{"id":"W2136515516","doi":"10.1139/tcsme-2013-0019","title":"DIFFERENTIAL EVOLUTION FOR OPTIMIZATION OF PID GAIN IN ELECTRICAL DISCHARGE MACHINING CONTROL SYSTEM","year":2013,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced Machining and Optimization Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"PID controller; Control theory (sociology); Electrical discharge machining; Actuator; Machining; Servo; Controller (irrigation); Differential evolution; Position (finance); Servo control; Computer science; Servomechanism; Process (computing); Control engineering; Engineering; Control (management); Mechanical engineering; Temperature control; Algorithm","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":[0.0001046937,0.0001177859,0.0002132984,0.00008579938,0.00008542424,0.000009938476,0.0001349274,0.000137293,0.00001100853],"category_scores_gemma":[0.00003200752,0.0001119646,0.0002832078,0.0002091731,0.00001302361,0.00009952638,0.000001874036,0.0001574198,1.00378e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004146915,"about_ca_system_score_gemma":0.00004502234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001074749,"about_ca_topic_score_gemma":0.0006409331,"domain_scores_codex":[0.9992488,0.000008305581,0.0003085443,0.0001055484,0.00008235157,0.00024645],"domain_scores_gemma":[0.9995866,0.00009779148,0.0000436015,0.0001259049,0.00006261601,0.00008343288],"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":[0.000003708267,0.000006982052,0.000003815543,0.0001549675,0.00003957498,1.000998e-8,0.00004482687,0.9900672,0.00559737,0.00379524,0.0000202988,0.0002659603],"study_design_scores_gemma":[0.0005099691,0.00003110972,0.00001426558,0.00007296338,0.00003996648,9.589696e-7,0.00003699899,0.9891713,0.009924306,0.00008350441,0.000009891169,0.000104769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001696638,0.00004347249,0.9970086,0.00004578849,0.0001820691,0.0007611286,0.00011273,0.0001380927,0.00001148148],"genre_scores_gemma":[0.9157046,0.000004881222,0.08392554,0.000006370062,0.0000198099,0.0002848304,0.00001041982,0.00003485612,0.00000869556],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.914008,"threshold_uncertainty_score":0.4565784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004082624201109068,"score_gpt":0.1837615674836697,"score_spread":0.1796789432825606,"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."}}