{"id":"W2288346758","doi":"10.1109/iemdc.2015.7409288","title":"Design optimization of switched reluctance machine using genetic algorithm","year":2015,"lang":"en","type":"article","venue":"","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Switched reluctance motor; Multi-objective optimization; Torque; Commutation; Genetic algorithm; Torque ripple; Envelope (radar); Computer science; Control theory (sociology); Optimal design; Mathematical optimization; Algorithm; Engineering; Direct torque control; Mathematics; Control (management); Induction motor","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006097991,0.0006594509,0.0006758441,0.0005479371,0.0002705251,0.0005229827,0.0004827669,0.0007984652,0.001158211],"category_scores_gemma":[0.000881292,0.0003936519,0.0005998433,0.0004017441,0.0004103432,0.0003108626,0.0002402105,0.0003692443,0.0002096441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004816143,"about_ca_system_score_gemma":0.0007274207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001958293,"about_ca_topic_score_gemma":0.001748129,"domain_scores_codex":[0.9997533,0.00008443223,0.00001002876,0.00004227831,0.00008632048,0.00002373544],"domain_scores_gemma":[0.9997733,0.0001233892,0.00003350939,0.00001010908,0.00005248125,0.000007227167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002502596,0.00002303236,0.000210649,0.00006882976,0.00002286088,0.00003723051,0.00003452962,0.9679025,0.004565814,0.002963041,0.0002063154,0.02394017],"study_design_scores_gemma":[0.0000133538,0.00004427581,0.00008389198,0.000007129492,0.000007491371,0.00001239821,0.000006865409,0.9975703,0.0008706797,0.0006870009,0.0006927667,0.000003652215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03462527,0.0003454731,0.9601543,0.00009287283,0.00002330884,0.00007506195,0.00002013234,0.0002730832,0.004390539],"genre_scores_gemma":[0.5721721,0.0005428942,0.4227227,0.00006609991,0.00002259763,0.0003805365,0.0000834975,0.00008373042,0.003925863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001958293,"threshold_uncertainty_score":0.003893852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02567617460232912,"score_gpt":0.2221945468029931,"score_spread":0.196518372200664,"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."}}