{"id":"https://openalex.org/W4401906634","doi":"https://doi.org/10.1109/access.2024.3450709","title":"Time-Series to Image-Transformed Adversarial Autoencoder for Anomaly Detection","display_name":"Time-Series to Image-Transformed Adversarial Autoencoder for Anomaly Detection","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401906634","doi":"https://doi.org/10.1109/access.2024.3450709"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3450709","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3450709","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":null,"license_id":null,"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.2024.3450709","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013669393","display_name":"Jiyoung Kang","orcid":"https://orcid.org/0000-0002-5364-3451"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jiyoung Kang","raw_affiliation_strings":["IP Prosecution Team, Samsung Electronics, Seocho-gu, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IP Prosecution Team, Samsung Electronics, Seocho-gu, Seoul, South Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Minseok Kim","orcid":"https://orcid.org/0009-0006-8705-3413"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Minseok Kim","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seodaemun-gu, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0006-8705-3413","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seodaemun-gu, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103061208","display_name":"Jinuk Park","orcid":"https://orcid.org/0000-0003-0424-8225"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinuk Park","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seodaemun-gu, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-0424-8225","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seodaemun-gu, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100322270","display_name":"Sanghyun Park","orcid":"https://orcid.org/0000-0002-5196-6193"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sanghyun Park","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seodaemun-gu, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-5196-6193","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seodaemun-gu, Seoul, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"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":0.8335,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77196148,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"12","issue":null,"first_page":"119671","last_page":"119684"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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":0.9905999898910522,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9775999784469604,"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/autoencoder","display_name":"Autoencoder","score":0.7679063081741333},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6966361403465271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6731654405593872},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6387362480163574},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.589138925075531},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.553534746170044},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.501795768737793},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4795239269733429},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.45710310339927673},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41734570264816284},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1815403401851654},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07441431283950806}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7679063081741333},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6966361403465271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6731654405593872},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6387362480163574},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.589138925075531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.553534746170044},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.501795768737793},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4795239269733429},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.45710310339927673},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41734570264816284},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1815403401851654},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07441431283950806},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3450709","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3450709","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:dc124fe0d12a435482128060ae370526","is_oa":true,"landing_page_url":"https://doaj.org/article/dc124fe0d12a435482128060ae370526","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 12, Pp 119671-119684 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3450709","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3450709","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":null,"license_id":null,"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":54,"referenced_works":["https://openalex.org/W2009662901","https://openalex.org/W2095705004","https://openalex.org/W2104410989","https://openalex.org/W2132870739","https://openalex.org/W2144182447","https://openalex.org/W2144200194","https://openalex.org/W2145561180","https://openalex.org/W2253669605","https://openalex.org/W2293689143","https://openalex.org/W2296719434","https://openalex.org/W2321990803","https://openalex.org/W2407991977","https://openalex.org/W2604247107","https://openalex.org/W2613328025","https://openalex.org/W2624190409","https://openalex.org/W2766436480","https://openalex.org/W2768947629","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2799884077","https://openalex.org/W2898322183","https://openalex.org/W2908896274","https://openalex.org/W2911200746","https://openalex.org/W2950361482","https://openalex.org/W2962734576","https://openalex.org/W2962736999","https://openalex.org/W2963166639","https://openalex.org/W2997638503","https://openalex.org/W3001279689","https://openalex.org/W3010246639","https://openalex.org/W3081497074","https://openalex.org/W3098957257","https://openalex.org/W3106543020","https://openalex.org/W3128634608","https://openalex.org/W3136173905","https://openalex.org/W3169450514","https://openalex.org/W3190748826","https://openalex.org/W3204263062","https://openalex.org/W4239954780","https://openalex.org/W4256177618","https://openalex.org/W4283069166","https://openalex.org/W4297814361","https://openalex.org/W4313554719","https://openalex.org/W4367050303","https://openalex.org/W4390024648","https://openalex.org/W4396805988","https://openalex.org/W6674330103","https://openalex.org/W6683965311","https://openalex.org/W6720514713","https://openalex.org/W6748102297","https://openalex.org/W6772383348","https://openalex.org/W6784869275","https://openalex.org/W6802061597","https://openalex.org/W6802821011"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W4363671829","https://openalex.org/W2780476542","https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928"],"abstract_inverted_index":{"The":[0],"automation":[1],"of":[2,17,24,85,88,134,187],"systems":[3,18],"and":[4,67,82,116,148,190],"the":[5,14,21,58,104,131,160,173,181,188,192],"accelerated":[6],"digital":[7],"transformations":[8],"across":[9],"various":[10],"industries":[11],"have":[12,36],"rendered":[13],"manual":[15],"monitoring":[16],"difficult.":[19],"Therefore,":[20],"automatic":[22],"detection":[23,41,90,166,182],"system":[25],"anomalies":[26,73],"is":[27],"essential":[28],"in":[29,42,56,71],"diverse":[30],"industries.":[31],"Various":[32],"deep":[33],"learning-based":[34],"techniques":[35,115,125],"been":[37],"developed":[38],"for":[39],"anomaly":[40,89,165],"multivariate":[43,156],"time-series":[44,61,105,157],"data":[45,62,76],"with":[46,169],"promising":[47],"performance.":[48],"However,":[49],"there":[50],"are":[51],"several":[52],"challenges:":[53],"1)":[54],"difficulty":[55],"understanding":[57],"relationships":[59],"among":[60],"due":[63],"to":[64,102,106,128,144,178],"their":[65],"complexity":[66],"high-dimensionality,":[68],"2)":[69],"limitation":[70],"distinguishing":[72],"from":[74],"normal":[75,147],"that":[77],"exhibit":[78],"similar":[79],"distributional":[80],"patterns,":[81],"3)":[83],"lack":[84],"intuitive":[86],"interpretation":[87],"results.":[91],"To":[92],"address":[93],"these":[94],"issues,":[95],"we":[96],"propose":[97],"a":[98],"novel":[99],"approach":[100],"referred":[101],"as":[103],"image-transformed":[107],"adversarial":[108,121,140],"autoencoder":[109],"(T2IAE),":[110],"which":[111],"adopts":[112],"image":[113],"transformation":[114,124],"convolutional":[117],"neural":[118],"network":[119],"(CNN)-based":[120],"learning.":[122],"Image":[123],"were":[126],"used":[127],"effectively":[129],"capture":[130],"local":[132],"features":[133],"adjacent":[135],"time":[136],"points.":[137],"Two":[138],"CNN-based":[139],"autoencoders":[141],"competitively":[142],"learned":[143],"distinguish":[145],"between":[146],"abnormal":[149],"data.":[150],"We":[151],"experimentally":[152],"analyzed":[153],"five":[154],"real-world":[155],"datasets,":[158],"wherein":[159],"proposed":[161,174],"model":[162,175],"achieved":[163],"superior":[164],"performance":[167],"compared":[168],"state-of-the-art":[170],"methods.":[171],"Moreover,":[172],"enables":[176],"humans":[177],"intuitively":[179],"interpret":[180],"results,":[183],"facilitating":[184],"appropriate":[185],"explanations":[186],"results":[189],"enhancing":[191],"model\u2019s":[193],"usability.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}