{"id":"W2401524101","doi":"10.2316/journal.201.2015.1.201-2599","title":"GENETIC ALGORITHM APPLIED TO CONTROL OF DC MOTOR WITH DISTURBANCE REJECTION BY FEEDFORWARD ACTION","year":2015,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feed forward; Rotor (electric); Computer science; Simplicity; Control engineering; Control theory (sociology); Variety (cybernetics); DC motor; Action (physics); Control (management); Engineering; Electrical engineering; Artificial intelligence; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001952172,0.0002597175,0.0005424876,0.000141386,0.00003695053,0.00005233719,0.0001157296,0.0001074808,0.000002729951],"category_scores_gemma":[0.00002852779,0.000216313,0.00004890108,0.0001848457,0.00003342377,0.00007589785,0.00000584673,0.000123686,0.00002004622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001668946,"about_ca_system_score_gemma":0.00002058319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000177033,"about_ca_topic_score_gemma":0.000006963374,"domain_scores_codex":[0.9986082,0.00004641886,0.0004511898,0.0002747042,0.0003036724,0.0003157862],"domain_scores_gemma":[0.9991493,0.00008119636,0.0001164971,0.0002361925,0.000156193,0.0002606672],"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":[0.003047601,0.0002390494,0.004610102,0.000727925,0.001986786,0.0000174932,0.001188473,0.3399426,0.328173,0.0005795974,0.01356282,0.3059245],"study_design_scores_gemma":[0.007215173,0.001538627,0.003058402,0.0001647936,0.0002878775,0.00005889236,0.0006362037,0.9430653,0.01291596,0.00004760452,0.030148,0.0008631539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1145001,0.005767736,0.8766627,0.00003973691,0.0007527389,0.001552315,0.00004911047,0.0001798952,0.0004957285],"genre_scores_gemma":[0.9988244,0.00007066191,0.0002500188,0.00004604398,0.0002813658,0.000256481,0.000004558433,0.00004581033,0.0002206597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8843243,"threshold_uncertainty_score":0.8820987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017025962494245,"score_gpt":0.1982910221290961,"score_spread":0.1881207625041536,"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."}}