Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method

A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the upload...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Guo Dongdong [verfasserIn] Yu Quanshun [verfasserIn] Ren Shuojin [verfasserIn] Wang Tao [verfasserIn] Shao Pengfei [verfasserIn] Yang Jianglong [verfasserIn] Shi Fulu [verfasserIn] Li Tengteng [verfasserIn] Zhang Chao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; Französisch

Erschienen:	2023

Schlagwörter:	heavy-duty vehicle moving average remote monitoring work-based window method

Übergeordnetes Werk:	In: E3S Web of Conferences - EDP Sciences, 2013, 438, p 01003(2023)
Übergeordnetes Werk:	volume:438, p 01003 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1051/e3sconf/202343801003

Katalog-ID:	DOAJ091056756

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ091056756
003	DE-627
005	20240413223138.0
007	cr uuu---uuuuu
008	240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1051/e3sconf/202343801003 \|2 doi
035			\|a (DE-627)DOAJ091056756
035			\|a (DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng \|a fre
050		0	\|a GE1-350
100	0		\|a Guo Dongdong \|e verfasserin \|4 aut
245	1	0	\|a Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions.
650		4	\|a heavy-duty vehicle
650		4	\|a moving average
650		4	\|a remote monitoring
650		4	\|a work-based window method
653		0	\|a Environmental sciences
700	0		\|a Yu Quanshun \|e verfasserin \|4 aut
700	0		\|a Ren Shuojin \|e verfasserin \|4 aut
700	0		\|a Wang Tao \|e verfasserin \|4 aut
700	0		\|a Shao Pengfei \|e verfasserin \|4 aut
700	0		\|a Yang Jianglong \|e verfasserin \|4 aut
700	0		\|a Shi Fulu \|e verfasserin \|4 aut
700	0		\|a Li Tengteng \|e verfasserin \|4 aut
700	0		\|a Zhang Chao \|e verfasserin \|4 aut
773	0	8	\|i In \|t E3S Web of Conferences \|d EDP Sciences, 2013 \|g 438, p 01003(2023) \|w (DE-627)778372081 \|w (DE-600)2755680-3 \|x 22671242 \|7 nnns
773	1	8	\|g volume:438, p 01003 \|g year:2023
856	4	0	\|u https://doi.org/10.1051/e3sconf/202343801003 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c \|z kostenfrei
856	4	0	\|u https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2267-1242 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 438, p 01003 \|j 2023

Indexfelder

author_variant	g d gd y q yq r s rs w t wt s p sp y j yj s f sf l t lt z c zc
matchkey_str	article:22671242:2023----::evdtvhcemsinhrceitcbsdnhrmtmntrntrei
hierarchy_sort_str	2023
callnumber-subject-code	GE
publishDate	2023
allfields	10.1051/e3sconf/202343801003 doi (DE-627)DOAJ091056756 (DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c DE-627 ger DE-627 rakwb eng fre GE1-350 Guo Dongdong verfasserin aut Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions. heavy-duty vehicle moving average remote monitoring work-based window method Environmental sciences Yu Quanshun verfasserin aut Ren Shuojin verfasserin aut Wang Tao verfasserin aut Shao Pengfei verfasserin aut Yang Jianglong verfasserin aut Shi Fulu verfasserin aut Li Tengteng verfasserin aut Zhang Chao verfasserin aut In E3S Web of Conferences EDP Sciences, 2013 438, p 01003(2023) (DE-627)778372081 (DE-600)2755680-3 22671242 nnns volume:438, p 01003 year:2023 https://doi.org/10.1051/e3sconf/202343801003 kostenfrei https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c kostenfrei https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf kostenfrei https://doaj.org/toc/2267-1242 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 438, p 01003 2023
spelling	10.1051/e3sconf/202343801003 doi (DE-627)DOAJ091056756 (DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c DE-627 ger DE-627 rakwb eng fre GE1-350 Guo Dongdong verfasserin aut Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions. heavy-duty vehicle moving average remote monitoring work-based window method Environmental sciences Yu Quanshun verfasserin aut Ren Shuojin verfasserin aut Wang Tao verfasserin aut Shao Pengfei verfasserin aut Yang Jianglong verfasserin aut Shi Fulu verfasserin aut Li Tengteng verfasserin aut Zhang Chao verfasserin aut In E3S Web of Conferences EDP Sciences, 2013 438, p 01003(2023) (DE-627)778372081 (DE-600)2755680-3 22671242 nnns volume:438, p 01003 year:2023 https://doi.org/10.1051/e3sconf/202343801003 kostenfrei https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c kostenfrei https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf kostenfrei https://doaj.org/toc/2267-1242 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 438, p 01003 2023
allfields_unstemmed	10.1051/e3sconf/202343801003 doi (DE-627)DOAJ091056756 (DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c DE-627 ger DE-627 rakwb eng fre GE1-350 Guo Dongdong verfasserin aut Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions. heavy-duty vehicle moving average remote monitoring work-based window method Environmental sciences Yu Quanshun verfasserin aut Ren Shuojin verfasserin aut Wang Tao verfasserin aut Shao Pengfei verfasserin aut Yang Jianglong verfasserin aut Shi Fulu verfasserin aut Li Tengteng verfasserin aut Zhang Chao verfasserin aut In E3S Web of Conferences EDP Sciences, 2013 438, p 01003(2023) (DE-627)778372081 (DE-600)2755680-3 22671242 nnns volume:438, p 01003 year:2023 https://doi.org/10.1051/e3sconf/202343801003 kostenfrei https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c kostenfrei https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf kostenfrei https://doaj.org/toc/2267-1242 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 438, p 01003 2023
allfieldsGer	10.1051/e3sconf/202343801003 doi (DE-627)DOAJ091056756 (DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c DE-627 ger DE-627 rakwb eng fre GE1-350 Guo Dongdong verfasserin aut Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions. heavy-duty vehicle moving average remote monitoring work-based window method Environmental sciences Yu Quanshun verfasserin aut Ren Shuojin verfasserin aut Wang Tao verfasserin aut Shao Pengfei verfasserin aut Yang Jianglong verfasserin aut Shi Fulu verfasserin aut Li Tengteng verfasserin aut Zhang Chao verfasserin aut In E3S Web of Conferences EDP Sciences, 2013 438, p 01003(2023) (DE-627)778372081 (DE-600)2755680-3 22671242 nnns volume:438, p 01003 year:2023 https://doi.org/10.1051/e3sconf/202343801003 kostenfrei https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c kostenfrei https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf kostenfrei https://doaj.org/toc/2267-1242 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 438, p 01003 2023
allfieldsSound	10.1051/e3sconf/202343801003 doi (DE-627)DOAJ091056756 (DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c DE-627 ger DE-627 rakwb eng fre GE1-350 Guo Dongdong verfasserin aut Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions. heavy-duty vehicle moving average remote monitoring work-based window method Environmental sciences Yu Quanshun verfasserin aut Ren Shuojin verfasserin aut Wang Tao verfasserin aut Shao Pengfei verfasserin aut Yang Jianglong verfasserin aut Shi Fulu verfasserin aut Li Tengteng verfasserin aut Zhang Chao verfasserin aut In E3S Web of Conferences EDP Sciences, 2013 438, p 01003(2023) (DE-627)778372081 (DE-600)2755680-3 22671242 nnns volume:438, p 01003 year:2023 https://doi.org/10.1051/e3sconf/202343801003 kostenfrei https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c kostenfrei https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf kostenfrei https://doaj.org/toc/2267-1242 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 438, p 01003 2023
language	English French
source	In E3S Web of Conferences 438, p 01003(2023) volume:438, p 01003 year:2023
sourceStr	In E3S Web of Conferences 438, p 01003(2023) volume:438, p 01003 year:2023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	heavy-duty vehicle moving average remote monitoring work-based window method Environmental sciences
isfreeaccess_bool	true
container_title	E3S Web of Conferences
authorswithroles_txt_mv	Guo Dongdong @@aut@@ Yu Quanshun @@aut@@ Ren Shuojin @@aut@@ Wang Tao @@aut@@ Shao Pengfei @@aut@@ Yang Jianglong @@aut@@ Shi Fulu @@aut@@ Li Tengteng @@aut@@ Zhang Chao @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	778372081
id	DOAJ091056756
language_de	englisch franzoesisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ091056756</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413223138.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1051/e3sconf/202343801003</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ091056756</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield><subfield code="a">fre</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Guo Dongdong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">heavy-duty vehicle</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">moving average</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">remote monitoring</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">work-based window method</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yu Quanshun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ren Shuojin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wang Tao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shao Pengfei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yang Jianglong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shi Fulu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Li Tengteng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhang Chao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">E3S Web of Conferences</subfield><subfield code="d">EDP Sciences, 2013</subfield><subfield code="g">438, p 01003(2023)</subfield><subfield code="w">(DE-627)778372081</subfield><subfield code="w">(DE-600)2755680-3</subfield><subfield code="x">22671242</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:438, p 01003</subfield><subfield code="g">year:2023</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1051/e3sconf/202343801003</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2267-1242</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">438, p 01003</subfield><subfield code="j">2023</subfield></datafield></record></collection>
callnumber-first	G - Geography, Anthropology, Recreation
author	Guo Dongdong
spellingShingle	Guo Dongdong misc GE1-350 misc heavy-duty vehicle misc moving average misc remote monitoring misc work-based window method misc Environmental sciences Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method
authorStr	Guo Dongdong
ppnlink_with_tag_str_mv	@@773@@(DE-627)778372081
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	GE1-350
illustrated	Not Illustrated
issn	22671242
topic_title	GE1-350 Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method heavy-duty vehicle moving average remote monitoring work-based window method
topic	misc GE1-350 misc heavy-duty vehicle misc moving average misc remote monitoring misc work-based window method misc Environmental sciences
topic_unstemmed	misc GE1-350 misc heavy-duty vehicle misc moving average misc remote monitoring misc work-based window method misc Environmental sciences
topic_browse	misc GE1-350 misc heavy-duty vehicle misc moving average misc remote monitoring misc work-based window method misc Environmental sciences
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	E3S Web of Conferences
hierarchy_parent_id	778372081
hierarchy_top_title	E3S Web of Conferences
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)778372081 (DE-600)2755680-3
title	Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method
ctrlnum	(DE-627)DOAJ091056756 (DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c
title_full	Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method
author_sort	Guo Dongdong
journal	E3S Web of Conferences
journalStr	E3S Web of Conferences
callnumber-first-code	G
lang_code	eng fre
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Guo Dongdong Yu Quanshun Ren Shuojin Wang Tao Shao Pengfei Yang Jianglong Shi Fulu Li Tengteng Zhang Chao
container_volume	438, p 01003
class	GE1-350
format_se	Elektronische Aufsätze
author-letter	Guo Dongdong
doi_str_mv	10.1051/e3sconf/202343801003
author2-role	verfasserin
title_sort	heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method
callnumber	GE1-350
title_auth	Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method
abstract	A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions.
abstractGer	A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions.
abstract_unstemmed	A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method
url	https://doi.org/10.1051/e3sconf/202343801003 https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf https://doaj.org/toc/2267-1242
remote_bool	true
author2	Yu Quanshun Ren Shuojin Wang Tao Shao Pengfei Yang Jianglong Shi Fulu Li Tengteng Zhang Chao
author2Str	Yu Quanshun Ren Shuojin Wang Tao Shao Pengfei Yang Jianglong Shi Fulu Li Tengteng Zhang Chao
ppnlink	778372081
callnumber-subject	GE - Environmental Sciences
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1051/e3sconf/202343801003
callnumber-a	GE1-350
up_date	2024-07-03T18:10:37.150Z
_version_	1803582425951895552
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ091056756</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413223138.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240412s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1051/e3sconf/202343801003</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ091056756</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ4884690c21194bd5950728b1afc6cb7c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield><subfield code="a">fre</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GE1-350</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Guo Dongdong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">A three-bin moving average window (3B-MAW) model was proposed and compared with the work-based window method (WB-WM) to investigate the on-road emission characteristics of heavy-duty vehicles. The invalid data of remote monitoring were mainly composed of the NOx sensor’s abnormal data and the uploaded data after the engine shutdown. In the 3B-MAW model, each data was attributed to one, two or three bins. The percentage of the three bins were linked to the vehicle’s real driving conditions. In order to gain the emission calculation accuracy and a smaller scale of required data, the value of the four main parameters, i.e., the minimum window number, the window width, the first cut-off and the second cut-off are set around 2 400 s, 300 s, 6% and 20%, respectively. Since the window power is no longer required, the 3B-MAW method is able to capture the low load emission characteristics more effectively, compared to the WB-WM. Therefore, the 3B-MAW method is a more appreciate approach to analyse on-road random driving conditions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">heavy-duty vehicle</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">moving average</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">remote monitoring</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">work-based window method</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yu Quanshun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ren Shuojin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wang Tao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shao Pengfei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yang Jianglong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shi Fulu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Li Tengteng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhang Chao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">E3S Web of Conferences</subfield><subfield code="d">EDP Sciences, 2013</subfield><subfield code="g">438, p 01003(2023)</subfield><subfield code="w">(DE-627)778372081</subfield><subfield code="w">(DE-600)2755680-3</subfield><subfield code="x">22671242</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:438, p 01003</subfield><subfield code="g">year:2023</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1051/e3sconf/202343801003</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/4884690c21194bd5950728b1afc6cb7c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/75/e3sconf_apee2023_01003.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2267-1242</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">438, p 01003</subfield><subfield code="j">2023</subfield></datafield></record></collection>
score	7.4016123

Nicht das Richtige dabei?

Schreiben Sie uns!

Heavy-duty vehicle emission characteristics based on the remote-monitoring three-bin moving-average window method

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?