{"id":"W4367277056","doi":"10.1109/tim.2023.3268445","title":"Resilient Dynamic State Estimation for Power System Using Cauchy-Kernel-Based Maximum Correntropy Cubature Kalman Filter","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Engineering and Physical Sciences Research Council; National Natural Science Foundation of China","keywords":"Kalman filter; Control theory (sociology); Cauchy distribution; Kernel density estimation; Mathematics; Kernel (algebra); Computer science; Mathematical optimization; Algorithm; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008322548,0.0006864043,0.0008579963,0.0005224815,0.0004520021,0.0007131328,0.0007820521,0.0006687205,0.001022965],"category_scores_gemma":[0.00267502,0.0003277964,0.0006221964,0.0005878888,0.0005222246,0.001178866,0.0006188178,0.000833596,0.0002978925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008340696,"about_ca_system_score_gemma":0.001293369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01202812,"about_ca_topic_score_gemma":0.007691501,"domain_scores_codex":[0.9995201,0.00009804398,0.0000341364,0.0001215272,0.0001826907,0.0000436782],"domain_scores_gemma":[0.9993007,0.0002628819,0.0001020278,0.0000762808,0.0002391717,0.000018979],"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":[0.0001131168,0.0000392606,0.001335148,0.00009752193,0.00006344288,0.00007248375,0.0001238718,0.8438405,0.008395315,0.01094046,0.001125357,0.1338536],"study_design_scores_gemma":[0.000002155936,0.00001034277,0.0001187754,0.000002655531,0.000003362869,0.00001169976,0.000002599935,0.9981112,0.000859209,0.000671027,0.0002007525,0.00000619816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00837398,0.0001287167,0.9905245,0.00005432187,0.00001540099,0.0000122943,0.00001469814,0.0003114459,0.0005647289],"genre_scores_gemma":[0.807385,0.0004123378,0.1892811,0.00008044649,0.00004443711,0.0001107602,0.0001792579,0.00009020731,0.002416496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01202812,"threshold_uncertainty_score":0.02391624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02451354745152353,"score_gpt":0.2504454562366902,"score_spread":0.2259319087851667,"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."}}