id	sid	tid	token	lemma	pos
fcis-14563	1	1	frontiers	frontier	NOUN
fcis-14563	1	2	in	in	ADP
fcis-14563	1	3	computing	computing	NOUN
fcis-14563	1	4	and	and	CCONJ
fcis-14563	1	5	intelligent	intelligent	ADJ
fcis-14563	1	6	systems	system	NOUN
fcis-14563	1	7	issn	issn	VERB
fcis-14563	1	8	:	:	PUNCT
fcis-14563	1	9	2832	2832	NUM
fcis-14563	1	10	-	-	SYM
fcis-14563	1	11	6024	6024	NUM
fcis-14563	1	12	|	|	NOUN
fcis-14563	1	13	vol	vol	NOUN
fcis-14563	1	14	.	.	PROPN
fcis-14563	2	1	6	6	NUM
fcis-14563	2	2	,	,	PUNCT
fcis-14563	2	3	no	no	INTJ
fcis-14563	2	4	.	.	NOUN
fcis-14563	2	5	1	1	NUM
fcis-14563	2	6	,	,	PUNCT
fcis-14563	2	7	2023	2023	NUM
fcis-14563	2	8	4	4	NUM
fcis-14563	2	9	power	power	NOUN
fcis-14563	2	10	quality	quality	NOUN
fcis-14563	2	11	disturbance	disturbance	NOUN
fcis-14563	2	12	recognition	recognition	NOUN
fcis-14563	2	13	based	base	VERB
fcis-14563	2	14	on	on	ADP
fcis-14563	2	15	deep	deep	ADJ
fcis-14563	2	16	neural	neural	ADJ
fcis-14563	2	17	network	network	NOUN
fcis-14563	2	18	and	and	CCONJ
fcis-14563	2	19	adaptive	adaptive	ADJ
fcis-14563	2	20	feature	feature	NOUN
fcis-14563	2	21	fusion	fusion	NOUN
fcis-14563	2	22	lei	lei	PROPN
fcis-14563	2	23	chen	chen	PROPN
fcis-14563	2	24	,	,	PUNCT
fcis-14563	2	25	chao	chao	PROPN
fcis-14563	2	26	zhou	zhou	PROPN
fcis-14563	3	1	*	*	PUNCT
fcis-14563	3	2	,	,	PUNCT
fcis-14563	3	3	jianjun	jianjun	PROPN
fcis-14563	3	4	chen	chen	PROPN
fcis-14563	3	5	department	department	PROPN
fcis-14563	3	6	of	of	ADP
fcis-14563	3	7	telecommunications	telecommunication	NOUN
fcis-14563	3	8	,	,	PUNCT
fcis-14563	3	9	qinhuangdao	qinhuangdao	PROPN
fcis-14563	3	10	campus	campus	PROPN
fcis-14563	3	11	,	,	PUNCT
fcis-14563	3	12	northeast	northeast	ADJ
fcis-14563	3	13	petroleum	petroleum	NOUN
fcis-14563	3	14	university	university	NOUN
fcis-14563	3	15	,	,	PUNCT
fcis-14563	3	16	qinhuangdao	qinhuangdao	PROPN
fcis-14563	3	17	,	,	PUNCT
fcis-14563	3	18	hebei	hebei	PROPN
fcis-14563	3	19	066044	066044	NUM
fcis-14563	3	20	,	,	PUNCT
fcis-14563	3	21	china	china	PROPN
fcis-14563	3	22	*	*	PUNCT
fcis-14563	3	23	corresponding	correspond	VERB
fcis-14563	3	24	author	author	NOUN
fcis-14563	3	25	:	:	PUNCT
fcis-14563	3	26	chao	chao	PROPN
fcis-14563	3	27	zhou	zhou	PROPN
fcis-14563	3	28	(	(	PUNCT
fcis-14563	3	29	email	email	NOUN
fcis-14563	3	30	:	:	PUNCT
fcis-14563	3	31	1239483277@qq.com	1239483277@qq.com	NUM
fcis-14563	3	32	)	)	PUNCT
fcis-14563	3	33	abstract	abstract	NOUN
fcis-14563	3	34	:	:	PUNCT
fcis-14563	3	35	aiming	aim	VERB
fcis-14563	3	36	at	at	ADP
fcis-14563	3	37	the	the	DET
fcis-14563	3	38	problems	problem	NOUN
fcis-14563	3	39	of	of	ADP
fcis-14563	3	40	low	low	ADJ
fcis-14563	3	41	recognition	recognition	NOUN
fcis-14563	3	42	accuracy	accuracy	NOUN
fcis-14563	3	43	and	and	CCONJ
fcis-14563	3	44	low	low	ADJ
fcis-14563	3	45	noise	noise	NOUN
fcis-14563	3	46	resistance	resistance	NOUN
fcis-14563	3	47	of	of	ADP
fcis-14563	3	48	current	current	ADJ
fcis-14563	3	49	power	power	NOUN
fcis-14563	3	50	quality	quality	NOUN
fcis-14563	3	51	disturbance	disturbance	NOUN
fcis-14563	3	52	recognition	recognition	NOUN
fcis-14563	3	53	algorithms	algorithm	NOUN
fcis-14563	3	54	,	,	PUNCT
fcis-14563	3	55	a	a	DET
fcis-14563	3	56	power	power	NOUN
fcis-14563	3	57	quality	quality	NOUN
fcis-14563	3	58	disturbance	disturbance	NOUN
fcis-14563	3	59	recognition	recognition	NOUN
fcis-14563	3	60	method	method	NOUN
fcis-14563	3	61	based	base	VERB
fcis-14563	3	62	on	on	ADP
fcis-14563	3	63	the	the	DET
fcis-14563	3	64	fusion	fusion	NOUN
fcis-14563	3	65	of	of	ADP
fcis-14563	3	66	neural	neural	ADJ
fcis-14563	3	67	network	network	NOUN
fcis-14563	3	68	and	and	CCONJ
fcis-14563	3	69	adaptive	adaptive	ADJ
fcis-14563	3	70	features	feature	NOUN
fcis-14563	3	71	is	be	AUX
fcis-14563	3	72	proposed	propose	VERB
fcis-14563	3	73	.	.	PUNCT
fcis-14563	4	1	firstly	firstly	ADV
fcis-14563	4	2	,	,	PUNCT
fcis-14563	4	3	one	one	NUM
fcis-14563	4	4	-	-	PUNCT
fcis-14563	4	5	dimensional	dimensional	ADJ
fcis-14563	4	6	features	feature	NOUN
fcis-14563	4	7	are	be	AUX
fcis-14563	4	8	extracted	extract	VERB
fcis-14563	4	9	by	by	ADP
fcis-14563	4	10	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	4	11	network	network	NOUN
fcis-14563	4	12	.	.	PUNCT
fcis-14563	5	1	the	the	DET
fcis-14563	5	2	gadf	gadf	PROPN
fcis-14563	5	3	algorithm	algorithm	NOUN
fcis-14563	5	4	and	and	CCONJ
fcis-14563	5	5	2d_cnn	2d_cnn	NUM
fcis-14563	5	6	are	be	AUX
fcis-14563	5	7	used	use	VERB
fcis-14563	5	8	to	to	PART
fcis-14563	5	9	extract	extract	VERB
fcis-14563	5	10	2d	2d	NUM
fcis-14563	5	11	features	feature	NOUN
fcis-14563	5	12	,	,	PUNCT
fcis-14563	5	13	and	and	CCONJ
fcis-14563	5	14	then	then	ADV
fcis-14563	5	15	the	the	DET
fcis-14563	5	16	extracted	extract	VERB
fcis-14563	5	17	1d	1d	NUM
fcis-14563	5	18	features	feature	NOUN
fcis-14563	5	19	and	and	CCONJ
fcis-14563	5	20	2d	2d	NOUN
fcis-14563	5	21	features	feature	NOUN
fcis-14563	5	22	are	be	AUX
fcis-14563	5	23	adaptively	adaptively	ADV
fcis-14563	5	24	fused	fuse	VERB
fcis-14563	5	25	into	into	ADP
fcis-14563	5	26	a	a	DET
fcis-14563	5	27	new	new	ADJ
fcis-14563	5	28	feature	feature	NOUN
fcis-14563	5	29	.	.	PUNCT
fcis-14563	6	1	finally	finally	ADV
fcis-14563	6	2	,	,	PUNCT
fcis-14563	6	3	the	the	DET
fcis-14563	6	4	new	new	ADJ
fcis-14563	6	5	features	feature	NOUN
fcis-14563	6	6	are	be	AUX
fcis-14563	6	7	input	input	VERB
fcis-14563	6	8	into	into	ADP
fcis-14563	6	9	the	the	DET
fcis-14563	6	10	channel	channel	NOUN
fcis-14563	6	11	attention	attention	NOUN
fcis-14563	6	12	mechanism	mechanism	NOUN
fcis-14563	6	13	and	and	CCONJ
fcis-14563	6	14	classified	classify	VERB
fcis-14563	6	15	through	through	ADP
fcis-14563	6	16	the	the	DET
fcis-14563	6	17	full	full	ADJ
fcis-14563	6	18	connection	connection	NOUN
fcis-14563	6	19	layer	layer	NOUN
fcis-14563	6	20	.	.	PUNCT
fcis-14563	7	1	the	the	DET
fcis-14563	7	2	experimental	experimental	ADJ
fcis-14563	7	3	results	result	NOUN
fcis-14563	7	4	show	show	VERB
fcis-14563	7	5	that	that	SCONJ
fcis-14563	7	6	the	the	DET
fcis-14563	7	7	classification	classification	NOUN
fcis-14563	7	8	accuracy	accuracy	NOUN
fcis-14563	7	9	of	of	ADP
fcis-14563	7	10	the	the	DET
fcis-14563	7	11	proposed	propose	VERB
fcis-14563	7	12	method	method	NOUN
fcis-14563	7	13	is	be	AUX
fcis-14563	7	14	above	above	ADP
fcis-14563	7	15	92	92	NUM
fcis-14563	7	16	%	%	NOUN
fcis-14563	7	17	for	for	ADP
fcis-14563	7	18	40	40	NUM
fcis-14563	7	19	disturbance	disturbance	NOUN
fcis-14563	7	20	types	type	NOUN
fcis-14563	7	21	containing	contain	VERB
fcis-14563	7	22	single	single	ADJ
fcis-14563	7	23	,	,	PUNCT
fcis-14563	7	24	double	double	ADJ
fcis-14563	7	25	,	,	PUNCT
fcis-14563	7	26	triple	triple	ADJ
fcis-14563	7	27	and	and	CCONJ
fcis-14563	7	28	quadruple	quadruple	ADJ
fcis-14563	7	29	disturbances	disturbance	NOUN
fcis-14563	7	30	,	,	PUNCT
fcis-14563	7	31	and	and	CCONJ
fcis-14563	7	32	above	above	ADP
fcis-14563	7	33	96	96	NUM
fcis-14563	7	34	%	%	NOUN
fcis-14563	7	35	for	for	ADP
fcis-14563	7	36	20	20	NUM
fcis-14563	7	37	disturbance	disturbance	NOUN
fcis-14563	7	38	types	type	NOUN
fcis-14563	7	39	containing	contain	VERB
fcis-14563	7	40	single	single	ADJ
fcis-14563	7	41	and	and	CCONJ
fcis-14563	7	42	double	double	ADJ
fcis-14563	7	43	disturbances	disturbance	NOUN
fcis-14563	7	44	.	.	PUNCT
fcis-14563	8	1	keywords	keyword	NOUN
fcis-14563	8	2	:	:	PUNCT
fcis-14563	8	3	power	power	NOUN
fcis-14563	8	4	quality	quality	NOUN
fcis-14563	8	5	disturbance	disturbance	NOUN
fcis-14563	8	6	;	;	PUNCT
fcis-14563	8	7	neural	neural	ADJ
fcis-14563	8	8	network	network	NOUN
fcis-14563	8	9	;	;	PUNCT
fcis-14563	8	10	feature	feature	NOUN
fcis-14563	8	11	fusion	fusion	NOUN
fcis-14563	8	12	;	;	PUNCT
fcis-14563	8	13	adaptive	adaptive	ADJ
fcis-14563	8	14	weights	weight	NOUN
fcis-14563	8	15	;	;	PUNCT
fcis-14563	8	16	gadf	gadf	NOUN
fcis-14563	8	17	algorithm	algorithm	NOUN
fcis-14563	8	18	.	.	PUNCT
fcis-14563	9	1	1	1	X
fcis-14563	9	2	.	.	X
fcis-14563	9	3	introduction	introduction	NOUN
fcis-14563	9	4	with	with	ADP
fcis-14563	9	5	the	the	DET
fcis-14563	9	6	increasing	increasing	NOUN
fcis-14563	9	7	of	of	ADP
fcis-14563	9	8	nonlinear	nonlinear	ADJ
fcis-14563	9	9	load	load	NOUN
fcis-14563	9	10	and	and	CCONJ
fcis-14563	9	11	the	the	DET
fcis-14563	9	12	rapid	rapid	ADJ
fcis-14563	9	13	development	development	NOUN
fcis-14563	9	14	of	of	ADP
fcis-14563	9	15	smart	smart	ADJ
fcis-14563	9	16	grid	grid	NOUN
fcis-14563	9	17	,	,	PUNCT
fcis-14563	9	18	power	power	NOUN
fcis-14563	9	19	enterprises	enterprise	NOUN
fcis-14563	9	20	and	and	CCONJ
fcis-14563	9	21	users	user	NOUN
fcis-14563	9	22	pay	pay	VERB
fcis-14563	9	23	more	more	ADJ
fcis-14563	9	24	and	and	CCONJ
fcis-14563	9	25	more	more	ADJ
fcis-14563	9	26	attention	attention	NOUN
fcis-14563	9	27	to	to	ADP
fcis-14563	9	28	power	power	NOUN
fcis-14563	9	29	quality[1	quality[1	PROPN
fcis-14563	9	30	]	]	PUNCT
fcis-14563	9	31	.	.	PUNCT
fcis-14563	10	1	in	in	ADP
fcis-14563	10	2	the	the	DET
fcis-14563	10	3	power	power	NOUN
fcis-14563	10	4	system	system	NOUN
fcis-14563	10	5	,	,	PUNCT
fcis-14563	10	6	after	after	SCONJ
fcis-14563	10	7	the	the	DET
fcis-14563	10	8	power	power	NOUN
fcis-14563	10	9	quality	quality	NOUN
fcis-14563	10	10	is	be	AUX
fcis-14563	10	11	disturbed	disturb	VERB
fcis-14563	10	12	,	,	PUNCT
fcis-14563	10	13	the	the	DET
fcis-14563	10	14	voltage	voltage	NOUN
fcis-14563	10	15	signal	signal	NOUN
fcis-14563	10	16	and	and	CCONJ
fcis-14563	10	17	the	the	DET
fcis-14563	10	18	current	current	ADJ
fcis-14563	10	19	signal	signal	NOUN
fcis-14563	10	20	will	will	AUX
fcis-14563	10	21	change	change	VERB
fcis-14563	10	22	[	[	X
fcis-14563	10	23	2	2	X
fcis-14563	10	24	]	]	PUNCT
fcis-14563	10	25	in	in	ADP
fcis-14563	10	26	different	different	ADJ
fcis-14563	10	27	forms	form	NOUN
fcis-14563	10	28	,	,	PUNCT
fcis-14563	10	29	so	so	SCONJ
fcis-14563	10	30	that	that	SCONJ
fcis-14563	10	31	the	the	DET
fcis-14563	10	32	electronic	electronic	ADJ
fcis-14563	10	33	equipment	equipment	NOUN
fcis-14563	10	34	is	be	AUX
fcis-14563	10	35	in	in	ADP
fcis-14563	10	36	the	the	DET
fcis-14563	10	37	abnormal	abnormal	ADJ
fcis-14563	10	38	working	work	VERB
fcis-14563	10	39	state	state	NOUN
fcis-14563	10	40	.	.	PUNCT
fcis-14563	11	1	the	the	DET
fcis-14563	11	2	identification	identification	NOUN
fcis-14563	11	3	and	and	CCONJ
fcis-14563	11	4	classification	classification	NOUN
fcis-14563	11	5	of	of	ADP
fcis-14563	11	6	power	power	NOUN
fcis-14563	11	7	quality	quality	NOUN
fcis-14563	11	8	disturbance	disturbance	NOUN
fcis-14563	11	9	is	be	AUX
fcis-14563	11	10	an	an	DET
fcis-14563	11	11	effective	effective	ADJ
fcis-14563	11	12	method	method	NOUN
fcis-14563	11	13	[	[	X
fcis-14563	11	14	3]to	3]to	NUM
fcis-14563	11	15	control	control	NOUN
fcis-14563	11	16	and	and	CCONJ
fcis-14563	11	17	improve	improve	VERB
fcis-14563	11	18	power	power	NOUN
fcis-14563	11	19	quality	quality	NOUN
fcis-14563	11	20	.	.	PUNCT
fcis-14563	12	1	the	the	DET
fcis-14563	12	2	problem	problem	NOUN
fcis-14563	12	3	of	of	ADP
fcis-14563	12	4	power	power	NOUN
fcis-14563	12	5	quality	quality	NOUN
fcis-14563	12	6	disturbance	disturbance	NOUN
fcis-14563	12	7	identification	identification	NOUN
fcis-14563	12	8	and	and	CCONJ
fcis-14563	12	9	classification	classification	NOUN
fcis-14563	12	10	is	be	AUX
fcis-14563	12	11	generally	generally	ADV
fcis-14563	12	12	divided	divide	VERB
fcis-14563	12	13	into	into	ADP
fcis-14563	12	14	two	two	NUM
fcis-14563	12	15	parts	part	NOUN
fcis-14563	12	16	,	,	PUNCT
fcis-14563	12	17	that	that	ADV
fcis-14563	12	18	is	is	ADV
fcis-14563	12	19	,	,	PUNCT
fcis-14563	12	20	feature	feature	NOUN
fcis-14563	12	21	extraction	extraction	NOUN
fcis-14563	12	22	and	and	CCONJ
fcis-14563	12	23	classifier	classifier	NOUN
fcis-14563	12	24	design	design	NOUN
fcis-14563	12	25	.	.	PUNCT
fcis-14563	13	1	usually	usually	ADV
fcis-14563	13	2	,	,	PUNCT
fcis-14563	13	3	feature	feature	NOUN
fcis-14563	13	4	extraction	extraction	NOUN
fcis-14563	13	5	is	be	AUX
fcis-14563	13	6	carried	carry	VERB
fcis-14563	13	7	out	out	ADP
fcis-14563	13	8	first	first	ADV
fcis-14563	13	9	,	,	PUNCT
fcis-14563	13	10	and	and	CCONJ
fcis-14563	13	11	then	then	ADV
fcis-14563	13	12	a	a	DET
fcis-14563	13	13	model	model	NOUN
fcis-14563	13	14	is	be	AUX
fcis-14563	13	15	established	establish	VERB
fcis-14563	13	16	to	to	PART
fcis-14563	13	17	classify	classify	VERB
fcis-14563	13	18	the	the	DET
fcis-14563	13	19	disturbance	disturbance	NOUN
fcis-14563	13	20	.	.	PUNCT
fcis-14563	14	1	the	the	DET
fcis-14563	14	2	existing	exist	VERB
fcis-14563	14	3	feature	feature	NOUN
fcis-14563	14	4	extraction	extraction	NOUN
fcis-14563	14	5	methods	method	NOUN
fcis-14563	14	6	are	be	AUX
fcis-14563	14	7	:	:	PUNCT
fcis-14563	14	8	fourier	fourier	ADJ
fcis-14563	14	9	transform[4	transform[4	PROPN
fcis-14563	14	10	]	]	PUNCT
fcis-14563	14	11	,	,	PUNCT
fcis-14563	14	12	wavelet	wavelet	NOUN
fcis-14563	14	13	transform	transform	NOUN
fcis-14563	14	14	,	,	PUNCT
fcis-14563	14	15	s	s	VERB
fcis-14563	14	16	transform[5][6	transform[5][6	NOUN
fcis-14563	14	17	]	]	X
fcis-14563	14	18	,	,	PUNCT
fcis-14563	14	19	hilbert	hilbert	NOUN
fcis-14563	14	20	-	-	PUNCT
fcis-14563	14	21	yellow	yellow	NOUN
fcis-14563	14	22	transform[7	transform[7	ADP
fcis-14563	14	23	]	]	PUNCT
fcis-14563	14	24	and	and	CCONJ
fcis-14563	14	25	so	so	ADV
fcis-14563	14	26	on	on	ADV
fcis-14563	14	27	.	.	PUNCT
fcis-14563	15	1	disturbance	disturbance	NOUN
fcis-14563	15	2	classification	classification	NOUN
fcis-14563	15	3	is	be	AUX
fcis-14563	15	4	based	base	VERB
fcis-14563	15	5	on	on	ADP
fcis-14563	15	6	feature	feature	NOUN
fcis-14563	15	7	extraction	extraction	NOUN
fcis-14563	15	8	,	,	PUNCT
fcis-14563	15	9	combined	combine	VERB
fcis-14563	15	10	with	with	ADP
fcis-14563	15	11	different	different	ADJ
fcis-14563	15	12	classification	classification	NOUN
fcis-14563	15	13	methods	method	NOUN
fcis-14563	15	14	,	,	PUNCT
fcis-14563	15	15	to	to	PART
fcis-14563	15	16	determine	determine	VERB
fcis-14563	15	17	the	the	DET
fcis-14563	15	18	category	category	NOUN
fcis-14563	15	19	of	of	ADP
fcis-14563	15	20	disturbance	disturbance	NOUN
fcis-14563	15	21	.	.	PUNCT
fcis-14563	16	1	the	the	DET
fcis-14563	16	2	commonly	commonly	ADV
fcis-14563	16	3	used	use	VERB
fcis-14563	16	4	classifiers	classifier	NOUN
fcis-14563	16	5	are	be	AUX
fcis-14563	16	6	:	:	PUNCT
fcis-14563	16	7	decision	decision	NOUN
fcis-14563	16	8	tree[8	tree[8	ADP
fcis-14563	16	9	]	]	PUNCT
fcis-14563	16	10	,	,	PUNCT
fcis-14563	16	11	artificial	artificial	ADJ
fcis-14563	16	12	neural	neural	ADJ
fcis-14563	16	13	network[9	network[9	NOUN
fcis-14563	16	14	]	]	PUNCT
fcis-14563	16	15	and	and	CCONJ
fcis-14563	16	16	support	support	VERB
fcis-14563	16	17	vector	vector	NOUN
fcis-14563	16	18	machine[10	machine[10	NOUN
fcis-14563	16	19	]	]	PUNCT
fcis-14563	16	20	.	.	PUNCT
fcis-14563	17	1	literature	literature	NOUN
fcis-14563	18	1	[	[	X
fcis-14563	18	2	11	11	NUM
fcis-14563	18	3	]	]	PUNCT
fcis-14563	18	4	uses	use	VERB
fcis-14563	18	5	s	s	NOUN
fcis-14563	18	6	-	-	PUNCT
fcis-14563	18	7	transform	transform	NOUN
fcis-14563	18	8	to	to	PART
fcis-14563	18	9	extract	extract	VERB
fcis-14563	18	10	the	the	DET
fcis-14563	18	11	characteristics	characteristic	NOUN
fcis-14563	18	12	of	of	ADP
fcis-14563	18	13	power	power	NOUN
fcis-14563	18	14	quality	quality	NOUN
fcis-14563	18	15	disturbance	disturbance	NOUN
fcis-14563	18	16	data	datum	NOUN
fcis-14563	18	17	and	and	CCONJ
fcis-14563	18	18	chaotic	chaotic	ADJ
fcis-14563	18	19	integrated	integrated	ADJ
fcis-14563	18	20	decision	decision	NOUN
fcis-14563	18	21	tree	tree	NOUN
fcis-14563	18	22	algorithm	algorithm	NOUN
fcis-14563	18	23	to	to	PART
fcis-14563	18	24	classify	classify	VERB
fcis-14563	18	25	the	the	DET
fcis-14563	18	26	disturbance	disturbance	NOUN
fcis-14563	18	27	.	.	PUNCT
fcis-14563	19	1	compared	compare	VERB
fcis-14563	19	2	with	with	ADP
fcis-14563	19	3	traditional	traditional	ADJ
fcis-14563	19	4	decision	decision	NOUN
fcis-14563	19	5	tree	tree	NOUN
fcis-14563	19	6	method	method	NOUN
fcis-14563	19	7	,	,	PUNCT
fcis-14563	19	8	the	the	DET
fcis-14563	19	9	recognition	recognition	NOUN
fcis-14563	19	10	accuracy	accuracy	NOUN
fcis-14563	19	11	of	of	ADP
fcis-14563	19	12	this	this	DET
fcis-14563	19	13	method	method	NOUN
fcis-14563	19	14	is	be	AUX
fcis-14563	19	15	greatly	greatly	ADV
fcis-14563	19	16	improved	improve	VERB
fcis-14563	19	17	.	.	PUNCT
fcis-14563	20	1	in	in	ADP
fcis-14563	20	2	literature	literature	NOUN
fcis-14563	20	3	[	[	X
fcis-14563	20	4	12	12	NUM
fcis-14563	20	5	]	]	PUNCT
fcis-14563	20	6	,	,	PUNCT
fcis-14563	20	7	mrmr	mrmr	PROPN
fcis-14563	20	8	algorithm	algorithm	PROPN
fcis-14563	20	9	is	be	AUX
fcis-14563	20	10	used	use	VERB
fcis-14563	20	11	to	to	PART
fcis-14563	20	12	extract	extract	VERB
fcis-14563	20	13	the	the	DET
fcis-14563	20	14	features	feature	NOUN
fcis-14563	20	15	of	of	ADP
fcis-14563	20	16	power	power	NOUN
fcis-14563	20	17	quality	quality	NOUN
fcis-14563	20	18	disturbance	disturbance	NOUN
fcis-14563	20	19	data	datum	NOUN
fcis-14563	20	20	,	,	PUNCT
fcis-14563	20	21	and	and	CCONJ
fcis-14563	20	22	then	then	ADV
fcis-14563	20	23	multicore	multicore	ADJ
fcis-14563	20	24	svm	svm	NOUN
fcis-14563	20	25	is	be	AUX
fcis-14563	20	26	used	use	VERB
fcis-14563	20	27	to	to	PART
fcis-14563	20	28	classify	classify	VERB
fcis-14563	20	29	the	the	DET
fcis-14563	20	30	extracted	extract	VERB
fcis-14563	20	31	disturbance	disturbance	NOUN
fcis-14563	20	32	features	feature	NOUN
fcis-14563	20	33	.	.	PUNCT
fcis-14563	21	1	this	this	DET
fcis-14563	21	2	method	method	NOUN
fcis-14563	21	3	improves	improve	VERB
fcis-14563	21	4	the	the	DET
fcis-14563	21	5	operation	operation	NOUN
fcis-14563	21	6	speed	speed	NOUN
fcis-14563	21	7	,	,	PUNCT
fcis-14563	21	8	has	have	VERB
fcis-14563	21	9	strong	strong	ADJ
fcis-14563	21	10	anti	anti	ADJ
fcis-14563	21	11	-	-	ADJ
fcis-14563	21	12	noise	noise	ADJ
fcis-14563	21	13	ability	ability	NOUN
fcis-14563	21	14	and	and	CCONJ
fcis-14563	21	15	good	good	ADJ
fcis-14563	21	16	stability	stability	NOUN
fcis-14563	21	17	.	.	PUNCT
fcis-14563	22	1	literature	literature	NOUN
fcis-14563	22	2	[	[	X
fcis-14563	22	3	13	13	NUM
fcis-14563	22	4	]	]	PUNCT
fcis-14563	22	5	uses	use	VERB
fcis-14563	22	6	improved	improved	ADJ
fcis-14563	22	7	s	s	NOUN
fcis-14563	22	8	-	-	PUNCT
fcis-14563	22	9	transform	transform	NOUN
fcis-14563	22	10	(	(	PUNCT
fcis-14563	22	11	smst	smst	NOUN
fcis-14563	22	12	)	)	PUNCT
fcis-14563	22	13	to	to	PART
fcis-14563	22	14	extract	extract	VERB
fcis-14563	22	15	the	the	DET
fcis-14563	22	16	timefrequency	timefrequency	NOUN
fcis-14563	22	17	features	feature	NOUN
fcis-14563	22	18	of	of	ADP
fcis-14563	22	19	the	the	DET
fcis-14563	22	20	power	power	NOUN
fcis-14563	22	21	quality	quality	NOUN
fcis-14563	22	22	disturbance	disturbance	NOUN
fcis-14563	22	23	signals	signal	NOUN
fcis-14563	22	24	to	to	PART
fcis-14563	22	25	be	be	AUX
fcis-14563	22	26	measured	measure	VERB
fcis-14563	22	27	,	,	PUNCT
fcis-14563	22	28	and	and	CCONJ
fcis-14563	22	29	uses	use	VERB
fcis-14563	22	30	the	the	DET
fcis-14563	22	31	improved	improved	ADJ
fcis-14563	22	32	cart	cart	NOUN
fcis-14563	22	33	algorithm	algorithm	NOUN
fcis-14563	22	34	to	to	PART
fcis-14563	22	35	build	build	VERB
fcis-14563	22	36	a	a	DET
fcis-14563	22	37	random	random	ADJ
fcis-14563	22	38	forest	forest	NOUN
fcis-14563	22	39	(	(	PUNCT
fcis-14563	22	40	rf	rf	NOUN
fcis-14563	22	41	)	)	PUNCT
fcis-14563	22	42	classifier	classifier	NOUN
fcis-14563	22	43	to	to	PART
fcis-14563	22	44	classify	classify	VERB
fcis-14563	22	45	the	the	DET
fcis-14563	22	46	disturbance	disturbance	NOUN
fcis-14563	22	47	features	feature	NOUN
fcis-14563	22	48	.	.	PUNCT
fcis-14563	23	1	compared	compare	VERB
fcis-14563	23	2	with	with	ADP
fcis-14563	23	3	the	the	DET
fcis-14563	23	4	traditional	traditional	ADJ
fcis-14563	23	5	cart	cart	NOUN
fcis-14563	23	6	algorithm	algorithm	NOUN
fcis-14563	23	7	,	,	PUNCT
fcis-14563	23	8	this	this	DET
fcis-14563	23	9	method	method	NOUN
fcis-14563	23	10	has	have	VERB
fcis-14563	23	11	a	a	DET
fcis-14563	23	12	great	great	ADJ
fcis-14563	23	13	improvement	improvement	NOUN
fcis-14563	23	14	in	in	ADP
fcis-14563	23	15	the	the	DET
fcis-14563	23	16	identification	identification	NOUN
fcis-14563	23	17	accuracy	accuracy	NOUN
fcis-14563	23	18	of	of	ADP
fcis-14563	23	19	single	single	ADJ
fcis-14563	23	20	and	and	CCONJ
fcis-14563	23	21	double	double	ADJ
fcis-14563	23	22	compound	compound	NOUN
fcis-14563	23	23	disturbances	disturbance	NOUN
fcis-14563	23	24	.	.	PUNCT
fcis-14563	24	1	in	in	ADP
fcis-14563	24	2	literature	literature	NOUN
fcis-14563	24	3	[	[	X
fcis-14563	24	4	14	14	NUM
fcis-14563	24	5	]	]	PUNCT
fcis-14563	24	6	,	,	PUNCT
fcis-14563	24	7	empirical	empirical	ADJ
fcis-14563	24	8	wavelet	wavelet	NOUN
fcis-14563	24	9	transform	transform	NOUN
fcis-14563	24	10	(	(	PUNCT
fcis-14563	24	11	ewt	ewt	PROPN
fcis-14563	24	12	)	)	PUNCT
fcis-14563	24	13	is	be	AUX
fcis-14563	24	14	used	use	VERB
fcis-14563	24	15	to	to	PART
fcis-14563	24	16	obtain	obtain	VERB
fcis-14563	24	17	the	the	DET
fcis-14563	24	18	eigenmatrix	eigenmatrix	NOUN
fcis-14563	24	19	of	of	ADP
fcis-14563	24	20	power	power	NOUN
fcis-14563	24	21	quality	quality	NOUN
fcis-14563	24	22	disturbance	disturbance	NOUN
fcis-14563	24	23	signals	signal	NOUN
fcis-14563	24	24	,	,	PUNCT
fcis-14563	24	25	and	and	CCONJ
fcis-14563	24	26	the	the	DET
fcis-14563	24	27	obtained	obtain	VERB
fcis-14563	24	28	eigenmatrix	eigenmatrix	NOUN
fcis-14563	24	29	is	be	AUX
fcis-14563	24	30	input	input	NOUN
fcis-14563	24	31	to	to	PART
fcis-14563	24	32	support	support	VERB
fcis-14563	24	33	vector	vector	NOUN
fcis-14563	24	34	machine	machine	NOUN
fcis-14563	24	35	(	(	PUNCT
fcis-14563	24	36	svm	svm	PROPN
fcis-14563	24	37	)	)	PUNCT
fcis-14563	24	38	for	for	ADP
fcis-14563	24	39	disturbance	disturbance	NOUN
fcis-14563	24	40	classification	classification	NOUN
fcis-14563	24	41	.	.	PUNCT
fcis-14563	25	1	ewt	ewt	PROPN
fcis-14563	25	2	is	be	AUX
fcis-14563	25	3	built	build	VERB
fcis-14563	25	4	on	on	ADP
fcis-14563	25	5	the	the	DET
fcis-14563	25	6	basis	basis	NOUN
fcis-14563	25	7	of	of	ADP
fcis-14563	25	8	wavelet	wavelet	NOUN
fcis-14563	25	9	transform	transform	NOUN
fcis-14563	25	10	(	(	PUNCT
fcis-14563	25	11	wt	wt	NOUN
fcis-14563	25	12	)	)	PUNCT
fcis-14563	25	13	.	.	PUNCT
fcis-14563	26	1	compared	compare	VERB
fcis-14563	26	2	with	with	ADP
fcis-14563	26	3	wt	wt	PROPN
fcis-14563	26	4	,	,	PUNCT
fcis-14563	26	5	ewt	ewt	PROPN
fcis-14563	26	6	has	have	VERB
fcis-14563	26	7	the	the	DET
fcis-14563	26	8	advantage	advantage	NOUN
fcis-14563	26	9	of	of	ADP
fcis-14563	26	10	empirical	empirical	ADJ
fcis-14563	26	11	mode	mode	NOUN
fcis-14563	26	12	decomposition	decomposition	NOUN
fcis-14563	26	13	(	(	PUNCT
fcis-14563	26	14	emd	emd	PROPN
fcis-14563	26	15	)	)	PUNCT
fcis-14563	26	16	,	,	PUNCT
fcis-14563	26	17	and	and	CCONJ
fcis-14563	26	18	the	the	DET
fcis-14563	26	19	calculation	calculation	NOUN
fcis-14563	26	20	speed	speed	NOUN
fcis-14563	26	21	is	be	AUX
fcis-14563	26	22	faster	fast	ADJ
fcis-14563	26	23	.	.	PUNCT
fcis-14563	27	1	the	the	DET
fcis-14563	27	2	author	author	NOUN
fcis-14563	27	3	simulated	simulate	VERB
fcis-14563	27	4	the	the	DET
fcis-14563	27	5	power	power	NOUN
fcis-14563	27	6	quality	quality	NOUN
fcis-14563	27	7	disturbance	disturbance	NOUN
fcis-14563	27	8	model	model	NOUN
fcis-14563	27	9	under	under	ADP
fcis-14563	27	10	different	different	ADJ
fcis-14563	27	11	conditions	condition	NOUN
fcis-14563	27	12	,	,	PUNCT
fcis-14563	27	13	and	and	CCONJ
fcis-14563	27	14	the	the	DET
fcis-14563	27	15	method	method	NOUN
fcis-14563	27	16	showed	show	VERB
fcis-14563	27	17	good	good	ADJ
fcis-14563	27	18	performance	performance	NOUN
fcis-14563	27	19	for	for	ADP
fcis-14563	27	20	single	single	ADJ
fcis-14563	27	21	disturbance	disturbance	NOUN
fcis-14563	27	22	identification	identification	NOUN
fcis-14563	27	23	accuracy	accuracy	NOUN
fcis-14563	27	24	and	and	CCONJ
fcis-14563	27	25	noise	noise	NOUN
fcis-14563	27	26	resistance	resistance	NOUN
fcis-14563	27	27	.	.	PUNCT
fcis-14563	28	1	the	the	DET
fcis-14563	28	2	above	above	ADJ
fcis-14563	28	3	feature	feature	NOUN
fcis-14563	28	4	extraction	extraction	NOUN
fcis-14563	28	5	methods	method	NOUN
fcis-14563	28	6	all	all	PRON
fcis-14563	28	7	require	require	VERB
fcis-14563	28	8	manual	manual	ADJ
fcis-14563	28	9	intervention	intervention	NOUN
fcis-14563	28	10	,	,	PUNCT
fcis-14563	28	11	and	and	CCONJ
fcis-14563	28	12	the	the	DET
fcis-14563	28	13	features	feature	NOUN
fcis-14563	28	14	determined	determine	VERB
fcis-14563	28	15	manually	manually	ADV
fcis-14563	28	16	are	be	AUX
fcis-14563	28	17	completely	completely	ADV
fcis-14563	28	18	dependent	dependent	ADJ
fcis-14563	28	19	on	on	ADP
fcis-14563	28	20	human	human	ADJ
fcis-14563	28	21	experience	experience	NOUN
fcis-14563	28	22	,	,	PUNCT
fcis-14563	28	23	which	which	PRON
fcis-14563	28	24	is	be	AUX
fcis-14563	28	25	difficult	difficult	ADJ
fcis-14563	28	26	to	to	PART
fcis-14563	28	27	fully	fully	ADV
fcis-14563	28	28	reflect	reflect	VERB
fcis-14563	28	29	the	the	DET
fcis-14563	28	30	key	key	ADJ
fcis-14563	28	31	features	feature	NOUN
fcis-14563	28	32	of	of	ADP
fcis-14563	28	33	the	the	DET
fcis-14563	28	34	disturbance	disturbance	NOUN
fcis-14563	28	35	,	,	PUNCT
fcis-14563	28	36	resulting	result	VERB
fcis-14563	28	37	in	in	ADP
fcis-14563	28	38	reduced	reduce	VERB
fcis-14563	28	39	accuracy	accuracy	NOUN
fcis-14563	28	40	of	of	ADP
fcis-14563	28	41	disturbance	disturbance	NOUN
fcis-14563	28	42	identification	identification	NOUN
fcis-14563	28	43	and	and	CCONJ
fcis-14563	28	44	weak	weak	ADJ
fcis-14563	28	45	adaptability	adaptability	NOUN
fcis-14563	28	46	.	.	PUNCT
fcis-14563	29	1	the	the	DET
fcis-14563	29	2	deep	deep	ADJ
fcis-14563	29	3	learning	learning	NOUN
fcis-14563	29	4	method	method	NOUN
fcis-14563	29	5	can	can	AUX
fcis-14563	29	6	eliminate	eliminate	VERB
fcis-14563	29	7	the	the	DET
fcis-14563	29	8	constraint	constraint	NOUN
fcis-14563	29	9	of	of	ADP
fcis-14563	29	10	human	human	ADJ
fcis-14563	29	11	factors	factor	NOUN
fcis-14563	29	12	and	and	CCONJ
fcis-14563	29	13	carry	carry	VERB
fcis-14563	29	14	out	out	ADP
fcis-14563	29	15	intelligent	intelligent	ADJ
fcis-14563	29	16	feature	feature	NOUN
fcis-14563	29	17	extraction	extraction	NOUN
fcis-14563	29	18	from	from	ADP
fcis-14563	29	19	a	a	DET
fcis-14563	29	20	large	large	ADJ
fcis-14563	29	21	number	number	NOUN
fcis-14563	29	22	of	of	ADP
fcis-14563	29	23	disturbance	disturbance	NOUN
fcis-14563	29	24	data	datum	NOUN
fcis-14563	29	25	.	.	PUNCT
fcis-14563	30	1	it	it	PRON
fcis-14563	30	2	can	can	AUX
fcis-14563	30	3	adapt	adapt	VERB
fcis-14563	30	4	to	to	ADP
fcis-14563	30	5	the	the	DET
fcis-14563	30	6	needs	need	NOUN
fcis-14563	30	7	of	of	ADP
fcis-14563	30	8	pq	pq	NOUN
fcis-14563	30	9	disturbance	disturbance	NOUN
fcis-14563	30	10	intelligent	intelligent	ADJ
fcis-14563	30	11	classification	classification	NOUN
fcis-14563	30	12	in	in	ADP
fcis-14563	30	13	the	the	DET
fcis-14563	30	14	current	current	ADJ
fcis-14563	30	15	era	era	NOUN
fcis-14563	30	16	of	of	ADP
fcis-14563	30	17	big	big	ADJ
fcis-14563	30	18	data	datum	NOUN
fcis-14563	30	19	.	.	PUNCT
fcis-14563	31	1	in	in	ADP
fcis-14563	31	2	this	this	DET
fcis-14563	31	3	paper	paper	NOUN
fcis-14563	31	4	,	,	PUNCT
fcis-14563	31	5	a	a	DET
fcis-14563	31	6	power	power	NOUN
fcis-14563	31	7	quality	quality	NOUN
fcis-14563	31	8	disturbance	disturbance	NOUN
fcis-14563	31	9	recognition	recognition	NOUN
fcis-14563	31	10	method	method	NOUN
fcis-14563	31	11	based	base	VERB
fcis-14563	31	12	on	on	ADP
fcis-14563	31	13	adaptive	adaptive	ADJ
fcis-14563	31	14	feature	feature	NOUN
fcis-14563	31	15	fusion	fusion	NOUN
fcis-14563	31	16	is	be	AUX
fcis-14563	31	17	proposed	propose	VERB
fcis-14563	31	18	,	,	PUNCT
fcis-14563	31	19	which	which	PRON
fcis-14563	31	20	realizes	realize	VERB
fcis-14563	31	21	the	the	DET
fcis-14563	31	22	recognition	recognition	NOUN
fcis-14563	31	23	and	and	CCONJ
fcis-14563	31	24	classification	classification	NOUN
fcis-14563	31	25	of	of	ADP
fcis-14563	31	26	power	power	NOUN
fcis-14563	31	27	quality	quality	NOUN
fcis-14563	31	28	disturbance	disturbance	NOUN
fcis-14563	31	29	data	datum	NOUN
fcis-14563	31	30	by	by	ADP
fcis-14563	31	31	adaptive	adaptive	ADJ
fcis-14563	31	32	fusion	fusion	NOUN
fcis-14563	31	33	of	of	ADP
fcis-14563	31	34	one	one	NUM
fcis-14563	31	35	-	-	PUNCT
fcis-14563	31	36	dimensional	dimensional	ADJ
fcis-14563	31	37	and	and	CCONJ
fcis-14563	31	38	twodimensional	twodimensional	ADJ
fcis-14563	31	39	features	feature	NOUN
fcis-14563	31	40	extracted	extract	VERB
fcis-14563	31	41	from	from	ADP
fcis-14563	31	42	two	two	NUM
fcis-14563	31	43	subnetworks	subnetwork	NOUN
fcis-14563	31	44	.	.	PUNCT
fcis-14563	32	1	2	2	X
fcis-14563	32	2	.	.	NOUN
fcis-14563	32	3	pqds	pqds	PROPN
fcis-14563	32	4	signal	signal	NOUN
fcis-14563	32	5	generation	generation	NOUN
fcis-14563	32	6	and	and	CCONJ
fcis-14563	32	7	processing	processing	NOUN
fcis-14563	32	8	2.1	2.1	NUM
fcis-14563	32	9	.	.	PUNCT
fcis-14563	33	1	gramian	gramian	ADJ
fcis-14563	33	2	angular	angular	ADJ
fcis-14563	33	3	field	field	NOUN
fcis-14563	33	4	(	(	PUNCT
fcis-14563	33	5	gaf	gaf	PROPN
fcis-14563	33	6	)	)	PUNCT
fcis-14563	33	7	the	the	DET
fcis-14563	33	8	gram	gram	PROPN
fcis-14563	33	9	angle	angle	NOUN
fcis-14563	33	10	field	field	NOUN
fcis-14563	33	11	algorithm	algorithm	NOUN
fcis-14563	33	12	can	can	AUX
fcis-14563	33	13	realize	realize	VERB
fcis-14563	33	14	the	the	DET
fcis-14563	33	15	reconstruction	reconstruction	NOUN
fcis-14563	33	16	of	of	ADP
fcis-14563	33	17	one	one	NUM
fcis-14563	33	18	-	-	PUNCT
fcis-14563	33	19	dimensional	dimensional	ADJ
fcis-14563	33	20	time	time	NOUN
fcis-14563	33	21	series	series	NOUN
fcis-14563	33	22	,	,	PUNCT
fcis-14563	33	23	and	and	CCONJ
fcis-14563	33	24	can	can	AUX
fcis-14563	33	25	avoid	avoid	VERB
fcis-14563	33	26	the	the	DET
fcis-14563	33	27	data	data	NOUN
fcis-14563	33	28	loss	loss	NOUN
fcis-14563	33	29	in	in	ADP
fcis-14563	33	30	the	the	DET
fcis-14563	33	31	reconstruction	reconstruction	NOUN
fcis-14563	33	32	process	process	NOUN
fcis-14563	33	33	.	.	PUNCT
fcis-14563	34	1	compared	compare	VERB
fcis-14563	34	2	with	with	ADP
fcis-14563	34	3	other	other	ADJ
fcis-14563	34	4	conversion	conversion	NOUN
fcis-14563	34	5	methods	method	NOUN
fcis-14563	34	6	,	,	PUNCT
fcis-14563	34	7	gaf	gaf	PROPN
fcis-14563	34	8	polar	polar	ADJ
fcis-14563	34	9	coordinates	coordinate	NOUN
fcis-14563	34	10	retain	retain	VERB
fcis-14563	34	11	the	the	DET
fcis-14563	34	12	absolute	absolute	ADJ
fcis-14563	34	13	value	value	NOUN
fcis-14563	34	14	of	of	ADP
fcis-14563	34	15	time	time	NOUN
fcis-14563	34	16	relation	relation	NOUN
fcis-14563	34	17	,	,	PUNCT
fcis-14563	34	18	and	and	CCONJ
fcis-14563	34	19	the	the	DET
fcis-14563	34	20	original	original	ADJ
fcis-14563	34	21	time	time	NOUN
fcis-14563	34	22	series	series	NOUN
fcis-14563	34	23	in	in	ADP
fcis-14563	34	24	cartesian	cartesian	ADJ
fcis-14563	34	25	coordinates	coordinate	NOUN
fcis-14563	34	26	can	can	AUX
fcis-14563	34	27	be	be	AUX
fcis-14563	34	28	restored	restore	VERB
fcis-14563	34	29	through	through	ADP
fcis-14563	34	30	the	the	DET
fcis-14563	34	31	main	main	ADJ
fcis-14563	34	32	diagonal	diagonal	NOUN
fcis-14563	34	33	of	of	ADP
fcis-14563	34	34	gaf	gaf	PROPN
fcis-14563	34	35	matrix	matrix	NOUN
fcis-14563	34	36	.	.	PUNCT
fcis-14563	35	1	the	the	DET
fcis-14563	35	2	principle	principle	NOUN
fcis-14563	35	3	of	of	ADP
fcis-14563	35	4	gram	gram	PROPN
fcis-14563	35	5	angular	angular	ADJ
fcis-14563	35	6	field	field	NOUN
fcis-14563	35	7	algorithm	algorithm	NOUN
fcis-14563	35	8	is	be	AUX
fcis-14563	35	9	as	as	SCONJ
fcis-14563	35	10	follows	follow	VERB
fcis-14563	35	11	:	:	PUNCT
fcis-14563	35	12	firstly	firstly	ADV
fcis-14563	35	13	,	,	PUNCT
fcis-14563	35	14	the	the	DET
fcis-14563	35	15	value	value	NOUN
fcis-14563	35	16	scaling	scaling	NOUN
fcis-14563	35	17	is	be	AUX
fcis-14563	35	18	carried	carry	VERB
fcis-14563	35	19	out	out	ADP
fcis-14563	35	20	;	;	PUNCT
fcis-14563	35	21	x	x	PUNCT
fcis-14563	35	22	x	x	X
fcis-14563	35	23	,	,	PUNCT
fcis-14563	35	24	x	x	X
fcis-14563	35	25	,	,	PUNCT
fcis-14563	35	26	⋯	⋯	PROPN
fcis-14563	35	27	,	,	PUNCT
fcis-14563	35	28	x	x	PROPN
fcis-14563	35	29	the	the	DET
fcis-14563	35	30	time	time	NOUN
fcis-14563	35	31	series	series	NOUN
fcis-14563	35	32	in	in	ADP
fcis-14563	35	33	the	the	DET
fcis-14563	35	34	cartesian	cartesian	ADJ
fcis-14563	35	35	coordinate	coordinate	NOUN
fcis-14563	35	36	5	5	NUM
fcis-14563	35	37	system	system	NOUN
fcis-14563	35	38	is	be	AUX
fcis-14563	35	39	scaled	scale	VERB
fcis-14563	35	40	to	to	ADP
fcis-14563	35	41	the	the	DET
fcis-14563	35	42	interval	interval	NOUN
fcis-14563	35	43	[	[	X
fcis-14563	35	44	1	1	NUM
fcis-14563	35	45	,	,	PUNCT
fcis-14563	35	46	-1	-1	PUNCT
fcis-14563	35	47	]	]	PUNCT
fcis-14563	35	48	by	by	ADP
fcis-14563	35	49	formula	formula	NOUN
fcis-14563	35	50	(	(	PUNCT
fcis-14563	35	51	1	1	NUM
fcis-14563	35	52	)	)	PUNCT
fcis-14563	35	53	,	,	PUNCT
fcis-14563	35	54	and	and	CCONJ
fcis-14563	35	55	then	then	ADV
fcis-14563	35	56	the	the	DET
fcis-14563	35	57	polar	polar	ADJ
fcis-14563	35	58	coordinates	coordinate	NOUN
fcis-14563	35	59	are	be	AUX
fcis-14563	35	60	converted	convert	VERB
fcis-14563	35	61	;	;	PUNCT
fcis-14563	35	62	formula	formula	NOUN
fcis-14563	35	63	(	(	PUNCT
fcis-14563	35	64	2	2	X
fcis-14563	35	65	)	)	PUNCT
fcis-14563	35	66	will	will	AUX
fcis-14563	35	67	be	be	AUX
fcis-14563	35	68	converted	convert	VERB
fcis-14563	35	69	to	to	ADP
fcis-14563	35	70	polar	polar	ADJ
fcis-14563	35	71	coordinate	coordinate	NOUN
fcis-14563	35	72	system	system	NOUN
fcis-14563	35	73	time	time	NOUN
fcis-14563	35	74	series.xx	series.xx	NUM
fcis-14563	35	75	max	max	PROPN
fcis-14563	35	76	min	min	PROPN
fcis-14563	35	77	max	max	PROPN
fcis-14563	35	78	min	min	PROPN
fcis-14563	36	1	i	i	PRON
fcis-14563	36	2	i	i	PRON
fcis-14563	36	3	i	i	VERB
fcis-14563	36	4	x	x	VERB
fcis-14563	36	5	x	x	PUNCT
fcis-14563	36	6	x	x	PUNCT
fcis-14563	36	7	x	x	PUNCT
fcis-14563	36	8	x	x	SYM
fcis-14563	36	9	x	x	PUNCT
fcis-14563	36	10	x	x	SYM
fcis-14563	36	11			PROPN
fcis-14563	36	12			VERB
fcis-14563	36	13			PROPN
fcis-14563	36	14			NUM
fcis-14563	36	15			PROPN
fcis-14563	36	16			NOUN
fcis-14563	36	17	(	(	PUNCT
fcis-14563	36	18	1	1	X
fcis-14563	36	19	)	)	PUNCT
fcis-14563	36	20	arccos	arcco	NOUN
fcis-14563	36	21	;	;	PUNCT
fcis-14563	36	22	1	1	NUM
fcis-14563	36	23	1	1	NUM
fcis-14563	36	24	,	,	PUNCT
fcis-14563	36	25	;	;	PUNCT
fcis-14563	36	26	i	i	PRON
fcis-14563	37	1	i	i	PRON
fcis-14563	37	2	i	i	PRON
fcis-14563	38	1	i	i	PRON
fcis-14563	38	2	i	i	VERB
fcis-14563	38	3	x	x	VERB
fcis-14563	39	1	x	x	PUNCT
fcis-14563	39	2	x	x	PUNCT
fcis-14563	39	3	x	x	SYM
fcis-14563	39	4	t	t	NOUN
fcis-14563	39	5	r	r	NOUN
fcis-14563	39	6	t	t	PROPN
fcis-14563	39	7	n	n	CCONJ
fcis-14563	39	8	n	n	ADV
fcis-14563	39	9			PROPN
fcis-14563	40	1			PROPN
fcis-14563	40	2			PROPN
fcis-14563	40	3			NOUN
fcis-14563	40	4			NOUN
fcis-14563	40	5			NOUN
fcis-14563	40	6			ADP
fcis-14563	40	7			NOUN
fcis-14563	40	8			ADP
fcis-14563	40	9			NUM
fcis-14563	40	10			NUM
fcis-14563	40	11			PROPN
fcis-14563	40	12			ADJ
fcis-14563	40	13			NOUN
fcis-14563	40	14	(	(	PUNCT
fcis-14563	40	15	2	2	NUM
fcis-14563	40	16	)	)	PUNCT
fcis-14563	40	17	where	where	SCONJ
fcis-14563	40	18	:	:	PUNCT
fcis-14563	40	19	is	be	AUX
fcis-14563	40	20	the	the	DET
fcis-14563	40	21	original	original	ADJ
fcis-14563	40	22	time	time	NOUN
fcis-14563	40	23	series	series	NOUN
fcis-14563	40	24	;	;	PUNCT
fcis-14563	40	25	x	x	SYM
fcis-14563	40	26	x	x	X
fcis-14563	40	27	is	be	AUX
fcis-14563	40	28	the	the	DET
fcis-14563	40	29	time	time	NOUN
fcis-14563	40	30	series	series	NOUN
fcis-14563	40	31	after	after	ADP
fcis-14563	40	32	reconstruction	reconstruction	NOUN
fcis-14563	40	33	;	;	PUNCT
fcis-14563	40	34	t	t	PROPN
fcis-14563	40	35	is	be	AUX
fcis-14563	40	36	the	the	DET
fcis-14563	40	37	time	time	NOUN
fcis-14563	40	38	stamp	stamp	NOUN
fcis-14563	40	39	;	;	PUNCT
fcis-14563	40	40	nand	nand	VERB
fcis-14563	40	41	the	the	DET
fcis-14563	40	42	constant	constant	ADJ
fcis-14563	40	43	factor	factor	NOUN
fcis-14563	40	44	of	of	ADP
fcis-14563	40	45	the	the	DET
fcis-14563	40	46	generated	generate	VERB
fcis-14563	40	47	space	space	NOUN
fcis-14563	40	48	for	for	ADP
fcis-14563	40	49	the	the	DET
fcis-14563	40	50	regularized	regularize	VERB
fcis-14563	40	51	polar	polar	ADJ
fcis-14563	40	52	coordinate	coordinate	NOUN
fcis-14563	40	53	system	system	NOUN
fcis-14563	40	54	.	.	PUNCT
fcis-14563	41	1	if	if	SCONJ
fcis-14563	41	2	the	the	DET
fcis-14563	41	3	cosine	cosine	NOUN
fcis-14563	41	4	function	function	NOUN
fcis-14563	41	5	of	of	ADP
fcis-14563	41	6	the	the	DET
fcis-14563	41	7	sum	sum	NOUN
fcis-14563	41	8	of	of	ADP
fcis-14563	41	9	two	two	NUM
fcis-14563	41	10	angles	angle	NOUN
fcis-14563	41	11	is	be	AUX
fcis-14563	41	12	used	use	VERB
fcis-14563	41	13	,	,	PUNCT
fcis-14563	41	14	the	the	DET
fcis-14563	41	15	gram	gram	NOUN
fcis-14563	41	16	angle	angle	NOUN
fcis-14563	41	17	and	and	CCONJ
fcis-14563	41	18	field	field	NOUN
fcis-14563	41	19	(	(	PUNCT
fcis-14563	41	20	gasf	gasf	NOUN
fcis-14563	41	21	)	)	PUNCT
fcis-14563	41	22	diagram	diagram	NOUN
fcis-14563	41	23	can	can	AUX
fcis-14563	41	24	be	be	AUX
fcis-14563	41	25	obtained	obtain	VERB
fcis-14563	41	26	,	,	PUNCT
fcis-14563	41	27	and	and	CCONJ
fcis-14563	41	28	if	if	SCONJ
fcis-14563	41	29	the	the	DET
fcis-14563	41	30	sine	sine	ADJ
fcis-14563	41	31	function	function	NOUN
fcis-14563	41	32	of	of	ADP
fcis-14563	41	33	the	the	DET
fcis-14563	41	34	difference	difference	NOUN
fcis-14563	41	35	of	of	ADP
fcis-14563	41	36	two	two	NUM
fcis-14563	41	37	angles	angle	NOUN
fcis-14563	41	38	is	be	AUX
fcis-14563	41	39	used	use	VERB
fcis-14563	41	40	,	,	PUNCT
fcis-14563	41	41	the	the	DET
fcis-14563	41	42	gram	gram	NOUN
fcis-14563	41	43	angle	angle	NOUN
fcis-14563	41	44	difference	difference	NOUN
fcis-14563	41	45	field	field	NOUN
fcis-14563	41	46	(	(	PUNCT
fcis-14563	41	47	gadf	gadf	PROPN
fcis-14563	41	48	)	)	PUNCT
fcis-14563	41	49	diagram	diagram	NOUN
fcis-14563	41	50	can	can	AUX
fcis-14563	41	51	be	be	AUX
fcis-14563	41	52	obtained	obtain	VERB
fcis-14563	41	53	.	.	PUNCT
fcis-14563	42	1	compared	compare	VERB
fcis-14563	42	2	with	with	ADP
fcis-14563	42	3	gasf	gasf	NOUN
fcis-14563	42	4	,	,	PUNCT
fcis-14563	42	5	gadf	gadf	PROPN
fcis-14563	42	6	has	have	VERB
fcis-14563	42	7	a	a	DET
fcis-14563	42	8	better	well	ADJ
fcis-14563	42	9	[	[	X
fcis-14563	42	10	17	17	NUM
fcis-14563	42	11	]	]	SYM
fcis-14563	42	12	ability	ability	NOUN
fcis-14563	42	13	to	to	PART
fcis-14563	42	14	express	express	VERB
fcis-14563	42	15	the	the	DET
fcis-14563	42	16	features	feature	NOUN
fcis-14563	42	17	of	of	ADP
fcis-14563	42	18	the	the	DET
fcis-14563	42	19	original	original	ADJ
fcis-14563	42	20	disturbed	disturbed	ADJ
fcis-14563	42	21	data	datum	NOUN
fcis-14563	42	22	.	.	PUNCT
fcis-14563	43	1	in	in	ADP
fcis-14563	43	2	this	this	DET
fcis-14563	43	3	paper	paper	NOUN
fcis-14563	43	4	,	,	PUNCT
fcis-14563	43	5	the	the	DET
fcis-14563	43	6	feature	feature	NOUN
fcis-14563	43	7	map	map	NOUN
fcis-14563	43	8	of	of	ADP
fcis-14563	43	9	gadf	gadf	NOUN
fcis-14563	43	10	is	be	AUX
fcis-14563	43	11	adopted	adopt	VERB
fcis-14563	43	12	.	.	PUNCT
fcis-14563	44	1	gadf	gadf	PROPN
fcis-14563	44	2	calculation	calculation	NOUN
fcis-14563	44	3	method	method	NOUN
fcis-14563	44	4	is	be	AUX
fcis-14563	44	5	as	as	SCONJ
fcis-14563	44	6	follows	follow	VERB
fcis-14563	44	7	:	:	PUNCT
fcis-14563	44	8			NOUN
fcis-14563	45	1			SYM
fcis-14563	45	2			NOUN
fcis-14563	45	3			SYM
fcis-14563	45	4			NOUN
fcis-14563	45	5			SYM
fcis-14563	45	6			NOUN
fcis-14563	45	7			PUNCT
fcis-14563	45	8			SYM
fcis-14563	45	9			PROPN
fcis-14563	45	10			X
fcis-14563	45	11			X
fcis-14563	45	12			NOUN
fcis-14563	45	13	1	1	NUM
fcis-14563	45	14	1	1	NUM
fcis-14563	45	15	1	1	NUM
fcis-14563	45	16	2	2	NUM
fcis-14563	45	17	2	2	NUM
fcis-14563	45	18	1	1	NUM
fcis-14563	45	19	sin	sin	NOUN
fcis-14563	45	20	sin	sin	NOUN
fcis-14563	45	21	sin	sin	NOUN
fcis-14563	45	22	sin	sin	NOUN
fcis-14563	45	23	n	n	ADP
fcis-14563	45	24	t	t	NOUN
fcis-14563	45	25	t	t	NOUN
fcis-14563	45	26	gadf	gadf	PROPN
fcis-14563	45	27	n	n	CCONJ
fcis-14563	45	28	n	n	CCONJ
fcis-14563	45	29	n	n	ADV
fcis-14563	45	30	g	g	NOUN
fcis-14563	46	1	i	i	NOUN
fcis-14563	46	2	x	x	NOUN
fcis-14563	47	1	x	x	PUNCT
fcis-14563	47	2	x	x	PUNCT
fcis-14563	47	3	i	i	NOUN
fcis-14563	47	4	x	x	X
fcis-14563	47	5			X
fcis-14563	47	6			X
fcis-14563	47	7			PROPN
fcis-14563	47	8			X
fcis-14563	47	9			PROPN
fcis-14563	47	10			PROPN
fcis-14563	47	11			PROPN
fcis-14563	47	12			PROPN
fcis-14563	47	13			PROPN
fcis-14563	47	14			PROPN
fcis-14563	47	15			PROPN
fcis-14563	47	16			PROPN
fcis-14563	47	17			NOUN
fcis-14563	47	18			PROPN
fcis-14563	47	19			PROPN
fcis-14563	47	20			PROPN
fcis-14563	47	21			PROPN
fcis-14563	47	22			PROPN
fcis-14563	47	23			PROPN
fcis-14563	47	24			NOUN
fcis-14563	47	25			NOUN
fcis-14563	47	26			NOUN
fcis-14563	47	27			NUM
fcis-14563	47	28			PROPN
fcis-14563	47	29			PROPN
fcis-14563	47	30			VERB
fcis-14563	47	31			NUM
fcis-14563	47	32			NUM
fcis-14563	47	33			NOUN
fcis-14563	47	34	(	(	PUNCT
fcis-14563	47	35	3	3	NUM
fcis-14563	47	36	)	)	PUNCT
fcis-14563	47	37	where	where	SCONJ
fcis-14563	47	38	:	:	PUNCT
fcis-14563	47	39	is	be	AUX
fcis-14563	47	40	the	the	DET
fcis-14563	47	41	unit	unit	NOUN
fcis-14563	47	42	row	row	NOUN
fcis-14563	47	43	vector	vector	NOUN
fcis-14563	47	44	;	;	PUNCT
fcis-14563	47	45	ix	ix	ADV
fcis-14563	47	46	is	be	AUX
fcis-14563	47	47	the	the	DET
fcis-14563	47	48	transpose	transpose	ADJ
fcis-14563	47	49	vector	vector	NOUN
fcis-14563	47	50	of.x	of.x	PROPN
fcis-14563	47	51	2.2	2.2	NUM
fcis-14563	47	52	.	.	PUNCT
fcis-14563	48	1	data	datum	NOUN
fcis-14563	48	2	generation	generation	NOUN
fcis-14563	48	3	and	and	CCONJ
fcis-14563	48	4	processing	processing	NOUN
fcis-14563	48	5	in	in	ADP
fcis-14563	48	6	this	this	DET
fcis-14563	48	7	paper	paper	NOUN
fcis-14563	48	8	,	,	PUNCT
fcis-14563	48	9	the	the	DET
fcis-14563	48	10	original	original	ADJ
fcis-14563	48	11	one	one	NUM
fcis-14563	48	12	-	-	PUNCT
fcis-14563	48	13	dimensional	dimensional	ADJ
fcis-14563	48	14	disturbance	disturbance	NOUN
fcis-14563	48	15	data	datum	NOUN
fcis-14563	48	16	are	be	AUX
fcis-14563	48	17	generated	generate	VERB
fcis-14563	48	18	by	by	ADP
fcis-14563	48	19	numerical	numerical	ADJ
fcis-14563	48	20	calculation	calculation	NOUN
fcis-14563	48	21	software	software	PROPN
fcis-14563	48	22	matlab	matlab	PROPN
fcis-14563	48	23	,	,	PUNCT
fcis-14563	48	24	including	include	VERB
fcis-14563	48	25	a	a	DET
fcis-14563	48	26	total	total	NOUN
fcis-14563	48	27	of	of	ADP
fcis-14563	48	28	40	40	NUM
fcis-14563	48	29	types	type	NOUN
fcis-14563	48	30	of	of	ADP
fcis-14563	48	31	power	power	NOUN
fcis-14563	48	32	quality	quality	NOUN
fcis-14563	48	33	disturbance	disturbance	NOUN
fcis-14563	48	34	data	datum	NOUN
fcis-14563	48	35	,	,	PUNCT
fcis-14563	48	36	including	include	VERB
fcis-14563	48	37	8	8	NUM
fcis-14563	48	38	types	type	NOUN
fcis-14563	48	39	of	of	ADP
fcis-14563	48	40	single	single	ADJ
fcis-14563	48	41	disturbance	disturbance	NOUN
fcis-14563	48	42	,	,	PUNCT
fcis-14563	48	43	15	15	NUM
fcis-14563	48	44	types	type	NOUN
fcis-14563	48	45	of	of	ADP
fcis-14563	48	46	double	double	ADJ
fcis-14563	48	47	disturbance	disturbance	NOUN
fcis-14563	48	48	,	,	PUNCT
fcis-14563	48	49	13	13	NUM
fcis-14563	48	50	types	type	NOUN
fcis-14563	48	51	of	of	ADP
fcis-14563	48	52	triple	triple	ADJ
fcis-14563	48	53	disturbance	disturbance	NOUN
fcis-14563	48	54	and	and	CCONJ
fcis-14563	48	55	4	4	NUM
fcis-14563	48	56	types	type	NOUN
fcis-14563	48	57	of	of	ADP
fcis-14563	48	58	quadruple	quadruple	NOUN
fcis-14563	48	59	disturbance	disturbance	NOUN
fcis-14563	48	60	,	,	PUNCT
fcis-14563	48	61	as	as	SCONJ
fcis-14563	48	62	shown	show	VERB
fcis-14563	48	63	in	in	ADP
fcis-14563	48	64	table	table	NOUN
fcis-14563	48	65	1	1	NUM
fcis-14563	48	66	.	.	PUNCT
fcis-14563	49	1	the	the	DET
fcis-14563	49	2	original	original	ADJ
fcis-14563	49	3	onedimensional	onedimensional	ADJ
fcis-14563	49	4	data	datum	NOUN
fcis-14563	49	5	is	be	AUX
fcis-14563	49	6	converted	convert	VERB
fcis-14563	49	7	into	into	ADP
fcis-14563	49	8	two	two	NUM
fcis-14563	49	9	-	-	PUNCT
fcis-14563	49	10	dimensional	dimensional	ADJ
fcis-14563	49	11	data	datum	NOUN
fcis-14563	49	12	by	by	ADP
fcis-14563	49	13	gadf	gadf	PROPN
fcis-14563	49	14	algorithm	algorithm	NOUN
fcis-14563	49	15	.	.	PUNCT
fcis-14563	50	1	fig	fig	NOUN
fcis-14563	50	2	.	.	PUNCT
fcis-14563	51	1	1	1	NUM
fcis-14563	51	2	shows	show	VERB
fcis-14563	51	3	the	the	DET
fcis-14563	51	4	gadf	gadf	PROPN
fcis-14563	51	5	diagram	diagram	NOUN
fcis-14563	51	6	of	of	ADP
fcis-14563	51	7	8	8	NUM
fcis-14563	51	8	common	common	ADJ
fcis-14563	51	9	single	single	ADJ
fcis-14563	51	10	disturbance	disturbance	NOUN
fcis-14563	51	11	and	and	CCONJ
fcis-14563	51	12	normal	normal	ADJ
fcis-14563	51	13	waveforms	waveform	NOUN
fcis-14563	51	14	,	,	PUNCT
fcis-14563	51	15	in	in	ADP
fcis-14563	51	16	which	which	PRON
fcis-14563	51	17	the	the	DET
fcis-14563	51	18	disturbance	disturbance	NOUN
fcis-14563	51	19	types	type	NOUN
fcis-14563	51	20	from	from	ADP
fcis-14563	51	21	left	left	ADJ
fcis-14563	51	22	to	to	ADP
fcis-14563	51	23	right	right	ADV
fcis-14563	51	24	in	in	ADP
fcis-14563	51	25	the	the	DET
fcis-14563	51	26	first	first	ADJ
fcis-14563	51	27	row	row	NOUN
fcis-14563	51	28	are	be	AUX
fcis-14563	51	29	interrupt	interrupt	ADJ
fcis-14563	51	30	(	(	PUNCT
fcis-14563	51	31	c3	c3	PROPN
fcis-14563	51	32	)	)	PUNCT
fcis-14563	51	33	,	,	PUNCT
fcis-14563	51	34	pulse	pulse	NOUN
fcis-14563	51	35	(	(	PUNCT
fcis-14563	51	36	c6	c6	PROPN
fcis-14563	51	37	)	)	PUNCT
fcis-14563	51	38	,	,	PUNCT
fcis-14563	51	39	dip	dip	NOUN
fcis-14563	51	40	+	+	CCONJ
fcis-14563	51	41	harmonic	harmonic	ADJ
fcis-14563	51	42	(	(	PUNCT
fcis-14563	51	43	c8	c8	PROPN
fcis-14563	51	44	)	)	PUNCT
fcis-14563	51	45	,	,	PUNCT
fcis-14563	51	46	and	and	CCONJ
fcis-14563	51	47	rise	rise	VERB
fcis-14563	51	48	+	+	CCONJ
fcis-14563	51	49	oscillation	oscillation	NOUN
fcis-14563	51	50	(	(	PUNCT
fcis-14563	51	51	c15	c15	PROPN
fcis-14563	51	52	)	)	PUNCT
fcis-14563	51	53	.	.	PUNCT
fcis-14563	52	1	in	in	ADP
fcis-14563	52	2	the	the	DET
fcis-14563	52	3	second	second	ADJ
fcis-14563	52	4	row	row	NOUN
fcis-14563	52	5	,	,	PUNCT
fcis-14563	52	6	the	the	DET
fcis-14563	52	7	disturbance	disturbance	NOUN
fcis-14563	52	8	types	type	NOUN
fcis-14563	52	9	from	from	ADP
fcis-14563	52	10	left	left	ADJ
fcis-14563	52	11	to	to	ADP
fcis-14563	52	12	right	right	NOUN
fcis-14563	52	13	are	be	AUX
fcis-14563	52	14	respectively	respectively	ADV
fcis-14563	52	15	sag	sag	ADJ
fcis-14563	52	16	+	+	CCONJ
fcis-14563	52	17	oscillation	oscillation	NOUN
fcis-14563	52	18	+	+	CCONJ
fcis-14563	52	19	pulse	pulse	NOUN
fcis-14563	52	20	(	(	PUNCT
fcis-14563	52	21	c24	c24	PROPN
fcis-14563	52	22	)	)	PUNCT
fcis-14563	52	23	,	,	PUNCT
fcis-14563	52	24	harmonics	harmonic	NOUN
fcis-14563	52	25	+	+	CCONJ
fcis-14563	52	26	interrupt	interrupt	ADJ
fcis-14563	52	27	+	+	CCONJ
fcis-14563	52	28	pulse	pulse	NOUN
fcis-14563	52	29	(	(	PUNCT
fcis-14563	52	30	c29	c29	NOUN
fcis-14563	52	31	)	)	PUNCT
fcis-14563	52	32	,	,	PUNCT
fcis-14563	52	33	harmonics	harmonic	NOUN
fcis-14563	52	34	+	+	CCONJ
fcis-14563	52	35	oscillation	oscillation	NOUN
fcis-14563	52	36	+	+	CCONJ
fcis-14563	52	37	pulse	pulse	NOUN
fcis-14563	52	38	+	+	CCONJ
fcis-14563	52	39	temporary	temporary	ADJ
fcis-14563	52	40	rise	rise	NOUN
fcis-14563	52	41	(	(	PUNCT
fcis-14563	52	42	c37	c37	NOUN
fcis-14563	52	43	)	)	PUNCT
fcis-14563	52	44	,	,	PUNCT
fcis-14563	52	45	harmonics	harmonic	NOUN
fcis-14563	52	46	+	+	CCONJ
fcis-14563	52	47	oscillation	oscillation	NOUN
fcis-14563	52	48	+	+	CCONJ
fcis-14563	52	49	wave	wave	NOUN
fcis-14563	52	50	+	+	CCONJ
fcis-14563	52	51	interrupt	interrupt	ADJ
fcis-14563	52	52	(	(	PUNCT
fcis-14563	52	53	c39	c39	NOUN
fcis-14563	52	54	)	)	PUNCT
fcis-14563	52	55	.	.	PUNCT
fcis-14563	53	1	c3	c3	PROPN
fcis-14563	53	2	c6	c6	PROPN
fcis-14563	53	3	c8	c8	PROPN
fcis-14563	53	4	c15	c15	PROPN
fcis-14563	53	5	c24	c24	PROPN
fcis-14563	53	6	c29	c29	PROPN
fcis-14563	53	7	c37	c37	NOUN
fcis-14563	53	8	c39	c39	NOUN
fcis-14563	53	9	fig	fig	NOUN
fcis-14563	53	10	1	1	NUM
fcis-14563	53	11	.	.	PUNCT
fcis-14563	53	12	partial	partial	ADJ
fcis-14563	53	13	disturbance	disturbance	NOUN
fcis-14563	53	14	gadf	gadf	PROPN
fcis-14563	53	15	diagram	diagram	NOUN
fcis-14563	53	16	table	table	NOUN
fcis-14563	53	17	1	1	NUM
fcis-14563	53	18	.	.	PUNCT
fcis-14563	53	19	perturbation	perturbation	NOUN
fcis-14563	53	20	types	type	NOUN
fcis-14563	53	21	correspond	correspond	VERB
fcis-14563	53	22	to	to	ADP
fcis-14563	53	23	the	the	DET
fcis-14563	53	24	symbols	symbol	NOUN
fcis-14563	53	25	in	in	ADP
fcis-14563	53	26	this	this	DET
fcis-14563	53	27	document	document	NOUN
fcis-14563	53	28	text	text	NOUN
fcis-14563	53	29	symbols	symbol	NOUN
fcis-14563	53	30	type	type	NOUN
fcis-14563	53	31	of	of	ADP
fcis-14563	53	32	disturbance	disturbance	NOUN
fcis-14563	53	33	c0	c0	PROPN
fcis-14563	53	34	normal	normal	ADJ
fcis-14563	53	35	waveform	waveform	VERB
fcis-14563	53	36	c1	c1	PROPN
fcis-14563	53	37	sagging	sag	VERB
fcis-14563	53	38	c2	c2	PROPN
fcis-14563	53	39	temporary	temporary	ADJ
fcis-14563	53	40	promotion	promotion	NOUN
fcis-14563	53	41	c3	c3	PROPN
fcis-14563	53	42	interrupt	interrupt	PROPN
fcis-14563	53	43	c4	c4	PROPN
fcis-14563	53	44	harmonics	harmonic	NOUN
fcis-14563	53	45	c5	c5	PROPN
fcis-14563	53	46	oscillations	oscillations	PROPN
fcis-14563	53	47	c6	c6	PROPN
fcis-14563	53	48	pulse	pulse	PROPN
fcis-14563	53	49	c7	c7	PROPN
fcis-14563	53	50	undulation	undulation	PROPN
fcis-14563	53	51	c8	c8	PROPN
fcis-14563	53	52	dip	dip	PROPN
fcis-14563	53	53	+	+	CCONJ
fcis-14563	53	54	harmonics	harmonic	NOUN
fcis-14563	53	55	c9	c9	NOUN
fcis-14563	53	56	temporary	temporary	ADJ
fcis-14563	53	57	rise	rise	NOUN
fcis-14563	54	1	+	+	CCONJ
fcis-14563	54	2	harmonics	harmonic	NOUN
fcis-14563	54	3	c10	c10	VERB
fcis-14563	54	4	interrupt	interrupt	ADJ
fcis-14563	54	5	+	+	CCONJ
fcis-14563	54	6	harmonics	harmonic	NOUN
fcis-14563	54	7	c11	c11	NOUN
fcis-14563	54	8	oscillations	oscillation	NOUN
fcis-14563	54	9	+	+	CCONJ
fcis-14563	54	10	harmonics	harmonic	NOUN
fcis-14563	54	11	c12	c12	PROPN
fcis-14563	54	12	pulse	pulse	NOUN
fcis-14563	54	13	+	+	NUM
fcis-14563	54	14	harmonics	harmonic	NOUN
fcis-14563	54	15	c13	c13	NOUN
fcis-14563	54	16	wave	wave	NOUN
fcis-14563	54	17	+	+	CCONJ
fcis-14563	54	18	harmonics	harmonic	NOUN
fcis-14563	54	19	c14	c14	NOUN
fcis-14563	54	20	dip	dip	NOUN
fcis-14563	54	21	+	+	CCONJ
fcis-14563	54	22	oscillation	oscillation	NOUN
fcis-14563	54	23	c15	c15	X
fcis-14563	54	24	temporary	temporary	ADJ
fcis-14563	54	25	rise	rise	NOUN
fcis-14563	54	26	+	+	CCONJ
fcis-14563	54	27	oscillation	oscillation	NOUN
fcis-14563	54	28	c16	c16	NOUN
fcis-14563	54	29	interrupt	interrupt	VERB
fcis-14563	54	30	+	+	CCONJ
fcis-14563	54	31	oscillation	oscillation	NOUN
fcis-14563	54	32	c17	c17	NOUN
fcis-14563	54	33	pulse	pulse	NOUN
fcis-14563	54	34	+	+	CCONJ
fcis-14563	54	35	oscillation	oscillation	NOUN
fcis-14563	54	36	c18	c18	NOUN
fcis-14563	54	37	wave	wave	VERB
fcis-14563	54	38	+	+	CCONJ
fcis-14563	54	39	oscillation	oscillation	NOUN
fcis-14563	54	40	c19	c19	NOUN
fcis-14563	54	41	volatility	volatility	NOUN
fcis-14563	54	42	+	+	CCONJ
fcis-14563	54	43	temporary	temporary	ADJ
fcis-14563	54	44	decline	decline	NOUN
fcis-14563	54	45	c20	c20	NOUN
fcis-14563	54	46	volatility	volatility	NOUN
fcis-14563	54	47	+	+	CCONJ
fcis-14563	54	48	temporary	temporary	ADJ
fcis-14563	54	49	rise	rise	NOUN
fcis-14563	54	50	c21	c21	NOUN
fcis-14563	54	51	volatility	volatility	NOUN
fcis-14563	54	52	+	+	CCONJ
fcis-14563	54	53	disruption	disruption	NOUN
fcis-14563	54	54	c22	c22	NOUN
fcis-14563	54	55	wave	wave	PROPN
fcis-14563	54	56	+	+	CCONJ
fcis-14563	54	57	pulse	pulse	NOUN
fcis-14563	54	58	c23	c23	NOUN
fcis-14563	54	59	harmonics	harmonic	NOUN
fcis-14563	54	60	+	+	CCONJ
fcis-14563	54	61	oscillations	oscillation	NOUN
fcis-14563	54	62	+	+	CCONJ
fcis-14563	54	63	pulses	pulse	NOUN
fcis-14563	54	64	c24	c24	NOUN
fcis-14563	54	65	dip	dip	NOUN
fcis-14563	54	66	+	+	CCONJ
fcis-14563	54	67	oscillation	oscillation	NOUN
fcis-14563	54	68	+	+	CCONJ
fcis-14563	54	69	pulse	pulse	NOUN
fcis-14563	54	70	c25	c25	NOUN
fcis-14563	54	71	temporary	temporary	ADJ
fcis-14563	54	72	rise	rise	NOUN
fcis-14563	54	73	+	+	CCONJ
fcis-14563	54	74	oscillation	oscillation	NOUN
fcis-14563	54	75	+	+	CCONJ
fcis-14563	54	76	c26	c26	NOUN
fcis-14563	54	77	interrupt	interrupt	VERB
fcis-14563	54	78	+	+	CCONJ
fcis-14563	54	79	oscillation	oscillation	NOUN
fcis-14563	54	80	+	+	CCONJ
fcis-14563	54	81	pulse	pulse	NOUN
fcis-14563	54	82	c27	c27	NOUN
fcis-14563	54	83	harmonics	harmonic	NOUN
fcis-14563	54	84	+	+	CCONJ
fcis-14563	54	85	dip	dip	NOUN
fcis-14563	54	86	+	+	CCONJ
fcis-14563	54	87	pulse	pulse	NOUN
fcis-14563	54	88	c28	c28	NOUN
fcis-14563	54	89	harmonics	harmonic	NOUN
fcis-14563	54	90	+	+	CCONJ
fcis-14563	54	91	temporary	temporary	ADJ
fcis-14563	54	92	rise	rise	NOUN
fcis-14563	54	93	+	+	CCONJ
fcis-14563	54	94	c29	c29	NOUN
fcis-14563	54	95	harmonics	harmonic	NOUN
fcis-14563	54	96	+	+	CCONJ
fcis-14563	54	97	interrupt	interrupt	ADJ
fcis-14563	54	98	+	+	CCONJ
fcis-14563	54	99	pulse	pulse	NOUN
fcis-14563	54	100	c30	c30	NOUN
fcis-14563	54	101	harmonics	harmonic	NOUN
fcis-14563	54	102	+	+	CCONJ
fcis-14563	54	103	oscillations	oscillation	NOUN
fcis-14563	54	104	+	+	CCONJ
fcis-14563	54	105	dips	dip	VERB
fcis-14563	54	106	c31	c31	NOUN
fcis-14563	54	107	harmonics	harmonic	NOUN
fcis-14563	54	108	+	+	CCONJ
fcis-14563	54	109	oscillations	oscillation	NOUN
fcis-14563	54	110	+	+	CCONJ
fcis-14563	54	111	c32	c32	NOUN
fcis-14563	54	112	harmonics	harmonic	NOUN
fcis-14563	54	113	+	+	CCONJ
fcis-14563	54	114	oscillation	oscillation	NOUN
fcis-14563	54	115	+	+	CCONJ
fcis-14563	54	116	interrupt	interrupt	ADJ
fcis-14563	54	117	c33	c33	NOUN
fcis-14563	54	118	harmonics	harmonic	NOUN
fcis-14563	54	119	+	+	CCONJ
fcis-14563	54	120	fluctuations	fluctuation	NOUN
fcis-14563	54	121	+	+	CCONJ
fcis-14563	54	122	c34	c34	PRON
fcis-14563	54	123	harmonic	harmonic	ADJ
fcis-14563	54	124	+	+	CCONJ
fcis-14563	54	125	wave	wave	NOUN
fcis-14563	54	126	+	+	CCONJ
fcis-14563	54	127	temporary	temporary	ADJ
fcis-14563	54	128	rise	rise	NOUN
fcis-14563	54	129	c35	c35	NOUN
fcis-14563	54	130	harmonics	harmonic	NOUN
fcis-14563	54	131	+	+	CCONJ
fcis-14563	54	132	wave	wave	NOUN
fcis-14563	54	133	+	+	CCONJ
fcis-14563	54	134	dip	dip	NOUN
fcis-14563	54	135	c36	c36	PROPN
fcis-14563	54	136	harmonic	harmonic	VERB
fcis-14563	54	137	+	+	CCONJ
fcis-14563	54	138	oscillation	oscillation	NOUN
fcis-14563	54	139	+	+	CCONJ
fcis-14563	54	140	pulse	pulse	NOUN
fcis-14563	54	141	+	+	CCONJ
fcis-14563	54	142	c37	c37	NOUN
fcis-14563	54	143	harmonic	harmonic	NOUN
fcis-14563	54	144	+	+	CCONJ
fcis-14563	54	145	oscillation	oscillation	NOUN
fcis-14563	54	146	+	+	CCONJ
fcis-14563	54	147	pulse	pulse	NOUN
fcis-14563	54	148	+	+	CCONJ
fcis-14563	54	149	c38	c38	NOUN
fcis-14563	54	150	harmonics	harmonic	NOUN
fcis-14563	54	151	+	+	CCONJ
fcis-14563	54	152	oscillations	oscillation	NOUN
fcis-14563	54	153	+	+	CCONJ
fcis-14563	54	154	pulses	pulse	NOUN
fcis-14563	54	155	c39	c39	NOUN
fcis-14563	54	156	harmonics	harmonic	NOUN
fcis-14563	54	157	+	+	CCONJ
fcis-14563	54	158	oscillations	oscillation	NOUN
fcis-14563	54	159	+	+	CCONJ
fcis-14563	54	160	waves	wave	NOUN
fcis-14563	54	161	3	3	NUM
fcis-14563	54	162	.	.	PUNCT
fcis-14563	54	163	neural	neural	ADJ
fcis-14563	54	164	network	network	NOUN
fcis-14563	54	165	3.1	3.1	NUM
fcis-14563	54	166	.	.	PUNCT
fcis-14563	55	1	convolutional	convolutional	ADJ
fcis-14563	55	2	neural	neural	ADJ
fcis-14563	55	3	networks	network	NOUN
fcis-14563	55	4	(	(	PUNCT
fcis-14563	55	5	cnn	cnn	PROPN
fcis-14563	55	6	)	)	PUNCT
fcis-14563	55	7	the	the	DET
fcis-14563	55	8	types	type	NOUN
fcis-14563	55	9	of	of	ADP
fcis-14563	55	10	cnns	cnn	NOUN
fcis-14563	55	11	used	use	VERB
fcis-14563	55	12	in	in	ADP
fcis-14563	55	13	this	this	DET
fcis-14563	55	14	paper	paper	NOUN
fcis-14563	55	15	are	be	AUX
fcis-14563	55	16	1d_cnn	1d_cnn	NUM
fcis-14563	55	17	and	and	CCONJ
fcis-14563	55	18	2d_cnn	2d_cnn	NUM
fcis-14563	55	19	.	.	PUNCT
fcis-14563	56	1	the	the	DET
fcis-14563	56	2	cnns	cnn	NOUN
fcis-14563	56	3	can	can	AUX
fcis-14563	56	4	be	be	AUX
fcis-14563	56	5	divided	divide	VERB
fcis-14563	56	6	into	into	ADP
fcis-14563	56	7	convolutional	convolutional	ADJ
fcis-14563	56	8	layer	layer	NOUN
fcis-14563	56	9	,	,	PUNCT
fcis-14563	56	10	pooling	pool	VERB
fcis-14563	56	11	layer	layer	NOUN
fcis-14563	56	12	,	,	PUNCT
fcis-14563	56	13	activation	activation	NOUN
fcis-14563	56	14	function	function	NOUN
fcis-14563	56	15	,	,	PUNCT
fcis-14563	56	16	batch	batch	VERB
fcis-14563	56	17	normalization	normalization	NOUN
fcis-14563	56	18	layer	layer	NOUN
fcis-14563	56	19	and	and	CCONJ
fcis-14563	56	20	fully	fully	ADV
fcis-14563	56	21	connected	connected	ADJ
fcis-14563	56	22	layer	layer	NOUN
fcis-14563	56	23	.	.	PUNCT
fcis-14563	57	1	3.1.1	3.1.1	X
fcis-14563	57	2	.	.	PUNCT
fcis-14563	57	3	convolutional	convolutional	ADJ
fcis-14563	57	4	layer	layer	NOUN
fcis-14563	57	5	the	the	DET
fcis-14563	57	6	convolution	convolution	NOUN
fcis-14563	57	7	kernel	kernel	NOUN
fcis-14563	57	8	in	in	ADP
fcis-14563	57	9	the	the	DET
fcis-14563	57	10	convolution	convolution	NOUN
fcis-14563	57	11	layer	layer	NOUN
fcis-14563	57	12	is	be	AUX
fcis-14563	57	13	the	the	DET
fcis-14563	57	14	computing	compute	VERB
fcis-14563	57	15	unit	unit	NOUN
fcis-14563	57	16	.	.	PUNCT
fcis-14563	58	1	the	the	DET
fcis-14563	58	2	convolution	convolution	NOUN
fcis-14563	58	3	kernel	kernel	NOUN
fcis-14563	58	4	moves	move	VERB
fcis-14563	58	5	on	on	ADP
fcis-14563	58	6	the	the	DET
fcis-14563	58	7	feature	feature	NOUN
fcis-14563	58	8	map	map	NOUN
fcis-14563	58	9	of	of	ADP
fcis-14563	58	10	the	the	DET
fcis-14563	58	11	input	input	NOUN
fcis-14563	58	12	convolution	convolution	NOUN
fcis-14563	58	13	layer	layer	NOUN
fcis-14563	58	14	and	and	CCONJ
fcis-14563	58	15	carries	carry	VERB
fcis-14563	58	16	out	out	ADP
fcis-14563	58	17	convolution	convolution	NOUN
fcis-14563	58	18	operations	operation	NOUN
fcis-14563	58	19	on	on	ADP
fcis-14563	58	20	each	each	DET
fcis-14563	58	21	region	region	NOUN
fcis-14563	58	22	of	of	ADP
fcis-14563	58	23	the	the	DET
fcis-14563	58	24	feature	feature	NOUN
fcis-14563	58	25	map	map	NOUN
fcis-14563	58	26	.	.	PUNCT
fcis-14563	59	1	the	the	DET
fcis-14563	59	2	calculation	calculation	NOUN
fcis-14563	59	3	results	result	NOUN
fcis-14563	59	4	of	of	ADP
fcis-14563	59	5	different	different	ADJ
fcis-14563	59	6	convolution	convolution	NOUN
fcis-14563	59	7	check	check	NOUN
fcis-14563	59	8	feature	feature	NOUN
fcis-14563	59	9	maps	map	NOUN
fcis-14563	59	10	are	be	AUX
fcis-14563	59	11	different	different	ADJ
fcis-14563	59	12	.	.	PUNCT
fcis-14563	60	1	the	the	DET
fcis-14563	60	2	result	result	NOUN
fcis-14563	60	3	of	of	ADP
fcis-14563	60	4	the	the	DET
fcis-14563	60	5	convolution	convolution	NOUN
fcis-14563	60	6	plus	plus	CCONJ
fcis-14563	60	7	the	the	DET
fcis-14563	60	8	offset	offset	NOUN
fcis-14563	60	9	of	of	ADP
fcis-14563	60	10	the	the	DET
fcis-14563	60	11	output	output	NOUN
fcis-14563	60	12	feature	feature	NOUN
fcis-14563	60	13	map	map	NOUN
fcis-14563	60	14	as	as	ADP
fcis-14563	60	15	the	the	DET
fcis-14563	60	16	output	output	NOUN
fcis-14563	60	17	of	of	ADP
fcis-14563	60	18	the	the	DET
fcis-14563	60	19	convolutional	convolutional	ADJ
fcis-14563	60	20	layer	layer	NOUN
fcis-14563	60	21	.	.	PUNCT
fcis-14563	61	1	the	the	DET
fcis-14563	61	2	convolutional	convolutional	ADJ
fcis-14563	61	3	layer	layer	NOUN
fcis-14563	61	4	is	be	AUX
fcis-14563	61	5	calculated	calculate	VERB
fcis-14563	61	6	as	as	SCONJ
fcis-14563	61	7	follows	follow	VERB
fcis-14563	61	8	:	:	PUNCT
fcis-14563	61	9	6	6	NUM
fcis-14563	61	10	1	1	NUM
fcis-14563	61	11	j	j	NOUN
fcis-14563	61	12	l	l	NOUN
fcis-14563	61	13	l	l	X
fcis-14563	62	1	l	l	X
fcis-14563	62	2	j	j	X
fcis-14563	63	1	j	j	NOUN
fcis-14563	63	2	i	i	PRON
fcis-14563	63	3	ij	ij	VERB
fcis-14563	63	4	ii	ii	NUM
fcis-14563	63	5	m	m	NOUN
fcis-14563	63	6	x	x	PUNCT
fcis-14563	63	7	x	x	PUNCT
fcis-14563	63	8	k	k	PROPN
fcis-14563	63	9	b	b	PROPN
fcis-14563	63	10			NOUN
fcis-14563	63	11			PROPN
fcis-14563	63	12			PROPN
fcis-14563	63	13			NOUN
fcis-14563	63	14	(	(	PUNCT
fcis-14563	63	15	4	4	NUM
fcis-14563	63	16	)	)	PUNCT
fcis-14563	63	17	where	where	SCONJ
fcis-14563	63	18	:	:	PUNCT
fcis-14563	63	19	represents	represent	VERB
fcis-14563	63	20	the	the	DET
fcis-14563	63	21	number	number	NOUN
fcis-14563	63	22	of	of	ADP
fcis-14563	63	23	layers	layer	NOUN
fcis-14563	63	24	;	;	PUNCT
fcis-14563	63	25	lm	lm	PUNCT
fcis-14563	63	26	represents	represent	VERB
fcis-14563	63	27	the	the	DET
fcis-14563	63	28	set	set	NOUN
fcis-14563	63	29	of	of	ADP
fcis-14563	63	30	feature	feature	NOUN
fcis-14563	63	31	maps	map	NOUN
fcis-14563	63	32	connected	connect	VERB
fcis-14563	63	33	with	with	ADP
fcis-14563	63	34	the	the	DET
fcis-14563	63	35	current	current	ADJ
fcis-14563	63	36	layer	layer	NOUN
fcis-14563	63	37	's	's	PART
fcis-14563	63	38	first	first	ADJ
fcis-14563	63	39	feature	feature	NOUN
fcis-14563	63	40	map	map	NOUN
fcis-14563	63	41	in	in	ADP
fcis-14563	63	42	the	the	DET
fcis-14563	63	43	previous	previous	ADJ
fcis-14563	63	44	layer	layer	NOUN
fcis-14563	63	45	;	;	PUNCT
fcis-14563	63	46	jx	jx	PROPN
fcis-14563	63	47	is	be	AUX
fcis-14563	63	48	the	the	DET
fcis-14563	63	49	first	first	ADJ
fcis-14563	63	50	feature	feature	NOUN
fcis-14563	63	51	map	map	NOUN
fcis-14563	63	52	output	output	NOUN
fcis-14563	63	53	of	of	ADP
fcis-14563	63	54	the	the	DET
fcis-14563	63	55	first	first	ADJ
fcis-14563	63	56	layer	layer	NOUN
fcis-14563	63	57	;	;	PUNCT
fcis-14563	63	58	ljx	ljx	VERB
fcis-14563	63	59	the	the	DET
fcis-14563	63	60	first	first	ADJ
fcis-14563	63	61	feature	feature	NOUN
fcis-14563	63	62	map	map	NOUN
fcis-14563	63	63	output	output	NOUN
fcis-14563	63	64	for	for	ADP
fcis-14563	63	65	the	the	DET
fcis-14563	63	66	first	first	ADJ
fcis-14563	63	67	layer	layer	NOUN
fcis-14563	63	68	;	;	PUNCT
fcis-14563	63	69	l	l	NOUN
fcis-14563	63	70	1ik	1ik	NOUN
fcis-14563	63	71	is	be	AUX
fcis-14563	63	72	the	the	DET
fcis-14563	63	73	convolution	convolution	NOUN
fcis-14563	63	74	kernel	kernel	NOUN
fcis-14563	63	75	between	between	ADP
fcis-14563	63	76	the	the	DET
fcis-14563	63	77	first	first	ADJ
fcis-14563	63	78	feature	feature	NOUN
fcis-14563	63	79	map	map	NOUN
fcis-14563	63	80	of	of	ADP
fcis-14563	63	81	the	the	DET
fcis-14563	63	82	first	first	ADJ
fcis-14563	63	83	layer	layer	NOUN
fcis-14563	63	84	and	and	CCONJ
fcis-14563	63	85	the	the	DET
fcis-14563	63	86	first	first	ADJ
fcis-14563	63	87	feature	feature	NOUN
fcis-14563	63	88	map	map	NOUN
fcis-14563	63	89	of	of	ADP
fcis-14563	63	90	the	the	DET
fcis-14563	63	91	previous	previous	ADJ
fcis-14563	63	92	layer	layer	NOUN
fcis-14563	63	93	;	;	PUNCT
fcis-14563	63	94	ljib	ljib	PROPN
fcis-14563	63	95	is	be	AUX
fcis-14563	63	96	the	the	DET
fcis-14563	63	97	bias	bias	NOUN
fcis-14563	63	98	of	of	ADP
fcis-14563	63	99	the	the	DET
fcis-14563	63	100	first	first	ADJ
fcis-14563	63	101	layer	layer	NOUN
fcis-14563	63	102	's	's	PART
fcis-14563	63	103	first	first	ADJ
fcis-14563	63	104	feature	feature	NOUN
fcis-14563	63	105	map.lj	map.lj	PROPN
fcis-14563	63	106	3.1.2	3.1.2	NUM
fcis-14563	63	107	.	.	PUNCT
fcis-14563	64	1	pool	pool	NOUN
fcis-14563	64	2	layer	layer	NOUN
fcis-14563	64	3	the	the	DET
fcis-14563	64	4	pooling	pooling	NOUN
fcis-14563	64	5	layer	layer	NOUN
fcis-14563	64	6	is	be	AUX
fcis-14563	64	7	located	locate	VERB
fcis-14563	64	8	after	after	ADP
fcis-14563	64	9	the	the	DET
fcis-14563	64	10	convolution	convolution	NOUN
fcis-14563	64	11	layer	layer	NOUN
fcis-14563	64	12	.	.	PUNCT
fcis-14563	65	1	since	since	SCONJ
fcis-14563	65	2	the	the	DET
fcis-14563	65	3	convolution	convolution	NOUN
fcis-14563	65	4	layer	layer	NOUN
fcis-14563	65	5	increases	increase	VERB
fcis-14563	65	6	the	the	DET
fcis-14563	65	7	number	number	NOUN
fcis-14563	65	8	and	and	CCONJ
fcis-14563	65	9	dimension	dimension	NOUN
fcis-14563	65	10	of	of	ADP
fcis-14563	65	11	feature	feature	NOUN
fcis-14563	65	12	maps	map	NOUN
fcis-14563	65	13	,	,	PUNCT
fcis-14563	65	14	in	in	ADP
fcis-14563	65	15	order	order	NOUN
fcis-14563	65	16	to	to	PART
fcis-14563	65	17	reduce	reduce	VERB
fcis-14563	65	18	the	the	DET
fcis-14563	65	19	number	number	NOUN
fcis-14563	65	20	and	and	CCONJ
fcis-14563	65	21	dimension	dimension	NOUN
fcis-14563	65	22	of	of	ADP
fcis-14563	65	23	feature	feature	NOUN
fcis-14563	65	24	maps	map	NOUN
fcis-14563	65	25	,	,	PUNCT
fcis-14563	65	26	and	and	CCONJ
fcis-14563	65	27	keep	keep	VERB
fcis-14563	65	28	the	the	DET
fcis-14563	65	29	scale	scale	NOUN
fcis-14563	65	30	invariance	invariance	NOUN
fcis-14563	65	31	of	of	ADP
fcis-14563	65	32	features	feature	NOUN
fcis-14563	65	33	as	as	ADV
fcis-14563	65	34	much	much	ADV
fcis-14563	65	35	as	as	ADP
fcis-14563	65	36	possible	possible	ADJ
fcis-14563	65	37	.	.	PUNCT
fcis-14563	66	1	the	the	DET
fcis-14563	66	2	pooling	pool	VERB
fcis-14563	66	3	layer	layer	NOUN
fcis-14563	66	4	can	can	AUX
fcis-14563	66	5	extract	extract	VERB
fcis-14563	66	6	the	the	DET
fcis-14563	66	7	features	feature	NOUN
fcis-14563	66	8	twice	twice	ADV
fcis-14563	66	9	after	after	ADP
fcis-14563	66	10	the	the	DET
fcis-14563	66	11	convolution	convolution	NOUN
fcis-14563	66	12	layer	layer	NOUN
fcis-14563	66	13	,	,	PUNCT
fcis-14563	66	14	which	which	PRON
fcis-14563	66	15	improves	improve	VERB
fcis-14563	66	16	the	the	DET
fcis-14563	66	17	generalization	generalization	NOUN
fcis-14563	66	18	ability	ability	NOUN
fcis-14563	66	19	of	of	ADP
fcis-14563	66	20	the	the	DET
fcis-14563	66	21	model	model	NOUN
fcis-14563	66	22	and	and	CCONJ
fcis-14563	66	23	is	be	AUX
fcis-14563	66	24	not	not	PART
fcis-14563	66	25	easy	easy	ADJ
fcis-14563	66	26	to	to	PART
fcis-14563	66	27	overfit	overfit	VERB
fcis-14563	66	28	.	.	PUNCT
fcis-14563	67	1	the	the	DET
fcis-14563	67	2	calculation	calculation	NOUN
fcis-14563	67	3	process	process	NOUN
fcis-14563	67	4	of	of	ADP
fcis-14563	67	5	the	the	DET
fcis-14563	67	6	pooling	pooling	NOUN
fcis-14563	67	7	layer	layer	NOUN
fcis-14563	67	8	is	be	AUX
fcis-14563	67	9	as	as	SCONJ
fcis-14563	67	10	follows	follow	VERB
fcis-14563	67	11	:	:	PUNCT
fcis-14563	68	1			NOUN
fcis-14563	68	2	1l	1l	PUNCT
fcis-14563	68	3	l	l	PUNCT
fcis-14563	68	4	l	l	NOUN
fcis-14563	68	5	l	l	X
fcis-14563	68	6	j	j	PROPN
fcis-14563	69	1	j	j	NOUN
fcis-14563	69	2	i	i	PRON
fcis-14563	69	3	jx	jx	PROPN
fcis-14563	69	4	subdown	subdown	VERB
fcis-14563	69	5	x	x	PROPN
fcis-14563	69	6	b	b	PROPN
fcis-14563	69	7			PROPN
fcis-14563	69	8			PUNCT
fcis-14563	69	9	(	(	PUNCT
fcis-14563	69	10	5	5	NUM
fcis-14563	69	11	)	)	PUNCT
fcis-14563	69	12	where	where	SCONJ
fcis-14563	69	13	:	:	PUNCT
fcis-14563	69	14	represents	represent	VERB
fcis-14563	69	15	the	the	DET
fcis-14563	69	16	pooled	pooled	ADJ
fcis-14563	69	17	downsampling	downsample	VERB
fcis-14563	69	18	function	function	NOUN
fcis-14563	69	19	;	;	PUNCT
fcis-14563	69	20	subdown	subdown	VERB
fcis-14563	69	21	∙	∙	PROPN
fcis-14563	69	22	β	β	X
fcis-14563	69	23	is	be	AUX
fcis-14563	69	24	proportional	proportional	ADJ
fcis-14563	69	25	bias	bias	NOUN
fcis-14563	69	26	;	;	PUNCT
fcis-14563	69	27	b	b	X
fcis-14563	69	28	is	be	AUX
fcis-14563	69	29	additive	additive	ADJ
fcis-14563	69	30	bias	bias	NOUN
fcis-14563	69	31	.	.	PUNCT
fcis-14563	70	1	3.1.3	3.1.3	NUM
fcis-14563	70	2	.	.	NOUN
fcis-14563	70	3	batch	batch	NOUN
fcis-14563	70	4	normalization	normalization	NOUN
fcis-14563	70	5	layer	layer	NOUN
fcis-14563	70	6	(	(	PUNCT
fcis-14563	70	7	bn	bn	NOUN
fcis-14563	70	8	)	)	PUNCT
fcis-14563	70	9	bn	bn	NOUN
fcis-14563	70	10	layer	layer	NOUN
fcis-14563	70	11	can	can	AUX
fcis-14563	70	12	overcome	overcome	VERB
fcis-14563	70	13	the	the	DET
fcis-14563	70	14	influence	influence	NOUN
fcis-14563	70	15	of	of	ADP
fcis-14563	70	16	covariance	covariance	NOUN
fcis-14563	70	17	deviation	deviation	NOUN
fcis-14563	70	18	,	,	PUNCT
fcis-14563	70	19	speed	speed	VERB
fcis-14563	70	20	up	up	ADP
fcis-14563	70	21	the	the	DET
fcis-14563	70	22	training	training	NOUN
fcis-14563	70	23	speed	speed	NOUN
fcis-14563	70	24	,	,	PUNCT
fcis-14563	70	25	and	and	CCONJ
fcis-14563	70	26	enhance	enhance	VERB
fcis-14563	70	27	the	the	DET
fcis-14563	70	28	generalization	generalization	NOUN
fcis-14563	70	29	ability	ability	NOUN
fcis-14563	70	30	of	of	ADP
fcis-14563	70	31	the	the	DET
fcis-14563	70	32	network	network	NOUN
fcis-14563	70	33	.	.	PUNCT
fcis-14563	71	1	3.1.4	3.1.4	NUM
fcis-14563	71	2	.	.	PUNCT
fcis-14563	71	3	activate	activate	VERB
fcis-14563	71	4	function	function	NOUN
fcis-14563	71	5	in	in	ADP
fcis-14563	71	6	order	order	NOUN
fcis-14563	71	7	to	to	PART
fcis-14563	71	8	avoid	avoid	VERB
fcis-14563	71	9	the	the	DET
fcis-14563	71	10	problem	problem	NOUN
fcis-14563	71	11	of	of	ADP
fcis-14563	71	12	insufficient	insufficient	ADJ
fcis-14563	71	13	expressiveness	expressiveness	NOUN
fcis-14563	71	14	of	of	ADP
fcis-14563	71	15	the	the	DET
fcis-14563	71	16	neural	neural	ADJ
fcis-14563	71	17	network	network	NOUN
fcis-14563	71	18	model	model	NOUN
fcis-14563	71	19	,	,	PUNCT
fcis-14563	71	20	a	a	DET
fcis-14563	71	21	nonlinear	nonlinear	ADJ
fcis-14563	71	22	function	function	NOUN
fcis-14563	71	23	is	be	AUX
fcis-14563	71	24	used	use	VERB
fcis-14563	71	25	as	as	ADP
fcis-14563	71	26	the	the	DET
fcis-14563	71	27	activation	activation	NOUN
fcis-14563	71	28	function	function	NOUN
fcis-14563	71	29	of	of	ADP
fcis-14563	71	30	the	the	DET
fcis-14563	71	31	feature	feature	NOUN
fcis-14563	71	32	graph	graph	NOUN
fcis-14563	71	33	output	output	NOUN
fcis-14563	71	34	by	by	ADP
fcis-14563	71	35	the	the	DET
fcis-14563	71	36	pooling	pool	VERB
fcis-14563	71	37	layer	layer	NOUN
fcis-14563	71	38	.	.	PUNCT
fcis-14563	72	1	commonly	commonly	ADV
fcis-14563	72	2	used	use	VERB
fcis-14563	72	3	activation	activation	NOUN
fcis-14563	72	4	functions	function	NOUN
fcis-14563	72	5	include	include	VERB
fcis-14563	72	6	sigmoid	sigmoid	NOUN
fcis-14563	72	7	activation	activation	NOUN
fcis-14563	72	8	function	function	NOUN
fcis-14563	72	9	,	,	PUNCT
fcis-14563	72	10	tanh	tanh	NOUN
fcis-14563	72	11	activation	activation	NOUN
fcis-14563	72	12	function	function	NOUN
fcis-14563	72	13	,	,	PUNCT
fcis-14563	72	14	relu	relu	NOUN
fcis-14563	72	15	activation	activation	NOUN
fcis-14563	72	16	function	function	NOUN
fcis-14563	72	17	,	,	PUNCT
fcis-14563	72	18	etc	etc	X
fcis-14563	72	19	.	.	X
fcis-14563	73	1	since	since	SCONJ
fcis-14563	73	2	the	the	DET
fcis-14563	73	3	first	first	ADJ
fcis-14563	73	4	two	two	NUM
fcis-14563	73	5	activation	activation	NOUN
fcis-14563	73	6	functions	function	NOUN
fcis-14563	73	7	belong	belong	VERB
fcis-14563	73	8	to	to	ADP
fcis-14563	73	9	saturation	saturation	NOUN
fcis-14563	73	10	activation	activation	NOUN
fcis-14563	73	11	function	function	NOUN
fcis-14563	73	12	,	,	PUNCT
fcis-14563	73	13	gradient	gradient	ADJ
fcis-14563	73	14	disappearance	disappearance	NOUN
fcis-14563	73	15	will	will	AUX
fcis-14563	73	16	occur	occur	VERB
fcis-14563	73	17	due	due	ADP
fcis-14563	73	18	to	to	ADP
fcis-14563	73	19	their	their	PRON
fcis-14563	73	20	saturation	saturation	NOUN
fcis-14563	73	21	,	,	PUNCT
fcis-14563	73	22	and	and	CCONJ
fcis-14563	73	23	the	the	DET
fcis-14563	73	24	convergence	convergence	NOUN
fcis-14563	73	25	speed	speed	NOUN
fcis-14563	73	26	is	be	AUX
fcis-14563	73	27	slow	slow	ADJ
fcis-14563	73	28	,	,	PUNCT
fcis-14563	73	29	so	so	SCONJ
fcis-14563	73	30	this	this	DET
fcis-14563	73	31	paper	paper	NOUN
fcis-14563	73	32	chooses	choose	VERB
fcis-14563	73	33	the	the	DET
fcis-14563	73	34	unsaturated	unsaturated	ADJ
fcis-14563	73	35	activation	activation	NOUN
fcis-14563	73	36	function	function	NOUN
fcis-14563	73	37	relu	relu	NOUN
fcis-14563	73	38	activation	activation	NOUN
fcis-14563	73	39	function	function	NOUN
fcis-14563	73	40	,	,	PUNCT
fcis-14563	73	41	which	which	PRON
fcis-14563	73	42	has	have	VERB
fcis-14563	73	43	a	a	DET
fcis-14563	73	44	faster	fast	ADJ
fcis-14563	73	45	convergence	convergence	NOUN
fcis-14563	73	46	speed	speed	NOUN
fcis-14563	73	47	.	.	PUNCT
fcis-14563	74	1	relu	relu	NOUN
fcis-14563	74	2	activation	activation	NOUN
fcis-14563	74	3	function	function	NOUN
fcis-14563	74	4	is	be	AUX
fcis-14563	74	5	defined	define	VERB
fcis-14563	74	6	as	as	SCONJ
fcis-14563	74	7	follows	follow	VERB
fcis-14563	74	8	:	:	PUNCT
fcis-14563	74	9	0	0	NUM
fcis-14563	74	10	(	(	PUNCT
fcis-14563	74	11	0	0	NUM
fcis-14563	74	12	)	)	PUNCT
fcis-14563	74	13	(	(	PUNCT
fcis-14563	74	14	0	0	NUM
fcis-14563	74	15	)	)	PUNCT
fcis-14563	74	16	(	(	PUNCT
fcis-14563	74	17	)	)	PUNCT
fcis-14563	75	1	x	x	SYM
fcis-14563	75	2	x	x	PUNCT
fcis-14563	75	3	xf	xf	PROPN
fcis-14563	75	4	x	x	PROPN
fcis-14563	75	5			PROPN
fcis-14563	75	6			INTJ
fcis-14563	75	7	(	(	PUNCT
fcis-14563	75	8	6	6	NUM
fcis-14563	75	9	)	)	PUNCT
fcis-14563	75	10	3.1.5	3.1.5	NOUN
fcis-14563	75	11	.	.	PUNCT
fcis-14563	75	12	full	full	ADJ
fcis-14563	75	13	connection	connection	NOUN
fcis-14563	75	14	layer	layer	NOUN
fcis-14563	75	15	after	after	ADP
fcis-14563	75	16	multiple	multiple	ADJ
fcis-14563	75	17	convolution	convolution	NOUN
fcis-14563	75	18	,	,	PUNCT
fcis-14563	75	19	pooling	pooling	NOUN
fcis-14563	75	20	and	and	CCONJ
fcis-14563	75	21	activation	activation	NOUN
fcis-14563	75	22	operations	operation	NOUN
fcis-14563	75	23	of	of	ADP
fcis-14563	75	24	the	the	DET
fcis-14563	75	25	input	input	NOUN
fcis-14563	75	26	data	datum	NOUN
fcis-14563	75	27	,	,	PUNCT
fcis-14563	75	28	the	the	DET
fcis-14563	75	29	resulting	result	VERB
fcis-14563	75	30	feature	feature	NOUN
fcis-14563	75	31	is	be	AUX
fcis-14563	75	32	expanded	expand	VERB
fcis-14563	75	33	into	into	ADP
fcis-14563	75	34	a	a	DET
fcis-14563	75	35	one	one	NUM
fcis-14563	75	36	-	-	PUNCT
fcis-14563	75	37	dimensional	dimensional	ADJ
fcis-14563	75	38	vector	vector	NOUN
fcis-14563	75	39	,	,	PUNCT
fcis-14563	75	40	so	so	SCONJ
fcis-14563	75	41	that	that	SCONJ
fcis-14563	75	42	the	the	DET
fcis-14563	75	43	feature	feature	NOUN
fcis-14563	75	44	corresponds	correspond	VERB
fcis-14563	75	45	to	to	ADP
fcis-14563	75	46	the	the	DET
fcis-14563	75	47	disturbance	disturbance	NOUN
fcis-14563	75	48	type	type	NOUN
fcis-14563	75	49	one	one	NUM
fcis-14563	75	50	by	by	ADP
fcis-14563	75	51	one	one	NUM
fcis-14563	75	52	,	,	PUNCT
fcis-14563	75	53	expressed	express	VERB
fcis-14563	75	54	as	as	ADP
fcis-14563	75	55	:	:	PUNCT
fcis-14563	75	56			NOUN
fcis-14563	75	57	1k	1k	PUNCT
fcis-14563	76	1	k	k	PROPN
fcis-14563	77	1	k	k	PROPN
fcis-14563	77	2	ky	ky	PROPN
fcis-14563	77	3	f	f	PROPN
fcis-14563	77	4	w	w	PROPN
fcis-14563	77	5	x	x	PROPN
fcis-14563	77	6	b	b	PROPN
fcis-14563	77	7			PUNCT
fcis-14563	77	8	(	(	PUNCT
fcis-14563	77	9	7	7	NUM
fcis-14563	77	10	)	)	PUNCT
fcis-14563	77	11	where	where	SCONJ
fcis-14563	77	12	:	:	PUNCT
fcis-14563	77	13	is	be	AUX
fcis-14563	77	14	the	the	DET
fcis-14563	77	15	number	number	NOUN
fcis-14563	77	16	of	of	ADP
fcis-14563	77	17	layers	layer	NOUN
fcis-14563	77	18	of	of	ADP
fcis-14563	77	19	the	the	DET
fcis-14563	77	20	network	network	NOUN
fcis-14563	77	21	;	;	PUNCT
fcis-14563	77	22	ky	ky	PROPN
fcis-14563	77	23	is	be	AUX
fcis-14563	77	24	the	the	DET
fcis-14563	77	25	total	total	ADJ
fcis-14563	77	26	output	output	NOUN
fcis-14563	77	27	of	of	ADP
fcis-14563	77	28	this	this	DET
fcis-14563	77	29	layer	layer	NOUN
fcis-14563	77	30	;	;	PUNCT
fcis-14563	77	31	x	x	X
fcis-14563	77	32	is	be	AUX
fcis-14563	77	33	the	the	DET
fcis-14563	77	34	initial	initial	ADJ
fcis-14563	77	35	input	input	NOUN
fcis-14563	77	36	of	of	ADP
fcis-14563	77	37	this	this	DET
fcis-14563	77	38	layer	layer	NOUN
fcis-14563	77	39	;	;	PUNCT
fcis-14563	77	40	w	w	NOUN
fcis-14563	77	41	is	be	AUX
fcis-14563	77	42	the	the	DET
fcis-14563	77	43	weight	weight	NOUN
fcis-14563	77	44	;	;	PUNCT
fcis-14563	77	45	b	b	X
fcis-14563	77	46	for	for	ADP
fcis-14563	77	47	network	network	NOUN
fcis-14563	77	48	bias	bias	NOUN
fcis-14563	77	49	.	.	PUNCT
fcis-14563	78	1	3.2	3.2	NUM
fcis-14563	78	2	.	.	PUNCT
fcis-14563	78	3	gated	gate	VERB
fcis-14563	78	4	cycle	cycle	NOUN
fcis-14563	78	5	unit	unit	NOUN
fcis-14563	78	6	(	(	PUNCT
fcis-14563	78	7	gru	gru	NOUN
fcis-14563	78	8	)	)	PUNCT
fcis-14563	78	9	grus	grus	NOUN
fcis-14563	78	10	were	be	AUX
fcis-14563	78	11	proposed	propose	VERB
fcis-14563	78	12	to	to	PART
fcis-14563	78	13	solve	solve	VERB
fcis-14563	78	14	problems	problem	NOUN
fcis-14563	78	15	such	such	ADJ
fcis-14563	78	16	as	as	ADP
fcis-14563	78	17	long	long	ADJ
fcis-14563	78	18	-	-	PUNCT
fcis-14563	78	19	term	term	NOUN
fcis-14563	78	20	memory	memory	NOUN
fcis-14563	78	21	and	and	CCONJ
fcis-14563	78	22	gradients	gradient	NOUN
fcis-14563	78	23	in	in	ADP
fcis-14563	78	24	backpropagation	backpropagation	NOUN
fcis-14563	78	25	.	.	PUNCT
fcis-14563	79	1	the	the	DET
fcis-14563	79	2	gru	gru	NOUN
fcis-14563	79	3	is	be	AUX
fcis-14563	79	4	composed	compose	VERB
fcis-14563	79	5	of	of	ADP
fcis-14563	79	6	two	two	NUM
fcis-14563	79	7	gates	gate	NOUN
fcis-14563	79	8	:	:	PUNCT
fcis-14563	79	9	update	update	NOUN
fcis-14563	79	10	gate	gate	NOUN
fcis-14563	79	11	and	and	CCONJ
fcis-14563	79	12	reset	reset	ADJ
fcis-14563	79	13	gate	gate	NOUN
fcis-14563	79	14	.	.	PUNCT
fcis-14563	80	1	the	the	DET
fcis-14563	80	2	update	update	NOUN
fcis-14563	80	3	gate	gate	NOUN
fcis-14563	80	4	controls	control	VERB
fcis-14563	80	5	the	the	DET
fcis-14563	80	6	influence	influence	NOUN
fcis-14563	80	7	of	of	ADP
fcis-14563	80	8	the	the	DET
fcis-14563	80	9	output	output	NOUN
fcis-14563	80	10	of	of	ADP
fcis-14563	80	11	the	the	DET
fcis-14563	80	12	hidden	hide	VERB
fcis-14563	80	13	layer	layer	NOUN
fcis-14563	80	14	at	at	ADP
fcis-14563	80	15	the	the	DET
fcis-14563	80	16	previous	previous	ADJ
fcis-14563	80	17	time	time	NOUN
fcis-14563	80	18	on	on	ADP
fcis-14563	80	19	the	the	DET
fcis-14563	80	20	output	output	NOUN
fcis-14563	80	21	of	of	ADP
fcis-14563	80	22	the	the	DET
fcis-14563	80	23	hidden	hide	VERB
fcis-14563	80	24	layer	layer	NOUN
fcis-14563	80	25	at	at	ADP
fcis-14563	80	26	the	the	DET
fcis-14563	80	27	same	same	ADJ
fcis-14563	80	28	time	time	NOUN
fcis-14563	80	29	,	,	PUNCT
fcis-14563	80	30	and	and	CCONJ
fcis-14563	80	31	the	the	DET
fcis-14563	80	32	reset	reset	NOUN
fcis-14563	80	33	gate	gate	NOUN
fcis-14563	80	34	indicates	indicate	VERB
fcis-14563	80	35	the	the	DET
fcis-14563	80	36	information	information	NOUN
fcis-14563	80	37	output	output	NOUN
fcis-14563	80	38	of	of	ADP
fcis-14563	80	39	the	the	DET
fcis-14563	80	40	hidden	hide	VERB
fcis-14563	80	41	layer	layer	NOUN
fcis-14563	80	42	at	at	ADP
fcis-14563	80	43	the	the	DET
fcis-14563	80	44	previous	previous	ADJ
fcis-14563	80	45	time	time	NOUN
fcis-14563	80	46	to	to	PART
fcis-14563	80	47	be	be	AUX
fcis-14563	80	48	deleted	delete	VERB
fcis-14563	80	49	at	at	ADP
fcis-14563	80	50	the	the	DET
fcis-14563	80	51	same	same	ADJ
fcis-14563	80	52	time	time	NOUN
fcis-14563	80	53	,	,	PUNCT
fcis-14563	80	54	which	which	PRON
fcis-14563	80	55	can	can	AUX
fcis-14563	80	56	effectively	effectively	ADV
fcis-14563	80	57	alleviate	alleviate	VERB
fcis-14563	80	58	the	the	DET
fcis-14563	80	59	problem	problem	NOUN
fcis-14563	81	1	[	[	X
fcis-14563	81	2	16	16	NUM
fcis-14563	81	3	]	]	PUNCT
fcis-14563	81	4	of	of	ADP
fcis-14563	81	5	"	"	PUNCT
fcis-14563	81	6	gradient	gradient	ADJ
fcis-14563	81	7	disappearance	disappearance	NOUN
fcis-14563	81	8	"	"	PUNCT
fcis-14563	81	9	in	in	ADP
fcis-14563	81	10	the	the	DET
fcis-14563	81	11	recurrent	recurrent	ADJ
fcis-14563	81	12	neural	neural	ADJ
fcis-14563	81	13	network	network	NOUN
fcis-14563	81	14	.	.	PUNCT
fcis-14563	82	1	4	4	X
fcis-14563	82	2	.	.	PUNCT
fcis-14563	82	3	model	model	NOUN
fcis-14563	82	4	establishment	establishment	NOUN
fcis-14563	82	5	and	and	CCONJ
fcis-14563	82	6	simulation	simulation	NOUN
fcis-14563	82	7	experiment	experiment	NOUN
fcis-14563	82	8	4.1	4.1	NUM
fcis-14563	82	9	.	.	PUNCT
fcis-14563	83	1	establishment	establishment	NOUN
fcis-14563	83	2	of	of	ADP
fcis-14563	83	3	adaptive	adaptive	ADJ
fcis-14563	83	4	feature	feature	NOUN
fcis-14563	83	5	fusion	fusion	NOUN
fcis-14563	83	6	model	model	NOUN
fcis-14563	83	7	4.1.1	4.1.1	NUM
fcis-14563	83	8	.	.	PUNCT
fcis-14563	84	1	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	84	2	the	the	DET
fcis-14563	84	3	module	module	NOUN
fcis-14563	84	4	consists	consist	VERB
fcis-14563	84	5	of	of	ADP
fcis-14563	84	6	1d_cnn	1d_cnn	NUM
fcis-14563	84	7	combined	combine	VERB
fcis-14563	84	8	with	with	ADP
fcis-14563	84	9	gru	gru	PROPN
fcis-14563	84	10	.	.	PROPN
fcis-14563	85	1	1d_cnn	1d_cnn	NUM
fcis-14563	85	2	consists	consist	VERB
fcis-14563	85	3	of	of	ADP
fcis-14563	85	4	5	5	NUM
fcis-14563	85	5	convolutional	convolutional	ADJ
fcis-14563	85	6	layers	layer	NOUN
fcis-14563	85	7	,	,	PUNCT
fcis-14563	85	8	5	5	NUM
fcis-14563	85	9	normalized	normalize	VERB
fcis-14563	85	10	layers	layer	NOUN
fcis-14563	85	11	,	,	PUNCT
fcis-14563	85	12	and	and	CCONJ
fcis-14563	85	13	no	no	DET
fcis-14563	85	14	pooling	pool	VERB
fcis-14563	85	15	layer	layer	NOUN
fcis-14563	85	16	.	.	PUNCT
fcis-14563	86	1	the	the	DET
fcis-14563	86	2	size	size	NOUN
fcis-14563	86	3	of	of	ADP
fcis-14563	86	4	the	the	DET
fcis-14563	86	5	convolutional	convolutional	ADJ
fcis-14563	86	6	kernel	kernel	NOUN
fcis-14563	86	7	is	be	AUX
fcis-14563	86	8	2×2	2×2	NUM
fcis-14563	86	9	,	,	PUNCT
fcis-14563	86	10	the	the	DET
fcis-14563	86	11	move	move	NOUN
fcis-14563	86	12	step	step	NOUN
fcis-14563	86	13	of	of	ADP
fcis-14563	86	14	the	the	DET
fcis-14563	86	15	convolutional	convolutional	ADJ
fcis-14563	86	16	kernel	kernel	NOUN
fcis-14563	86	17	is	be	AUX
fcis-14563	86	18	set	set	VERB
fcis-14563	86	19	to	to	ADP
fcis-14563	86	20	2	2	NUM
fcis-14563	86	21	and	and	CCONJ
fcis-14563	86	22	no	no	DET
fcis-14563	86	23	filling	filling	NOUN
fcis-14563	86	24	is	be	AUX
fcis-14563	86	25	required	require	VERB
fcis-14563	86	26	,	,	PUNCT
fcis-14563	86	27	and	and	CCONJ
fcis-14563	86	28	the	the	DET
fcis-14563	86	29	parameters	parameter	NOUN
fcis-14563	86	30	of	of	ADP
fcis-14563	86	31	dropout	dropout	NOUN
fcis-14563	86	32	are	be	AUX
fcis-14563	86	33	set	set	VERB
fcis-14563	86	34	to	to	ADP
fcis-14563	86	35	0.5	0.5	NUM
fcis-14563	86	36	.	.	PUNCT
fcis-14563	87	1	the	the	DET
fcis-14563	87	2	one	one	NUM
fcis-14563	87	3	-	-	PUNCT
fcis-14563	87	4	dimensional	dimensional	ADJ
fcis-14563	87	5	data	datum	NOUN
fcis-14563	87	6	is	be	AUX
fcis-14563	87	7	taken	take	VERB
fcis-14563	87	8	as	as	ADP
fcis-14563	87	9	the	the	DET
fcis-14563	87	10	input	input	NOUN
fcis-14563	87	11	of	of	ADP
fcis-14563	87	12	1d_cnn	1d_cnn	NUM
fcis-14563	87	13	,	,	PUNCT
fcis-14563	87	14	and	and	CCONJ
fcis-14563	87	15	then	then	ADV
fcis-14563	87	16	the	the	DET
fcis-14563	87	17	cnn	cnn	PROPN
fcis-14563	87	18	output	output	NOUN
fcis-14563	87	19	is	be	AUX
fcis-14563	87	20	taken	take	VERB
fcis-14563	87	21	as	as	ADP
fcis-14563	87	22	the	the	DET
fcis-14563	87	23	input	input	NOUN
fcis-14563	87	24	of	of	ADP
fcis-14563	87	25	gru	gru	PROPN
fcis-14563	87	26	,	,	PUNCT
fcis-14563	87	27	and	and	CCONJ
fcis-14563	87	28	the	the	DET
fcis-14563	87	29	output	output	NOUN
fcis-14563	87	30	of	of	ADP
fcis-14563	87	31	gru	gru	PROPN
fcis-14563	87	32	is	be	AUX
fcis-14563	87	33	the	the	DET
fcis-14563	87	34	one	one	NUM
fcis-14563	87	35	-	-	PUNCT
fcis-14563	87	36	dimensional	dimensional	ADJ
fcis-14563	87	37	data	datum	NOUN
fcis-14563	87	38	feature	feature	NOUN
fcis-14563	87	39	.	.	PUNCT
fcis-14563	88	1	the	the	DET
fcis-14563	88	2	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	88	3	structure	structure	NOUN
fcis-14563	88	4	is	be	AUX
fcis-14563	88	5	shown	show	VERB
fcis-14563	88	6	in	in	ADP
fcis-14563	88	7	figure	figure	NOUN
fcis-14563	88	8	2	2	NUM
fcis-14563	88	9	.	.	PUNCT
fcis-14563	88	10	fig	fig	NOUN
fcis-14563	88	11	2	2	NUM
fcis-14563	88	12	.	.	X
fcis-14563	89	1	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	89	2	structure	structure	NOUN
fcis-14563	89	3	diagram	diagram	PROPN
fcis-14563	89	4	4.1.2	4.1.2	NUM
fcis-14563	89	5	.	.	PUNCT
fcis-14563	90	1	2d_cnn	2d_cnn	NUM
fcis-14563	90	2	this	this	DET
fcis-14563	90	3	module	module	NOUN
fcis-14563	90	4	consists	consist	VERB
fcis-14563	90	5	of	of	ADP
fcis-14563	90	6	2d_cnn	2d_cnn	NUM
fcis-14563	90	7	,	,	PUNCT
fcis-14563	90	8	obtained	obtain	VERB
fcis-14563	90	9	by	by	ADP
fcis-14563	90	10	modifying	modify	VERB
fcis-14563	90	11	the	the	DET
fcis-14563	90	12	classical	classical	ADJ
fcis-14563	90	13	neural	neural	ADJ
fcis-14563	90	14	network	network	NOUN
fcis-14563	90	15	model	model	NOUN
fcis-14563	90	16	structure	structure	PROPN
fcis-14563	90	17	vgg16	vgg16	PROPN
fcis-14563	90	18	.	.	PUNCT
fcis-14563	91	1	this	this	DET
fcis-14563	91	2	module	module	NOUN
fcis-14563	91	3	consists	consist	VERB
fcis-14563	91	4	of	of	ADP
fcis-14563	91	5	6	6	NUM
fcis-14563	91	6	convolutional	convolutional	ADJ
fcis-14563	91	7	layers	layer	NOUN
fcis-14563	91	8	,	,	PUNCT
fcis-14563	91	9	3	3	NUM
fcis-14563	91	10	pooled	pool	VERB
fcis-14563	91	11	layers	layer	NOUN
fcis-14563	91	12	and	and	CCONJ
fcis-14563	91	13	6	6	NUM
fcis-14563	91	14	normalization	normalization	NOUN
fcis-14563	91	15	layers	layer	NOUN
fcis-14563	91	16	.	.	PUNCT
fcis-14563	92	1	the	the	DET
fcis-14563	92	2	convolutional	convolutional	ADJ
fcis-14563	92	3	kernel	kernel	NOUN
fcis-14563	92	4	size	size	NOUN
fcis-14563	92	5	of	of	ADP
fcis-14563	92	6	the	the	DET
fcis-14563	92	7	convolutional	convolutional	ADJ
fcis-14563	92	8	layer	layer	NOUN
fcis-14563	92	9	is	be	AUX
fcis-14563	92	10	3×3	3×3	NUM
fcis-14563	92	11	,	,	PUNCT
fcis-14563	92	12	the	the	DET
fcis-14563	92	13	convolutional	convolutional	ADJ
fcis-14563	92	14	kernel	kernel	NOUN
fcis-14563	92	15	moving	move	VERB
fcis-14563	92	16	step	step	NOUN
fcis-14563	92	17	is	be	AUX
fcis-14563	92	18	1	1	NUM
fcis-14563	92	19	,	,	PUNCT
fcis-14563	92	20	the	the	DET
fcis-14563	92	21	picture	picture	NOUN
fcis-14563	92	22	filling	fill	VERB
fcis-14563	92	23	is	be	AUX
fcis-14563	92	24	1	1	NUM
fcis-14563	92	25	,	,	PUNCT
fcis-14563	92	26	the	the	DET
fcis-14563	92	27	pooled	pooled	ADJ
fcis-14563	92	28	window	window	NOUN
fcis-14563	92	29	of	of	ADP
fcis-14563	92	30	the	the	DET
fcis-14563	92	31	pooled	pool	VERB
fcis-14563	92	32	layer	layer	NOUN
fcis-14563	92	33	is	be	AUX
fcis-14563	92	34	2×2	2×2	NUM
fcis-14563	92	35	,	,	PUNCT
fcis-14563	92	36	and	and	CCONJ
fcis-14563	92	37	the	the	DET
fcis-14563	92	38	pooling	pool	VERB
fcis-14563	92	39	window	window	NOUN
fcis-14563	92	40	moving	moving	NOUN
fcis-14563	92	41	step	step	NOUN
fcis-14563	92	42	is	be	AUX
fcis-14563	92	43	2	2	NUM
fcis-14563	92	44	.	.	PUNCT
fcis-14563	92	45	through	through	ADP
fcis-14563	92	46	the	the	DET
fcis-14563	92	47	above	above	ADJ
fcis-14563	92	48	parameter	parameter	NOUN
fcis-14563	92	49	settings	setting	NOUN
fcis-14563	92	50	,	,	PUNCT
fcis-14563	92	51	the	the	DET
fcis-14563	92	52	size	size	NOUN
fcis-14563	92	53	of	of	ADP
fcis-14563	92	54	the	the	DET
fcis-14563	92	55	input	input	NOUN
fcis-14563	92	56	through	through	ADP
fcis-14563	92	57	the	the	DET
fcis-14563	92	58	convolutional	convolutional	ADJ
fcis-14563	92	59	layer	layer	NOUN
fcis-14563	92	60	remains	remain	VERB
fcis-14563	92	61	unchanged	unchanged	ADJ
fcis-14563	92	62	,	,	PUNCT
fcis-14563	92	63	and	and	CCONJ
fcis-14563	92	64	the	the	DET
fcis-14563	92	65	size	size	NOUN
fcis-14563	92	66	is	be	AUX
fcis-14563	92	67	changed	change	VERB
fcis-14563	92	68	only	only	ADV
fcis-14563	92	69	through	through	ADP
fcis-14563	92	70	the	the	DET
fcis-14563	92	71	pooled	pooled	ADJ
fcis-14563	92	72	layer	layer	NOUN
fcis-14563	92	73	.	.	PUNCT
fcis-14563	93	1	the	the	DET
fcis-14563	93	2	parameters	parameter	NOUN
fcis-14563	93	3	of	of	ADP
fcis-14563	93	4	dropout	dropout	NOUN
fcis-14563	93	5	are	be	AUX
fcis-14563	93	6	set	set	VERB
fcis-14563	93	7	to	to	ADP
fcis-14563	93	8	0.5	0.5	NUM
fcis-14563	93	9	,	,	PUNCT
fcis-14563	93	10	and	and	CCONJ
fcis-14563	93	11	finally	finally	ADV
fcis-14563	93	12	the	the	DET
fcis-14563	93	13	output	output	NOUN
fcis-14563	93	14	is	be	AUX
fcis-14563	93	15	activated	activate	VERB
fcis-14563	93	16	by	by	ADP
fcis-14563	93	17	the	the	DET
fcis-14563	93	18	relu	relu	NOUN
fcis-14563	93	19	function	function	NOUN
fcis-14563	93	20	.	.	PUNCT
fcis-14563	94	1	the	the	DET
fcis-14563	94	2	2d	2d	PROPN
fcis-14563	94	3	data	data	NOUN
fcis-14563	94	4	is	be	AUX
fcis-14563	94	5	input	input	NOUN
fcis-14563	94	6	as	as	ADP
fcis-14563	94	7	this	this	DET
fcis-14563	94	8	module	module	NOUN
fcis-14563	94	9	,	,	PUNCT
fcis-14563	94	10	the	the	DET
fcis-14563	94	11	output	output	NOUN
fcis-14563	94	12	shape	shape	NOUN
fcis-14563	94	13	is	be	AUX
fcis-14563	94	14	(	(	PUNCT
fcis-14563	94	15	64,60,8,8	64,60,8,8	PROPN
fcis-14563	94	16	)	)	PUNCT
fcis-14563	94	17	,	,	PUNCT
fcis-14563	94	18	and	and	CCONJ
fcis-14563	94	19	the	the	DET
fcis-14563	94	20	output	output	NOUN
fcis-14563	94	21	shape	shape	NOUN
fcis-14563	94	22	is	be	AUX
fcis-14563	94	23	changed	change	VERB
fcis-14563	94	24	to	to	ADP
fcis-14563	94	25	(	(	PUNCT
fcis-14563	94	26	64,60,64	64,60,64	NUM
fcis-14563	94	27	)	)	PUNCT
fcis-14563	94	28	by	by	ADP
fcis-14563	94	29	changing	change	VERB
fcis-14563	94	30	the	the	DET
fcis-14563	94	31	array	array	NOUN
fcis-14563	94	32	shape	shape	NOUN
fcis-14563	94	33	function	function	NOUN
fcis-14563	94	34	,	,	PUNCT
fcis-14563	94	35	as	as	ADP
fcis-14563	94	36	the	the	DET
fcis-14563	94	37	2d	2d	PROPN
fcis-14563	94	38	data	datum	NOUN
fcis-14563	94	39	feature	feature	NOUN
fcis-14563	94	40	.	.	PUNCT
fcis-14563	95	1	the	the	DET
fcis-14563	95	2	2d_cnn	2d_cnn	NUM
fcis-14563	95	3	structure	structure	NOUN
fcis-14563	95	4	is	be	AUX
fcis-14563	95	5	shown	show	VERB
fcis-14563	95	6	in	in	ADP
fcis-14563	95	7	figure	figure	NOUN
fcis-14563	95	8	3	3	NUM
fcis-14563	95	9	.	.	NOUN
fcis-14563	95	10	7	7	NUM
fcis-14563	95	11	fig	fig	NOUN
fcis-14563	95	12	3	3	NUM
fcis-14563	95	13	.	.	PUNCT
fcis-14563	96	1	2d_cnn	2d_cnn	NUM
fcis-14563	96	2	structure	structure	NOUN
fcis-14563	96	3	diagram	diagram	NOUN
fcis-14563	96	4	4.1.3	4.1.3	NUM
fcis-14563	96	5	.	.	PUNCT
fcis-14563	97	1	adaptive	adaptive	ADJ
fcis-14563	97	2	feature	feature	NOUN
fcis-14563	97	3	fusion	fusion	NOUN
fcis-14563	97	4	in	in	ADP
fcis-14563	97	5	order	order	NOUN
fcis-14563	97	6	to	to	PART
fcis-14563	97	7	make	make	VERB
fcis-14563	97	8	better	well	ADJ
fcis-14563	97	9	use	use	NOUN
fcis-14563	97	10	of	of	ADP
fcis-14563	97	11	the	the	DET
fcis-14563	97	12	extracted	extract	VERB
fcis-14563	97	13	onedimensional	onedimensional	ADJ
fcis-14563	97	14	and	and	CCONJ
fcis-14563	97	15	two	two	NUM
fcis-14563	97	16	-	-	PUNCT
fcis-14563	97	17	dimensional	dimensional	ADJ
fcis-14563	97	18	feature	feature	NOUN
fcis-14563	97	19	vectors	vector	NOUN
fcis-14563	97	20	,	,	PUNCT
fcis-14563	97	21	adaptive	adaptive	ADJ
fcis-14563	97	22	feature	feature	NOUN
fcis-14563	97	23	weights	weight	NOUN
fcis-14563	97	24	are	be	AUX
fcis-14563	97	25	introduced	introduce	VERB
fcis-14563	97	26	to	to	PART
fcis-14563	97	27	make	make	VERB
fcis-14563	97	28	the	the	DET
fcis-14563	97	29	model	model	NOUN
fcis-14563	97	30	allocate	allocate	VERB
fcis-14563	97	31	different	different	ADJ
fcis-14563	97	32	weights	weight	NOUN
fcis-14563	97	33	according	accord	VERB
fcis-14563	97	34	to	to	ADP
fcis-14563	97	35	the	the	DET
fcis-14563	97	36	feature	feature	NOUN
fcis-14563	97	37	distribution	distribution	NOUN
fcis-14563	97	38	to	to	PART
fcis-14563	97	39	carry	carry	VERB
fcis-14563	97	40	out	out	ADP
fcis-14563	97	41	feature	feature	NOUN
fcis-14563	97	42	fusion	fusion	NOUN
fcis-14563	98	1	[	[	X
fcis-14563	98	2	17	17	NUM
fcis-14563	98	3	]	]	PUNCT
fcis-14563	98	4	.	.	PUNCT
fcis-14563	99	1	the	the	DET
fcis-14563	99	2	structure	structure	NOUN
fcis-14563	99	3	of	of	ADP
fcis-14563	99	4	this	this	DET
fcis-14563	99	5	module	module	NOUN
fcis-14563	99	6	is	be	AUX
fcis-14563	99	7	shown	show	VERB
fcis-14563	99	8	in	in	ADP
fcis-14563	99	9	figure	figure	NOUN
fcis-14563	99	10	4	4	NUM
fcis-14563	99	11	.	.	PUNCT
fcis-14563	100	1	the	the	DET
fcis-14563	100	2	one	one	NUM
fcis-14563	100	3	-	-	PUNCT
fcis-14563	100	4	dimensional	dimensional	ADJ
fcis-14563	100	5	and	and	CCONJ
fcis-14563	100	6	two	two	NUM
fcis-14563	100	7	-	-	PUNCT
fcis-14563	100	8	dimensional	dimensional	ADJ
fcis-14563	100	9	features	feature	NOUN
fcis-14563	100	10	are	be	AUX
fcis-14563	100	11	multiplied	multiply	VERB
fcis-14563	100	12	by	by	ADP
fcis-14563	100	13	w1	w1	NOUN
fcis-14563	100	14	and	and	CCONJ
fcis-14563	100	15	w2	w2	NOUN
fcis-14563	100	16	respectively	respectively	ADV
fcis-14563	100	17	and	and	CCONJ
fcis-14563	100	18	then	then	ADV
fcis-14563	100	19	added	add	VERB
fcis-14563	100	20	,	,	PUNCT
fcis-14563	100	21	and	and	CCONJ
fcis-14563	100	22	the	the	DET
fcis-14563	100	23	added	add	VERB
fcis-14563	100	24	result	result	NOUN
fcis-14563	100	25	is	be	AUX
fcis-14563	100	26	taken	take	VERB
fcis-14563	100	27	as	as	ADP
fcis-14563	100	28	the	the	DET
fcis-14563	100	29	input	input	NOUN
fcis-14563	100	30	of	of	ADP
fcis-14563	100	31	senet	senet	NOUN
fcis-14563	100	32	,	,	PUNCT
fcis-14563	100	33	and	and	CCONJ
fcis-14563	100	34	finally	finally	ADV
fcis-14563	100	35	the	the	DET
fcis-14563	100	36	fused	fuse	VERB
fcis-14563	100	37	feature	feature	NOUN
fcis-14563	100	38	vector	vector	NOUN
fcis-14563	100	39	is	be	AUX
fcis-14563	100	40	output	output	NOUN
fcis-14563	100	41	.	.	PUNCT
fcis-14563	101	1	the	the	DET
fcis-14563	101	2	initial	initial	ADJ
fcis-14563	101	3	weights	weight	NOUN
fcis-14563	101	4	w1	w1	NOUN
fcis-14563	101	5	and	and	CCONJ
fcis-14563	101	6	w2	w2	NOUN
fcis-14563	101	7	are	be	AUX
fcis-14563	101	8	initialized	initialize	VERB
fcis-14563	101	9	by	by	ADP
fcis-14563	101	10	parameter	parameter	NOUN
fcis-14563	101	11	function	function	NOUN
fcis-14563	101	12	,	,	PUNCT
fcis-14563	101	13	both	both	PRON
fcis-14563	101	14	of	of	ADP
fcis-14563	101	15	which	which	PRON
fcis-14563	101	16	are	be	AUX
fcis-14563	101	17	0.5	0.5	NUM
fcis-14563	101	18	.	.	PUNCT
fcis-14563	102	1	in	in	ADP
fcis-14563	102	2	the	the	DET
fcis-14563	102	3	network	network	NOUN
fcis-14563	102	4	,	,	PUNCT
fcis-14563	102	5	the	the	DET
fcis-14563	102	6	initial	initial	ADJ
fcis-14563	102	7	weight	weight	NOUN
fcis-14563	102	8	parameter	parameter	NOUN
fcis-14563	102	9	will	will	AUX
fcis-14563	102	10	be	be	AUX
fcis-14563	102	11	added	add	VERB
fcis-14563	102	12	to	to	ADP
fcis-14563	102	13	the	the	DET
fcis-14563	102	14	update	update	NOUN
fcis-14563	102	15	parameter	parameter	NOUN
fcis-14563	102	16	of	of	ADP
fcis-14563	102	17	the	the	DET
fcis-14563	102	18	optimizer	optimizer	NOUN
fcis-14563	102	19	,	,	PUNCT
fcis-14563	102	20	so	so	SCONJ
fcis-14563	102	21	that	that	SCONJ
fcis-14563	102	22	when	when	SCONJ
fcis-14563	102	23	the	the	DET
fcis-14563	102	24	optimizer	optimizer	NOUN
fcis-14563	102	25	is	be	AUX
fcis-14563	102	26	updated	update	VERB
fcis-14563	102	27	,	,	PUNCT
fcis-14563	102	28	the	the	DET
fcis-14563	102	29	optimization	optimization	NOUN
fcis-14563	102	30	direction	direction	NOUN
fcis-14563	102	31	is	be	AUX
fcis-14563	102	32	to	to	PART
fcis-14563	102	33	minimize	minimize	VERB
fcis-14563	102	34	the	the	DET
fcis-14563	102	35	loss	loss	NOUN
fcis-14563	102	36	function	function	NOUN
fcis-14563	102	37	.	.	PUNCT
fcis-14563	103	1	senet	senet	PROPN
fcis-14563	103	2	is	be	AUX
fcis-14563	103	3	the	the	DET
fcis-14563	103	4	channel	channel	NOUN
fcis-14563	103	5	attention	attention	NOUN
fcis-14563	103	6	module	module	NOUN
fcis-14563	103	7	,	,	PUNCT
fcis-14563	103	8	which	which	PRON
fcis-14563	103	9	can	can	AUX
fcis-14563	103	10	obtain	obtain	VERB
fcis-14563	103	11	the	the	DET
fcis-14563	103	12	importance	importance	NOUN
fcis-14563	103	13	degree	degree	NOUN
fcis-14563	103	14	of	of	ADP
fcis-14563	103	15	each	each	DET
fcis-14563	103	16	channel	channel	NOUN
fcis-14563	103	17	in	in	ADP
fcis-14563	103	18	the	the	DET
fcis-14563	103	19	feature	feature	NOUN
fcis-14563	103	20	graph	graph	NOUN
fcis-14563	103	21	and	and	CCONJ
fcis-14563	103	22	automatically	automatically	ADV
fcis-14563	103	23	assign	assign	VERB
fcis-14563	103	24	different	different	ADJ
fcis-14563	103	25	weights	weight	NOUN
fcis-14563	103	26	according	accord	VERB
fcis-14563	103	27	to	to	ADP
fcis-14563	103	28	the	the	DET
fcis-14563	103	29	importance	importance	NOUN
fcis-14563	103	30	degree	degree	NOUN
fcis-14563	103	31	.	.	PUNCT
fcis-14563	104	1	the	the	DET
fcis-14563	104	2	scope	scope	NOUN
fcis-14563	104	3	of	of	ADP
fcis-14563	104	4	adaptive	adaptive	ADJ
fcis-14563	104	5	feature	feature	NOUN
fcis-14563	104	6	weights	weight	NOUN
fcis-14563	104	7	is	be	AUX
fcis-14563	104	8	the	the	DET
fcis-14563	104	9	overall	overall	ADJ
fcis-14563	104	10	operation	operation	NOUN
fcis-14563	104	11	of	of	ADP
fcis-14563	104	12	one	one	NUM
fcis-14563	104	13	-	-	PUNCT
fcis-14563	104	14	dimensional	dimensional	ADJ
fcis-14563	104	15	and	and	CCONJ
fcis-14563	104	16	two	two	NUM
fcis-14563	104	17	-	-	PUNCT
fcis-14563	104	18	dimensional	dimensional	ADJ
fcis-14563	104	19	features	feature	NOUN
fcis-14563	104	20	,	,	PUNCT
fcis-14563	104	21	while	while	SCONJ
fcis-14563	104	22	the	the	DET
fcis-14563	104	23	scope	scope	NOUN
fcis-14563	104	24	of	of	ADP
fcis-14563	104	25	senet	senet	NOUN
fcis-14563	104	26	is	be	AUX
fcis-14563	104	27	the	the	DET
fcis-14563	104	28	internal	internal	ADJ
fcis-14563	104	29	channel	channel	NOUN
fcis-14563	104	30	of	of	ADP
fcis-14563	104	31	the	the	DET
fcis-14563	104	32	feature	feature	NOUN
fcis-14563	104	33	vector	vector	NOUN
fcis-14563	104	34	.	.	PUNCT
fcis-14563	104	35	fig	fig	PROPN
fcis-14563	104	36	4	4	NUM
fcis-14563	104	37	.	.	PUNCT
fcis-14563	104	38	structure	structure	NOUN
fcis-14563	104	39	diagram	diagram	NOUN
fcis-14563	104	40	of	of	ADP
fcis-14563	104	41	adaptive	adaptive	ADJ
fcis-14563	104	42	feature	feature	NOUN
fcis-14563	104	43	fusion	fusion	NOUN
fcis-14563	104	44	module	module	NOUN
fcis-14563	104	45	4.1.4	4.1.4	NOUN
fcis-14563	104	46	.	.	PUNCT
fcis-14563	105	1	overall	overall	ADJ
fcis-14563	105	2	flow	flow	NOUN
fcis-14563	105	3	diagram	diagram	NOUN
fcis-14563	105	4	in	in	ADP
fcis-14563	105	5	this	this	DET
fcis-14563	105	6	paper	paper	NOUN
fcis-14563	105	7	,	,	PUNCT
fcis-14563	105	8	the	the	DET
fcis-14563	105	9	error	error	NOUN
fcis-14563	105	10	backpropagation	backpropagation	NOUN
fcis-14563	105	11	method	method	NOUN
fcis-14563	105	12	is	be	AUX
fcis-14563	105	13	used	use	VERB
fcis-14563	105	14	to	to	PART
fcis-14563	105	15	train	train	VERB
fcis-14563	105	16	the	the	DET
fcis-14563	105	17	convolutional	convolutional	ADJ
fcis-14563	105	18	neural	neural	ADJ
fcis-14563	105	19	network	network	NOUN
fcis-14563	105	20	.	.	PUNCT
fcis-14563	106	1	the	the	DET
fcis-14563	106	2	training	training	NOUN
fcis-14563	106	3	samples	sample	NOUN
fcis-14563	106	4	pass	pass	VERB
fcis-14563	106	5	through	through	ADP
fcis-14563	106	6	the	the	DET
fcis-14563	106	7	convolutional	convolutional	ADJ
fcis-14563	106	8	layer	layer	NOUN
fcis-14563	106	9	,	,	PUNCT
fcis-14563	106	10	the	the	DET
fcis-14563	106	11	pooling	pool	VERB
fcis-14563	106	12	layer	layer	NOUN
fcis-14563	106	13	,	,	PUNCT
fcis-14563	106	14	the	the	DET
fcis-14563	106	15	activation	activation	NOUN
fcis-14563	106	16	function	function	NOUN
fcis-14563	106	17	activation	activation	NOUN
fcis-14563	106	18	and	and	CCONJ
fcis-14563	106	19	the	the	DET
fcis-14563	106	20	full	full	ADJ
fcis-14563	106	21	connection	connection	NOUN
fcis-14563	106	22	layer	layer	NOUN
fcis-14563	106	23	,	,	PUNCT
fcis-14563	106	24	and	and	CCONJ
fcis-14563	106	25	the	the	DET
fcis-14563	106	26	weight	weight	NOUN
fcis-14563	106	27	and	and	CCONJ
fcis-14563	106	28	bias	bias	NOUN
fcis-14563	106	29	of	of	ADP
fcis-14563	106	30	each	each	DET
fcis-14563	106	31	layer	layer	NOUN
fcis-14563	106	32	are	be	AUX
fcis-14563	106	33	updated	update	VERB
fcis-14563	106	34	by	by	ADP
fcis-14563	106	35	the	the	DET
fcis-14563	106	36	random	random	ADJ
fcis-14563	106	37	gradient	gradient	ADJ
fcis-14563	106	38	descent	descent	NOUN
fcis-14563	106	39	algorithm	algorithm	NOUN
fcis-14563	106	40	.	.	PUNCT
fcis-14563	107	1	the	the	DET
fcis-14563	107	2	overall	overall	ADJ
fcis-14563	107	3	process	process	NOUN
fcis-14563	107	4	is	be	AUX
fcis-14563	107	5	shown	show	VERB
fcis-14563	107	6	in	in	ADP
fcis-14563	107	7	fig	fig	NOUN
fcis-14563	107	8	.	.	PUNCT
fcis-14563	108	1	5	5	NUM
fcis-14563	108	2	.	.	X
fcis-14563	108	3	one	one	NUM
fcis-14563	108	4	-	-	PUNCT
fcis-14563	108	5	dimensional	dimensional	ADJ
fcis-14563	108	6	data	datum	NOUN
fcis-14563	108	7	is	be	AUX
fcis-14563	108	8	extracted	extract	VERB
fcis-14563	108	9	by	by	ADP
fcis-14563	108	10	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	108	11	module	module	NOUN
fcis-14563	108	12	with	with	ADP
fcis-14563	108	13	one	one	NUM
fcis-14563	108	14	-	-	PUNCT
fcis-14563	108	15	dimensional	dimensional	ADJ
fcis-14563	108	16	features	feature	NOUN
fcis-14563	108	17	;	;	PUNCT
fcis-14563	108	18	onedimensional	onedimensional	ADJ
fcis-14563	108	19	data	datum	NOUN
fcis-14563	108	20	is	be	AUX
fcis-14563	108	21	converted	convert	VERB
fcis-14563	108	22	into	into	ADP
fcis-14563	108	23	two	two	NUM
fcis-14563	108	24	-	-	PUNCT
fcis-14563	108	25	dimensional	dimensional	ADJ
fcis-14563	108	26	data	datum	NOUN
fcis-14563	108	27	by	by	ADP
fcis-14563	108	28	gadf	gadf	PROPN
fcis-14563	108	29	algorithm	algorithm	NOUN
fcis-14563	108	30	,	,	PUNCT
fcis-14563	108	31	two	two	NUM
fcis-14563	108	32	-	-	PUNCT
fcis-14563	108	33	dimensional	dimensional	ADJ
fcis-14563	108	34	features	feature	NOUN
fcis-14563	108	35	are	be	AUX
fcis-14563	108	36	extracted	extract	VERB
fcis-14563	108	37	by	by	ADP
fcis-14563	108	38	2d_cnn	2d_cnn	NUM
fcis-14563	108	39	module	module	NOUN
fcis-14563	108	40	,	,	PUNCT
fcis-14563	108	41	and	and	CCONJ
fcis-14563	108	42	feature	feature	NOUN
fcis-14563	108	43	fusion	fusion	NOUN
fcis-14563	108	44	is	be	AUX
fcis-14563	108	45	carried	carry	VERB
fcis-14563	108	46	out	out	ADP
fcis-14563	108	47	by	by	ADP
fcis-14563	108	48	adaptive	adaptive	ADJ
fcis-14563	108	49	feature	feature	NOUN
fcis-14563	108	50	fusion	fusion	NOUN
fcis-14563	108	51	module	module	NOUN
fcis-14563	108	52	.	.	PUNCT
fcis-14563	109	1	after	after	ADP
fcis-14563	109	2	fusion	fusion	NOUN
fcis-14563	109	3	,	,	PUNCT
fcis-14563	109	4	classification	classification	NOUN
fcis-14563	109	5	is	be	AUX
fcis-14563	109	6	output	output	NOUN
fcis-14563	109	7	through	through	ADP
fcis-14563	109	8	the	the	DET
fcis-14563	109	9	full	full	ADJ
fcis-14563	109	10	connection	connection	NOUN
fcis-14563	109	11	layer	layer	NOUN
fcis-14563	109	12	.	.	PUNCT
fcis-14563	110	1	fig	fig	NOUN
fcis-14563	110	2	5	5	NUM
fcis-14563	110	3	.	.	PUNCT
fcis-14563	111	1	overall	overall	ADJ
fcis-14563	111	2	flowchart	flowchart	PROPN
fcis-14563	111	3	4.2	4.2	NUM
fcis-14563	111	4	.	.	PUNCT
fcis-14563	111	5	training	training	NOUN
fcis-14563	111	6	and	and	CCONJ
fcis-14563	111	7	testing	testing	NOUN
fcis-14563	111	8	of	of	ADP
fcis-14563	111	9	the	the	DET
fcis-14563	111	10	model	model	NOUN
fcis-14563	111	11	in	in	ADP
fcis-14563	111	12	this	this	DET
fcis-14563	111	13	paper	paper	NOUN
fcis-14563	111	14	,	,	PUNCT
fcis-14563	111	15	500	500	NUM
fcis-14563	111	16	pieces	piece	NOUN
fcis-14563	111	17	of	of	ADP
fcis-14563	111	18	data	datum	NOUN
fcis-14563	111	19	were	be	AUX
fcis-14563	111	20	selected	select	VERB
fcis-14563	111	21	for	for	ADP
fcis-14563	111	22	each	each	DET
fcis-14563	111	23	disturbance	disturbance	NOUN
fcis-14563	111	24	,	,	PUNCT
fcis-14563	111	25	with	with	ADP
fcis-14563	111	26	a	a	DET
fcis-14563	111	27	total	total	NOUN
fcis-14563	111	28	of	of	ADP
fcis-14563	111	29	20,000	20,000	NUM
fcis-14563	111	30	pieces	piece	NOUN
fcis-14563	111	31	of	of	ADP
fcis-14563	111	32	data	datum	NOUN
fcis-14563	111	33	for	for	ADP
fcis-14563	111	34	40	40	NUM
fcis-14563	111	35	kinds	kind	NOUN
fcis-14563	111	36	of	of	ADP
fcis-14563	111	37	disturbances	disturbance	NOUN
fcis-14563	111	38	.	.	PUNCT
fcis-14563	112	1	2048	2048	NUM
fcis-14563	112	2	data	datum	NOUN
fcis-14563	112	3	points	point	NOUN
fcis-14563	112	4	were	be	AUX
fcis-14563	112	5	collected	collect	VERB
fcis-14563	112	6	for	for	ADP
fcis-14563	112	7	each	each	DET
fcis-14563	112	8	disturbance	disturbance	NOUN
fcis-14563	112	9	by	by	ADP
fcis-14563	112	10	matlab	matlab	PROPN
fcis-14563	112	11	with	with	ADP
fcis-14563	112	12	a	a	DET
fcis-14563	112	13	total	total	ADJ
fcis-14563	112	14	sampling	sampling	NOUN
fcis-14563	112	15	time	time	NOUN
fcis-14563	112	16	of	of	ADP
fcis-14563	112	17	0.2s	0.2s	PROPN
fcis-14563	112	18	.	.	PUNCT
fcis-14563	113	1	the	the	DET
fcis-14563	113	2	original	original	ADJ
fcis-14563	113	3	data	datum	NOUN
fcis-14563	113	4	was	be	AUX
fcis-14563	113	5	divided	divide	VERB
fcis-14563	113	6	into	into	ADP
fcis-14563	113	7	training	training	NOUN
fcis-14563	113	8	set	set	NOUN
fcis-14563	113	9	,	,	PUNCT
fcis-14563	113	10	verification	verification	NOUN
fcis-14563	113	11	set	set	NOUN
fcis-14563	113	12	and	and	CCONJ
fcis-14563	113	13	test	test	NOUN
fcis-14563	113	14	set	set	VERB
fcis-14563	113	15	according	accord	VERB
fcis-14563	113	16	to	to	ADP
fcis-14563	113	17	the	the	DET
fcis-14563	113	18	ratio	ratio	NOUN
fcis-14563	113	19	of	of	ADP
fcis-14563	113	20	8:1:1	8:1:1	NUM
fcis-14563	113	21	.	.	PUNCT
fcis-14563	114	1	in	in	ADP
fcis-14563	114	2	order	order	NOUN
fcis-14563	114	3	to	to	PART
fcis-14563	114	4	test	test	VERB
fcis-14563	114	5	the	the	DET
fcis-14563	114	6	antinoise	antinoise	ADJ
fcis-14563	114	7	ability	ability	NOUN
fcis-14563	114	8	of	of	ADP
fcis-14563	114	9	the	the	DET
fcis-14563	114	10	model	model	NOUN
fcis-14563	114	11	,	,	PUNCT
fcis-14563	114	12	white	white	ADJ
fcis-14563	114	13	noise	noise	NOUN
fcis-14563	114	14	is	be	AUX
fcis-14563	114	15	superimposed	superimpose	VERB
fcis-14563	114	16	on	on	ADP
fcis-14563	114	17	the	the	DET
fcis-14563	114	18	disturbance	disturbance	NOUN
fcis-14563	114	19	signal	signal	NOUN
fcis-14563	114	20	,	,	PUNCT
fcis-14563	114	21	and	and	CCONJ
fcis-14563	114	22	the	the	DET
fcis-14563	114	23	signal	signal	NOUN
fcis-14563	114	24	-	-	PUNCT
fcis-14563	114	25	to	to	ADP
fcis-14563	114	26	-	-	PUNCT
fcis-14563	114	27	noise	noise	NOUN
fcis-14563	114	28	ratio	ratio	NOUN
fcis-14563	114	29	is	be	AUX
fcis-14563	114	30	20db	20db	ADJ
fcis-14563	114	31	and	and	CCONJ
fcis-14563	114	32	40db	40db	NOUN
fcis-14563	114	33	respectively	respectively	ADV
fcis-14563	114	34	.	.	PUNCT
fcis-14563	115	1	the	the	DET
fcis-14563	115	2	model	model	NOUN
fcis-14563	115	3	was	be	AUX
fcis-14563	115	4	trained	train	VERB
fcis-14563	115	5	and	and	CCONJ
fcis-14563	115	6	tested	test	VERB
fcis-14563	115	7	by	by	ADP
fcis-14563	115	8	three	three	NUM
fcis-14563	115	9	mixed	mixed	ADJ
fcis-14563	115	10	modes	mode	NOUN
fcis-14563	115	11	:	:	PUNCT
fcis-14563	115	12	single	single	ADJ
fcis-14563	115	13	disturbance	disturbance	NOUN
fcis-14563	115	14	(	(	PUNCT
fcis-14563	115	15	t1	t1	NOUN
fcis-14563	115	16	)	)	PUNCT
fcis-14563	115	17	,	,	PUNCT
fcis-14563	115	18	single	single	ADJ
fcis-14563	115	19	disturbance	disturbance	NOUN
fcis-14563	115	20	and	and	CCONJ
fcis-14563	115	21	double	double	ADJ
fcis-14563	115	22	disturbance	disturbance	NOUN
fcis-14563	115	23	(	(	PUNCT
fcis-14563	115	24	t2	t2	NOUN
fcis-14563	115	25	)	)	PUNCT
fcis-14563	115	26	,	,	PUNCT
fcis-14563	115	27	and	and	CCONJ
fcis-14563	115	28	multiple	multiple	ADJ
fcis-14563	115	29	compound	compound	NOUN
fcis-14563	115	30	disturbance	disturbance	NOUN
fcis-14563	115	31	(	(	PUNCT
fcis-14563	115	32	t3	t3	PROPN
fcis-14563	115	33	)	)	PUNCT
fcis-14563	115	34	with	with	ADP
fcis-14563	115	35	single	single	ADJ
fcis-14563	115	36	,	,	PUNCT
fcis-14563	115	37	double	double	ADJ
fcis-14563	115	38	,	,	PUNCT
fcis-14563	115	39	triple	triple	ADJ
fcis-14563	115	40	and	and	CCONJ
fcis-14563	115	41	quadruple	quadruple	ADJ
fcis-14563	115	42	disturbance	disturbance	NOUN
fcis-14563	115	43	.	.	PUNCT
fcis-14563	116	1	60	60	NUM
fcis-14563	116	2	cycles	cycle	NOUN
fcis-14563	116	3	were	be	AUX
fcis-14563	116	4	set	set	VERB
fcis-14563	116	5	for	for	ADP
fcis-14563	116	6	the	the	DET
fcis-14563	116	7	training	training	NOUN
fcis-14563	116	8	and	and	CCONJ
fcis-14563	116	9	testing	testing	NOUN
fcis-14563	116	10	of	of	ADP
fcis-14563	116	11	this	this	DET
fcis-14563	116	12	model	model	NOUN
fcis-14563	116	13	.	.	PUNCT
fcis-14563	117	1	during	during	ADP
fcis-14563	117	2	the	the	DET
fcis-14563	117	3	training	training	NOUN
fcis-14563	117	4	process	process	NOUN
fcis-14563	117	5	,	,	PUNCT
fcis-14563	117	6	gaussian	gaussian	ADJ
fcis-14563	117	7	noise	noise	NOUN
fcis-14563	117	8	or	or	CCONJ
fcis-14563	117	9	salt	salt	NOUN
fcis-14563	117	10	and	and	CCONJ
fcis-14563	117	11	pepper	pepper	NOUN
fcis-14563	117	12	noise	noise	NOUN
fcis-14563	117	13	were	be	AUX
fcis-14563	117	14	added	add	VERB
fcis-14563	117	15	to	to	ADP
fcis-14563	117	16	the	the	DET
fcis-14563	117	17	data	datum	NOUN
fcis-14563	117	18	respectively	respectively	ADV
fcis-14563	117	19	to	to	PART
fcis-14563	117	20	achieve	achieve	VERB
fcis-14563	117	21	data	datum	NOUN
fcis-14563	117	22	enhancement	enhancement	NOUN
fcis-14563	117	23	.	.	PUNCT
fcis-14563	118	1	the	the	DET
fcis-14563	118	2	loss	loss	NOUN
fcis-14563	118	3	function	function	NOUN
fcis-14563	118	4	was	be	AUX
fcis-14563	118	5	cross	cross	ADJ
fcis-14563	118	6	-	-	ADJ
fcis-14563	118	7	entropy	entropy	ADJ
fcis-14563	118	8	function	function	NOUN
fcis-14563	118	9	and	and	CCONJ
fcis-14563	118	10	the	the	DET
fcis-14563	118	11	optimizer	optimizer	NOUN
fcis-14563	118	12	was	be	AUX
fcis-14563	118	13	adam	adam	PROPN
fcis-14563	118	14	.	.	PUNCT
fcis-14563	119	1	the	the	DET
fcis-14563	119	2	learning	learning	NOUN
fcis-14563	119	3	rate	rate	NOUN
fcis-14563	119	4	was	be	AUX
fcis-14563	119	5	reduced	reduce	VERB
fcis-14563	119	6	from	from	ADP
fcis-14563	119	7	0.01	0.01	NUM
fcis-14563	119	8	to	to	ADP
fcis-14563	119	9	0.003	0.003	NUM
fcis-14563	119	10	by	by	ADP
fcis-14563	119	11	exponential	exponential	ADJ
fcis-14563	119	12	decay	decay	NOUN
fcis-14563	119	13	learning	learn	VERB
fcis-14563	119	14	rate	rate	NOUN
fcis-14563	119	15	function	function	NOUN
fcis-14563	119	16	exponentiallr	exponentiallr	NOUN
fcis-14563	119	17	function	function	NOUN
fcis-14563	119	18	.	.	PUNCT
fcis-14563	120	1	figure	figure	VERB
fcis-14563	120	2	7	7	NUM
fcis-14563	120	3	to	to	PART
fcis-14563	120	4	9	9	NUM
fcis-14563	120	5	shows	show	VERB
fcis-14563	120	6	the	the	DET
fcis-14563	120	7	training	training	NOUN
fcis-14563	120	8	process	process	NOUN
fcis-14563	120	9	of	of	ADP
fcis-14563	120	10	t1	t1	PROPN
fcis-14563	120	11	,	,	PUNCT
fcis-14563	120	12	t2	t2	NOUN
fcis-14563	120	13	and	and	CCONJ
fcis-14563	120	14	t3	t3	NOUN
fcis-14563	120	15	with	with	ADP
fcis-14563	120	16	no	no	DET
fcis-14563	120	17	noise	noise	NOUN
fcis-14563	120	18	and	and	CCONJ
fcis-14563	120	19	signalto	signalto	NOUN
fcis-14563	120	20	-	-	PUNCT
fcis-14563	120	21	noise	noise	NOUN
fcis-14563	120	22	ratio	ratio	NOUN
fcis-14563	120	23	of	of	ADP
fcis-14563	120	24	20db	20db	ADJ
fcis-14563	120	25	and	and	CCONJ
fcis-14563	120	26	40db	40db	NOUN
fcis-14563	120	27	respectively	respectively	ADV
fcis-14563	120	28	.	.	PUNCT
fcis-14563	121	1	4.2.1	4.2.1	X
fcis-14563	121	2	.	.	PUNCT
fcis-14563	122	1	classification	classification	NOUN
fcis-14563	122	2	of	of	ADP
fcis-14563	122	3	t1	t1	PROPN
fcis-14563	122	4	(	(	PUNCT
fcis-14563	122	5	a	a	NOUN
fcis-14563	122	6	)	)	PUNCT
fcis-14563	122	7	no	no	DET
fcis-14563	122	8	noise	noise	NOUN
fcis-14563	122	9	(	(	PUNCT
fcis-14563	122	10	b	b	NOUN
fcis-14563	122	11	)	)	PUNCT
fcis-14563	122	12	20db	20db	NOUN
fcis-14563	122	13	(	(	PUNCT
fcis-14563	122	14	c	c	NOUN
fcis-14563	122	15	)	)	PUNCT
fcis-14563	122	16	40db	40db	NOUN
fcis-14563	122	17	(	(	PUNCT
fcis-14563	122	18	d	d	NOUN
fcis-14563	122	19	)	)	PUNCT
fcis-14563	122	20	validation	validation	NOUN
fcis-14563	122	21	-	-	PUNCT
fcis-14563	122	22	acc	acc	NOUN
fcis-14563	122	23	curve	curve	NOUN
fcis-14563	122	24	with	with	ADP
fcis-14563	122	25	different	different	ADJ
fcis-14563	122	26	signal	signal	NOUN
fcis-14563	122	27	-	-	PUNCT
fcis-14563	122	28	to	to	ADP
fcis-14563	122	29	-	-	PUNCT
fcis-14563	122	30	noise	noise	NOUN
fcis-14563	122	31	ratio	ratio	NOUN
fcis-14563	122	32	fig	fig	NOUN
fcis-14563	122	33	6	6	NUM
fcis-14563	122	34	.	.	PUNCT
fcis-14563	123	1	training	training	NOUN
fcis-14563	123	2	process	process	NOUN
fcis-14563	123	3	of	of	ADP
fcis-14563	123	4	8	8	NUM
fcis-14563	123	5	classification	classification	NOUN
fcis-14563	123	6	with	with	ADP
fcis-14563	123	7	different	different	ADJ
fcis-14563	123	8	signal	signal	NOUN
fcis-14563	123	9	-	-	PUNCT
fcis-14563	123	10	tonoise	tonoise	NOUN
fcis-14563	123	11	ratios	ratio	NOUN
fcis-14563	123	12	8	8	NUM
fcis-14563	123	13	the	the	DET
fcis-14563	123	14	confusion	confusion	NOUN
fcis-14563	123	15	matrix	matrix	NOUN
fcis-14563	123	16	can	can	AUX
fcis-14563	123	17	intuitively	intuitively	ADV
fcis-14563	123	18	observe	observe	VERB
fcis-14563	123	19	the	the	DET
fcis-14563	123	20	classification	classification	NOUN
fcis-14563	123	21	of	of	ADP
fcis-14563	123	22	the	the	DET
fcis-14563	123	23	model	model	NOUN
fcis-14563	123	24	.	.	PUNCT
fcis-14563	124	1	the	the	DET
fcis-14563	124	2	horizontal	horizontal	ADJ
fcis-14563	124	3	coordinate	coordinate	NOUN
fcis-14563	124	4	of	of	ADP
fcis-14563	124	5	the	the	DET
fcis-14563	124	6	matrix	matrix	NOUN
fcis-14563	124	7	is	be	AUX
fcis-14563	124	8	the	the	DET
fcis-14563	124	9	type	type	NOUN
fcis-14563	124	10	predicted	predict	VERB
fcis-14563	124	11	by	by	ADP
fcis-14563	124	12	the	the	DET
fcis-14563	124	13	model	model	NOUN
fcis-14563	124	14	,	,	PUNCT
fcis-14563	124	15	and	and	CCONJ
fcis-14563	124	16	the	the	DET
fcis-14563	124	17	vertical	vertical	ADJ
fcis-14563	124	18	coordinate	coordinate	NOUN
fcis-14563	124	19	is	be	AUX
fcis-14563	124	20	the	the	DET
fcis-14563	124	21	real	real	ADJ
fcis-14563	124	22	category	category	NOUN
fcis-14563	124	23	,	,	PUNCT
fcis-14563	124	24	in	in	ADP
fcis-14563	124	25	which	which	PRON
fcis-14563	124	26	the	the	DET
fcis-14563	124	27	diagonal	diagonal	ADJ
fcis-14563	124	28	elements	element	NOUN
fcis-14563	124	29	are	be	AUX
fcis-14563	124	30	the	the	DET
fcis-14563	124	31	number	number	NOUN
fcis-14563	124	32	of	of	ADP
fcis-14563	124	33	correct	correct	ADJ
fcis-14563	124	34	classification	classification	NOUN
fcis-14563	124	35	,	,	PUNCT
fcis-14563	124	36	and	and	CCONJ
fcis-14563	124	37	the	the	DET
fcis-14563	124	38	non	non	ADJ
fcis-14563	124	39	-	-	ADJ
fcis-14563	124	40	diagonal	diagonal	ADJ
fcis-14563	124	41	elements	element	NOUN
fcis-14563	124	42	are	be	AUX
fcis-14563	124	43	wrong	wrong	ADJ
fcis-14563	124	44	judgment	judgment	NOUN
fcis-14563	124	45	and	and	CCONJ
fcis-14563	124	46	missing	miss	VERB
fcis-14563	124	47	judgment	judgment	NOUN
fcis-14563	124	48	.	.	PUNCT
fcis-14563	125	1	figure	figure	NOUN
fcis-14563	125	2	6	6	NUM
fcis-14563	125	3	shows	show	VERB
fcis-14563	125	4	the	the	DET
fcis-14563	125	5	confusion	confusion	NOUN
fcis-14563	125	6	matrix	matrix	NOUN
fcis-14563	125	7	and	and	CCONJ
fcis-14563	125	8	training	training	NOUN
fcis-14563	125	9	curve	curve	NOUN
fcis-14563	125	10	of	of	ADP
fcis-14563	125	11	the	the	DET
fcis-14563	125	12	model	model	NOUN
fcis-14563	125	13	for	for	ADP
fcis-14563	125	14	t1	t1	NOUN
fcis-14563	125	15	when	when	SCONJ
fcis-14563	125	16	there	there	PRON
fcis-14563	125	17	is	be	VERB
fcis-14563	125	18	no	no	DET
fcis-14563	125	19	noise	noise	NOUN
fcis-14563	125	20	and	and	CCONJ
fcis-14563	125	21	the	the	DET
fcis-14563	125	22	snr	snr	NOUN
fcis-14563	125	23	is	be	AUX
fcis-14563	125	24	20db	20db	ADJ
fcis-14563	125	25	and	and	CCONJ
fcis-14563	125	26	40db	40db	NOUN
fcis-14563	125	27	respectively	respectively	ADV
fcis-14563	125	28	.	.	PUNCT
fcis-14563	126	1	it	it	PRON
fcis-14563	126	2	can	can	AUX
fcis-14563	126	3	be	be	AUX
fcis-14563	126	4	seen	see	VERB
fcis-14563	126	5	from	from	ADP
fcis-14563	126	6	the	the	DET
fcis-14563	126	7	matrix	matrix	NOUN
fcis-14563	126	8	that	that	PRON
fcis-14563	126	9	at	at	ADP
fcis-14563	126	10	20db	20db	ADJ
fcis-14563	126	11	signal	signal	NOUN
fcis-14563	126	12	-	-	PUNCT
fcis-14563	126	13	to	to	ADP
fcis-14563	126	14	-	-	PUNCT
fcis-14563	126	15	noise	noise	NOUN
fcis-14563	126	16	ratio	ratio	NOUN
fcis-14563	126	17	,	,	PUNCT
fcis-14563	126	18	the	the	DET
fcis-14563	126	19	number	number	NOUN
fcis-14563	126	20	of	of	ADP
fcis-14563	126	21	false	false	ADJ
fcis-14563	126	22	judgments	judgment	NOUN
fcis-14563	126	23	and	and	CCONJ
fcis-14563	126	24	missed	miss	VERB
fcis-14563	126	25	judgments	judgment	NOUN
fcis-14563	126	26	in	in	ADP
fcis-14563	126	27	the	the	DET
fcis-14563	126	28	proposed	propose	VERB
fcis-14563	126	29	method	method	NOUN
fcis-14563	126	30	is	be	AUX
fcis-14563	126	31	low	low	ADJ
fcis-14563	126	32	,	,	PUNCT
fcis-14563	126	33	especially	especially	ADV
fcis-14563	126	34	the	the	DET
fcis-14563	126	35	recognition	recognition	NOUN
fcis-14563	126	36	accuracy	accuracy	NOUN
fcis-14563	126	37	of	of	ADP
fcis-14563	126	38	temporary	temporary	ADJ
fcis-14563	126	39	rise	rise	NOUN
fcis-14563	126	40	and	and	CCONJ
fcis-14563	126	41	fluctuation	fluctuation	NOUN
fcis-14563	126	42	is	be	AUX
fcis-14563	126	43	the	the	DET
fcis-14563	126	44	highest	high	ADJ
fcis-14563	126	45	,	,	PUNCT
fcis-14563	126	46	which	which	PRON
fcis-14563	126	47	are	be	AUX
fcis-14563	126	48	99.96	99.96	NUM
fcis-14563	126	49	%	%	NOUN
fcis-14563	126	50	and	and	CCONJ
fcis-14563	126	51	100	100	NUM
fcis-14563	126	52	%	%	NOUN
fcis-14563	126	53	respectively	respectively	ADV
fcis-14563	126	54	.	.	PUNCT
fcis-14563	127	1	4.2.2	4.2.2	X
fcis-14563	127	2	.	.	PUNCT
fcis-14563	127	3	classification	classification	NOUN
fcis-14563	127	4	of	of	ADP
fcis-14563	127	5	t2	t2	PROPN
fcis-14563	127	6	the	the	DET
fcis-14563	127	7	first	first	ADJ
fcis-14563	127	8	20	20	NUM
fcis-14563	127	9	types	type	NOUN
fcis-14563	127	10	of	of	ADP
fcis-14563	127	11	disturbance	disturbance	NOUN
fcis-14563	127	12	in	in	ADP
fcis-14563	127	13	this	this	DET
fcis-14563	127	14	paper	paper	NOUN
fcis-14563	127	15	include	include	VERB
fcis-14563	127	16	single	single	ADJ
fcis-14563	127	17	disturbance	disturbance	NOUN
fcis-14563	127	18	and	and	CCONJ
fcis-14563	127	19	double	double	ADJ
fcis-14563	127	20	disturbance	disturbance	NOUN
fcis-14563	127	21	.	.	PUNCT
fcis-14563	128	1	the	the	DET
fcis-14563	128	2	training	training	NOUN
fcis-14563	128	3	process	process	NOUN
fcis-14563	128	4	of	of	ADP
fcis-14563	128	5	classifying	classify	VERB
fcis-14563	128	6	the	the	DET
fcis-14563	128	7	first	first	ADJ
fcis-14563	128	8	20	20	NUM
fcis-14563	128	9	types	type	NOUN
fcis-14563	128	10	of	of	ADP
fcis-14563	128	11	disturbance	disturbance	NOUN
fcis-14563	128	12	under	under	ADP
fcis-14563	128	13	different	different	ADJ
fcis-14563	128	14	snr	snr	NOUN
fcis-14563	128	15	is	be	AUX
fcis-14563	128	16	shown	show	VERB
fcis-14563	128	17	in	in	ADP
fcis-14563	128	18	figure	figure	NOUN
fcis-14563	128	19	7	7	NUM
fcis-14563	128	20	.	.	PUNCT
fcis-14563	129	1	(	(	PUNCT
fcis-14563	129	2	a	a	X
fcis-14563	129	3	)	)	PUNCT
fcis-14563	129	4	train	train	NOUN
fcis-14563	129	5	-	-	PUNCT
fcis-14563	129	6	loss	loss	NOUN
fcis-14563	129	7	curve	curve	NOUN
fcis-14563	129	8	under	under	ADP
fcis-14563	129	9	different	different	ADJ
fcis-14563	129	10	noises	noise	NOUN
fcis-14563	129	11	(	(	PUNCT
fcis-14563	129	12	b	b	NOUN
fcis-14563	129	13	)	)	PUNCT
fcis-14563	129	14	validation	validation	NOUN
fcis-14563	129	15	-	-	PUNCT
fcis-14563	129	16	loss	loss	NOUN
fcis-14563	129	17	curve	curve	NOUN
fcis-14563	129	18	under	under	ADP
fcis-14563	129	19	different	different	ADJ
fcis-14563	129	20	noises	noise	NOUN
fcis-14563	129	21	(	(	PUNCT
fcis-14563	129	22	c	c	NOUN
fcis-14563	129	23	)	)	PUNCT
fcis-14563	129	24	validation	validation	NOUN
fcis-14563	129	25	-	-	PUNCT
fcis-14563	129	26	acc	acc	NOUN
fcis-14563	129	27	curve	curve	NOUN
fcis-14563	129	28	under	under	ADP
fcis-14563	129	29	different	different	ADJ
fcis-14563	129	30	noises	noise	NOUN
fcis-14563	129	31	fig	fig	NOUN
fcis-14563	129	32	7	7	NUM
fcis-14563	129	33	.	.	PUNCT
fcis-14563	129	34	training	training	NOUN
fcis-14563	129	35	process	process	NOUN
fcis-14563	129	36	of	of	ADP
fcis-14563	129	37	20	20	NUM
fcis-14563	129	38	classification	classification	NOUN
fcis-14563	129	39	with	with	ADP
fcis-14563	129	40	different	different	ADJ
fcis-14563	129	41	signal	signal	NOUN
fcis-14563	129	42	-	-	PUNCT
fcis-14563	129	43	tonoise	tonoise	NOUN
fcis-14563	129	44	ratios	ratio	NOUN
fcis-14563	129	45	4.2.3	4.2.3	NUM
fcis-14563	129	46	.	.	PUNCT
fcis-14563	130	1	classification	classification	NOUN
fcis-14563	130	2	of	of	ADP
fcis-14563	130	3	t3	t3	PROPN
fcis-14563	130	4	this	this	DET
fcis-14563	130	5	paper	paper	NOUN
fcis-14563	130	6	has	have	VERB
fcis-14563	130	7	a	a	DET
fcis-14563	130	8	total	total	NOUN
fcis-14563	130	9	of	of	ADP
fcis-14563	130	10	40	40	NUM
fcis-14563	130	11	types	type	NOUN
fcis-14563	130	12	of	of	ADP
fcis-14563	130	13	disturbance	disturbance	NOUN
fcis-14563	130	14	data	datum	NOUN
fcis-14563	130	15	,	,	PUNCT
fcis-14563	130	16	including	include	VERB
fcis-14563	130	17	a	a	DET
fcis-14563	130	18	variety	variety	NOUN
fcis-14563	130	19	of	of	ADP
fcis-14563	130	20	compound	compound	NOUN
fcis-14563	130	21	disturbances	disturbance	NOUN
fcis-14563	130	22	including	include	VERB
fcis-14563	130	23	single	single	ADJ
fcis-14563	130	24	,	,	PUNCT
fcis-14563	130	25	double	double	ADJ
fcis-14563	130	26	,	,	PUNCT
fcis-14563	130	27	triple	triple	ADJ
fcis-14563	130	28	and	and	CCONJ
fcis-14563	130	29	quadruple	quadruple	ADJ
fcis-14563	130	30	disturbances	disturbance	NOUN
fcis-14563	130	31	.	.	PUNCT
fcis-14563	131	1	the	the	DET
fcis-14563	131	2	training	training	NOUN
fcis-14563	131	3	process	process	NOUN
fcis-14563	131	4	of	of	ADP
fcis-14563	131	5	classifying	classify	VERB
fcis-14563	131	6	40	40	NUM
fcis-14563	131	7	types	type	NOUN
fcis-14563	131	8	of	of	ADP
fcis-14563	131	9	disturbances	disturbance	NOUN
fcis-14563	131	10	under	under	ADP
fcis-14563	131	11	different	different	ADJ
fcis-14563	131	12	signal	signal	NOUN
fcis-14563	131	13	-	-	PUNCT
fcis-14563	131	14	to	to	ADP
fcis-14563	131	15	-	-	PUNCT
fcis-14563	131	16	noise	noise	NOUN
fcis-14563	131	17	ratio	ratio	NOUN
fcis-14563	131	18	is	be	AUX
fcis-14563	131	19	shown	show	VERB
fcis-14563	131	20	in	in	ADP
fcis-14563	131	21	figure	figure	NOUN
fcis-14563	131	22	8	8	NUM
fcis-14563	131	23	.	.	PUNCT
fcis-14563	131	24	table	table	NOUN
fcis-14563	131	25	2	2	NUM
fcis-14563	131	26	shows	show	VERB
fcis-14563	131	27	the	the	DET
fcis-14563	131	28	average	average	ADJ
fcis-14563	131	29	recognition	recognition	NOUN
fcis-14563	131	30	accuracy	accuracy	NOUN
fcis-14563	131	31	of	of	ADP
fcis-14563	131	32	the	the	DET
fcis-14563	131	33	model	model	NOUN
fcis-14563	131	34	in	in	ADP
fcis-14563	131	35	this	this	DET
fcis-14563	131	36	paper	paper	NOUN
fcis-14563	131	37	for	for	ADP
fcis-14563	131	38	8	8	NUM
fcis-14563	131	39	classification	classification	NOUN
fcis-14563	131	40	with	with	ADP
fcis-14563	131	41	different	different	ADJ
fcis-14563	131	42	signal	signal	NOUN
fcis-14563	131	43	,	,	PUNCT
fcis-14563	131	44	20	20	NUM
fcis-14563	131	45	classification	classification	NOUN
fcis-14563	131	46	and	and	CCONJ
fcis-14563	131	47	40	40	NUM
fcis-14563	131	48	classification	classification	NOUN
fcis-14563	131	49	under	under	ADP
fcis-14563	131	50	three	three	NUM
fcis-14563	131	51	different	different	ADJ
fcis-14563	131	52	noise	noise	NOUN
fcis-14563	131	53	environments	environment	NOUN
fcis-14563	131	54	.	.	PUNCT
fcis-14563	132	1	(	(	PUNCT
fcis-14563	132	2	a	a	X
fcis-14563	132	3	)	)	PUNCT
fcis-14563	132	4	train	train	NOUN
fcis-14563	132	5	-	-	PUNCT
fcis-14563	132	6	loss	loss	NOUN
fcis-14563	132	7	curve	curve	NOUN
fcis-14563	132	8	under	under	ADP
fcis-14563	132	9	different	different	ADJ
fcis-14563	132	10	noises	noise	NOUN
fcis-14563	132	11	(	(	PUNCT
fcis-14563	132	12	b	b	NOUN
fcis-14563	132	13	)	)	PUNCT
fcis-14563	132	14	validation	validation	NOUN
fcis-14563	132	15	-	-	PUNCT
fcis-14563	132	16	loss	loss	NOUN
fcis-14563	132	17	curve	curve	NOUN
fcis-14563	132	18	under	under	ADP
fcis-14563	132	19	different	different	ADJ
fcis-14563	132	20	noises	noise	NOUN
fcis-14563	132	21	(	(	PUNCT
fcis-14563	132	22	c	c	NOUN
fcis-14563	132	23	)	)	PUNCT
fcis-14563	132	24	validation	validation	NOUN
fcis-14563	132	25	-	-	PUNCT
fcis-14563	132	26	acc	acc	NOUN
fcis-14563	132	27	curve	curve	NOUN
fcis-14563	132	28	under	under	ADP
fcis-14563	132	29	different	different	ADJ
fcis-14563	132	30	noises	noise	NOUN
fcis-14563	132	31	fig	fig	NOUN
fcis-14563	132	32	8	8	NUM
fcis-14563	132	33	.	.	PUNCT
fcis-14563	133	1	training	training	NOUN
fcis-14563	133	2	process	process	NOUN
fcis-14563	133	3	of	of	ADP
fcis-14563	133	4	40	40	NUM
fcis-14563	133	5	classification	classification	NOUN
fcis-14563	133	6	with	with	ADP
fcis-14563	133	7	different	different	ADJ
fcis-14563	133	8	signal	signal	NOUN
fcis-14563	133	9	-	-	PUNCT
fcis-14563	133	10	tonoise	tonoise	NOUN
fcis-14563	133	11	ratios	ratio	NOUN
fcis-14563	133	12	table	table	VERB
fcis-14563	133	13	2	2	NUM
fcis-14563	133	14	.	.	PUNCT
fcis-14563	134	1	the	the	DET
fcis-14563	134	2	classification	classification	NOUN
fcis-14563	134	3	accuracy	accuracy	NOUN
fcis-14563	134	4	of	of	ADP
fcis-14563	134	5	the	the	DET
fcis-14563	134	6	method	method	NOUN
fcis-14563	134	7	in	in	ADP
fcis-14563	134	8	this	this	DET
fcis-14563	134	9	paper	paper	NOUN
fcis-14563	134	10	number	number	NOUN
fcis-14563	134	11	of	of	ADP
fcis-14563	134	12	disturbance	disturbance	NOUN
fcis-14563	134	13	types	type	NOUN
fcis-14563	134	14	average	average	ADJ
fcis-14563	134	15	recognition	recognition	NOUN
fcis-14563	134	16	accuracy	accuracy	NOUN
fcis-14563	134	17	%	%	NOUN
fcis-14563	134	18	noise20db	noise20db	NOUN
fcis-14563	134	19	40db	40db	NOUN
fcis-14563	134	20	8	8	NUM
fcis-14563	134	21	99	99	NUM
fcis-14563	134	22	.	.	PUNCT
fcis-14563	135	1	94	94	NUM
fcis-14563	135	2	99	99	NUM
fcis-14563	135	3	.	.	PUNCT
fcis-14563	136	1	81	81	NUM
fcis-14563	136	2	99	99	NUM
fcis-14563	136	3	.	.	PUNCT
fcis-14563	137	1	87	87	NUM
fcis-14563	137	2	20	20	NUM
fcis-14563	137	3	99	99	NUM
fcis-14563	137	4	.	.	PUNCT
fcis-14563	137	5	00	00	NUM
fcis-14563	138	1	96	96	NUM
fcis-14563	138	2	.	.	PUNCT
fcis-14563	139	1	73	73	NUM
fcis-14563	139	2	98	98	NUM
fcis-14563	139	3	.	.	PUNCT
fcis-14563	140	1	32	32	NUM
fcis-14563	140	2	40	40	NUM
fcis-14563	140	3	95	95	NUM
fcis-14563	140	4	.	.	PUNCT
fcis-14563	141	1	80	80	NUM
fcis-14563	141	2	92	92	NUM
fcis-14563	141	3	.	.	PUNCT
fcis-14563	142	1	57	57	NUM
fcis-14563	142	2	93	93	NUM
fcis-14563	142	3	.	.	PUNCT
fcis-14563	143	1	62	62	NUM
fcis-14563	143	2	as	as	SCONJ
fcis-14563	143	3	can	can	AUX
fcis-14563	143	4	be	be	AUX
fcis-14563	143	5	seen	see	VERB
fcis-14563	143	6	from	from	ADP
fcis-14563	143	7	the	the	DET
fcis-14563	143	8	above	above	ADJ
fcis-14563	143	9	table	table	NOUN
fcis-14563	143	10	,	,	PUNCT
fcis-14563	143	11	this	this	DET
fcis-14563	143	12	model	model	NOUN
fcis-14563	143	13	can	can	AUX
fcis-14563	143	14	effectively	effectively	ADV
fcis-14563	143	15	identify	identify	VERB
fcis-14563	143	16	power	power	NOUN
fcis-14563	143	17	quality	quality	NOUN
fcis-14563	143	18	disturbance	disturbance	NOUN
fcis-14563	143	19	signals	signal	NOUN
fcis-14563	143	20	regardless	regardless	ADV
fcis-14563	143	21	of	of	ADP
fcis-14563	143	22	8	8	NUM
fcis-14563	143	23	classification	classification	NOUN
fcis-14563	143	24	,	,	PUNCT
fcis-14563	143	25	20	20	NUM
fcis-14563	143	26	classification	classification	NOUN
fcis-14563	143	27	or	or	CCONJ
fcis-14563	143	28	40	40	NUM
fcis-14563	143	29	classification	classification	NOUN
fcis-14563	143	30	.	.	PUNCT
fcis-14563	144	1	under	under	ADP
fcis-14563	144	2	the	the	DET
fcis-14563	144	3	three	three	NUM
fcis-14563	144	4	different	different	ADJ
fcis-14563	144	5	noise	noise	NOUN
fcis-14563	144	6	environments	environment	NOUN
fcis-14563	144	7	,	,	PUNCT
fcis-14563	144	8	the	the	DET
fcis-14563	144	9	average	average	ADJ
fcis-14563	144	10	accuracy	accuracy	NOUN
fcis-14563	144	11	of	of	ADP
fcis-14563	144	12	8	8	NUM
fcis-14563	144	13	classification	classification	NOUN
fcis-14563	144	14	is	be	AUX
fcis-14563	144	15	higher	high	ADJ
fcis-14563	144	16	than	than	ADP
fcis-14563	144	17	99	99	NUM
fcis-14563	144	18	%	%	NOUN
fcis-14563	144	19	,	,	PUNCT
fcis-14563	144	20	the	the	DET
fcis-14563	144	21	average	average	ADJ
fcis-14563	144	22	accuracy	accuracy	NOUN
fcis-14563	144	23	of	of	ADP
fcis-14563	144	24	20	20	NUM
fcis-14563	144	25	classification	classification	NOUN
fcis-14563	144	26	is	be	AUX
fcis-14563	144	27	higher	high	ADJ
fcis-14563	144	28	than	than	ADP
fcis-14563	144	29	96	96	NUM
fcis-14563	144	30	%	%	NOUN
fcis-14563	144	31	,	,	PUNCT
fcis-14563	144	32	and	and	CCONJ
fcis-14563	144	33	the	the	DET
fcis-14563	144	34	accuracy	accuracy	NOUN
fcis-14563	144	35	of	of	ADP
fcis-14563	144	36	40	40	NUM
fcis-14563	144	37	classification	classification	NOUN
fcis-14563	144	38	is	be	AUX
fcis-14563	144	39	higher	high	ADJ
fcis-14563	144	40	than	than	ADP
fcis-14563	144	41	92	92	NUM
fcis-14563	144	42	%	%	NOUN
fcis-14563	144	43	.	.	PUNCT
fcis-14563	145	1	table	table	NOUN
fcis-14563	145	2	3	3	NUM
fcis-14563	145	3	shows	show	VERB
fcis-14563	145	4	the	the	DET
fcis-14563	145	5	classification	classification	NOUN
fcis-14563	145	6	results	result	NOUN
fcis-14563	145	7	of	of	ADP
fcis-14563	145	8	20	20	NUM
fcis-14563	145	9	types	type	NOUN
fcis-14563	145	10	of	of	ADP
fcis-14563	145	11	disturbances	disturbance	NOUN
fcis-14563	145	12	under	under	ADP
fcis-14563	145	13	different	different	ADJ
fcis-14563	145	14	signal	signal	NOUN
fcis-14563	145	15	-	-	PUNCT
fcis-14563	145	16	to	to	ADP
fcis-14563	145	17	-	-	PUNCT
fcis-14563	145	18	noise	noise	NOUN
fcis-14563	145	19	ratios	ratio	NOUN
fcis-14563	145	20	.	.	PUNCT
fcis-14563	146	1	as	as	SCONJ
fcis-14563	146	2	can	can	AUX
fcis-14563	146	3	be	be	AUX
fcis-14563	146	4	seen	see	VERB
fcis-14563	146	5	from	from	ADP
fcis-14563	146	6	the	the	DET
fcis-14563	146	7	above	above	ADJ
fcis-14563	146	8	table	table	NOUN
fcis-14563	146	9	,	,	PUNCT
fcis-14563	146	10	the	the	DET
fcis-14563	146	11	proposed	propose	VERB
fcis-14563	146	12	model	model	NOUN
fcis-14563	146	13	can	can	AUX
fcis-14563	146	14	effectively	effectively	ADV
fcis-14563	146	15	identify	identify	VERB
fcis-14563	146	16	t1	t1	NOUN
fcis-14563	146	17	and	and	CCONJ
fcis-14563	146	18	t2	t2	NOUN
fcis-14563	146	19	,	,	PUNCT
fcis-14563	146	20	and	and	CCONJ
fcis-14563	146	21	the	the	DET
fcis-14563	146	22	average	average	ADJ
fcis-14563	146	23	recognition	recognition	NOUN
fcis-14563	146	24	accuracy	accuracy	NOUN
fcis-14563	146	25	is	be	AUX
fcis-14563	146	26	above	above	ADP
fcis-14563	146	27	96	96	NUM
fcis-14563	146	28	%	%	NOUN
fcis-14563	146	29	under	under	ADP
fcis-14563	146	30	three	three	NUM
fcis-14563	146	31	different	different	ADJ
fcis-14563	146	32	snr	snr	NOUN
fcis-14563	146	33	conditions	condition	NOUN
fcis-14563	146	34	.	.	PUNCT
fcis-14563	147	1	9	9	NUM
fcis-14563	147	2	table	table	NOUN
fcis-14563	147	3	3	3	NUM
fcis-14563	147	4	.	.	PUNCT
fcis-14563	148	1	the	the	DET
fcis-14563	148	2	classification	classification	NOUN
fcis-14563	148	3	accuracy	accuracy	NOUN
fcis-14563	148	4	of	of	ADP
fcis-14563	148	5	this	this	DET
fcis-14563	148	6	method	method	NOUN
fcis-14563	148	7	20	20	NUM
fcis-14563	148	8	under	under	ADP
fcis-14563	148	9	different	different	ADJ
fcis-14563	148	10	signal	signal	NOUN
fcis-14563	148	11	-	-	PUNCT
fcis-14563	148	12	to	to	ADP
fcis-14563	148	13	-	-	PUNCT
fcis-14563	148	14	noise	noise	NOUN
fcis-14563	148	15	ratios	ratio	NOUN
fcis-14563	148	16	type	type	NOUN
fcis-14563	148	17	of	of	ADP
fcis-14563	148	18	disturbance	disturbance	NOUN
fcis-14563	148	19	recognition	recognition	NOUN
fcis-14563	148	20	accuracy	accuracy	NOUN
fcis-14563	148	21	%	%	INTJ
fcis-14563	148	22	no	no	DET
fcis-14563	148	23	noise	noise	NOUN
fcis-14563	148	24	20db	20db	ADJ
fcis-14563	148	25	40db	40db	PROPN
fcis-14563	148	26	c0	c0	NOUN
fcis-14563	148	27	100	100	NUM
fcis-14563	148	28	.	.	PUNCT
fcis-14563	148	29	00	00	NUM
fcis-14563	149	1	99	99	NUM
fcis-14563	149	2	.	.	PUNCT
fcis-14563	150	1	26	26	NUM
fcis-14563	150	2	99	99	NUM
fcis-14563	150	3	.	.	PUNCT
fcis-14563	151	1	96	96	NUM
fcis-14563	151	2	c1	c1	PROPN
fcis-14563	151	3	99.66	99.66	NUM
fcis-14563	151	4	91	91	NUM
fcis-14563	151	5	.	.	PUNCT
fcis-14563	152	1	33	33	NUM
fcis-14563	152	2	96	96	NUM
fcis-14563	152	3	.	.	PUNCT
fcis-14563	153	1	76	76	NUM
fcis-14563	153	2	c2	c2	PROPN
fcis-14563	153	3	99.56	99.56	NUM
fcis-14563	153	4	95	95	NUM
fcis-14563	153	5	.	.	PUNCT
fcis-14563	154	1	43	43	NUM
fcis-14563	154	2	96	96	NUM
fcis-14563	154	3	.	.	PUNCT
fcis-14563	155	1	83	83	NUM
fcis-14563	155	2	c3	c3	NOUN
fcis-14563	155	3	99	99	NUM
fcis-14563	155	4	.	.	PUNCT
fcis-14563	156	1	53	53	NUM
fcis-14563	156	2	97	97	NUM
fcis-14563	156	3	.	.	PUNCT
fcis-14563	157	1	33	33	NUM
fcis-14563	157	2	97	97	NUM
fcis-14563	157	3	.	.	PUNCT
fcis-14563	158	1	73	73	NUM
fcis-14563	158	2	c4	c4	NOUN
fcis-14563	158	3	99	99	NUM
fcis-14563	158	4	.	.	PUNCT
fcis-14563	158	5	86	86	NUM
fcis-14563	158	6	95	95	NUM
fcis-14563	158	7	.	.	PUNCT
fcis-14563	159	1	03	03	NUM
fcis-14563	159	2	99	99	NUM
fcis-14563	159	3	.	.	PUNCT
fcis-14563	160	1	76	76	NUM
fcis-14563	160	2	c5	c5	PROPN
fcis-14563	160	3	99	99	NUM
fcis-14563	160	4	.	.	PUNCT
fcis-14563	160	5	86	86	NUM
fcis-14563	160	6	99	99	NUM
fcis-14563	160	7	.	.	PUNCT
fcis-14563	160	8	26	26	NUM
fcis-14563	160	9	99	99	NUM
fcis-14563	160	10	.	.	PUNCT
fcis-14563	161	1	70	70	NUM
fcis-14563	161	2	c6	c6	PROPN
fcis-14563	161	3	99	99	NUM
fcis-14563	161	4	.	.	PROPN
fcis-14563	161	5	83	83	NUM
fcis-14563	161	6	99	99	NUM
fcis-14563	161	7	.	.	PUNCT
fcis-14563	161	8	20	20	NUM
fcis-14563	161	9	99	99	NUM
fcis-14563	161	10	.	.	PUNCT
fcis-14563	162	1	70	70	NUM
fcis-14563	162	2	c7	c7	PROPN
fcis-14563	162	3	100	100	NUM
fcis-14563	162	4	.	.	PUNCT
fcis-14563	162	5	00	00	NUM
fcis-14563	162	6	99	99	NUM
fcis-14563	162	7	.	.	PUNCT
fcis-14563	162	8	96	96	NUM
fcis-14563	162	9	100	100	NUM
fcis-14563	162	10	.	.	PUNCT
fcis-14563	162	11	00	00	PUNCT
fcis-14563	163	1	c8	c8	PROPN
fcis-14563	163	2	92	92	NUM
fcis-14563	163	3	.	.	PUNCT
fcis-14563	163	4	80	80	NUM
fcis-14563	163	5	93	93	NUM
fcis-14563	163	6	.	.	PUNCT
fcis-14563	164	1	43	43	NUM
fcis-14563	164	2	95	95	NUM
fcis-14563	164	3	.	.	PUNCT
fcis-14563	165	1	63	63	NUM
fcis-14563	165	2	c9	c9	NOUN
fcis-14563	165	3	99	99	NUM
fcis-14563	165	4	.	.	PUNCT
fcis-14563	165	5	86	86	NUM
fcis-14563	165	6	98	98	NUM
fcis-14563	165	7	.	.	PUNCT
fcis-14563	166	1	56	56	NUM
fcis-14563	166	2	99	99	NUM
fcis-14563	166	3	.	.	PUNCT
fcis-14563	167	1	60	60	NUM
fcis-14563	167	2	c10	c10	VERB
fcis-14563	167	3	93.46	93.46	NUM
fcis-14563	167	4	95	95	NUM
fcis-14563	167	5	.	.	PUNCT
fcis-14563	168	1	16	16	NUM
fcis-14563	168	2	95	95	NUM
fcis-14563	168	3	.	.	PUNCT
fcis-14563	169	1	10	10	NUM
fcis-14563	169	2	c11	c11	NOUN
fcis-14563	169	3	99	99	NUM
fcis-14563	169	4	.	.	PUNCT
fcis-14563	169	5	83	83	NUM
fcis-14563	169	6	94	94	NUM
fcis-14563	169	7	.	.	PUNCT
fcis-14563	170	1	73	73	NUM
fcis-14563	170	2	98	98	NUM
fcis-14563	170	3	.	.	PUNCT
fcis-14563	171	1	60	60	NUM
fcis-14563	171	2	c12	c12	PROPN
fcis-14563	171	3	99	99	NUM
fcis-14563	171	4	.	.	PUNCT
fcis-14563	171	5	90	90	NUM
fcis-14563	171	6	98	98	NUM
fcis-14563	171	7	.	.	PUNCT
fcis-14563	172	1	16	16	NUM
fcis-14563	172	2	99	99	NUM
fcis-14563	172	3	.	.	PUNCT
fcis-14563	173	1	50	50	NUM
fcis-14563	173	2	c13	c13	NOUN
fcis-14563	173	3	100	100	NUM
fcis-14563	173	4	.	.	PUNCT
fcis-14563	173	5	00	00	NUM
fcis-14563	173	6	98	98	NUM
fcis-14563	173	7	.	.	PUNCT
fcis-14563	173	8	96	96	NUM
fcis-14563	173	9	99	99	NUM
fcis-14563	173	10	.	.	PUNCT
fcis-14563	173	11	93	93	NUM
fcis-14563	173	12	c14	c14	NOUN
fcis-14563	173	13	98.40	98.40	NUM
fcis-14563	173	14	94	94	NUM
fcis-14563	173	15	.	.	PUNCT
fcis-14563	174	1	16	16	NUM
fcis-14563	174	2	95	95	NUM
fcis-14563	174	3	.	.	PUNCT
fcis-14563	175	1	46	46	NUM
fcis-14563	175	2	c15	c15	NOUN
fcis-14563	175	3	99	99	NUM
fcis-14563	175	4	.	.	PUNCT
fcis-14563	175	5	40	40	NUM
fcis-14563	175	6	95	95	NUM
fcis-14563	175	7	.	.	PUNCT
fcis-14563	176	1	46	46	NUM
fcis-14563	176	2	97	97	NUM
fcis-14563	176	3	.	.	PUNCT
fcis-14563	176	4	13	13	NUM
fcis-14563	176	5	c16	c16	PROPN
fcis-14563	176	6	98	98	NUM
fcis-14563	176	7	.	.	PUNCT
fcis-14563	177	1	53	53	NUM
fcis-14563	177	2	93	93	NUM
fcis-14563	177	3	.	.	PUNCT
fcis-14563	178	1	30	30	NUM
fcis-14563	178	2	95	95	NUM
fcis-14563	178	3	.	.	PUNCT
fcis-14563	179	1	80	80	NUM
fcis-14563	179	2	c17	c17	NOUN
fcis-14563	179	3	99	99	NUM
fcis-14563	179	4	.	.	PUNCT
fcis-14563	179	5	86	86	NUM
fcis-14563	179	6	99	99	NUM
fcis-14563	179	7	.	.	PUNCT
fcis-14563	180	1	56	56	NUM
fcis-14563	180	2	99	99	NUM
fcis-14563	180	3	.	.	PUNCT
fcis-14563	181	1	83	83	NUM
fcis-14563	181	2	c18	c18	NOUN
fcis-14563	181	3	99	99	NUM
fcis-14563	181	4	.	.	PUNCT
fcis-14563	181	5	86	86	NUM
fcis-14563	181	6	98	98	NUM
fcis-14563	181	7	.	.	PUNCT
fcis-14563	181	8	30	30	NUM
fcis-14563	181	9	99	99	NUM
fcis-14563	181	10	.	.	PUNCT
fcis-14563	182	1	76	76	NUM
fcis-14563	182	2	c19	c19	NOUN
fcis-14563	182	3	99	99	NUM
fcis-14563	182	4	.	.	PUNCT
fcis-14563	182	5	86	86	NUM
fcis-14563	182	6	97	97	NUM
fcis-14563	182	7	.	.	PUNCT
fcis-14563	183	1	93	93	NUM
fcis-14563	183	2	99	99	NUM
fcis-14563	183	3	.	.	PUNCT
fcis-14563	184	1	70	70	NUM
fcis-14563	184	2	average	average	ADJ
fcis-14563	184	3	99	99	NUM
fcis-14563	184	4	.	.	PUNCT
fcis-14563	184	5	00	00	NUM
fcis-14563	185	1	96	96	NUM
fcis-14563	185	2	.	.	PUNCT
fcis-14563	186	1	73	73	NUM
fcis-14563	186	2	98	98	NUM
fcis-14563	186	3	.	.	PUNCT
fcis-14563	187	1	32	32	NUM
fcis-14563	187	2	4.3	4.3	NUM
fcis-14563	187	3	.	.	PUNCT
fcis-14563	188	1	comparison	comparison	NOUN
fcis-14563	188	2	of	of	ADP
fcis-14563	188	3	3	3	NUM
fcis-14563	188	4	methods	method	NOUN
fcis-14563	188	5	the	the	DET
fcis-14563	188	6	proposed	propose	VERB
fcis-14563	188	7	method	method	NOUN
fcis-14563	188	8	is	be	AUX
fcis-14563	188	9	compared	compare	VERB
fcis-14563	188	10	with	with	ADP
fcis-14563	188	11	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	188	12	,	,	PUNCT
fcis-14563	188	13	2d_cnn	2d_cnn	NUM
fcis-14563	188	14	and	and	CCONJ
fcis-14563	188	15	2d_resnet	2d_resnet	NUM
fcis-14563	189	1	[	[	X
fcis-14563	189	2	18	18	NUM
fcis-14563	189	3	]	]	PUNCT
fcis-14563	189	4	,	,	PUNCT
fcis-14563	189	5	respectively	respectively	ADV
fcis-14563	189	6	,	,	PUNCT
fcis-14563	189	7	and	and	CCONJ
fcis-14563	189	8	the	the	DET
fcis-14563	189	9	classification	classification	NOUN
fcis-14563	189	10	accuracy	accuracy	NOUN
fcis-14563	189	11	of	of	ADP
fcis-14563	189	12	40	40	NUM
fcis-14563	189	13	kinds	kind	NOUN
fcis-14563	189	14	of	of	ADP
fcis-14563	189	15	multiple	multiple	ADJ
fcis-14563	189	16	composite	composite	ADJ
fcis-14563	189	17	disturbances	disturbance	NOUN
fcis-14563	189	18	under	under	ADP
fcis-14563	189	19	three	three	NUM
fcis-14563	189	20	snr	snr	NOUN
fcis-14563	189	21	conditions	condition	NOUN
fcis-14563	189	22	of	of	ADP
fcis-14563	189	23	0db	0db	ADJ
fcis-14563	189	24	,	,	PUNCT
fcis-14563	189	25	20db	20db	ADJ
fcis-14563	189	26	and	and	CCONJ
fcis-14563	189	27	40db	40db	NOUN
fcis-14563	189	28	are	be	AUX
fcis-14563	189	29	respectively	respectively	ADV
fcis-14563	189	30	used	use	VERB
fcis-14563	189	31	by	by	ADP
fcis-14563	189	32	these	these	DET
fcis-14563	189	33	four	four	NUM
fcis-14563	189	34	methods	method	NOUN
fcis-14563	189	35	.	.	PUNCT
fcis-14563	190	1	the	the	DET
fcis-14563	190	2	results	result	NOUN
fcis-14563	190	3	are	be	AUX
fcis-14563	190	4	shown	show	VERB
fcis-14563	190	5	in	in	ADP
fcis-14563	190	6	table	table	NOUN
fcis-14563	190	7	4	4	NUM
fcis-14563	190	8	.	.	PUNCT
fcis-14563	190	9	table	table	NOUN
fcis-14563	190	10	4	4	NUM
fcis-14563	190	11	.	.	PUNCT
fcis-14563	190	12	classification	classification	NOUN
fcis-14563	190	13	accuracy	accuracy	NOUN
fcis-14563	190	14	of	of	ADP
fcis-14563	190	15	the	the	DET
fcis-14563	190	16	four	four	NUM
fcis-14563	190	17	methods	method	NOUN
fcis-14563	190	18	models	model	NOUN
fcis-14563	190	19	average	average	ADJ
fcis-14563	190	20	recognition	recognition	NOUN
fcis-14563	190	21	noise20db	noise20db	NOUN
fcis-14563	190	22	40db	40db	ADJ
fcis-14563	190	23	method	method	NOUN
fcis-14563	190	24	of	of	ADP
fcis-14563	190	25	this	this	DET
fcis-14563	190	26	95	95	NUM
fcis-14563	190	27	.	.	PUNCT
fcis-14563	191	1	80	80	NUM
fcis-14563	191	2	92	92	NUM
fcis-14563	191	3	.	.	PUNCT
fcis-14563	192	1	93	93	NUM
fcis-14563	192	2	.	.	PUNCT
fcis-14563	193	1	62	62	NUM
fcis-14563	193	2	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	193	3	91.71	91.71	NUM
fcis-14563	193	4	83.64	83.64	NUM
fcis-14563	193	5	91.29	91.29	NUM
fcis-14563	193	6	2d_cnn	2d_cnn	NUM
fcis-14563	193	7	93.95	93.95	NUM
fcis-14563	193	8	92.28	92.28	NUM
fcis-14563	193	9	93.57	93.57	NUM
fcis-14563	193	10	2d_resnet	2d_resnet	NUM
fcis-14563	193	11	86.39	86.39	NUM
fcis-14563	193	12	81.51	81.51	NUM
fcis-14563	193	13	85.28	85.28	NUM
fcis-14563	193	14	as	as	SCONJ
fcis-14563	193	15	can	can	AUX
fcis-14563	193	16	be	be	AUX
fcis-14563	193	17	seen	see	VERB
fcis-14563	193	18	from	from	ADP
fcis-14563	193	19	table	table	NOUN
fcis-14563	193	20	4	4	NUM
fcis-14563	193	21	,	,	PUNCT
fcis-14563	193	22	the	the	DET
fcis-14563	193	23	average	average	ADJ
fcis-14563	193	24	recognition	recognition	NOUN
fcis-14563	193	25	accuracy	accuracy	NOUN
fcis-14563	193	26	of	of	ADP
fcis-14563	193	27	the	the	DET
fcis-14563	193	28	proposed	propose	VERB
fcis-14563	193	29	method	method	NOUN
fcis-14563	193	30	is	be	AUX
fcis-14563	193	31	higher	high	ADJ
fcis-14563	193	32	than	than	ADP
fcis-14563	193	33	that	that	PRON
fcis-14563	193	34	of	of	ADP
fcis-14563	193	35	the	the	DET
fcis-14563	193	36	other	other	ADJ
fcis-14563	193	37	three	three	NUM
fcis-14563	193	38	under	under	ADP
fcis-14563	193	39	three	three	NUM
fcis-14563	193	40	different	different	ADJ
fcis-14563	193	41	noise	noise	NOUN
fcis-14563	193	42	conditions	condition	NOUN
fcis-14563	193	43	.	.	PUNCT
fcis-14563	194	1	when	when	SCONJ
fcis-14563	194	2	there	there	PRON
fcis-14563	194	3	is	be	VERB
fcis-14563	194	4	no	no	DET
fcis-14563	194	5	noise	noise	NOUN
fcis-14563	194	6	,	,	PUNCT
fcis-14563	194	7	the	the	DET
fcis-14563	194	8	recognition	recognition	NOUN
fcis-14563	194	9	accuracy	accuracy	NOUN
fcis-14563	194	10	of	of	ADP
fcis-14563	194	11	the	the	DET
fcis-14563	194	12	proposed	propose	VERB
fcis-14563	194	13	method	method	NOUN
fcis-14563	194	14	is	be	AUX
fcis-14563	194	15	increased	increase	VERB
fcis-14563	194	16	by	by	ADP
fcis-14563	194	17	4.45	4.45	NUM
fcis-14563	194	18	%	%	NOUN
fcis-14563	194	19	,	,	PUNCT
fcis-14563	194	20	1.97	1.97	NUM
fcis-14563	194	21	%	%	NOUN
fcis-14563	194	22	and	and	CCONJ
fcis-14563	194	23	10.89	10.89	NUM
fcis-14563	194	24	%	%	NOUN
fcis-14563	194	25	compared	compare	VERB
fcis-14563	194	26	with	with	ADP
fcis-14563	194	27	the	the	DET
fcis-14563	194	28	methods	method	NOUN
fcis-14563	194	29	1d_cnn+gru	1d_cnn+gru	NUM
fcis-14563	194	30	,	,	PUNCT
fcis-14563	194	31	2d_cnn	2d_cnn	NUM
fcis-14563	194	32	and	and	CCONJ
fcis-14563	194	33	2d_resnet	2d_resnet	NUM
fcis-14563	194	34	,	,	PUNCT
fcis-14563	194	35	respectively	respectively	ADV
fcis-14563	194	36	.	.	PUNCT
fcis-14563	195	1	when	when	SCONJ
fcis-14563	195	2	the	the	DET
fcis-14563	195	3	snr	snr	NOUN
fcis-14563	195	4	is	be	AUX
fcis-14563	195	5	20db	20db	ADJ
fcis-14563	195	6	,	,	PUNCT
fcis-14563	195	7	the	the	DET
fcis-14563	195	8	recognition	recognition	NOUN
fcis-14563	195	9	accuracy	accuracy	NOUN
fcis-14563	195	10	of	of	ADP
fcis-14563	195	11	the	the	DET
fcis-14563	195	12	proposed	propose	VERB
fcis-14563	195	13	method	method	NOUN
fcis-14563	195	14	is	be	AUX
fcis-14563	195	15	improved	improve	VERB
fcis-14563	195	16	by	by	ADP
fcis-14563	195	17	10.67	10.67	NUM
fcis-14563	195	18	%	%	NOUN
fcis-14563	195	19	,	,	PUNCT
fcis-14563	195	20	0.31	0.31	NUM
fcis-14563	195	21	%	%	NOUN
fcis-14563	195	22	and	and	CCONJ
fcis-14563	195	23	13.56	13.56	NUM
fcis-14563	195	24	%	%	NOUN
fcis-14563	195	25	,	,	PUNCT
fcis-14563	195	26	respectively	respectively	ADV
fcis-14563	195	27	,	,	PUNCT
fcis-14563	195	28	compared	compare	VERB
fcis-14563	195	29	with	with	ADP
fcis-14563	195	30	the	the	DET
fcis-14563	195	31	other	other	ADJ
fcis-14563	195	32	three	three	NUM
fcis-14563	195	33	methods	method	NOUN
fcis-14563	195	34	.	.	PUNCT
fcis-14563	196	1	when	when	SCONJ
fcis-14563	196	2	the	the	DET
fcis-14563	196	3	snr	snr	NOUN
fcis-14563	196	4	is	be	AUX
fcis-14563	196	5	40db	40db	ADJ
fcis-14563	196	6	,	,	PUNCT
fcis-14563	196	7	the	the	DET
fcis-14563	196	8	recognition	recognition	NOUN
fcis-14563	196	9	accuracy	accuracy	NOUN
fcis-14563	196	10	is	be	AUX
fcis-14563	196	11	improved	improve	VERB
fcis-14563	196	12	by	by	ADP
fcis-14563	196	13	2.55	2.55	NUM
fcis-14563	196	14	%	%	NOUN
fcis-14563	196	15	,	,	PUNCT
fcis-14563	196	16	0.05	0.05	NUM
fcis-14563	196	17	%	%	NOUN
fcis-14563	196	18	and	and	CCONJ
fcis-14563	196	19	9.77	9.77	NUM
fcis-14563	196	20	%	%	NOUN
fcis-14563	196	21	,	,	PUNCT
fcis-14563	196	22	respectively	respectively	ADV
fcis-14563	196	23	.	.	PUNCT
fcis-14563	197	1	it	it	PRON
fcis-14563	197	2	can	can	AUX
fcis-14563	197	3	be	be	AUX
fcis-14563	197	4	seen	see	VERB
fcis-14563	197	5	from	from	ADP
fcis-14563	197	6	the	the	DET
fcis-14563	197	7	first	first	ADJ
fcis-14563	197	8	three	three	NUM
fcis-14563	197	9	methods	method	NOUN
fcis-14563	197	10	that	that	PRON
fcis-14563	197	11	the	the	DET
fcis-14563	197	12	recognition	recognition	NOUN
fcis-14563	197	13	accuracy	accuracy	NOUN
fcis-14563	197	14	and	and	CCONJ
fcis-14563	197	15	noise	noise	NOUN
fcis-14563	197	16	resistance	resistance	NOUN
fcis-14563	197	17	of	of	ADP
fcis-14563	197	18	the	the	DET
fcis-14563	197	19	model	model	NOUN
fcis-14563	197	20	after	after	ADP
fcis-14563	197	21	feature	feature	NOUN
fcis-14563	197	22	fusion	fusion	NOUN
fcis-14563	197	23	are	be	AUX
fcis-14563	197	24	significantly	significantly	ADV
fcis-14563	197	25	improved	improve	VERB
fcis-14563	197	26	compared	compare	VERB
fcis-14563	197	27	with	with	ADP
fcis-14563	197	28	the	the	DET
fcis-14563	197	29	model	model	NOUN
fcis-14563	197	30	without	without	ADP
fcis-14563	197	31	feature	feature	NOUN
fcis-14563	197	32	fusion	fusion	NOUN
fcis-14563	197	33	.	.	PUNCT
fcis-14563	198	1	5	5	X
fcis-14563	198	2	.	.	X
fcis-14563	198	3	conclusion	conclusion	NOUN
fcis-14563	198	4	in	in	ADP
fcis-14563	198	5	order	order	NOUN
fcis-14563	198	6	to	to	PART
fcis-14563	198	7	avoid	avoid	VERB
fcis-14563	198	8	the	the	DET
fcis-14563	198	9	decrease	decrease	NOUN
fcis-14563	198	10	of	of	ADP
fcis-14563	198	11	recognition	recognition	NOUN
fcis-14563	198	12	accuracy	accuracy	NOUN
fcis-14563	198	13	caused	cause	VERB
fcis-14563	198	14	by	by	ADP
fcis-14563	198	15	manual	manual	ADJ
fcis-14563	198	16	extraction	extraction	NOUN
fcis-14563	198	17	of	of	ADP
fcis-14563	198	18	disturbed	disturbed	ADJ
fcis-14563	198	19	features	feature	NOUN
fcis-14563	198	20	,	,	PUNCT
fcis-14563	198	21	this	this	DET
fcis-14563	198	22	paper	paper	NOUN
fcis-14563	198	23	uses	use	VERB
fcis-14563	198	24	deep	deep	ADJ
fcis-14563	198	25	neural	neural	ADJ
fcis-14563	198	26	network	network	NOUN
fcis-14563	198	27	to	to	PART
fcis-14563	198	28	automatically	automatically	ADV
fcis-14563	198	29	extract	extract	VERB
fcis-14563	198	30	features	feature	NOUN
fcis-14563	198	31	.	.	PUNCT
fcis-14563	199	1	in	in	ADP
fcis-14563	199	2	order	order	NOUN
fcis-14563	199	3	to	to	PART
fcis-14563	199	4	make	make	VERB
fcis-14563	199	5	the	the	DET
fcis-14563	199	6	model	model	NOUN
fcis-14563	199	7	have	have	VERB
fcis-14563	199	8	higher	high	ADJ
fcis-14563	199	9	recognition	recognition	NOUN
fcis-14563	199	10	accuracy	accuracy	NOUN
fcis-14563	199	11	and	and	CCONJ
fcis-14563	199	12	noise	noise	NOUN
fcis-14563	199	13	resistance	resistance	NOUN
fcis-14563	199	14	,	,	PUNCT
fcis-14563	199	15	this	this	DET
fcis-14563	199	16	paper	paper	NOUN
fcis-14563	199	17	proposes	propose	VERB
fcis-14563	199	18	a	a	DET
fcis-14563	199	19	deep	deep	ADJ
fcis-14563	199	20	neural	neural	ADJ
fcis-14563	199	21	network	network	NOUN
fcis-14563	199	22	algorithm	algorithm	NOUN
fcis-14563	199	23	of	of	ADP
fcis-14563	199	24	adaptive	adaptive	ADJ
fcis-14563	199	25	feature	feature	NOUN
fcis-14563	199	26	fusion	fusion	NOUN
fcis-14563	199	27	.	.	PUNCT
fcis-14563	200	1	the	the	DET
fcis-14563	200	2	experimental	experimental	ADJ
fcis-14563	200	3	results	result	NOUN
fcis-14563	200	4	show	show	VERB
fcis-14563	200	5	that	that	SCONJ
fcis-14563	200	6	under	under	ADP
fcis-14563	200	7	three	three	NUM
fcis-14563	200	8	different	different	ADJ
fcis-14563	200	9	snr	snr	NOUN
fcis-14563	200	10	conditions	condition	NOUN
fcis-14563	200	11	,	,	PUNCT
fcis-14563	200	12	the	the	DET
fcis-14563	200	13	recognition	recognition	NOUN
fcis-14563	200	14	accuracy	accuracy	NOUN
fcis-14563	200	15	of	of	ADP
fcis-14563	200	16	the	the	DET
fcis-14563	200	17	model	model	NOUN
fcis-14563	200	18	for	for	ADP
fcis-14563	200	19	8	8	NUM
fcis-14563	200	20	types	type	NOUN
fcis-14563	200	21	of	of	ADP
fcis-14563	200	22	disturbance	disturbance	NOUN
fcis-14563	200	23	with	with	ADP
fcis-14563	200	24	only	only	ADV
fcis-14563	200	25	one	one	NUM
fcis-14563	200	26	disturbance	disturbance	NOUN
fcis-14563	200	27	is	be	AUX
fcis-14563	200	28	higher	high	ADJ
fcis-14563	200	29	than	than	ADP
fcis-14563	200	30	99	99	NUM
fcis-14563	200	31	%	%	NOUN
fcis-14563	200	32	.	.	PUNCT
fcis-14563	201	1	the	the	DET
fcis-14563	201	2	recognition	recognition	NOUN
fcis-14563	201	3	accuracy	accuracy	NOUN
fcis-14563	201	4	of	of	ADP
fcis-14563	201	5	20	20	NUM
fcis-14563	201	6	kinds	kind	NOUN
fcis-14563	201	7	of	of	ADP
fcis-14563	201	8	disturbances	disturbance	NOUN
fcis-14563	201	9	including	include	VERB
fcis-14563	201	10	single	single	ADJ
fcis-14563	201	11	disturbance	disturbance	NOUN
fcis-14563	201	12	and	and	CCONJ
fcis-14563	201	13	double	double	ADJ
fcis-14563	201	14	compound	compound	NOUN
fcis-14563	201	15	disturbance	disturbance	NOUN
fcis-14563	201	16	is	be	AUX
fcis-14563	201	17	greater	great	ADJ
fcis-14563	201	18	than	than	ADP
fcis-14563	201	19	96	96	NUM
fcis-14563	201	20	%	%	NOUN
fcis-14563	201	21	;	;	PUNCT
fcis-14563	201	22	for	for	ADP
fcis-14563	201	23	40	40	NUM
fcis-14563	201	24	types	type	NOUN
fcis-14563	201	25	of	of	ADP
fcis-14563	201	26	multiple	multiple	ADJ
fcis-14563	201	27	complex	complex	ADJ
fcis-14563	201	28	disturbances	disturbance	NOUN
fcis-14563	201	29	containing	contain	VERB
fcis-14563	201	30	single	single	ADJ
fcis-14563	201	31	,	,	PUNCT
fcis-14563	201	32	double	double	ADJ
fcis-14563	201	33	,	,	PUNCT
fcis-14563	201	34	triple	triple	ADJ
fcis-14563	201	35	and	and	CCONJ
fcis-14563	201	36	quadruple	quadruple	ADJ
fcis-14563	201	37	disturbances	disturbance	NOUN
fcis-14563	201	38	,	,	PUNCT
fcis-14563	201	39	the	the	DET
fcis-14563	201	40	recognition	recognition	NOUN
fcis-14563	201	41	accuracy	accuracy	NOUN
fcis-14563	201	42	is	be	AUX
fcis-14563	201	43	above	above	ADP
fcis-14563	201	44	92	92	NUM
fcis-14563	201	45	%	%	NOUN
fcis-14563	201	46	.	.	PUNCT
fcis-14563	202	1	compared	compare	VERB
fcis-14563	202	2	with	with	ADP
fcis-14563	202	3	the	the	DET
fcis-14563	202	4	other	other	ADJ
fcis-14563	202	5	three	three	NUM
fcis-14563	202	6	methods	method	NOUN
fcis-14563	202	7	,	,	PUNCT
fcis-14563	202	8	the	the	DET
fcis-14563	202	9	recognition	recognition	NOUN
fcis-14563	202	10	accuracy	accuracy	NOUN
fcis-14563	202	11	and	and	CCONJ
fcis-14563	202	12	noise	noise	NOUN
fcis-14563	202	13	resistance	resistance	NOUN
fcis-14563	202	14	of	of	ADP
fcis-14563	202	15	the	the	DET
fcis-14563	202	16	proposed	propose	VERB
fcis-14563	202	17	method	method	NOUN
fcis-14563	202	18	are	be	AUX
fcis-14563	202	19	improved	improve	VERB
fcis-14563	202	20	in	in	ADP
fcis-14563	202	21	different	different	ADJ
fcis-14563	202	22	degrees	degree	NOUN
fcis-14563	202	23	,	,	PUNCT
fcis-14563	202	24	which	which	PRON
fcis-14563	202	25	proves	prove	VERB
fcis-14563	202	26	the	the	DET
fcis-14563	202	27	feasibility	feasibility	NOUN
fcis-14563	202	28	of	of	ADP
fcis-14563	202	29	the	the	DET
fcis-14563	202	30	model	model	NOUN
fcis-14563	202	31	and	and	CCONJ
fcis-14563	202	32	provides	provide	VERB
fcis-14563	202	33	a	a	DET
fcis-14563	202	34	new	new	ADJ
fcis-14563	202	35	idea	idea	NOUN
fcis-14563	202	36	for	for	ADP
fcis-14563	202	37	dealing	deal	VERB
fcis-14563	202	38	with	with	ADP
fcis-14563	202	39	today	today	NOUN
fcis-14563	202	40	's	's	PART
fcis-14563	202	41	complex	complex	ADJ
fcis-14563	202	42	types	type	NOUN
fcis-14563	202	43	of	of	ADP
fcis-14563	202	44	power	power	NOUN
fcis-14563	202	45	quality	quality	NOUN
fcis-14563	202	46	disturbances	disturbance	NOUN
fcis-14563	202	47	.	.	PUNCT
fcis-14563	203	1	references	reference	NOUN
fcis-14563	203	2	[	[	X
fcis-14563	203	3	1	1	NUM
fcis-14563	203	4	]	]	PUNCT
fcis-14563	203	5	wang	wang	PROPN
fcis-14563	203	6	weibo	weibo	PROPN
fcis-14563	203	7	,	,	PUNCT
fcis-14563	203	8	zhang	zhang	PROPN
fcis-14563	203	9	bin	bin	PROPN
fcis-14563	203	10	,	,	PUNCT
fcis-14563	203	11	zeng	zeng	PROPN
fcis-14563	203	12	wenyu	wenyu	PROPN
fcis-14563	203	13	,	,	PUNCT
fcis-14563	203	14	et	et	PROPN
fcis-14563	203	15	al	al	PROPN
fcis-14563	203	16	.	.	PUNCT
fcis-14563	203	17	power	power	NOUN
fcis-14563	203	18	quality	quality	NOUN
fcis-14563	203	19	disturbance	disturbance	NOUN
fcis-14563	203	20	classification	classification	NOUN
fcis-14563	203	21	based	base	VERB
fcis-14563	203	22	on	on	ADP
fcis-14563	203	23	feature	feature	NOUN
fcis-14563	203	24	fusion	fusion	NOUN
fcis-14563	203	25	onedimensional	onedimensional	ADJ
fcis-14563	203	26	convolutional	convolutional	ADJ
fcis-14563	203	27	neural	neural	ADJ
fcis-14563	203	28	network	network	NOUN
fcis-14563	203	29	[	[	X
fcis-14563	203	30	j	j	X
fcis-14563	203	31	]	]	X
fcis-14563	203	32	.	.	PUNCT
fcis-14563	204	1	power	power	NOUN
fcis-14563	204	2	system	system	NOUN
fcis-14563	204	3	protection	protection	NOUN
fcis-14563	204	4	and	and	CCONJ
fcis-14563	204	5	control	control	NOUN
fcis-14563	204	6	,	,	PUNCT
fcis-14563	204	7	2020	2020	NUM
fcis-14563	204	8	,	,	PUNCT
fcis-14563	204	9	48(06	48(06	NUM
fcis-14563	204	10	):	):	PUNCT
fcis-14563	204	11	53	53	NUM
fcis-14563	204	12	-	-	SYM
fcis-14563	204	13	60	60	NUM
fcis-14563	204	14	.	.	PUNCT
fcis-14563	205	1	[	[	X
fcis-14563	205	2	2	2	NUM
fcis-14563	205	3	]	]	PUNCT
fcis-14563	205	4	sebastijan	sebastijan	NOUN
fcis-14563	205	5	s	s	PROPN
fcis-14563	205	6	,	,	PUNCT
fcis-14563	205	7	niko	niko	PROPN
fcis-14563	205	8	l	l	PROPN
fcis-14563	205	9	,	,	PUNCT
fcis-14563	205	10	boheme	boheme	PROPN
fcis-14563	205	11	,	,	PUNCT
fcis-14563	205	12	et	et	PROPN
fcis-14563	205	13	al	al	PROPN
fcis-14563	205	14	.	.	PUNCT
fcis-14563	205	15	power	power	NOUN
fcis-14563	205	16	quality	quality	NOUN
fcis-14563	205	17	experimental	experimental	ADJ
fcis-14563	205	18	analysis	analysis	NOUN
fcis-14563	205	19	of	of	ADP
fcis-14563	205	20	grid	grid	NOUN
fcis-14563	205	21	-	-	PUNCT
fcis-14563	205	22	connected	connect	VERB
fcis-14563	205	23	photovoltaic	photovoltaic	NOUN
fcis-14563	205	24	systems	system	NOUN
fcis-14563	205	25	in	in	ADP
fcis-14563	205	26	urban	urban	ADJ
fcis-14563	205	27	distribution	distribution	NOUN
fcis-14563	205	28	networks[j	networks[j	PROPN
fcis-14563	205	29	]	]	PUNCT
fcis-14563	205	30	.	.	PUNCT
fcis-14563	206	1	energy	energy	NOUN
fcis-14563	206	2	,	,	PUNCT
fcis-14563	206	3	2017	2017	NUM
fcis-14563	206	4	,	,	PUNCT
fcis-14563	206	5	139	139	NUM
fcis-14563	206	6	.	.	PUNCT
fcis-14563	207	1	[	[	X
fcis-14563	207	2	3	3	X
fcis-14563	207	3	]	]	X
fcis-14563	207	4	jin	jin	NOUN
fcis-14563	207	5	guo	guo	PROPN
fcis-14563	207	6	,	,	PUNCT
fcis-14563	207	7	zhu	zhu	PROPN
fcis-14563	207	8	qing	qing	PROPN
fcis-14563	207	9	-	-	PUNCT
fcis-14563	207	10	zhi	zhi	PROPN
fcis-14563	207	11	,	,	PUNCT
fcis-14563	207	12	meng	meng	PROPN
fcis-14563	207	13	yang	yang	PROPN
fcis-14563	207	14	,	,	PUNCT
fcis-14563	207	15	et	et	PROPN
fcis-14563	207	16	al	al	PROPN
fcis-14563	207	17	.	.	PUNCT
fcis-14563	207	18	power	power	NOUN
fcis-14563	207	19	quality	quality	PROPN
fcis-14563	207	20	disturbance	disturbance	NOUN
fcis-14563	207	21	multi	multi	ADJ
fcis-14563	207	22	-	-	ADJ
fcis-14563	207	23	label	label	ADJ
fcis-14563	207	24	classification	classification	NOUN
fcis-14563	207	25	algorithm	algorithm	NOUN
fcis-14563	207	26	based	base	VERB
fcis-14563	207	27	on	on	ADP
fcis-14563	207	28	multilayer	multilayer	ADJ
fcis-14563	207	29	extreme	extreme	ADJ
fcis-14563	207	30	learning	learning	NOUN
fcis-14563	207	31	machine	machine	NOUN
fcis-14563	208	1	[	[	X
fcis-14563	208	2	j	j	X
fcis-14563	208	3	]	]	X
fcis-14563	208	4	.	.	PUNCT
fcis-14563	209	1	power	power	NOUN
fcis-14563	209	2	system	system	NOUN
fcis-14563	209	3	protection	protection	NOUN
fcis-14563	209	4	and	and	CCONJ
fcis-14563	209	5	control	control	NOUN
fcis-14563	209	6	,	,	PUNCT
fcis-14563	209	7	2020	2020	NUM
fcis-14563	209	8	,	,	PUNCT
fcis-14563	209	9	48(08	48(08	NUM
fcis-14563	209	10	):	):	PUNCT
fcis-14563	209	11	96	96	NUM
fcis-14563	209	12	-	-	SYM
fcis-14563	209	13	105	105	NUM
fcis-14563	209	14	.	.	PUNCT
fcis-14563	210	1	[	[	X
fcis-14563	210	2	4	4	X
fcis-14563	210	3	]	]	X
fcis-14563	210	4	huang	huang	PROPN
fcis-14563	210	5	jianming	jianming	PROPN
fcis-14563	210	6	,	,	PUNCT
fcis-14563	210	7	li	li	PROPN
fcis-14563	210	8	xiaoming	xiaoming	PROPN
fcis-14563	210	9	.	.	PUNCT
fcis-14563	211	1	harmonic	harmonic	ADJ
fcis-14563	211	2	detection	detection	NOUN
fcis-14563	211	3	method	method	NOUN
fcis-14563	211	4	of	of	ADP
fcis-14563	211	5	power	power	NOUN
fcis-14563	211	6	system	system	NOUN
fcis-14563	211	7	combined	combine	VERB
fcis-14563	211	8	with	with	ADP
fcis-14563	211	9	short	short	ADJ
fcis-14563	211	10	-	-	PUNCT
fcis-14563	211	11	time	time	NOUN
fcis-14563	211	12	fourier	fourier	NOUN
fcis-14563	211	13	transform	transform	NOUN
fcis-14563	211	14	and	and	CCONJ
fcis-14563	211	15	spectral	spectral	ADJ
fcis-14563	211	16	steepness[j	steepness[j	PROPN
fcis-14563	211	17	]	]	PUNCT
fcis-14563	211	18	.	.	PUNCT
fcis-14563	212	1	power	power	NOUN
fcis-14563	212	2	system	system	NOUN
fcis-14563	212	3	protection	protection	NOUN
fcis-14563	212	4	and	and	CCONJ
fcis-14563	212	5	control	control	NOUN
fcis-14563	212	6	,	,	PUNCT
fcis-14563	212	7	2017	2017	NUM
fcis-14563	212	8	,	,	PUNCT
fcis-14563	212	9	45(07	45(07	NUM
fcis-14563	212	10	):	):	PUNCT
fcis-14563	212	11	43	43	NUM
fcis-14563	212	12	-	-	SYM
fcis-14563	212	13	50	50	NUM
fcis-14563	212	14	.	.	PUNCT
fcis-14563	213	1	[	[	X
fcis-14563	213	2	5	5	NUM
fcis-14563	213	3	]	]	PUNCT
fcis-14563	213	4	tao	tao	PROPN
fcis-14563	213	5	caixia	caixia	PROPN
fcis-14563	213	6	,	,	PUNCT
fcis-14563	213	7	du	du	PROPN
fcis-14563	213	8	xue	xue	PROPN
fcis-14563	213	9	,	,	PUNCT
fcis-14563	213	10	gao	gao	PROPN
fcis-14563	213	11	fengyang	fengyang	PROPN
fcis-14563	213	12	,	,	PUNCT
fcis-14563	213	13	et	et	PROPN
fcis-14563	213	14	al	al	PROPN
fcis-14563	213	15	.	.	PUNCT
fcis-14563	214	1	fault	fault	VERB
fcis-14563	214	2	location	location	NOUN
fcis-14563	214	3	for	for	ADP
fcis-14563	214	4	single	single	ADJ
fcis-14563	214	5	-	-	PUNCT
fcis-14563	214	6	phase	phase	NOUN
fcis-14563	214	7	grounding	grounding	NOUN
fcis-14563	214	8	of	of	ADP
fcis-14563	214	9	hybrid	hybrid	ADJ
fcis-14563	214	10	transmission	transmission	NOUN
fcis-14563	214	11	lines	line	NOUN
fcis-14563	214	12	based	base	VERB
fcis-14563	214	13	on	on	ADP
fcis-14563	214	14	empirical	empirical	ADJ
fcis-14563	214	15	wavelet	wavelet	NOUN
fcis-14563	214	16	transform	transform	NOUN
fcis-14563	214	17	[	[	X
fcis-14563	214	18	j	j	X
fcis-14563	214	19	]	]	X
fcis-14563	214	20	.	.	PUNCT
fcis-14563	215	1	power	power	NOUN
fcis-14563	215	2	system	system	NOUN
fcis-14563	215	3	protection	protection	NOUN
fcis-14563	215	4	and	and	CCONJ
fcis-14563	215	5	control	control	NOUN
fcis-14563	215	6	,	,	PUNCT
fcis-14563	215	7	2021	2021	NUM
fcis-14563	215	8	,	,	PUNCT
fcis-14563	215	9	49(10	49(10	NUM
fcis-14563	215	10	):	):	PUNCT
fcis-14563	215	11	105	105	NUM
fcis-14563	215	12	-	-	SYM
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fcis-14563	215	14	.	.	PUNCT
fcis-14563	216	1	[	[	X
fcis-14563	216	2	6	6	NUM
fcis-14563	216	3	]	]	X
fcis-14563	216	4	cheng	cheng	PROPN
fcis-14563	216	5	zhiyou	zhiyou	PROPN
fcis-14563	216	6	,	,	PUNCT
fcis-14563	216	7	yang	yang	PROPN
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fcis-14563	216	9	.	.	PUNCT
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fcis-14563	217	4	type	type	NOUN
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fcis-14563	217	8	two	two	NUM
fcis-14563	217	9	-	-	PUNCT
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fcis-14563	217	11	discrete	discrete	ADJ
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fcis-14563	217	13	s	s	PART
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fcis-14563	217	17	]	]	X
fcis-14563	217	18	.	.	PUNCT
fcis-14563	218	1	power	power	NOUN
fcis-14563	218	2	system	system	NOUN
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fcis-14563	218	4	and	and	CCONJ
fcis-14563	218	5	control	control	NOUN
fcis-14563	218	6	,	,	PUNCT
fcis-14563	218	7	2021	2021	NUM
fcis-14563	218	8	,	,	PUNCT
fcis-14563	218	9	49(17	49(17	PROPN
fcis-14563	218	10	):	):	PUNCT
fcis-14563	218	11	85	85	NUM
fcis-14563	218	12	-	-	SYM
fcis-14563	218	13	92	92	NUM
fcis-14563	218	14	.	.	PUNCT
fcis-14563	219	1	(	(	PUNCT
fcis-14563	219	2	in	in	ADP
fcis-14563	219	3	chinese	chinese	PROPN
fcis-14563	219	4	)	)	PUNCT
fcis-14563	220	1	[	[	X
fcis-14563	220	2	7	7	X
fcis-14563	220	3	]	]	X
fcis-14563	220	4	luo	luo	PROPN
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fcis-14563	220	6	,	,	PUNCT
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fcis-14563	220	9	,	,	PUNCT
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fcis-14563	220	12	.	.	PUNCT
fcis-14563	220	13	research	research	NOUN
fcis-14563	220	14	on	on	ADP
fcis-14563	220	15	detection	detection	NOUN
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fcis-14563	220	17	identification	identification	NOUN
fcis-14563	220	18	of	of	ADP
fcis-14563	220	19	transient	transient	ADJ
fcis-14563	220	20	power	power	NOUN
fcis-14563	220	21	quality	quality	NOUN
fcis-14563	220	22	disturbance	disturbance	NOUN
fcis-14563	220	23	in	in	ADP
fcis-14563	220	24	microgrid	microgrid	NOUN
fcis-14563	220	25	based	base	VERB
fcis-14563	220	26	on	on	ADP
fcis-14563	220	27	wavelet	wavelet	NOUN
fcis-14563	220	28	transform	transform	NOUN
fcis-14563	220	29	and	and	CCONJ
fcis-14563	220	30	hilbert	hilbert	NOUN
fcis-14563	220	31	-	-	PUNCT
fcis-14563	220	32	yellow	yellow	ADJ
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fcis-14563	220	34	[	[	X
fcis-14563	220	35	j	j	X
fcis-14563	220	36	]	]	X
fcis-14563	220	37	.	.	PUNCT
fcis-14563	221	1	power	power	NOUN
fcis-14563	221	2	capacitors	capacitor	NOUN
fcis-14563	221	3	and	and	CCONJ
fcis-14563	221	4	reactive	reactive	ADJ
fcis-14563	221	5	power	power	NOUN
fcis-14563	221	6	compensation	compensation	NOUN
fcis-14563	221	7	,	,	PUNCT
fcis-14563	221	8	2020	2020	NUM
fcis-14563	221	9	,	,	PUNCT
fcis-14563	221	10	41(03	41(03	NUM
fcis-14563	221	11	):	):	PUNCT
fcis-14563	221	12	182	182	NUM
fcis-14563	221	13	-	-	SYM
fcis-14563	221	14	188	188	NUM
fcis-14563	221	15	.	.	PUNCT
fcis-14563	222	1	[	[	X
fcis-14563	222	2	8	8	NUM
fcis-14563	222	3	]	]	X
fcis-14563	222	4	li	li	PROPN
fcis-14563	222	5	xiaona	xiaona	PROPN
fcis-14563	222	6	,	,	PUNCT
fcis-14563	222	7	shen	shen	PROPN
fcis-14563	222	8	xing	xing	PROPN
fcis-14563	222	9	-	-	PUNCT
fcis-14563	222	10	lai	lai	PROPN
fcis-14563	222	11	,	,	PUNCT
fcis-14563	222	12	xue	xue	PROPN
fcis-14563	222	13	xue	xue	PROPN
fcis-14563	222	14	,	,	PUNCT
fcis-14563	222	15	et	et	PROPN
fcis-14563	222	16	al	al	PROPN
fcis-14563	222	17	.	.	PUNCT
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fcis-14563	222	19	quality	quality	NOUN
fcis-14563	222	20	disturbance	disturbance	NOUN
fcis-14563	222	21	identification	identification	NOUN
fcis-14563	222	22	based	base	VERB
fcis-14563	222	23	on	on	ADP
fcis-14563	222	24	improved	improve	VERB
fcis-14563	222	25	hht	hht	PROPN
fcis-14563	222	26	and	and	CCONJ
fcis-14563	222	27	decision	decision	NOUN
fcis-14563	222	28	tree	tree	NOUN
fcis-14563	223	1	[	[	X
fcis-14563	223	2	j	j	X
fcis-14563	223	3	]	]	X
fcis-14563	223	4	.	.	PUNCT
fcis-14563	224	1	journal	journal	PROPN
fcis-14563	224	2	of	of	ADP
fcis-14563	224	3	electric	electric	ADJ
fcis-14563	224	4	power	power	NOUN
fcis-14563	224	5	construction	construction	NOUN
fcis-14563	224	6	,	,	PUNCT
fcis-14563	224	7	2017	2017	NUM
fcis-14563	224	8	,	,	PUNCT
fcis-14563	224	9	38(02	38(02	NUM
fcis-14563	224	10	):	):	PUNCT
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fcis-14563	224	12	-	-	SYM
fcis-14563	224	13	121	121	NUM
fcis-14563	224	14	.	.	PUNCT
fcis-14563	225	1	[	[	X
fcis-14563	225	2	9	9	NUM
fcis-14563	225	3	]	]	X
fcis-14563	225	4	wang	wang	PROPN
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fcis-14563	225	6	,	,	PUNCT
fcis-14563	225	7	zhu	zhu	PROPN
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fcis-14563	225	9	,	,	PUNCT
fcis-14563	225	10	zhao	zhao	PROPN
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fcis-14563	225	12	,	,	PUNCT
fcis-14563	225	13	et	et	PROPN
fcis-14563	225	14	al	al	PROPN
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fcis-14563	225	19	outage	outage	NOUN
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fcis-14563	225	21	based	base	VERB
fcis-14563	225	22	on	on	ADP
fcis-14563	225	23	deep	deep	ADJ
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fcis-14563	225	25	neural	neural	ADJ
fcis-14563	225	26	network	network	NOUN
fcis-14563	225	27	and	and	CCONJ
fcis-14563	225	28	gis	gis	PROPN
fcis-14563	225	29	data	datum	NOUN
fcis-14563	226	1	[	[	X
fcis-14563	226	2	j	j	X
fcis-14563	226	3	]	]	X
fcis-14563	226	4	.	.	PUNCT
fcis-14563	227	1	power	power	NOUN
fcis-14563	227	2	system	system	NOUN
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fcis-14563	227	6	,	,	PUNCT
fcis-14563	227	7	2019	2019	NUM
fcis-14563	227	8	,	,	PUNCT
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fcis-14563	227	10	):	):	PUNCT
fcis-14563	227	11	58	58	NUM
fcis-14563	227	12	-	-	SYM
fcis-14563	227	13	63	63	NUM
fcis-14563	227	14	.	.	PUNCT
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fcis-14563	228	2	10	10	NUM
fcis-14563	228	3	]	]	X
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fcis-14563	228	6	,	,	PUNCT
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fcis-14563	228	9	.	.	PUNCT
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fcis-14563	229	8	svm	svm	PROPN
fcis-14563	229	9	[	[	X
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fcis-14563	229	11	]	]	X
fcis-14563	229	12	.	.	PUNCT
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fcis-14563	230	5	,	,	PUNCT
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fcis-14563	230	7	,	,	PUNCT
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fcis-14563	230	9	):	):	PUNCT
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fcis-14563	230	11	-	-	SYM
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fcis-14563	230	15	.	.	PUNCT
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fcis-14563	232	1	[	[	X
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fcis-14563	232	4	.	.	PUNCT
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fcis-14563	233	6	,	,	PUNCT
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fcis-14563	233	8	,	,	PUNCT
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fcis-14563	233	10	):	):	PUNCT
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fcis-14563	233	12	-	-	SYM
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fcis-14563	233	14	.	.	PUNCT
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fcis-14563	235	1	[	[	X
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fcis-14563	235	4	.	.	PUNCT
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fcis-14563	236	6	,	,	PUNCT
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fcis-14563	236	8	,	,	PUNCT
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fcis-14563	236	10	):	):	PUNCT
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fcis-14563	236	12	-	-	SYM
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fcis-14563	236	14	.	.	PUNCT
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fcis-14563	239	1	[	[	X
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fcis-14563	239	3	]	]	X
fcis-14563	239	4	.	.	PUNCT
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fcis-14563	239	11	,	,	PUNCT
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fcis-14563	239	13	,	,	PUNCT
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fcis-14563	239	15	):	):	PUNCT
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fcis-14563	239	17	-	-	SYM
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fcis-14563	239	19	.	.	PUNCT
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fcis-14563	240	3	]	]	PUNCT
fcis-14563	240	4	xu	xu	PROPN
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fcis-14563	240	9	,	,	PUNCT
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fcis-14563	240	13	et	et	PROPN
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fcis-14563	240	33	cmpe	cmpe	NOUN
fcis-14563	241	1	[	[	X
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fcis-14563	241	3	]	]	X
fcis-14563	241	4	.	.	PUNCT
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fcis-14563	242	4	,	,	PUNCT
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fcis-14563	242	6	,	,	PUNCT
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fcis-14563	242	8	):	):	PUNCT
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fcis-14563	242	10	-	-	SYM
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fcis-14563	242	12	.	.	PUNCT
fcis-14563	243	1	[	[	X
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fcis-14563	243	3	]	]	X
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fcis-14563	243	12	.	.	PUNCT
fcis-14563	244	1	fault	fault	VERB
fcis-14563	244	2	diagnosis	diagnosis	NOUN
fcis-14563	244	3	method	method	NOUN
fcis-14563	244	4	of	of	ADP
fcis-14563	244	5	rolling	rolling	ADJ
fcis-14563	244	6	bearing	bearing	NOUN
fcis-14563	244	7	based	base	VERB
fcis-14563	244	8	on	on	ADP
fcis-14563	244	9	gram	gram	NOUN
fcis-14563	244	10	angle	angle	NOUN
fcis-14563	244	11	field	field	NOUN
fcis-14563	244	12	and	and	CCONJ
fcis-14563	244	13	cnn	cnn	PROPN
fcis-14563	244	14	-	-	PUNCT
fcis-14563	244	15	rnn	rnn	PROPN
fcis-14563	245	1	[	[	X
fcis-14563	245	2	j	j	X
fcis-14563	245	3	]	]	X
fcis-14563	245	4	.	.	PUNCT
fcis-14563	246	1	journal	journal	PROPN
fcis-14563	246	2	of	of	ADP
fcis-14563	246	3	bearings	bearing	NOUN
fcis-14563	246	4	,	,	PUNCT
fcis-14563	246	5	2022(02	2022(02	NUM
fcis-14563	246	6	):	):	PUNCT
fcis-14563	246	7	61	61	NUM
fcis-14563	246	8	-	-	SYM
fcis-14563	246	9	67	67	NUM
fcis-14563	246	10	.	.	PUNCT
fcis-14563	247	1	[	[	X
fcis-14563	247	2	16	16	NUM
fcis-14563	247	3	]	]	X
fcis-14563	247	4	yang	yang	PROPN
fcis-14563	247	5	wei	wei	PROPN
fcis-14563	247	6	,	,	PUNCT
fcis-14563	247	7	pu	pu	PROPN
fcis-14563	247	8	caixia	caixia	PROPN
fcis-14563	247	9	,	,	PUNCT
fcis-14563	247	10	yang	yang	PROPN
fcis-14563	247	11	kun	kun	PROPN
fcis-14563	247	12	,	,	PUNCT
fcis-14563	247	13	et	et	PROPN
fcis-14563	247	14	al	al	PROPN
fcis-14563	247	15	.	.	PUNCT
fcis-14563	247	16	short	short	ADJ
fcis-14563	247	17	-	-	PUNCT
fcis-14563	247	18	term	term	NOUN
fcis-14563	247	19	fault	fault	NOUN
fcis-14563	247	20	prediction	prediction	NOUN
fcis-14563	247	21	method	method	NOUN
fcis-14563	247	22	of	of	ADP
fcis-14563	247	23	transformer	transformer	NOUN
fcis-14563	247	24	based	base	VERB
fcis-14563	247	25	on	on	ADP
fcis-14563	247	26	cnn	cnn	PROPN
fcis-14563	247	27	-	-	PUNCT
fcis-14563	247	28	gru	gru	PROPN
fcis-14563	247	29	combined	combine	VERB
fcis-14563	247	30	neural	neural	ADJ
fcis-14563	247	31	network	network	NOUN
fcis-14563	247	32	[	[	X
fcis-14563	247	33	j	j	X
fcis-14563	247	34	]	]	X
fcis-14563	247	35	.	.	PUNCT
fcis-14563	248	1	power	power	NOUN
fcis-14563	248	2	systems	system	NOUN
fcis-14563	248	3	protection	protection	NOUN
fcis-14563	248	4	and	and	CCONJ
fcis-14563	248	5	control	control	NOUN
fcis-14563	248	6	,	,	PUNCT
fcis-14563	248	7	2022	2022	NUM
fcis-14563	248	8	,	,	PUNCT
fcis-14563	248	9	50(06	50(06	NUM
fcis-14563	248	10	):	):	PUNCT
fcis-14563	248	11	107	107	NUM
fcis-14563	248	12	-	-	SYM
fcis-14563	248	13	116	116	NUM
fcis-14563	248	14	.	.	PUNCT
fcis-14563	249	1	[	[	X
fcis-14563	249	2	17	17	NUM
fcis-14563	249	3	]	]	X
fcis-14563	249	4	xu	xu	PROPN
fcis-14563	249	5	dong	dong	PROPN
fcis-14563	249	6	,	,	PUNCT
fcis-14563	249	7	yang	yang	PROPN
fcis-14563	249	8	guan	guan	PROPN
fcis-14563	249	9	,	,	PUNCT
fcis-14563	249	10	liu	liu	PROPN
fcis-14563	249	11	xiaoming	xiaoming	PROPN
fcis-14563	249	12	,	,	PUNCT
fcis-14563	249	13	et	et	PROPN
fcis-14563	249	14	al	al	PROPN
fcis-14563	249	15	.	.	PUNCT
fcis-14563	250	1	small	small	ADJ
fcis-14563	250	2	sample	sample	NOUN
fcis-14563	250	3	image	image	NOUN
fcis-14563	250	4	classification	classification	NOUN
fcis-14563	250	5	based	base	VERB
fcis-14563	250	6	on	on	ADP
fcis-14563	250	7	adaptive	adaptive	ADJ
fcis-14563	250	8	feature	feature	NOUN
fcis-14563	250	9	fusion	fusion	NOUN
fcis-14563	250	10	and	and	CCONJ
fcis-14563	250	11	transformation	transformation	NOUN
fcis-14563	251	1	[	[	X
fcis-14563	251	2	j	j	X
fcis-14563	251	3	]	]	X
fcis-14563	251	4	.	.	PUNCT
fcis-14563	252	1	computer	computer	NOUN
fcis-14563	252	2	engineering	engineering	NOUN
fcis-14563	252	3	and	and	CCONJ
fcis-14563	252	4	applications	application	NOUN
fcis-14563	252	5	,	,	PUNCT
fcis-14563	252	6	2022	2022	NUM
fcis-14563	252	7	,	,	PUNCT
fcis-14563	252	8	58(24	58(24	NUM
fcis-14563	252	9	):	):	PUNCT
fcis-14563	252	10	223	223	NUM
fcis-14563	252	11	-	-	SYM
fcis-14563	252	12	232	232	NUM
fcis-14563	252	13	.	.	PUNCT
fcis-14563	253	1	[	[	X
fcis-14563	253	2	18	18	NUM
fcis-14563	253	3	]	]	PUNCT
fcis-14563	253	4	song	song	NOUN
fcis-14563	253	5	tiewei	tiewei	PROPN
fcis-14563	253	6	,	,	PUNCT
fcis-14563	253	7	shi	shi	PROPN
fcis-14563	253	8	weifeng	weifeng	PROPN
fcis-14563	253	9	,	,	PUNCT
fcis-14563	253	10	bi	bi	PROPN
fcis-14563	253	11	zong	zong	PROPN
fcis-14563	253	12	,	,	PUNCT
fcis-14563	253	13	et	et	PROPN
fcis-14563	253	14	al	al	PROPN
fcis-14563	253	15	.	.	PUNCT
fcis-14563	253	16	power	power	NOUN
fcis-14563	253	17	quality	quality	NOUN
fcis-14563	253	18	disturbance	disturbance	NOUN
fcis-14563	253	19	identification	identification	NOUN
fcis-14563	253	20	of	of	ADP
fcis-14563	253	21	marine	marine	ADJ
fcis-14563	253	22	power	power	NOUN
fcis-14563	253	23	system	system	NOUN
fcis-14563	253	24	based	base	VERB
fcis-14563	253	25	on	on	ADP
fcis-14563	253	26	2d	2d	NOUN
fcis-14563	253	27	-	-	PUNCT
fcis-14563	253	28	resnet	resnet	NOUN
fcis-14563	253	29	[	[	X
fcis-14563	253	30	j	j	X
fcis-14563	253	31	]	]	X
fcis-14563	253	32	.	.	PUNCT
fcis-14563	254	1	power	power	NOUN
fcis-14563	254	2	system	system	NOUN
fcis-14563	254	3	protection	protection	NOUN
fcis-14563	254	4	and	and	CCONJ
fcis-14563	254	5	control	control	NOUN
fcis-14563	254	6	,	,	PUNCT
fcis-14563	254	7	2022	2022	NUM
fcis-14563	254	8	,	,	PUNCT
fcis-14563	254	9	50(10	50(10	NUM
fcis-14563	254	10	):	):	PUNCT
fcis-14563	254	11	94	94	NUM
fcis-14563	254	12	-	-	SYM
fcis-14563	254	13	103	103	NUM
fcis-14563	254	14	.	.	PUNCT