{"id":"W4412748054","doi":"10.1109/tie.2025.3589383","title":"An Adaptive Moment Estimation-Based Sine Cosine Algorithm With Historical Updates for Robust Second Harmonic Current Suppression","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Motors (Canada)","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Sine; Harmonic; Trigonometric functions; Current (fluid); Harmonic analysis; Moment (physics); Algorithm; Control theory (sociology); Computer science; Mathematics; Physics; Acoustics; Engineering; Electrical engineering; Artificial intelligence; Mathematical analysis; Control (management)","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":[0.000113425,0.0003344934,0.0003138435,0.0002888599,0.0002031497,0.0000401153,0.0002001063,0.0002038507,0.00005182735],"category_scores_gemma":[0.000003996189,0.0003260868,0.00008583802,0.0004068131,0.00003974565,0.0002351837,0.000001111778,0.0007748783,0.000001644016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002560806,"about_ca_system_score_gemma":0.0002845254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000862795,"about_ca_topic_score_gemma":0.00003312717,"domain_scores_codex":[0.9986113,0.0000391529,0.0003379481,0.0003693835,0.0001941255,0.0004480435],"domain_scores_gemma":[0.9992437,0.0001383096,0.0000654086,0.0003289822,0.0001224703,0.0001011396],"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":[0.0004528275,0.0002698216,3.835146e-7,0.00002650932,0.00007165093,8.640711e-7,0.00001137437,0.7632479,0.002830179,0.0001062945,0.001448333,0.2315339],"study_design_scores_gemma":[0.001645224,0.001382176,0.000001297989,0.0001693456,0.00007900174,0.00000129472,0.000004879449,0.7148337,0.2673098,0.0002021242,0.01408472,0.0002863464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001405657,0.0002566987,0.9955361,0.000124957,0.0007334618,0.0009519572,0.0002249337,0.0007461014,0.00002013691],"genre_scores_gemma":[0.7885863,0.00008759656,0.2092616,0.00004506133,0.0001804084,0.001283384,0.0001614193,0.0001387327,0.0002555625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7871806,"threshold_uncertainty_score":0.9999191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0293057419173628,"score_gpt":0.2636938982840074,"score_spread":0.2343881563666446,"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."}}