{"id":"W4405717800","doi":"10.1109/tec.2024.3521289","title":"A Neural-Network-Based Electric Machine Emulator Using Neuro-Fuzzy Controller for Power-Hardware-in-the-Loop Testing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Energy Conversion","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial neural network; Neuro-fuzzy; Control engineering; Computer science; Control theory (sociology); Controller (irrigation); Electric power system; Fuzzy control system; Fuzzy logic; Power (physics); Artificial intelligence; Engineering; Control (management); 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":[0.0002367562,0.000271588,0.0002835118,0.0003329627,0.0001975638,0.000132361,0.0001566807,0.0001241279,0.00004087478],"category_scores_gemma":[0.0000100403,0.0002327314,0.0002272069,0.0008279345,0.00001424295,0.0001786247,6.100667e-7,0.0002567912,0.00001326843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405617,"about_ca_system_score_gemma":0.00004152742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000155502,"about_ca_topic_score_gemma":0.00002008479,"domain_scores_codex":[0.9985528,0.000110168,0.0003766684,0.0003139646,0.000242271,0.0004041397],"domain_scores_gemma":[0.9986216,0.0009892931,0.00003847012,0.0002138538,0.00005831519,0.00007842689],"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":[0.00009958987,0.00002931357,0.00001064354,0.00005477868,0.00004575519,0.00001607042,0.00003027569,0.9773499,0.01343476,0.00006198127,0.0002040148,0.00866291],"study_design_scores_gemma":[0.001619362,0.0001334923,0.0000223813,0.00009011431,0.00007505688,0.00001494607,0.00001150502,0.9928656,0.001995506,0.00003225018,0.002909981,0.0002297556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03777659,0.0006496155,0.9576068,0.0001754977,0.002001587,0.0005603372,0.00002191236,0.0006217068,0.0005859798],"genre_scores_gemma":[0.9990063,0.00001044098,0.0001787089,0.0003817148,0.0001326212,0.00007735329,0.000006168954,0.00007253519,0.0001342104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9612297,"threshold_uncertainty_score":0.9490511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156662120511787,"score_gpt":0.2254363720590244,"score_spread":0.2097701600078457,"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."}}