{"id":"W2899142389","doi":"10.23919/ipec.2018.8507794","title":"Opportunities for Leveraging Low-Voltage GaN Devices in Modular Multi-level Converters for Electric-Vehicle Charging Applications","year":2018,"lang":"en","type":"article","venue":"2018 International Power Electronics Conference (IPEC-Niigata 2018 -ECCE Asia)","topic":"HVDC Systems and Fault Protection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Converters; Decoupling (probability); Modular design; EMI; Voltage; Electrical engineering; Power (physics); Low voltage; Electromagnetic interference; High voltage; Electronic engineering; Computer science; Electric vehicle; Power module; Materials science; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002238456,0.0003231813,0.0002210285,0.0001957258,0.00009928017,0.0005293337,0.000558386,0.0003365679,0.001999562],"category_scores_gemma":[0.0002792141,0.0001091102,0.0002357376,0.0002181911,0.0001909863,0.0005836489,0.0001842355,0.0003837314,0.000468337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002983513,"about_ca_system_score_gemma":0.0001090738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002401508,"about_ca_topic_score_gemma":0.0006700931,"domain_scores_codex":[0.999908,0.00002119987,0.000003354666,0.00001627933,0.00003114383,0.00002008797],"domain_scores_gemma":[0.9998381,0.00003637843,0.00003254063,0.000032815,0.00004533046,0.00001483316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002552317,0.0001597571,0.002616259,0.0003361879,0.00006612879,0.0003301258,0.0001159156,0.04162638,0.8814328,0.008381783,0.002354665,0.06232477],"study_design_scores_gemma":[0.0001241796,0.002425044,0.01006156,0.000131459,0.0001201158,0.001274821,0.0001543613,0.2555181,0.6732662,0.007573586,0.04927755,0.0000729666],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.653652,0.002449435,0.2984003,0.0008460711,0.0002873004,0.0001590233,0.0002935268,0.002249258,0.04166314],"genre_scores_gemma":[0.9786953,0.0002224959,0.01963075,0.00006733579,0.00002117493,0.00001556617,0.00004364599,0.00004222817,0.001261398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001999562,"threshold_uncertainty_score":0.006689191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07150399481294961,"score_gpt":0.2827226120862864,"score_spread":0.2112186172733368,"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."}}