{"id":"W3017161861","doi":"10.1016/j.automatica.2020.108963","title":"Stabilization of non-homogeneous hidden semi-Markov Jump systems with limited sojourn-time information","year":2020,"lang":"en","type":"article","venue":"Automatica","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Defense Basic Scientific Research Program of China","keywords":"Markov chain; Hidden Markov model; Lyapunov function; Markov process; Homogeneous; Computer science; Control theory (sociology); Mode (computer interface); Probability density function; Controller (irrigation); Jump; Mathematics; Markov model; Stability (learning theory); Statistical physics; Control (management); Artificial intelligence; Statistics; Machine learning; Physics; Combinatorics","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":[0.0009775497,0.0008365482,0.001133495,0.0004902658,0.0005135559,0.00106563,0.001000541,0.0008463782,0.0016127],"category_scores_gemma":[0.002242949,0.0003566584,0.0005604337,0.0003330053,0.001418105,0.0007237594,0.001391817,0.0007128528,0.0001270095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008986361,"about_ca_system_score_gemma":0.0008911599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007448408,"about_ca_topic_score_gemma":0.004665151,"domain_scores_codex":[0.9995765,0.0001012319,0.00001960418,0.0001143166,0.00007827148,0.0001100066],"domain_scores_gemma":[0.9985334,0.0007978813,0.0002981459,0.00006013234,0.0002229105,0.00008746801],"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":[0.0007652997,0.0001137448,0.001293409,0.0002109317,0.0001549768,0.0002795751,0.0002088886,0.9315318,0.01577199,0.03936111,0.000634221,0.009673952],"study_design_scores_gemma":[0.0000116991,0.00003880307,0.0002002604,0.000002916488,0.000009904569,0.000003982296,0.00000895294,0.9966592,0.0003979146,0.002619502,0.00004284067,0.00000410747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.47451,0.0007658149,0.5147579,0.0007383039,0.0002065484,0.0000543381,0.0001708418,0.000242289,0.008554126],"genre_scores_gemma":[0.996892,0.00008376893,0.0010628,0.00001924331,0.00001838672,0.00001471288,0.00002923919,0.000006599615,0.001873289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007448408,"threshold_uncertainty_score":0.01481009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004919156893252298,"score_gpt":0.169652573265601,"score_spread":0.1647334163723488,"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."}}