{"id":"W4385627288","doi":"10.1109/tie.2023.3296824","title":"Model Predictive Control With Inherent CMV Reduction Capability for Multilevel Inverters","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Central Power Research Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Reduction (mathematics); Model predictive control; Weighting; Total harmonic distortion; Computational complexity theory; Control theory (sociology); DSPACE; Computer science; Harmonic; Voltage; Electronic engineering; Mathematics; Engineering; Control (management); Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001831356,0.0003228997,0.0003183514,0.0002173869,0.000199985,0.00003606451,0.0001647408,0.0003137879,0.0000207117],"category_scores_gemma":[0.000005857215,0.0003077752,0.000159653,0.0003408863,0.00009916675,0.0002136173,6.476295e-7,0.0006627686,0.00001887093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006203166,"about_ca_system_score_gemma":0.0002262263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000036492,"about_ca_topic_score_gemma":0.00006566413,"domain_scores_codex":[0.9983344,0.00003905094,0.0003403091,0.0003925081,0.0002603505,0.0006333949],"domain_scores_gemma":[0.9993097,0.00009810078,0.00005075044,0.0003024473,0.00008664744,0.0001523339],"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":[0.0007530604,0.00007059985,0.000003239319,0.00002375977,0.0002665371,6.47466e-7,0.0003746495,0.8695866,0.002775044,0.00002833803,0.00238996,0.1237275],"study_design_scores_gemma":[0.004340927,0.0005355197,0.000007193689,0.00003554122,0.0001382196,0.000004341056,0.0001975477,0.9766912,0.01719991,0.0001644311,0.0003599845,0.0003252018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04226611,0.00001179864,0.9539968,0.0001545644,0.001090106,0.001353239,0.0003316566,0.0007309486,0.00006471181],"genre_scores_gemma":[0.9984,0.0000490479,0.0001603404,0.00005856391,0.0001249723,0.0008865559,0.0000242505,0.00008005439,0.0002162428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9561338,"threshold_uncertainty_score":0.9999374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04652319779362699,"score_gpt":0.2374700823187751,"score_spread":0.1909468845251481,"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."}}