{"id":"W4311978990","doi":"10.3390/en15249335","title":"End-to-End Deep Neural Network Based Nonlinear Model Predictive Control: Experimental Implementation on Diesel Engine Emission Control","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Deutsche Forschungsgemeinschaft; University of Alberta","keywords":"Model predictive control; Robustness (evolution); Diesel engine; Artificial neural network; Control theory (sociology); Computer science; Controller (irrigation); PID controller; Fuel efficiency; Engineering; Automotive engineering; Control engineering; Artificial intelligence; Temperature control","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001624196,0.000290373,0.0003183839,0.0001338673,0.0002943597,0.00003622052,0.000174808,0.00004713828,0.0001452472],"category_scores_gemma":[0.00001889372,0.0003082566,0.00008956014,0.0002117747,0.00001758989,0.0002026056,0.00004436951,0.0001993942,0.000003492778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004106988,"about_ca_system_score_gemma":0.00002479205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001744076,"about_ca_topic_score_gemma":0.000009105709,"domain_scores_codex":[0.9983481,0.000109479,0.0003780766,0.0003113955,0.0004193259,0.000433608],"domain_scores_gemma":[0.9993544,0.000143003,0.00008387888,0.0002520222,0.00004689578,0.000119867],"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":[0.0003623355,0.00003393807,0.00009144864,0.000007044374,0.00006967583,0.00000483126,0.0003102036,0.9798771,0.01719241,0.0001392899,0.0004424968,0.001469277],"study_design_scores_gemma":[0.003685159,0.0003317195,0.00009852366,0.0000092258,0.0000353175,0.000001786039,0.0005104047,0.9899369,0.004460795,0.00002354593,0.000632731,0.0002739208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1154276,0.001008355,0.8795081,0.0002128374,0.0009775275,0.001141641,0.000361319,0.0008867136,0.0004759218],"genre_scores_gemma":[0.9928952,0.000003639653,0.005014917,0.0003864711,0.0003562389,0.0009825169,0.0002226541,0.00008848054,0.00004992457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8774676,"threshold_uncertainty_score":0.9999369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006160999257184174,"score_gpt":0.2352391563806345,"score_spread":0.2290781571234504,"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."}}