{"id":"W2923735614","doi":"10.1109/tpel.2019.2906557","title":"Investigation of a Practical Convex-Optimization-Based Sensorless Scheme for IPMSM Drives","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Scheme (mathematics); Control theory (sociology); Rotor (electric); Reduction (mathematics); Convex optimization; Process (computing); Torque; Variable (mathematics); Optimization problem; Regular polygon; Computer science; Engineering; Mathematics; Algorithm; Physics; Control (management)","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":[0.0001455839,0.0002470778,0.000337238,0.0002710607,0.0000585903,0.00002470116,0.0001159479,0.0001942903,0.0001417684],"category_scores_gemma":[0.00002010319,0.0002750621,0.0001681493,0.0003780641,0.00005069044,0.0001778362,3.22557e-7,0.0003641378,0.00002412455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002112549,"about_ca_system_score_gemma":0.000241264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002920118,"about_ca_topic_score_gemma":0.000008548675,"domain_scores_codex":[0.9985891,0.00004466153,0.0003874535,0.0002741343,0.0002647303,0.00043991],"domain_scores_gemma":[0.998717,0.000561435,0.00009705266,0.0003419012,0.0001835704,0.00009909927],"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":[0.0001339974,0.0000530152,0.00003944405,0.00007706292,0.0001522909,6.042761e-7,0.00009201026,0.9397443,0.05878891,0.0005642176,0.0001301869,0.0002239666],"study_design_scores_gemma":[0.001328432,0.0003515443,0.0000156791,0.00002650271,0.00005731119,0.000003398847,0.0000185329,0.7483647,0.2489987,0.00006341463,0.0005394023,0.0002323497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1447359,0.0001101654,0.8531659,0.0002559049,0.0004172617,0.0007820451,0.00002568852,0.0002473892,0.0002597202],"genre_scores_gemma":[0.983369,0.00003811553,0.01611724,0.0001439093,0.00001700314,0.00009305668,0.00001179007,0.00008263698,0.0001272577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8386331,"threshold_uncertainty_score":0.9999701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008807386521159,"score_gpt":0.2253173869406111,"score_spread":0.2152293130753995,"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."}}