{"id":"W4244695226","doi":"10.2139/ssrn.3935560","title":"Self-Adapting Anti-Surge Intelligence Control and Numerical Simulation of Centrifugal Compressors Based on RBF Neural Network","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Sensor and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Centrifugal compressor; Surge; Computer science; Control (management); Artificial intelligence; Gas compressor; Control engineering; Engineering; Mechanical engineering; Electrical engineering","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":[0.0004121146,0.0001655784,0.000336152,0.00004678682,0.0001082045,0.00003229825,0.00008730758,0.00006715349,0.000006522351],"category_scores_gemma":[0.00005178451,0.0001597497,0.00009898467,0.0001797833,0.0000155568,0.0001013728,0.000008208737,0.0008787248,0.000001675499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964046,"about_ca_system_score_gemma":0.0001417777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004102809,"about_ca_topic_score_gemma":0.00001025775,"domain_scores_codex":[0.9979627,0.0001352028,0.0003940713,0.0001524221,0.0002337354,0.001121846],"domain_scores_gemma":[0.9992701,0.0003189906,0.0001145503,0.0001231985,0.00008985732,0.00008333584],"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":[0.00005527605,0.00002371171,0.002781605,0.00001723789,0.00008418312,0.00001037679,0.0000306379,0.988287,0.0007778395,0.001601162,0.000001802591,0.006329132],"study_design_scores_gemma":[0.0007311998,0.0001001775,0.0005740834,0.0000394688,0.00003570102,0.00007779705,0.0001301275,0.9965309,0.0002701365,0.001123343,0.0002395681,0.0001474803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1248098,0.004628317,0.8697168,0.00006706074,0.0003466178,0.0001367045,0.000003268592,0.0000970191,0.0001944778],"genre_scores_gemma":[0.9990987,0.0002594014,0.000265767,0.00004007079,0.0002950071,0.000001435938,0.000002226672,0.00002797438,0.000009487069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8742889,"threshold_uncertainty_score":0.6514406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006152551695927465,"score_gpt":0.2170701232679665,"score_spread":0.2109175715720391,"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."}}