A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise

Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature ex...
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Autor*in:	Yu-xing Li [verfasserIn] Shang-bin Jiao [verfasserIn] Bo Geng [verfasserIn] Qing Zhang [verfasserIn] You-min Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise

Übergeordnetes Werk:	In: Defence Technology - KeAi Communications Co., Ltd., 2015, 18(2022), 2, Seite 183-193
Übergeordnetes Werk:	volume:18 ; year:2022 ; number:2 ; pages:183-193

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.dt.2020.11.011

Katalog-ID:	DOAJ01753187X

Internformat


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245	1	2	\|a A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise
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520			\|a Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate.
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author_variant	y x l yxl s b j sbj b g bg q z qz y m z ymz
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allfields	10.1016/j.dt.2020.11.011 doi (DE-627)DOAJ01753187X (DE-599)DOAJ00c3c02c84f14b099af501d1d18feced DE-627 ger DE-627 rakwb eng Yu-xing Li verfasserin aut A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate. Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise Military Science U Shang-bin Jiao verfasserin aut Bo Geng verfasserin aut Qing Zhang verfasserin aut You-min Zhang verfasserin aut In Defence Technology KeAi Communications Co., Ltd., 2015 18(2022), 2, Seite 183-193 (DE-627)774106905 (DE-600)2745453-8 22149147 nnns volume:18 year:2022 number:2 pages:183-193 https://doi.org/10.1016/j.dt.2020.11.011 kostenfrei https://doaj.org/article/00c3c02c84f14b099af501d1d18feced kostenfrei http://www.sciencedirect.com/science/article/pii/S2214914720304785 kostenfrei https://doaj.org/toc/2214-9147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 18 2022 2 183-193
spelling	10.1016/j.dt.2020.11.011 doi (DE-627)DOAJ01753187X (DE-599)DOAJ00c3c02c84f14b099af501d1d18feced DE-627 ger DE-627 rakwb eng Yu-xing Li verfasserin aut A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate. Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise Military Science U Shang-bin Jiao verfasserin aut Bo Geng verfasserin aut Qing Zhang verfasserin aut You-min Zhang verfasserin aut In Defence Technology KeAi Communications Co., Ltd., 2015 18(2022), 2, Seite 183-193 (DE-627)774106905 (DE-600)2745453-8 22149147 nnns volume:18 year:2022 number:2 pages:183-193 https://doi.org/10.1016/j.dt.2020.11.011 kostenfrei https://doaj.org/article/00c3c02c84f14b099af501d1d18feced kostenfrei http://www.sciencedirect.com/science/article/pii/S2214914720304785 kostenfrei https://doaj.org/toc/2214-9147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 18 2022 2 183-193
allfields_unstemmed	10.1016/j.dt.2020.11.011 doi (DE-627)DOAJ01753187X (DE-599)DOAJ00c3c02c84f14b099af501d1d18feced DE-627 ger DE-627 rakwb eng Yu-xing Li verfasserin aut A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate. Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise Military Science U Shang-bin Jiao verfasserin aut Bo Geng verfasserin aut Qing Zhang verfasserin aut You-min Zhang verfasserin aut In Defence Technology KeAi Communications Co., Ltd., 2015 18(2022), 2, Seite 183-193 (DE-627)774106905 (DE-600)2745453-8 22149147 nnns volume:18 year:2022 number:2 pages:183-193 https://doi.org/10.1016/j.dt.2020.11.011 kostenfrei https://doaj.org/article/00c3c02c84f14b099af501d1d18feced kostenfrei http://www.sciencedirect.com/science/article/pii/S2214914720304785 kostenfrei https://doaj.org/toc/2214-9147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 18 2022 2 183-193
allfieldsGer	10.1016/j.dt.2020.11.011 doi (DE-627)DOAJ01753187X (DE-599)DOAJ00c3c02c84f14b099af501d1d18feced DE-627 ger DE-627 rakwb eng Yu-xing Li verfasserin aut A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate. Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise Military Science U Shang-bin Jiao verfasserin aut Bo Geng verfasserin aut Qing Zhang verfasserin aut You-min Zhang verfasserin aut In Defence Technology KeAi Communications Co., Ltd., 2015 18(2022), 2, Seite 183-193 (DE-627)774106905 (DE-600)2745453-8 22149147 nnns volume:18 year:2022 number:2 pages:183-193 https://doi.org/10.1016/j.dt.2020.11.011 kostenfrei https://doaj.org/article/00c3c02c84f14b099af501d1d18feced kostenfrei http://www.sciencedirect.com/science/article/pii/S2214914720304785 kostenfrei https://doaj.org/toc/2214-9147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 18 2022 2 183-193
allfieldsSound	10.1016/j.dt.2020.11.011 doi (DE-627)DOAJ01753187X (DE-599)DOAJ00c3c02c84f14b099af501d1d18feced DE-627 ger DE-627 rakwb eng Yu-xing Li verfasserin aut A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate. Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise Military Science U Shang-bin Jiao verfasserin aut Bo Geng verfasserin aut Qing Zhang verfasserin aut You-min Zhang verfasserin aut In Defence Technology KeAi Communications Co., Ltd., 2015 18(2022), 2, Seite 183-193 (DE-627)774106905 (DE-600)2745453-8 22149147 nnns volume:18 year:2022 number:2 pages:183-193 https://doi.org/10.1016/j.dt.2020.11.011 kostenfrei https://doaj.org/article/00c3c02c84f14b099af501d1d18feced kostenfrei http://www.sciencedirect.com/science/article/pii/S2214914720304785 kostenfrei https://doaj.org/toc/2214-9147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 18 2022 2 183-193
language	English
source	In Defence Technology 18(2022), 2, Seite 183-193 volume:18 year:2022 number:2 pages:183-193
sourceStr	In Defence Technology 18(2022), 2, Seite 183-193 volume:18 year:2022 number:2 pages:183-193
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise Military Science U
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container_title	Defence Technology
authorswithroles_txt_mv	Yu-xing Li @@aut@@ Shang-bin Jiao @@aut@@ Bo Geng @@aut@@ Qing Zhang @@aut@@ You-min Zhang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	774106905
id	DOAJ01753187X
language_de	englisch
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author	Yu-xing Li
spellingShingle	Yu-xing Li misc Nonlinear dynamic misc Refined composite multi-scale dispersion entropy (RCMDE) misc Multi-scale dispersion entropy (MDE) misc Multi-scale weighted-permutation entropy (MW-PE) misc Multi-scale permutation entropy (MPE) misc Classification of ship-radiated noise misc Military Science misc U A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise
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topic_title	A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise Nonlinear dynamic Refined composite multi-scale dispersion entropy (RCMDE) Multi-scale dispersion entropy (MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy (MPE) Classification of ship-radiated noise
topic	misc Nonlinear dynamic misc Refined composite multi-scale dispersion entropy (RCMDE) misc Multi-scale dispersion entropy (MDE) misc Multi-scale weighted-permutation entropy (MW-PE) misc Multi-scale permutation entropy (MPE) misc Classification of ship-radiated noise misc Military Science misc U
topic_unstemmed	misc Nonlinear dynamic misc Refined composite multi-scale dispersion entropy (RCMDE) misc Multi-scale dispersion entropy (MDE) misc Multi-scale weighted-permutation entropy (MW-PE) misc Multi-scale permutation entropy (MPE) misc Classification of ship-radiated noise misc Military Science misc U
topic_browse	misc Nonlinear dynamic misc Refined composite multi-scale dispersion entropy (RCMDE) misc Multi-scale dispersion entropy (MDE) misc Multi-scale weighted-permutation entropy (MW-PE) misc Multi-scale permutation entropy (MPE) misc Classification of ship-radiated noise misc Military Science misc U
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title	A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise
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title_full	A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise
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author_browse	Yu-xing Li Shang-bin Jiao Bo Geng Qing Zhang You-min Zhang
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title_sort	comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise
title_auth	A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise
abstract	Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate.
abstractGer	Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate.
abstract_unstemmed	Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate.
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title_short	A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise
url	https://doi.org/10.1016/j.dt.2020.11.011 https://doaj.org/article/00c3c02c84f14b099af501d1d18feced http://www.sciencedirect.com/science/article/pii/S2214914720304785 https://doaj.org/toc/2214-9147
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up_date	2024-07-04T01:58:47.471Z
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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">DOAJ01753187X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502154209.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.dt.2020.11.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ01753187X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ00c3c02c84f14b099af501d1d18feced</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></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yu-xing Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. 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A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise

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