{"id":"W2892597487","doi":"10.1109/tie.2018.2870366","title":"An Efficient Hierarchical Zonal Method for Large-Scale Circuit Simulation and Its Real-Time Application on More Electric Aircraft Microgrid","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Microgrid; Field-programmable gate array; Computation; Computer science; Waveform; Voltage; Computational complexity theory; Process (computing); Electronic engineering; Scale (ratio); Virtex; Gate array; Computer engineering; Topology (electrical circuits); Algorithm; Engineering; Electrical engineering; Embedded 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":[0.0002514448,0.0005306334,0.0003605177,0.0005906538,0.0004712564,0.0003866335,0.0006988614,0.0004318185,0.005164864],"category_scores_gemma":[0.0006702023,0.0002521706,0.000438437,0.000561645,0.0003166451,0.0006535215,0.0004598053,0.0004609966,0.0005662307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004797454,"about_ca_system_score_gemma":0.0008110529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008788242,"about_ca_topic_score_gemma":0.01471732,"domain_scores_codex":[0.9999238,0.00002017617,0.000003876977,0.00001240685,0.00003023013,0.000009367947],"domain_scores_gemma":[0.9997764,0.00009160963,0.00002599441,0.00003785325,0.00005406386,0.00001401718],"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":[0.00004581327,0.00003516081,0.0007333224,0.0001117619,0.00002344975,0.00008175016,0.00009075649,0.9067916,0.01014573,0.01552141,0.001375321,0.06504391],"study_design_scores_gemma":[0.000004204976,0.000007160754,0.00005299666,0.000001741488,0.00000205737,0.000008074443,0.000006532077,0.9980336,0.0004487967,0.0007502084,0.0006822451,0.000002326961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009142608,0.00006212643,0.9873167,0.00004154077,0.00001411565,0.00003764973,0.00004956365,0.0008376942,0.002497985],"genre_scores_gemma":[0.3584928,0.0001695239,0.637507,0.00004678868,0.00001652781,0.0002222707,0.0001815601,0.0002650259,0.003098544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008788242,"threshold_uncertainty_score":0.01747417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697487265943867,"score_gpt":0.2835949695265909,"score_spread":0.2666200968671522,"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."}}