{"id":"https://openalex.org/W3203003849","doi":"https://doi.org/10.1007/978-3-030-86486-6_29","title":"Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise","display_name":"Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise","publication_year":2021,"publication_date":"2021-05-01","ids":{"openalex":"https://openalex.org/W3203003849","doi":"https://doi.org/10.1007/978-3-030-86486-6_29","mag":"3203003849"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2105.00349","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.00349","pdf_url":"https://arxiv.org/pdf/2105.00349","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"type":"book-chapter","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2105.00349","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015381830","display_name":"Andrea Castellani","orcid":"https://orcid.org/0000-0003-0476-5978"},"institutions":[{"id":"https://openalex.org/I20121455","display_name":"Bielefeld University","ror":"https://ror.org/02hpadn98","country_code":"DE","type":"education","lineage":["https://openalex.org/I20121455"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Castellani, Andrea","raw_affiliation_strings":["Bielefeld University, Bielefeld, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bielefeld University, Bielefeld, Germany","institution_ids":["https://openalex.org/I20121455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101529349","display_name":"Sebastian Schmitt","orcid":"https://orcid.org/0000-0001-7130-5483"},"institutions":[{"id":"https://openalex.org/I4210112253","display_name":"Honda (Germany)","ror":"https://ror.org/022c1xk47","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210112253"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Schmitt, Sebastian","raw_affiliation_strings":["Honda Research Institute Europe, Offenbach, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Honda Research Institute Europe, Offenbach, Germany","institution_ids":["https://openalex.org/I4210112253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091180862","display_name":"Barbara Hammer","orcid":"https://orcid.org/0000-0002-0935-5591"},"institutions":[{"id":"https://openalex.org/I20121455","display_name":"Bielefeld University","ror":"https://ror.org/02hpadn98","country_code":"DE","type":"education","lineage":["https://openalex.org/I20121455"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hammer, Barbara","raw_affiliation_strings":["Bielefeld University, Bielefeld, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bielefeld University, Bielefeld, Germany","institution_ids":["https://openalex.org/I20121455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015381830"],"corresponding_institution_ids":["https://openalex.org/I20121455"],"apc_list":null,"apc_paid":null,"fwci":2.3133,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.89316296,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9973000288009644,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9830999970436096,"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/computer-science","display_name":"Computer science","score":0.7936969995498657},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6304858326911926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5468012690544128},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5372346043586731},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.535986065864563},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.4522460401058197},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.42969322204589844},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.41963914036750793},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4191347360610962},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.29219672083854675}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7936969995498657},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6304858326911926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5468012690544128},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5372346043586731},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.535986065864563},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.4522460401058197},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.42969322204589844},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.41963914036750793},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4191347360610962},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.29219672083854675},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:arXiv.org:2105.00349","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.00349","pdf_url":"https://arxiv.org/pdf/2105.00349","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2105.00349","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.00349","pdf_url":"https://arxiv.org/pdf/2105.00349","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1565746575","https://openalex.org/W1577581905","https://openalex.org/W1836465849","https://openalex.org/W2087996001","https://openalex.org/W2121056381","https://openalex.org/W2167460663","https://openalex.org/W2292353510","https://openalex.org/W2338255398","https://openalex.org/W2551393996","https://openalex.org/W2618574054","https://openalex.org/W2682189153","https://openalex.org/W2754051771","https://openalex.org/W2765407302","https://openalex.org/W2786672974","https://openalex.org/W2803187616","https://openalex.org/W2804525582","https://openalex.org/W2885593519","https://openalex.org/W2889326414","https://openalex.org/W2892035503","https://openalex.org/W2907003022","https://openalex.org/W2943865428","https://openalex.org/W2946686788","https://openalex.org/W2950220847","https://openalex.org/W2951863938","https://openalex.org/W2952133801","https://openalex.org/W2952361104","https://openalex.org/W2954540134","https://openalex.org/W2962762541","https://openalex.org/W2963081269","https://openalex.org/W2963096987","https://openalex.org/W2963735582","https://openalex.org/W2963762717","https://openalex.org/W2963859210","https://openalex.org/W2964316188","https://openalex.org/W2978426779","https://openalex.org/W2989929945","https://openalex.org/W2990083057","https://openalex.org/W2995066777","https://openalex.org/W2995624272","https://openalex.org/W2996108195","https://openalex.org/W2998531464","https://openalex.org/W3002709689","https://openalex.org/W3005731330","https://openalex.org/W3008626953","https://openalex.org/W3009953871","https://openalex.org/W3023534255","https://openalex.org/W3036586801","https://openalex.org/W3086836049","https://openalex.org/W3089703222","https://openalex.org/W3092841986","https://openalex.org/W3096215432","https://openalex.org/W3103846556","https://openalex.org/W3108807663","https://openalex.org/W3124328990","https://openalex.org/W3137695714","https://openalex.org/W3150919918","https://openalex.org/W3185109982","https://openalex.org/W3203003849","https://openalex.org/W4287716221","https://openalex.org/W4288095202","https://openalex.org/W4295312788","https://openalex.org/W4300089971","https://openalex.org/W4307775329","https://openalex.org/W4307823382"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4321789545"],"abstract_inverted_index":{"In":[0,33],"complex":[1],"industrial":[2],"settings,":[3],"it":[4,188],"is":[5,84],"common":[6],"practice":[7],"to":[8,16,121,154,189],"monitor":[9],"the":[10,40,58,65,73,77,82,91,97,105,126,157,172,199,204,211,218,223,226,237],"operation":[11],"of":[12,30,43,51,174,202,225,239],"machines":[13],"in":[14,100,113,163,236],"order":[15],"detect":[17,122],"undesired":[18],"states,":[19],"adjust":[20],"maintenance":[21],"schedules,":[22],"optimize":[23],"system":[24],"performance":[25],"or":[26],"collect":[27],"usage":[28],"statistics":[29],"individual":[31],"machines.":[32],"this":[34],"work,":[35],"we":[36,95,129,208],"focus":[37],"on":[38,171,181],"estimating":[39],"power":[41,61,79,206],"output":[42],"a":[44,52,68,131,140,146,164],"Combined":[45],"Heat":[46],"and":[47,87,123,142,186,192,215,229],"Power":[48],"(CHP)":[49],"machine":[50],"medium-sized":[53],"company":[54],"facility":[55,60,83],"by":[56],"analyzing":[57],"total":[59],"consumption.":[62],"We":[63,177],"formulate":[64],"problem":[66,71],"as":[67,241,243],"time-series":[69,183],"classification":[70,184],"where":[72],"class":[74],"label":[75,175,245],"represents":[76],"CHP":[78,92,106,205],"output.":[80],"As":[81],"fully":[85],"instrumented":[86],"sensor":[88,107,110],"measurements":[89],"from":[90,104,125],"are":[93,119],"available,":[94],"generate":[96],"training":[98,115,162],"labels":[99],"an":[101,143],"automated":[102],"fashion":[103],"readings.":[108],"However,":[109],"failures":[111],"result":[112],"mislabeled":[114],"data":[116,159],"samples":[117,160],"which":[118],"hard":[120],"remove":[124],"dataset.":[127],"Therefore,":[128],"propose":[130],"novel":[132],"multi-task":[133],"deep":[134],"learning":[135,227],"approach":[136,152,180],"that":[137,217],"jointly":[138],"trains":[139],"classifier":[141],"autoencoder":[144],"with":[145],"shared":[147],"embedding":[148],"representation.":[149],"The":[150],"proposed":[151],"targets":[153],"gradually":[155],"correct":[156],"mislabelled":[158],"during":[161],"self-supervised":[165],"fashion,":[166],"without":[167],"any":[168],"prior":[169],"assumption":[170],"amount":[173],"noise.":[176,246],"benchmark":[178],"our":[179],"several":[182],"datasets":[185],"find":[187],"be":[190],"comparable":[191],"sometimes":[193],"better":[194],"than":[195],"state-of-the-art":[196,234],"methods.":[197],"On":[198],"real-world":[200],"use-case":[201],"predicting":[203],"output,":[207],"thoroughly":[209],"evaluate":[210],"architectural":[212],"design":[213],"choices":[214],"show":[216],"final":[219],"architecture":[220],"considerably":[221],"increases":[222],"robustness":[224],"process":[228],"consistently":[230],"beats":[231],"other":[232],"recent":[233],"algorithms":[235],"presence":[238],"unstructured":[240],"well":[242],"structured":[244]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2021-10-11T00:00:00"}