{"id":"W4396234361","doi":"10.1139/tcsme-2023-0190","title":"Characteristics analysis and nonlinear control of a digital hydraulic pressure tracking system for high-speed helicopter wet friction clutch","year":2024,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Clutch; Nonlinear system; Tracking (education); Control theory (sociology); Hydraulic pressure; Hydraulic machinery; Hydraulic fluid; Engineering; Control engineering; Computer science; Mechanical engineering; Control (management); Automotive engineering; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002361511,0.0002745005,0.0002460472,0.0001979493,0.0003692063,0.0005316216,0.0003291549,0.0003493607,0.001523721],"category_scores_gemma":[0.0004198948,0.0001398027,0.0002382396,0.0001435252,0.0003150591,0.0003834058,0.0002630421,0.0002802377,0.0001664067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000414716,"about_ca_system_score_gemma":0.0004616052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004373069,"about_ca_topic_score_gemma":0.002591091,"domain_scores_codex":[0.9997968,0.00002750183,0.00001444148,0.00004778425,0.00009188834,0.00002165452],"domain_scores_gemma":[0.9997424,0.00007826611,0.00006066832,0.00002557648,0.00008304263,0.000009934177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001098424,0.000189646,0.0060054,0.0006831465,0.0001364983,0.0007448265,0.00106172,0.4776914,0.3772469,0.008884634,0.00155459,0.1247029],"study_design_scores_gemma":[0.00003139357,0.0002962484,0.002479946,0.00000995744,0.00002040301,0.00008722631,0.00005491513,0.9803866,0.0152524,0.0003606452,0.001004674,0.00001566754],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4669617,0.0004499558,0.5181332,0.0003335826,0.0000963465,0.0001145354,0.00009890266,0.0007471833,0.01306465],"genre_scores_gemma":[0.9967008,0.00004711835,0.00213166,0.00001142197,0.000004190667,0.00002148514,0.00001383897,0.000004554921,0.001064993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004373069,"threshold_uncertainty_score":0.008695245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006048778656705646,"score_gpt":0.1853639054728894,"score_spread":0.1793151268161838,"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."}}