{"id":"W3104960959","doi":"10.1109/ojpel.2020.3039117","title":"Machine Learning Building Blocks for Real-Time Emulation of Advanced Transport Power Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Open Journal of Power Electronics","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Emulation; Field-programmable gate array; Transient (computer programming); Computer science; Embedded system; Hardware emulation; Solver; Consistency (knowledge bases); Artificial neural network; Component (thermodynamics); Computer architecture; Simulation; Control engineering; Artificial intelligence; Engineering; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003086542,0.000552643,0.0002776098,0.0003062918,0.0002222567,0.000417273,0.0006917759,0.0004112996,0.003707492],"category_scores_gemma":[0.0007785021,0.0002453091,0.0003798891,0.0002553129,0.0002056871,0.0005917825,0.0003083419,0.0008718154,0.0009060524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004397968,"about_ca_system_score_gemma":0.0004918613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003054613,"about_ca_topic_score_gemma":0.002993069,"domain_scores_codex":[0.9998541,0.00004129388,0.000009070352,0.0000174643,0.00006478967,0.00001324492],"domain_scores_gemma":[0.9998049,0.00007756049,0.00002370904,0.00003775831,0.00004740522,0.000008677689],"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":[0.00002867586,0.00002879186,0.0003440643,0.0000550031,0.00001738456,0.00004127989,0.00003468926,0.9485621,0.006135297,0.005615077,0.0005297255,0.03860787],"study_design_scores_gemma":[0.000003315693,0.00001930952,0.00009027014,0.000006156238,0.000003657311,0.000009027372,0.00000319077,0.9939761,0.002898648,0.001145247,0.001842694,0.000002380437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01349126,0.0001262491,0.9798809,0.00007328253,0.00002921911,0.00003791206,0.00007025836,0.001778672,0.004512376],"genre_scores_gemma":[0.6920398,0.0003113485,0.302367,0.00005714537,0.0000203761,0.0002226502,0.0003405559,0.0002787866,0.004362416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003707492,"threshold_uncertainty_score":0.01240283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009124373979468029,"score_gpt":0.2457163175107159,"score_spread":0.2365919435312479,"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."}}