{"id":"W4320018431","doi":"10.1109/tcns.2023.3244108","title":"A Mean-Rate Event-Triggered Mechanism for Nonlinear Plants With Weak Time Regularization","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Control of Network Systems","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Regularization (linguistics); Control theory (sociology); Computer science; Event (particle physics); Stability (learning theory); Mathematics; Artificial intelligence; Control (management)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001250496,0.0007311438,0.0005681431,0.0004151853,0.0004020962,0.0006825936,0.001430606,0.0009277689,0.001396565],"category_scores_gemma":[0.002430277,0.0002221955,0.0007907758,0.0002551037,0.0009015872,0.001256845,0.001076212,0.001073392,0.0002204163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000489782,"about_ca_system_score_gemma":0.0004707901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003167381,"about_ca_topic_score_gemma":0.0002423463,"domain_scores_codex":[0.999299,0.000157011,0.00004812807,0.0001794367,0.0002580008,0.00005829403],"domain_scores_gemma":[0.9989057,0.0003871378,0.0002846333,0.0001701096,0.0001914948,0.00006096794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005595743,0.0001479262,0.00117854,0.0003190235,0.0001039088,0.0006231136,0.0003699294,0.3457901,0.1926312,0.3725978,0.001339028,0.0843398],"study_design_scores_gemma":[0.00002677983,0.0002029544,0.0002365028,0.0000109526,0.00002057723,0.0001295143,0.0000100933,0.9686409,0.009221791,0.01996433,0.001501722,0.00003390822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01592772,0.00007813812,0.982031,0.00007685983,0.00005089852,0.00003178503,0.00001428047,0.0001517908,0.001637536],"genre_scores_gemma":[0.9051008,0.000181227,0.09104104,0.0001226391,0.00009463362,0.0001222638,0.00003759519,0.00004814587,0.003251648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001430606,"threshold_uncertainty_score":0.006613314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104352751741799,"score_gpt":0.2038393559725321,"score_spread":0.1934040807983522,"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."}}