{"id":"W2802841716","doi":"10.1109/tpel.2018.2835773","title":"An Improved Torque Sharing Function for Torque Ripple Reduction in Switched Reluctance Machines","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":248,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Switched reluctance motor; Control theory (sociology); Torque; Torque ripple; Flux linkage; Ripple; Direct torque control; Copper loss; Reduction (mathematics); Computer science; Engineering; Mathematics; Physics; Control (management); Voltage; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002303063,0.0002473554,0.0002349418,0.0003045611,0.0001691511,0.00005403842,0.0001767403,0.0001805884,0.00009847486],"category_scores_gemma":[0.000004077556,0.0002713505,0.000129654,0.0006515996,0.00002708125,0.0003462707,4.348983e-7,0.0003907086,0.00001204199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004799882,"about_ca_system_score_gemma":0.00005243817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006566908,"about_ca_topic_score_gemma":0.0009147006,"domain_scores_codex":[0.9985901,0.00001992475,0.0003265409,0.0004175573,0.0001273753,0.0005185104],"domain_scores_gemma":[0.9993684,0.00002706932,0.00004572577,0.0003829187,0.00009024416,0.00008562628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004321447,0.000245503,0.00001810212,0.00004201743,0.0001897381,8.37185e-7,0.0004776348,0.02735675,0.9211985,0.0003003404,0.0002250832,0.0495133],"study_design_scores_gemma":[0.0008914933,0.001359897,0.00006398252,0.00002420675,0.00009988638,0.00001107891,0.00002832049,0.759337,0.2345389,0.001359317,0.001828645,0.0004572984],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.180171,0.0002653128,0.8179063,0.00004402492,0.000726295,0.0003607371,0.000009989333,0.000362881,0.0001535139],"genre_scores_gemma":[0.9974661,0.0001453879,0.001514065,0.00004241286,0.0001616973,0.0001903357,0.00001146232,0.00007381915,0.0003947098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8172951,"threshold_uncertainty_score":0.9999739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006979524495821087,"score_gpt":0.2285365486102478,"score_spread":0.2215570241144267,"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."}}