{"id":"W2092547378","doi":"10.1049/iet-epa.2012.0116","title":"Multi‐rate real‐time model‐based parameter estimation and state identification for induction motors","year":2013,"lang":"en","type":"article","venue":"IET Electric Power Applications","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Centre National de la Recherche Scientifique; Else Kröner-Fresenius-Stiftung; National Natural Science Foundation of China","keywords":"Induction motor; Identification (biology); Estimation theory; State (computer science); Control theory (sociology); Control engineering; Computer science; System identification; Estimation; Engineering; Artificial intelligence; Algorithm; Data modeling; Control (management); Electrical engineering; Systems engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006306376,0.0006303942,0.000603731,0.0004169177,0.0002100555,0.000591239,0.000724468,0.0005484991,0.001034777],"category_scores_gemma":[0.001971763,0.0003821494,0.0005585605,0.0003209393,0.0002689525,0.00115716,0.0004713968,0.0007019282,0.0005841077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002516182,"about_ca_system_score_gemma":0.000315136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001654332,"about_ca_topic_score_gemma":0.001063979,"domain_scores_codex":[0.9995388,0.00009156934,0.00003556014,0.0001120829,0.0001926767,0.0000293643],"domain_scores_gemma":[0.9995388,0.0001255839,0.0001038191,0.000108859,0.0001101055,0.00001287244],"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":[0.0002292559,0.00007976229,0.001161239,0.0002966897,0.00009828647,0.0001178849,0.0001548843,0.5158995,0.03791612,0.007346884,0.001119847,0.4355796],"study_design_scores_gemma":[0.000006820326,0.00004376022,0.0003585395,0.000009890901,0.00001063672,0.0000488693,0.000005949009,0.9914783,0.005690562,0.0007292123,0.001602658,0.00001489544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006761559,0.0004131015,0.991651,0.00003786715,0.00003574893,0.00001035989,0.00001251236,0.0004871905,0.0005905438],"genre_scores_gemma":[0.7434943,0.0009959185,0.2516846,0.00004975306,0.00008900207,0.00009593766,0.0001438939,0.0001210372,0.003325621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001654332,"threshold_uncertainty_score":0.003461659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008621477994273527,"score_gpt":0.2279585796959108,"score_spread":0.2193371017016373,"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."}}