{"id":"W4205250713","doi":"10.1109/ias48185.2021.9677195","title":"Computationally Efficient MPC Technique for PUC-Based Inverters Without Weighting Factors","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Industry Applications Society Annual Meeting (IAS)","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Weighting; Control theory (sociology); Model predictive control; Benchmark (surveying); Capacitor; Correctness; Inverter; Engineering; Selection (genetic algorithm); Network topology; Voltage; Computer science; Topology (electrical circuits); Electronic engineering; Control (management); Algorithm; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000317177,0.0003584189,0.0003352071,0.00005903448,0.0004584126,0.0001038302,0.0003175813,0.0005896913,0.00008266052],"category_scores_gemma":[0.00003958409,0.0004014594,0.0003421472,0.0005714514,0.0001115279,0.0001122432,0.00004365311,0.0007511725,0.00001968025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002605043,"about_ca_system_score_gemma":0.0002288816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001931888,"about_ca_topic_score_gemma":0.000004810874,"domain_scores_codex":[0.9980512,0.00004983535,0.0005275683,0.0005427905,0.0003258655,0.0005027079],"domain_scores_gemma":[0.9985367,0.0003305172,0.0001378249,0.0004033086,0.0003960828,0.0001955682],"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":[0.00001467675,0.0003988352,0.0044973,0.0007609121,0.0005478833,0.000005639235,0.00515535,0.8375611,0.08280858,0.000454717,0.04395701,0.023838],"study_design_scores_gemma":[0.0005234571,0.00001507096,0.0002984369,0.000168826,0.00006739626,0.000004824145,0.00471779,0.9031924,0.08062057,0.00007871108,0.009778377,0.0005341651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05872429,0.00007775184,0.9378617,0.0004982206,0.0003813829,0.001106949,0.0004141294,0.0003859949,0.0005495319],"genre_scores_gemma":[0.9563075,0.000008615754,0.03969,0.000716237,0.0003969867,0.002271761,0.0003257325,0.0001027943,0.0001803249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8981718,"threshold_uncertainty_score":0.9998437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752541341954434,"score_gpt":0.2501120006468113,"score_spread":0.2325865872272669,"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."}}