{"id":"https://openalex.org/W4391697791","doi":"https://doi.org/10.1109/icsai61474.2023.10423370","title":"An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder","display_name":"An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder","publication_year":2023,"publication_date":"2023-12-16","ids":{"openalex":"https://openalex.org/W4391697791","doi":"https://doi.org/10.1109/icsai61474.2023.10423370"},"language":"en","primary_location":{"id":"doi:10.1109/icsai61474.2023.10423370","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsai61474.2023.10423370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5109693859","display_name":"Mingwei Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingwei Yang","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China","College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100317723","display_name":"Yanhua Liu","orcid":"https://orcid.org/0000-0002-6076-9968"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"YanHua Liu","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China","College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100420464","display_name":"Hong Chen","orcid":"https://orcid.org/0000-0003-1546-4791"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong Chen","raw_affiliation_strings":["State Grid Info-Telecom Great Power Science and Technology CO.,LTD,Fuzhou,China,350000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Info-Telecom Great Power Science and Technology CO.,LTD,Fuzhou,China,350000","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101303212","display_name":"Jiefei Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiefei Lin","raw_affiliation_strings":["State Grid Info-Telecom Great Power Science and Technology CO.,LTD,Fuzhou,China,350000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Info-Telecom Great Power Science and Technology CO.,LTD,Fuzhou,China,350000","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032205250","display_name":"Haoqiang Lin","orcid":"https://orcid.org/0009-0000-5768-5467"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoqiang Lin","raw_affiliation_strings":["State Grid Info-Telecom Great Power Science and Technology CO.,LTD,Fuzhou,China,350000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Info-Telecom Great Power Science and Technology CO.,LTD,Fuzhou,China,350000","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9898999929428101,"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/T14470","display_name":"Advanced Data Processing Techniques","score":0.9822999835014343,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.9096510410308838},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7771224975585938},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6402093768119812},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.622277557849884},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6164089441299438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6048012971878052},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5983908176422119},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5564209818840027},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5379510521888733},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5020244121551514},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5009682178497314},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45653021335601807},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.439360648393631},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3197941780090332},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1982519030570984},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1560378074645996},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08075618743896484}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9096510410308838},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7771224975585938},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6402093768119812},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.622277557849884},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6164089441299438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6048012971878052},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5983908176422119},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5564209818840027},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5379510521888733},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5020244121551514},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5009682178497314},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45653021335601807},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.439360648393631},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3197941780090332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1982519030570984},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1560378074645996},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08075618743896484},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai61474.2023.10423370","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icsai61474.2023.10423370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6200000047683716}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1975630243","https://openalex.org/W1994505190","https://openalex.org/W1997208277","https://openalex.org/W2431410052","https://openalex.org/W2511461628","https://openalex.org/W2612554669","https://openalex.org/W2762709466","https://openalex.org/W2765226309","https://openalex.org/W2782209003","https://openalex.org/W2789634333","https://openalex.org/W2792972374","https://openalex.org/W2919054368","https://openalex.org/W2939985247","https://openalex.org/W2952598456","https://openalex.org/W2972034512","https://openalex.org/W3160417254","https://openalex.org/W3185421118","https://openalex.org/W4221113159","https://openalex.org/W4323926444"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829"],"abstract_inverted_index":{"With":[0],"the":[1,12,43,47,52,57,63,67,76,82,86,90,96,116,119,122],"increasing":[2],"complexity":[3],"of":[4,14,118,125],"industrial":[5,15,58],"systems,":[6],"new":[7],"challenges":[8],"are":[9],"posed":[10],"to":[11,21,50,70,74,84,88,130],"monitoring":[13],"process":[16],"data,":[17,59],"which":[18,45,114],"often":[19],"appear":[20],"be":[22],"characterized":[23],"by":[24],"nonlinear":[25,53],"and":[26,60,66,79,104,112,127],"strong":[27],"feature":[28,54,64],"correlation.":[29],"Therefore,":[30],"a":[31,72],"sparse":[32],"stacked":[33],"denoise":[34],"autoencoder(SSDAE)":[35],"based":[36],"anomaly":[37],"detection":[38,123],"model":[39,49],"is":[40,101,128],"proposed":[41],"in":[42,56],"paper,":[44,94],"uses":[46],"autoencoder":[48],"capture":[51,75],"structure":[55],"we":[61],"extract":[62],"space":[65,69],"residual":[68],"build":[71],"statistic":[73],"system":[77],"changes,":[78],"finally":[80],"use":[81],"KDE":[83],"determine":[85],"threshold":[87],"detect":[89],"anomalies.":[91],"In":[92],"this":[93],"using":[95],"Tennesse-Eastman":[97],"dataset,":[98],"method":[99],"validation":[100],"carried":[102],"out":[103],"compared":[105],"with":[106],"algorithms":[107],"such":[108],"as":[109],"PCA,":[110],"DAE":[111],"LRAE,":[113],"verifies":[115],"effectiveness":[117],"algorithm,":[120],"improves":[121],"rate":[124],"faults,":[126],"able":[129],"identify":[131],"more":[132],"faults.":[133]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
