{"id":"W4295768341","doi":"10.1109/fuzz-ieee55066.2022.9882748","title":"AVOA-Based Tuning of Low-Cost Fuzzy Controllers for Tower Crane Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Ministry of Education","keywords":"Payload (computing); Control theory (sociology); Fuzzy logic; Fuzzy control system; Metaheuristic; Computer science; Controller (irrigation); Mathematical optimization; Optimization problem; Tower; Position (finance); PID controller; Control engineering; Mathematics; Engineering; Control (management); Temperature control; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005683342,0.0006976873,0.0006668729,0.0004701557,0.0003928836,0.0006206506,0.0007374483,0.0006125479,0.0006867789],"category_scores_gemma":[0.001118587,0.0002880682,0.0004735518,0.0002803114,0.0003972669,0.0003365627,0.0004102744,0.0005321763,0.0001063417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004462334,"about_ca_system_score_gemma":0.0005192187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003655886,"about_ca_topic_score_gemma":0.003116715,"domain_scores_codex":[0.9997633,0.00005970615,0.0000144563,0.00004866196,0.00008324917,0.00003061171],"domain_scores_gemma":[0.9997393,0.0001259806,0.00004406042,0.00001785129,0.00006076443,0.00001189454],"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":[0.00005988873,0.00002811558,0.0003655132,0.00006183336,0.00004118412,0.00002834163,0.00004370043,0.9311752,0.007414755,0.001977663,0.0002152381,0.0585885],"study_design_scores_gemma":[0.00000740827,0.00003375633,0.0001135921,0.000003895836,0.000004986663,0.0000106483,0.000004721178,0.9982548,0.001031675,0.0002765419,0.0002545413,0.000003408381],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05587087,0.0005038024,0.9396768,0.00005740684,0.00005036316,0.00004015739,0.00001466279,0.0002739733,0.003511893],"genre_scores_gemma":[0.8916752,0.0001280018,0.1069298,0.00003975003,0.0000208345,0.00007565195,0.00002943535,0.00003440606,0.001066938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003655886,"threshold_uncertainty_score":0.007269204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05141880838025346,"score_gpt":0.2806221593956375,"score_spread":0.229203351015384,"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."}}