{"id":"W4401433452","doi":"10.1002/cjce.25445","title":"Integrating autoencoder with Koopman operator to design a linear data‐driven model predictive controller","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Autoencoder; Operator (biology); Control theory (sociology); Model predictive control; Linear model; Computer science; Controller (irrigation); Econometrics; Artificial intelligence; Mathematics; Machine learning; Control (management); Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007315205,0.0006573836,0.0005306774,0.0003157766,0.0003312069,0.0006920287,0.0008240231,0.0008378729,0.001224068],"category_scores_gemma":[0.0009266757,0.0003738897,0.0005504718,0.0002878389,0.0004618594,0.0005159754,0.0005311106,0.001017394,0.0003339483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005236746,"about_ca_system_score_gemma":0.0009946228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008573228,"about_ca_topic_score_gemma":0.006097502,"domain_scores_codex":[0.9996598,0.00006401483,0.00002348945,0.00007592652,0.0001408223,0.00003591595],"domain_scores_gemma":[0.9995561,0.0001764901,0.00004960493,0.00003186864,0.0001714201,0.00001442845],"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":[0.00004017189,0.00003983678,0.0002247077,0.00007586095,0.00003495504,0.00004245466,0.0000435069,0.9384533,0.007291022,0.002528315,0.0004221177,0.05080364],"study_design_scores_gemma":[0.000001727865,0.00001274736,0.00002498097,0.000002382643,0.000002600925,0.000003291244,9.738161e-7,0.9988025,0.0008123997,0.0001728736,0.0001617797,0.000001664979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01158308,0.0001700548,0.9855049,0.0000828846,0.00003682429,0.00004636024,0.00001930591,0.0004499107,0.002106759],"genre_scores_gemma":[0.8076281,0.0002253539,0.1881153,0.0001479266,0.00003811502,0.0002790626,0.00008440862,0.00005561984,0.003426135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008573228,"threshold_uncertainty_score":0.01704669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464325665765088,"score_gpt":0.2088224432656843,"score_spread":0.1941791866080334,"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."}}