{"id":"W1843549889","doi":"10.1109/pesc.2006.1711959","title":"Genetic algorithm optimization for high-performance VSI-Fed permanent magnet synchronous motor drives","year":2006,"lang":"en","type":"article","venue":"","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Control theory (sociology); Vector control; Genetic algorithm; Computer science; Sorting; Controller (irrigation); Digital signal processor; Inverter; Control engineering; Synchronous motor; Permanent magnet synchronous motor; Digital signal processing; Voltage; Magnet; Engineering; Induction motor; Control (management); Algorithm; Computer hardware","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002334025,0.0001682055,0.0001410734,0.00006312813,0.00006188377,0.00004126756,0.0001123368,0.00006698801,0.0003788238],"category_scores_gemma":[8.764601e-7,0.000161269,0.00004096496,0.00005105195,0.00002167023,0.0001197885,0.00001251566,0.00004426757,0.00003524085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006776013,"about_ca_system_score_gemma":0.000009332085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009896164,"about_ca_topic_score_gemma":0.000005420582,"domain_scores_codex":[0.9992492,0.000005130885,0.0002098767,0.0001706179,0.00009810673,0.00026711],"domain_scores_gemma":[0.9997269,0.00001414353,0.00002085058,0.0001584377,0.00003272962,0.00004693638],"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":[0.00001020682,0.00003660642,0.0002550075,0.0001315545,0.00003127371,0.000002730282,0.00006865527,0.7222902,0.001412038,0.00006383297,0.005058544,0.2706393],"study_design_scores_gemma":[0.0005248117,0.00008474603,0.00738114,0.00001080051,0.00001787232,0.000003808346,0.00001630744,0.9892252,0.002010612,0.00001699504,0.000477599,0.0002301188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1396482,0.00012074,0.8582492,0.00002415939,0.0004402771,0.0003670692,0.00002575944,0.0002885887,0.0008359826],"genre_scores_gemma":[0.8522374,0.00007416751,0.1463551,0.00007511656,0.0001899215,0.0001115281,0.0000631665,0.0000413101,0.0008522802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7125891,"threshold_uncertainty_score":0.6576358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004118664850320122,"score_gpt":0.1710900206064054,"score_spread":0.1669713557560853,"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."}}