A robust stochastic model updating method with resampling processing

A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problem...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhao, Yanlin [verfasserIn] Deng, Zhongmin [verfasserIn] Zhang, Xinjie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance

Übergeordnetes Werk:	Enthalten in: Mechanical systems and signal processing - Amsterdam [u.a.] : Elsevier, 1987, 136
Übergeordnetes Werk:	volume:136

DOI / URN:	10.1016/j.ymssp.2019.106494

Katalog-ID:	ELV003476340

Internformat


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245	1	0	\|a A robust stochastic model updating method with resampling processing
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520			\|a A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample.
650		4	\|a Stochastic model updating
650		4	\|a Robustness
650		4	\|a Resampling
650		4	\|a Bhattacharyya distance
650		4	\|a Euclidian distance
700	1		\|a Deng, Zhongmin \|e verfasserin \|4 aut
700	1		\|a Zhang, Xinjie \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Mechanical systems and signal processing \|d Amsterdam [u.a.] : Elsevier, 1987 \|g 136 \|h Online-Ressource \|w (DE-627)267838670 \|w (DE-600)1471003-1 \|w (DE-576)253127629 \|x 1096-1216 \|7 nnns
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936	b	k	\|a 50.32 \|j Dynamik \|j Schwingungslehre \|x Technische Mechanik
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allfields	10.1016/j.ymssp.2019.106494 doi (DE-627)ELV003476340 (ELSEVIER)S0888-3270(19)30715-0 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Zhao, Yanlin verfasserin aut A robust stochastic model updating method with resampling processing 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample. Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance Deng, Zhongmin verfasserin aut Zhang, Xinjie verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 136 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:136 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 136
spelling	10.1016/j.ymssp.2019.106494 doi (DE-627)ELV003476340 (ELSEVIER)S0888-3270(19)30715-0 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Zhao, Yanlin verfasserin aut A robust stochastic model updating method with resampling processing 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample. Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance Deng, Zhongmin verfasserin aut Zhang, Xinjie verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 136 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:136 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 136
allfields_unstemmed	10.1016/j.ymssp.2019.106494 doi (DE-627)ELV003476340 (ELSEVIER)S0888-3270(19)30715-0 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Zhao, Yanlin verfasserin aut A robust stochastic model updating method with resampling processing 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample. Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance Deng, Zhongmin verfasserin aut Zhang, Xinjie verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 136 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:136 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 136
allfieldsGer	10.1016/j.ymssp.2019.106494 doi (DE-627)ELV003476340 (ELSEVIER)S0888-3270(19)30715-0 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Zhao, Yanlin verfasserin aut A robust stochastic model updating method with resampling processing 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample. Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance Deng, Zhongmin verfasserin aut Zhang, Xinjie verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 136 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:136 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 136
allfieldsSound	10.1016/j.ymssp.2019.106494 doi (DE-627)ELV003476340 (ELSEVIER)S0888-3270(19)30715-0 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Zhao, Yanlin verfasserin aut A robust stochastic model updating method with resampling processing 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample. Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance Deng, Zhongmin verfasserin aut Zhang, Xinjie verfasserin aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 136 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:136 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 136
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sourceStr	Enthalten in Mechanical systems and signal processing 136 volume:136
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bklname	Dynamik Schwingungslehre Technische Zuverlässigkeit Instandhaltung
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topic_facet	Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance
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container_title	Mechanical systems and signal processing
authorswithroles_txt_mv	Zhao, Yanlin @@aut@@ Deng, Zhongmin @@aut@@ Zhang, Xinjie @@aut@@
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author	Zhao, Yanlin
spellingShingle	Zhao, Yanlin ddc 004 bkl 50.32 bkl 50.16 misc Stochastic model updating misc Robustness misc Resampling misc Bhattacharyya distance misc Euclidian distance A robust stochastic model updating method with resampling processing
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topic_title	004 DE-600 50.32 bkl 50.16 bkl A robust stochastic model updating method with resampling processing Stochastic model updating Robustness Resampling Bhattacharyya distance Euclidian distance
topic	ddc 004 bkl 50.32 bkl 50.16 misc Stochastic model updating misc Robustness misc Resampling misc Bhattacharyya distance misc Euclidian distance
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title	A robust stochastic model updating method with resampling processing
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title_full	A robust stochastic model updating method with resampling processing
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title_sort	a robust stochastic model updating method with resampling processing
title_auth	A robust stochastic model updating method with resampling processing
abstract	A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample.
abstractGer	A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample.
abstract_unstemmed	A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample.
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title_short	A robust stochastic model updating method with resampling processing
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up_date	2024-07-06T19:45:07.224Z
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A robust stochastic model updating method with resampling processing

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