{"id":"W3041581117","doi":"10.1177/0954407020933364","title":"Ride comfort control of in-wheel motor drive unmanned ground vehicles with energy regeneration","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Vibration Control and Rheological Fluids","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Automotive engineering; Acceleration; Suspension (topology); Vibration; Ride quality; Regenerative brake; Engineering; Sprung mass; Controller (irrigation); Vehicle dynamics; Power (physics); Control engineering; Damper; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003233342,0.0002096423,0.0005941855,0.0001649924,0.00002506805,0.00001237726,0.0003253447,0.000141707,0.000009399445],"category_scores_gemma":[0.0002715083,0.0001494212,0.0001877157,0.0004324688,0.00006633274,0.0003701383,0.00003095331,0.0003117342,2.13956e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006263676,"about_ca_system_score_gemma":0.00005030951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000328312,"about_ca_topic_score_gemma":8.206292e-7,"domain_scores_codex":[0.9982975,0.000008514049,0.0009697088,0.0001233066,0.0004039483,0.0001969879],"domain_scores_gemma":[0.9991241,0.00007931241,0.0003018273,0.00007356681,0.0002982738,0.000122965],"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":[0.0001173106,0.00003290691,0.00008305607,0.0001615354,0.0001030642,0.000001868781,0.00007433298,0.6426233,0.3349439,0.02164388,0.00004178241,0.0001730938],"study_design_scores_gemma":[0.0016011,0.0005111725,0.0002444656,0.0003703799,0.00006971943,0.00002441146,0.00006346717,0.6746976,0.3218468,0.00009363839,0.000329052,0.0001482755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6642921,0.000542778,0.3338641,0.0003674629,0.0004756368,0.0002693143,0.00001760988,0.00008236785,0.00008864747],"genre_scores_gemma":[0.997564,0.0001086574,0.002103067,0.00003128984,0.000154171,0.00001207156,0.000001085346,0.00002147736,0.000004219198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3332718,"threshold_uncertainty_score":0.609322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006233984321779021,"score_gpt":0.169247673156993,"score_spread":0.1630136888352139,"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."}}