{"id":"W4406131155","doi":"10.18280/jesa.570622","title":"Three-Phase PWM Rectifier Control: Enhanced Direct Power Control with Neural Networks from Theory to Superior Reality Performance","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"PWM rectifier; Rectifier (neural networks); Pulse-width modulation; Control theory (sociology); Control (management); Artificial neural network; Power control; Power (physics); Computer science; Phase (matter); Three-phase; Engineering; Physics; Electrical engineering; Artificial intelligence; Voltage; Recurrent neural network; Types of artificial neural networks","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005148327,0.0002526269,0.0002546335,0.000116521,0.0001644673,0.0006129803,0.0003730543,0.0004069896,0.001485163],"category_scores_gemma":[0.0008664098,0.0001090763,0.0001869087,0.0001768382,0.0002888077,0.000552883,0.0004036739,0.0005104377,0.0001850005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002058346,"about_ca_system_score_gemma":0.0002259511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001252647,"about_ca_topic_score_gemma":0.001266628,"domain_scores_codex":[0.9998758,0.00003432198,0.000009235434,0.00002565998,0.00004446753,0.00001053123],"domain_scores_gemma":[0.9997724,0.0001083071,0.00002605948,0.00002512189,0.00006108134,0.000006922238],"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":[0.0004593933,0.00017724,0.0007336637,0.0003751836,0.00006528583,0.0001050383,0.0001775702,0.5440248,0.05980752,0.04100671,0.001190812,0.3518767],"study_design_scores_gemma":[0.00001168275,0.0000817219,0.000234396,0.000007670853,0.000008244423,0.00002087834,0.00000474821,0.9921112,0.004389558,0.002566885,0.0005577924,0.00000525086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06145765,0.0004554105,0.9227846,0.0002109038,0.0000676706,0.00003348886,0.00001499799,0.0001829877,0.01479236],"genre_scores_gemma":[0.9646158,0.0001667705,0.03257095,0.00002976717,0.00001918846,0.00001938438,0.00001493165,0.00001842037,0.002544802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001485163,"threshold_uncertainty_score":0.004968405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008742443921106255,"score_gpt":0.236723513503159,"score_spread":0.2279810695820527,"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."}}