{"id":"https://openalex.org/W4414603474","doi":"https://doi.org/10.1109/access.2025.3615243","title":"Variable-Guided Attention for Multichannel Signal Anomaly Detection","display_name":"Variable-Guided Attention for Multichannel Signal Anomaly Detection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414603474","doi":"https://doi.org/10.1109/access.2025.3615243"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3615243","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615243","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3615243","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018142925","display_name":"Chunghyup Mok","orcid":"https://orcid.org/0000-0003-2967-1492"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chunghyup Mok","raw_affiliation_strings":["School of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2967-1492","affiliations":[{"raw_affiliation_string":"School of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033915072","display_name":"Seokho Moon","orcid":"https://orcid.org/0000-0002-8986-9954"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seokho Moon","raw_affiliation_strings":["School of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078484139","display_name":"Eun Soo Choi","orcid":"https://orcid.org/0000-0002-7493-6763"},"institutions":[{"id":"https://openalex.org/I49946491","display_name":"Hyundai Motors (South Korea)","ror":"https://ror.org/016kvft77","country_code":"KR","type":"company","lineage":["https://openalex.org/I197312522","https://openalex.org/I49946491"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Eun Soo Choi","raw_affiliation_strings":["Hyundai Motor Company, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hyundai Motor Company, Seoul, South Korea","institution_ids":["https://openalex.org/I49946491"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035367790","display_name":"Hong Gi Shim","orcid":null},"institutions":[{"id":"https://openalex.org/I49946491","display_name":"Hyundai Motors (South Korea)","ror":"https://ror.org/016kvft77","country_code":"KR","type":"company","lineage":["https://openalex.org/I197312522","https://openalex.org/I49946491"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hong Gi Shim","raw_affiliation_strings":["Hyundai Motor Company, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hyundai Motor Company, Seoul, South Korea","institution_ids":["https://openalex.org/I49946491"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058258354","display_name":"Seoung Bum Kim","orcid":"https://orcid.org/0000-0002-2205-8516"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seoung Bum Kim","raw_affiliation_strings":["School of Industrial and Management Engineering, Korea University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-2205-8516","affiliations":[{"raw_affiliation_string":"School of Industrial and Management Engineering, Korea University, Seoul, South Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13297111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"170862","last_page":"170875"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.984499990940094,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9702000021934509,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/interpretability","display_name":"Interpretability","score":0.9329000115394592},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8274999856948853},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5325000286102295},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5277000069618225},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5273000001907349},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4731000065803528},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.45750001072883606}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9329000115394592},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8274999856948853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7411999702453613},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5325000286102295},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5277000069618225},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5273000001907349},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5015000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4869999885559082},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4120999872684479},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.3287000060081482},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3615243","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615243","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8522380428dd41a6bc844409ebcc3f65","is_oa":true,"landing_page_url":"https://doaj.org/article/8522380428dd41a6bc844409ebcc3f65","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 170862-170875 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3615243","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615243","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1970088130","https://openalex.org/W2045049630","https://openalex.org/W2075698219","https://openalex.org/W2085794166","https://openalex.org/W2122646361","https://openalex.org/W2144182447","https://openalex.org/W2296719434","https://openalex.org/W2533835508","https://openalex.org/W2786827964","https://openalex.org/W2883554196","https://openalex.org/W2907492528","https://openalex.org/W3118738952","https://openalex.org/W3128634608","https://openalex.org/W3138161023","https://openalex.org/W3169450514","https://openalex.org/W3177318507","https://openalex.org/W3184127157","https://openalex.org/W4283318673","https://openalex.org/W4283324222","https://openalex.org/W4294167343","https://openalex.org/W4384917038","https://openalex.org/W4385245566","https://openalex.org/W4394939074","https://openalex.org/W4395697368"],"related_works":[],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,134,185],"in":[2,13,48,186],"multichannel":[3],"sensor":[4,74,121,188],"data":[5,162],"is":[6],"essential":[7],"for":[8,72,183,193],"ensuring":[9],"safety":[10],"and":[11,21,51,77,116,123,136,153,168,180],"reliability":[12],"domains":[14],"such":[15],"as":[16],"industrial":[17],"monitoring,":[18],"automotive":[19,160],"systems,":[20],"healthcare.":[22],"Traditional":[23],"time-series":[24],"models":[25,110],"often":[26],"focus":[27],"solely":[28],"on":[29,91,125,139,158],"temporal":[30,92],"patterns,":[31,150],"struggling":[32],"to":[33,96,146],"capture":[34],"the":[35,53,107,128,143,164],"complex":[36,154],"inter-variable":[37,126],"relationships":[38],"among":[39,85],"highly":[40],"correlated":[41],"sensors.":[42],"This":[43],"limitation":[44],"hinders":[45],"their":[46],"effectiveness":[47],"detecting":[49],"anomalies":[50],"identifying":[52],"key":[54],"contributing":[55],"variables.":[56],"To":[57],"address":[58],"this":[59],"issue,":[60],"we":[61],"propose":[62],"a":[63,70,78,178],"transformer-based":[64,98],"framework":[65,130],"that":[66,82],"integrates":[67],"variable":[68],"encoding,":[69],"method":[71],"embedding":[73],"type":[75],"information,":[76],"variable-guided":[79],"attention":[80],"mechanism":[81],"emphasizes":[83],"interactions":[84],"variables":[86],"rather":[87],"than":[88],"focusing":[89,124],"only":[90],"patterns.":[93],"In":[94],"contrast":[95],"prior":[97],"models,":[99],"which":[100],"typically":[101],"apply":[102],"self-attention":[103],"across":[104],"time":[105],"steps,":[106],"proposed":[108,129],"approach":[109,176],"intervariable":[111],"dependencies":[112],"directly,":[113],"enhancing":[114],"interpretability":[115],"robustness.":[117],"By":[118],"explicitly":[119],"modeling":[120],"types":[122],"dependencies,":[127],"improves":[131],"both":[132],"anomaly":[133,149,184],"accuracy":[135],"interpretability.":[137],"Experiments":[138],"synthetic":[140],"datasets":[141],"demonstrate":[142],"model\u2019s":[144,165],"ability":[145],"detect":[147],"diverse":[148],"including":[151],"subtle":[152],"group":[155],"anomalies.":[156],"Validation":[157],"real-world":[159],"battery":[161],"demonstrates":[163],"high":[166],"performance":[167],"robustness,":[169],"achieving":[170],"an":[171],"AUROC":[172],"of":[173],"0.96.":[174],"Our":[175],"offers":[177],"practical":[179],"efficient":[181],"solution":[182],"high-dimensional":[187],"data,":[189],"with":[190],"significant":[191],"potential":[192],"real-time":[194],"monitoring":[195],"applications.":[196]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
