{"id":"https://openalex.org/W4210680043","doi":"https://doi.org/10.1109/tii.2022.3145834","title":"A Hybrid Algorithm Incorporating Vector Quantization and One-Class Support Vector Machine for Industrial Anomaly Detection","display_name":"A Hybrid Algorithm Incorporating Vector Quantization and One-Class Support Vector Machine for Industrial Anomaly Detection","publication_year":2022,"publication_date":"2022-01-25","ids":{"openalex":"https://openalex.org/W4210680043","doi":"https://doi.org/10.1109/tii.2022.3145834"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2022.3145834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3145834","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"},"type":"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/A5053807794","display_name":"Jingxuan Pang","orcid":null},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingxuan Pang","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","Ningbo Research Institute, Zhejiang University, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Ningbo Research Institute, Zhejiang University, Ningbo, China","institution_ids":["https://openalex.org/I109935558","https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074259393","display_name":"Xiaokun Pu","orcid":"https://orcid.org/0000-0001-7071-7822"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaokun Pu","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","Ningbo Research Institute, Zhejiang University, Ningbo, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Ningbo Research Institute, Zhejiang University, Ningbo, China","institution_ids":["https://openalex.org/I109935558","https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100654349","display_name":"Chunguang Li","orcid":"https://orcid.org/0000-0003-3147-1553"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunguang Li","raw_affiliation_strings":["College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","Ningbo Research Institute, Zhejiang University, Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0003-3147-1553","affiliations":[{"raw_affiliation_string":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Ningbo Research Institute, Zhejiang University, Ningbo, China","institution_ids":["https://openalex.org/I109935558","https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5053807794"],"corresponding_institution_ids":["https://openalex.org/I109935558","https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":9.8359,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.9839747,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"18","issue":"12","first_page":"8786","last_page":"8796"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.987500011920929,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.6695355176925659},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6464711427688599},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6396411061286926},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6311237215995789},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6297510266304016},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.546066403388977},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.534096896648407},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4822193384170532},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.4163908064365387},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25183725357055664}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6695355176925659},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6464711427688599},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6396411061286926},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6311237215995789},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6297510266304016},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.546066403388977},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.534096896648407},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4822193384170532},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.4163908064365387},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25183725357055664},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2022.3145834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3145834","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G6610172906","display_name":null,"funder_award_id":"U20A20158","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W46659105","https://openalex.org/W818098075","https://openalex.org/W1480376833","https://openalex.org/W1533208079","https://openalex.org/W1535668279","https://openalex.org/W1634005169","https://openalex.org/W1970088130","https://openalex.org/W2016654142","https://openalex.org/W2019014808","https://openalex.org/W2023469250","https://openalex.org/W2045079045","https://openalex.org/W2071820974","https://openalex.org/W2083003257","https://openalex.org/W2095345875","https://openalex.org/W2119222175","https://openalex.org/W2122646361","https://openalex.org/W2123792643","https://openalex.org/W2132870739","https://openalex.org/W2134383396","https://openalex.org/W2134490011","https://openalex.org/W2135046866","https://openalex.org/W2153477625","https://openalex.org/W2153635508","https://openalex.org/W2163816481","https://openalex.org/W2166152502","https://openalex.org/W2287985126","https://openalex.org/W2296719434","https://openalex.org/W2336788611","https://openalex.org/W2752856697","https://openalex.org/W2793988933","https://openalex.org/W2950772912","https://openalex.org/W2963445059","https://openalex.org/W2963878656","https://openalex.org/W3007048760","https://openalex.org/W3042859852","https://openalex.org/W3086419524","https://openalex.org/W3095320854","https://openalex.org/W3128465814","https://openalex.org/W3144619878","https://openalex.org/W4244017338","https://openalex.org/W4254182148","https://openalex.org/W6602002561","https://openalex.org/W6632180709","https://openalex.org/W6677957879","https://openalex.org/W6682894622","https://openalex.org/W6696581697","https://openalex.org/W6703470281","https://openalex.org/W6757844995"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W156213964","https://openalex.org/W2050960118"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,28,90],"plays":[2],"an":[3,166,230],"important":[4],"role":[5],"in":[6,9,23,69,119,133,182],"industry,":[7],"especially":[8],"ensuring":[10],"system":[11],"safety":[12],"and":[13,96,108,144,159,222,235,260,267],"product":[14],"quality.":[15],"Due":[16],"to":[17,115,152,164,170,186],"the":[18,70,74,129,160,183,192,197,203,209,214,218,223,246,265,270],"unavailability":[19],"of":[20,76,156,208,232,241,269],"anomalous":[21],"data":[22,62,98,101,172,219,256],"many":[24],"practical":[25],"cases,":[26],"anomaly":[27,57,89],"is":[29,51,85,150,180],"usually":[30],"solved":[31],"by":[32,64],"one-class":[33,46],"classification":[34],"(OCC)":[35],"methods":[36],"using":[37,122],"only":[38],"normal":[39,157],"data.":[40],"As":[41],"a":[42,52,66,123,138,174,188],"classical":[43,210],"OCC":[44],"method,":[45],"support":[47],"vector":[48,142,148],"machine":[49],"(OCSVM)":[50],"popular":[53],"discriminative":[54,236],"approach":[55],"for":[56,88,93,113,254],"detection,":[58],"which":[59],"detects":[60],"abnormal":[61],"points":[63],"establishing":[65],"decision":[67],"boundary":[68],"kernel":[71,81,125,204],"space.":[72,177],"However,":[73],"performance":[75],"OCSVM":[77,114,145,179,211],"heavily":[78],"relies":[79],"on":[80],"parameters,":[82],"whose":[83],"selection":[84,206],"not":[86],"trivial":[87],"problems.":[91],"Moreover,":[92],"some":[94],"uneven":[95],"complex":[97,255],"distributions,":[99],"different":[100,106],"regions":[102,121],"may":[103],"have":[104,250],"quite":[105],"densities":[107],"shapes,":[109],"making":[110],"it":[111],"difficult":[112],"obtain":[116],"good":[117],"boundaries":[118],"all":[120],"global":[124],"parameter.":[126],"To":[127],"address":[128],"above":[130],"two":[131,243],"issues,":[132],"this":[134],"article,":[135],"we":[136],"propose":[137],"hybrid":[139],"algorithm":[140,249],"incorporating":[141],"quantization":[143,149],"(VQ-OCSVM).":[146],"Specifically,":[147],"used":[151,163],"extract":[153],"distribution":[154,220],"information":[155],"data,":[158],"results":[161,263],"are":[162],"construct":[165],"explicit":[167,193],"mapping":[168,194,216],"function":[169],"map":[171],"into":[173,195],"high-dimensional":[175],"feature":[176,184],"Then,":[178],"performed":[181],"space":[185],"build":[187],"classifier.":[189],"By":[190],"introducing":[191],"OCSVM,":[196],"proposed":[198,247,271],"method":[199],"can":[200,226],"effectively":[201],"bypass":[202],"parameter":[205],"problem":[207],"method.":[212,272],"Furthermore,":[213],"constructed":[215],"carries":[217],"information,":[221],"VQ-OCSVM":[224,248],"model":[225],"be":[227],"regarded":[228],"as":[229],"integration":[231],"generative":[233],"learning":[234],"learning.":[237],"The":[238],"complementary":[239],"properties":[240],"these":[242],"paradigms":[244],"make":[245],"better":[251],"generalization":[252],"capacity":[253],"distribution.":[257],"Both":[258],"qualitative":[259],"quantitative":[261],"experimental":[262],"demonstrate":[264],"effectiveness":[266],"advantages":[268]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":4}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
