{"id":"W2123702245","doi":"10.1109/tpel.2002.1004250","title":"High performance predictive dead-beat digital controller for DC power supplies","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":231,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Control theory (sociology); Inductor; Digital control; Model predictive control; Beat (acoustics); Dead time; Computer science; Power control; Engineering; Power (physics); Electronic engineering; Control (management); Electrical engineering; Voltage; Mathematics; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00003954851,0.0003472082,0.0003065437,0.0001528015,0.0001693056,0.0000638551,0.0001882173,0.0001493687,0.0003022952],"category_scores_gemma":[0.000003540675,0.0003619309,0.0001690384,0.000193538,0.00006380118,0.0005932171,9.145924e-7,0.0004371365,0.0001556875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003917443,"about_ca_system_score_gemma":0.00002020908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.573453e-7,"about_ca_topic_score_gemma":0.000005752255,"domain_scores_codex":[0.9984411,0.000007576396,0.0002872904,0.0003205774,0.0002202467,0.0007232004],"domain_scores_gemma":[0.9993588,0.0001312814,0.00003498573,0.0002918874,0.00007295658,0.0001100429],"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":[0.001447728,0.0008931931,0.00003933042,0.0001562267,0.001893012,0.00001059377,0.002648468,0.6648228,0.01446913,0.001027811,0.006376139,0.3062156],"study_design_scores_gemma":[0.005221824,0.002303777,0.00006510611,0.00005078536,0.0001615567,0.00004133079,0.0001451924,0.8778254,0.06749024,0.0004396209,0.04510335,0.001151766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04252623,0.0004344967,0.9539998,0.0001040871,0.0007869796,0.0005800847,0.0002258162,0.0005792907,0.0007632857],"genre_scores_gemma":[0.9974622,0.0003451596,0.0003626075,0.0001011018,0.00002696161,0.0002432949,0.000008431889,0.0001105346,0.001339675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.954936,"threshold_uncertainty_score":0.9998833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004596324452141679,"score_gpt":0.1801592985693995,"score_spread":0.1755629741172578,"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."}}