{"id":"https://openalex.org/W4401879202","doi":"https://doi.org/10.1109/tim.2024.3449964","title":"Soft Sensor Enhancement for Multimodal Industrial Process Data: Meta Regression Gaussian Mixture Variational Autoencoder","display_name":"Soft Sensor Enhancement for Multimodal Industrial Process Data: Meta Regression Gaussian Mixture Variational Autoencoder","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401879202","doi":"https://doi.org/10.1109/tim.2024.3449964"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2024.3449964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3449964","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/A5100333592","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0002-1449-3016"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1449-3016","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060223712","display_name":"Yuan Xu","orcid":"https://orcid.org/0000-0001-5490-2892"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Xu","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5490-2892","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054385364","display_name":"Qunxiong Zhu","orcid":"https://orcid.org/0000-0002-2719-0516"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qun-Xiong Zhu","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2719-0516","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045331954","display_name":"Yan\u2010Lin He","orcid":"https://orcid.org/0000-0001-9999-3679"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan-Lin He","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9999-3679","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":null,"apc_paid":null,"fwci":3.1251,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.91976484,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"73","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9991000294685364,"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":0.9991000294685364,"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9682999849319458,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.965399980545044,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8908612728118896},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.6812576651573181},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5539747476577759},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5203877687454224},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.483163058757782},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45284852385520935},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.448293000459671},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4392852485179901},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4301716685295105},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.42761659622192383},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3756950795650482},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3506365418434143},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3255729079246521},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3242397904396057},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.25232285261154175},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1663753092288971}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8908612728118896},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6812576651573181},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5539747476577759},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5203877687454224},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.483163058757782},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45284852385520935},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.448293000459671},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4392852485179901},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4301716685295105},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.42761659622192383},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3756950795650482},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3506365418434143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3255729079246521},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3242397904396057},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25232285261154175},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1663753092288971},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2024.3449964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3449964","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":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G2174988415","display_name":"\u590d\u6742\u8fc7\u7a0b\u7cfb\u7edf\u865a\u62df\u6837\u672c\u751f\u6210\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61973024","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5348053237","display_name":"\u4e0d\u5e73\u8861\u5c0f\u6837\u672c\u6570\u636e\u4e0b\u590d\u6742\u77f3\u5316\u5de5\u4e1a\u5371\u5316\u54c1\u707e\u5bb3\u98ce\u9669\u6f14\u5316\u9884\u6d4b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61973022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6371134328","display_name":null,"funder_award_id":"JD2327","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8751590961","display_name":null,"funder_award_id":"62073022","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":30,"referenced_works":["https://openalex.org/W1965733010","https://openalex.org/W2962069445","https://openalex.org/W3023056142","https://openalex.org/W3036721652","https://openalex.org/W3042609801","https://openalex.org/W3080648230","https://openalex.org/W3120987150","https://openalex.org/W3215430566","https://openalex.org/W4200181476","https://openalex.org/W4205099792","https://openalex.org/W4214676882","https://openalex.org/W4233524360","https://openalex.org/W4280561235","https://openalex.org/W4285287611","https://openalex.org/W4292348134","https://openalex.org/W4294068740","https://openalex.org/W4312237393","https://openalex.org/W4312843836","https://openalex.org/W4313306326","https://openalex.org/W4327521490","https://openalex.org/W4368232735","https://openalex.org/W4379382541","https://openalex.org/W4382999352","https://openalex.org/W4383337691","https://openalex.org/W4385245566","https://openalex.org/W4388505146","https://openalex.org/W4392567286","https://openalex.org/W6640963894","https://openalex.org/W6730333270","https://openalex.org/W6736057607"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4312950481","https://openalex.org/W2989872913","https://openalex.org/W2157136988","https://openalex.org/W2115519811","https://openalex.org/W1993264360"],"abstract_inverted_index":{"Traditional":[0],"industrial":[1,6,23,150],"soft":[2,39],"sensors":[3],"often":[4],"treat":[5],"process":[7,24,95],"data":[8,25,35,131],"as":[9],"uniformly":[10],"distributed":[11],"or":[12],"unimodal.":[13],"However,":[14],"in":[15,20],"reality,":[16],"due":[17],"to":[18,38,43,87,102,105,123],"variations":[19],"operating":[21,52],"conditions,":[22,53],"frequently":[26],"exhibit":[27],"multimodal":[28,32,89,118],"characteristics.":[29],"Overlooking":[30],"the":[31,56,94,109,121,130,136,141],"nature":[33],"of":[34,58,111,140,149],"can":[36],"lead":[37],"sensor":[40],"models":[41],"failing":[42],"accurately":[44],"capture":[45],"changing":[46],"patterns":[47],"and":[48,91,138],"relationships":[49],"under":[50],"different":[51,106,133],"thereby":[54],"compromising":[55],"accuracy":[57],"prediction":[59],"results.":[60],"To":[61],"address":[62],"this":[63],"issue,":[64],"a":[65,78],"method":[66],"called":[67],"meta":[68],"regression":[69,79],"Gaussian":[70,80],"mixture":[71,81],"variational":[72,82],"autoencoder":[73,83],"(MR-GMVAE)":[74],"is":[75,85,100,113],"proposed.":[76],"First,":[77],"(R-GMVAE)":[84],"introduced":[86,114],"extract":[88],"distributions":[90],"dynamically":[92],"model":[93],"variables.":[96],"An":[97],"adaptive":[98],"module":[99],"incorporated":[101],"allocate":[103],"importance":[104],"modes.":[107,134],"Second,":[108],"concept":[110],"meta-learning":[112],"for":[115],"training":[116,127],"on":[117,129],"data,":[119],"enabling":[120],"R-GMVAE":[122],"adaptively":[124],"customize":[125],"its":[126],"based":[128],"from":[132],"Finally,":[135],"effectiveness":[137],"adaptability":[139],"proposed":[142],"MR-GMVAE":[143],"are":[144],"validated":[145],"using":[146],"two":[147],"sets":[148],"data.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":11}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}