{"id":"https://openalex.org/W3027298110","doi":"https://doi.org/10.1145/3388818.3388823","title":"Condition Monitoring for Confined Industrial Process Based on Infrared Images by Using Deep Neural Network and Variants","display_name":"Condition Monitoring for Confined Industrial Process Based on Infrared Images by Using Deep Neural Network and Variants","publication_year":2020,"publication_date":"2020-03-20","ids":{"openalex":"https://openalex.org/W3027298110","doi":"https://doi.org/10.1145/3388818.3388823","mag":"3027298110"},"language":"en","primary_location":{"id":"doi:10.1145/3388818.3388823","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3388818.3388823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing","raw_type":"proceedings-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/A5070382887","display_name":"Yuchong Zhang","orcid":"https://orcid.org/0000-0003-1804-6296"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Yuchong Zhang","raw_affiliation_strings":["Chalmers University of Technology, Gothenburg, Sweden"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079816004","display_name":"Morten Fjeld","orcid":"https://orcid.org/0000-0002-9562-5147"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Morten Fjeld","raw_affiliation_strings":["Chalmers University of Technology, Gothenburg, Sweden"],"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology, Gothenburg, Sweden","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5070382887"],"corresponding_institution_ids":["https://openalex.org/I66862912"],"apc_list":null,"apc_paid":null,"fwci":2.1397,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.89115865,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"99","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9951000213623047,"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"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9951000213623047,"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"}},{"id":"https://openalex.org/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T10188","display_name":"Advanced machining processes and optimization","score":0.9843000173568726,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7765345573425293},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7609139084815979},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.7432861328125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6984845399856567},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6688838005065918},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5996912121772766},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5859637260437012},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5333734750747681},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.5046936273574829},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4551215171813965},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4258892238140106},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4205607771873474},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3540504574775696},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1905648112297058},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10474491119384766}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7765345573425293},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7609139084815979},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7432861328125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6984845399856567},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6688838005065918},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5996912121772766},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5859637260437012},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5333734750747681},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.5046936273574829},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4551215171813965},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4258892238140106},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4205607771873474},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3540504574775696},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1905648112297058},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10474491119384766},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","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},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3388818.3388823","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3388818.3388823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:research.chalmers.se:517387","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/517387","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5799999833106995,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W298212978","https://openalex.org/W1169095854","https://openalex.org/W1269224004","https://openalex.org/W1830582801","https://openalex.org/W1980438367","https://openalex.org/W1982589161","https://openalex.org/W1992139774","https://openalex.org/W2012355778","https://openalex.org/W2031329301","https://openalex.org/W2053741029","https://openalex.org/W2063217105","https://openalex.org/W2076063813","https://openalex.org/W2117130368","https://openalex.org/W2119821739","https://openalex.org/W2119884533","https://openalex.org/W2126584714","https://openalex.org/W2149933564","https://openalex.org/W2163605009","https://openalex.org/W2171928131","https://openalex.org/W2194775991","https://openalex.org/W2285341434","https://openalex.org/W2572334665","https://openalex.org/W2724573302","https://openalex.org/W2738563279","https://openalex.org/W2757455114","https://openalex.org/W2810292802","https://openalex.org/W2936573025","https://openalex.org/W3015751240","https://openalex.org/W3103586777","https://openalex.org/W4205947740","https://openalex.org/W4297816008","https://openalex.org/W4300011764"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W3183901164","https://openalex.org/W2951211570","https://openalex.org/W3176438653","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3167935049","https://openalex.org/W3003905048","https://openalex.org/W2253429366","https://openalex.org/W3127975138"],"abstract_inverted_index":{"Some":[0],"industrial":[1],"processes":[2,17],"take":[3,18],"place":[4,19],"in":[5,144,170],"confined":[6,82],"settings":[7],"only":[8],"observable":[9],"by":[10,25,60],"sensors,":[11],"e.g.":[12],"infrared":[13],"(IR)":[14],"cameras.":[15,38],"Drying":[16],"while":[20],"a":[21,28,31,121],"material":[22],"is":[23,48],"transported":[24],"means":[26],"of":[27,85,123,148],"conveyor":[29],"through":[30],"\"black":[32],"box\"":[33],"equipped":[34],"with":[35,88],"internal":[36],"IR":[37,67],"While":[39],"such":[40],"sensors":[41],"deliver":[42],"data":[43],"at":[44,92],"high":[45],"rates,":[46],"this":[47,72],"beyond":[49],"what":[50],"human":[51],"operators":[52],"can":[53,77],"analyze":[54],"and":[55,105,116,163],"calls":[56],"for":[57,126],"automation.":[58],"Inspired":[59],"numerous":[61],"implementations":[62],"monitoring":[63,94],"techniques":[64,136,167],"that":[65,132,159],"analyse":[66],"images":[68],"using":[69,120],"deep":[70,106,134,164],"learning,":[71,104],"paper":[73],"shows":[74,131],"how":[75],"they":[76],"be":[78],"applied":[79],"to":[80,95,150],"the":[81],"microwave":[83],"drying":[84],"porous":[86],"foams,":[87],"benchmarking":[89],"their":[90],"effectiveness":[91],"condition":[93,139],"conduct":[96],"fault":[97,145],"detection.":[98],"Convolutional":[99],"neural":[100,108,179],"networks,":[101],"derived":[102,160],"transfer":[103,161],"residual":[107,165],"network":[109,166],"methods":[110],"are":[111,117],"already":[112],"regarded":[113],"as":[114],"cutting-edge":[115],"studied":[118],"here,":[119],"set":[122],"conventional":[124,153],"approaches":[125],"comparative":[127],"evaluation.":[128],"Our":[129],"comparison":[130],"state-of-the-art":[133],"learning":[135,162],"significantly":[137],"benefit":[138],"monitoring,":[140],"providing":[141],"an":[142],"increase":[143],"finding":[146],"accuracy":[147],"up":[149],"48%":[151],"over":[152,176],"methods.":[154],"Nevertheless,":[155],"we":[156],"also":[157],"found":[158],"do":[168],"not":[169],"our":[171],"case":[172],"yield":[173],"increased":[174],"performance":[175],"normal":[177],"convolutional":[178],"networks.":[180]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}