{"id":"W3033221360","doi":"10.1177/1687814020923178","title":"Design optimization of multi-objective proportional–integral–derivative controllers for enhanced handling quality of a twin-engine, propeller-driven airplane","year":2014,"lang":"en","type":"article","venue":"Advances in Mechanical Engineering","topic":"Advanced Aircraft Design and Technologies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Propeller; Airplane; Particle swarm optimization; Control theory (sociology); Genetic algorithm; Sensitivity (control systems); Engineering; Stability (learning theory); Propulsion; Stability derivatives; Aerodynamics; Range (aeronautics); Computer science; Mathematical optimization; Mathematics; Aerospace engineering; Control (management); Marine engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000489129,0.0001856176,0.0004424794,0.00007986857,0.00003185404,0.000003737728,0.0002166387,0.0001021355,0.00002354824],"category_scores_gemma":[0.001531146,0.000162746,0.00007126328,0.0002852298,0.0001221383,0.0003077898,0.00006221021,0.000155597,0.000001402598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000105699,"about_ca_system_score_gemma":0.00001075519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009247665,"about_ca_topic_score_gemma":0.000008796153,"domain_scores_codex":[0.9986165,0.00005404338,0.0005421316,0.0003264085,0.0002091213,0.0002517964],"domain_scores_gemma":[0.998939,0.0005377398,0.0002625274,0.0001777043,0.00004512925,0.00003788284],"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":[0.0001233321,0.00006197226,0.0000452399,0.00005196486,0.00001083334,2.291516e-7,0.0000719091,0.8268818,0.1652742,0.001523143,4.043636e-7,0.005954982],"study_design_scores_gemma":[0.00110914,0.0002182122,0.00006230169,0.00008584374,0.000006051503,3.832653e-7,0.00007808366,0.694445,0.3024184,0.001397194,0.00002640637,0.0001529556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005975388,0.00009326515,0.9928342,0.00001945434,0.00007731718,0.0008523987,0.000006586025,0.00008740248,0.00005397608],"genre_scores_gemma":[0.576166,0.00004913385,0.4236419,0.000005297714,0.000006002584,0.0001066154,0.000003726331,0.00001247115,0.00000881697],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5701906,"threshold_uncertainty_score":0.6636589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01743395911139756,"score_gpt":0.2669283205936873,"score_spread":0.2494943614822898,"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."}}