{"id":"W1540887169","doi":"10.1007/3-540-39205-x_27","title":"Adaptive Granular Control of an HVDC System: A Rough Set Approach","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Control theory (sociology); Controller (irrigation); PID controller; High-voltage direct current; Adaptive control; Control engineering; Rough set; Computer science; Control system; Set (abstract data type); Voltage; Engineering; Control (management); Artificial intelligence; Direct current; Temperature control","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":[0.001011091,0.0008001321,0.00212481,0.0007765053,0.0006547828,0.002935612,0.001376095,0.0008453231,0.001316377],"category_scores_gemma":[0.001708634,0.0005248426,0.001084515,0.001103292,0.001197445,0.001554593,0.001046368,0.00103248,0.0001301503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090441,"about_ca_system_score_gemma":0.0007036921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005612902,"about_ca_topic_score_gemma":0.002933788,"domain_scores_codex":[0.9993342,0.0001556348,0.00004851368,0.00009918881,0.0002703902,0.00009203627],"domain_scores_gemma":[0.9995019,0.0001978046,0.00009790896,0.00006235913,0.0001125203,0.00002751271],"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":[0.00006234018,0.0000257894,0.0001418648,0.0001160392,0.00005435452,0.00008770013,0.0000709876,0.9467123,0.002952489,0.02714681,0.0005455067,0.02208379],"study_design_scores_gemma":[0.000005760749,0.00003633558,0.0001547983,0.00001218062,0.00002072394,0.00001515863,0.00001890719,0.9868564,0.0003674692,0.01212429,0.0003714681,0.00001651311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01748518,0.0007146387,0.9758518,0.0001748395,0.0001024443,0.00004284574,0.00003925216,0.0001609021,0.005428188],"genre_scores_gemma":[0.9426675,0.0008660927,0.05388782,0.00005613404,0.00008523017,0.00007730306,0.00004141501,0.00003746547,0.002281092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005612902,"threshold_uncertainty_score":0.01116049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03093827597032713,"score_gpt":0.2442652648386202,"score_spread":0.2133269888682931,"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."}}