{"id":"https://openalex.org/W4318586084","doi":"https://doi.org/10.1109/tim.2023.3239908","title":"Process Operation Performance Assessment Based on Semi-Supervised Fine-Grained Generative Adversarial Network for EFMF","display_name":"Process Operation Performance Assessment Based on Semi-Supervised Fine-Grained Generative Adversarial Network for EFMF","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4318586084","doi":"https://doi.org/10.1109/tim.2023.3239908"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3239908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3239908","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5007510043","display_name":"Kaiqing Bu","orcid":"https://orcid.org/0000-0002-4818-6938"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaiqing Bu","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-4818-6938","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071875933","display_name":"Yan Liu","orcid":"https://orcid.org/0000-0002-8003-8222"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Liu","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-8003-8222","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075396691","display_name":"Fuli Wang","orcid":"https://orcid.org/0000-0002-1107-7893"},"institutions":[{"id":"https://openalex.org/I4391767858","display_name":"State Key Laboratory of Synthetical Automation for Process Industries","ror":"https://ror.org/0380ng272","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767858","https://openalex.org/I9224756"]},{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuli Wang","raw_affiliation_strings":["State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-1107-7893","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756","https://openalex.org/I4391767858"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007510043"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":1.2592,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.75501554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9955999851226807,"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"}},"topics":[{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9955999851226807,"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"}},{"id":"https://openalex.org/T10670","display_name":"Aluminum Alloy Microstructure Properties","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10736","display_name":"Hydrogen embrittlement and corrosion behaviors in metals","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/2506","display_name":"Metals and Alloys"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7305089831352234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7102524042129517},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5992233753204346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5352806448936462},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.507595419883728},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4971161186695099},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4891107678413391},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47372427582740784},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4692996144294739},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.43108266592025757},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39350542426109314},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3144208788871765}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7305089831352234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7102524042129517},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5992233753204346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5352806448936462},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.507595419883728},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4971161186695099},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4891107678413391},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47372427582740784},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4692996144294739},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.43108266592025757},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39350542426109314},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3144208788871765},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3239908","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3239908","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1657799955","display_name":null,"funder_award_id":"N2104011","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3703343603","display_name":null,"funder_award_id":"62073060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6223266527","display_name":null,"funder_award_id":"61973057","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6508879597","display_name":null,"funder_award_id":"61621004","funder_id":"https://openalex.org/F4320322271","funder_display_name":"Science Fund for Creative Research Groups"},{"id":"https://openalex.org/G7713164500","display_name":null,"funder_award_id":"2019YFE0105000","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program 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/F4320322271","display_name":"Science Fund for Creative Research Groups","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2075349211","https://openalex.org/W2187089797","https://openalex.org/W2726291047","https://openalex.org/W2793485796","https://openalex.org/W2967818120","https://openalex.org/W2998115938","https://openalex.org/W2999414995","https://openalex.org/W3000160980","https://openalex.org/W3000569854","https://openalex.org/W3047819323","https://openalex.org/W3048602189","https://openalex.org/W3084582826","https://openalex.org/W3095770430","https://openalex.org/W3112055870","https://openalex.org/W3116325243","https://openalex.org/W3126600466","https://openalex.org/W3160848244","https://openalex.org/W3172814384","https://openalex.org/W3186613042","https://openalex.org/W3210336390","https://openalex.org/W4206199419","https://openalex.org/W4206258998","https://openalex.org/W4210541032","https://openalex.org/W4213159173","https://openalex.org/W4214676882","https://openalex.org/W4226070402","https://openalex.org/W6639480849","https://openalex.org/W6718379498","https://openalex.org/W6729482032","https://openalex.org/W6743338426"],"related_works":["https://openalex.org/W2995777218","https://openalex.org/W2596763562","https://openalex.org/W3217069185","https://openalex.org/W2964218010","https://openalex.org/W3049340819","https://openalex.org/W4308928038","https://openalex.org/W4200430540","https://openalex.org/W3141413246","https://openalex.org/W2888032422","https://openalex.org/W2808862658"],"abstract_inverted_index":{"The":[0,23,99,191],"process":[1],"operation":[2,125],"performance":[3,126],"assessment":[4,172],"(POPA)":[5],"of":[6,26,34,42,60,65,70,92,94,104,123,140,163,181,188,193,208],"electro-fused":[7],"magnesium":[8],"furnace":[9],"(EFMF)":[10],"plays":[11],"an":[12],"irreplaceable":[13],"role":[14],"in":[15,55,128,149],"improving":[16],"production":[17],"efficiency":[18],"and":[19,73,109,112,132,156,184,202,218],"ensuring":[20],"product":[21],"quality.":[22],"existing":[24],"methods":[25],"POPA":[27,93,162],"seriously":[28],"rely":[29],"on":[30],"a":[31,47,80,141,178,185],"large":[32,186],"number":[33,180,187],"manually":[35],"marked":[36],"label":[37],"data.":[38,190],"However,":[39],"manual":[40],"marking":[41],"all":[43],"samples":[44],"will":[45],"pay":[46],"very":[48],"high":[49],"price,":[50],"which":[51,118],"is":[52,214,225],"not":[53],"applicable":[54],"the":[56,66,90,95,105,116,120,145,166,171,194,206,228],"actual":[57],"industrial":[58],"application":[59],"EFMF.":[61],"Therefore,":[62,144],"taking":[63],"advantage":[64],"multisource":[67],"heterogeneous":[68],"information":[69],"image,":[71],"sound,":[72],"current":[74],"generated":[75],"by":[76],"EFMF,":[77],"we":[78],"propose":[79],"novel":[81],"semi-supervised":[82,110],"fine-grained":[83,142],"generative":[84,106],"adversarial":[85,107,138],"network":[86,108],"(SSFGGAN)":[87],"that":[88,170],"addresses":[89],"issue":[91],"EFMF":[96],"smelting":[97],"process.":[98],"SSFGGAN":[100,195],"method":[101,196],"makes":[102,119],"use":[103],"strategy":[111],"works":[113],"together":[114],"with":[115,177],"classifier,":[117],"feature":[121],"distribution":[122],"each":[124,221],"grade":[127],"source":[129],"domain":[130,134],"(SD)":[131],"target":[133],"(TD)":[135],"consistent":[136],"through":[137],"learning":[139],"discriminator.":[143],"comprehensive":[146],"features":[147],"learned":[148],"SD":[150],"remain":[151],"effective":[152],"for":[153],"TD":[154],"data":[155,183,210,213],"can":[157,197],"be":[158],"directly":[159],"applied":[160],"to":[161,211],"TD.":[164],"Finally,":[165],"simulation":[167],"results":[168],"show":[169],"accuracy":[173,192],"has":[174],"been":[175],"improved":[176],"small":[179],"labeled":[182,209],"unlabeled":[189,212],"reach":[198],"99.875%,":[199],"99.75%,":[200],"99.625%,":[201],"99.125%,":[203],"respectively,":[204],"when":[205],"ratio":[207],"1:2,":[215],"1:5,":[216],"1:10,":[217],"1:20.":[219],"In":[220],"evaluation":[222],"index,":[223],"it":[224],"better":[226],"than":[227],"compared":[229],"methods.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}