{"id":"https://openalex.org/W4388820783","doi":"https://doi.org/10.1109/apsipaasc58517.2023.10317439","title":"Soft-Sensor Construction Method Based on Adaptive Modeling and Transfer Learning for Manufacturing Process Including Maintenance Periods","display_name":"Soft-Sensor Construction Method Based on Adaptive Modeling and Transfer Learning for Manufacturing Process Including Maintenance Periods","publication_year":2023,"publication_date":"2023-10-31","ids":{"openalex":"https://openalex.org/W4388820783","doi":"https://doi.org/10.1109/apsipaasc58517.2023.10317439"},"language":"en","primary_location":{"id":"doi:10.1109/apsipaasc58517.2023.10317439","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/apsipaasc58517.2023.10317439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5109656916","display_name":"Kaito Katayama","orcid":null},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kaito Katayama","raw_affiliation_strings":["Nagoya University,Japan","Nagoya University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University,Japan","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"Nagoya University, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062335613","display_name":"Koichi Fujiwara","orcid":"https://orcid.org/0000-0002-2929-0561"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koichi Fujiwara","raw_affiliation_strings":["Nagoya University,Japan","Nagoya University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University,Japan","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"Nagoya University, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065866506","display_name":"Kazuki Yamamoto","orcid":"https://orcid.org/0000-0003-1320-1664"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kazuki Yamamoto","raw_affiliation_strings":["AGC Inc.,Japan","AGC Inc., Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AGC Inc.,Japan","institution_ids":[]},{"raw_affiliation_string":"AGC Inc., Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5845,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6143368,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"abs/0907.1815","issue":null,"first_page":"325","last_page":"328"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9993000030517578,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9607999920845032,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/soft-sensor","display_name":"Soft sensor","score":0.7629023790359497},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.7542905211448669},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7226524353027344},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5653732419013977},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.47810834646224976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4748292863368988},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.47337716817855835}],"concepts":[{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.7629023790359497},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.7542905211448669},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7226524353027344},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5653732419013977},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.47810834646224976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4748292863368988},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.47337716817855835},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc58517.2023.10317439","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/apsipaasc58517.2023.10317439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4000000059604645,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1966089218","https://openalex.org/W1981552604","https://openalex.org/W2077496324","https://openalex.org/W2120354757","https://openalex.org/W2165698076","https://openalex.org/W2803462565","https://openalex.org/W2804564777","https://openalex.org/W2965420085","https://openalex.org/W3030618279","https://openalex.org/W3104887532","https://openalex.org/W4281698770","https://openalex.org/W4288828557","https://openalex.org/W6793289451"],"related_works":["https://openalex.org/W2097933059","https://openalex.org/W2374290272","https://openalex.org/W4386158748","https://openalex.org/W1970956258","https://openalex.org/W3127658115","https://openalex.org/W3152705324","https://openalex.org/W2408812858","https://openalex.org/W2737942854","https://openalex.org/W2618126831","https://openalex.org/W578198117"],"abstract_inverted_index":{"Various":[0],"soft-sensor":[1,60,91,131,153],"construction":[2,92],"methods":[3,29,55],"based":[4,103],"on":[5,104,188],"transfer":[6,31,114],"learning":[7,32,115],"have":[8],"been":[9],"proposed":[10,119,162],"to":[11,15,44,56,58,64,77,143,146,156,167],"quickly":[12],"adapt":[13,145],"soft-sensors":[14,142],"changes":[16,147],"in":[17,148,190],"process":[18,22,38,47,67,149,174],"characteristics":[19,39,68,150],"caused":[20],"by":[21,186],"maintenance.":[23,36],"However,":[24],"most":[25],"of":[26,83,113,124,160,171],"the":[27,35,53,65,70,80,118,121,158,161,184,193],"conventional":[28,54,194],"adopt":[30],"immediately":[33],"after":[34],"Since":[37],"may":[40],"also":[41],"change":[42],"due":[43],"other":[45],"than":[46],"maintenance,":[48],"it":[49,74,164],"is":[50,62,75,110,126,137],"necessary":[51],"for":[52],"switch":[57],"a":[59,88,111,130,133,172],"that":[61,181],"applicable":[63],"current":[66],"at":[69],"appropriate":[71],"time;":[72],"however,":[73],"difficult":[76],"properly":[78],"determine":[79],"switching":[81],"timings":[82],"soft-sensors.":[84],"This":[85,139],"study":[86],"proposes":[87],"new":[89,134],"adaptive":[90,105],"method":[93],"called":[94],"Latest":[95],"Sample":[96],"Targeting":[97],"Frustratingly":[98],"Easy":[99],"Domain":[100],"Adaptation":[101],"(LST-FEDA)":[102],"modeling":[106],"and":[107,128],"FEDA":[108,125],"which":[109],"kind":[112],"method.":[116],"In":[117],"LST-FEDA,":[120,163],"application":[122],"range":[123],"updated":[127],"rebuilds":[129],"when":[132],"query":[135],"sample":[136],"measured.":[138],"approach":[140],"enables":[141],"automatically":[144],"without":[151],"intentional":[152],"switching.In":[154],"order":[155],"demonstrate":[157],"effectiveness":[159],"was":[165],"applied":[166],"actual":[168],"operation":[169],"data":[170],"distillation":[173],"including":[175],"maintenance":[176],"periods.":[177],"The":[178],"results":[179],"demonstrated":[180],"LST-FEDA":[182],"improved":[183],"RMSE":[185],"19%":[187],"average":[189],"comparison":[191],"with":[192],"methods.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}