{"id":"https://openalex.org/W4322628665","doi":"https://doi.org/10.3390/s23052658","title":"Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns","display_name":"Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns","publication_year":2023,"publication_date":"2023-02-28","ids":{"openalex":"https://openalex.org/W4322628665","doi":"https://doi.org/10.3390/s23052658","pmid":"https://pubmed.ncbi.nlm.nih.gov/36904861"},"language":"en","primary_location":{"id":"doi:10.3390/s23052658","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23052658","pdf_url":"https://www.mdpi.com/1424-8220/23/5/2658/pdf?version=1677575699","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/23/5/2658/pdf?version=1677575699","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100739893","display_name":"Yi Liu","orcid":"https://orcid.org/0000-0002-4066-689X"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Liu","raw_affiliation_strings":["Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China"],"affiliations":[{"raw_affiliation_string":"Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101815953","display_name":"Yuxin Jiang","orcid":"https://orcid.org/0000-0002-9797-5116"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Jiang","raw_affiliation_strings":["Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China"],"affiliations":[{"raw_affiliation_string":"Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038738520","display_name":"Zengliang Gao","orcid":"https://orcid.org/0000-0003-2943-8332"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengliang Gao","raw_affiliation_strings":["Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China"],"affiliations":[{"raw_affiliation_string":"Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100674710","display_name":"Kaixin Liu","orcid":"https://orcid.org/0000-0001-5573-1781"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaixin Liu","raw_affiliation_strings":["Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan 030051, China"],"affiliations":[{"raw_affiliation_string":"Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan 030051, China","institution_ids":["https://openalex.org/I135714990"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000492991","display_name":"Yuan Yao","orcid":"https://orcid.org/0000-0002-0025-6175"},"institutions":[{"id":"https://openalex.org/I25846049","display_name":"National Tsing Hua University","ror":"https://ror.org/00zdnkx70","country_code":"TW","type":"education","lineage":["https://openalex.org/I25846049"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Yuan Yao","raw_affiliation_strings":["Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan","institution_ids":["https://openalex.org/I25846049"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5000492991","https://openalex.org/A5100674710"],"corresponding_institution_ids":["https://openalex.org/I135714990","https://openalex.org/I25846049"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":0.3657,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55528799,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"23","issue":"5","first_page":"2658","last_page":"2658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.991599977016449,"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":0.991599977016449,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9878000020980835,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9861000180244446,"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/flooding","display_name":"Flooding (psychology)","score":0.7823151350021362},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6590859293937683},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5762348175048828},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5540958642959595},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.504536509513855},{"id":"https://openalex.org/keywords/column","display_name":"Column (typography)","score":0.49741604924201965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46155428886413574},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4598664939403534},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4519084692001343},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4025549590587616},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35794705152511597},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.2682233452796936}],"concepts":[{"id":"https://openalex.org/C186594467","wikidata":"https://www.wikidata.org/wiki/Q1429176","display_name":"Flooding (psychology)","level":2,"score":0.7823151350021362},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6590859293937683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5762348175048828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5540958642959595},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.504536509513855},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.49741604924201965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46155428886413574},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4598664939403534},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4519084692001343},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4025549590587616},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35794705152511597},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2682233452796936},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s23052658","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23052658","pdf_url":"https://www.mdpi.com/1424-8220/23/5/2658/pdf?version=1677575699","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36904861","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36904861","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10007398","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10007398","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10007398/pdf/sensors-23-02658.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:c4dba7c7294b4521a467c6e3b8e4c5d9","is_oa":true,"landing_page_url":"https://doaj.org/article/c4dba7c7294b4521a467c6e3b8e4c5d9","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 23, Iss 5, p 2658 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/23/5/2658/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s23052658","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s23052658","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23052658","pdf_url":"https://www.mdpi.com/1424-8220/23/5/2658/pdf?version=1677575699","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4947568728","display_name":null,"funder_award_id":"61873241","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8856357754","display_name":null,"funder_award_id":"62022073","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":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4322628665.pdf"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1920661243","https://openalex.org/W1980697376","https://openalex.org/W2037019685","https://openalex.org/W2133393721","https://openalex.org/W2136922672","https://openalex.org/W2163922914","https://openalex.org/W2322622188","https://openalex.org/W2342028629","https://openalex.org/W2492169898","https://openalex.org/W2521703687","https://openalex.org/W2626190321","https://openalex.org/W2737248315","https://openalex.org/W2757962973","https://openalex.org/W2759373267","https://openalex.org/W2789440825","https://openalex.org/W2903712458","https://openalex.org/W2904067769","https://openalex.org/W2919115771","https://openalex.org/W2965059701","https://openalex.org/W2970007912","https://openalex.org/W2997616577","https://openalex.org/W2998320282","https://openalex.org/W3094510613","https://openalex.org/W3095103355","https://openalex.org/W3168997536","https://openalex.org/W3195912117","https://openalex.org/W3206066207","https://openalex.org/W4281837623","https://openalex.org/W4285170881","https://openalex.org/W4295308203","https://openalex.org/W4297518356","https://openalex.org/W6739508189","https://openalex.org/W6785403596","https://openalex.org/W6842098365"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W4293226380","https://openalex.org/W2181722423","https://openalex.org/W148178222","https://openalex.org/W2386325437","https://openalex.org/W2353043494","https://openalex.org/W2104657898","https://openalex.org/W2347222412","https://openalex.org/W1948992892","https://openalex.org/W2085601491"],"abstract_inverted_index":{"In":[0],"chemical":[1],"processes,":[2],"packed":[3,39,93,99,162],"columns":[4,22],"are":[5,23],"frequently":[6],"employed":[7],"in":[8,20,47,92],"various":[9],"unit":[10],"operations.":[11],"However,":[12],"the":[13,27,33,68,98,152,168],"flow":[14],"rates":[15],"of":[16,29,38,71,90,97,120,139,151],"gas":[17],"and":[18,35,107,135,143,149],"liquid":[19],"these":[21],"often":[24],"constrained":[25],"by":[26],"risk":[28],"flooding.":[30,125],"To":[31,73],"ensure":[32],"safe":[34],"efficient":[36],"operation":[37],"columns,":[40],"it":[41],"is":[42],"crucial":[43],"to":[44,123,182,185],"detect":[45],"flooding":[46,51,91,187],"real":[48,161],"time.":[49],"Conventional":[50],"monitoring":[52],"methods":[53],"rely":[54],"heavily":[55],"on":[56,117,159],"manual":[57],"visual":[58],"inspections":[59],"or":[60],"indirect":[61],"information":[62],"from":[63],"process":[64,180],"variables,":[65],"which":[66,113],"limit":[67],"real-time":[69,173],"accuracy":[70],"results.":[72],"address":[74],"this":[75],"challenge,":[76],"we":[77],"proposed":[78,127,153,169],"a":[79,104,110,118,160,172],"convolutional":[80],"neural":[81],"network":[82],"(CNN)-based":[83],"machine":[84],"vision":[85],"approach":[86,128,138,175],"for":[87,176],"non-destructive":[88],"detection":[89],"columns.":[94],"Real-time":[95],"images":[96,122],"column":[100],"were":[101,155],"captured":[102],"using":[103],"digital":[105],"camera":[106],"analyzed":[108],"with":[109,131],"CNN":[111],"model,":[112],"was":[114,129],"been":[115],"trained":[116],"dataset":[119],"recorded":[121],"identify":[124],"The":[126,147,164],"compared":[130],"deep":[132],"belief":[133],"networks":[134],"an":[136],"integrated":[137],"principal":[140],"component":[141],"analysis":[142],"support":[144],"vector":[145],"machines.":[146],"feasibility":[148],"advantages":[150],"method":[154,170],"demonstrated":[156],"through":[157],"experiments":[158],"column.":[163],"results":[165],"showed":[166],"that":[167],"provides":[171],"pre-alarm":[174],"detecting":[177],"flooding,":[178],"enabling":[179],"engineers":[181],"quickly":[183],"respond":[184],"potential":[186],"events.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
