{"id":"https://openalex.org/W3010625323","doi":"https://doi.org/10.1109/tii.2020.2978944","title":"Multitask-Based Temporal-Channelwise CNN for Parameter Prediction of Two-Phase Flows","display_name":"Multitask-Based Temporal-Channelwise CNN for Parameter Prediction of Two-Phase Flows","publication_year":2020,"publication_date":"2020-03-06","ids":{"openalex":"https://openalex.org/W3010625323","doi":"https://doi.org/10.1109/tii.2020.2978944","mag":"3010625323"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2020.2978944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.2978944","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/A5082820311","display_name":"Zhongke Gao","orcid":"https://orcid.org/0000-0002-9551-202X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongke Gao","raw_affiliation_strings":["Ministry of Education, Key Laboratory of Efficient Utilization of Low and Medium Grade Energy (Tianjin University), Tianjin, China","School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-9551-202X","affiliations":[{"raw_affiliation_string":"Ministry of Education, Key Laboratory of Efficient Utilization of Low and Medium Grade Energy (Tianjin University), Tianjin, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021590751","display_name":"Linhua Hou","orcid":"https://orcid.org/0000-0001-5868-2604"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linhua Hou","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0001-5868-2604","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080578053","display_name":"Weidong Dang","orcid":"https://orcid.org/0000-0002-5898-4853"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weidong Dang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-5898-4853","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006199471","display_name":"Xinmin Wang","orcid":"https://orcid.org/0000-0003-1675-327X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinmin Wang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110375288","display_name":"Xiaolin Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolin Hong","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-0128-3036","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085440585","display_name":"Xiong Yang","orcid":"https://orcid.org/0000-0002-0128-3036"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiong Yang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024633466","display_name":"Guanrong Chen","orcid":"https://orcid.org/0000-0003-1381-7418"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Guanrong Chen","raw_affiliation_strings":["Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-1381-7418","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4341,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.85563954,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"17","issue":"9","first_page":"6329","last_page":"6336"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9948999881744385,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9948999881744385,"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/T11407","display_name":"Innovative Microfluidic and Catalytic Techniques Innovation","score":0.9932000041007996,"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/T10864","display_name":"Fluid Dynamics and Mixing","score":0.9883000254631042,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6066577434539795},{"id":"https://openalex.org/keywords/two-phase-flow","display_name":"Two-phase flow","score":0.540296196937561},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5272485613822937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4816366732120514},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41885802149772644},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.41407352685928345},{"id":"https://openalex.org/keywords/fraction","display_name":"Fraction (chemistry)","score":0.4121086597442627},{"id":"https://openalex.org/keywords/mechanics","display_name":"Mechanics","score":0.20985591411590576},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.12207755446434021},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12095236778259277}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6066577434539795},{"id":"https://openalex.org/C144308804","wikidata":"https://www.wikidata.org/wiki/Q232997","display_name":"Two-phase flow","level":3,"score":0.540296196937561},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5272485613822937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4816366732120514},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41885802149772644},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.41407352685928345},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.4121086597442627},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.20985591411590576},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.12207755446434021},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12095236778259277},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2020.2978944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.2978944","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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5400000214576721}],"awards":[{"id":"https://openalex.org/G5928784654","display_name":null,"funder_award_id":"61873181","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6723776895","display_name":null,"funder_award_id":"61922062","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":33,"referenced_works":["https://openalex.org/W1170214759","https://openalex.org/W1536680647","https://openalex.org/W1836465849","https://openalex.org/W1969771299","https://openalex.org/W2052678124","https://openalex.org/W2092024822","https://openalex.org/W2097117768","https://openalex.org/W2098472208","https://openalex.org/W2136922672","https://openalex.org/W2172414120","https://openalex.org/W2204679745","https://openalex.org/W2268914338","https://openalex.org/W2319555658","https://openalex.org/W2565419829","https://openalex.org/W2604096629","https://openalex.org/W2742947407","https://openalex.org/W2753709519","https://openalex.org/W2778901641","https://openalex.org/W2791482549","https://openalex.org/W2794086095","https://openalex.org/W2796096089","https://openalex.org/W2800428573","https://openalex.org/W2884001105","https://openalex.org/W2887581812","https://openalex.org/W2891719020","https://openalex.org/W2908578648","https://openalex.org/W2921755989","https://openalex.org/W2926004035","https://openalex.org/W2963446712","https://openalex.org/W2964350391","https://openalex.org/W2964901258","https://openalex.org/W6638667902","https://openalex.org/W6694260854"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W1971346788","https://openalex.org/W2320316818","https://openalex.org/W2002601706","https://openalex.org/W2064547363"],"abstract_inverted_index":{"Gas-liquid":[0],"two-phase":[1,21,54,62],"flow":[2,16,22,63,68],"is":[3,47],"of":[4,128],"great":[5],"importance":[6],"in":[7,18,51,171],"various":[8],"industrial":[9],"processes.":[10],"How":[11],"to":[12,40,65,88,103,124,149],"accurately":[13],"measure":[14,38,66],"the":[15,19,42,58,67,72,90,99,117,129,151],"parameters":[17],"gas-liquid":[20,53,61],"remains":[23],"a":[24,32,52,80,168],"challenging":[25],"problem.":[26],"In":[27,94],"this":[28],"article,":[29],"we":[30,78,96,120],"develop":[31],"novel":[33,81],"deep":[34],"learning":[35,123],"based":[36],"soft":[37],"technique":[39],"predict":[41,89],"gas":[43,91,137,172],"void":[44,92,138,173],"fraction,":[45],"which":[46,162],"one":[48],"key":[49],"parameter":[50],"flow.":[55],"We":[56,142,154],"conduct":[57],"vertical":[59],"upward":[60],"experiments":[64],"signals":[69],"by":[70,116],"using":[71],"four-sector":[73],"distributed":[74],"conductance":[75],"sensor.":[76],"Then,":[77],"design":[79],"multitask-based":[82],"temporal-channelwise":[83],"convolutional":[84,101],"neural":[85],"network":[86],"(MTCCNN)":[87],"fraction.":[93],"MTCCNN,":[95],"first":[97],"utilize":[98],"decomposed":[100],"block":[102],"extract":[104],"temporal":[105],"dependence":[106],"and":[107,136],"channel":[108],"connection":[109],"from":[110],"fluid":[111],"data.":[112],"After":[113],"further":[114],"fusion":[115],"dense":[118],"layer,":[119],"apply":[121],"multitask":[122],"make":[125],"full":[126],"use":[127],"extracted":[130],"features":[131],"through":[132],"both":[133],"classification":[134],"branch":[135],"fraction":[139,174],"prediction":[140],"branch.":[141],"compare":[143],"our":[144,165],"MTCCNN":[145,166],"with":[146],"its":[147],"variations":[148],"demonstrate":[150],"proposed":[152],"improvements.":[153],"also":[155],"present":[156],"other":[157],"competitive":[158],"methods":[159],"for":[160],"comparisons,":[161],"shows":[163],"that":[164],"presents":[167],"better":[169],"performance":[170],"prediction.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
