{"id":"W2804640572","doi":"10.1155/2018/7308058","title":"Optimal Operation of High-Speed Trains Using Hybrid Model Predictive Control","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Model predictive control; Control theory (sociology); Train; Nonlinear system; Linearization; Controller (irrigation); Piecewise linear function; Hybrid system; Piecewise; Computer science; Computation; Process (computing); Mathematical optimization; Control engineering; Optimal control; Engineering; Control (management); Algorithm; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001283962,0.00009938828,0.0002349019,0.0001090203,0.00003791747,0.000007535418,0.00006699029,0.00003805888,0.000007855529],"category_scores_gemma":[0.000006681063,0.00009105881,0.0000751905,0.00008730917,0.00003520222,0.000489354,3.521065e-7,0.0000863134,3.963282e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005080848,"about_ca_system_score_gemma":0.00004143527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008147534,"about_ca_topic_score_gemma":0.0000112867,"domain_scores_codex":[0.9990412,0.00001055884,0.0005501023,0.00007158033,0.0002107079,0.00011583],"domain_scores_gemma":[0.999392,0.00001269432,0.0001988388,0.00006385655,0.0002822429,0.00005037788],"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":[0.0001059263,0.00001966981,0.00003037807,0.00001938522,0.00003117372,0.000003394991,0.001093025,0.848712,0.1493712,0.0001312287,0.000005975481,0.0004766507],"study_design_scores_gemma":[0.001391664,0.0002951371,0.004112471,0.00007836348,0.00005917969,0.00001076462,0.0002304096,0.9612678,0.03237496,0.00007185177,0.00001787283,0.00008947057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.536951,0.00004752937,0.4626294,0.00000485648,0.0002489147,0.00004734624,0.00002454504,0.00001172864,0.00003468695],"genre_scores_gemma":[0.9804099,0.0000246191,0.01933674,0.000005696794,0.0001894808,8.864068e-7,0.000008570162,0.00001879142,0.000005320596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4434589,"threshold_uncertainty_score":0.3713271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008341301952758097,"score_gpt":0.2210256140474038,"score_spread":0.2126843120946457,"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."}}