{"id":"https://openalex.org/W4415398671","doi":"https://doi.org/10.1109/etfa65518.2025.11205672","title":"Creating Virtual Sensors Using Neural Networks","display_name":"Creating Virtual Sensors Using Neural Networks","publication_year":2025,"publication_date":"2025-09-09","ids":{"openalex":"https://openalex.org/W4415398671","doi":"https://doi.org/10.1109/etfa65518.2025.11205672"},"language":null,"primary_location":{"id":"doi:10.1109/etfa65518.2025.11205672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa65518.2025.11205672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)","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/A5114286384","display_name":"Bj\u00f6rn Ludwig","orcid":null},"institutions":[{"id":"https://openalex.org/I190134885","display_name":"Helmut Schmidt University","ror":"https://ror.org/04e8jbs38","country_code":"DE","type":"education","lineage":["https://openalex.org/I190134885"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Bj\u00f6rn Ludwig","raw_affiliation_strings":["Helmut-Schmidt-University,Hamburg,Germany"],"affiliations":[{"raw_affiliation_string":"Helmut-Schmidt-University,Hamburg,Germany","institution_ids":["https://openalex.org/I190134885"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018465759","display_name":"Jonas Ehrhardt","orcid":"https://orcid.org/0000-0001-5023-839X"},"institutions":[{"id":"https://openalex.org/I190134885","display_name":"Helmut Schmidt University","ror":"https://ror.org/04e8jbs38","country_code":"DE","type":"education","lineage":["https://openalex.org/I190134885"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jonas Ehrhardt","raw_affiliation_strings":["Helmut-Schmidt-University,Hamburg,Germany"],"affiliations":[{"raw_affiliation_string":"Helmut-Schmidt-University,Hamburg,Germany","institution_ids":["https://openalex.org/I190134885"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012395966","display_name":"Oliver Niggemann","orcid":"https://orcid.org/0000-0001-8747-3596"},"institutions":[{"id":"https://openalex.org/I190134885","display_name":"Helmut Schmidt University","ror":"https://ror.org/04e8jbs38","country_code":"DE","type":"education","lineage":["https://openalex.org/I190134885"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Oliver Niggemann","raw_affiliation_strings":["Helmut-Schmidt-University,Hamburg,Germany"],"affiliations":[{"raw_affiliation_string":"Helmut-Schmidt-University,Hamburg,Germany","institution_ids":["https://openalex.org/I190134885"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114286384"],"corresponding_institution_ids":["https://openalex.org/I190134885"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16546943,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.5365999937057495,"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/T10320","display_name":"Neural Networks and Applications","score":0.5365999937057495,"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/modular-design","display_name":"Modular design","score":0.6743999719619751},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.586899995803833},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5784000158309937},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.5202999711036682},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.5142999887466431},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49639999866485596},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4794999957084656},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.46209999918937683}],"concepts":[{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.6743999719619751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6406999826431274},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.586899995803833},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5784000158309937},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.5142999887466431},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4794999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4702000021934509},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4106999933719635},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4034000039100647},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C2781121602","wikidata":"https://www.wikidata.org/wiki/Q3504403","display_name":"Modular neural network","level":4,"score":0.39089998602867126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32820001244544983},{"id":"https://openalex.org/C176563091","wikidata":"https://www.wikidata.org/wiki/Q669238","display_name":"Intelligent sensor","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2818000018596649},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2784999907016754},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C174998907","wikidata":"https://www.wikidata.org/wiki/Q357662","display_name":"Work in process","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25459998846054077},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/etfa65518.2025.11205672","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa65518.2025.11205672","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reliable":[0],"sensor":[1,54,82,100,129,172],"data":[2,55,83,118],"are":[3],"essential":[4],"for":[5,159],"the":[6,34,104,135,146],"effective":[7],"operation":[8],"and":[9,74,93,115,148,163],"safety":[10],"of":[11,36,49,137],"cyber-physical":[12],"systems":[13],"(CPS)":[14],"in":[15,167],"industrial":[16],"environments.":[17],"However,":[18],"sensors":[19,47],"frequently":[20],"experience":[21],"faults":[22],"or":[23,52],"degradation,":[24],"leading":[25],"to":[26,32,44,79,96,151],"compromised":[27],"system":[28,161],"performance.":[29],"In":[30],"order":[31],"increase":[33],"resilience":[35,162],"CPS,":[37],"this":[38,109],"paper":[39],"proposes":[40],"a":[41,76],"novel":[42],"approach":[43,110],"creating":[45],"virtual":[46,171],"capable":[48],"reconstructing":[50],"missing":[51,99],"faulty":[53],"through":[56,169],"gradient-based":[57],"input":[58],"reconstruction":[59,125],"by":[60,103],"leveraging":[61],"neural":[62],"networks.":[63],"Specifically,":[64],"we":[65,133],"employ":[66],"an":[67],"LSTM-based":[68],"autoencoder":[69],"architecture":[70],"trained":[71],"both":[72,113],"conventionally":[73],"with":[75],"masking":[77],"strategy":[78],"handle":[80],"potential":[81,158],"loss":[84],"scenarios":[85],"effectively.":[86],"Our":[87,154],"method":[88],"involves":[89],"using":[90],"automatic":[91],"differentiation":[92],"gradient":[94],"descent":[95],"iteratively":[97],"optimize":[98],"inputs,":[101],"guided":[102],"pretrained":[105],"network.":[106],"We":[107],"evaluate":[108],"comprehensively":[111],"on":[112],"simulated":[114],"real-world":[116],"plant":[117],"from":[119],"cyber-phyiscal":[120],"process":[121],"plants,":[122],"demonstrating":[123],"robust":[124],"performance":[126],"across":[127],"various":[128],"failure":[130],"scenarios.":[131],"Additionally,":[132],"explore":[134],"efficacy":[136],"modular":[138],"clustering":[139],"methods":[140],"versus":[141],"single":[142],"comprehensive":[143],"models,":[144],"highlighting":[145],"advantages":[147],"limitations":[149],"inherent":[150],"each":[152],"approach.":[153],"findings":[155],"reveal":[156],"significant":[157],"improving":[160],"maintaining":[164],"operational":[165],"continuity":[166],"CPS":[168],"advanced":[170],"implementations.":[173]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-22T00:00:00"}