id	sid	tid	token	lemma	pos
ajst-26203	1	1	academic	academic	ADJ
ajst-26203	1	2	journal	journal	NOUN
ajst-26203	1	3	of	of	ADP
ajst-26203	1	4	science	science	NOUN
ajst-26203	1	5	and	and	CCONJ
ajst-26203	1	6	technology	technology	NOUN
ajst-26203	1	7	issn	issn	NOUN
ajst-26203	1	8	:	:	PUNCT
ajst-26203	1	9	2771	2771	NUM
ajst-26203	1	10	-	-	SYM
ajst-26203	1	11	3032	3032	NUM
ajst-26203	1	12	|	|	NOUN
ajst-26203	1	13	vol	vol	NOUN
ajst-26203	1	14	.	.	PROPN
ajst-26203	2	1	12	12	NUM
ajst-26203	2	2	,	,	PUNCT
ajst-26203	2	3	no	no	INTJ
ajst-26203	2	4	.	.	NOUN
ajst-26203	2	5	3	3	NUM
ajst-26203	2	6	,	,	PUNCT
ajst-26203	2	7	2024	2024	NUM
ajst-26203	2	8	125	125	NUM
ajst-26203	2	9	sf6	sf6	NOUN
ajst-26203	2	10	gas	gas	NOUN
ajst-26203	2	11	humidity	humidity	NOUN
ajst-26203	2	12	prediction	prediction	NOUN
ajst-26203	2	13	model	model	NOUN
ajst-26203	2	14	based	base	VERB
ajst-26203	2	15	on	on	ADP
ajst-26203	2	16	deep	deep	ADJ
ajst-26203	2	17	learning	learning	NOUN
ajst-26203	2	18	xiaomin	xiaomin	NOUN
ajst-26203	2	19	chen1	chen1	PROPN
ajst-26203	2	20	,	,	PUNCT
ajst-26203	2	21	*	*	PUNCT
ajst-26203	2	22	,	,	PUNCT
ajst-26203	2	23	zhangyun	zhangyun	NOUN
ajst-26203	2	24	li2	li2	PROPN
ajst-26203	2	25	1guangzhou	1guangzhou	NUM
ajst-26203	2	26	power	power	NOUN
ajst-26203	2	27	supply	supply	PROPN
ajst-26203	2	28	bureau	bureau	PROPN
ajst-26203	2	29	of	of	ADP
ajst-26203	2	30	guangdong	guangdong	PROPN
ajst-26203	2	31	power	power	PROPN
ajst-26203	2	32	grid	grid	PROPN
ajst-26203	2	33	co.	co.	PROPN
ajst-26203	2	34	,	,	PUNCT
ajst-26203	2	35	ltd	ltd	PROPN
ajst-26203	2	36	,	,	PUNCT
ajst-26203	2	37	guangzhou	guangzhou	PROPN
ajst-26203	2	38	510000	510000	NUM
ajst-26203	2	39	,	,	PUNCT
ajst-26203	2	40	china	china	PROPN
ajst-26203	2	41	2electric	2electric	NUM
ajst-26203	2	42	power	power	NOUN
ajst-26203	2	43	science	science	PROPN
ajst-26203	2	44	research	research	PROPN
ajst-26203	2	45	institute	institute	PROPN
ajst-26203	2	46	of	of	ADP
ajst-26203	2	47	southern	southern	ADJ
ajst-26203	2	48	power	power	NOUN
ajst-26203	2	49	grid	grid	PROPN
ajst-26203	2	50	ultra	ultra	ADJ
ajst-26203	2	51	high	high	ADJ
ajst-26203	2	52	voltage	voltage	NOUN
ajst-26203	2	53	transmission	transmission	NOUN
ajst-26203	2	54	company	company	NOUN
ajst-26203	2	55	,	,	PUNCT
ajst-26203	2	56	guangzhou	guangzhou	PROPN
ajst-26203	2	57	510000	510000	NUM
ajst-26203	2	58	,	,	PUNCT
ajst-26203	2	59	china	china	PROPN
ajst-26203	2	60	*	*	PUNCT
ajst-26203	2	61	corresponding	correspond	VERB
ajst-26203	2	62	author	author	NOUN
ajst-26203	2	63	abstract	abstract	NOUN
ajst-26203	2	64	:	:	PUNCT
ajst-26203	3	1	sf6	sf6	PROPN
ajst-26203	3	2	gas	gas	NOUN
ajst-26203	3	3	is	be	AUX
ajst-26203	3	4	widely	widely	ADV
ajst-26203	3	5	used	use	VERB
ajst-26203	3	6	in	in	ADP
ajst-26203	3	7	gas	gas	NOUN
ajst-26203	3	8	insulated	insulate	VERB
ajst-26203	3	9	switchgear(gis	switchgear(gis	NOUN
ajst-26203	3	10	)	)	PUNCT
ajst-26203	3	11	as	as	ADP
ajst-26203	3	12	an	an	DET
ajst-26203	3	13	insulating	insulating	NOUN
ajst-26203	3	14	and	and	CCONJ
ajst-26203	3	15	arc	arc	NOUN
ajst-26203	3	16	extinguishing	extinguish	VERB
ajst-26203	3	17	medium	medium	NOUN
ajst-26203	3	18	in	in	ADP
ajst-26203	3	19	electric	electric	ADJ
ajst-26203	3	20	power	power	NOUN
ajst-26203	3	21	industry	industry	NOUN
ajst-26203	3	22	.	.	PUNCT
ajst-26203	4	1	however	however	ADV
ajst-26203	4	2	,	,	PUNCT
ajst-26203	4	3	sf6	sf6	PROPN
ajst-26203	4	4	gas	gas	NOUN
ajst-26203	4	5	humidity	humidity	NOUN
ajst-26203	4	6	has	have	VERB
ajst-26203	4	7	a	a	DET
ajst-26203	4	8	significant	significant	ADJ
ajst-26203	4	9	impact	impact	NOUN
ajst-26203	4	10	on	on	ADP
ajst-26203	4	11	the	the	DET
ajst-26203	4	12	performance	performance	NOUN
ajst-26203	4	13	and	and	CCONJ
ajst-26203	4	14	reliability	reliability	NOUN
ajst-26203	4	15	of	of	ADP
ajst-26203	4	16	gis	gis	NOUN
ajst-26203	4	17	equipment	equipment	NOUN
ajst-26203	4	18	.	.	PUNCT
ajst-26203	5	1	the	the	DET
ajst-26203	5	2	accurate	accurate	ADJ
ajst-26203	5	3	prediction	prediction	NOUN
ajst-26203	5	4	of	of	ADP
ajst-26203	5	5	humidity	humidity	NOUN
ajst-26203	5	6	level	level	NOUN
ajst-26203	5	7	is	be	AUX
ajst-26203	5	8	one	one	NUM
ajst-26203	5	9	of	of	ADP
ajst-26203	5	10	the	the	DET
ajst-26203	5	11	keys	key	NOUN
ajst-26203	5	12	to	to	PART
ajst-26203	5	13	ensure	ensure	VERB
ajst-26203	5	14	the	the	DET
ajst-26203	5	15	long	long	ADJ
ajst-26203	5	16	-	-	PUNCT
ajst-26203	5	17	term	term	NOUN
ajst-26203	5	18	stable	stable	ADJ
ajst-26203	5	19	operation	operation	NOUN
ajst-26203	5	20	of	of	ADP
ajst-26203	5	21	gis	gis	PROPN
ajst-26203	5	22	equipment	equipment	NOUN
ajst-26203	5	23	.	.	PUNCT
ajst-26203	6	1	traditional	traditional	ADJ
ajst-26203	6	2	humidity	humidity	NOUN
ajst-26203	6	3	prediction	prediction	NOUN
ajst-26203	6	4	methods	method	NOUN
ajst-26203	6	5	are	be	AUX
ajst-26203	6	6	often	often	ADV
ajst-26203	6	7	limited	limit	VERB
ajst-26203	6	8	by	by	ADP
ajst-26203	6	9	model	model	NOUN
ajst-26203	6	10	complexity	complexity	NOUN
ajst-26203	6	11	and	and	CCONJ
ajst-26203	6	12	data	datum	NOUN
ajst-26203	6	13	processing	processing	NOUN
ajst-26203	6	14	ability	ability	NOUN
ajst-26203	6	15	,	,	PUNCT
ajst-26203	6	16	which	which	PRON
ajst-26203	6	17	is	be	AUX
ajst-26203	6	18	difficult	difficult	ADJ
ajst-26203	6	19	to	to	PART
ajst-26203	6	20	meet	meet	VERB
ajst-26203	6	21	the	the	DET
ajst-26203	6	22	actual	actual	ADJ
ajst-26203	6	23	demand	demand	NOUN
ajst-26203	6	24	.	.	PUNCT
ajst-26203	7	1	in	in	ADP
ajst-26203	7	2	this	this	DET
ajst-26203	7	3	paper	paper	NOUN
ajst-26203	7	4	,	,	PUNCT
ajst-26203	7	5	deep	deep	ADJ
ajst-26203	7	6	learning	learning	NOUN
ajst-26203	7	7	,	,	PUNCT
ajst-26203	7	8	as	as	ADP
ajst-26203	7	9	a	a	DET
ajst-26203	7	10	powerful	powerful	ADJ
ajst-26203	7	11	data	data	NOUN
ajst-26203	7	12	-	-	PUNCT
ajst-26203	7	13	driven	drive	VERB
ajst-26203	7	14	approach	approach	NOUN
ajst-26203	7	15	,	,	PUNCT
ajst-26203	7	16	shows	show	VERB
ajst-26203	7	17	great	great	ADJ
ajst-26203	7	18	potential	potential	NOUN
ajst-26203	7	19	in	in	ADP
ajst-26203	7	20	gas	gas	NOUN
ajst-26203	7	21	humidity	humidity	NOUN
ajst-26203	7	22	prediction	prediction	NOUN
ajst-26203	7	23	.	.	PUNCT
ajst-26203	8	1	an	an	DET
ajst-26203	8	2	efficient	efficient	ADJ
ajst-26203	8	3	sf6	sf6	PROPN
ajst-26203	8	4	gas	gas	NOUN
ajst-26203	8	5	humidity	humidity	NOUN
ajst-26203	8	6	prediction	prediction	NOUN
ajst-26203	8	7	model	model	NOUN
ajst-26203	8	8	for	for	ADP
ajst-26203	8	9	gis	gis	NOUN
ajst-26203	8	10	equipment	equipment	NOUN
ajst-26203	8	11	is	be	AUX
ajst-26203	8	12	constructed	construct	VERB
ajst-26203	8	13	based	base	VERB
ajst-26203	8	14	on	on	ADP
ajst-26203	8	15	deep	deep	ADJ
ajst-26203	8	16	learning	learning	NOUN
ajst-26203	8	17	algorithm	algorithm	NOUN
ajst-26203	8	18	.	.	PUNCT
ajst-26203	9	1	the	the	DET
ajst-26203	9	2	deep	deep	ADJ
ajst-26203	9	3	learning	learning	NOUN
ajst-26203	9	4	model	model	NOUN
ajst-26203	9	5	can	can	AUX
ajst-26203	9	6	learn	learn	VERB
ajst-26203	9	7	complex	complex	ADJ
ajst-26203	9	8	feature	feature	NOUN
ajst-26203	9	9	representations	representation	NOUN
ajst-26203	9	10	from	from	ADP
ajst-26203	9	11	a	a	DET
ajst-26203	9	12	large	large	ADJ
ajst-26203	9	13	number	number	NOUN
ajst-26203	9	14	of	of	ADP
ajst-26203	9	15	historical	historical	ADJ
ajst-26203	9	16	data	datum	NOUN
ajst-26203	9	17	,	,	PUNCT
ajst-26203	9	18	and	and	CCONJ
ajst-26203	9	19	then	then	ADV
ajst-26203	9	20	accurately	accurately	ADV
ajst-26203	9	21	predict	predict	VERB
ajst-26203	9	22	sf6	sf6	PROPN
ajst-26203	9	23	gas	gas	NOUN
ajst-26203	9	24	humidity	humidity	NOUN
ajst-26203	9	25	.	.	PUNCT
ajst-26203	10	1	keywords	keyword	NOUN
ajst-26203	10	2	:	:	PUNCT
ajst-26203	10	3	sf6	sf6	PROPN
ajst-26203	10	4	gas	gas	NOUN
ajst-26203	10	5	;	;	PUNCT
ajst-26203	10	6	gis	gis	NOUN
ajst-26203	10	7	equipment	equipment	NOUN
ajst-26203	10	8	;	;	PUNCT
ajst-26203	10	9	humidity	humidity	NOUN
ajst-26203	10	10	prediction	prediction	NOUN
ajst-26203	10	11	;	;	PUNCT
ajst-26203	10	12	deep	deep	ADJ
ajst-26203	10	13	learning	learning	NOUN
ajst-26203	10	14	.	.	PUNCT
ajst-26203	11	1	1	1	X
ajst-26203	11	2	.	.	X
ajst-26203	11	3	introduction	introduction	NOUN
ajst-26203	11	4	gis	gis	PROPN
ajst-26203	11	5	equipment	equipment	NOUN
ajst-26203	11	6	is	be	AUX
ajst-26203	11	7	one	one	NUM
ajst-26203	11	8	of	of	ADP
ajst-26203	11	9	the	the	DET
ajst-26203	11	10	key	key	ADJ
ajst-26203	11	11	equipment	equipment	NOUN
ajst-26203	11	12	in	in	ADP
ajst-26203	11	13	electric	electric	ADJ
ajst-26203	11	14	power	power	NOUN
ajst-26203	11	15	industry	industry	NOUN
ajst-26203	11	16	,	,	PUNCT
ajst-26203	11	17	its	its	PRON
ajst-26203	11	18	monitoring	monitoring	NOUN
ajst-26203	11	19	and	and	CCONJ
ajst-26203	11	20	maintenance	maintenance	NOUN
ajst-26203	11	21	has	have	AUX
ajst-26203	11	22	been	be	AUX
ajst-26203	11	23	concerned	concern	VERB
ajst-26203	11	24	.	.	PUNCT
ajst-26203	12	1	the	the	DET
ajst-26203	12	2	current	current	ADJ
ajst-26203	12	3	research	research	NOUN
ajst-26203	12	4	has	have	AUX
ajst-26203	12	5	deeply	deeply	ADV
ajst-26203	12	6	discussed	discuss	VERB
ajst-26203	12	7	the	the	DET
ajst-26203	12	8	status	status	NOUN
ajst-26203	12	9	and	and	CCONJ
ajst-26203	12	10	challenges	challenge	NOUN
ajst-26203	12	11	of	of	ADP
ajst-26203	12	12	gis	gis	NOUN
ajst-26203	12	13	equipment	equipment	NOUN
ajst-26203	12	14	monitoring	monitoring	NOUN
ajst-26203	12	15	and	and	CCONJ
ajst-26203	12	16	maintenance	maintenance	NOUN
ajst-26203	12	17	.	.	PUNCT
ajst-26203	13	1	these	these	DET
ajst-26203	13	2	studies	study	NOUN
ajst-26203	13	3	point	point	VERB
ajst-26203	13	4	out	out	ADP
ajst-26203	13	5	that	that	SCONJ
ajst-26203	13	6	during	during	ADP
ajst-26203	13	7	the	the	DET
ajst-26203	13	8	longterm	longterm	PROPN
ajst-26203	13	9	operation	operation	NOUN
ajst-26203	13	10	of	of	ADP
ajst-26203	13	11	gis	gis	PROPN
ajst-26203	13	12	equipment	equipment	NOUN
ajst-26203	13	13	,	,	PUNCT
ajst-26203	13	14	the	the	DET
ajst-26203	13	15	change	change	NOUN
ajst-26203	13	16	of	of	ADP
ajst-26203	13	17	sf6	sf6	PROPN
ajst-26203	13	18	gas	gas	NOUN
ajst-26203	13	19	humidity	humidity	NOUN
ajst-26203	13	20	has	have	VERB
ajst-26203	13	21	an	an	DET
ajst-26203	13	22	important	important	ADJ
ajst-26203	13	23	impact	impact	NOUN
ajst-26203	13	24	on	on	ADP
ajst-26203	13	25	the	the	DET
ajst-26203	13	26	performance	performance	NOUN
ajst-26203	13	27	and	and	CCONJ
ajst-26203	13	28	reliability	reliability	NOUN
ajst-26203	13	29	of	of	ADP
ajst-26203	13	30	the	the	DET
ajst-26203	13	31	equipment	equipment	NOUN
ajst-26203	13	32	,	,	PUNCT
ajst-26203	13	33	so	so	SCONJ
ajst-26203	13	34	humidity	humidity	NOUN
ajst-26203	13	35	prediction	prediction	NOUN
ajst-26203	13	36	becomes	become	VERB
ajst-26203	13	37	an	an	DET
ajst-26203	13	38	important	important	ADJ
ajst-26203	13	39	part	part	NOUN
ajst-26203	13	40	of	of	ADP
ajst-26203	13	41	gis	gis	NOUN
ajst-26203	13	42	equipment	equipment	NOUN
ajst-26203	13	43	monitoring	monitoring	NOUN
ajst-26203	13	44	.	.	PUNCT
ajst-26203	14	1	in	in	ADP
ajst-26203	14	2	the	the	DET
ajst-26203	14	3	field	field	NOUN
ajst-26203	14	4	of	of	ADP
ajst-26203	14	5	humidity	humidity	NOUN
ajst-26203	14	6	prediction	prediction	NOUN
ajst-26203	14	7	,	,	PUNCT
ajst-26203	14	8	a	a	DET
ajst-26203	14	9	series	series	NOUN
ajst-26203	14	10	of	of	ADP
ajst-26203	14	11	traditional	traditional	ADJ
ajst-26203	14	12	methods	method	NOUN
ajst-26203	14	13	have	have	AUX
ajst-26203	14	14	been	be	AUX
ajst-26203	14	15	applied	apply	VERB
ajst-26203	14	16	to	to	ADP
ajst-26203	14	17	gis	gis	NOUN
ajst-26203	14	18	equipment	equipment	NOUN
ajst-26203	14	19	sf6	sf6	PROPN
ajst-26203	14	20	gas	gas	PROPN
ajst-26203	14	21	humidity	humidity	NOUN
ajst-26203	14	22	prediction	prediction	NOUN
ajst-26203	14	23	.	.	PUNCT
ajst-26203	15	1	these	these	DET
ajst-26203	15	2	methods	method	NOUN
ajst-26203	15	3	include	include	VERB
ajst-26203	15	4	statistics	statistic	NOUN
ajst-26203	15	5	-	-	PUNCT
ajst-26203	15	6	based	base	VERB
ajst-26203	15	7	models	model	NOUN
ajst-26203	15	8	,	,	PUNCT
ajst-26203	15	9	machine	machine	NOUN
ajst-26203	15	10	learning	learning	NOUN
ajst-26203	15	11	methods	method	NOUN
ajst-26203	15	12	,	,	PUNCT
ajst-26203	15	13	and	and	CCONJ
ajst-26203	15	14	physical	physical	ADJ
ajst-26203	15	15	models	model	NOUN
ajst-26203	15	16	.	.	PUNCT
ajst-26203	16	1	however	however	ADV
ajst-26203	16	2	,	,	PUNCT
ajst-26203	16	3	the	the	DET
ajst-26203	16	4	traditional	traditional	ADJ
ajst-26203	16	5	method	method	NOUN
ajst-26203	16	6	has	have	VERB
ajst-26203	16	7	some	some	DET
ajst-26203	16	8	limitations	limitation	NOUN
ajst-26203	16	9	in	in	ADP
ajst-26203	16	10	processing	processing	NOUN
ajst-26203	16	11	complex	complex	ADJ
ajst-26203	16	12	time	time	NOUN
ajst-26203	16	13	series	series	NOUN
ajst-26203	16	14	data	datum	NOUN
ajst-26203	16	15	and	and	CCONJ
ajst-26203	16	16	extracting	extract	VERB
ajst-26203	16	17	advanced	advanced	ADJ
ajst-26203	16	18	features	feature	NOUN
ajst-26203	16	19	,	,	PUNCT
ajst-26203	16	20	which	which	PRON
ajst-26203	16	21	is	be	AUX
ajst-26203	16	22	difficult	difficult	ADJ
ajst-26203	16	23	to	to	PART
ajst-26203	16	24	meet	meet	VERB
ajst-26203	16	25	the	the	DET
ajst-26203	16	26	needs	need	NOUN
ajst-26203	16	27	of	of	ADP
ajst-26203	16	28	practical	practical	ADJ
ajst-26203	16	29	applications	application	NOUN
ajst-26203	16	30	.	.	PUNCT
ajst-26203	17	1	in	in	ADP
ajst-26203	17	2	recent	recent	ADJ
ajst-26203	17	3	years	year	NOUN
ajst-26203	17	4	,	,	PUNCT
ajst-26203	17	5	deep	deep	ADJ
ajst-26203	17	6	learning	learning	NOUN
ajst-26203	17	7	,	,	PUNCT
ajst-26203	17	8	as	as	ADP
ajst-26203	17	9	a	a	DET
ajst-26203	17	10	powerful	powerful	ADJ
ajst-26203	17	11	data	data	NOUN
ajst-26203	17	12	-	-	PUNCT
ajst-26203	17	13	driven	drive	VERB
ajst-26203	17	14	method	method	NOUN
ajst-26203	17	15	,	,	PUNCT
ajst-26203	17	16	has	have	AUX
ajst-26203	17	17	gradually	gradually	ADV
ajst-26203	17	18	emerged	emerge	VERB
ajst-26203	17	19	in	in	ADP
ajst-26203	17	20	the	the	DET
ajst-26203	17	21	field	field	NOUN
ajst-26203	17	22	of	of	ADP
ajst-26203	17	23	sf6	sf6	PROPN
ajst-26203	17	24	gas	gas	PROPN
ajst-26203	17	25	humidity	humidity	NOUN
ajst-26203	17	26	prediction	prediction	NOUN
ajst-26203	17	27	.	.	PUNCT
ajst-26203	18	1	deep	deep	ADJ
ajst-26203	18	2	learning	learning	NOUN
ajst-26203	18	3	models	model	NOUN
ajst-26203	18	4	can	can	AUX
ajst-26203	18	5	accurately	accurately	ADV
ajst-26203	18	6	predict	predict	VERB
ajst-26203	18	7	the	the	DET
ajst-26203	18	8	humidity	humidity	NOUN
ajst-26203	18	9	of	of	ADP
ajst-26203	18	10	sf6	sf6	PROPN
ajst-26203	18	11	gas	gas	NOUN
ajst-26203	18	12	by	by	ADP
ajst-26203	18	13	learning	learn	VERB
ajst-26203	18	14	complex	complex	ADJ
ajst-26203	18	15	features	feature	NOUN
ajst-26203	18	16	in	in	ADP
ajst-26203	18	17	large	large	ADJ
ajst-26203	18	18	-	-	PUNCT
ajst-26203	18	19	scale	scale	NOUN
ajst-26203	18	20	data	datum	NOUN
ajst-26203	18	21	.	.	PUNCT
ajst-26203	19	1	these	these	DET
ajst-26203	19	2	models	model	NOUN
ajst-26203	19	3	are	be	AUX
ajst-26203	19	4	usually	usually	ADV
ajst-26203	19	5	constructed	construct	VERB
ajst-26203	19	6	based	base	VERB
ajst-26203	19	7	on	on	ADP
ajst-26203	19	8	deep	deep	ADJ
ajst-26203	19	9	learning	learning	NOUN
ajst-26203	19	10	algorithms	algorithm	NOUN
ajst-26203	19	11	such	such	ADJ
ajst-26203	19	12	as	as	ADP
ajst-26203	19	13	convolutional	convolutional	ADJ
ajst-26203	19	14	neural	neural	ADJ
ajst-26203	19	15	networks	network	NOUN
ajst-26203	19	16	(	(	PUNCT
ajst-26203	19	17	cnn	cnn	PROPN
ajst-26203	19	18	)	)	PUNCT
ajst-26203	19	19	and	and	CCONJ
ajst-26203	19	20	recurrent	recurrent	ADJ
ajst-26203	19	21	neural	neural	ADJ
ajst-26203	19	22	networks	network	NOUN
ajst-26203	19	23	(	(	PUNCT
ajst-26203	19	24	rnn	rnn	PROPN
ajst-26203	19	25	)	)	PUNCT
ajst-26203	19	26	,	,	PUNCT
ajst-26203	19	27	which	which	PRON
ajst-26203	19	28	have	have	VERB
ajst-26203	19	29	high	high	ADJ
ajst-26203	19	30	flexibility	flexibility	NOUN
ajst-26203	19	31	and	and	CCONJ
ajst-26203	19	32	predictive	predictive	ADJ
ajst-26203	19	33	performance	performance	NOUN
ajst-26203	19	34	[	[	X
ajst-26203	19	35	1	1	NUM
ajst-26203	19	36	]	]	PUNCT
ajst-26203	19	37	.	.	PUNCT
ajst-26203	20	1	although	although	SCONJ
ajst-26203	20	2	deep	deep	ADJ
ajst-26203	20	3	learning	learning	NOUN
ajst-26203	20	4	has	have	AUX
ajst-26203	20	5	made	make	VERB
ajst-26203	20	6	remarkable	remarkable	ADJ
ajst-26203	20	7	achievements	achievement	NOUN
ajst-26203	20	8	in	in	ADP
ajst-26203	20	9	the	the	DET
ajst-26203	20	10	field	field	NOUN
ajst-26203	20	11	of	of	ADP
ajst-26203	20	12	humidity	humidity	NOUN
ajst-26203	20	13	prediction	prediction	NOUN
ajst-26203	20	14	,	,	PUNCT
ajst-26203	20	15	its	its	PRON
ajst-26203	20	16	application	application	NOUN
ajst-26203	20	17	in	in	ADP
ajst-26203	20	18	the	the	DET
ajst-26203	20	19	gis	gis	PROPN
ajst-26203	20	20	equipment	equipment	NOUN
ajst-26203	20	21	sf6	sf6	PROPN
ajst-26203	20	22	gas	gas	NOUN
ajst-26203	20	23	humidity	humidity	NOUN
ajst-26203	20	24	prediction	prediction	NOUN
ajst-26203	20	25	is	be	AUX
ajst-26203	20	26	still	still	ADV
ajst-26203	20	27	in	in	ADP
ajst-26203	20	28	the	the	DET
ajst-26203	20	29	preliminary	preliminary	ADJ
ajst-26203	20	30	stage	stage	NOUN
ajst-26203	20	31	.	.	PUNCT
ajst-26203	21	1	therefore	therefore	ADV
ajst-26203	21	2	,	,	PUNCT
ajst-26203	21	3	it	it	PRON
ajst-26203	21	4	is	be	AUX
ajst-26203	21	5	of	of	ADP
ajst-26203	21	6	great	great	ADJ
ajst-26203	21	7	significance	significance	NOUN
ajst-26203	21	8	to	to	PART
ajst-26203	21	9	deeply	deeply	ADV
ajst-26203	21	10	study	study	VERB
ajst-26203	21	11	the	the	DET
ajst-26203	21	12	sf6	sf6	PROPN
ajst-26203	21	13	gas	gas	PROPN
ajst-26203	21	14	humidity	humidity	NOUN
ajst-26203	21	15	prediction	prediction	NOUN
ajst-26203	21	16	model	model	NOUN
ajst-26203	21	17	of	of	ADP
ajst-26203	21	18	gis	gis	NOUN
ajst-26203	21	19	equipment	equipment	NOUN
ajst-26203	21	20	based	base	VERB
ajst-26203	21	21	on	on	ADP
ajst-26203	21	22	deep	deep	ADJ
ajst-26203	21	23	learning	learning	NOUN
ajst-26203	21	24	,	,	PUNCT
ajst-26203	21	25	and	and	CCONJ
ajst-26203	21	26	it	it	PRON
ajst-26203	21	27	is	be	AUX
ajst-26203	21	28	expected	expect	VERB
ajst-26203	21	29	to	to	PART
ajst-26203	21	30	provide	provide	VERB
ajst-26203	21	31	a	a	DET
ajst-26203	21	32	new	new	ADJ
ajst-26203	21	33	solution	solution	NOUN
ajst-26203	21	34	for	for	ADP
ajst-26203	21	35	gis	gis	NOUN
ajst-26203	21	36	equipment	equipment	NOUN
ajst-26203	21	37	monitoring	monitoring	NOUN
ajst-26203	21	38	and	and	CCONJ
ajst-26203	21	39	maintenance	maintenance	NOUN
ajst-26203	21	40	.	.	PUNCT
ajst-26203	22	1	2	2	X
ajst-26203	22	2	.	.	X
ajst-26203	22	3	system	system	NOUN
ajst-26203	22	4	module	module	NOUN
ajst-26203	22	5	function	function	NOUN
ajst-26203	22	6	(	(	PUNCT
ajst-26203	22	7	1	1	NUM
ajst-26203	22	8	)	)	PUNCT
ajst-26203	22	9	data	datum	NOUN
ajst-26203	22	10	input	input	NOUN
ajst-26203	22	11	and	and	CCONJ
ajst-26203	22	12	processing	processing	NOUN
ajst-26203	22	13	module	module	NOUN
ajst-26203	22	14	design	design	NOUN
ajst-26203	22	15	data	datum	NOUN
ajst-26203	22	16	input	input	NOUN
ajst-26203	22	17	and	and	CCONJ
ajst-26203	22	18	processing	processing	NOUN
ajst-26203	22	19	module	module	NOUN
ajst-26203	22	20	is	be	AUX
ajst-26203	22	21	an	an	DET
ajst-26203	22	22	important	important	ADJ
ajst-26203	22	23	part	part	NOUN
ajst-26203	22	24	of	of	ADP
ajst-26203	22	25	sf6	sf6	PROPN
ajst-26203	22	26	gas	gas	PROPN
ajst-26203	22	27	humidity	humidity	NOUN
ajst-26203	22	28	prediction	prediction	NOUN
ajst-26203	22	29	model	model	NOUN
ajst-26203	22	30	of	of	ADP
ajst-26203	22	31	gis	gis	NOUN
ajst-26203	22	32	equipment	equipment	NOUN
ajst-26203	22	33	based	base	VERB
ajst-26203	22	34	on	on	ADP
ajst-26203	22	35	deep	deep	ADJ
ajst-26203	22	36	learning	learning	NOUN
ajst-26203	22	37	.	.	PUNCT
ajst-26203	23	1	the	the	DET
ajst-26203	23	2	module	module	NOUN
ajst-26203	23	3	is	be	AUX
ajst-26203	23	4	designed	design	VERB
ajst-26203	23	5	to	to	PART
ajst-26203	23	6	achieve	achieve	VERB
ajst-26203	23	7	effective	effective	ADJ
ajst-26203	23	8	acquisition	acquisition	NOUN
ajst-26203	23	9	,	,	PUNCT
ajst-26203	23	10	cleaning	cleaning	NOUN
ajst-26203	23	11	and	and	CCONJ
ajst-26203	23	12	preprocessing	preprocessing	NOUN
ajst-26203	23	13	of	of	ADP
ajst-26203	23	14	sf6	sf6	PROPN
ajst-26203	23	15	gas	gas	PROPN
ajst-26203	23	16	humidity	humidity	NOUN
ajst-26203	23	17	data	datum	NOUN
ajst-26203	23	18	,	,	PUNCT
ajst-26203	23	19	and	and	CCONJ
ajst-26203	23	20	provide	provide	VERB
ajst-26203	23	21	reliable	reliable	ADJ
ajst-26203	23	22	data	datum	NOUN
ajst-26203	23	23	support	support	NOUN
ajst-26203	23	24	for	for	ADP
ajst-26203	23	25	subsequent	subsequent	ADJ
ajst-26203	23	26	model	model	NOUN
ajst-26203	23	27	training	training	NOUN
ajst-26203	23	28	and	and	CCONJ
ajst-26203	23	29	prediction	prediction	NOUN
ajst-26203	23	30	[	[	X
ajst-26203	23	31	2	2	NUM
ajst-26203	23	32	]	]	PUNCT
ajst-26203	23	33	.	.	PUNCT
ajst-26203	24	1	in	in	ADP
ajst-26203	24	2	the	the	DET
ajst-26203	24	3	data	datum	NOUN
ajst-26203	24	4	input	input	NOUN
ajst-26203	24	5	phase	phase	NOUN
ajst-26203	24	6	,	,	PUNCT
ajst-26203	24	7	the	the	DET
ajst-26203	24	8	module	module	NOUN
ajst-26203	24	9	will	will	AUX
ajst-26203	24	10	obtain	obtain	VERB
ajst-26203	24	11	sf6	sf6	NOUN
ajst-26203	24	12	gas	gas	NOUN
ajst-26203	24	13	humidity	humidity	NOUN
ajst-26203	24	14	data	datum	NOUN
ajst-26203	24	15	from	from	ADP
ajst-26203	24	16	various	various	ADJ
ajst-26203	24	17	data	datum	NOUN
ajst-26203	24	18	sources	source	NOUN
ajst-26203	24	19	.	.	PUNCT
ajst-26203	25	1	these	these	DET
ajst-26203	25	2	data	datum	NOUN
ajst-26203	25	3	sources	source	NOUN
ajst-26203	25	4	can	can	AUX
ajst-26203	25	5	include	include	VERB
ajst-26203	25	6	sensor	sensor	NOUN
ajst-26203	25	7	data	datum	NOUN
ajst-26203	25	8	in	in	ADP
ajst-26203	25	9	gis	gis	PROPN
ajst-26203	25	10	equipment	equipment	NOUN
ajst-26203	25	11	,	,	PUNCT
ajst-26203	25	12	historical	historical	ADJ
ajst-26203	25	13	data	datum	NOUN
ajst-26203	25	14	collected	collect	VERB
ajst-26203	25	15	by	by	ADP
ajst-26203	25	16	monitoring	monitor	VERB
ajst-26203	25	17	systems	system	NOUN
ajst-26203	25	18	,	,	PUNCT
ajst-26203	25	19	and	and	CCONJ
ajst-26203	25	20	external	external	ADJ
ajst-26203	25	21	weather	weather	NOUN
ajst-26203	25	22	stations	station	NOUN
ajst-26203	25	23	.	.	PUNCT
ajst-26203	26	1	the	the	DET
ajst-26203	26	2	data	datum	NOUN
ajst-26203	26	3	input	input	NOUN
ajst-26203	26	4	module	module	NOUN
ajst-26203	26	5	with	with	ADP
ajst-26203	26	6	different	different	ADJ
ajst-26203	26	7	data	datum	NOUN
ajst-26203	26	8	sources	source	NOUN
ajst-26203	26	9	and	and	CCONJ
ajst-26203	26	10	real	real	ADJ
ajst-26203	26	11	-	-	PUNCT
ajst-26203	26	12	time	time	NOUN
ajst-26203	26	13	data	datum	NOUN
ajst-26203	26	14	acquisition	acquisition	NOUN
ajst-26203	26	15	.	.	PUNCT
ajst-26203	27	1	in	in	ADP
ajst-26203	27	2	the	the	DET
ajst-26203	27	3	data	data	NOUN
ajst-26203	27	4	processing	processing	NOUN
ajst-26203	27	5	stage	stage	NOUN
ajst-26203	27	6	,	,	PUNCT
ajst-26203	27	7	the	the	DET
ajst-26203	27	8	module	module	NOUN
ajst-26203	27	9	will	will	AUX
ajst-26203	27	10	clean	clean	VERB
ajst-26203	27	11	and	and	CCONJ
ajst-26203	27	12	preprocess	preprocess	NOUN
ajst-26203	27	13	the	the	DET
ajst-26203	27	14	obtained	obtain	VERB
ajst-26203	27	15	sf6	sf6	PROPN
ajst-26203	27	16	gas	gas	NOUN
ajst-26203	27	17	humidity	humidity	NOUN
ajst-26203	27	18	data	datum	NOUN
ajst-26203	27	19	to	to	PART
ajst-26203	27	20	improve	improve	VERB
ajst-26203	27	21	the	the	DET
ajst-26203	27	22	quality	quality	NOUN
ajst-26203	27	23	and	and	CCONJ
ajst-26203	27	24	availability	availability	NOUN
ajst-26203	27	25	of	of	ADP
ajst-26203	27	26	the	the	DET
ajst-26203	27	27	data	datum	NOUN
ajst-26203	27	28	.	.	PUNCT
ajst-26203	28	1	the	the	DET
ajst-26203	28	2	cleaning	cleaning	NOUN
ajst-26203	28	3	process	process	NOUN
ajst-26203	28	4	includes	include	VERB
ajst-26203	28	5	removing	remove	VERB
ajst-26203	28	6	duplicate	duplicate	ADJ
ajst-26203	28	7	data	datum	NOUN
ajst-26203	28	8	,	,	PUNCT
ajst-26203	28	9	filling	fill	VERB
ajst-26203	28	10	in	in	ADP
ajst-26203	28	11	missing	miss	VERB
ajst-26203	28	12	values	value	NOUN
ajst-26203	28	13	,	,	PUNCT
ajst-26203	28	14	and	and	CCONJ
ajst-26203	28	15	handling	handle	VERB
ajst-26203	28	16	outliers	outlier	NOUN
ajst-26203	28	17	to	to	PART
ajst-26203	28	18	ensure	ensure	VERB
ajst-26203	28	19	data	datum	NOUN
ajst-26203	28	20	integrity	integrity	NOUN
ajst-26203	28	21	and	and	CCONJ
ajst-26203	28	22	accuracy	accuracy	NOUN
ajst-26203	28	23	.	.	PUNCT
ajst-26203	29	1	the	the	DET
ajst-26203	29	2	preprocessing	preprocessing	NOUN
ajst-26203	29	3	involves	involve	VERB
ajst-26203	29	4	data	datum	NOUN
ajst-26203	29	5	normalization	normalization	NOUN
ajst-26203	29	6	,	,	PUNCT
ajst-26203	29	7	feature	feature	NOUN
ajst-26203	29	8	extraction	extraction	NOUN
ajst-26203	29	9	and	and	CCONJ
ajst-26203	29	10	data	datum	NOUN
ajst-26203	29	11	conversion	conversion	NOUN
ajst-26203	29	12	,	,	PUNCT
ajst-26203	29	13	etc	etc	X
ajst-26203	29	14	.	.	X
ajst-26203	29	15	,	,	PUNCT
ajst-26203	29	16	and	and	CCONJ
ajst-26203	29	17	the	the	DET
ajst-26203	29	18	prepared	prepare	VERB
ajst-26203	29	19	data	data	NOUN
ajst-26203	29	20	format	format	NOUN
ajst-26203	29	21	is	be	AUX
ajst-26203	29	22	input	input	VERB
ajst-26203	29	23	into	into	ADP
ajst-26203	29	24	the	the	DET
ajst-26203	29	25	deep	deep	ADJ
ajst-26203	29	26	learning	learning	NOUN
ajst-26203	29	27	model	model	NOUN
ajst-26203	29	28	for	for	ADP
ajst-26203	29	29	training	training	NOUN
ajst-26203	29	30	[	[	X
ajst-26203	29	31	3	3	NUM
ajst-26203	29	32	-	-	SYM
ajst-26203	29	33	5	5	NUM
ajst-26203	29	34	]	]	PUNCT
ajst-26203	29	35	.	.	PUNCT
ajst-26203	30	1	(	(	PUNCT
ajst-26203	30	2	2	2	X
ajst-26203	30	3	)	)	PUNCT
ajst-26203	30	4	the	the	DET
ajst-26203	30	5	significance	significance	NOUN
ajst-26203	30	6	and	and	CCONJ
ajst-26203	30	7	implementation	implementation	NOUN
ajst-26203	30	8	of	of	ADP
ajst-26203	30	9	time	time	NOUN
ajst-26203	30	10	step	step	NOUN
ajst-26203	30	11	alternate	alternate	NOUN
ajst-26203	30	12	setting	set	VERB
ajst-26203	30	13	the	the	DET
ajst-26203	30	14	main	main	ADJ
ajst-26203	30	15	purpose	purpose	NOUN
ajst-26203	30	16	of	of	ADP
ajst-26203	30	17	the	the	DET
ajst-26203	30	18	alternate	alternate	ADJ
ajst-26203	30	19	time	time	NOUN
ajst-26203	30	20	step	step	NOUN
ajst-26203	30	21	setup	setup	NOUN
ajst-26203	30	22	is	be	AUX
ajst-26203	30	23	to	to	PART
ajst-26203	30	24	explore	explore	VERB
ajst-26203	30	25	the	the	DET
ajst-26203	30	26	changes	change	NOUN
ajst-26203	30	27	in	in	ADP
ajst-26203	30	28	model	model	NOUN
ajst-26203	30	29	performance	performance	NOUN
ajst-26203	30	30	under	under	ADP
ajst-26203	30	31	different	different	ADJ
ajst-26203	30	32	time	time	NOUN
ajst-26203	30	33	steps	step	NOUN
ajst-26203	30	34	to	to	PART
ajst-26203	30	35	better	well	ADV
ajst-26203	30	36	understand	understand	VERB
ajst-26203	30	37	and	and	CCONJ
ajst-26203	30	38	optimize	optimize	VERB
ajst-26203	30	39	the	the	DET
ajst-26203	30	40	predictive	predictive	ADJ
ajst-26203	30	41	power	power	NOUN
ajst-26203	30	42	of	of	ADP
ajst-26203	30	43	the	the	DET
ajst-26203	30	44	model	model	NOUN
ajst-26203	30	45	.	.	PUNCT
ajst-26203	31	1	the	the	DET
ajst-26203	31	2	significance	significance	NOUN
ajst-26203	31	3	of	of	ADP
ajst-26203	31	4	time	time	NOUN
ajst-26203	31	5	step	step	NOUN
ajst-26203	31	6	alternate	alternate	ADJ
ajst-26203	31	7	setting	setting	NOUN
ajst-26203	31	8	is	be	AUX
ajst-26203	31	9	reflected	reflect	VERB
ajst-26203	31	10	in	in	ADP
ajst-26203	31	11	the	the	DET
ajst-26203	31	12	model	model	NOUN
ajst-26203	31	13	stability	stability	NOUN
ajst-26203	31	14	and	and	CCONJ
ajst-26203	31	15	generalization	generalization	NOUN
ajst-26203	31	16	ability	ability	NOUN
ajst-26203	31	17	,	,	PUNCT
ajst-26203	31	18	the	the	DET
ajst-26203	31	19	exploration	exploration	NOUN
ajst-26203	31	20	and	and	CCONJ
ajst-26203	31	21	adaptability	adaptability	NOUN
ajst-26203	31	22	of	of	ADP
ajst-26203	31	23	time	time	NOUN
ajst-26203	31	24	scale	scale	NOUN
ajst-26203	31	25	,	,	PUNCT
ajst-26203	31	26	the	the	DET
ajst-26203	31	27	optimization	optimization	NOUN
ajst-26203	31	28	and	and	CCONJ
ajst-26203	31	29	adjustment	adjustment	NOUN
ajst-26203	31	30	of	of	ADP
ajst-26203	31	31	prediction	prediction	NOUN
ajst-26203	31	32	effect	effect	NOUN
ajst-26203	31	33	,	,	PUNCT
ajst-26203	31	34	and	and	CCONJ
ajst-26203	31	35	the	the	DET
ajst-26203	31	36	optimization	optimization	NOUN
ajst-26203	31	37	and	and	CCONJ
ajst-26203	31	38	adjustment	adjustment	NOUN
ajst-26203	31	39	of	of	ADP
ajst-26203	31	40	prediction	prediction	NOUN
ajst-26203	31	41	effect	effect	NOUN
ajst-26203	31	42	.	.	PUNCT
ajst-26203	32	1	therefore	therefore	ADV
ajst-26203	32	2	,	,	PUNCT
ajst-26203	32	3	the	the	DET
ajst-26203	32	4	implementation	implementation	NOUN
ajst-26203	32	5	of	of	ADP
ajst-26203	32	6	an	an	DET
ajst-26203	32	7	alternate	alternate	ADJ
ajst-26203	32	8	time	time	NOUN
ajst-26203	32	9	-	-	PUNCT
ajst-26203	32	10	step	step	NOUN
ajst-26203	32	11	setup	setup	NOUN
ajst-26203	32	12	usually	usually	ADV
ajst-26203	32	13	consists	consist	VERB
ajst-26203	32	14	of	of	ADP
ajst-26203	32	15	the	the	DET
ajst-26203	32	16	following	follow	VERB
ajst-26203	32	17	steps	step	NOUN
ajst-26203	32	18	:	:	PUNCT
ajst-26203	32	19	1	1	NUM
ajst-26203	32	20	)	)	PUNCT
ajst-26203	32	21	data	datum	NOUN
ajst-26203	32	22	division	division	NOUN
ajst-26203	32	23	:	:	PUNCT
ajst-26203	32	24	historical	historical	ADJ
ajst-26203	32	25	data	datum	NOUN
ajst-26203	32	26	is	be	AUX
ajst-26203	32	27	divided	divide	VERB
ajst-26203	32	28	according	accord	VERB
ajst-26203	32	29	to	to	ADP
ajst-26203	32	30	different	different	ADJ
ajst-26203	32	31	time	time	NOUN
ajst-26203	32	32	steps	step	NOUN
ajst-26203	32	33	to	to	PART
ajst-26203	32	34	form	form	VERB
ajst-26203	32	35	training	training	NOUN
ajst-26203	32	36	sets	set	NOUN
ajst-26203	32	37	,	,	PUNCT
ajst-26203	32	38	verification	verification	NOUN
ajst-26203	32	39	sets	set	NOUN
ajst-26203	32	40	and	and	CCONJ
ajst-26203	32	41	test	test	NOUN
ajst-26203	32	42	sets	set	NOUN
ajst-26203	32	43	of	of	ADP
ajst-26203	32	44	different	different	ADJ
ajst-26203	32	45	time	time	NOUN
ajst-26203	32	46	scales	scale	NOUN
ajst-26203	32	47	.	.	PUNCT
ajst-26203	33	1	2)model	2)model	NUM
ajst-26203	33	2	training	training	NOUN
ajst-26203	33	3	and	and	CCONJ
ajst-26203	33	4	evaluation	evaluation	NOUN
ajst-26203	33	5	:	:	PUNCT
ajst-26203	33	6	data	datum	NOUN
ajst-26203	33	7	sets	set	NOUN
ajst-26203	33	8	with	with	ADP
ajst-26203	33	9	different	different	ADJ
ajst-26203	33	10	126	126	NUM
ajst-26203	33	11	time	time	NOUN
ajst-26203	33	12	steps	step	NOUN
ajst-26203	33	13	are	be	AUX
ajst-26203	33	14	used	use	VERB
ajst-26203	33	15	to	to	PART
ajst-26203	33	16	train	train	VERB
ajst-26203	33	17	and	and	CCONJ
ajst-26203	33	18	evaluate	evaluate	VERB
ajst-26203	33	19	the	the	DET
ajst-26203	33	20	model	model	NOUN
ajst-26203	33	21	.	.	PUNCT
ajst-26203	34	1	in	in	ADP
ajst-26203	34	2	the	the	DET
ajst-26203	34	3	training	training	NOUN
ajst-26203	34	4	process	process	NOUN
ajst-26203	34	5	,	,	PUNCT
ajst-26203	34	6	the	the	DET
ajst-26203	34	7	model	model	NOUN
ajst-26203	34	8	parameters	parameter	NOUN
ajst-26203	34	9	and	and	CCONJ
ajst-26203	34	10	training	training	NOUN
ajst-26203	34	11	strategies	strategy	NOUN
ajst-26203	34	12	should	should	AUX
ajst-26203	34	13	be	be	AUX
ajst-26203	34	14	adjusted	adjust	VERB
ajst-26203	34	15	according	accord	VERB
ajst-26203	34	16	to	to	ADP
ajst-26203	34	17	the	the	DET
ajst-26203	34	18	characteristics	characteristic	NOUN
ajst-26203	34	19	of	of	ADP
ajst-26203	34	20	different	different	ADJ
ajst-26203	34	21	time	time	NOUN
ajst-26203	34	22	steps	step	NOUN
ajst-26203	34	23	.	.	PUNCT
ajst-26203	35	1	3)result	3)result	NUM
ajst-26203	35	2	analysis	analysis	NOUN
ajst-26203	35	3	and	and	CCONJ
ajst-26203	35	4	comparison	comparison	NOUN
ajst-26203	35	5	:	:	PUNCT
ajst-26203	35	6	the	the	DET
ajst-26203	35	7	prediction	prediction	NOUN
ajst-26203	35	8	results	result	NOUN
ajst-26203	35	9	of	of	ADP
ajst-26203	35	10	the	the	DET
ajst-26203	35	11	model	model	NOUN
ajst-26203	35	12	under	under	ADP
ajst-26203	35	13	different	different	ADJ
ajst-26203	35	14	time	time	NOUN
ajst-26203	35	15	steps	step	NOUN
ajst-26203	35	16	were	be	AUX
ajst-26203	35	17	analyzed	analyze	VERB
ajst-26203	35	18	and	and	CCONJ
ajst-26203	35	19	compared	compare	VERB
ajst-26203	35	20	to	to	PART
ajst-26203	35	21	evaluate	evaluate	VERB
ajst-26203	35	22	the	the	DET
ajst-26203	35	23	performance	performance	NOUN
ajst-26203	35	24	of	of	ADP
ajst-26203	35	25	the	the	DET
ajst-26203	35	26	model	model	NOUN
ajst-26203	35	27	under	under	ADP
ajst-26203	35	28	different	different	ADJ
ajst-26203	35	29	time	time	NOUN
ajst-26203	35	30	scales	scale	NOUN
ajst-26203	35	31	.	.	PUNCT
ajst-26203	36	1	based	base	VERB
ajst-26203	36	2	on	on	ADP
ajst-26203	36	3	the	the	DET
ajst-26203	36	4	analysis	analysis	NOUN
ajst-26203	36	5	results	result	NOUN
ajst-26203	36	6	,	,	PUNCT
ajst-26203	36	7	the	the	DET
ajst-26203	36	8	optimal	optimal	ADJ
ajst-26203	36	9	time	time	NOUN
ajst-26203	36	10	step	step	NOUN
ajst-26203	36	11	setting	set	VERB
ajst-26203	36	12	can	can	AUX
ajst-26203	36	13	be	be	AUX
ajst-26203	36	14	selected	select	VERB
ajst-26203	36	15	to	to	PART
ajst-26203	36	16	optimize	optimize	VERB
ajst-26203	36	17	the	the	DET
ajst-26203	36	18	prediction	prediction	NOUN
ajst-26203	36	19	effect	effect	NOUN
ajst-26203	36	20	of	of	ADP
ajst-26203	36	21	the	the	DET
ajst-26203	36	22	model	model	NOUN
ajst-26203	36	23	.	.	PUNCT
ajst-26203	37	1	(	(	PUNCT
ajst-26203	37	2	3	3	X
ajst-26203	37	3	)	)	PUNCT
ajst-26203	37	4	lstm	lstm	NOUN
ajst-26203	37	5	network	network	NOUN
ajst-26203	37	6	architecture	architecture	NOUN
ajst-26203	37	7	definition	definition	NOUN
ajst-26203	37	8	and	and	CCONJ
ajst-26203	37	9	training	training	NOUN
ajst-26203	37	10	process	process	NOUN
ajst-26203	37	11	long	long	ADJ
ajst-26203	37	12	short	short	ADJ
ajst-26203	37	13	-	-	PUNCT
ajst-26203	37	14	term	term	NOUN
ajst-26203	37	15	memory	memory	NOUN
ajst-26203	37	16	network	network	NOUN
ajst-26203	37	17	(	(	PUNCT
ajst-26203	37	18	lstm	lstm	PROPN
ajst-26203	37	19	)	)	PUNCT
ajst-26203	37	20	is	be	AUX
ajst-26203	37	21	a	a	DET
ajst-26203	37	22	kind	kind	NOUN
ajst-26203	37	23	of	of	ADP
ajst-26203	37	24	deep	deep	ADJ
ajst-26203	37	25	learning	learning	NOUN
ajst-26203	37	26	model	model	NOUN
ajst-26203	37	27	commonly	commonly	ADV
ajst-26203	37	28	used	use	VERB
ajst-26203	37	29	for	for	ADP
ajst-26203	37	30	processing	process	VERB
ajst-26203	37	31	sequence	sequence	NOUN
ajst-26203	37	32	data	datum	NOUN
ajst-26203	37	33	,	,	PUNCT
ajst-26203	37	34	which	which	PRON
ajst-26203	37	35	can	can	AUX
ajst-26203	37	36	effectively	effectively	ADV
ajst-26203	37	37	capture	capture	VERB
ajst-26203	37	38	long	long	ADJ
ajst-26203	37	39	-	-	PUNCT
ajst-26203	37	40	term	term	NOUN
ajst-26203	37	41	dependency	dependency	NOUN
ajst-26203	37	42	and	and	CCONJ
ajst-26203	37	43	memory	memory	NOUN
ajst-26203	37	44	information	information	NOUN
ajst-26203	37	45	,	,	PUNCT
ajst-26203	37	46	and	and	CCONJ
ajst-26203	37	47	is	be	AUX
ajst-26203	37	48	suitable	suitable	ADJ
ajst-26203	37	49	for	for	ADP
ajst-26203	37	50	modeling	modeling	NOUN
ajst-26203	37	51	and	and	CCONJ
ajst-26203	37	52	forecasting	forecasting	NOUN
ajst-26203	37	53	time	time	NOUN
ajst-26203	37	54	series	series	PROPN
ajst-26203	37	55	data	data	PROPN
ajst-26203	37	56	.	.	PUNCT
ajst-26203	38	1	in	in	ADP
ajst-26203	38	2	the	the	DET
ajst-26203	38	3	sf6	sf6	PROPN
ajst-26203	38	4	gas	gas	PROPN
ajst-26203	38	5	humidity	humidity	NOUN
ajst-26203	38	6	prediction	prediction	NOUN
ajst-26203	38	7	model	model	NOUN
ajst-26203	38	8	of	of	ADP
ajst-26203	38	9	gis	gis	NOUN
ajst-26203	38	10	equipment	equipment	NOUN
ajst-26203	38	11	based	base	VERB
ajst-26203	38	12	on	on	ADP
ajst-26203	38	13	deep	deep	ADJ
ajst-26203	38	14	learning	learning	NOUN
ajst-26203	38	15	,	,	PUNCT
ajst-26203	38	16	lstm	lstm	ADJ
ajst-26203	38	17	network	network	NOUN
ajst-26203	38	18	is	be	AUX
ajst-26203	38	19	often	often	ADV
ajst-26203	38	20	selected	select	VERB
ajst-26203	38	21	as	as	ADP
ajst-26203	38	22	the	the	DET
ajst-26203	38	23	main	main	ADJ
ajst-26203	38	24	prediction	prediction	NOUN
ajst-26203	38	25	model	model	NOUN
ajst-26203	38	26	,	,	PUNCT
ajst-26203	38	27	and	and	CCONJ
ajst-26203	38	28	the	the	DET
ajst-26203	38	29	lstm	lstm	ADJ
ajst-26203	38	30	network	network	NOUN
ajst-26203	38	31	architecture	architecture	NOUN
ajst-26203	38	32	is	be	AUX
ajst-26203	38	33	shown	show	VERB
ajst-26203	38	34	in	in	ADP
ajst-26203	38	35	figure	figure	NOUN
ajst-26203	38	36	1	1	NUM
ajst-26203	38	37	.	.	PUNCT
ajst-26203	39	1	figure	figure	NOUN
ajst-26203	39	2	1	1	NUM
ajst-26203	39	3	.	.	PUNCT
ajst-26203	39	4	lstm	lstm	ADJ
ajst-26203	39	5	network	network	NOUN
ajst-26203	39	6	architecture	architecture	NOUN
ajst-26203	39	7	diagram	diagram	VERB
ajst-26203	39	8	the	the	DET
ajst-26203	39	9	lstm	lstm	PROPN
ajst-26203	39	10	network	network	NOUN
ajst-26203	39	11	training	training	NOUN
ajst-26203	39	12	process	process	NOUN
ajst-26203	39	13	is	be	AUX
ajst-26203	39	14	shown	show	VERB
ajst-26203	39	15	in	in	ADP
ajst-26203	39	16	figure	figure	NOUN
ajst-26203	39	17	2	2	NUM
ajst-26203	39	18	.	.	NOUN
ajst-26203	39	19	3	3	NUM
ajst-26203	39	20	.	.	NOUN
ajst-26203	39	21	method	method	NOUN
ajst-26203	39	22	and	and	CCONJ
ajst-26203	39	23	experiment	experiment	NOUN
ajst-26203	39	24	(	(	PUNCT
ajst-26203	39	25	1	1	NUM
ajst-26203	39	26	)	)	PUNCT
ajst-26203	39	27	data	datum	NOUN
ajst-26203	39	28	collection	collection	NOUN
ajst-26203	39	29	and	and	CCONJ
ajst-26203	39	30	preprocessing	preprocesse	VERB
ajst-26203	39	31	1)data	1)data	NUM
ajst-26203	39	32	sources	source	NOUN
ajst-26203	39	33	and	and	CCONJ
ajst-26203	39	34	methods	method	NOUN
ajst-26203	39	35	of	of	ADP
ajst-26203	39	36	acquisition	acquisition	NOUN
ajst-26203	39	37	the	the	DET
ajst-26203	39	38	establishment	establishment	NOUN
ajst-26203	39	39	of	of	ADP
ajst-26203	39	40	sf6	sf6	PROPN
ajst-26203	39	41	gas	gas	PROPN
ajst-26203	39	42	humidity	humidity	NOUN
ajst-26203	39	43	prediction	prediction	NOUN
ajst-26203	39	44	model	model	NOUN
ajst-26203	39	45	requires	require	VERB
ajst-26203	39	46	a	a	DET
ajst-26203	39	47	large	large	ADJ
ajst-26203	39	48	amount	amount	NOUN
ajst-26203	39	49	of	of	ADP
ajst-26203	39	50	sf6	sf6	PROPN
ajst-26203	39	51	gas	gas	NOUN
ajst-26203	39	52	humidity	humidity	NOUN
ajst-26203	39	53	data	datum	NOUN
ajst-26203	39	54	as	as	ADP
ajst-26203	39	55	the	the	DET
ajst-26203	39	56	basis	basis	NOUN
ajst-26203	39	57	for	for	ADP
ajst-26203	39	58	training	training	NOUN
ajst-26203	39	59	and	and	CCONJ
ajst-26203	39	60	verification	verification	NOUN
ajst-26203	39	61	.	.	PUNCT
ajst-26203	40	1	the	the	DET
ajst-26203	40	2	data	data	NOUN
ajst-26203	40	3	sources	source	NOUN
ajst-26203	40	4	mainly	mainly	ADV
ajst-26203	40	5	include	include	VERB
ajst-26203	40	6	gis	gis	NOUN
ajst-26203	40	7	equipment	equipment	NOUN
ajst-26203	40	8	sensors	sensor	NOUN
ajst-26203	40	9	,	,	PUNCT
ajst-26203	40	10	monitoring	monitoring	NOUN
ajst-26203	40	11	systems	system	NOUN
ajst-26203	40	12	,	,	PUNCT
ajst-26203	40	13	weather	weather	NOUN
ajst-26203	40	14	station	station	NOUN
ajst-26203	40	15	data	datum	NOUN
ajst-26203	40	16	and	and	CCONJ
ajst-26203	40	17	so	so	ADV
ajst-26203	40	18	on	on	ADV
ajst-26203	40	19	.	.	PUNCT
ajst-26203	41	1	data	datum	NOUN
ajst-26203	41	2	acquisition	acquisition	NOUN
ajst-26203	41	3	methods	method	NOUN
ajst-26203	41	4	mainly	mainly	ADV
ajst-26203	41	5	include	include	VERB
ajst-26203	41	6	realtime	realtime	NOUN
ajst-26203	41	7	data	datum	NOUN
ajst-26203	41	8	acquisition	acquisition	NOUN
ajst-26203	41	9	,	,	PUNCT
ajst-26203	41	10	historical	historical	ADJ
ajst-26203	41	11	data	datum	NOUN
ajst-26203	41	12	query	query	NOUN
ajst-26203	41	13	,	,	PUNCT
ajst-26203	41	14	and	and	CCONJ
ajst-26203	41	15	external	external	ADJ
ajst-26203	41	16	data	datum	NOUN
ajst-26203	41	17	acquisition	acquisition	NOUN
ajst-26203	41	18	.	.	PUNCT
ajst-26203	42	1	2)data	2)data	NUM
ajst-26203	42	2	preprocessing	preprocessing	NOUN
ajst-26203	42	3	procedure	procedure	NOUN
ajst-26203	42	4	before	before	ADP
ajst-26203	42	5	constructing	construct	VERB
ajst-26203	42	6	the	the	DET
ajst-26203	42	7	sf6	sf6	PROPN
ajst-26203	42	8	gas	gas	NOUN
ajst-26203	42	9	humidity	humidity	NOUN
ajst-26203	42	10	prediction	prediction	NOUN
ajst-26203	42	11	model	model	NOUN
ajst-26203	42	12	of	of	ADP
ajst-26203	42	13	gis	gis	NOUN
ajst-26203	42	14	equipment	equipment	NOUN
ajst-26203	42	15	based	base	VERB
ajst-26203	42	16	on	on	ADP
ajst-26203	42	17	deep	deep	ADJ
ajst-26203	42	18	learning	learning	NOUN
ajst-26203	42	19	,	,	PUNCT
ajst-26203	42	20	it	it	PRON
ajst-26203	42	21	is	be	AUX
ajst-26203	42	22	necessary	necessary	ADJ
ajst-26203	42	23	to	to	PART
ajst-26203	42	24	preprocess	preprocess	VERB
ajst-26203	42	25	the	the	DET
ajst-26203	42	26	original	original	ADJ
ajst-26203	42	27	data	datum	NOUN
ajst-26203	42	28	to	to	PART
ajst-26203	42	29	improve	improve	VERB
ajst-26203	42	30	the	the	DET
ajst-26203	42	31	quality	quality	NOUN
ajst-26203	42	32	and	and	CCONJ
ajst-26203	42	33	applicability	applicability	NOUN
ajst-26203	42	34	of	of	ADP
ajst-26203	42	35	the	the	DET
ajst-26203	42	36	data	datum	NOUN
ajst-26203	42	37	.	.	PUNCT
ajst-26203	43	1	figure	figure	NOUN
ajst-26203	43	2	2	2	NUM
ajst-26203	43	3	.	.	PUNCT
ajst-26203	43	4	lstm	lstm	PROPN
ajst-26203	43	5	network	network	NOUN
ajst-26203	43	6	training	training	NOUN
ajst-26203	43	7	process	process	NOUN
ajst-26203	43	8	a	a	NOUN
ajst-26203	43	9	)	)	PUNCT
ajst-26203	43	10	missing	miss	VERB
ajst-26203	43	11	value	value	NOUN
ajst-26203	43	12	processing	processing	NOUN
ajst-26203	43	13	missing	miss	VERB
ajst-26203	43	14	value	value	NOUN
ajst-26203	43	15	is	be	AUX
ajst-26203	43	16	a	a	DET
ajst-26203	43	17	common	common	ADJ
ajst-26203	43	18	problem	problem	NOUN
ajst-26203	43	19	in	in	ADP
ajst-26203	43	20	data	datum	NOUN
ajst-26203	43	21	preprocessing	preprocessing	NOUN
ajst-26203	43	22	,	,	PUNCT
ajst-26203	43	23	which	which	PRON
ajst-26203	43	24	may	may	AUX
ajst-26203	43	25	affect	affect	VERB
ajst-26203	43	26	the	the	DET
ajst-26203	43	27	performance	performance	NOUN
ajst-26203	43	28	and	and	CCONJ
ajst-26203	43	29	stability	stability	NOUN
ajst-26203	43	30	of	of	ADP
ajst-26203	43	31	the	the	DET
ajst-26203	43	32	model	model	NOUN
ajst-26203	43	33	.	.	PUNCT
ajst-26203	44	1	common	common	ADJ
ajst-26203	44	2	missing	missing	ADJ
ajst-26203	44	3	value	value	NOUN
ajst-26203	44	4	handling	handling	NOUN
ajst-26203	44	5	methods	method	NOUN
ajst-26203	44	6	include	include	VERB
ajst-26203	44	7	：	：	PUNCT
ajst-26203	44	8	delete	delete	ADJ
ajst-26203	44	9	missing	miss	VERB
ajst-26203	44	10	values	value	NOUN
ajst-26203	44	11	:	:	PUNCT
ajst-26203	44	12	in	in	ADP
ajst-26203	44	13	cases	case	NOUN
ajst-26203	44	14	where	where	SCONJ
ajst-26203	44	15	there	there	PRON
ajst-26203	44	16	are	be	VERB
ajst-26203	44	17	fewer	few	ADJ
ajst-26203	44	18	missing	missing	ADJ
ajst-26203	44	19	values	value	NOUN
ajst-26203	44	20	,	,	PUNCT
ajst-26203	44	21	you	you	PRON
ajst-26203	44	22	can	can	AUX
ajst-26203	44	23	simply	simply	ADV
ajst-26203	44	24	delete	delete	VERB
ajst-26203	44	25	the	the	DET
ajst-26203	44	26	sample	sample	NOUN
ajst-26203	44	27	or	or	CCONJ
ajst-26203	44	28	feature	feature	NOUN
ajst-26203	44	29	that	that	PRON
ajst-26203	44	30	contains	contain	VERB
ajst-26203	44	31	the	the	DET
ajst-26203	44	32	missing	miss	VERB
ajst-26203	44	33	value	value	NOUN
ajst-26203	44	34	.	.	PUNCT
ajst-26203	45	1	interpolation	interpolation	NOUN
ajst-26203	45	2	filling	filling	NOUN
ajst-26203	45	3	:	:	PUNCT
ajst-26203	45	4	using	use	VERB
ajst-26203	45	5	the	the	DET
ajst-26203	45	6	information	information	NOUN
ajst-26203	45	7	of	of	ADP
ajst-26203	45	8	the	the	DET
ajst-26203	45	9	existing	exist	VERB
ajst-26203	45	10	data	datum	NOUN
ajst-26203	45	11	,	,	PUNCT
ajst-26203	45	12	the	the	DET
ajst-26203	45	13	missing	miss	VERB
ajst-26203	45	14	value	value	NOUN
ajst-26203	45	15	is	be	AUX
ajst-26203	45	16	estimated	estimate	VERB
ajst-26203	45	17	by	by	ADP
ajst-26203	45	18	interpolation	interpolation	NOUN
ajst-26203	45	19	methods	method	NOUN
ajst-26203	45	20	(	(	PUNCT
ajst-26203	45	21	such	such	ADJ
ajst-26203	45	22	as	as	ADP
ajst-26203	45	23	linear	linear	PROPN
ajst-26203	45	24	interpolation	interpolation	NOUN
ajst-26203	45	25	,	,	PUNCT
ajst-26203	45	26	polynomial	polynomial	ADJ
ajst-26203	45	27	interpolation	interpolation	NOUN
ajst-26203	45	28	,	,	PUNCT
ajst-26203	45	29	etc	etc	X
ajst-26203	45	30	.	.	X
ajst-26203	45	31	)	)	PUNCT
ajst-26203	45	32	.	.	PUNCT
ajst-26203	46	1	fill	fill	VERB
ajst-26203	46	2	a	a	DET
ajst-26203	46	3	constant	constant	ADJ
ajst-26203	46	4	value	value	NOUN
ajst-26203	46	5	:	:	PUNCT
ajst-26203	46	6	replaces	replace	VERB
ajst-26203	46	7	the	the	DET
ajst-26203	46	8	missing	miss	VERB
ajst-26203	46	9	value	value	NOUN
ajst-26203	46	10	with	with	ADP
ajst-26203	46	11	some	some	DET
ajst-26203	46	12	fixed	fix	VERB
ajst-26203	46	13	constant	constant	ADJ
ajst-26203	46	14	value	value	NOUN
ajst-26203	46	15	,	,	PUNCT
ajst-26203	46	16	such	such	ADJ
ajst-26203	46	17	as	as	ADP
ajst-26203	46	18	0	0	NUM
ajst-26203	46	19	or	or	CCONJ
ajst-26203	46	20	an	an	DET
ajst-26203	46	21	average	average	NOUN
ajst-26203	46	22	.	.	PUNCT
ajst-26203	47	1	b	b	X
ajst-26203	47	2	)	)	PUNCT
ajst-26203	47	3	outlier	outlier	NOUN
ajst-26203	47	4	detection	detection	NOUN
ajst-26203	47	5	and	and	CCONJ
ajst-26203	47	6	processing	processing	NOUN
ajst-26203	47	7	outliers	outlier	NOUN
ajst-26203	47	8	may	may	AUX
ajst-26203	47	9	interfere	interfere	VERB
ajst-26203	47	10	with	with	ADP
ajst-26203	47	11	model	model	NOUN
ajst-26203	47	12	training	training	NOUN
ajst-26203	47	13	and	and	CCONJ
ajst-26203	47	14	prediction	prediction	NOUN
ajst-26203	47	15	,	,	PUNCT
ajst-26203	47	16	so	so	SCONJ
ajst-26203	47	17	outliers	outlier	NOUN
ajst-26203	47	18	need	need	VERB
ajst-26203	47	19	to	to	PART
ajst-26203	47	20	be	be	AUX
ajst-26203	47	21	detected	detect	VERB
ajst-26203	47	22	and	and	CCONJ
ajst-26203	47	23	processed	process	VERB
ajst-26203	47	24	.	.	PUNCT
ajst-26203	48	1	common	common	ADJ
ajst-26203	48	2	methods	method	NOUN
ajst-26203	48	3	include	include	VERB
ajst-26203	48	4	:	:	PUNCT
ajst-26203	48	5	statistical	statistical	ADJ
ajst-26203	48	6	methods	method	NOUN
ajst-26203	48	7	:	:	PUNCT
ajst-26203	48	8	statistical	statistical	ADJ
ajst-26203	48	9	methods	method	NOUN
ajst-26203	48	10	are	be	AUX
ajst-26203	48	11	used	use	VERB
ajst-26203	48	12	to	to	PART
ajst-26203	48	13	identify	identify	VERB
ajst-26203	48	14	outliers	outlier	NOUN
ajst-26203	48	15	in	in	ADP
ajst-26203	48	16	the	the	DET
ajst-26203	48	17	data	datum	NOUN
ajst-26203	48	18	.	.	PUNCT
ajst-26203	49	1	outlier	outlier	NOUN
ajst-26203	49	2	detection	detection	NOUN
ajst-26203	49	3	algorithm	algorithm	NOUN
ajst-26203	49	4	:	:	PUNCT
ajst-26203	49	5	the	the	DET
ajst-26203	49	6	outlier	outlier	ADJ
ajst-26203	49	7	detection	detection	NOUN
ajst-26203	49	8	algorithm	algorithm	NOUN
ajst-26203	49	9	is	be	AUX
ajst-26203	49	10	used	use	VERB
ajst-26203	49	11	to	to	PART
ajst-26203	49	12	automatically	automatically	ADV
ajst-26203	49	13	identify	identify	VERB
ajst-26203	49	14	outliers	outlier	NOUN
ajst-26203	49	15	.	.	PUNCT
ajst-26203	50	1	model	model	NOUN
ajst-26203	50	2	-	-	PUNCT
ajst-26203	50	3	based	base	VERB
ajst-26203	50	4	approach	approach	NOUN
ajst-26203	50	5	:	:	PUNCT
ajst-26203	50	6	outlier	outlier	NOUN
ajst-26203	50	7	detection	detection	NOUN
ajst-26203	50	8	using	use	VERB
ajst-26203	50	9	machine	machine	NOUN
ajst-26203	50	10	learning	learning	NOUN
ajst-26203	50	11	models	model	NOUN
ajst-26203	50	12	.	.	PUNCT
ajst-26203	51	1	delete	delete	ADJ
ajst-26203	51	2	outliers	outlier	NOUN
ajst-26203	51	3	:	:	PUNCT
ajst-26203	51	4	delete	delete	ADJ
ajst-26203	51	5	outliers	outlier	NOUN
ajst-26203	51	6	from	from	ADP
ajst-26203	51	7	data	datum	NOUN
ajst-26203	51	8	.	.	PUNCT
ajst-26203	52	1	replace	replace	VERB
ajst-26203	52	2	outliers	outlier	NOUN
ajst-26203	52	3	:	:	PUNCT
ajst-26203	52	4	replace	replace	VERB
ajst-26203	52	5	outliers	outlier	NOUN
ajst-26203	52	6	with	with	ADP
ajst-26203	52	7	the	the	DET
ajst-26203	52	8	mean	mean	ADJ
ajst-26203	52	9	,	,	PUNCT
ajst-26203	52	10	median	median	NOUN
ajst-26203	52	11	,	,	PUNCT
ajst-26203	52	12	127	127	NUM
ajst-26203	52	13	or	or	CCONJ
ajst-26203	52	14	other	other	ADJ
ajst-26203	52	15	appropriate	appropriate	ADJ
ajst-26203	52	16	value	value	NOUN
ajst-26203	52	17	of	of	ADP
ajst-26203	52	18	the	the	DET
ajst-26203	52	19	data	datum	NOUN
ajst-26203	52	20	.	.	PUNCT
ajst-26203	53	1	c	c	X
ajst-26203	53	2	)	)	PUNCT
ajst-26203	53	3	feature	feature	NOUN
ajst-26203	53	4	selection	selection	NOUN
ajst-26203	53	5	feature	feature	NOUN
ajst-26203	53	6	selection	selection	NOUN
ajst-26203	53	7	is	be	AUX
ajst-26203	53	8	to	to	PART
ajst-26203	53	9	select	select	VERB
ajst-26203	53	10	the	the	DET
ajst-26203	53	11	most	most	ADV
ajst-26203	53	12	relevant	relevant	ADJ
ajst-26203	53	13	and	and	CCONJ
ajst-26203	53	14	useful	useful	ADJ
ajst-26203	53	15	features	feature	NOUN
ajst-26203	53	16	from	from	ADP
ajst-26203	53	17	the	the	DET
ajst-26203	53	18	original	original	ADJ
ajst-26203	53	19	features	feature	NOUN
ajst-26203	53	20	to	to	PART
ajst-26203	53	21	reduce	reduce	VERB
ajst-26203	53	22	the	the	DET
ajst-26203	53	23	complexity	complexity	NOUN
ajst-26203	53	24	of	of	ADP
ajst-26203	53	25	the	the	DET
ajst-26203	53	26	model	model	NOUN
ajst-26203	53	27	and	and	CCONJ
ajst-26203	53	28	improve	improve	VERB
ajst-26203	53	29	the	the	DET
ajst-26203	53	30	prediction	prediction	NOUN
ajst-26203	53	31	performance	performance	NOUN
ajst-26203	53	32	.	.	PUNCT
ajst-26203	54	1	common	common	ADJ
ajst-26203	54	2	feature	feature	NOUN
ajst-26203	54	3	selection	selection	NOUN
ajst-26203	54	4	methods	method	NOUN
ajst-26203	54	5	include	include	VERB
ajst-26203	54	6	：	：	PUNCT
ajst-26203	54	7	filtering	filter	VERB
ajst-26203	54	8	method	method	NOUN
ajst-26203	54	9	:	:	PUNCT
ajst-26203	54	10	according	accord	VERB
ajst-26203	54	11	to	to	ADP
ajst-26203	54	12	the	the	DET
ajst-26203	54	13	correlation	correlation	NOUN
ajst-26203	54	14	between	between	ADP
ajst-26203	54	15	the	the	DET
ajst-26203	54	16	feature	feature	NOUN
ajst-26203	54	17	and	and	CCONJ
ajst-26203	54	18	the	the	DET
ajst-26203	54	19	target	target	NOUN
ajst-26203	54	20	variable	variable	NOUN
ajst-26203	54	21	,	,	PUNCT
ajst-26203	54	22	the	the	DET
ajst-26203	54	23	commonly	commonly	ADV
ajst-26203	54	24	used	use	VERB
ajst-26203	54	25	indicators	indicator	NOUN
ajst-26203	54	26	include	include	VERB
ajst-26203	54	27	correlation	correlation	NOUN
ajst-26203	54	28	coefficient	coefficient	NOUN
ajst-26203	54	29	,	,	PUNCT
ajst-26203	54	30	chi	chi	ADJ
ajst-26203	54	31	-	-	PUNCT
ajst-26203	54	32	square	square	NOUN
ajst-26203	54	33	test	test	NOUN
ajst-26203	54	34	and	and	CCONJ
ajst-26203	54	35	so	so	ADV
ajst-26203	54	36	on	on	ADV
ajst-26203	54	37	.	.	PUNCT
ajst-26203	55	1	packaging	packaging	NOUN
ajst-26203	55	2	method	method	NOUN
ajst-26203	55	3	:	:	PUNCT
ajst-26203	55	4	use	use	VERB
ajst-26203	55	5	the	the	DET
ajst-26203	55	6	performance	performance	NOUN
ajst-26203	55	7	of	of	ADP
ajst-26203	55	8	the	the	DET
ajst-26203	55	9	model	model	NOUN
ajst-26203	55	10	to	to	PART
ajst-26203	55	11	evaluate	evaluate	VERB
ajst-26203	55	12	the	the	DET
ajst-26203	55	13	importance	importance	NOUN
ajst-26203	55	14	of	of	ADP
ajst-26203	55	15	features	feature	NOUN
ajst-26203	55	16	.	.	PUNCT
ajst-26203	56	1	common	common	ADJ
ajst-26203	56	2	methods	method	NOUN
ajst-26203	56	3	include	include	VERB
ajst-26203	56	4	recursive	recursive	ADJ
ajst-26203	56	5	feature	feature	NOUN
ajst-26203	56	6	elimination	elimination	NOUN
ajst-26203	56	7	.	.	PUNCT
ajst-26203	57	1	embedding	embed	VERB
ajst-26203	57	2	method	method	NOUN
ajst-26203	57	3	:	:	PUNCT
ajst-26203	57	4	features	feature	NOUN
ajst-26203	57	5	are	be	AUX
ajst-26203	57	6	automatically	automatically	ADV
ajst-26203	57	7	selected	select	VERB
ajst-26203	57	8	during	during	ADP
ajst-26203	57	9	model	model	NOUN
ajst-26203	57	10	training	training	NOUN
ajst-26203	57	11	.	.	PUNCT
ajst-26203	58	1	common	common	ADJ
ajst-26203	58	2	methods	method	NOUN
ajst-26203	58	3	include	include	VERB
ajst-26203	58	4	l1	l1	PROPN
ajst-26203	58	5	regularization	regularization	NOUN
ajst-26203	58	6	,	,	PUNCT
ajst-26203	58	7	decision	decision	NOUN
ajst-26203	58	8	tree	tree	NOUN
ajst-26203	58	9	,	,	PUNCT
ajst-26203	58	10	etc	etc	X
ajst-26203	58	11	.	.	X
ajst-26203	59	1	(	(	PUNCT
ajst-26203	59	2	2	2	X
ajst-26203	59	3	)	)	PUNCT
ajst-26203	59	4	deep	deep	ADJ
ajst-26203	59	5	learning	learning	NOUN
ajst-26203	59	6	model	model	NOUN
ajst-26203	59	7	design	design	NOUN
ajst-26203	59	8	and	and	CCONJ
ajst-26203	59	9	training	train	VERB
ajst-26203	59	10	1)model	1)model	NUM
ajst-26203	59	11	selection	selection	NOUN
ajst-26203	59	12	it	it	PRON
ajst-26203	59	13	is	be	AUX
ajst-26203	59	14	very	very	ADV
ajst-26203	59	15	important	important	ADJ
ajst-26203	59	16	to	to	PART
ajst-26203	59	17	select	select	VERB
ajst-26203	59	18	the	the	DET
ajst-26203	59	19	appropriate	appropriate	ADJ
ajst-26203	59	20	deep	deep	ADJ
ajst-26203	59	21	learning	learning	NOUN
ajst-26203	59	22	model	model	NOUN
ajst-26203	59	23	when	when	SCONJ
ajst-26203	59	24	constructing	construct	VERB
ajst-26203	59	25	the	the	DET
ajst-26203	59	26	prediction	prediction	NOUN
ajst-26203	59	27	model	model	NOUN
ajst-26203	59	28	of	of	ADP
ajst-26203	59	29	sf6	sf6	PROPN
ajst-26203	59	30	gas	gas	PROPN
ajst-26203	59	31	humidity	humidity	NOUN
ajst-26203	59	32	of	of	ADP
ajst-26203	59	33	gis	gis	NOUN
ajst-26203	59	34	equipment	equipment	NOUN
ajst-26203	59	35	based	base	VERB
ajst-26203	59	36	on	on	ADP
ajst-26203	59	37	deep	deep	ADJ
ajst-26203	59	38	learning	learning	NOUN
ajst-26203	59	39	.	.	PUNCT
ajst-26203	60	1	common	common	ADJ
ajst-26203	60	2	deep	deep	ADJ
ajst-26203	60	3	learning	learning	NOUN
ajst-26203	60	4	models	model	NOUN
ajst-26203	60	5	include	include	VERB
ajst-26203	60	6	convolutional	convolutional	ADJ
ajst-26203	60	7	neural	neural	ADJ
ajst-26203	60	8	network	network	NOUN
ajst-26203	60	9	(	(	PUNCT
ajst-26203	60	10	cnn	cnn	PROPN
ajst-26203	60	11	)	)	PUNCT
ajst-26203	60	12	and	and	CCONJ
ajst-26203	60	13	recurrent	recurrent	ADJ
ajst-26203	60	14	neural	neural	ADJ
ajst-26203	60	15	network	network	NOUN
ajst-26203	60	16	(	(	PUNCT
ajst-26203	60	17	rnn	rnn	PROPN
ajst-26203	60	18	)	)	PUNCT
ajst-26203	60	19	,	,	PUNCT
ajst-26203	60	20	which	which	PRON
ajst-26203	60	21	each	each	PRON
ajst-26203	60	22	have	have	VERB
ajst-26203	60	23	their	their	PRON
ajst-26203	60	24	own	own	ADJ
ajst-26203	60	25	characteristics	characteristic	NOUN
ajst-26203	60	26	and	and	CCONJ
ajst-26203	60	27	are	be	AUX
ajst-26203	60	28	suitable	suitable	ADJ
ajst-26203	60	29	for	for	ADP
ajst-26203	60	30	different	different	ADJ
ajst-26203	60	31	types	type	NOUN
ajst-26203	60	32	of	of	ADP
ajst-26203	60	33	data	datum	NOUN
ajst-26203	60	34	and	and	CCONJ
ajst-26203	60	35	problems	problem	NOUN
ajst-26203	60	36	.	.	PUNCT
ajst-26203	61	1	convolutional	convolutional	ADJ
ajst-26203	61	2	neural	neural	ADJ
ajst-26203	61	3	network	network	NOUN
ajst-26203	61	4	(	(	PUNCT
ajst-26203	61	5	cnn	cnn	PROPN
ajst-26203	61	6	)	)	PUNCT
ajst-26203	61	7	is	be	AUX
ajst-26203	61	8	mainly	mainly	ADV
ajst-26203	61	9	used	use	VERB
ajst-26203	61	10	to	to	PART
ajst-26203	61	11	process	process	VERB
ajst-26203	61	12	image	image	NOUN
ajst-26203	61	13	data	datum	NOUN
ajst-26203	61	14	and	and	CCONJ
ajst-26203	61	15	has	have	VERB
ajst-26203	61	16	good	good	ADJ
ajst-26203	61	17	feature	feature	NOUN
ajst-26203	61	18	extraction	extraction	NOUN
ajst-26203	61	19	ability	ability	NOUN
ajst-26203	61	20	.	.	PUNCT
ajst-26203	62	1	in	in	ADP
ajst-26203	62	2	the	the	DET
ajst-26203	62	3	sf6	sf6	PROPN
ajst-26203	62	4	gas	gas	PROPN
ajst-26203	62	5	humidity	humidity	NOUN
ajst-26203	62	6	prediction	prediction	NOUN
ajst-26203	62	7	model	model	NOUN
ajst-26203	62	8	of	of	ADP
ajst-26203	62	9	gis	gis	PROPN
ajst-26203	62	10	equipment	equipment	NOUN
ajst-26203	62	11	,	,	PUNCT
ajst-26203	62	12	if	if	SCONJ
ajst-26203	62	13	the	the	DET
ajst-26203	62	14	data	datum	NOUN
ajst-26203	62	15	may	may	AUX
ajst-26203	62	16	have	have	VERB
ajst-26203	62	17	temporal	temporal	ADJ
ajst-26203	62	18	and	and	CCONJ
ajst-26203	62	19	spatial	spatial	ADJ
ajst-26203	62	20	characteristics	characteristic	NOUN
ajst-26203	62	21	(	(	PUNCT
ajst-26203	62	22	such	such	ADJ
ajst-26203	62	23	as	as	ADP
ajst-26203	62	24	spatial	spatial	ADJ
ajst-26203	62	25	relationship	relationship	NOUN
ajst-26203	62	26	and	and	CCONJ
ajst-26203	62	27	time	time	NOUN
ajst-26203	62	28	series	series	PROPN
ajst-26203	62	29	)	)	PUNCT
ajst-26203	62	30	are	be	AUX
ajst-26203	62	31	considered	consider	VERB
ajst-26203	62	32	,	,	PUNCT
ajst-26203	62	33	the	the	DET
ajst-26203	62	34	sf6	sf6	PROPN
ajst-26203	62	35	gas	gas	PROPN
ajst-26203	62	36	humidity	humidity	NOUN
ajst-26203	62	37	data	datum	NOUN
ajst-26203	62	38	can	can	AUX
ajst-26203	62	39	be	be	AUX
ajst-26203	62	40	organized	organize	VERB
ajst-26203	62	41	into	into	ADP
ajst-26203	62	42	a	a	DET
ajst-26203	62	43	form	form	NOUN
ajst-26203	62	44	similar	similar	ADJ
ajst-26203	62	45	to	to	ADP
ajst-26203	62	46	images	image	NOUN
ajst-26203	62	47	,	,	PUNCT
ajst-26203	62	48	and	and	CCONJ
ajst-26203	62	49	then	then	ADV
ajst-26203	62	50	processed	process	VERB
ajst-26203	62	51	by	by	ADP
ajst-26203	62	52	cnn	cnn	PROPN
ajst-26203	62	53	.	.	PUNCT
ajst-26203	63	1	the	the	DET
ajst-26203	63	2	advantage	advantage	NOUN
ajst-26203	63	3	of	of	ADP
ajst-26203	63	4	cnn	cnn	PROPN
ajst-26203	63	5	is	be	AUX
ajst-26203	63	6	that	that	SCONJ
ajst-26203	63	7	it	it	PRON
ajst-26203	63	8	can	can	AUX
ajst-26203	63	9	automatically	automatically	ADV
ajst-26203	63	10	extract	extract	VERB
ajst-26203	63	11	local	local	ADJ
ajst-26203	63	12	features	feature	NOUN
ajst-26203	63	13	and	and	CCONJ
ajst-26203	63	14	spatial	spatial	ADJ
ajst-26203	63	15	correlations	correlation	NOUN
ajst-26203	63	16	in	in	ADP
ajst-26203	63	17	the	the	DET
ajst-26203	63	18	data	datum	NOUN
ajst-26203	63	19	,	,	PUNCT
ajst-26203	63	20	which	which	PRON
ajst-26203	63	21	is	be	AUX
ajst-26203	63	22	suitable	suitable	ADJ
ajst-26203	63	23	for	for	ADP
ajst-26203	63	24	processing	process	VERB
ajst-26203	63	25	twodimensional	twodimensional	ADJ
ajst-26203	63	26	or	or	CCONJ
ajst-26203	63	27	three	three	NUM
ajst-26203	63	28	-	-	PUNCT
ajst-26203	63	29	dimensional	dimensional	ADJ
ajst-26203	63	30	data	datum	NOUN
ajst-26203	63	31	structures	structure	NOUN
ajst-26203	63	32	.	.	PUNCT
ajst-26203	64	1	recurrent	recurrent	ADJ
ajst-26203	64	2	neural	neural	ADJ
ajst-26203	64	3	network	network	NOUN
ajst-26203	64	4	(	(	PUNCT
ajst-26203	64	5	rnn	rnn	PROPN
ajst-26203	64	6	)	)	PUNCT
ajst-26203	64	7	are	be	AUX
ajst-26203	64	8	neural	neural	ADJ
ajst-26203	64	9	network	network	NOUN
ajst-26203	64	10	structures	structure	NOUN
ajst-26203	64	11	specifically	specifically	ADV
ajst-26203	64	12	designed	design	VERB
ajst-26203	64	13	to	to	PART
ajst-26203	64	14	process	process	VERB
ajst-26203	64	15	sequence	sequence	NOUN
ajst-26203	64	16	data	datum	NOUN
ajst-26203	64	17	with	with	ADP
ajst-26203	64	18	memory	memory	NOUN
ajst-26203	64	19	and	and	CCONJ
ajst-26203	64	20	dynamic	dynamic	ADJ
ajst-26203	64	21	modeling	modeling	NOUN
ajst-26203	64	22	capabilities	capability	NOUN
ajst-26203	64	23	.	.	PUNCT
ajst-26203	65	1	in	in	ADP
ajst-26203	65	2	the	the	DET
ajst-26203	65	3	sf6	sf6	PROPN
ajst-26203	65	4	gas	gas	PROPN
ajst-26203	65	5	humidity	humidity	NOUN
ajst-26203	65	6	prediction	prediction	NOUN
ajst-26203	65	7	model	model	NOUN
ajst-26203	65	8	of	of	ADP
ajst-26203	65	9	gis	gis	PROPN
ajst-26203	65	10	equipment	equipment	NOUN
ajst-26203	65	11	,	,	PUNCT
ajst-26203	65	12	sf6	sf6	PROPN
ajst-26203	65	13	gas	gas	NOUN
ajst-26203	65	14	humidity	humidity	NOUN
ajst-26203	65	15	data	datum	NOUN
ajst-26203	65	16	are	be	AUX
ajst-26203	65	17	usually	usually	ADV
ajst-26203	65	18	collected	collect	VERB
ajst-26203	65	19	in	in	ADP
ajst-26203	65	20	time	time	NOUN
ajst-26203	65	21	order	order	NOUN
ajst-26203	65	22	,	,	PUNCT
ajst-26203	65	23	which	which	PRON
ajst-26203	65	24	is	be	AUX
ajst-26203	65	25	sequential	sequential	ADJ
ajst-26203	65	26	and	and	CCONJ
ajst-26203	65	27	series	series	NOUN
ajst-26203	65	28	-	-	PUNCT
ajst-26203	65	29	dependent	dependent	ADJ
ajst-26203	65	30	,	,	PUNCT
ajst-26203	65	31	so	so	ADV
ajst-26203	65	32	rnn	rnn	NOUN
ajst-26203	65	33	is	be	AUX
ajst-26203	65	34	a	a	DET
ajst-26203	65	35	natural	natural	ADJ
ajst-26203	65	36	choice	choice	NOUN
ajst-26203	65	37	.	.	PUNCT
ajst-26203	66	1	with	with	ADP
ajst-26203	66	2	rnn	rnn	PROPN
ajst-26203	66	3	,	,	PUNCT
ajst-26203	66	4	models	model	NOUN
ajst-26203	66	5	can	can	AUX
ajst-26203	66	6	automatically	automatically	ADV
ajst-26203	66	7	capture	capture	VERB
ajst-26203	66	8	temporal	temporal	ADJ
ajst-26203	66	9	correlations	correlation	NOUN
ajst-26203	66	10	in	in	ADP
ajst-26203	66	11	data	datum	NOUN
ajst-26203	66	12	,	,	PUNCT
ajst-26203	66	13	model	model	NOUN
ajst-26203	66	14	historical	historical	ADJ
ajst-26203	66	15	data	datum	NOUN
ajst-26203	66	16	,	,	PUNCT
ajst-26203	66	17	and	and	CCONJ
ajst-26203	66	18	make	make	VERB
ajst-26203	66	19	future	future	ADJ
ajst-26203	66	20	predictions	prediction	NOUN
ajst-26203	66	21	.	.	PUNCT
ajst-26203	67	1	2	2	X
ajst-26203	67	2	)	)	PUNCT
ajst-26203	67	3	model	model	NOUN
ajst-26203	67	4	architecture	architecture	NOUN
ajst-26203	67	5	design	design	NOUN
ajst-26203	67	6	and	and	CCONJ
ajst-26203	67	7	optimization	optimization	NOUN
ajst-26203	67	8	when	when	SCONJ
ajst-26203	67	9	designing	design	VERB
ajst-26203	67	10	the	the	DET
ajst-26203	67	11	architecture	architecture	NOUN
ajst-26203	67	12	of	of	ADP
ajst-26203	67	13	a	a	DET
ajst-26203	67	14	deep	deep	ADJ
ajst-26203	67	15	learning	learning	NOUN
ajst-26203	67	16	model	model	NOUN
ajst-26203	67	17	,	,	PUNCT
ajst-26203	67	18	key	key	ADJ
ajst-26203	67	19	factors	factor	NOUN
ajst-26203	67	20	such	such	ADJ
ajst-26203	67	21	as	as	ADP
ajst-26203	67	22	the	the	DET
ajst-26203	67	23	number	number	NOUN
ajst-26203	67	24	of	of	ADP
ajst-26203	67	25	layers	layer	NOUN
ajst-26203	67	26	of	of	ADP
ajst-26203	67	27	the	the	DET
ajst-26203	67	28	network	network	NOUN
ajst-26203	67	29	,	,	PUNCT
ajst-26203	67	30	the	the	DET
ajst-26203	67	31	number	number	NOUN
ajst-26203	67	32	of	of	ADP
ajst-26203	67	33	nodes	node	NOUN
ajst-26203	67	34	in	in	ADP
ajst-26203	67	35	each	each	DET
ajst-26203	67	36	layer	layer	NOUN
ajst-26203	67	37	,	,	PUNCT
ajst-26203	67	38	and	and	CCONJ
ajst-26203	67	39	the	the	DET
ajst-26203	67	40	choice	choice	NOUN
ajst-26203	67	41	of	of	ADP
ajst-26203	67	42	activation	activation	NOUN
ajst-26203	67	43	functions	function	NOUN
ajst-26203	67	44	need	need	VERB
ajst-26203	67	45	to	to	PART
ajst-26203	67	46	be	be	AUX
ajst-26203	67	47	taken	take	VERB
ajst-26203	67	48	into	into	ADP
ajst-26203	67	49	account	account	NOUN
ajst-26203	67	50	.	.	PUNCT
ajst-26203	68	1	these	these	DET
ajst-26203	68	2	factors	factor	NOUN
ajst-26203	68	3	directly	directly	ADV
ajst-26203	68	4	affect	affect	VERB
ajst-26203	68	5	the	the	DET
ajst-26203	68	6	representation	representation	NOUN
ajst-26203	68	7	ability	ability	NOUN
ajst-26203	68	8	,	,	PUNCT
ajst-26203	68	9	complexity	complexity	NOUN
ajst-26203	68	10	and	and	CCONJ
ajst-26203	68	11	prediction	prediction	NOUN
ajst-26203	68	12	performance	performance	NOUN
ajst-26203	68	13	of	of	ADP
ajst-26203	68	14	the	the	DET
ajst-26203	68	15	model	model	NOUN
ajst-26203	68	16	.	.	PUNCT
ajst-26203	69	1	the	the	DET
ajst-26203	69	2	number	number	NOUN
ajst-26203	69	3	of	of	ADP
ajst-26203	69	4	layers	layer	NOUN
ajst-26203	69	5	of	of	ADP
ajst-26203	69	6	the	the	DET
ajst-26203	69	7	network	network	NOUN
ajst-26203	69	8	determines	determine	VERB
ajst-26203	69	9	the	the	DET
ajst-26203	69	10	depth	depth	NOUN
ajst-26203	69	11	of	of	ADP
ajst-26203	69	12	the	the	DET
ajst-26203	69	13	model	model	NOUN
ajst-26203	69	14	,	,	PUNCT
ajst-26203	69	15	and	and	CCONJ
ajst-26203	69	16	deep	deep	ADJ
ajst-26203	69	17	networks	network	NOUN
ajst-26203	69	18	are	be	AUX
ajst-26203	69	19	often	often	ADV
ajst-26203	69	20	able	able	ADJ
ajst-26203	69	21	to	to	PART
ajst-26203	69	22	learn	learn	VERB
ajst-26203	69	23	more	more	ADJ
ajst-26203	69	24	complex	complex	ADJ
ajst-26203	69	25	feature	feature	NOUN
ajst-26203	69	26	representations	representation	NOUN
ajst-26203	69	27	,	,	PUNCT
ajst-26203	69	28	but	but	CCONJ
ajst-26203	69	29	are	be	AUX
ajst-26203	69	30	also	also	ADV
ajst-26203	69	31	prone	prone	ADJ
ajst-26203	69	32	to	to	ADP
ajst-26203	69	33	problems	problem	NOUN
ajst-26203	69	34	such	such	ADJ
ajst-26203	69	35	as	as	ADP
ajst-26203	69	36	disappearing	disappear	VERB
ajst-26203	69	37	gradients	gradient	NOUN
ajst-26203	69	38	or	or	CCONJ
ajst-26203	69	39	explosions	explosion	NOUN
ajst-26203	69	40	.	.	PUNCT
ajst-26203	70	1	when	when	SCONJ
ajst-26203	70	2	choosing	choose	VERB
ajst-26203	70	3	the	the	DET
ajst-26203	70	4	number	number	NOUN
ajst-26203	70	5	of	of	ADP
ajst-26203	70	6	layers	layer	NOUN
ajst-26203	70	7	,	,	PUNCT
ajst-26203	70	8	you	you	PRON
ajst-26203	70	9	need	need	VERB
ajst-26203	70	10	to	to	PART
ajst-26203	70	11	make	make	VERB
ajst-26203	70	12	trade	trade	NOUN
ajst-26203	70	13	-	-	PUNCT
ajst-26203	70	14	offs	off	NOUN
ajst-26203	70	15	based	base	VERB
ajst-26203	70	16	on	on	ADP
ajst-26203	70	17	the	the	DET
ajst-26203	70	18	complexity	complexity	NOUN
ajst-26203	70	19	of	of	ADP
ajst-26203	70	20	the	the	DET
ajst-26203	70	21	data	datum	NOUN
ajst-26203	70	22	and	and	CCONJ
ajst-26203	70	23	the	the	DET
ajst-26203	70	24	difficulty	difficulty	NOUN
ajst-26203	70	25	of	of	ADP
ajst-26203	70	26	the	the	DET
ajst-26203	70	27	problem	problem	NOUN
ajst-26203	70	28	.	.	PUNCT
ajst-26203	71	1	typically	typically	ADV
ajst-26203	71	2	,	,	PUNCT
ajst-26203	71	3	you	you	PRON
ajst-26203	71	4	can	can	AUX
ajst-26203	71	5	start	start	VERB
ajst-26203	71	6	with	with	ADP
ajst-26203	71	7	a	a	DET
ajst-26203	71	8	shallower	shallower	NOUN
ajst-26203	71	9	network	network	NOUN
ajst-26203	71	10	initially	initially	ADV
ajst-26203	71	11	and	and	CCONJ
ajst-26203	71	12	gradually	gradually	ADV
ajst-26203	71	13	increase	increase	VERB
ajst-26203	71	14	the	the	DET
ajst-26203	71	15	number	number	NOUN
ajst-26203	71	16	of	of	ADP
ajst-26203	71	17	layers	layer	NOUN
ajst-26203	71	18	until	until	SCONJ
ajst-26203	71	19	you	you	PRON
ajst-26203	71	20	reach	reach	VERB
ajst-26203	71	21	a	a	DET
ajst-26203	71	22	stable	stable	ADJ
ajst-26203	71	23	point	point	NOUN
ajst-26203	71	24	of	of	ADP
ajst-26203	71	25	performance	performance	NOUN
ajst-26203	71	26	.	.	PUNCT
ajst-26203	72	1	the	the	DET
ajst-26203	72	2	number	number	NOUN
ajst-26203	72	3	of	of	ADP
ajst-26203	72	4	nodes	node	NOUN
ajst-26203	72	5	in	in	ADP
ajst-26203	72	6	each	each	DET
ajst-26203	72	7	layer	layer	NOUN
ajst-26203	72	8	affects	affect	VERB
ajst-26203	72	9	the	the	DET
ajst-26203	72	10	capacity	capacity	NOUN
ajst-26203	72	11	and	and	CCONJ
ajst-26203	72	12	complexity	complexity	NOUN
ajst-26203	72	13	of	of	ADP
ajst-26203	72	14	the	the	DET
ajst-26203	72	15	network	network	NOUN
ajst-26203	72	16	.	.	PUNCT
ajst-26203	73	1	the	the	DET
ajst-26203	73	2	more	more	ADJ
ajst-26203	73	3	nodes	node	NOUN
ajst-26203	73	4	,	,	PUNCT
ajst-26203	73	5	the	the	PRON
ajst-26203	73	6	stronger	strong	ADJ
ajst-26203	73	7	the	the	DET
ajst-26203	73	8	expression	expression	NOUN
ajst-26203	73	9	ability	ability	NOUN
ajst-26203	73	10	of	of	ADP
ajst-26203	73	11	the	the	DET
ajst-26203	73	12	network	network	NOUN
ajst-26203	73	13	.	.	PUNCT
ajst-26203	74	1	however	however	ADV
ajst-26203	74	2	,	,	PUNCT
ajst-26203	74	3	too	too	ADV
ajst-26203	74	4	many	many	ADJ
ajst-26203	74	5	nodes	node	NOUN
ajst-26203	74	6	can	can	AUX
ajst-26203	74	7	lead	lead	VERB
ajst-26203	74	8	to	to	ADP
ajst-26203	74	9	overfitting	overfitte	VERB
ajst-26203	74	10	problems	problem	NOUN
ajst-26203	74	11	,	,	PUNCT
ajst-26203	74	12	while	while	SCONJ
ajst-26203	74	13	too	too	ADV
ajst-26203	74	14	few	few	ADJ
ajst-26203	74	15	nodes	node	NOUN
ajst-26203	74	16	can	can	AUX
ajst-26203	74	17	lead	lead	VERB
ajst-26203	74	18	to	to	ADP
ajst-26203	74	19	underfitting	underfitting	NOUN
ajst-26203	74	20	.	.	PUNCT
ajst-26203	75	1	when	when	SCONJ
ajst-26203	75	2	selecting	select	VERB
ajst-26203	75	3	the	the	DET
ajst-26203	75	4	number	number	NOUN
ajst-26203	75	5	of	of	ADP
ajst-26203	75	6	nodes	node	NOUN
ajst-26203	75	7	,	,	PUNCT
ajst-26203	75	8	we	we	PRON
ajst-26203	75	9	can	can	AUX
ajst-26203	75	10	find	find	VERB
ajst-26203	75	11	the	the	DET
ajst-26203	75	12	appropriate	appropriate	ADJ
ajst-26203	75	13	number	number	NOUN
ajst-26203	75	14	of	of	ADP
ajst-26203	75	15	nodes	node	NOUN
ajst-26203	75	16	to	to	PART
ajst-26203	75	17	balance	balance	VERB
ajst-26203	75	18	the	the	DET
ajst-26203	75	19	performance	performance	NOUN
ajst-26203	75	20	and	and	CCONJ
ajst-26203	75	21	generalization	generalization	NOUN
ajst-26203	75	22	ability	ability	NOUN
ajst-26203	75	23	of	of	ADP
ajst-26203	75	24	the	the	DET
ajst-26203	75	25	model	model	NOUN
ajst-26203	75	26	through	through	ADP
ajst-26203	75	27	cross	cross	ADJ
ajst-26203	75	28	-	-	ADJ
ajst-26203	75	29	validation	validation	ADJ
ajst-26203	75	30	and	and	CCONJ
ajst-26203	75	31	other	other	ADJ
ajst-26203	75	32	methods	method	NOUN
ajst-26203	75	33	.	.	PUNCT
ajst-26203	76	1	activation	activation	NOUN
ajst-26203	76	2	function	function	NOUN
ajst-26203	76	3	plays	play	VERB
ajst-26203	76	4	the	the	DET
ajst-26203	76	5	role	role	NOUN
ajst-26203	76	6	of	of	ADP
ajst-26203	76	7	nonlinear	nonlinear	ADJ
ajst-26203	76	8	mapping	mapping	NOUN
ajst-26203	76	9	in	in	ADP
ajst-26203	76	10	neural	neural	ADJ
ajst-26203	76	11	network	network	NOUN
ajst-26203	76	12	,	,	PUNCT
ajst-26203	76	13	which	which	PRON
ajst-26203	76	14	can	can	AUX
ajst-26203	76	15	increase	increase	VERB
ajst-26203	76	16	the	the	DET
ajst-26203	76	17	expressive	expressive	ADJ
ajst-26203	76	18	power	power	NOUN
ajst-26203	76	19	of	of	ADP
ajst-26203	76	20	the	the	DET
ajst-26203	76	21	network	network	NOUN
ajst-26203	76	22	.	.	PUNCT
ajst-26203	77	1	common	common	ADJ
ajst-26203	77	2	activation	activation	NOUN
ajst-26203	77	3	functions	function	NOUN
ajst-26203	77	4	include	include	VERB
ajst-26203	77	5	relu	relu	NOUN
ajst-26203	77	6	,	,	PUNCT
ajst-26203	77	7	sigmoid	sigmoid	NOUN
ajst-26203	77	8	,	,	PUNCT
ajst-26203	77	9	tanh	tanh	NOUN
ajst-26203	77	10	,	,	PUNCT
ajst-26203	77	11	and	and	CCONJ
ajst-26203	77	12	so	so	ADV
ajst-26203	77	13	on	on	ADV
ajst-26203	77	14	.	.	PUNCT
ajst-26203	78	1	when	when	SCONJ
ajst-26203	78	2	selecting	select	VERB
ajst-26203	78	3	the	the	DET
ajst-26203	78	4	activation	activation	NOUN
ajst-26203	78	5	function	function	NOUN
ajst-26203	78	6	,	,	PUNCT
ajst-26203	78	7	it	it	PRON
ajst-26203	78	8	is	be	AUX
ajst-26203	78	9	necessary	necessary	ADJ
ajst-26203	78	10	to	to	PART
ajst-26203	78	11	consider	consider	VERB
ajst-26203	78	12	the	the	DET
ajst-26203	78	13	problems	problem	NOUN
ajst-26203	78	14	such	such	ADJ
ajst-26203	78	15	as	as	ADP
ajst-26203	78	16	gradient	gradient	ADJ
ajst-26203	78	17	disappearance	disappearance	NOUN
ajst-26203	78	18	and	and	CCONJ
ajst-26203	78	19	gradient	gradient	NOUN
ajst-26203	78	20	explosion	explosion	NOUN
ajst-26203	78	21	,	,	PUNCT
ajst-26203	78	22	and	and	CCONJ
ajst-26203	78	23	make	make	VERB
ajst-26203	78	24	appropriate	appropriate	ADJ
ajst-26203	78	25	selection	selection	NOUN
ajst-26203	78	26	according	accord	VERB
ajst-26203	78	27	to	to	ADP
ajst-26203	78	28	the	the	DET
ajst-26203	78	29	actual	actual	ADJ
ajst-26203	78	30	situation	situation	NOUN
ajst-26203	78	31	.	.	PUNCT
ajst-26203	79	1	in	in	ADP
ajst-26203	79	2	addition	addition	NOUN
ajst-26203	79	3	to	to	ADP
ajst-26203	79	4	architectural	architectural	ADJ
ajst-26203	79	5	design	design	NOUN
ajst-26203	79	6	,	,	PUNCT
ajst-26203	79	7	regularization	regularization	NOUN
ajst-26203	79	8	and	and	CCONJ
ajst-26203	79	9	optimization	optimization	NOUN
ajst-26203	79	10	techniques	technique	NOUN
ajst-26203	79	11	need	need	VERB
ajst-26203	79	12	to	to	PART
ajst-26203	79	13	be	be	AUX
ajst-26203	79	14	considered	consider	VERB
ajst-26203	79	15	.	.	PUNCT
ajst-26203	80	1	regularization	regularization	NOUN
ajst-26203	80	2	methods	method	NOUN
ajst-26203	80	3	such	such	ADJ
ajst-26203	80	4	as	as	ADP
ajst-26203	80	5	dropout	dropout	NOUN
ajst-26203	80	6	and	and	CCONJ
ajst-26203	80	7	l2	l2	NOUN
ajst-26203	80	8	regularization	regularization	NOUN
ajst-26203	80	9	can	can	AUX
ajst-26203	80	10	be	be	AUX
ajst-26203	80	11	used	use	VERB
ajst-26203	80	12	to	to	PART
ajst-26203	80	13	prevent	prevent	VERB
ajst-26203	80	14	overfitting	overfitting	NOUN
ajst-26203	80	15	,	,	PUNCT
ajst-26203	80	16	and	and	CCONJ
ajst-26203	80	17	optimization	optimization	NOUN
ajst-26203	80	18	techniques	technique	NOUN
ajst-26203	80	19	such	such	ADJ
ajst-26203	80	20	as	as	ADP
ajst-26203	80	21	adam	adam	PROPN
ajst-26203	80	22	and	and	CCONJ
ajst-26203	80	23	sgd	sgd	PROPN
ajst-26203	80	24	can	can	AUX
ajst-26203	80	25	be	be	AUX
ajst-26203	80	26	used	use	VERB
ajst-26203	80	27	to	to	PART
ajst-26203	80	28	accelerate	accelerate	VERB
ajst-26203	80	29	network	network	NOUN
ajst-26203	80	30	convergence	convergence	NOUN
ajst-26203	80	31	and	and	CCONJ
ajst-26203	80	32	improve	improve	VERB
ajst-26203	80	33	training	training	NOUN
ajst-26203	80	34	efficiency	efficiency	NOUN
ajst-26203	80	35	.	.	PUNCT
ajst-26203	81	1	finally	finally	ADV
ajst-26203	81	2	,	,	PUNCT
ajst-26203	81	3	it	it	PRON
ajst-26203	81	4	is	be	AUX
ajst-26203	81	5	necessary	necessary	ADJ
ajst-26203	81	6	to	to	PART
ajst-26203	81	7	optimize	optimize	VERB
ajst-26203	81	8	the	the	DET
ajst-26203	81	9	hyper	hyper	ADJ
ajst-26203	81	10	parameters	parameter	NOUN
ajst-26203	81	11	of	of	ADP
ajst-26203	81	12	the	the	DET
ajst-26203	81	13	model	model	NOUN
ajst-26203	81	14	,	,	PUNCT
ajst-26203	81	15	including	include	VERB
ajst-26203	81	16	learning	learn	VERB
ajst-26203	81	17	rate	rate	NOUN
ajst-26203	81	18	,	,	PUNCT
ajst-26203	81	19	batch	batch	NOUN
ajst-26203	81	20	size	size	NOUN
ajst-26203	81	21	,	,	PUNCT
ajst-26203	81	22	optimizer	optimizer	NOUN
ajst-26203	81	23	selection	selection	NOUN
ajst-26203	81	24	,	,	PUNCT
ajst-26203	81	25	etc	etc	X
ajst-26203	81	26	.	.	X
ajst-26203	81	27	through	through	ADP
ajst-26203	81	28	cross	cross	ADJ
ajst-26203	81	29	-	-	ADJ
ajst-26203	81	30	validation	validation	ADJ
ajst-26203	81	31	and	and	CCONJ
ajst-26203	81	32	other	other	ADJ
ajst-26203	81	33	methods	method	NOUN
ajst-26203	81	34	,	,	PUNCT
ajst-26203	81	35	the	the	DET
ajst-26203	81	36	optimal	optimal	ADJ
ajst-26203	81	37	combination	combination	NOUN
ajst-26203	81	38	of	of	ADP
ajst-26203	81	39	hyper	hyper	ADJ
ajst-26203	81	40	parameters	parameter	NOUN
ajst-26203	81	41	can	can	AUX
ajst-26203	81	42	be	be	AUX
ajst-26203	81	43	found	find	VERB
ajst-26203	81	44	to	to	PART
ajst-26203	81	45	further	far	ADV
ajst-26203	81	46	improve	improve	VERB
ajst-26203	81	47	the	the	DET
ajst-26203	81	48	performance	performance	NOUN
ajst-26203	81	49	of	of	ADP
ajst-26203	81	50	the	the	DET
ajst-26203	81	51	model	model	NOUN
ajst-26203	81	52	.	.	PUNCT
ajst-26203	82	1	(	(	PUNCT
ajst-26203	82	2	3)model	3)model	NUM
ajst-26203	82	3	performance	performance	NOUN
ajst-26203	82	4	evaluation	evaluation	NOUN
ajst-26203	82	5	and	and	CCONJ
ajst-26203	82	6	result	result	VERB
ajst-26203	82	7	analysis	analysis	NOUN
ajst-26203	82	8	1	1	NUM
ajst-26203	82	9	)	)	PUNCT
ajst-26203	82	10	experimental	experimental	ADJ
ajst-26203	82	11	settings	setting	NOUN
ajst-26203	82	12	when	when	SCONJ
ajst-26203	82	13	evaluating	evaluate	VERB
ajst-26203	82	14	the	the	DET
ajst-26203	82	15	performance	performance	NOUN
ajst-26203	82	16	and	and	CCONJ
ajst-26203	82	17	analyzing	analyze	VERB
ajst-26203	82	18	the	the	DET
ajst-26203	82	19	results	result	NOUN
ajst-26203	82	20	of	of	ADP
ajst-26203	82	21	sf6	sf6	PROPN
ajst-26203	82	22	gas	gas	NOUN
ajst-26203	82	23	humidity	humidity	NOUN
ajst-26203	82	24	prediction	prediction	NOUN
ajst-26203	82	25	model	model	NOUN
ajst-26203	82	26	of	of	ADP
ajst-26203	82	27	gis	gis	NOUN
ajst-26203	82	28	equipment	equipment	NOUN
ajst-26203	82	29	based	base	VERB
ajst-26203	82	30	on	on	ADP
ajst-26203	82	31	deep	deep	ADJ
ajst-26203	82	32	learning	learning	NOUN
ajst-26203	82	33	,	,	PUNCT
ajst-26203	82	34	it	it	PRON
ajst-26203	82	35	is	be	AUX
ajst-26203	82	36	necessary	necessary	ADJ
ajst-26203	82	37	to	to	PART
ajst-26203	82	38	carry	carry	VERB
ajst-26203	82	39	out	out	ADP
ajst-26203	82	40	appropriate	appropriate	ADJ
ajst-26203	82	41	experimental	experimental	ADJ
ajst-26203	82	42	settings	setting	NOUN
ajst-26203	82	43	,	,	PUNCT
ajst-26203	82	44	including	include	VERB
ajst-26203	82	45	the	the	DET
ajst-26203	82	46	division	division	NOUN
ajst-26203	82	47	of	of	ADP
ajst-26203	82	48	data	datum	NOUN
ajst-26203	82	49	sets	set	NOUN
ajst-26203	82	50	and	and	CCONJ
ajst-26203	82	51	the	the	DET
ajst-26203	82	52	selection	selection	NOUN
ajst-26203	82	53	of	of	ADP
ajst-26203	82	54	evaluation	evaluation	NOUN
ajst-26203	82	55	indicators	indicator	NOUN
ajst-26203	82	56	.	.	PUNCT
ajst-26203	83	1	among	among	ADP
ajst-26203	83	2	them	they	PRON
ajst-26203	83	3	,	,	PUNCT
ajst-26203	83	4	data	datum	NOUN
ajst-26203	83	5	set	set	VERB
ajst-26203	83	6	partitioning	partitioning	NOUN
ajst-26203	83	7	mainly	mainly	ADV
ajst-26203	83	8	includes	include	VERB
ajst-26203	83	9	training	training	NOUN
ajst-26203	83	10	set	set	NOUN
ajst-26203	83	11	,	,	PUNCT
ajst-26203	83	12	verification	verification	NOUN
ajst-26203	83	13	set	set	NOUN
ajst-26203	83	14	,	,	PUNCT
ajst-26203	83	15	test	test	NOUN
ajst-26203	83	16	set	set	VERB
ajst-26203	83	17	and	and	CCONJ
ajst-26203	83	18	so	so	ADV
ajst-26203	83	19	on	on	ADV
ajst-26203	83	20	.	.	PUNCT
ajst-26203	84	1	the	the	DET
ajst-26203	84	2	training	training	NOUN
ajst-26203	84	3	set	set	NOUN
ajst-26203	84	4	is	be	AUX
ajst-26203	84	5	used	use	VERB
ajst-26203	84	6	to	to	PART
ajst-26203	84	7	train	train	VERB
ajst-26203	84	8	the	the	DET
ajst-26203	84	9	model	model	NOUN
ajst-26203	84	10	and	and	CCONJ
ajst-26203	84	11	usually	usually	ADV
ajst-26203	84	12	accounts	account	VERB
ajst-26203	84	13	for	for	ADP
ajst-26203	84	14	the	the	DET
ajst-26203	84	15	majority	majority	NOUN
ajst-26203	84	16	of	of	ADP
ajst-26203	84	17	the	the	DET
ajst-26203	84	18	total	total	ADJ
ajst-26203	84	19	data	datum	NOUN
ajst-26203	84	20	,	,	PUNCT
ajst-26203	84	21	which	which	PRON
ajst-26203	84	22	can	can	AUX
ajst-26203	84	23	be	be	AUX
ajst-26203	84	24	around	around	ADV
ajst-26203	84	25	70	70	NUM
ajst-26203	84	26	%	%	NOUN
ajst-26203	84	27	to	to	PART
ajst-26203	84	28	80	80	NUM
ajst-26203	84	29	%	%	NOUN
ajst-26203	84	30	of	of	ADP
ajst-26203	84	31	the	the	DET
ajst-26203	84	32	data	datum	NOUN
ajst-26203	84	33	.	.	PUNCT
ajst-26203	85	1	validation	validation	NOUN
ajst-26203	85	2	sets	set	NOUN
ajst-26203	85	3	are	be	AUX
ajst-26203	85	4	used	use	VERB
ajst-26203	85	5	for	for	ADP
ajst-26203	85	6	the	the	DET
ajst-26203	85	7	tuning	tuning	NOUN
ajst-26203	85	8	and	and	CCONJ
ajst-26203	85	9	parameter	parameter	NOUN
ajst-26203	85	10	selection	selection	NOUN
ajst-26203	85	11	of	of	ADP
ajst-26203	85	12	the	the	DET
ajst-26203	85	13	model	model	NOUN
ajst-26203	85	14	and	and	CCONJ
ajst-26203	85	15	can	can	AUX
ajst-26203	85	16	account	account	VERB
ajst-26203	85	17	for	for	ADP
ajst-26203	85	18	around	around	ADV
ajst-26203	85	19	10	10	NUM
ajst-26203	85	20	%	%	NOUN
ajst-26203	85	21	to	to	PART
ajst-26203	85	22	20	20	NUM
ajst-26203	85	23	%	%	NOUN
ajst-26203	85	24	of	of	ADP
ajst-26203	85	25	the	the	DET
ajst-26203	85	26	total	total	ADJ
ajst-26203	85	27	data	datum	NOUN
ajst-26203	85	28	as	as	SCONJ
ajst-26203	85	29	needed	need	VERB
ajst-26203	85	30	.	.	PUNCT
ajst-26203	86	1	test	test	NOUN
ajst-26203	86	2	sets	set	NOUN
ajst-26203	86	3	are	be	AUX
ajst-26203	86	4	used	use	VERB
ajst-26203	86	5	to	to	PART
ajst-26203	86	6	ultimately	ultimately	ADV
ajst-26203	86	7	evaluate	evaluate	VERB
ajst-26203	86	8	the	the	DET
ajst-26203	86	9	performance	performance	NOUN
ajst-26203	86	10	of	of	ADP
ajst-26203	86	11	the	the	DET
ajst-26203	86	12	model	model	NOUN
ajst-26203	86	13	and	and	CCONJ
ajst-26203	86	14	can	can	AUX
ajst-26203	86	15	not	not	PART
ajst-26203	86	16	be	be	AUX
ajst-26203	86	17	used	use	VERB
ajst-26203	86	18	for	for	ADP
ajst-26203	86	19	training	training	NOUN
ajst-26203	86	20	and	and	CCONJ
ajst-26203	86	21	tuning	tuning	NOUN
ajst-26203	86	22	of	of	ADP
ajst-26203	86	23	the	the	DET
ajst-26203	86	24	model	model	NOUN
ajst-26203	86	25	,	,	PUNCT
ajst-26203	86	26	usually	usually	ADV
ajst-26203	86	27	accounting	account	VERB
ajst-26203	86	28	for	for	ADP
ajst-26203	86	29	about	about	ADV
ajst-26203	86	30	10	10	NUM
ajst-26203	86	31	%	%	NOUN
ajst-26203	86	32	of	of	ADP
ajst-26203	86	33	the	the	DET
ajst-26203	86	34	total	total	ADJ
ajst-26203	86	35	data	datum	NOUN
ajst-26203	86	36	.	.	PUNCT
ajst-26203	87	1	the	the	DET
ajst-26203	87	2	randomness	randomness	NOUN
ajst-26203	87	3	and	and	CCONJ
ajst-26203	87	4	representations	representation	NOUN
ajst-26203	87	5	of	of	ADP
ajst-26203	87	6	samples	sample	NOUN
ajst-26203	87	7	should	should	AUX
ajst-26203	87	8	be	be	AUX
ajst-26203	87	9	taken	take	VERB
ajst-26203	87	10	into	into	ADP
ajst-26203	87	11	account	account	NOUN
ajst-26203	87	12	in	in	ADP
ajst-26203	87	13	the	the	DET
ajst-26203	87	14	partitioning	partitioning	NOUN
ajst-26203	87	15	of	of	ADP
ajst-26203	87	16	data	datum	NOUN
ajst-26203	87	17	sets	set	NOUN
ajst-26203	87	18	to	to	PART
ajst-26203	87	19	ensure	ensure	VERB
ajst-26203	87	20	the	the	DET
ajst-26203	87	21	objectivity	objectivity	NOUN
ajst-26203	87	22	and	and	CCONJ
ajst-26203	87	23	reliability	reliability	NOUN
ajst-26203	87	24	of	of	ADP
ajst-26203	87	25	model	model	NOUN
ajst-26203	87	26	evaluation	evaluation	NOUN
ajst-26203	87	27	.	.	PUNCT
ajst-26203	88	1	in	in	ADP
ajst-26203	88	2	terms	term	NOUN
ajst-26203	88	3	of	of	ADP
ajst-26203	88	4	the	the	DET
ajst-26203	88	5	selection	selection	NOUN
ajst-26203	88	6	of	of	ADP
ajst-26203	88	7	evaluation	evaluation	NOUN
ajst-26203	88	8	indicators	indicator	NOUN
ajst-26203	88	9	,	,	PUNCT
ajst-26203	88	10	root	root	NOUN
ajst-26203	88	11	mean	mean	VERB
ajst-26203	88	12	square	square	ADJ
ajst-26203	88	13	error	error	NOUN
ajst-26203	88	14	(	(	PUNCT
ajst-26203	88	15	rmse	rmse	NOUN
ajst-26203	88	16	)	)	PUNCT
ajst-26203	88	17	is	be	AUX
ajst-26203	88	18	the	the	DET
ajst-26203	88	19	most	most	ADV
ajst-26203	88	20	commonly	commonly	ADV
ajst-26203	88	21	used	use	VERB
ajst-26203	88	22	regression	regression	NOUN
ajst-26203	88	23	model	model	NOUN
ajst-26203	88	24	evaluation	evaluation	NOUN
ajst-26203	88	25	indicator	indicator	NOUN
ajst-26203	88	26	to	to	PART
ajst-26203	88	27	measure	measure	VERB
ajst-26203	88	28	the	the	DET
ajst-26203	88	29	deviation	deviation	NOUN
ajst-26203	88	30	between	between	ADP
ajst-26203	88	31	the	the	DET
ajst-26203	88	32	predicted	predict	VERB
ajst-26203	88	33	value	value	NOUN
ajst-26203	88	34	and	and	CCONJ
ajst-26203	88	35	the	the	DET
ajst-26203	88	36	actual	actual	ADJ
ajst-26203	88	37	value	value	NOUN
ajst-26203	88	38	.	.	PUNCT
ajst-26203	89	1	the	the	DET
ajst-26203	89	2	formula	formula	NOUN
ajst-26203	89	3	is	be	AUX
ajst-26203	89	4	shown	show	VERB
ajst-26203	89	5	in	in	ADP
ajst-26203	89	6	equation	equation	NOUN
ajst-26203	89	7	1	1	NUM
ajst-26203	89	8	.	.	PUNCT
ajst-26203	90	1	rmse	rmse	PROPN
ajst-26203	90	2	∑	∑	PROPN
ajst-26203	90	3	𝑛	𝑛	PROPN
ajst-26203	90	4	𝑖	𝑖	SYM
ajst-26203	90	5	1	1	NUM
ajst-26203	90	6	𝑦	𝑦	NUM
ajst-26203	90	7	𝑦𝚤	𝑦𝚤	NOUN
ajst-26203	90	8	(	(	PUNCT
ajst-26203	90	9	equation	equation	NOUN
ajst-26203	90	10	1	1	NUM
ajst-26203	90	11	)	)	PUNCT
ajst-26203	90	12	then	then	ADV
ajst-26203	90	13	,	,	PUNCT
ajst-26203	90	14	the	the	DET
ajst-26203	90	15	mean	mean	ADJ
ajst-26203	90	16	absolute	absolute	ADJ
ajst-26203	90	17	error	error	NOUN
ajst-26203	90	18	(	(	PUNCT
ajst-26203	90	19	mae	mae	PROPN
ajst-26203	90	20	)	)	PUNCT
ajst-26203	90	21	is	be	AUX
ajst-26203	90	22	used	use	VERB
ajst-26203	90	23	to	to	PART
ajst-26203	90	24	measure	measure	VERB
ajst-26203	90	25	the	the	DET
ajst-26203	90	26	mean	mean	ADJ
ajst-26203	90	27	absolute	absolute	ADJ
ajst-26203	90	28	deviation	deviation	NOUN
ajst-26203	90	29	between	between	ADP
ajst-26203	90	30	the	the	DET
ajst-26203	90	31	predicted	predict	VERB
ajst-26203	90	32	value	value	NOUN
ajst-26203	90	33	and	and	CCONJ
ajst-26203	90	34	the	the	DET
ajst-26203	90	35	actual	actual	ADJ
ajst-26203	90	36	value	value	NOUN
ajst-26203	90	37	of	of	ADP
ajst-26203	90	38	the	the	DET
ajst-26203	90	39	model	model	NOUN
ajst-26203	90	40	,	,	PUNCT
ajst-26203	90	41	as	as	SCONJ
ajst-26203	90	42	shown	show	VERB
ajst-26203	90	43	in	in	ADP
ajst-26203	90	44	equation	equation	NOUN
ajst-26203	90	45	2	2	NUM
ajst-26203	90	46	.	.	PUNCT
ajst-26203	90	47	mae	mae	PROPN
ajst-26203	90	48	∑	∑	PROPN
ajst-26203	90	49	𝑛	𝑛	PROPN
ajst-26203	90	50	𝑖	𝑖	SYM
ajst-26203	90	51	1|𝑦	1|𝑦	NUM
ajst-26203	91	1	𝑦𝚤|	𝑦𝚤|	CCONJ
ajst-26203	91	2	(	(	PUNCT
ajst-26203	91	3	equation	equation	NOUN
ajst-26203	91	4	2	2	NUM
ajst-26203	91	5	)	)	PUNCT
ajst-26203	91	6	128	128	NUM
ajst-26203	91	7	correlation	correlation	NOUN
ajst-26203	91	8	coefficient	coefficient	NOUN
ajst-26203	91	9	is	be	AUX
ajst-26203	91	10	used	use	VERB
ajst-26203	91	11	to	to	PART
ajst-26203	91	12	measure	measure	VERB
ajst-26203	91	13	the	the	DET
ajst-26203	91	14	degree	degree	NOUN
ajst-26203	91	15	of	of	ADP
ajst-26203	91	16	linear	linear	ADJ
ajst-26203	91	17	correlation	correlation	NOUN
ajst-26203	91	18	between	between	ADP
ajst-26203	91	19	the	the	DET
ajst-26203	91	20	predicted	predict	VERB
ajst-26203	91	21	value	value	NOUN
ajst-26203	91	22	of	of	ADP
ajst-26203	91	23	the	the	DET
ajst-26203	91	24	model	model	NOUN
ajst-26203	91	25	and	and	CCONJ
ajst-26203	91	26	the	the	DET
ajst-26203	91	27	actual	actual	ADJ
ajst-26203	91	28	value	value	NOUN
ajst-26203	91	29	,	,	PUNCT
ajst-26203	91	30	and	and	CCONJ
ajst-26203	91	31	the	the	DET
ajst-26203	91	32	value	value	NOUN
ajst-26203	91	33	range	range	NOUN
ajst-26203	91	34	is	be	AUX
ajst-26203	91	35	[	[	X
ajst-26203	91	36	-1	-1	ADJ
ajst-26203	91	37	,	,	PUNCT
ajst-26203	91	38	1	1	NUM
ajst-26203	91	39	]	]	PUNCT
ajst-26203	91	40	.	.	PUNCT
ajst-26203	92	1	the	the	PRON
ajst-26203	92	2	closer	close	ADV
ajst-26203	92	3	the	the	DET
ajst-26203	92	4	value	value	NOUN
ajst-26203	92	5	is	be	AUX
ajst-26203	92	6	to	to	ADP
ajst-26203	92	7	1	1	NUM
ajst-26203	92	8	,	,	PUNCT
ajst-26203	92	9	the	the	PRON
ajst-26203	92	10	better	well	ADJ
ajst-26203	92	11	the	the	DET
ajst-26203	92	12	prediction	prediction	NOUN
ajst-26203	92	13	effect	effect	NOUN
ajst-26203	92	14	is	be	AUX
ajst-26203	92	15	.	.	PUNCT
ajst-26203	93	1	finally	finally	ADV
ajst-26203	93	2	,	,	PUNCT
ajst-26203	93	3	goodness	goodness	NOUN
ajst-26203	93	4	of	of	ADP
ajst-26203	93	5	fit	fit	ADJ
ajst-26203	93	6	(	(	PUNCT
ajst-26203	93	7	r	r	NOUN
ajst-26203	93	8	-	-	PUNCT
ajst-26203	93	9	squared	square	VERB
ajst-26203	93	10	)	)	PUNCT
ajst-26203	93	11	measures	measure	NOUN
ajst-26203	93	12	the	the	DET
ajst-26203	93	13	fitting	fitting	ADJ
ajst-26203	93	14	degree	degree	NOUN
ajst-26203	93	15	of	of	ADP
ajst-26203	93	16	the	the	DET
ajst-26203	93	17	model	model	NOUN
ajst-26203	93	18	to	to	ADP
ajst-26203	93	19	the	the	DET
ajst-26203	93	20	data	datum	NOUN
ajst-26203	93	21	,	,	PUNCT
ajst-26203	93	22	and	and	CCONJ
ajst-26203	93	23	the	the	DET
ajst-26203	93	24	value	value	NOUN
ajst-26203	93	25	range	range	NOUN
ajst-26203	93	26	is	be	AUX
ajst-26203	93	27	[	[	X
ajst-26203	93	28	0	0	NUM
ajst-26203	93	29	,	,	PUNCT
ajst-26203	93	30	1	1	NUM
ajst-26203	93	31	]	]	PUNCT
ajst-26203	93	32	.	.	PUNCT
ajst-26203	94	1	the	the	PRON
ajst-26203	94	2	closer	close	ADV
ajst-26203	94	3	the	the	DET
ajst-26203	94	4	value	value	NOUN
ajst-26203	94	5	is	be	AUX
ajst-26203	94	6	to	to	ADP
ajst-26203	94	7	1	1	NUM
ajst-26203	94	8	,	,	PUNCT
ajst-26203	94	9	the	the	PRON
ajst-26203	94	10	better	well	ADJ
ajst-26203	94	11	the	the	DET
ajst-26203	94	12	fitting	fitting	ADJ
ajst-26203	94	13	effect	effect	NOUN
ajst-26203	94	14	of	of	ADP
ajst-26203	94	15	the	the	DET
ajst-26203	94	16	model	model	NOUN
ajst-26203	94	17	is	be	AUX
ajst-26203	94	18	.	.	PUNCT
ajst-26203	94	19	2	2	X
ajst-26203	94	20	)	)	PUNCT
ajst-26203	94	21	model	model	NOUN
ajst-26203	94	22	training	training	NOUN
ajst-26203	94	23	and	and	CCONJ
ajst-26203	94	24	evaluation	evaluation	NOUN
ajst-26203	94	25	during	during	ADP
ajst-26203	94	26	model	model	NOUN
ajst-26203	94	27	training	training	NOUN
ajst-26203	94	28	and	and	CCONJ
ajst-26203	94	29	evaluation	evaluation	NOUN
ajst-26203	94	30	,	,	PUNCT
ajst-26203	94	31	data	datum	NOUN
ajst-26203	94	32	is	be	AUX
ajst-26203	94	33	first	first	ADV
ajst-26203	94	34	prepared	prepare	VERB
ajst-26203	94	35	,	,	PUNCT
ajst-26203	94	36	loaded	load	VERB
ajst-26203	94	37	according	accord	VERB
ajst-26203	94	38	to	to	ADP
ajst-26203	94	39	the	the	DET
ajst-26203	94	40	previously	previously	ADV
ajst-26203	94	41	divided	divide	VERB
ajst-26203	94	42	training	training	NOUN
ajst-26203	94	43	set	set	NOUN
ajst-26203	94	44	,	,	PUNCT
ajst-26203	94	45	verification	verification	NOUN
ajst-26203	94	46	set	set	NOUN
ajst-26203	94	47	and	and	CCONJ
ajst-26203	94	48	test	test	NOUN
ajst-26203	94	49	set	set	VERB
ajst-26203	94	50	,	,	PUNCT
ajst-26203	94	51	and	and	CCONJ
ajst-26203	94	52	necessary	necessary	ADJ
ajst-26203	94	53	preprocessing	preprocessing	NOUN
ajst-26203	94	54	is	be	AUX
ajst-26203	94	55	carried	carry	VERB
ajst-26203	94	56	out	out	ADP
ajst-26203	94	57	,	,	PUNCT
ajst-26203	94	58	such	such	ADJ
ajst-26203	94	59	as	as	ADP
ajst-26203	94	60	data	datum	NOUN
ajst-26203	94	61	normalization	normalization	NOUN
ajst-26203	94	62	and	and	CCONJ
ajst-26203	94	63	standardization	standardization	NOUN
ajst-26203	94	64	.	.	PUNCT
ajst-26203	95	1	secondly	secondly	ADV
ajst-26203	95	2	,	,	PUNCT
ajst-26203	95	3	model	model	NOUN
ajst-26203	95	4	initialization	initialization	NOUN
ajst-26203	95	5	initializes	initialize	VERB
ajst-26203	95	6	the	the	DET
ajst-26203	95	7	structure	structure	NOUN
ajst-26203	95	8	and	and	CCONJ
ajst-26203	95	9	parameters	parameter	NOUN
ajst-26203	95	10	of	of	ADP
ajst-26203	95	11	the	the	DET
ajst-26203	95	12	deep	deep	ADJ
ajst-26203	95	13	learning	learning	NOUN
ajst-26203	95	14	model	model	NOUN
ajst-26203	95	15	,	,	PUNCT
ajst-26203	95	16	and	and	CCONJ
ajst-26203	95	17	selects	select	VERB
ajst-26203	95	18	the	the	DET
ajst-26203	95	19	appropriate	appropriate	ADJ
ajst-26203	95	20	optimizer	optimizer	NOUN
ajst-26203	95	21	and	and	CCONJ
ajst-26203	95	22	loss	loss	NOUN
ajst-26203	95	23	function	function	NOUN
ajst-26203	95	24	.	.	PUNCT
ajst-26203	96	1	the	the	DET
ajst-26203	96	2	process	process	NOUN
ajst-26203	96	3	is	be	AUX
ajst-26203	96	4	then	then	ADV
ajst-26203	96	5	trained	train	VERB
ajst-26203	96	6	,	,	PUNCT
ajst-26203	96	7	the	the	DET
ajst-26203	96	8	training	training	NOUN
ajst-26203	96	9	set	set	NOUN
ajst-26203	96	10	is	be	AUX
ajst-26203	96	11	input	input	NOUN
ajst-26203	96	12	into	into	ADP
ajst-26203	96	13	the	the	DET
ajst-26203	96	14	model	model	NOUN
ajst-26203	96	15	,	,	PUNCT
ajst-26203	96	16	and	and	CCONJ
ajst-26203	96	17	the	the	DET
ajst-26203	96	18	model	model	NOUN
ajst-26203	96	19	parameters	parameter	NOUN
ajst-26203	96	20	are	be	AUX
ajst-26203	96	21	constantly	constantly	ADV
ajst-26203	96	22	adjusted	adjust	VERB
ajst-26203	96	23	through	through	ADP
ajst-26203	96	24	a	a	DET
ajst-26203	96	25	backpropagation	backpropagation	NOUN
ajst-26203	96	26	algorithm	algorithm	NOUN
ajst-26203	96	27	to	to	PART
ajst-26203	96	28	minimize	minimize	VERB
ajst-26203	96	29	the	the	DET
ajst-26203	96	30	loss	loss	NOUN
ajst-26203	96	31	function	function	NOUN
ajst-26203	96	32	.	.	PUNCT
ajst-26203	97	1	at	at	ADP
ajst-26203	97	2	the	the	DET
ajst-26203	97	3	same	same	ADJ
ajst-26203	97	4	time	time	NOUN
ajst-26203	97	5	,	,	PUNCT
ajst-26203	97	6	the	the	DET
ajst-26203	97	7	validation	validation	NOUN
ajst-26203	97	8	set	set	NOUN
ajst-26203	97	9	is	be	AUX
ajst-26203	97	10	used	use	VERB
ajst-26203	97	11	to	to	PART
ajst-26203	97	12	verify	verify	VERB
ajst-26203	97	13	the	the	DET
ajst-26203	97	14	model	model	NOUN
ajst-26203	97	15	and	and	CCONJ
ajst-26203	97	16	monitor	monitor	VERB
ajst-26203	97	17	the	the	DET
ajst-26203	97	18	performance	performance	NOUN
ajst-26203	97	19	of	of	ADP
ajst-26203	97	20	the	the	DET
ajst-26203	97	21	model	model	NOUN
ajst-26203	97	22	.	.	PUNCT
ajst-26203	98	1	in	in	ADP
ajst-26203	98	2	the	the	DET
ajst-26203	98	3	training	training	NOUN
ajst-26203	98	4	process	process	NOUN
ajst-26203	98	5	,	,	PUNCT
ajst-26203	98	6	the	the	DET
ajst-26203	98	7	parameters	parameter	NOUN
ajst-26203	98	8	and	and	CCONJ
ajst-26203	98	9	weights	weight	NOUN
ajst-26203	98	10	of	of	ADP
ajst-26203	98	11	the	the	DET
ajst-26203	98	12	model	model	NOUN
ajst-26203	98	13	are	be	AUX
ajst-26203	98	14	saved	save	VERB
ajst-26203	98	15	regularly	regularly	ADV
ajst-26203	98	16	,	,	PUNCT
ajst-26203	98	17	so	so	SCONJ
ajst-26203	98	18	that	that	SCONJ
ajst-26203	98	19	the	the	DET
ajst-26203	98	20	model	model	NOUN
ajst-26203	98	21	can	can	AUX
ajst-26203	98	22	be	be	AUX
ajst-26203	98	23	restored	restore	VERB
ajst-26203	98	24	and	and	CCONJ
ajst-26203	98	25	the	the	DET
ajst-26203	98	26	training	training	NOUN
ajst-26203	98	27	can	can	AUX
ajst-26203	98	28	continue	continue	VERB
ajst-26203	98	29	when	when	SCONJ
ajst-26203	98	30	the	the	DET
ajst-26203	98	31	training	training	NOUN
ajst-26203	98	32	is	be	AUX
ajst-26203	98	33	interrupted	interrupt	VERB
ajst-26203	98	34	.	.	PUNCT
ajst-26203	99	1	at	at	ADP
ajst-26203	99	2	the	the	DET
ajst-26203	99	3	end	end	NOUN
ajst-26203	99	4	of	of	ADP
ajst-26203	99	5	the	the	DET
ajst-26203	99	6	training	training	NOUN
ajst-26203	99	7	,	,	PUNCT
ajst-26203	99	8	when	when	SCONJ
ajst-26203	99	9	the	the	DET
ajst-26203	99	10	model	model	NOUN
ajst-26203	99	11	achieves	achieve	VERB
ajst-26203	99	12	satisfactory	satisfactory	ADJ
ajst-26203	99	13	performance	performance	NOUN
ajst-26203	99	14	on	on	ADP
ajst-26203	99	15	the	the	DET
ajst-26203	99	16	verification	verification	NOUN
ajst-26203	99	17	set	set	NOUN
ajst-26203	99	18	,	,	PUNCT
ajst-26203	99	19	the	the	DET
ajst-26203	99	20	training	training	NOUN
ajst-26203	99	21	process	process	NOUN
ajst-26203	99	22	is	be	AUX
ajst-26203	99	23	stopped	stop	VERB
ajst-26203	99	24	and	and	CCONJ
ajst-26203	99	25	the	the	DET
ajst-26203	99	26	optimal	optimal	ADJ
ajst-26203	99	27	model	model	NOUN
ajst-26203	99	28	is	be	AUX
ajst-26203	99	29	saved	save	VERB
ajst-26203	99	30	.	.	PUNCT
ajst-26203	100	1	in	in	ADP
ajst-26203	100	2	the	the	DET
ajst-26203	100	3	model	model	NOUN
ajst-26203	100	4	evaluation	evaluation	NOUN
ajst-26203	100	5	,	,	PUNCT
ajst-26203	100	6	the	the	DET
ajst-26203	100	7	test	test	NOUN
ajst-26203	100	8	set	set	NOUN
ajst-26203	100	9	evaluation	evaluation	NOUN
ajst-26203	100	10	is	be	AUX
ajst-26203	100	11	carried	carry	VERB
ajst-26203	100	12	out	out	ADP
ajst-26203	100	13	first	first	ADV
ajst-26203	100	14	,	,	PUNCT
ajst-26203	100	15	and	and	CCONJ
ajst-26203	100	16	the	the	DET
ajst-26203	100	17	trained	train	VERB
ajst-26203	100	18	model	model	NOUN
ajst-26203	100	19	is	be	AUX
ajst-26203	100	20	evaluated	evaluate	VERB
ajst-26203	100	21	with	with	ADP
ajst-26203	100	22	the	the	DET
ajst-26203	100	23	test	test	NOUN
ajst-26203	100	24	set	set	NOUN
ajst-26203	100	25	.	.	PUNCT
ajst-26203	101	1	the	the	DET
ajst-26203	101	2	predictive	predictive	ADJ
ajst-26203	101	3	performance	performance	NOUN
ajst-26203	101	4	indicators	indicator	NOUN
ajst-26203	101	5	of	of	ADP
ajst-26203	101	6	the	the	DET
ajst-26203	101	7	model	model	NOUN
ajst-26203	101	8	on	on	ADP
ajst-26203	101	9	the	the	DET
ajst-26203	101	10	test	test	NOUN
ajst-26203	101	11	set	set	NOUN
ajst-26203	101	12	,	,	PUNCT
ajst-26203	101	13	such	such	ADJ
ajst-26203	101	14	as	as	ADP
ajst-26203	101	15	rmse	rmse	NOUN
ajst-26203	101	16	and	and	CCONJ
ajst-26203	101	17	mae	mae	PROPN
ajst-26203	101	18	,	,	PUNCT
ajst-26203	101	19	are	be	AUX
ajst-26203	101	20	calculated	calculate	VERB
ajst-26203	101	21	.	.	PUNCT
ajst-26203	102	1	secondly	secondly	ADV
ajst-26203	102	2	,	,	PUNCT
ajst-26203	102	3	the	the	DET
ajst-26203	102	4	result	result	NOUN
ajst-26203	102	5	analysis	analysis	NOUN
ajst-26203	102	6	analyzes	analyze	VERB
ajst-26203	102	7	the	the	DET
ajst-26203	102	8	prediction	prediction	NOUN
ajst-26203	102	9	results	result	NOUN
ajst-26203	102	10	of	of	ADP
ajst-26203	102	11	the	the	DET
ajst-26203	102	12	model	model	NOUN
ajst-26203	102	13	on	on	ADP
ajst-26203	102	14	the	the	DET
ajst-26203	102	15	test	test	NOUN
ajst-26203	102	16	set	set	NOUN
ajst-26203	102	17	,	,	PUNCT
ajst-26203	102	18	compares	compare	VERB
ajst-26203	102	19	the	the	DET
ajst-26203	102	20	difference	difference	NOUN
ajst-26203	102	21	between	between	ADP
ajst-26203	102	22	the	the	DET
ajst-26203	102	23	predicted	predict	VERB
ajst-26203	102	24	value	value	NOUN
ajst-26203	102	25	and	and	CCONJ
ajst-26203	102	26	the	the	DET
ajst-26203	102	27	real	real	ADJ
ajst-26203	102	28	value	value	NOUN
ajst-26203	102	29	,	,	PUNCT
ajst-26203	102	30	discusses	discuss	VERB
ajst-26203	102	31	the	the	DET
ajst-26203	102	32	advantages	advantage	NOUN
ajst-26203	102	33	and	and	CCONJ
ajst-26203	102	34	limitations	limitation	NOUN
ajst-26203	102	35	of	of	ADP
ajst-26203	102	36	the	the	DET
ajst-26203	102	37	model	model	NOUN
ajst-26203	102	38	,	,	PUNCT
ajst-26203	102	39	and	and	CCONJ
ajst-26203	102	40	further	far	ADV
ajst-26203	102	41	optimizes	optimize	VERB
ajst-26203	102	42	the	the	DET
ajst-26203	102	43	model	model	NOUN
ajst-26203	102	44	or	or	CCONJ
ajst-26203	102	45	improves	improve	VERB
ajst-26203	102	46	the	the	DET
ajst-26203	102	47	prediction	prediction	NOUN
ajst-26203	102	48	method	method	NOUN
ajst-26203	102	49	.	.	PUNCT
ajst-26203	103	1	then	then	ADV
ajst-26203	103	2	the	the	DET
ajst-26203	103	3	visualization	visualization	NOUN
ajst-26203	103	4	analysis	analysis	NOUN
ajst-26203	103	5	can	can	AUX
ajst-26203	103	6	be	be	AUX
ajst-26203	103	7	carried	carry	VERB
ajst-26203	103	8	out	out	ADP
ajst-26203	103	9	by	by	ADP
ajst-26203	103	10	drawing	draw	VERB
ajst-26203	103	11	the	the	DET
ajst-26203	103	12	prediction	prediction	NOUN
ajst-26203	103	13	curve	curve	NOUN
ajst-26203	103	14	,	,	PUNCT
ajst-26203	103	15	residual	residual	ADJ
ajst-26203	103	16	diagram	diagram	NOUN
ajst-26203	103	17	and	and	CCONJ
ajst-26203	103	18	other	other	ADJ
ajst-26203	103	19	ways	way	NOUN
ajst-26203	103	20	to	to	PART
ajst-26203	103	21	visualize	visualize	VERB
ajst-26203	103	22	the	the	DET
ajst-26203	103	23	model	model	NOUN
ajst-26203	103	24	to	to	PART
ajst-26203	103	25	show	show	VERB
ajst-26203	103	26	the	the	DET
ajst-26203	103	27	prediction	prediction	NOUN
ajst-26203	103	28	effect	effect	NOUN
ajst-26203	103	29	of	of	ADP
ajst-26203	103	30	the	the	DET
ajst-26203	103	31	model	model	NOUN
ajst-26203	103	32	.	.	PUNCT
ajst-26203	104	1	finally	finally	ADV
ajst-26203	104	2	,	,	PUNCT
ajst-26203	104	3	model	model	ADJ
ajst-26203	104	4	comparison	comparison	NOUN
ajst-26203	104	5	.	.	PUNCT
ajst-26203	105	1	if	if	SCONJ
ajst-26203	105	2	there	there	PRON
ajst-26203	105	3	are	be	VERB
ajst-26203	105	4	multiple	multiple	ADJ
ajst-26203	105	5	models	model	NOUN
ajst-26203	105	6	,	,	PUNCT
ajst-26203	105	7	their	their	PRON
ajst-26203	105	8	performance	performance	NOUN
ajst-26203	105	9	on	on	ADP
ajst-26203	105	10	the	the	DET
ajst-26203	105	11	same	same	ADJ
ajst-26203	105	12	data	datum	NOUN
ajst-26203	105	13	set	set	NOUN
ajst-26203	105	14	can	can	AUX
ajst-26203	105	15	be	be	AUX
ajst-26203	105	16	compared	compare	VERB
ajst-26203	105	17	,	,	PUNCT
ajst-26203	105	18	and	and	CCONJ
ajst-26203	105	19	the	the	DET
ajst-26203	105	20	optimal	optimal	ADJ
ajst-26203	105	21	model	model	NOUN
ajst-26203	105	22	can	can	AUX
ajst-26203	105	23	be	be	AUX
ajst-26203	105	24	selected	select	VERB
ajst-26203	105	25	for	for	ADP
ajst-26203	105	26	practical	practical	ADJ
ajst-26203	105	27	application	application	NOUN
ajst-26203	105	28	.	.	PUNCT
ajst-26203	106	1	3	3	X
ajst-26203	106	2	)	)	PUNCT
ajst-26203	106	3	result	result	VERB
ajst-26203	106	4	analysis	analysis	NOUN
ajst-26203	106	5	and	and	CCONJ
ajst-26203	106	6	discussion	discussion	NOUN
ajst-26203	106	7	the	the	DET
ajst-26203	106	8	lstm	lstm	ADJ
ajst-26203	106	9	network	network	NOUN
ajst-26203	106	10	architecture	architecture	NOUN
ajst-26203	106	11	is	be	AUX
ajst-26203	106	12	defined	define	VERB
ajst-26203	106	13	and	and	CCONJ
ajst-26203	106	14	trained	train	VERB
ajst-26203	106	15	,	,	PUNCT
ajst-26203	106	16	and	and	CCONJ
ajst-26203	106	17	the	the	DET
ajst-26203	106	18	single	single	ADJ
ajst-26203	106	19	step	step	NOUN
ajst-26203	106	20	prediction	prediction	NOUN
ajst-26203	106	21	is	be	AUX
ajst-26203	106	22	realized	realize	VERB
ajst-26203	106	23	by	by	ADP
ajst-26203	106	24	using	use	VERB
ajst-26203	106	25	lstm	lstm	NOUN
ajst-26203	106	26	network.the	network.the	DET
ajst-26203	106	27	prediction	prediction	NOUN
ajst-26203	106	28	results	result	NOUN
ajst-26203	106	29	are	be	AUX
ajst-26203	106	30	compared	compare	VERB
ajst-26203	106	31	with	with	ADP
ajst-26203	106	32	the	the	DET
ajst-26203	106	33	actual	actual	ADJ
ajst-26203	106	34	data	datum	NOUN
ajst-26203	106	35	.	.	PUNCT
ajst-26203	107	1	the	the	DET
ajst-26203	107	2	300	300	NUM
ajst-26203	107	3	predictive	predictive	ADJ
ajst-26203	107	4	trainings	training	NOUN
ajst-26203	107	5	are	be	AUX
ajst-26203	107	6	shown	show	VERB
ajst-26203	107	7	in	in	ADP
ajst-26203	107	8	figure	figure	NOUN
ajst-26203	107	9	3	3	NUM
ajst-26203	107	10	,	,	PUNCT
ajst-26203	107	11	and	and	CCONJ
ajst-26203	107	12	the	the	DET
ajst-26203	107	13	300	300	NUM
ajst-26203	107	14	test	test	NOUN
ajst-26203	107	15	results	result	NOUN
ajst-26203	107	16	since	since	SCONJ
ajst-26203	107	17	2000	2000	NUM
ajst-26203	107	18	are	be	AUX
ajst-26203	107	19	shown	show	VERB
ajst-26203	107	20	in	in	ADP
ajst-26203	107	21	figure	figure	NOUN
ajst-26203	107	22	4	4	NUM
ajst-26203	107	23	.	.	PUNCT
ajst-26203	107	24	figure	figure	VERB
ajst-26203	107	25	3	3	NUM
ajst-26203	107	26	.	.	PUNCT
ajst-26203	107	27	prediction	prediction	NOUN
ajst-26203	107	28	training	training	NOUN
ajst-26203	107	29	diagram	diagram	NOUN
ajst-26203	107	30	figure	figure	NOUN
ajst-26203	107	31	4	4	NUM
ajst-26203	107	32	.	.	PUNCT
ajst-26203	107	33	test	test	NOUN
ajst-26203	107	34	result	result	PROPN
ajst-26203	107	35	diagram	diagram	PROPN
ajst-26203	107	36	129	129	NUM
ajst-26203	107	37	as	as	SCONJ
ajst-26203	107	38	can	can	AUX
ajst-26203	107	39	be	be	AUX
ajst-26203	107	40	seen	see	VERB
ajst-26203	107	41	from	from	ADP
ajst-26203	107	42	figure	figure	NOUN
ajst-26203	107	43	4	4	NUM
ajst-26203	107	44	,	,	PUNCT
ajst-26203	107	45	in	in	ADP
ajst-26203	107	46	the	the	DET
ajst-26203	107	47	prediction	prediction	NOUN
ajst-26203	107	48	process	process	NOUN
ajst-26203	107	49	,	,	PUNCT
ajst-26203	107	50	the	the	DET
ajst-26203	107	51	model	model	NOUN
ajst-26203	107	52	predicted	predict	VERB
ajst-26203	107	53	300	300	NUM
ajst-26203	107	54	data	datum	NOUN
ajst-26203	107	55	points	point	NOUN
ajst-26203	107	56	after	after	ADP
ajst-26203	107	57	2000	2000	NUM
ajst-26203	107	58	data	datum	NOUN
ajst-26203	107	59	points	point	NOUN
ajst-26203	107	60	,	,	PUNCT
ajst-26203	107	61	and	and	CCONJ
ajst-26203	107	62	the	the	DET
ajst-26203	107	63	data	data	NOUN
ajst-26203	107	64	points	point	NOUN
ajst-26203	107	65	verified	verify	VERB
ajst-26203	107	66	the	the	DET
ajst-26203	107	67	training	training	NOUN
ajst-26203	107	68	results	result	NOUN
ajst-26203	107	69	of	of	ADP
ajst-26203	107	70	the	the	DET
ajst-26203	107	71	neural	neural	ADJ
ajst-26203	107	72	network	network	NOUN
ajst-26203	107	73	,	,	PUNCT
ajst-26203	107	74	showing	show	VERB
ajst-26203	107	75	that	that	SCONJ
ajst-26203	107	76	the	the	DET
ajst-26203	107	77	results	result	NOUN
ajst-26203	107	78	were	be	AUX
ajst-26203	107	79	good	good	ADJ
ajst-26203	107	80	4	4	NUM
ajst-26203	107	81	.	.	PUNCT
ajst-26203	107	82	conclusion	conclusion	NOUN
ajst-26203	107	83	through	through	ADP
ajst-26203	107	84	the	the	DET
ajst-26203	107	85	construction	construction	NOUN
ajst-26203	107	86	of	of	ADP
ajst-26203	107	87	gis	gis	PROPN
ajst-26203	107	88	equipment	equipment	NOUN
ajst-26203	107	89	sf6	sf6	PROPN
ajst-26203	107	90	gas	gas	PROPN
ajst-26203	107	91	humidity	humidity	NOUN
ajst-26203	107	92	prediction	prediction	NOUN
ajst-26203	107	93	model	model	NOUN
ajst-26203	107	94	based	base	VERB
ajst-26203	107	95	on	on	ADP
ajst-26203	107	96	deep	deep	ADJ
ajst-26203	107	97	learning	learning	NOUN
ajst-26203	107	98	,	,	PUNCT
ajst-26203	107	99	some	some	DET
ajst-26203	107	100	results	result	NOUN
ajst-26203	107	101	and	and	CCONJ
ajst-26203	107	102	enlightenment	enlightenment	NOUN
ajst-26203	107	103	have	have	AUX
ajst-26203	107	104	been	be	AUX
ajst-26203	107	105	obtained	obtain	VERB
ajst-26203	107	106	.	.	PUNCT
ajst-26203	108	1	in	in	ADP
ajst-26203	108	2	the	the	DET
ajst-26203	108	3	process	process	NOUN
ajst-26203	108	4	of	of	ADP
ajst-26203	108	5	model	model	NOUN
ajst-26203	108	6	design	design	NOUN
ajst-26203	108	7	and	and	CCONJ
ajst-26203	108	8	training	training	NOUN
ajst-26203	108	9	,	,	PUNCT
ajst-26203	108	10	the	the	DET
ajst-26203	108	11	advantage	advantage	NOUN
ajst-26203	108	12	of	of	ADP
ajst-26203	108	13	deep	deep	ADJ
ajst-26203	108	14	learning	learning	NOUN
ajst-26203	108	15	algorithm	algorithm	NOUN
ajst-26203	108	16	is	be	AUX
ajst-26203	108	17	fully	fully	ADV
ajst-26203	108	18	utilized	utilize	VERB
ajst-26203	108	19	,	,	PUNCT
ajst-26203	108	20	and	and	CCONJ
ajst-26203	108	21	the	the	DET
ajst-26203	108	22	accurate	accurate	ADJ
ajst-26203	108	23	prediction	prediction	NOUN
ajst-26203	108	24	of	of	ADP
ajst-26203	108	25	sf6	sf6	PROPN
ajst-26203	108	26	gas	gas	NOUN
ajst-26203	108	27	humidity	humidity	NOUN
ajst-26203	108	28	is	be	AUX
ajst-26203	108	29	realized	realize	VERB
ajst-26203	108	30	through	through	ADP
ajst-26203	108	31	lstm	lstm	NOUN
ajst-26203	108	32	network	network	NOUN
ajst-26203	108	33	and	and	CCONJ
ajst-26203	108	34	other	other	ADJ
ajst-26203	108	35	models	model	NOUN
ajst-26203	108	36	.	.	PUNCT
ajst-26203	109	1	in	in	ADP
ajst-26203	109	2	the	the	DET
ajst-26203	109	3	experiment	experiment	NOUN
ajst-26203	109	4	and	and	CCONJ
ajst-26203	109	5	result	result	VERB
ajst-26203	109	6	analysis	analysis	NOUN
ajst-26203	109	7	,	,	PUNCT
ajst-26203	109	8	it	it	PRON
ajst-26203	109	9	is	be	AUX
ajst-26203	109	10	found	find	VERB
ajst-26203	109	11	that	that	SCONJ
ajst-26203	109	12	the	the	DET
ajst-26203	109	13	model	model	NOUN
ajst-26203	109	14	has	have	VERB
ajst-26203	109	15	good	good	ADJ
ajst-26203	109	16	performance	performance	NOUN
ajst-26203	109	17	in	in	ADP
ajst-26203	109	18	the	the	DET
ajst-26203	109	19	forecasting	forecasting	NOUN
ajst-26203	109	20	process	process	NOUN
ajst-26203	109	21	,	,	PUNCT
ajst-26203	109	22	and	and	CCONJ
ajst-26203	109	23	can	can	AUX
ajst-26203	109	24	predict	predict	VERB
ajst-26203	109	25	the	the	DET
ajst-26203	109	26	future	future	ADJ
ajst-26203	109	27	humidity	humidity	NOUN
ajst-26203	109	28	level	level	NOUN
ajst-26203	109	29	more	more	ADV
ajst-26203	109	30	accurately	accurately	ADV
ajst-26203	109	31	.	.	PUNCT
ajst-26203	110	1	however	however	ADV
ajst-26203	110	2	,	,	PUNCT
ajst-26203	110	3	there	there	PRON
ajst-26203	110	4	are	be	VERB
ajst-26203	110	5	still	still	ADV
ajst-26203	110	6	some	some	DET
ajst-26203	110	7	challenges	challenge	NOUN
ajst-26203	110	8	and	and	CCONJ
ajst-26203	110	9	shortcomings	shortcoming	NOUN
ajst-26203	110	10	in	in	ADP
ajst-26203	110	11	the	the	DET
ajst-26203	110	12	construction	construction	NOUN
ajst-26203	110	13	and	and	CCONJ
ajst-26203	110	14	training	training	NOUN
ajst-26203	110	15	of	of	ADP
ajst-26203	110	16	deep	deep	ADJ
ajst-26203	110	17	learning	learning	NOUN
ajst-26203	110	18	models	model	NOUN
ajst-26203	110	19	.	.	PUNCT
ajst-26203	111	1	for	for	ADP
ajst-26203	111	2	example	example	NOUN
ajst-26203	111	3	,	,	PUNCT
ajst-26203	111	4	the	the	DET
ajst-26203	111	5	prediction	prediction	NOUN
ajst-26203	111	6	accuracy	accuracy	NOUN
ajst-26203	111	7	of	of	ADP
ajst-26203	111	8	the	the	DET
ajst-26203	111	9	model	model	NOUN
ajst-26203	111	10	is	be	AUX
ajst-26203	111	11	limited	limit	VERB
ajst-26203	111	12	by	by	ADP
ajst-26203	111	13	the	the	DET
ajst-26203	111	14	quality	quality	NOUN
ajst-26203	111	15	and	and	CCONJ
ajst-26203	111	16	quantity	quantity	NOUN
ajst-26203	111	17	of	of	ADP
ajst-26203	111	18	training	training	NOUN
ajst-26203	111	19	data	datum	NOUN
ajst-26203	111	20	,	,	PUNCT
ajst-26203	111	21	and	and	CCONJ
ajst-26203	111	22	the	the	DET
ajst-26203	111	23	processing	processing	NOUN
ajst-26203	111	24	of	of	ADP
ajst-26203	111	25	complex	complex	ADJ
ajst-26203	111	26	scenarios	scenario	NOUN
ajst-26203	111	27	and	and	CCONJ
ajst-26203	111	28	anomalies	anomaly	NOUN
ajst-26203	111	29	needs	need	VERB
ajst-26203	111	30	to	to	PART
ajst-26203	111	31	be	be	AUX
ajst-26203	111	32	further	far	ADV
ajst-26203	111	33	improved	improve	VERB
ajst-26203	111	34	.	.	PUNCT
ajst-26203	112	1	in	in	ADP
ajst-26203	112	2	addition	addition	NOUN
ajst-26203	112	3	,	,	PUNCT
ajst-26203	112	4	the	the	DET
ajst-26203	112	5	generalization	generalization	NOUN
ajst-26203	112	6	ability	ability	NOUN
ajst-26203	112	7	and	and	CCONJ
ajst-26203	112	8	stability	stability	NOUN
ajst-26203	112	9	of	of	ADP
ajst-26203	112	10	the	the	DET
ajst-26203	112	11	model	model	NOUN
ajst-26203	112	12	also	also	ADV
ajst-26203	112	13	need	need	VERB
ajst-26203	112	14	to	to	PART
ajst-26203	112	15	be	be	AUX
ajst-26203	112	16	further	far	ADV
ajst-26203	112	17	improved	improve	VERB
ajst-26203	112	18	to	to	PART
ajst-26203	112	19	adapt	adapt	VERB
ajst-26203	112	20	to	to	ADP
ajst-26203	112	21	the	the	DET
ajst-26203	112	22	actual	actual	ADJ
ajst-26203	112	23	application	application	NOUN
ajst-26203	112	24	requirements	requirement	NOUN
ajst-26203	112	25	in	in	ADP
ajst-26203	112	26	different	different	ADJ
ajst-26203	112	27	environments	environment	NOUN
ajst-26203	112	28	.	.	PUNCT
ajst-26203	113	1	in	in	ADP
ajst-26203	113	2	future	future	ADJ
ajst-26203	113	3	studies	study	NOUN
ajst-26203	113	4	,	,	PUNCT
ajst-26203	113	5	it	it	PRON
ajst-26203	113	6	is	be	AUX
ajst-26203	113	7	necessary	necessary	ADJ
ajst-26203	113	8	to	to	PART
ajst-26203	113	9	continue	continue	VERB
ajst-26203	113	10	to	to	PART
ajst-26203	113	11	optimize	optimize	VERB
ajst-26203	113	12	the	the	DET
ajst-26203	113	13	design	design	NOUN
ajst-26203	113	14	and	and	CCONJ
ajst-26203	113	15	training	training	NOUN
ajst-26203	113	16	methods	method	NOUN
ajst-26203	113	17	of	of	ADP
ajst-26203	113	18	deep	deep	ADJ
ajst-26203	113	19	learning	learning	NOUN
ajst-26203	113	20	models	model	NOUN
ajst-26203	113	21	to	to	PART
ajst-26203	113	22	further	far	ADV
ajst-26203	113	23	improve	improve	VERB
ajst-26203	113	24	the	the	DET
ajst-26203	113	25	performance	performance	NOUN
ajst-26203	113	26	and	and	CCONJ
ajst-26203	113	27	stability	stability	NOUN
ajst-26203	113	28	of	of	ADP
ajst-26203	113	29	predictive	predictive	ADJ
ajst-26203	113	30	models	model	NOUN
ajst-26203	113	31	.	.	PUNCT
ajst-26203	114	1	at	at	ADP
ajst-26203	114	2	the	the	DET
ajst-26203	114	3	same	same	ADJ
ajst-26203	114	4	time	time	NOUN
ajst-26203	114	5	,	,	PUNCT
ajst-26203	114	6	more	more	ADJ
ajst-26203	114	7	data	datum	NOUN
ajst-26203	114	8	sources	source	NOUN
ajst-26203	114	9	and	and	CCONJ
ajst-26203	114	10	feature	feature	NOUN
ajst-26203	114	11	representation	representation	NOUN
ajst-26203	114	12	methods	method	NOUN
ajst-26203	114	13	will	will	AUX
ajst-26203	114	14	be	be	AUX
ajst-26203	114	15	explored	explore	VERB
ajst-26203	114	16	to	to	PART
ajst-26203	114	17	improve	improve	VERB
ajst-26203	114	18	the	the	DET
ajst-26203	114	19	adaptability	adaptability	NOUN
ajst-26203	114	20	and	and	CCONJ
ajst-26203	114	21	generalization	generalization	NOUN
ajst-26203	114	22	ability	ability	NOUN
ajst-26203	114	23	of	of	ADP
ajst-26203	114	24	the	the	DET
ajst-26203	114	25	model	model	NOUN
ajst-26203	114	26	.	.	PUNCT
ajst-26203	115	1	with	with	ADP
ajst-26203	115	2	the	the	DET
ajst-26203	115	3	continuous	continuous	ADJ
ajst-26203	115	4	progress	progress	NOUN
ajst-26203	115	5	of	of	ADP
ajst-26203	115	6	technology	technology	NOUN
ajst-26203	115	7	and	and	CCONJ
ajst-26203	115	8	in	in	ADP
ajst-26203	115	9	-	-	PUNCT
ajst-26203	115	10	depth	depth	NOUN
ajst-26203	115	11	research	research	NOUN
ajst-26203	115	12	,	,	PUNCT
ajst-26203	115	13	gis	gis	PROPN
ajst-26203	115	14	equipment	equipment	NOUN
ajst-26203	115	15	sf6	sf6	PROPN
ajst-26203	115	16	gas	gas	PROPN
ajst-26203	115	17	humidity	humidity	NOUN
ajst-26203	115	18	prediction	prediction	NOUN
ajst-26203	115	19	model	model	NOUN
ajst-26203	115	20	based	base	VERB
ajst-26203	115	21	on	on	ADP
ajst-26203	115	22	deep	deep	ADJ
ajst-26203	115	23	learning	learning	NOUN
ajst-26203	115	24	will	will	AUX
ajst-26203	115	25	play	play	VERB
ajst-26203	115	26	an	an	DET
ajst-26203	115	27	increasingly	increasingly	ADV
ajst-26203	115	28	important	important	ADJ
ajst-26203	115	29	role	role	NOUN
ajst-26203	115	30	in	in	ADP
ajst-26203	115	31	the	the	DET
ajst-26203	115	32	monitoring	monitoring	NOUN
ajst-26203	115	33	and	and	CCONJ
ajst-26203	115	34	maintenance	maintenance	NOUN
ajst-26203	115	35	of	of	ADP
ajst-26203	115	36	power	power	NOUN
ajst-26203	115	37	industry	industry	NOUN
ajst-26203	115	38	.	.	PUNCT
ajst-26203	116	1	references	reference	NOUN
ajst-26203	116	2	[	[	X
ajst-26203	116	3	1	1	NUM
ajst-26203	116	4	]	]	X
ajst-26203	116	5	liu	liu	PROPN
ajst-26203	116	6	zongyi	zongyi	PROPN
ajst-26203	116	7	,	,	PUNCT
ajst-26203	116	8	zhang	zhang	PROPN
ajst-26203	116	9	caihong	caihong	PROPN
ajst-26203	116	10	,	,	PUNCT
ajst-26203	116	11	jiang	jiang	PROPN
ajst-26203	116	12	jianjian	jianjian	PROPN
ajst-26203	116	13	,	,	PUNCT
ajst-26203	116	14	et	et	PROPN
ajst-26203	116	15	al	al	PROPN
ajst-26203	116	16	.	.	PROPN
ajst-26203	117	1	rapid	rapid	ADJ
ajst-26203	117	2	detection	detection	NOUN
ajst-26203	117	3	of	of	ADP
ajst-26203	117	4	total	total	ADJ
ajst-26203	117	5	flavonoids	flavonoid	NOUN
ajst-26203	117	6	in	in	ADP
ajst-26203	117	7	dendrobium	dendrobium	NOUN
ajst-26203	117	8	officinale	officinale	NOUN
ajst-26203	117	9	based	base	VERB
ajst-26203	117	10	on	on	ADP
ajst-26203	117	11	raman	raman	NOUN
ajst-26203	117	12	spectroscopy	spectroscopy	NOUN
ajst-26203	117	13	combined	combine	VERB
ajst-26203	117	14	with	with	ADP
ajst-26203	117	15	cnn	cnn	PROPN
ajst-26203	117	16	-	-	PUNCT
ajst-26203	117	17	lstm	lstm	ADJ
ajst-26203	117	18	deep	deep	ADJ
ajst-26203	117	19	learning	learning	NOUN
ajst-26203	117	20	[	[	X
ajst-26203	117	21	j	j	X
ajst-26203	117	22	]	]	X
ajst-26203	117	23	.	.	PUNCT
ajst-26203	117	24	spectroscopy	spectroscopy	NOUN
ajst-26203	117	25	and	and	CCONJ
ajst-26203	117	26	spectral	spectral	ADJ
ajst-26203	117	27	analysis	analysis	NOUN
ajst-26203	117	28	.	.	PUNCT
ajst-26203	117	29	2024	2024	NUM
ajst-26203	117	30	,	,	PUNCT
ajst-26203	117	31	44	44	NUM
ajst-26203	117	32	(	(	PUNCT
ajst-26203	117	33	04	04	NUM
ajst-26203	117	34	):	):	PUNCT
ajst-26203	117	35	1018	1018	NUM
ajst-26203	117	36	-	-	SYM
ajst-26203	117	37	1024	1024	NUM
ajst-26203	117	38	.	.	PUNCT
ajst-26203	118	1	[	[	X
ajst-26203	118	2	2	2	X
ajst-26203	118	3	]	]	X
ajst-26203	118	4	huang	huang	PROPN
ajst-26203	118	5	zehui	zehui	PROPN
ajst-26203	118	6	,	,	PUNCT
ajst-26203	118	7	tang	tang	PROPN
ajst-26203	118	8	hongbin	hongbin	PROPN
ajst-26203	118	9	,	,	PUNCT
ajst-26203	118	10	wang	wang	PROPN
ajst-26203	118	11	xuesong	xuesong	PROPN
ajst-26203	118	12	et	et	PROPN
ajst-26203	118	13	al	al	PROPN
ajst-26203	118	14	.	.	PROPN
ajst-26203	118	15	research	research	NOUN
ajst-26203	118	16	on	on	ADP
ajst-26203	118	17	occupant	occupant	ADJ
ajst-26203	118	18	injury	injury	NOUN
ajst-26203	118	19	prediction	prediction	NOUN
ajst-26203	118	20	method	method	NOUN
ajst-26203	118	21	of	of	ADP
ajst-26203	118	22	vehicle	vehicle	NOUN
ajst-26203	118	23	collision	collision	NOUN
ajst-26203	118	24	based	base	VERB
ajst-26203	118	25	on	on	ADP
ajst-26203	118	26	deep	deep	ADJ
ajst-26203	118	27	learning	learning	NOUN
ajst-26203	119	1	[	[	X
ajst-26203	119	2	j	j	X
ajst-26203	119	3	]	]	X
ajst-26203	119	4	.	.	PUNCT
ajst-26203	120	1	automotive	automotive	ADJ
ajst-26203	120	2	engineer	engineer	NOUN
ajst-26203	120	3	.	.	PUNCT
ajst-26203	121	1	2024	2024	NUM
ajst-26203	121	2	(	(	PUNCT
ajst-26203	121	3	04):8	04):8	NUM
ajst-26203	121	4	-	-	SYM
ajst-26203	121	5	11	11	NUM
ajst-26203	121	6	.	.	PUNCT
ajst-26203	122	1	[	[	X
ajst-26203	122	2	3	3	NUM
ajst-26203	122	3	]	]	PUNCT
ajst-26203	122	4	zou	zou	PROPN
ajst-26203	122	5	yutan	yutan	PROPN
ajst-26203	122	6	.	.	PUNCT
ajst-26203	123	1	prediction	prediction	NOUN
ajst-26203	123	2	model	model	NOUN
ajst-26203	123	3	of	of	ADP
ajst-26203	123	4	soil	soil	NOUN
ajst-26203	123	5	mechanical	mechanical	ADJ
ajst-26203	123	6	properties	property	NOUN
ajst-26203	123	7	of	of	ADP
ajst-26203	123	8	highway	highway	NOUN
ajst-26203	123	9	subgrade	subgrade	NOUN
ajst-26203	123	10	based	base	VERB
ajst-26203	123	11	on	on	ADP
ajst-26203	123	12	machine	machine	NOUN
ajst-26203	123	13	learning	learn	VERB
ajst-26203	124	1	[	[	X
ajst-26203	124	2	j	j	X
ajst-26203	124	3	]	]	X
ajst-26203	124	4	.	.	PUNCT
ajst-26203	125	1	transportation	transportation	NOUN
ajst-26203	125	2	technology	technology	NOUN
ajst-26203	125	3	and	and	CCONJ
ajst-26203	125	4	management	management	NOUN
ajst-26203	125	5	,	,	PUNCT
ajst-26203	125	6	2019	2019	NUM
ajst-26203	125	7	,	,	PUNCT
ajst-26203	125	8	5(07):1416	5(07):1416	NUM
ajst-26203	125	9	.	.	PUNCT
ajst-26203	126	1	[	[	X
ajst-26203	126	2	4	4	X
ajst-26203	126	3	]	]	X
ajst-26203	126	4	chen	chen	PROPN
ajst-26203	126	5	xiaomin	xiaomin	PROPN
ajst-26203	126	6	,	,	PUNCT
ajst-26203	126	7	wang	wang	PROPN
ajst-26203	126	8	gang	gang	PROPN
ajst-26203	126	9	,	,	PUNCT
ajst-26203	126	10	wang	wang	PROPN
ajst-26203	126	11	longjun	longjun	PROPN
ajst-26203	126	12	.	.	PROPN
ajst-26203	126	13	ordered	order	VERB
ajst-26203	126	14	charging	charge	VERB
ajst-26203	126	15	strategy	strategy	NOUN
ajst-26203	126	16	of	of	ADP
ajst-26203	126	17	electric	electric	ADJ
ajst-26203	126	18	vehicle	vehicle	NOUN
ajst-26203	126	19	based	base	VERB
ajst-26203	126	20	on	on	ADP
ajst-26203	126	21	vehicle	vehicle	NOUN
ajst-26203	126	22	networking	network	VERB
ajst-26203	126	23	framework	framework	NOUN
ajst-26203	126	24	[	[	X
ajst-26203	126	25	j	j	X
ajst-26203	126	26	]	]	X
ajst-26203	126	27	.	.	PUNCT
ajst-26203	127	1	modern	modern	ADJ
ajst-26203	127	2	electric	electric	ADJ
ajst-26203	127	3	power	power	NOUN
ajst-26203	127	4	.	.	PUNCT
ajst-26203	128	1	2018	2018	NUM
ajst-26203	128	2	,	,	PUNCT
ajst-26203	128	3	35(4):1	35(4):1	NUM
ajst-26203	128	4	-	-	SYM
ajst-26203	128	5	7	7	NUM
ajst-26203	128	6	.	.	PUNCT
ajst-26203	129	1	[	[	X
ajst-26203	129	2	5	5	X
ajst-26203	129	3	]	]	X
ajst-26203	129	4	xiaomin	xiaomin	PROPN
ajst-26203	129	5	chen	chen	PROPN
ajst-26203	129	6	,	,	PUNCT
ajst-26203	129	7	gang	gang	NOUN
ajst-26203	129	8	wang.optimal	wang.optimal	PROPN
ajst-26203	129	9	control	control	NOUN
ajst-26203	129	10	of	of	ADP
ajst-26203	129	11	micro	micro	PROPN
ajst-26203	129	12	grid	grid	PROPN
ajst-26203	129	13	operation	operation	NOUN
ajst-26203	129	14	mode	mode	NOUN
ajst-26203	129	15	seamless	seamless	NOUN
ajst-26203	129	16	switching	switching	NOUN
ajst-26203	129	17	based	base	VERB
ajst-26203	129	18	on	on	ADP
ajst-26203	129	19	radau	radau	NOUN
ajst-26203	129	20	allocation	allocation	NOUN
ajst-26203	129	21	method	method	NOUN
ajst-26203	129	22	[	[	PUNCT
ajst-26203	129	23	c].iop	c].iop	NUM
ajst-26203	129	24	conference	conference	NOUN
ajst-26203	129	25	series	series	NOUN
ajst-26203	129	26	:	:	PUNCT
ajst-26203	129	27	materials	material	NOUN
ajst-26203	129	28	science	science	NOUN
ajst-26203	129	29	and	and	CCONJ
ajst-26203	129	30	engineering.2017,199(1):012053	engineering.2017,199(1):012053	ADJ