{"id":"https://openalex.org/W7165645371","doi":"https://doi.org/10.48550/arxiv.2606.21230","title":"Comparative Evaluation of Machine Learning and Deep Learning Models for Wound-Rotor Synchronous Motor Performance Prediction","display_name":"Comparative Evaluation of Machine Learning and Deep Learning Models for Wound-Rotor Synchronous Motor Performance Prediction","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165645371","doi":"https://doi.org/10.48550/arxiv.2606.21230"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21230","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21230","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.21230","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043168534","display_name":"K\u0131van\u00e7 Do\u011fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Do\u011fan, K\u0131van\u00e7","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139213588","display_name":"Ahmet Orhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Orhan, Ahmet","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.24539999663829803,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.24539999663829803,"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/T10278","display_name":"Electric Motor Design and Analysis","score":0.23849999904632568,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11602","display_name":"Magnetic Bearings and Levitation Dynamics","score":0.0722000002861023,"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/hyperparameter","display_name":"Hyperparameter","score":0.7311000227928162},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6349999904632568},{"id":"https://openalex.org/keywords/latin-hypercube-sampling","display_name":"Latin hypercube sampling","score":0.5666000247001648},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5315999984741211},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5088000297546387},{"id":"https://openalex.org/keywords/rotor","display_name":"Rotor (electric)","score":0.4212000072002411},{"id":"https://openalex.org/keywords/torque","display_name":"Torque","score":0.34130001068115234},{"id":"https://openalex.org/keywords/performance-prediction","display_name":"Performance prediction","score":0.34040001034736633}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7311000227928162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7114999890327454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013000249862671},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.659500002861023},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6349999904632568},{"id":"https://openalex.org/C20820323","wikidata":"https://www.wikidata.org/wiki/Q6496514","display_name":"Latin hypercube sampling","level":3,"score":0.5666000247001648},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5088000297546387},{"id":"https://openalex.org/C17281054","wikidata":"https://www.wikidata.org/wiki/Q193466","display_name":"Rotor (electric)","level":2,"score":0.4212000072002411},{"id":"https://openalex.org/C144171764","wikidata":"https://www.wikidata.org/wiki/Q48103","display_name":"Torque","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3294000029563904},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.32829999923706055},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21230","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21230","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.21230","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21230","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Wound":[0],"rotor":[1],"synchronous":[2],"motors":[3],"have":[4],"emerged":[5],"as":[6,165],"a":[7,62,75,87,115,155,168,173,224],"strong":[8],"alternative":[9],"that":[10,184],"eliminates":[11],"dependence":[12],"on":[13],"REEs.":[14],"However,":[15],"WRSM":[16,109],"design":[17],"requires":[18],"the":[19,29,40,44,56,84,106,126,148,177,195],"simultaneous":[20],"optimization":[21],"of":[22,33,43,65,86,89,108,117,176,213],"numerous":[23],"geometric":[24],"and":[25,28,71,93,111,140,172,208,231],"electromagnetic":[26],"parameters,":[27],"high":[30],"computational":[31,178],"cost":[32],"conventional":[34],"finite":[35],"element":[36],"analysis":[37,175],"severely":[38],"limits":[39],"rapid":[41],"exploration":[42],"large":[45],"parameter":[46],"space.":[47],"Although":[48],"there":[49],"are":[50],"machine-learning-based":[51],"surrogate":[52],"modeling":[53],"studies":[54],"in":[55,125,205,223],"literature,":[57,150],"they":[58],"generally":[59],"compare":[60],"only":[61],"limited":[63],"number":[64],"models,":[66],"exclude":[67],"deep":[68,94,161],"learning":[69,92,95,162],"architectures,":[70],"do":[72],"not":[73],"provide":[74],"comprehensive":[76],"benchmark":[77],"specific":[78],"to":[79,217],"WRSM.":[80],"In":[81],"this":[82,151],"study,":[83],"performance":[85],"total":[88],"eight":[90],"machine":[91],"models":[96,186],"from":[97,147],"four":[98],"different":[99,136],"algorithmic":[100],"families":[101],"was":[102,132],"systematically":[103,187],"compared":[104,216],"for":[105],"prediction":[107],"torque":[110],"motor":[112],"efficiency.":[113],"On":[114],"dataset":[116],"3351":[118],"samples":[119],"generated":[120],"using":[121,227],"Latin":[122],"Hypercube":[123],"Sampling":[124],"Motor-CAD":[127],"simulation":[128],"environment,":[129],"each":[130],"model":[131,157,193],"trained":[133],"with":[134,199],"10":[135],"random":[137],"seed":[138],"values":[139],"tuned":[141],"via":[142],"Optuna":[143],"hyperparameter":[144],"optimization.":[145],"Different":[146],"existing":[149],"study":[152],"jointly":[153],"offers":[154],"broad":[156],"spectrum":[158],"including":[159],"recent":[160],"architectures":[163],"such":[164],"FT":[166],"Transformer,":[167],"multi-seed":[169],"reproducibility":[170],"protocol,":[171],"Pareto":[174],"cost-accuracy":[179],"trade-off.":[180],"The":[181,191],"results":[182],"revealed":[183],"neural-network-based":[185],"outperform":[188],"tree-based":[189],"models.":[190],"FT-Transformer":[192],"achieved":[194],"highest":[196],"single-model":[197],"accuracy":[198],"R^2":[200],"=":[201],"0.9928,":[202],"producing":[203],"predictions":[204],"0.33":[206],"milliseconds":[207],"thus":[209],"obtaining":[210],"several":[211],"orders":[212],"magnitude":[214],"speedup":[215],"FEA.":[218],"Model":[219],"performances":[220],"were":[221],"evaluated":[222],"multidimensional":[225],"manner":[226],"R^2,":[228],"MAE,":[229],"RMSE,":[230],"MAPE":[232],"metrics.":[233]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}
