{"id":"https://openalex.org/W4396941473","doi":"https://doi.org/10.1109/jiot.2024.3401217","title":"Patch-Masked Visual Inspection via Parallel Deep Tensor Factorization in Industrial Internet of Things","display_name":"Patch-Masked Visual Inspection via Parallel Deep Tensor Factorization in Industrial Internet of Things","publication_year":2024,"publication_date":"2024-05-15","ids":{"openalex":"https://openalex.org/W4396941473","doi":"https://doi.org/10.1109/jiot.2024.3401217"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2024.3401217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3401217","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5083177788","display_name":"Gang Yue","orcid":"https://orcid.org/0000-0001-5646-0944"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Gang Yue","raw_affiliation_strings":["WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","Institute of Systems Engineering, Academy of Military Sciences, Beijing, China","BUPT, China"],"raw_orcid":"https://orcid.org/0000-0001-5646-0944","affiliations":[{"raw_affiliation_string":"WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Institute of Systems Engineering, Academy of Military Sciences, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"BUPT, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041007168","display_name":"Zhuo Sun","orcid":"https://orcid.org/0000-0002-3333-722X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuo Sun","raw_affiliation_strings":["WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3333-722X","affiliations":[{"raw_affiliation_string":"WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036297719","display_name":"Jinpo Fan","orcid":"https://orcid.org/0000-0003-0346-0558"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinpo Fan","raw_affiliation_strings":["WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0346-0558","affiliations":[{"raw_affiliation_string":"WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":null,"display_name":"Di Peng","orcid":"https://orcid.org/0009-0007-8798-9163"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Peng","raw_affiliation_strings":["WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-8798-9163","affiliations":[{"raw_affiliation_string":"WSPN Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083177788"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.2381,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47176514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"11","issue":"17","first_page":"28200","last_page":"28212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9897000193595886,"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/inpainting","display_name":"Inpainting","score":0.8386746048927307},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8148230314254761},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6941350698471069},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4645020663738251},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4399656355381012},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.4345601797103882},{"id":"https://openalex.org/keywords/structure-tensor","display_name":"Structure tensor","score":0.41103675961494446},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3527786135673523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3203684687614441},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2502748370170593}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.8386746048927307},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8148230314254761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6941350698471069},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4645020663738251},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4399656355381012},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.4345601797103882},{"id":"https://openalex.org/C113315163","wikidata":"https://www.wikidata.org/wiki/Q7625159","display_name":"Structure tensor","level":3,"score":0.41103675961494446},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3527786135673523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3203684687614441},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2502748370170593}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2024.3401217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3401217","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G7019433403","display_name":null,"funder_award_id":"62171063","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":40,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1963826206","https://openalex.org/W1992426838","https://openalex.org/W2024165284","https://openalex.org/W2043571470","https://openalex.org/W2091449379","https://openalex.org/W2133665775","https://openalex.org/W2336406062","https://openalex.org/W2599354622","https://openalex.org/W2613645387","https://openalex.org/W2765145408","https://openalex.org/W2935842115","https://openalex.org/W2948982773","https://openalex.org/W2963885538","https://openalex.org/W2964214749","https://openalex.org/W2979396152","https://openalex.org/W2987228832","https://openalex.org/W2995081268","https://openalex.org/W2997965215","https://openalex.org/W3007171445","https://openalex.org/W3025377753","https://openalex.org/W3039088060","https://openalex.org/W3092704883","https://openalex.org/W3094502228","https://openalex.org/W3127751679","https://openalex.org/W3165355476","https://openalex.org/W3168052222","https://openalex.org/W3168686693","https://openalex.org/W3175238080","https://openalex.org/W4285293775","https://openalex.org/W4286883132","https://openalex.org/W4292264335","https://openalex.org/W4312433903","https://openalex.org/W4313156423","https://openalex.org/W4376626035","https://openalex.org/W4390875033","https://openalex.org/W4400071817","https://openalex.org/W6784333009","https://openalex.org/W6803640930","https://openalex.org/W6849540298"],"related_works":["https://openalex.org/W2371759427","https://openalex.org/W4206236856","https://openalex.org/W2507507643","https://openalex.org/W2575794905","https://openalex.org/W3204028237","https://openalex.org/W3145291360","https://openalex.org/W2939230802","https://openalex.org/W2261546675","https://openalex.org/W3103070857","https://openalex.org/W3083789332"],"abstract_inverted_index":{"Visual":[0],"inspection":[1,45,147,184],"is":[2,18,95,122,160],"an":[3,54],"effective":[4,79],"approach":[5,142],"for":[6,21,201],"anomaly":[7],"detection":[8],"in":[9,81,134],"the":[10,26,30,44,48,66,86,99,126,131,150,154,163,174],"industrial":[11,22],"Internet":[12],"of":[13,29,47,162],"Things.":[14],"The":[15],"inpainting-based":[16],"strategy":[17,76],"widely":[19],"adopted":[20],"visual":[23,146],"inspection.":[24],"However,":[25],"training":[27,194],"process":[28],"semantic-based":[31,187],"inpainting":[32,80,188],"model":[33,49,94,104,181],"requires":[34],"heavy":[35],"resource":[36],"consumption":[37],"due":[38],"to":[39,77,97,124,143],"its":[40],"complex":[41],"structure.":[42],"Additionally,":[43],"performance":[46],"degrades":[50],"when":[51],"it":[52,190,198],"encounters":[53],"out-of-scope":[55],"image.":[56],"Image":[57],"can":[58],"be":[59],"naturally":[60],"deemed":[61],"as":[62,167],"a":[63,73,89,106,140,168],"tensor":[64,92],"with":[65,109],"global":[67],"low-rank":[68],"property.":[69],"Therefore,":[70],"we":[71,138],"propose":[72],"data":[74],"structure-based":[75],"implement":[78,144],"this":[82],"paper.":[83],"Inspired":[84],"by":[85,148],"tensor-tensor":[87],"product,":[88],"patch-masked":[90,145],"deep":[91],"factorization":[93],"constructed":[96],"reconstruct":[98],"intentionally":[100],"masked":[101],"region.":[102],"This":[103],"has":[105,182,191],"simplified":[107],"structure":[108],"three":[110],"feed-forward":[111],"neural":[112],"subnets":[113],"sharing":[114],"one":[115],"trainable":[116],"low-dimensional":[117],"input.":[118],"Besides,":[119],"Laplacian":[120],"regularization":[121],"imposed":[123],"improve":[125],"recovery":[127],"accuracy":[128],"based":[129],"on":[130,173],"local":[132],"smoothness":[133],"images.":[135],"Above":[136],"all,":[137],"apply":[139],"parallel":[141],"comparing":[149],"structural":[151],"similarity":[152],"between":[153],"recovered":[155],"and":[156,165],"original":[157],"patches.":[158],"It":[159],"independent":[161],"dataset":[164,176],"works":[166],"sample-related":[169],"pattern.":[170],"Experiments":[171],"conducted":[172],"MVTec":[175],"demonstrate":[177],"that,":[178],"although":[179],"our":[180],"lower":[183],"resolution":[185],"than":[186],"models,":[189],"much":[192],"higher":[193],"efficiency,":[195],"which":[196],"makes":[197],"more":[199],"beneficial":[200],"practical":[202],"deployment.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
