{"id":"W2911373596","doi":"10.1155/2019/6270515","title":"Optimal Utilization of Adhesion Force for Heavy-Haul Electric Locomotive Based on Extremum Seeking with Sliding Mode and Asymmetric Barrier Lyapunov Function","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Electrical Contact Performance and Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province","keywords":"Lyapunov function; Control theory (sociology); Sliding mode control; Slip (aerodynamics); Traction (geology); Optimal control; Tractive force; Adhesion; Slip ratio; Computer science; Engineering; Materials science; Mathematics; Structural engineering; Automotive engineering; Physics; Mechanical engineering; Control (management); Mathematical optimization; Nonlinear system; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002936418,0.0005194215,0.0004121782,0.000316474,0.0002665752,0.0004767047,0.0004877076,0.0003129638,0.0008317223],"category_scores_gemma":[0.000403336,0.0001840476,0.0003226628,0.0001786854,0.0003166265,0.0004198054,0.0005313934,0.000331661,0.0001327668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003029491,"about_ca_system_score_gemma":0.0005429053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002537468,"about_ca_topic_score_gemma":0.001757217,"domain_scores_codex":[0.9998173,0.00003120563,0.00001153761,0.00003731116,0.00007175389,0.00003092325],"domain_scores_gemma":[0.9998499,0.00003328387,0.00002828809,0.00001071059,0.00006391688,0.00001392345],"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":[0.0003102632,0.0001252515,0.003972101,0.0004311589,0.00008594131,0.0003318232,0.0003328854,0.6531036,0.1415733,0.03588791,0.002029334,0.1618165],"study_design_scores_gemma":[0.00001498323,0.0001220288,0.0004424051,0.000006902675,0.000009578682,0.00003351816,0.000023096,0.9939517,0.003587725,0.001157657,0.0006422313,0.000008164806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06582497,0.0002370471,0.9291729,0.00007818347,0.00003776122,0.00003512171,0.00001476965,0.0001520817,0.004447198],"genre_scores_gemma":[0.9817456,0.0001225408,0.01638636,0.00001614387,0.00001078777,0.00005048101,0.00002031693,0.000008994454,0.001638859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002537468,"threshold_uncertainty_score":0.005045414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007250199227162389,"score_gpt":0.2233510038759526,"score_spread":0.2161008046487902,"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."}}