{"id":"W1492911430","doi":"10.1002/acs.2401","title":"Active fault tolerant control systems by the semi‐Markov model approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Controller (irrigation); Markov chain; Markov process; Fault tolerance; Actuator; Process (computing); Fault (geology); Control engineering; Convex optimization; Stability (learning theory); Computer science; Markov model; Markov decision process; Engineering; Fault detection and isolation; Regular polygon; Control (management); Distributed computing; Mathematics; Artificial intelligence","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":[0.0003375822,0.0002240315,0.0003871635,0.000116437,0.000123026,0.000377125,0.0003235165,0.00009424711,0.00001171629],"category_scores_gemma":[0.00002302111,0.0001448043,0.000124281,0.00006930219,0.00007583514,0.0006667081,0.00001272403,0.0004094268,0.000004389351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000120336,"about_ca_system_score_gemma":0.00005634313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005089949,"about_ca_topic_score_gemma":0.000001274157,"domain_scores_codex":[0.9983641,0.00009482019,0.0005916972,0.000147079,0.000577376,0.0002249374],"domain_scores_gemma":[0.9985166,0.0001261396,0.0003202734,0.00006041188,0.0008482093,0.0001284122],"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":[0.0005715506,0.00007487755,0.00004060438,0.00004323889,0.001122604,0.00001254867,0.0008263073,0.8111102,0.06361232,0.0001187945,0.002491563,0.1199754],"study_design_scores_gemma":[0.003026911,0.00007691811,0.00007628328,0.0001116,0.00005754441,0.0002539188,0.001193715,0.9941694,0.0001663844,0.0001272943,0.0005746982,0.0001653366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03457764,0.007009279,0.9539373,0.0005533076,0.0005009851,0.0004566838,0.00005681361,0.00005631255,0.002851661],"genre_scores_gemma":[0.9988733,0.00003278682,0.0001020489,0.000281498,0.0004620892,0.00005670034,0.000001696981,0.00002536799,0.0001645341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9642956,"threshold_uncertainty_score":0.5904948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008295072750641567,"score_gpt":0.2081627937128227,"score_spread":0.1998677209621811,"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."}}