{"id":"W2993254404","doi":"10.1109/tie.2019.2952820","title":"A Simple Technique for Fundamental Harmonic Approximation Analysis in Parallel and Series–Parallel Resonant Converters","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Advanced DC-DC Converters","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Converters; Rectifier (neural networks); Harmonic; Series and parallel circuits; Capacitive sensing; Harmonic analysis; Electronic engineering; Series (stratigraphy); Filter (signal processing); Simple (philosophy); Total harmonic distortion; Voltage; Computer science; State space; Topology (electrical circuits); Control theory (sociology); Engineering; Physics; Mathematics; Electrical engineering; Acoustics","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":[0.0002326904,0.0006068329,0.0003614605,0.0004510581,0.0005101563,0.0005749727,0.0006184325,0.0004482036,0.005739148],"category_scores_gemma":[0.0004704893,0.000225497,0.0007383919,0.0004383507,0.0003416113,0.0007410934,0.0003563639,0.001080104,0.00181753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002726475,"about_ca_system_score_gemma":0.0003691919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000762936,"about_ca_topic_score_gemma":0.000769719,"domain_scores_codex":[0.9997928,0.00003162815,0.00001244079,0.00003741264,0.000113051,0.00001266916],"domain_scores_gemma":[0.9999012,0.00002413961,0.000009559615,0.00003279122,0.00002895985,0.000003349109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001344415,0.00013248,0.0005828581,0.0004953493,0.0000804116,0.0004548703,0.0004298423,0.1043192,0.2181173,0.2707094,0.009564007,0.3949799],"study_design_scores_gemma":[0.00004039696,0.0002297086,0.0006284348,0.00006944904,0.00005757071,0.001167613,0.00008741576,0.756447,0.06639668,0.07555891,0.0992537,0.00006320519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001968133,0.0001273195,0.9916374,0.00005264828,0.00008188318,0.00003232967,0.00003718399,0.0005635595,0.005499539],"genre_scores_gemma":[0.3111053,0.000738104,0.6708556,0.000162719,0.0001431365,0.0001738587,0.0001880966,0.0003731837,0.0162599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005739148,"threshold_uncertainty_score":0.01919937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01632755689044074,"score_gpt":0.2335196605276499,"score_spread":0.2171921036372092,"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."}}