{"id":"W4388017400","doi":"10.1109/tpel.2023.3327956","title":"Flux Linkage Tracking-Based Permanent Magnet Temperature Hybrid Modeling and Estimation for PMSMs With Data-Driven-Based Core Loss Compensation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Flux linkage; Control theory (sociology); Magnet; Compensation (psychology); Linkage (software); Core (optical fiber); Computer science; Engineering; Control engineering; Mechanical engineering; Direct torque control","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":[0.0001810631,0.0002643989,0.0002442065,0.0003102983,0.0002297664,0.000093625,0.0001709807,0.0001073685,0.00002712148],"category_scores_gemma":[0.000004097815,0.000256823,0.00007549443,0.0005165851,0.00002584433,0.0001857701,6.543999e-7,0.0003886046,0.00001037517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001518214,"about_ca_system_score_gemma":0.0001162669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005200507,"about_ca_topic_score_gemma":0.00006026204,"domain_scores_codex":[0.9986687,0.00002453746,0.0002527112,0.0003841909,0.000254441,0.0004154307],"domain_scores_gemma":[0.9992546,0.0001221279,0.00003983602,0.0004083251,0.00008984564,0.00008522049],"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":[0.00007083626,0.00005208699,0.000003355031,0.00008656743,0.00008082564,0.000006570726,0.00005025785,0.9873265,0.007206276,0.00001984526,0.0001997151,0.004897153],"study_design_scores_gemma":[0.0009825819,0.0003426905,0.000008891842,0.00005281062,0.0001863624,0.000007914061,0.000008483215,0.9822622,0.01566672,0.00004714059,0.0001487055,0.0002855172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2114922,0.0001300825,0.7871575,0.0001261971,0.00007790084,0.0003839029,0.0001769127,0.0004452712,0.00001000837],"genre_scores_gemma":[0.9945803,0.00006990868,0.004096189,0.00008979348,0.00001939892,0.00008565355,0.0009193594,0.00007539077,0.00006397761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7830881,"threshold_uncertainty_score":0.9999884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02067839106308738,"score_gpt":0.2432431642721697,"score_spread":0.2225647732090823,"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."}}