{"id":"https://openalex.org/W2908249601","doi":"https://doi.org/10.1109/iecon.2018.8591599","title":"Hysteresis Compensation in Force/Torque Sensor based on Machine Learning","display_name":"Hysteresis Compensation in Force/Torque Sensor based on Machine Learning","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2908249601","doi":"https://doi.org/10.1109/iecon.2018.8591599","mag":"2908249601"},"language":"en","primary_location":{"id":"doi:10.1109/iecon.2018.8591599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2018.8591599","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society","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/A5064688836","display_name":"Ryuichiro Koike","orcid":null},"institutions":[{"id":"https://openalex.org/I72253084","display_name":"Saitama University","ror":"https://ror.org/02evnh647","country_code":"JP","type":"education","lineage":["https://openalex.org/I72253084"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryuichiro Koike","raw_affiliation_strings":["Graduate School of Science and Engineering, Saitama University, Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Saitama University, Saitama, Japan","institution_ids":["https://openalex.org/I72253084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065050034","display_name":"Sho Sakaino","orcid":"https://orcid.org/0000-0002-5182-5649"},"institutions":[{"id":"https://openalex.org/I72253084","display_name":"Saitama University","ror":"https://ror.org/02evnh647","country_code":"JP","type":"education","lineage":["https://openalex.org/I72253084"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sho Sakaino","raw_affiliation_strings":["Graduate School of Science and Engineering, Saitama University, Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Saitama University, Saitama, Japan","institution_ids":["https://openalex.org/I72253084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017159721","display_name":"Toshiaki Tsuji","orcid":"https://orcid.org/0000-0002-4532-4514"},"institutions":[{"id":"https://openalex.org/I72253084","display_name":"Saitama University","ror":"https://ror.org/02evnh647","country_code":"JP","type":"education","lineage":["https://openalex.org/I72253084"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshiaki Tsuji","raw_affiliation_strings":["Graduate School of Science and Engineering, Saitama University, Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Saitama University, Saitama, Japan","institution_ids":["https://openalex.org/I72253084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1873,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.5441152,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2769","last_page":"2774"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.982200026512146,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.982200026512146,"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/T12564","display_name":"Sensor Technology and Measurement Systems","score":0.97079998254776,"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"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9581999778747559,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hysteresis","display_name":"Hysteresis","score":0.8452922701835632},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7123318910598755},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7027276754379272},{"id":"https://openalex.org/keywords/torque","display_name":"Torque","score":0.6117408275604248},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5966908931732178},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5655844211578369},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.524857759475708},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.5135679244995117},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.4911520183086395},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42079296708106995},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.41260528564453125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.393961638212204},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3405173420906067},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27229392528533936},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18717560172080994},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10303011536598206},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.06537696719169617}],"concepts":[{"id":"https://openalex.org/C123299182","wikidata":"https://www.wikidata.org/wiki/Q190837","display_name":"Hysteresis","level":2,"score":0.8452922701835632},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7123318910598755},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7027276754379272},{"id":"https://openalex.org/C144171764","wikidata":"https://www.wikidata.org/wiki/Q48103","display_name":"Torque","level":2,"score":0.6117408275604248},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5966908931732178},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5655844211578369},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.524857759475708},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.5135679244995117},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.4911520183086395},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42079296708106995},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.41260528564453125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.393961638212204},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3405173420906067},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27229392528533936},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18717560172080994},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10303011536598206},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.06537696719169617},{"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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon.2018.8591599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon.2018.8591599","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society","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":16,"referenced_works":["https://openalex.org/W1964357740","https://openalex.org/W1989927461","https://openalex.org/W2010777729","https://openalex.org/W2018310749","https://openalex.org/W2019617942","https://openalex.org/W2025365464","https://openalex.org/W2058811414","https://openalex.org/W2060477610","https://openalex.org/W2134167019","https://openalex.org/W2156909104","https://openalex.org/W2328899747","https://openalex.org/W2332548465","https://openalex.org/W2562319536","https://openalex.org/W2744224469","https://openalex.org/W2745261858","https://openalex.org/W4239944110"],"related_works":["https://openalex.org/W2899703592","https://openalex.org/W3128948325","https://openalex.org/W3123452979","https://openalex.org/W2526752050","https://openalex.org/W2145032470","https://openalex.org/W31220157","https://openalex.org/W2288557197","https://openalex.org/W4233024177","https://openalex.org/W2101914902","https://openalex.org/W3174613421"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,36],"method":[4,85],"to":[5],"improve":[6],"the":[7,10,97],"accuracy":[8],"of":[9,30,38],"force/torque":[11],"(F/T)":[12],"sensor":[13],"based":[14],"on":[15,41,50],"machine":[16,71],"learning":[17,72],"considering":[18,61,126],"time":[19,62,127],"series":[20,63,66,128],"data.":[21,64],"There":[22,44],"are":[23],"several":[24],"problems":[25],"with":[26,86],"F/T":[27,54,92],"sensors,":[28],"one":[29],"which":[31],"is":[32,35,109],"hysteresis.":[33],"Hysteresis":[34],"factor":[37],"error":[39,98],"dependent":[40],"force":[42,105],"history.":[43],"have":[45],"been":[46],"few":[47],"researches":[48],"focusing":[49],"hysteresis":[51],"in":[52,100,111],"an":[53,87],"sensor.":[55,93],"We":[56,82,94],"solved":[57],"this":[58,84],"problem":[59],"by":[60],"Time":[65],"data":[67,136,143],"was":[68,129],"put":[69],"into":[70],"such":[73],"as":[74],"linear":[75,122],"regression":[76,125],"and":[77,103,140],"Support":[78],"Vector":[79],"Regression":[80],"(SVR).":[81],"evaluated":[83],"existing":[88],"high":[89,102],"dynamic":[90],"range":[91],"confirmed":[95],"that":[96,115],"decreased":[99],"both":[101],"low":[104],"ranges.":[106],"Since":[107],"there":[108],"nonlinearity":[110],"hysteresis,":[112],"we":[113],"predicted":[114],"SVR":[116,132],"will":[117],"be":[118],"more":[119],"accurate":[120],"than":[121,131],"regression.":[123],"Linear":[124],"better":[130],"when":[133],"loading":[134,141],"training":[135],"at":[137,144],"random":[138],"intervals":[139],"test":[142],"constant":[145],"intervals.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
