{"id":"W2775765383","doi":"10.1109/iecon.2017.8216332","title":"High-resolution low-cost rotor position sensor for traction applications","year":2017,"lang":"en","type":"article","venue":"IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Position sensor; Total harmonic distortion; Position (finance); Rotor (electric); Resolver; Hall effect sensor; Encoder; Torque; Control theory (sociology); Traction (geology); Automotive engineering; Engineering; Computer science; Magnet; Electrical engineering; Voltage; Mechanical engineering; Control (management); Physics; 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":[],"consensus_categories":[],"category_scores_codex":[0.00040654,0.0002360775,0.0003418277,0.00004028516,0.0007449658,0.0001832378,0.0006532656,0.0004315294,0.00002048036],"category_scores_gemma":[0.00007856169,0.0002117379,0.0004357403,0.0001191326,0.000124735,0.0004347467,0.00002380822,0.0005679266,0.000008836279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003433865,"about_ca_system_score_gemma":0.0002837274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001223603,"about_ca_topic_score_gemma":0.00006349192,"domain_scores_codex":[0.9985636,0.0000479144,0.0003990552,0.00028043,0.0002457087,0.0004633264],"domain_scores_gemma":[0.9983824,0.00009079248,0.000427323,0.0006591749,0.0003576338,0.00008261138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004431886,0.000572928,0.0003754686,0.0003547815,0.002209289,0.000001178631,0.001482548,0.03531583,0.5628915,0.01153173,0.1316557,0.2531658],"study_design_scores_gemma":[0.007253153,0.0006264889,0.00113015,0.0002958337,0.001223869,0.00001779154,0.0003910869,0.4603844,0.4630971,0.006246949,0.05771343,0.001619739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6892679,0.0004135006,0.2932428,0.003149444,0.003276865,0.007390606,0.001638043,0.0004962409,0.001124679],"genre_scores_gemma":[0.997241,0.0002447052,0.0004000554,0.00003133238,0.0009103112,0.0003584803,0.00005314776,0.0000337942,0.0007272378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4250686,"threshold_uncertainty_score":0.8634421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04101703322007917,"score_gpt":0.2628373605609181,"score_spread":0.2218203273408389,"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."}}