{"id":"W4404317422","doi":"10.1109/sepoc63090.2024.10747468","title":"Enhancing quarter car active suspension performance using walrus optimization algorithm-based PID controller","year":2024,"lang":"en","type":"article","venue":"","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"PID controller; Active suspension; Control theory (sociology); Computer science; Suspension (topology); Controller (irrigation); Quarter (Canadian coin); Control engineering; Algorithm; Engineering; Control (management); Temperature control; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001333509,0.0001701383,0.0002093289,0.0001175794,0.00007207817,0.0001150966,0.00006209237,0.0000909553,0.00007714135],"category_scores_gemma":[0.000003057345,0.0001500797,0.00007559545,0.0001687146,0.00001011937,0.0002364998,0.00000825541,0.0001371601,0.00002880473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001874442,"about_ca_system_score_gemma":0.00002962375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001203488,"about_ca_topic_score_gemma":0.00002607559,"domain_scores_codex":[0.9991325,0.00002163655,0.0002428835,0.0001962278,0.0001539273,0.0002528805],"domain_scores_gemma":[0.9996981,0.00004595067,0.00001882148,0.0001208741,0.00005906136,0.00005716998],"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":[0.000009791908,0.00000515891,0.00000759472,0.0000660665,0.00004852473,0.000006625271,0.0001231975,0.9509827,0.0290829,0.00003941626,0.00003108486,0.01959692],"study_design_scores_gemma":[0.0005011457,0.0000336076,0.00004112159,0.0001270003,0.00002904919,0.000006329282,0.00008734733,0.9928336,0.005967738,0.00000320173,0.0001743714,0.0001955612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1583097,0.0003104032,0.8386117,0.00002978871,0.0007382922,0.0002540508,0.000009151623,0.0004462368,0.001290714],"genre_scores_gemma":[0.994633,0.00001507271,0.004945749,0.00003450111,0.0001851614,0.0000179837,0.00001526331,0.00004679936,0.0001064673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8363233,"threshold_uncertainty_score":0.6120073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004864472994573438,"score_gpt":0.192561074150868,"score_spread":0.1876966011562946,"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."}}