{"id":"W4313169467","doi":"10.1109/access.2022.3229043","title":"Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Unsupervised learning; Artificial neural network; Online machine learning; Switched reluctance motor; Active learning (machine learning); Feedforward neural network; Bayesian optimization; Wake-sleep algorithm; Algorithm; Reinforcement learning; Engineering; Rotor (electric)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008453244,0.001225669,0.001270757,0.001392798,0.0002563188,0.0008956153,0.001096252,0.0008664833,0.003067287],"category_scores_gemma":[0.00192157,0.0005886169,0.000981992,0.002507614,0.000311176,0.001109321,0.0005122476,0.001081052,0.00165401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004676435,"about_ca_system_score_gemma":0.0006620144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001315885,"about_ca_topic_score_gemma":0.001178228,"domain_scores_codex":[0.9994655,0.0001201819,0.00007459417,0.00009314886,0.0002255126,0.00002097864],"domain_scores_gemma":[0.9989868,0.0005509425,0.00005815523,0.00005422277,0.0003324995,0.00001741191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00005954424,0.00007393825,0.0005875768,0.00834682,0.0002280484,0.0001380889,0.0000815336,0.1233151,0.003540161,0.02959863,0.02039144,0.813639],"study_design_scores_gemma":[0.00002242461,0.0002616266,0.001902879,0.00262071,0.0002425387,0.0005977789,0.00008506209,0.3651132,0.006582633,0.03928163,0.5831766,0.0001129158],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003320609,0.5599828,0.4195512,0.0009980559,0.001044694,0.00005423544,0.0002790155,0.0005342885,0.01423504],"genre_scores_gemma":[0.06324543,0.7681643,0.1536414,0.0005467811,0.002519409,0.0001347026,0.0008723076,0.0002449437,0.01063071],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003067287,"threshold_uncertainty_score":0.01026112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02836045808783972,"score_gpt":0.2655003853151119,"score_spread":0.2371399272272722,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}