{"id":"W2965674009","doi":"10.1109/compel.2019.8769689","title":"Simplified SVPWM Method for the Vienna Rectifier","year":2019,"lang":"en","type":"article","venue":"","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Total harmonic distortion; Robustness (evolution); Rectifier (neural networks); Power factor; MATLAB; Space vector; Pulse-width modulation; Control theory (sociology); Computer science; Harmonic analysis; Capacitor; Voltage; Space vector modulation; Electronic engineering; Engineering; Electrical engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001341381,0.00008717936,0.00009754767,0.00001992182,0.00003257747,0.00002552616,0.0001394694,0.0000441951,0.0008433771],"category_scores_gemma":[0.000005716517,0.00005450574,0.00007313981,0.00005065524,0.000006294306,0.00005056741,0.00001127612,0.00006356409,0.0002075377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001592582,"about_ca_system_score_gemma":0.000004518846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003221887,"about_ca_topic_score_gemma":0.00001479337,"domain_scores_codex":[0.9995569,0.00000802676,0.00009801811,0.0001099631,0.00005527449,0.0001718464],"domain_scores_gemma":[0.9994905,0.0002130504,0.000008131957,0.0002401896,0.00001736904,0.00003073868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002154584,0.0000078944,0.0001353556,0.00007426958,0.0001491772,3.176494e-7,0.0005346359,0.003537413,0.01737351,0.004044733,0.07227257,0.9018486],"study_design_scores_gemma":[0.0002256148,0.000009918179,0.0003110582,0.000003186356,0.00001060225,0.000001056873,0.0001385691,0.8690733,0.003580746,0.0002634141,0.126286,0.00009646593],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001417165,0.00006226855,0.9782855,0.0004330652,0.001148671,0.0004081091,0.000004314317,0.0001763287,0.0180646],"genre_scores_gemma":[0.9687053,0.0000265917,0.01619998,0.001890064,0.0001153896,0.0001161216,0.000004434591,0.00005085547,0.01289127],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9672881,"threshold_uncertainty_score":0.9234387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403887985135864,"score_gpt":0.2450394891691215,"score_spread":0.2310006093177628,"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."}}