{"id":"https://openalex.org/W3158786118","doi":"https://doi.org/10.1145/3412841.3441916","title":"Flight data anomaly detection and diagnosis with variable association change","display_name":"Flight data anomaly detection and diagnosis with variable association change","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3158786118","doi":"https://doi.org/10.1145/3412841.3441916","mag":"3158786118"},"language":"en","primary_location":{"id":"doi:10.1145/3412841.3441916","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3441916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","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/A5102427250","display_name":"Sijie He","orcid":null},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sijie He","raw_affiliation_strings":["University of Minnesota"],"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082976295","display_name":"Hao Huang","orcid":"https://orcid.org/0000-0002-3777-1488"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao Huang","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048176207","display_name":"Shinjae Yoo","orcid":"https://orcid.org/0000-0003-4378-6448"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shinjae Yoo","raw_affiliation_strings":["Stony Brook University"],"affiliations":[{"raw_affiliation_string":"Stony Brook University","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073342710","display_name":"Weizhong Yan","orcid":"https://orcid.org/0000-0002-7916-8476"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weizhong Yan","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008564522","display_name":"Feng Xue","orcid":"https://orcid.org/0000-0003-2828-1476"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feng Xue","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112225428","display_name":"Tianyi Wang","orcid":"https://orcid.org/0009-0007-1844-8582"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Wang","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112351454","display_name":"Chenxiao Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenxiao Xu","raw_affiliation_strings":["Stony Brook University"],"affiliations":[{"raw_affiliation_string":"Stony Brook University","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5102427250"],"corresponding_institution_ids":["https://openalex.org/I2800403580"],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51927393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"346","last_page":"354"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9754999876022339,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7066724300384521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5576283931732178},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.548436164855957},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.4695817828178406},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.43953683972358704},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.4215327501296997},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3697505593299866},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11218518018722534},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07782799005508423},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.075003981590271}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7066724300384521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5576283931732178},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.548436164855957},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.4695817828178406},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.43953683972358704},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.4215327501296997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3697505593299866},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11218518018722534},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07782799005508423},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.075003981590271},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3412841.3441916","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3441916","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1788497923","https://openalex.org/W2031175485","https://openalex.org/W2033390084","https://openalex.org/W2041782669","https://openalex.org/W2083494231","https://openalex.org/W2093606067","https://openalex.org/W2113762408","https://openalex.org/W2117360769","https://openalex.org/W2118418963","https://openalex.org/W2120681761","https://openalex.org/W2122361470","https://openalex.org/W2123993001","https://openalex.org/W2127838378","https://openalex.org/W2135039490","https://openalex.org/W2146152876","https://openalex.org/W2149649623","https://openalex.org/W2156803951","https://openalex.org/W2194775991","https://openalex.org/W2267902091","https://openalex.org/W2383005638","https://openalex.org/W2411741275","https://openalex.org/W2614367676","https://openalex.org/W2676890506","https://openalex.org/W2743138268","https://openalex.org/W2767094836","https://openalex.org/W2911200746","https://openalex.org/W2947626232","https://openalex.org/W2950361482","https://openalex.org/W2962736999","https://openalex.org/W2975649990","https://openalex.org/W3098590676","https://openalex.org/W3200711003"],"related_works":["https://openalex.org/W2347219288","https://openalex.org/W1969663039","https://openalex.org/W2383003961","https://openalex.org/W2377356555","https://openalex.org/W1521770704","https://openalex.org/W2130317780","https://openalex.org/W2371659003","https://openalex.org/W4290998432","https://openalex.org/W2170089425","https://openalex.org/W2142729206"],"abstract_inverted_index":{"Aircraft":[0],"sensors":[1],"generate":[2],"multivariate":[3,116],"time":[4,53,117],"series":[5,118],"during":[6],"flights,":[7],"where":[8],"each":[9],"sensor":[10],"corresponds":[11],"to":[12,37,50,58,74,149],"one":[13],"variable.":[14],"During":[15],"normal":[16,134,147],"operation":[17],"mode,":[18],"the":[19,52,65,79,88,96,123,128,137,143,151,155],"associations":[20,81,131],"(dependencies)":[21],"among":[22],"variables":[23,56],"are":[24,82,91],"mainly":[25],"stationary.":[26],"One":[27],"type":[28,46,112],"of":[29,34,44,47,68,113,154],"flight":[30,162,172],"anomaly":[31,48,173],"that":[32,107,165],"is":[33,72],"interest":[35],"relates":[36],"variable":[38,80,130],"association":[39,59,124,153],"change.":[40],"Detection":[41],"and":[42,85,87,136,160,175],"diagnosis":[43],"such":[45,76],"need":[49],"pinpoint":[51],"series,":[54],"i.e.,":[55],"related":[57],"change,":[60],"which":[61],"helps":[62],"in":[63,171],"understanding":[64],"underlying":[66],"mechanisms":[67],"anomalies.":[69,139,156],"However,":[70],"it":[71],"hard":[73],"detect":[75,110],"change":[77,125],"because":[78],"usually":[83,92],"unknown":[84],"complicated,":[86],"anomalous":[89],"samples":[90],"insufficient":[93],"for":[94],"learning":[95,127],"substandard":[97],"association.":[98],"In":[99],"this":[100,111],"work,":[101],"we":[102,141],"present":[103],"a":[104],"neural":[105],"network":[106],"can":[108],"1)":[109],"anomalies":[114],"given":[115],"as":[119],"input;":[120],"2)":[121],"locate":[122],"by":[126],"nonlinear":[129],"from":[132,146],"both":[133],"data":[135,148,163],"detected":[138],"Specifically,":[140],"leverage":[142],"learned":[144],"model":[145,167],"learn":[150],"faulty":[152],"Experiments":[157],"using":[158],"simulated":[159],"real-world":[161],"show":[164],"our":[166],"outperforms":[168],"existing":[169],"methods":[170],"detection":[174],"diagnosis.":[176]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
