{"id":"W4312439090","doi":"10.1109/tie.2022.3220893","title":"Deep Reinforcement Learning Aided Variable-Frequency Triple-Phase-Shift Control for Dual-Active-Bridge Converter","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Control theory (sociology); Reinforcement learning; Computer science; Converters; Automatic frequency control; Voltage; Control (management); Engineering; Artificial intelligence; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003793784,0.000348332,0.0003421269,0.0001426034,0.0001845887,0.0003615716,0.0005092378,0.000312609,0.001055897],"category_scores_gemma":[0.0004379318,0.0001445851,0.000175662,0.0001167353,0.0002675907,0.0002665592,0.0004224366,0.000587202,0.0001375084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002824859,"about_ca_system_score_gemma":0.0004477576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001803241,"about_ca_topic_score_gemma":0.001914518,"domain_scores_codex":[0.9998958,0.00002093438,0.000006107574,0.00001971177,0.0000387094,0.00001867168],"domain_scores_gemma":[0.9998679,0.00004572911,0.00002189874,0.00001394136,0.00003941028,0.00001104567],"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":[0.0001677696,0.0001532286,0.0009349959,0.0001198371,0.00003605464,0.0001251528,0.00008736502,0.7895258,0.02975051,0.007140134,0.001089507,0.1708697],"study_design_scores_gemma":[0.000006528472,0.00003264962,0.00006347125,0.000002145262,0.000002477541,0.000009162948,0.00000168315,0.9974638,0.001801112,0.0003953172,0.0002196485,0.000001993319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0691899,0.0002619147,0.9245111,0.0001541421,0.00006480345,0.00004745876,0.00001493403,0.0004839562,0.00527169],"genre_scores_gemma":[0.9732633,0.00004677304,0.0256136,0.0000310665,0.000006944406,0.00002875761,0.000009096738,0.000009543524,0.0009909831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001803241,"threshold_uncertainty_score":0.003585458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215688983913335,"score_gpt":0.2467519997058214,"score_spread":0.2245951098666881,"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."}}