{"id":"W2999315501","doi":"10.1109/tmag.2019.2957162","title":"Efficiency Map Prediction of Motor Drives Using Deep Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Flux linkage; Torque; Operating point; Feed forward; Process (computing); Artificial intelligence; Task (project management); Deep learning; Induction motor; Topology (electrical circuits); Control engineering; Direct torque control; Voltage; Electronic engineering; Mathematics; Engineering; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.00003398708,0.0001129781,0.0001448952,0.0001194196,0.00006997711,0.00001302037,0.00007963899,0.00006643692,0.0001638365],"category_scores_gemma":[0.000003893046,0.0001242912,0.00009878434,0.0003825413,0.00002667659,0.00004742395,3.535777e-7,0.0002300453,0.00001572521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002774565,"about_ca_system_score_gemma":0.000007356275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005334311,"about_ca_topic_score_gemma":0.000001360762,"domain_scores_codex":[0.9993215,0.00002753819,0.000211441,0.0001278037,0.0001616937,0.0001500795],"domain_scores_gemma":[0.999734,0.00003744912,0.0000275492,0.00008966903,0.00003273003,0.00007857807],"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":[0.000007625966,0.00002936116,0.00002917863,0.00005569597,0.00003459613,0.000001363474,0.000373022,0.7493272,0.2121433,0.000002790759,0.00001033825,0.03798554],"study_design_scores_gemma":[0.0001697277,0.0003096127,0.00006870107,0.00001286449,0.0001062726,0.000001197687,0.000044607,0.9507589,0.0481983,0.000004348406,0.0002349417,0.00009054721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06850129,0.0002191893,0.9306208,0.00002597685,0.0001505795,0.00008629372,0.00001077636,0.0001966292,0.0001884773],"genre_scores_gemma":[0.9944084,0.000167462,0.005213442,0.00001563978,0.00004938251,0.000004252985,0.00000128821,0.00002457131,0.0001156097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9259071,"threshold_uncertainty_score":0.5068447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359283302437747,"score_gpt":0.1973423382826389,"score_spread":0.1837495052582614,"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."}}