{"id":"W2579650447","doi":"10.1109/cefc.2016.7816158","title":"A computational-analytical approach to efficiently locate optimum objective spaces of permanent magnet motors in transient, rated and flux weakening operations","year":2016,"lang":"en","type":"article","venue":"","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Flux linkage; Magnet; Transient (computer programming); Computation; Torque; Control theory (sociology); Flux (metallurgy); Constraint (computer-aided design); Magnetic flux; Pareto principle; Process (computing); Computer science; Monotonic function; Mathematical optimization; Mathematics; Engineering; Physics; Algorithm; Mathematical analysis; Voltage; Direct torque control; Mechanical engineering; Induction motor; Magnetic field; Geometry; Electrical engineering; Materials science; Artificial intelligence","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":[0.0006950824,0.00127617,0.0008643821,0.001206837,0.0005731291,0.0009820571,0.0007907957,0.0006978894,0.00341826],"category_scores_gemma":[0.002101966,0.0005876934,0.0007890091,0.000735276,0.000637525,0.0006672732,0.0006157458,0.0008995754,0.0005815956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006094554,"about_ca_system_score_gemma":0.001350901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002360039,"about_ca_topic_score_gemma":0.002840748,"domain_scores_codex":[0.9998092,0.00005611395,0.000008347582,0.0000217119,0.00007873873,0.00002592247],"domain_scores_gemma":[0.9994766,0.0003340512,0.00005055304,0.00002764628,0.00009330415,0.00001787474],"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":[0.00002626761,0.00003300715,0.0001395807,0.0001359546,0.00001739806,0.00003808568,0.00004165291,0.9458492,0.00229679,0.01279005,0.0005273221,0.03810473],"study_design_scores_gemma":[0.000003632989,0.00001600491,0.00003855621,0.000007433652,0.000004168104,0.00001292392,0.00001132865,0.9962522,0.0006682858,0.002320031,0.0006618724,0.000003474413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003262367,0.00009345794,0.9936506,0.00004484358,0.00001051692,0.00003213901,0.00001945353,0.000124948,0.002761657],"genre_scores_gemma":[0.2673324,0.0003307652,0.728089,0.00007030574,0.000045118,0.0004433652,0.0001281624,0.0001557209,0.003405085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00341826,"threshold_uncertainty_score":0.01143527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120366148940266,"score_gpt":0.2164028595887657,"score_spread":0.205199198099363,"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."}}