{"id":"W3023205979","doi":"10.1109/itec-india48457.2019.itecindia2019-40","title":"Design & Development of On-Board DC Fast Chargers for E-Rickshaw","year":2019,"lang":"en","type":"article","venue":"2019 IEEE Transportation Electrification Conference (ITEC-India)","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Battery charger; Power factor; Rectifier (neural networks); Battery (electricity); Buck converter; Voltage; Topology (electrical circuits); Buck–boost converter; Inductance; Electrical engineering; Power (physics); Three-phase; Boost converter; Electronic engineering; Engineering; Forward converter; Computer science; 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":[0.0002702269,0.0003449419,0.0004006131,0.000352865,0.00008136548,0.00002832774,0.0003189221,0.0001974994,0.0001516762],"category_scores_gemma":[0.00001559419,0.0003967457,0.0001063618,0.0004332556,0.00003706156,0.0002973651,4.155251e-7,0.0002205731,0.000389211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001575486,"about_ca_system_score_gemma":0.0002734419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004729031,"about_ca_topic_score_gemma":0.00002072586,"domain_scores_codex":[0.997883,0.00003070656,0.0007461446,0.000478039,0.0003925981,0.0004694854],"domain_scores_gemma":[0.9986425,0.0002049904,0.0002629104,0.0004437276,0.000324029,0.0001218592],"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":[0.0001895762,0.00008899144,0.0007795187,0.0002527899,0.0001335316,4.331569e-7,0.004272184,0.01207231,0.8990049,0.001603304,0.0005685234,0.08103392],"study_design_scores_gemma":[0.001995428,0.0002006969,0.02682669,0.0001441868,0.0000739188,7.953057e-7,0.0001998751,0.02916456,0.9341463,0.0002617071,0.006000169,0.0009856829],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1369824,0.0000416809,0.8605241,0.00005279447,0.0004344109,0.001406896,0.00007733285,0.0002645465,0.0002158281],"genre_scores_gemma":[0.9766142,0.00007740259,0.02143263,0.00006274536,0.00003218416,0.0003030257,0.0006908918,0.00007422092,0.0007126867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8396318,"threshold_uncertainty_score":0.9998484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02723178599497626,"score_gpt":0.2434010056500919,"score_spread":0.2161692196551157,"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."}}