{"id":"https://openalex.org/W3157761116","doi":"https://doi.org/10.1109/tim.2021.3075515","title":"Augmented Multidimensional Convolutional Neural Network for Industrial Soft Sensing","display_name":"Augmented Multidimensional Convolutional Neural Network for Industrial Soft Sensing","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3157761116","doi":"https://doi.org/10.1109/tim.2021.3075515","mag":"3157761116"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2021.3075515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3075515","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/A5012291182","display_name":"Xiaoyu Jiang","orcid":"https://orcid.org/0000-0003-4170-5579"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyu Jiang","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China","State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067726465","display_name":"Zhiqiang Ge","orcid":"https://orcid.org/0000-0002-2071-4380"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Ge","raw_affiliation_strings":["Peng Cheng Laboratory, Shenzhen, China","State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5012291182"],"corresponding_institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I4391767838"],"apc_list":null,"apc_paid":null,"fwci":8.7889,"has_fulltext":false,"cited_by_count":84,"citation_normalized_percentile":{"value":0.98396558,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"70","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9994999766349792,"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":0.9994999766349792,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9944000244140625,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/computer-science","display_name":"Computer science","score":0.7444320917129517},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7210890650749207},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6133792400360107},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.5988589525222778},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5527344346046448},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5487412810325623},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4878575801849365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.451913446187973},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3867616355419159},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3602342903614044}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7444320917129517},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7210890650749207},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6133792400360107},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.5988589525222778},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5527344346046448},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5487412810325623},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4878575801849365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.451913446187973},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3867616355419159},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3602342903614044},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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.2021.3075515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3075515","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":[{"id":"https://metadata.un.org/sdg/9","score":0.550000011920929,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G7655635370","display_name":null,"funder_award_id":"LR18F030001","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"},{"id":"https://openalex.org/G7875806344","display_name":null,"funder_award_id":"61722310","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"},{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W169539560","https://openalex.org/W1528140509","https://openalex.org/W1836465849","https://openalex.org/W2000651380","https://openalex.org/W2018411202","https://openalex.org/W2062368510","https://openalex.org/W2073503722","https://openalex.org/W2099471712","https://openalex.org/W2112796928","https://openalex.org/W2183341477","https://openalex.org/W2379133119","https://openalex.org/W2740586183","https://openalex.org/W2768753204","https://openalex.org/W2774816978","https://openalex.org/W2794908468","https://openalex.org/W2884001105","https://openalex.org/W2886243431","https://openalex.org/W2919115771","https://openalex.org/W2940589124","https://openalex.org/W2946994806","https://openalex.org/W2954996726","https://openalex.org/W2964350391","https://openalex.org/W2982252423","https://openalex.org/W2989872913","https://openalex.org/W3003166104","https://openalex.org/W3025505824","https://openalex.org/W3028207611","https://openalex.org/W3034654297","https://openalex.org/W3045997366","https://openalex.org/W3049640903","https://openalex.org/W3123899295","https://openalex.org/W4295521014","https://openalex.org/W4301971222","https://openalex.org/W4320013936","https://openalex.org/W6606918967","https://openalex.org/W6638667902","https://openalex.org/W6694260854","https://openalex.org/W6735913928","https://openalex.org/W6779669310"],"related_works":["https://openalex.org/W2374290272","https://openalex.org/W2097933059","https://openalex.org/W4386158748","https://openalex.org/W1970956258","https://openalex.org/W2408812858","https://openalex.org/W3127658115","https://openalex.org/W3152705324","https://openalex.org/W2359154573","https://openalex.org/W2737942854","https://openalex.org/W2964954556"],"abstract_inverted_index":{"In":[0,122],"the":[1,24,30,41,48,81,98,102,107,125,146,161,165],"era":[2],"of":[3,26,43,56,100,105,109,119,127,148,164,171],"industrial":[4,31,57,71],"big":[5],"data,":[6,59,106],"data-driven":[7],"soft-sensor":[8],"models":[9],"have":[10,36],"become":[11],"an":[12,63],"important":[13],"method":[14],"to":[15,23,84,93,114,140,160,176],"guide":[16],"production":[17,168],"and":[18,53,88,144],"optimize":[19],"control.":[20],"However,":[21],"due":[22],"limitation":[25],"data":[27,32,77,83,87,130,135],"acquisition":[28],"methods,":[29],"obtained":[33],"in":[34],"practice":[35],"unavoidable":[37],"defects":[38],"that":[39],"affect":[40],"performance":[42,147],"soft":[44,72,152],"sensors.":[45,153],"Aiming":[46],"at":[47],"unbalanced":[49],"sampling,":[50],"inaccurate":[51],"matching,":[52],"partial":[54,128],"missing":[55,129],"process":[58,76,120],"this":[60,123,142],"article":[61],"proposes":[62],"augmented":[64,156],"multidimensional":[65,110,149],"convolutional":[66],"neural":[67],"network":[68],"(CNN)":[69],"for":[70],"sensing.":[73],"For":[74],"complete":[75],"information,":[78],"we":[79],"stitch":[80],"fine-grained":[82],"obtain":[85],"coarse-grained":[86],"then":[89],"use":[90],"a":[91],"CNN":[92,150],"extract":[94],"deep":[95],"features.":[96],"On":[97],"basis":[99],"analyzing":[101],"physical":[103],"meaning":[104],"structure":[108],"convolution":[111],"is":[112,131,158],"designed":[113],"focus":[115],"on":[116],"different":[117],"details":[118],"information.":[121],"framework,":[124],"problem":[126,143],"emphasized.":[132],"Then,":[133],"mix-grained":[134],"augmentation":[136],"strategies":[137],"are":[138],"invented":[139],"solve":[141],"improve":[145],"(MDCNN)":[151],"The":[154],"proposed":[155],"MDCNN":[157],"applied":[159],"f-CaO":[162],"prediction":[163],"cement":[166],"clinker":[167],"process,":[169],"results":[170],"which":[172],"show":[173],"superiority":[174],"compared":[175],"existing":[177],"methods.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}