{"id":"https://openalex.org/W2784248958","doi":"https://doi.org/10.1109/etfa.2017.8247695","title":"Using self-organizing maps to learn hybrid timed automata in absence of discrete events","display_name":"Using self-organizing maps to learn hybrid timed automata in absence of discrete events","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2784248958","doi":"https://doi.org/10.1109/etfa.2017.8247695","mag":"2784248958"},"language":"en","primary_location":{"id":"doi:10.1109/etfa.2017.8247695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa.2017.8247695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","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/A5013308810","display_name":"Alexander von Birgelen","orcid":null},"institutions":[{"id":"https://openalex.org/I5209920","display_name":"Ostwestfalen-Lippe University of Applied Sciences and Arts","ror":"https://ror.org/04eka8j06","country_code":"DE","type":"education","lineage":["https://openalex.org/I5209920"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander von Birgelen","raw_affiliation_strings":["Ostwestfalen-Lippe of Applied Sciences, Institute Industrial IT, Lemgo, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ostwestfalen-Lippe of Applied Sciences, Institute Industrial IT, Lemgo, Germany","institution_ids":["https://openalex.org/I5209920"]}]},{"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/I5209920","display_name":"Ostwestfalen-Lippe University of Applied Sciences and Arts","ror":"https://ror.org/04eka8j06","country_code":"DE","type":"education","lineage":["https://openalex.org/I5209920"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Oliver Niggemann","raw_affiliation_strings":["Ostwestfalen-Lippe of Applied Sciences, Institute Industrial IT, Lemgo, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ostwestfalen-Lippe of Applied Sciences, Institute Industrial IT, Lemgo, Germany","institution_ids":["https://openalex.org/I5209920"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I5209920"],"apc_list":null,"apc_paid":null,"fwci":2.2079,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88371984,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9995999932289124,"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":0.9995999932289124,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9922999739646912,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9879999756813049,"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/automaton","display_name":"Automaton","score":0.7786811590194702},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7348881363868713},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6914184093475342},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6174761056900024},{"id":"https://openalex.org/keywords/hybrid-system","display_name":"Hybrid system","score":0.511427104473114},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5098497271537781},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.4706864655017853},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4693796634674072},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.43296903371810913},{"id":"https://openalex.org/keywords/cellular-automaton","display_name":"Cellular automaton","score":0.4116480350494385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3796544075012207},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37474489212036133},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3012869656085968},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2679302990436554},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.1049889326095581},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10156941413879395}],"concepts":[{"id":"https://openalex.org/C112505250","wikidata":"https://www.wikidata.org/wiki/Q787116","display_name":"Automaton","level":2,"score":0.7786811590194702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7348881363868713},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6914184093475342},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6174761056900024},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.511427104473114},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5098497271537781},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.4706864655017853},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4693796634674072},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.43296903371810913},{"id":"https://openalex.org/C35527583","wikidata":"https://www.wikidata.org/wiki/Q189156","display_name":"Cellular automaton","level":2,"score":0.4116480350494385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3796544075012207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37474489212036133},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3012869656085968},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2679302990436554},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.1049889326095581},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10156941413879395},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/etfa.2017.8247695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa.2017.8247695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"},{"id":"pmh:oai:fraunhofer.de:N-479985","is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-479985.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer IOSB","raw_type":"Conference Paper"},{"id":"pmh:oai:publica.fraunhofer.de:publica/399153","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/399153","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1990707482","https://openalex.org/W2001230460","https://openalex.org/W2025818287","https://openalex.org/W2056254566","https://openalex.org/W2124260943","https://openalex.org/W2147340401","https://openalex.org/W2158363765","https://openalex.org/W2399591989","https://openalex.org/W2403264072","https://openalex.org/W2406562161","https://openalex.org/W2593031622","https://openalex.org/W3036846224","https://openalex.org/W3183452638","https://openalex.org/W6713036356","https://openalex.org/W6713511804","https://openalex.org/W6779887223","https://openalex.org/W6798651032"],"related_works":["https://openalex.org/W1495105673","https://openalex.org/W2095644384","https://openalex.org/W42165","https://openalex.org/W2020061635","https://openalex.org/W1968401573","https://openalex.org/W2029666815","https://openalex.org/W2047013661","https://openalex.org/W1524267824","https://openalex.org/W2066057891","https://openalex.org/W1483796952"],"abstract_inverted_index":{"Modern":[0],"industrial":[1],"plants":[2,72],"become":[3],"more":[4],"complex":[5],"and":[6,27,54,76,219,226,255,266,274],"consequently":[7],"monitoring":[8],"them":[9],"often":[10,40],"exceeds":[11],"the":[12,34,63,85,105,109,122,134,137,150,175,192,197,214,223,227,232,243,253,279],"capabilities":[13],"of":[14,36,52,66,121,128,131,136,146,174,216,229,281,289],"human":[15],"operators.":[16],"Model-based":[17],"diagnosis":[18],"is":[19,55,133,161],"a":[20,31,50,112,126],"commonly":[21],"used":[22],"approach":[23,264],"to":[24,107,111,195,203,241,277],"identify":[25],"anomalies":[26,98,230],"root":[28],"causes":[29],"within":[30],"system":[32],"through":[33,163,178],"use":[35,280],"models,":[37],"which":[38,132,148,166],"are":[39,143,167,188,248],"times":[41],"manually":[42],"created":[43,170],"by":[44,70,183],"experts.":[45,184],"However,":[46,235],"manual":[47,86],"modelling":[48,87],"takes":[49],"lot":[51],"effort":[53],"not":[56,249,258,292],"suitable":[57],"for":[58,168,209],"today's":[59],"fast-changing":[60],"systems.":[61],"Today,":[62],"large":[64],"amount":[65],"sensor":[67],"data":[68,286],"provided":[69],"modern":[71],"enables":[73],"data-driven":[74,90],"solutions":[75,91],"models":[77],"can":[78,99,257],"be":[79,100,259],"learned":[80],"from":[81,171,252],"data,":[82],"significantly":[83],"reducing":[84],"efforts.":[88],"These":[89],"enable":[92],"tasks":[93],"such":[94],"as":[95],"condition":[96],"monitoring:":[97],"detected":[101],"automatically,":[102],"giving":[103],"operators":[104],"chance":[106],"restore":[108],"plant":[110,176,254],"working":[113],"state":[114],"before":[115],"production":[116],"losses":[117],"occur.":[118],"The":[119,185,206,261],"choice":[120],"model":[123,147],"depends":[124],"on":[125,285],"couple":[127],"factors,":[129],"one":[130,144],"type":[135,145],"available":[138,251],"signals.":[139,234],"Hybrid":[140],"timed":[141,211,283],"automata":[142,212,256,284,290],"separate":[149],"systems":[151],"behaviour":[152],"into":[153,191],"different":[154],"modes,":[155],"e.g.":[156],"`valve":[157],"open'":[158],"or":[159,177,238],"`motor":[160],"running'":[162],"discrete":[164,246],"events":[165,247],"example":[169],"binary":[172,236],"signals":[173,187,237],"real-valued":[179,186,233],"signal":[180],"thresholds,":[181],"defined":[182],"then":[189],"separated":[190],"corresponding":[193],"modes":[194],"improve":[196],"anomaly":[198,207],"detection":[199,208,215,228],"process":[200],"in":[201,222,231,268],"comparison":[202],"unseparated":[204],"data.":[205],"hybrid":[210,282],"combines":[213],"timing":[217],"errors":[218,221],"sequence":[220],"mode":[224],"changes":[225],"expert":[239],"knowledge":[240],"generate":[242],"much":[244],"needed":[245],"always":[250],"learned.":[260],"unsupervised,":[262],"nonparametric":[263],"presented":[265],"evaluated":[267],"this":[269],"paper":[270],"uses":[271],"self-organizing":[272],"maps":[273],"watershed":[275],"transformations":[276],"allow":[278],"where":[287],"learning":[288],"was":[291],"possible":[293],"before.":[294]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2018-01-26T00:00:00"}
