562	106	43	more	more	ADV
ajst-23562	106	44	concentrated	concentrated	ADJ
ajst-23562	106	45	,	,	PUNCT
ajst-23562	106	46	and	and	CCONJ
ajst-23562	106	47	the	the	DET
ajst-23562	106	48	overall	overall	ADJ
ajst-23562	106	49	error	error	NOUN
ajst-23562	106	50	is	be	AUX
ajst-23562	106	51	distributed	distribute	VERB
ajst-23562	106	52	under	under	ADP
ajst-23562	106	53	2	2	NUM
ajst-23562	106	54	m	m	NOUN
ajst-23562	106	55	,	,	PUNCT
ajst-23562	106	56	which	which	PRON
ajst-23562	106	57	achieves	achieve	VERB
ajst-23562	106	58	a	a	DET
ajst-23562	106	59	better	well	ADJ
ajst-23562	106	60	performance	performance	NOUN
ajst-23562	106	61	than	than	ADP
ajst-23562	106	62	the	the	DET
ajst-23562	106	63	other	other	ADJ
ajst-23562	106	64	models	model	NOUN
ajst-23562	106	65	.	.	PUNCT
ajst-23562	107	1	4	4	X
ajst-23562	107	2	.	.	X
ajst-23562	107	3	summary	summary	NOUN
ajst-23562	107	4	wireless	wireless	NOUN
ajst-23562	107	5	-	-	PUNCT
ajst-23562	107	6	aware	aware	ADJ
ajst-23562	107	7	indoor	indoor	ADJ
ajst-23562	107	8	localization	localization	NOUN
ajst-23562	107	9	architecture	architecture	NOUN
ajst-23562	107	10	based	base	VERB
ajst-23562	107	11	on	on	ADP
ajst-23562	107	12	stacked	stack	VERB
ajst-23562	107	13	self	self	NOUN
ajst-23562	107	14	-	-	PUNCT
ajst-23562	107	15	encoder	encoder	NOUN
ajst-23562	107	16	feature	feature	NOUN
ajst-23562	107	17	learning	learn	VERB
ajst-23562	107	18	cascade	cascade	NOUN
ajst-23562	107	19	bilstm	bilstm	NOUN
ajst-23562	107	20	network	network	NOUN
ajst-23562	107	21	improves	improve	VERB
ajst-23562	107	22	the	the	DET
ajst-23562	107	23	localization	localization	NOUN
ajst-23562	107	24	performance	performance	NOUN
ajst-23562	107	25	of	of	ADP
ajst-23562	107	26	the	the	DET
ajst-23562	107	27	localization	localization	NOUN
ajst-23562	107	28	model	model	NOUN
ajst-23562	107	29	and	and	CCONJ
ajst-23562	107	30	has	have	VERB
ajst-23562	107	31	better	well	ADJ
ajst-23562	107	32	performance	performance	NOUN
ajst-23562	107	33	on	on	ADP
ajst-23562	107	34	the	the	DET
ajst-23562	107	35	validation	validation	NOUN
ajst-23562	107	36	set	set	NOUN
ajst-23562	107	37	.	.	PUNCT
ajst-23562	108	1	compared	compare	VERB
ajst-23562	108	2	with	with	ADP
ajst-23562	108	3	traditional	traditional	ADJ
ajst-23562	108	4	indoor	indoor	ADJ
ajst-23562	108	5	localization	localization	NOUN
ajst-23562	108	6	methods	method	NOUN
ajst-23562	108	7	,	,	PUNCT
ajst-23562	108	8	this	this	DET
ajst-23562	108	9	method	method	NOUN
ajst-23562	108	10	not	not	PART
ajst-23562	108	11	only	only	ADV
ajst-23562	108	12	achieves	achieve	VERB
ajst-23562	108	13	higher	high	ADJ
ajst-23562	108	14	localization	localization	NOUN
ajst-23562	108	15	accuracy	accuracy	NOUN
ajst-23562	108	16	,	,	PUNCT
ajst-23562	108	17	but	but	CCONJ
ajst-23562	108	18	also	also	ADV
ajst-23562	108	19	effectively	effectively	ADV
ajst-23562	108	20	mitigates	mitigate	VERB
ajst-23562	108	21	the	the	DET
ajst-23562	108	22	large	large	ADJ
ajst-23562	108	23	-	-	PUNCT
ajst-23562	108	24	scale	scale	NOUN
ajst-23562	108	25	differences	difference	NOUN
ajst-23562	108	26	in	in	ADP
ajst-23562	108	27	rssi	rssi	NOUN
ajst-23562	108	28	,	,	PUNCT
ajst-23562	108	29	attenuates	attenuate	VERB
ajst-23562	108	30	the	the	DET
ajst-23562	108	31	small	small	ADJ
ajst-23562	108	32	-	-	PUNCT
ajst-23562	108	33	scale	scale	NOUN
ajst-23562	108	34	differences	difference	NOUN
ajst-23562	108	35	caused	cause	VERB
ajst-23562	108	36	by	by	ADP
ajst-23562	108	37	noise	noise	NOUN
ajst-23562	108	38	,	,	PUNCT
ajst-23562	108	39	and	and	CCONJ
ajst-23562	108	40	maintains	maintain	VERB
ajst-23562	108	41	good	good	ADJ
ajst-23562	108	42	robustness	robustness	NOUN
ajst-23562	108	43	of	of	ADP
ajst-23562	108	44	the	the	DET
ajst-23562	108	45	model	model	NOUN
ajst-23562	108	46	.	.	PUNCT
ajst-23562	109	1	meanwhile	meanwhile	ADV
ajst-23562	109	2	,	,	PUNCT
ajst-23562	109	3	due	due	ADP
ajst-23562	109	4	to	to	ADP
ajst-23562	109	5	the	the	DET
ajst-23562	109	6	low	low	ADJ
ajst-23562	109	7	cost	cost	NOUN
ajst-23562	109	8	,	,	PUNCT
ajst-23562	109	9	low	low	ADJ
ajst-23562	109	10	power	power	NOUN
ajst-23562	109	11	consumption	consumption	NOUN
ajst-23562	109	12	and	and	CCONJ
ajst-23562	109	13	scalability	scalability	NOUN
ajst-23562	109	14	of	of	ADP
ajst-23562	109	15	ibeacon	ibeacon	NOUN
ajst-23562	109	16	devices	device	NOUN
ajst-23562	109	17	,	,	PUNCT
ajst-23562	109	18	the	the	DET
ajst-23562	109	19	modeling	modeling	NOUN
ajst-23562	109	20	of	of	ADP
ajst-23562	109	21	localization	localization	NOUN
ajst-23562	109	22	areas	area	NOUN
ajst-23562	109	23	is	be	AUX
ajst-23562	109	24	cost	cost	NOUN
ajst-23562	109	25	-	-	PUNCT
ajst-23562	109	26	effective	effective	ADJ
ajst-23562	109	27	and	and	CCONJ
ajst-23562	109	28	easy	easy	ADJ
ajst-23562	109	29	to	to	PART
ajst-23562	109	30	implement	implement	VERB
ajst-23562	109	31	.	.	PUNCT
ajst-23562	110	1	solving	solve	VERB
ajst-23562	110	2	the	the	DET
ajst-23562	110	3	regression	regression	NOUN
ajst-23562	110	4	task	task	NOUN
ajst-23562	110	5	using	use	VERB
ajst-23562	110	6	deep	deep	ADJ
ajst-23562	110	7	learning	learning	NOUN
ajst-23562	110	8	to	to	PART
ajst-23562	110	9	predict	predict	VERB
ajst-23562	110	10	the	the	DET
ajst-23562	110	11	user	user	NOUN
ajst-23562	110	12	's	's	PART
ajst-23562	110	13	final	final	ADJ
ajst-23562	110	14	location	location	NOUN
ajst-23562	110	15	and	and	CCONJ
ajst-23562	110	16	applying	apply	VERB
ajst-23562	110	17	the	the	DET
ajst-23562	110	18	proposed	propose	VERB
ajst-23562	110	19	deep	deep	ADJ
ajst-23562	110	20	neural	neural	ADJ
ajst-23562	110	21	network	network	NOUN
ajst-23562	110	22	architecture	architecture	NOUN
ajst-23562	110	23	to	to	PART
ajst-23562	110	24	predict	predict	VERB
ajst-23562	110	25	the	the	DET
ajst-23562	110	26	user	user	NOUN
ajst-23562	110	27	's	's	PART
ajst-23562	110	28	location	location	NOUN
ajst-23562	110	29	in	in	ADP
ajst-23562	110	30	large	large	ADJ
ajst-23562	110	31	-	-	PUNCT
ajst-23562	110	32	scale	scale	NOUN
ajst-23562	110	33	dynamically	dynamically	ADV
ajst-23562	110	34	changing	change	VERB
ajst-23562	110	35	indoor	indoor	ADJ
ajst-23562	110	36	environments	environment	NOUN
ajst-23562	110	37	are	be	AUX
ajst-23562	110	38	the	the	DET
ajst-23562	110	39	next	next	ADJ
ajst-23562	110	40	research	research	NOUN
ajst-23562	110	41	directions	direction	NOUN
ajst-23562	110	42	.	.	PUNCT
ajst-23562	111	1	references	reference	NOUN
ajst-23562	111	2	[	[	X
ajst-23562	111	3	1	1	NUM
ajst-23562	111	4	]	]	PUNCT
ajst-23562	111	5	zhuang	zhuang	PROPN
ajst-23562	111	6	c	c	PROPN
ajst-23562	111	7	s	s	PROPN
ajst-23562	111	8	,	,	PUNCT
ajst-23562	111	9	zhang	zhang	PROPN
ajst-23562	112	1	d	d	PROPN
ajst-23562	112	2	y.	y.	PROPN
ajst-23562	112	3	a	a	DET
ajst-23562	112	4	robust	robust	ADJ
ajst-23562	112	5	wifi	wifi	NOUN
ajst-23562	112	6	localization	localization	NOUN
ajst-23562	112	7	algorithm	algorithm	NOUN
ajst-23562	112	8	using	use	VERB
ajst-23562	112	9	data	datum	NOUN
ajst-23562	112	10	augmentation	augmentation	NOUN
ajst-23562	112	11	and	and	CCONJ
ajst-23562	112	12	stacked	stack	VERB
ajst-23562	112	13	denoising	denoise	VERB
ajst-23562	112	14	autoencoder	autoencoder	NOUN
ajst-23562	113	1	[	[	X
ajst-23562	113	2	c	c	X
ajst-23562	113	3	]	]	X
ajst-23562	113	4	.	.	PUNCT
ajst-23562	114	1	2023	2023	NUM
ajst-23562	114	2	35th	35th	ADJ
ajst-23562	114	3	chinese	chinese	ADJ
ajst-23562	114	4	control	control	NOUN
ajst-23562	114	5	and	and	CCONJ
ajst-23562	114	6	decision	decision	NOUN
ajst-23562	114	7	conference	conference	NOUN
ajst-23562	114	8	(	(	PUNCT
ajst-23562	114	9	ccdc	ccdc	PROPN
ajst-23562	114	10	)	)	PUNCT
ajst-23562	114	11	(	(	PUNCT
ajst-23562	114	12	2023	2023	NUM
ajst-23562	114	13	):	):	PUNCT
ajst-23562	114	14	1445	1445	NUM
ajst-23562	114	15	-	-	SYM
ajst-23562	114	16	1450	1450	NUM
ajst-23562	114	17	.	.	PUNCT
ajst-23562	115	1	[	[	X
ajst-23562	115	2	2	2	NUM
ajst-23562	115	3	]	]	X
ajst-23562	115	4	yong	yong	PROPN
ajst-23562	115	5	l	l	PROPN
ajst-23562	115	6	,	,	PUNCT
ajst-23562	115	7	sun	sun	PROPN
ajst-23562	115	8	,	,	PUNCT
ajst-23562	115	9	wei	wei	PROPN
ajst-23562	115	10	x	x	PROPN
ajst-23562	115	11	,	,	PUNCT
ajst-23562	115	12	et	et	PROPN
ajst-23562	115	13	al	al	PROPN
ajst-23562	115	14	.	.	PUNCT
ajst-23562	116	1	voronoi	voronoi	PROPN
ajst-23562	116	2	diagram	diagram	PROPN
ajst-23562	116	3	and	and	CCONJ
ajst-23562	116	4	crowdsourcing	crowdsourcing	NOUN
ajst-23562	116	5	-	-	PUNCT
ajst-23562	116	6	based	base	VERB
ajst-23562	116	7	radio	radio	NOUN
ajst-23562	116	8	map	map	NOUN
ajst-23562	116	9	interpolation	interpolation	NOUN
ajst-23562	116	10	for	for	ADP
ajst-23562	116	11	grnn	grnn	NOUN
ajst-23562	116	12	fingerprinting	fingerprint	VERB
ajst-23562	116	13	localization	localization	NOUN
ajst-23562	116	14	using	use	VERB
ajst-23562	116	15	wlan[j	wlan[j	PROPN
ajst-23562	116	16	]	]	PUNCT
ajst-23562	116	17	.	.	PUNCT
ajst-23562	117	1	sensors	sensor	NOUN
ajst-23562	117	2	(	(	PUNCT
ajst-23562	117	3	basel	basel	PROPN
ajst-23562	117	4	,	,	PUNCT
ajst-23562	117	5	switzerland	switzerland	PROPN
ajst-23562	117	6	)	)	PUNCT
ajst-23562	117	7	,	,	PUNCT
ajst-23562	117	8	2018	2018	NUM
ajst-23562	117	9	.	.	PUNCT
ajst-23562	118	1	[	[	X
ajst-23562	118	2	3	3	X
ajst-23562	118	3	]	]	PUNCT
ajst-23562	118	4	alqahtani	alqahtani	VERB
ajst-23562	118	5	a	a	DET
ajst-23562	118	6	a	a	DET
ajst-23562	118	7	s	s	PROPN
ajst-23562	118	8	,	,	PUNCT
ajst-23562	118	9	choudhury	choudhury	PROPN
ajst-23562	118	10	n.	n.	PROPN
ajst-23562	118	11	machine	machine	NOUN
ajst-23562	118	12	learning	learn	VERB
ajst-23562	118	13	for	for	ADP
ajst-23562	118	14	location	location	NOUN
ajst-23562	118	15	prediction	prediction	NOUN
ajst-23562	118	16	using	use	VERB
ajst-23562	118	17	rssi	rssi	NOUN
ajst-23562	118	18	on	on	ADP
ajst-23562	118	19	wi	wi	PROPN
ajst-23562	118	20	-	-	PUNCT
ajst-23562	118	21	fi	fi	NOUN
ajst-23562	118	22	2.4	2.4	NUM
ajst-23562	118	23	ghz	ghz	NOUN
ajst-23562	118	24	frequency	frequency	NOUN
ajst-23562	118	25	band	band	NOUN
ajst-23562	119	1	[	[	X
ajst-23562	119	2	c	c	X
ajst-23562	119	3	]	]	X
ajst-23562	119	4	,	,	PUNCT
ajst-23562	119	5	2021	2021	NUM
ajst-23562	119	6	ieee	ieee	NOUN
ajst-23562	119	7	12th	12th	NOUN
ajst-23562	119	8	annual	annual	ADJ
ajst-23562	119	9	information	information	NOUN
ajst-23562	119	10	technology	technology	NOUN
ajst-23562	119	11	,	,	PUNCT
ajst-23562	119	12	electronics	electronic	NOUN
ajst-23562	119	13	and	and	CCONJ
ajst-23562	119	14	mobile	mobile	ADJ
ajst-23562	119	15	communication	communication	NOUN
ajst-23562	119	16	conference	conference	NOUN
ajst-23562	119	17	(	(	PUNCT
ajst-23562	119	18	iemcon	iemcon	NOUN
ajst-23562	119	19	)	)	PUNCT
ajst-23562	119	20	,	,	PUNCT
ajst-23562	119	21	2021	2021	NUM
ajst-23562	119	22	.	.	PUNCT
ajst-23562	120	1	[	[	X
ajst-23562	120	2	4	4	X
ajst-23562	120	3	]	]	PUNCT
ajst-23562	120	4	aggarwal	aggarwal	NOUN
ajst-23562	120	5	c	c	PROPN
ajst-23562	120	6	c.	c.	PROPN
ajst-23562	120	7	machine	machine	NOUN
ajst-23562	120	8	learning	learn	VERB
ajst-23562	120	9	with	with	ADP
ajst-23562	120	10	shallow	shallow	ADJ
ajst-23562	120	11	neural	neural	ADJ
ajst-23562	120	12	networks	network	NOUN
ajst-23562	120	13	[	[	X
ajst-23562	120	14	m	m	X
ajst-23562	120	15	]	]	X
ajst-23562	120	16	.	.	PUNCT
ajst-23562	121	1	neural	neural	ADJ
ajst-23562	121	2	networks	network	NOUN
ajst-23562	121	3	and	and	CCONJ
ajst-23562	121	4	deep	deep	ADJ
ajst-23562	121	5	learning	learning	NOUN
ajst-23562	121	6	:	:	PUNCT
ajst-23562	121	7	a	a	DET
ajst-23562	121	8	textbook	textbook	NOUN
ajst-23562	121	9	.	.	PUNCT
ajst-23562	122	1	cham	cham	PROPN
ajst-23562	122	2	;	;	PUNCT
ajst-23562	122	3	springer	springer	NOUN
ajst-23562	122	4	international	international	ADJ
ajst-23562	122	5	publishing	publishing	NOUN
ajst-23562	122	6	.	.	PUNCT
ajst-23562	123	1	2018	2018	NUM
ajst-23562	123	2	:	:	PUNCT
ajst-23562	123	3	53104	53104	NUM
ajst-23562	123	4	.	.	PUNCT
ajst-23562	124	1	[	[	X
ajst-23562	124	2	5	5	NUM
ajst-23562	124	3	]	]	PUNCT
ajst-23562	124	4	qiao	qiao	PROPN
ajst-23562	124	5	f	f	PROPN
ajst-23562	124	6	,	,	PUNCT
ajst-23562	124	7	wu	wu	PROPN
ajst-23562	124	8	j	j	PROPN
ajst-23562	124	9	,	,	PUNCT
ajst-23562	124	10	li	li	PROPN
ajst-23562	124	11	j	j	PROPN
ajst-23562	124	12	,	,	PUNCT
ajst-23562	124	13	et	et	PROPN
ajst-23562	124	14	al	al	PROPN
ajst-23562	124	15	.	.	PROPN
ajst-23562	124	16	trustworthy	trustworthy	ADJ
ajst-23562	124	17	edge	edge	NOUN
ajst-23562	124	18	storage	storage	NOUN
ajst-23562	124	19	orchestration	orchestration	NOUN
ajst-23562	124	20	in	in	ADP
ajst-23562	124	21	intelligent	intelligent	ADJ
ajst-23562	124	22	transportation	transportation	NOUN
ajst-23562	124	23	systems	system	NOUN
ajst-23562	124	24	using	use	VERB
ajst-23562	124	25	reinforcement	reinforcement	NOUN
ajst-23562	124	26	learning[j	learning[j	NOUN
ajst-23562	124	27	]	]	PUNCT
ajst-23562	124	28	.	.	PUNCT
ajst-23562	125	1	ieee	ieee	NOUN
ajst-23562	125	2	transactions	transaction	NOUN
ajst-23562	125	3	on	on	ADP
ajst-23562	125	4	intelligent	intelligent	ADJ
ajst-23562	125	5	transportation	transportation	NOUN
ajst-23562	125	6	systems	system	NOUN
ajst-23562	125	7	,	,	PUNCT
ajst-23562	125	8	2021	2021	NUM
ajst-23562	125	9	,	,	PUNCT
ajst-23562	125	10	22(7	22(7	NUM
ajst-23562	125	11	):	):	PUNCT
ajst-23562	125	12	4443	4443	NUM
ajst-23562	125	13	-	-	SYM
ajst-23562	125	14	56	56	NUM
ajst-23562	125	15	.	.	PUNCT
ajst-23562	126	1	[	[	X
ajst-23562	126	2	6	6	NUM
ajst-23562	126	3	]	]	PUNCT
ajst-23562	126	4	he	he	PRON
ajst-23562	126	5	s	s	PROPN
ajst-23562	126	6	,	,	PUNCT
ajst-23562	126	7	chan	chan	PROPN
ajst-23562	126	8	s	s	PROPN
ajst-23562	126	9	h	h	PROPN
ajst-23562	126	10	g.	g.	PROPN
ajst-23562	126	11	wi	wi	PROPN
ajst-23562	126	12	-	-	PUNCT
ajst-23562	126	13	fi	fi	NOUN
ajst-23562	126	14	fingerprint	fingerprint	NOUN
ajst-23562	126	15	-	-	PUNCT
ajst-23562	126	16	based	base	VERB
ajst-23562	126	17	indoor	indoor	ADJ
ajst-23562	126	18	positioning	positioning	NOUN
ajst-23562	126	19	:	:	PUNCT
ajst-23562	126	20	recent	recent	ADJ
ajst-23562	126	21	advances	advance	NOUN
ajst-23562	126	22	and	and	CCONJ
ajst-23562	126	23	comparisons[j].ieee	comparisons[j].ieee	NOUN
ajst-23562	126	24	communications	communication	NOUN
ajst-23562	126	25	surveys	survey	NOUN
ajst-23562	126	26	&	&	CCONJ
ajst-23562	126	27	tutorials	tutorial	NOUN
ajst-23562	126	28	,	,	PUNCT
ajst-23562	126	29	2017	2017	NUM
ajst-23562	126	30	,	,	PUNCT
ajst-23562	126	31	18(1):466	18(1):466	NUM
ajst-23562	126	32	-	-	SYM
ajst-23562	126	33	490	490	NUM
ajst-23562	126	34	.	.	PUNCT
ajst-23562	127	1	[	[	X
ajst-23562	127	2	7	7	X
ajst-23562	127	3	]	]	X
ajst-23562	127	4	jiao	jiao	PROPN
ajst-23562	127	5	j	j	PROPN
ajst-23562	127	6	,	,	PUNCT
ajst-23562	127	7	li	li	PROPN
ajst-23562	127	8	f	f	PROPN
ajst-23562	127	9	,	,	PUNCT
ajst-23562	127	10	deng	deng	PROPN
ajst-23562	127	11	z	z	PROPN
ajst-23562	127	12	l	l	PROPN
ajst-23562	127	13	,	,	PUNCT
ajst-23562	127	14	et	et	PROPN
ajst-23562	127	15	al	al	PROPN
ajst-23562	127	16	.	.	PUNCT
ajst-23562	128	1	a	a	DET
ajst-23562	128	2	smartphone	smartphone	NOUN
ajst-23562	128	3	camera	camera	NOUN
ajst-23562	128	4	-	-	PUNCT
ajst-23562	128	5	based	base	VERB
ajst-23562	128	6	indoor	indoor	ADJ
ajst-23562	128	7	positioning	positioning	NOUN
ajst-23562	128	8	algorithm	algorithm	NOUN
ajst-23562	128	9	of	of	ADP
ajst-23562	128	10	crowded	crowded	ADJ
ajst-23562	128	11	scenarios	scenario	NOUN
ajst-23562	128	12	with	with	ADP
ajst-23562	128	13	the	the	DET
ajst-23562	128	14	assistance	assistance	NOUN
ajst-23562	128	15	of	of	ADP
ajst-23562	128	16	deep	deep	ADJ
ajst-23562	128	17	cnn[j	cnn[j	NOUN
ajst-23562	128	18	]	]	PUNCT
ajst-23562	128	19	.	.	PROPN
ajst-23562	129	1	2017	2017	NUM
ajst-23562	129	2	,	,	PUNCT
ajst-23562	129	3	17	17	NUM
ajst-23562	129	4	.	.	PUNCT
ajst-23562	130	1	[	[	X
ajst-23562	130	2	8	8	NUM
ajst-23562	130	3	]	]	X
ajst-23562	130	4	das	das	PROPN
ajst-23562	130	5	s	s	PROPN
ajst-23562	130	6	,	,	PUNCT
ajst-23562	130	7	tariq	tariq	PROPN
ajst-23562	130	8	a	a	X
ajst-23562	130	9	,	,	PUNCT
ajst-23562	130	10	santos	santos	PROPN
ajst-23562	130	11	t	t	PROPN
ajst-23562	130	12	,	,	PUNCT
ajst-23562	130	13	et	et	PROPN
ajst-23562	130	14	al	al	PROPN
ajst-23562	130	15	.	.	PROPN
ajst-23562	131	1	recurrent	recurrent	ADJ
ajst-23562	131	2	neural	neural	ADJ
ajst-23562	131	3	networks	network	NOUN
ajst-23562	131	4	(	(	PUNCT
ajst-23562	131	5	rnns	rnns	PROPN
ajst-23562	131	6	):	):	PUNCT
ajst-23562	131	7	architectures	architecture	NOUN
ajst-23562	131	8	,	,	PUNCT
ajst-23562	131	9	training	training	NOUN
ajst-23562	131	10	tricks	trick	NOUN
ajst-23562	131	11	,	,	PUNCT
ajst-23562	131	12	and	and	CCONJ
ajst-23562	131	13	introduction	introduction	NOUN
ajst-23562	131	14	to	to	ADP
ajst-23562	131	15	influential	influential	ADJ
ajst-23562	131	16	research[m	research[m	NOUN
ajst-23562	131	17	]	]	PUNCT
ajst-23562	131	18	,	,	PUNCT
ajst-23562	131	19	machine	machine	NOUN
ajst-23562	131	20	learning	learn	VERB
ajst-23562	131	21	for	for	ADP
ajst-23562	131	22	brain	brain	NOUN
ajst-23562	131	23	disorders	disorder	NOUN
ajst-23562	131	24	,	,	PUNCT
ajst-23562	131	25	2023	2023	NUM
ajst-23562	131	26	:	:	PUNCT
ajst-23562	131	27	117	117	NUM
ajst-23562	131	28	-	-	SYM
ajst-23562	131	29	38	38	NUM
ajst-23562	131	30	.	.	PUNCT
ajst-23562	132	1	[	[	X
ajst-23562	132	2	9	9	NUM
ajst-23562	132	3	]	]	X
ajst-23562	132	4	sherstinsky	sherstinsky	ADJ
ajst-23562	132	5	a.	a.	NOUN
ajst-23562	132	6	fundamentals	fundamental	NOUN
ajst-23562	132	7	of	of	ADP
ajst-23562	132	8	recurrent	recurrent	ADJ
ajst-23562	132	9	neural	neural	ADJ
ajst-23562	132	10	network	network	NOUN
ajst-23562	132	11	(	(	PUNCT
ajst-23562	132	12	rnn	rnn	PROPN
ajst-23562	132	13	)	)	PUNCT
ajst-23562	132	14	and	and	CCONJ
ajst-23562	132	15	long	long	ADJ
ajst-23562	132	16	short	short	ADJ
ajst-23562	132	17	-	-	PUNCT
ajst-23562	132	18	term	term	NOUN
ajst-23562	132	19	memory	memory	NOUN
ajst-23562	132	20	(	(	PUNCT
ajst-23562	132	21	lstm	lstm	NOUN
ajst-23562	132	22	)	)	PUNCT
ajst-23562	132	23	network[j	network[j	PROPN
ajst-23562	132	24	]	]	PUNCT
ajst-23562	132	25	.	.	PUNCT
ajst-23562	133	1	physica	physica	PROPN
ajst-23562	133	2	d	d	NOUN
ajst-23562	133	3	:	:	PUNCT
ajst-23562	133	4	nonlinear	nonlinear	ADJ
ajst-23562	133	5	phenomena	phenomenon	NOUN
ajst-23562	133	6	,	,	PUNCT
ajst-23562	133	7	2020	2020	NUM
ajst-23562	133	8	,	,	PUNCT
ajst-23562	133	9	404	404	NUM
ajst-23562	133	10	:	:	PUNCT
ajst-23562	133	11	132306	132306	NUM
ajst-23562	133	12	.	.	PUNCT
ajst-23562	134	1	[	[	X
ajst-23562	134	2	10	10	NUM
ajst-23562	134	3	]	]	X
ajst-23562	134	4	kingma	kingma	PROPN
ajst-23562	135	1	d	d	X
ajst-23562	135	2	p	p	PROPN
ajst-23562	135	3	,	,	PUNCT
ajst-23562	135	4	ba	ba	PROPN
ajst-23562	135	5	j	j	PROPN
ajst-23562	135	6	j	j	PROPN
ajst-23562	135	7	c.	c.	PROPN
ajst-23562	135	8	adam	adam	PROPN
ajst-23562	135	9	:	:	PUNCT
ajst-23562	135	10	a	a	DET
ajst-23562	135	11	method	method	NOUN
ajst-23562	135	12	for	for	ADP
ajst-23562	135	13	stochastic	stochastic	ADJ
ajst-23562	135	14	optimization[j	optimization[j	NOUN
ajst-23562	135	15	]	]	X
ajst-23562	135	16	.	.	PUNCT
ajst-23562	135	17	2014	2014	NUM
ajst-23562	135	18	,	,	PUNCT
ajst-23562	135	19	abs/1412.6980	abs/1412.6980	X
ajst-23562	135	20	.	.	PUNCT