{"id":"https://openalex.org/W2913113846","doi":"https://doi.org/10.3390/e21020176","title":"Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy","display_name":"Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy","publication_year":2019,"publication_date":"2019-02-14","ids":{"openalex":"https://openalex.org/W2913113846","doi":"https://doi.org/10.3390/e21020176","mag":"2913113846","pmid":"https://pubmed.ncbi.nlm.nih.gov/33266892"},"language":"en","primary_location":{"id":"doi:10.3390/e21020176","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21020176","pdf_url":"https://www.mdpi.com/1099-4300/21/2/176/pdf?version=1550127551","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/21/2/176/pdf?version=1550127551","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100337868","display_name":"Guohui Li","orcid":"https://orcid.org/0000-0001-8175-4311"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guohui Li","raw_affiliation_strings":["School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China"],"raw_orcid":"https://orcid.org/0000-0001-8175-4311","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101431410","display_name":"Zhichao Yang","orcid":"https://orcid.org/0000-0002-8719-2708"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhichao Yang","raw_affiliation_strings":["School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China"],"raw_orcid":"https://orcid.org/0000-0002-8719-2708","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062896696","display_name":"Hong Yang","orcid":"https://orcid.org/0000-0002-7028-5879"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hong Yang","raw_affiliation_strings":["School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062896696","https://openalex.org/A5100337868"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":1.3488,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.80534568,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"21","issue":"2","first_page":"176","last_page":"176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.857324481010437},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.6447564959526062},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6192828416824341},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5619654655456543},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5587789416313171},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5477362275123596},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4882955551147461},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47467899322509766},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.469748318195343},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4547414779663086},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40223872661590576},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.36879944801330566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36462557315826416},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2104523777961731},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.14723888039588928},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13979166746139526}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.857324481010437},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.6447564959526062},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6192828416824341},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5619654655456543},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5587789416313171},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5477362275123596},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4882955551147461},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47467899322509766},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.469748318195343},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4547414779663086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40223872661590576},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36879944801330566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36462557315826416},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2104523777961731},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.14723888039588928},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13979166746139526},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e21020176","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21020176","pdf_url":"https://www.mdpi.com/1099-4300/21/2/176/pdf?version=1550127551","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33266892","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33266892","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:209ee880d1454f42a63441c9be2ce926","is_oa":true,"landing_page_url":"https://doaj.org/article/209ee880d1454f42a63441c9be2ce926","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 21, Iss 2, p 176 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/21/2/176/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/e21020176","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7514658","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7514658","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e21020176","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e21020176","pdf_url":"https://www.mdpi.com/1099-4300/21/2/176/pdf?version=1550127551","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8095425598","display_name":null,"funder_award_id":"No. 51709228","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8685861336","display_name":null,"funder_award_id":"51709228","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325367","display_name":"Xi'an University of Posts and Telecommunications","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2913113846.pdf","grobid_xml":"https://content.openalex.org/works/W2913113846.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1529119041","https://openalex.org/W1964860933","https://openalex.org/W2007221293","https://openalex.org/W2012078644","https://openalex.org/W2014683958","https://openalex.org/W2021887391","https://openalex.org/W2040162990","https://openalex.org/W2049941387","https://openalex.org/W2054946297","https://openalex.org/W2095526670","https://openalex.org/W2118183148","https://openalex.org/W2120390927","https://openalex.org/W2138402108","https://openalex.org/W2154053567","https://openalex.org/W2181210515","https://openalex.org/W2280719928","https://openalex.org/W2308133951","https://openalex.org/W2355875820","https://openalex.org/W2555142204","https://openalex.org/W2607330859","https://openalex.org/W2727813793","https://openalex.org/W2737867005","https://openalex.org/W2755192388","https://openalex.org/W2784928507","https://openalex.org/W2805941080","https://openalex.org/W2808286220","https://openalex.org/W2887002979","https://openalex.org/W2898157310","https://openalex.org/W2902033916","https://openalex.org/W2902329119","https://openalex.org/W3104131705","https://openalex.org/W3122798562"],"related_works":["https://openalex.org/W2361368121","https://openalex.org/W2036846997","https://openalex.org/W2112223184","https://openalex.org/W2347586617","https://openalex.org/W2161963661","https://openalex.org/W2125614474","https://openalex.org/W2295845123","https://openalex.org/W2358054814","https://openalex.org/W2090882960","https://openalex.org/W2889544313"],"abstract_inverted_index":{"To":[0],"improve":[1],"the":[2,44,57,74,82,98,106,112,117,124,139,144,148,159,179,183,189,196],"recognition":[3,180],"accuracy":[4],"of":[5,48,59,77,90,100,119,171,182,198],"ship-radiated":[6,83,172],"noise,":[7],"a":[8,52,88],"feature":[9],"extraction":[10],"method":[11,185,192],"based":[12],"on":[13],"regenerated":[14],"phase-shifted":[15],"sinusoid-assisted":[16],"empirical":[17,38],"mode":[18,39,45,92],"decomposition":[19,40],"(RPSEMD),":[20],"mutual":[21],"information":[22],"(MI),":[23],"and":[24,97,111,126,147,167],"differential":[25],"symbolic":[26],"entropy":[27],"(DSE)":[28],"is":[29,35,51,85,103,115,121,131,136,152],"proposed":[30,184,190],"in":[31],"this":[32],"paper.":[33],"RPSEMD":[34],"an":[36],"improved":[37],"(EMD)":[41],"that":[42,178],"alleviates":[43],"mixing":[46],"problem":[47],"EMD.":[49],"DSE":[50,99,150],"new":[53],"tool":[54],"to":[55,142,165],"quantify":[56],"complexity":[58,76],"nonlinear":[60,75],"time":[61,79],"series.":[62,80],"It":[63],"not":[64],"only":[65],"has":[66],"high":[67],"computational":[68],"efficiency,":[69],"but":[70],"also":[71],"can":[72,193],"measure":[73],"short":[78],"Firstly,":[81],"noise":[84],"decomposed":[86],"into":[87,158],"series":[89],"intrinsic":[91],"functions":[93],"(IMFs)":[94],"by":[95],"RPSEMD,":[96],"each":[101,109,127,134],"IMF":[102,110],"calculated.":[104],"Then,":[105],"MI":[107,129],"between":[108],"original":[113],"signal":[114],"calculated;":[116],"sum":[118],"MIs":[120],"taken":[122],"as":[123,138],"denominator;":[125],"normalized":[128],"(norMI)":[130],"obtained.":[132,153],"Finally,":[133],"norMI":[135],"used":[137],"weight":[140,143],"coefficient":[141],"corresponding":[145],"DSE,":[146],"weighted":[149],"(WDSE)":[151],"The":[154,174],"WDSEs":[155],"are":[156],"sent":[157],"support":[160],"vector":[161],"machine":[162],"(SVM)":[163],"classifier":[164],"classify":[166],"recognize":[168],"three":[169],"types":[170],"noise.":[173],"experimental":[175],"results":[176],"demonstrate":[177],"rate":[181],"reaches":[186],"98.3333%.":[187],"Consequently,":[188],"WDSE":[191],"effectively":[194],"achieve":[195],"classification":[197],"ships.":[199]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-10T14:10:52.464848","created_date":"2025-10-10T00:00:00"}
