乐胖代购免代理版

{"id":"https://openalex.org/W2977602779","doi":"https://doi.org/10.1109/ijcnn.2019.8852305","title":"Multi-Objective Autoencoder for Fault Detection and Diagnosis in Higher-Order Data","display_name":"Multi-Objective Autoencoder for Fault Detection and Diagnosis in Higher-Order Data","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977602779","doi":"https://doi.org/10.1109/ijcnn.2019.8852305","mag":"2977602779"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852305","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005831779","display_name":"Ali Anaissi","orcid":"https://orcid.org/0000-0002-8864-0314"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ali Anaissi","raw_affiliation_strings":["School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044200395","display_name":"Seid Miad Zandavi","orcid":"https://orcid.org/0000-0003-3015-5962"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Seid Miad Zandavi","raw_affiliation_strings":["School of Computer Science, The University of Sydney, Camperdown, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Camperdown, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.007,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.947609,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":87,"max":88},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9983,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9983,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9975,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9883,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/autoencoder","display_name":"Autoencoder","score":0.9700567},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5079728}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9700567},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7586959},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71938574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.599031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5775721},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5337907},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5079728},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.48584196},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45805228},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4426038},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852305","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, justice, and strong institutions","score":0.61,"id":"https://metadata.un.org/sdg/16"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":31,"referenced_works":["https://openalex.org/W1556024794","https://openalex.org/W1866230956","https://openalex.org/W1925417509","https://openalex.org/W1963802829","https://openalex.org/W1965833531","https://openalex.org/W1978680118","https://openalex.org/W1980221648","https://openalex.org/W2051203581","https://openalex.org/W2066301115","https://openalex.org/W2120617515","https://openalex.org/W2130942839","https://openalex.org/W2140862024","https://openalex.org/W2154234219","https://openalex.org/W2158199200","https://openalex.org/W2160815625","https://openalex.org/W2326670233","https://openalex.org/W2396976214","https://openalex.org/W2579718262","https://openalex.org/W2605444120","https://openalex.org/W2620705027","https://openalex.org/W2765994138","https://openalex.org/W2767258879","https://openalex.org/W2781761393","https://openalex.org/W2806295708","https://openalex.org/W2889234259","https://openalex.org/W2896787006","https://openalex.org/W2901168797","https://openalex.org/W2952838738","https://openalex.org/W3139214261","https://openalex.org/W3153872861","https://openalex.org/W4237826793"],"related_works":["https://openalex.org/W4363671829","https://openalex.org/W4290647774","https://openalex.org/W3210364259","https://openalex.org/W3207797160","https://openalex.org/W3194885736","https://openalex.org/W3189286258","https://openalex.org/W3186512740","https://openalex.org/W3046391934","https://openalex.org/W3017266184","https://openalex.org/W2806741695"],"abstract_inverted_index":{"We":[0,88],"propose":[1],"a":[2,82],"multi-objective":[3],"autoencoder":[4,20],"method":[5,42,91,112,145],"for":[6,33,60,102],"fault":[7],"detection":[8,104,153],"and":[9,58,78,130,154],"diagnosis":[10],"in":[11,37,48,85,95,134,149],"multi-way":[12,93],"data":[13,26,39,44,77],"based":[14],"on":[15,92],"the":[16,66,72,75,86,96,110,124,140],"reconstruction":[17],"error":[18],"of":[19,74,98,127,151],"deep":[21],"neural":[22],"network":[23],"(ADNN).":[24],"Multi-way":[25],"analysis":[27],"has":[28],"become":[29],"an":[30,135],"essential":[31],"tool":[32],"capturing":[34],"underlying":[35],"structures":[36],"higher-order":[38],"sets.":[40],"Our":[41],"fuses":[43],"from":[45],"multiple":[46],"sources":[47],"one":[49],"ADNN":[50],"at":[51],"which":[52],"informative":[53],"features":[54],"are":[55],"being":[56],"extracted":[57],"utilized":[59],"anomaly":[61,68],"detection.":[62],"It":[63,118],"also":[64,120],"uses":[65],"generated":[67],"scores":[69],"to":[70,122,139],"asses":[71],"severity":[73],"anomalous":[76],"localize":[79],"it":[80],"via":[81],"localization":[83],"layer":[84],"autoencoder.":[87],"evaluated":[89],"our":[90,143],"datasets":[94],"area":[97],"structural":[99,116],"health":[100],"monitoring":[101],"damage":[103,128,132,152],"purposes.":[105],"Experimental":[106],"results":[107],"show":[108],"that":[109],"proposed":[111,144],"can":[113],"accurately":[114],"detect":[115],"damage.":[117],"was":[119],"able":[121],"estimate":[123],"different":[125],"levels":[126],"severity,":[129],"capture":[131],"locations":[133],"unsupervised":[136],"aspect.":[137],"Compared":[138],"state-of-the-art":[141],"approaches,":[142],"shows":[146],"better":[147],"performance":[148],"terms":[150],"localization.":[155]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2977602779","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2025-01-18T00:09:13.497203","created_date":"2019-10-10"}