{"id":"W4409014696","doi":"10.1109/ojpel.2025.3556430","title":"Dropout Kolmogorov–Arnold Networks: A Novel Data-Driven Impedance Modeling Approach for Voltage-Source Converters","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of Power Electronics","topic":"Advancements in Semiconductor Devices and Circuit Design","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dropout (neural networks); Converters; Electrical impedance; Voltage source; Output impedance; Computer science; Voltage; Electronic engineering; Electrical engineering; Engineering; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006694483,0.0002945293,0.0005448392,0.0001187532,0.0001230929,0.0002246632,0.002217116,0.00012649,0.00001317104],"category_scores_gemma":[0.00003005581,0.0002859544,0.0001258968,0.0002646895,0.00003359328,0.000967065,0.0001216066,0.0006057653,0.000001230345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003100686,"about_ca_system_score_gemma":0.0002319396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005078924,"about_ca_topic_score_gemma":0.00000498201,"domain_scores_codex":[0.9980343,0.00002241351,0.000745906,0.0003337911,0.0002292771,0.0006343092],"domain_scores_gemma":[0.9987152,0.00008491785,0.0002512797,0.000642693,0.0001962103,0.0001096819],"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":[0.000076611,0.00007676635,0.00003292545,0.00006537062,0.0004970263,0.000002372995,0.0001192608,0.9772404,0.009893989,0.000899998,0.004023611,0.00707168],"study_design_scores_gemma":[0.001519583,0.0001317755,6.071929e-7,0.0001204971,0.0001425832,0.00002986002,0.0002707073,0.9660432,0.0007015382,0.000327712,0.03042264,0.000289276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005369531,0.006565264,0.9854071,0.00002215766,0.001156563,0.0005382937,0.0000252495,0.00004010946,0.00087574],"genre_scores_gemma":[0.9822922,0.0009622962,0.0156057,0.0003374922,0.000219102,0.0000233452,0.00003087915,0.00008952186,0.000439482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9769226,"threshold_uncertainty_score":0.9999593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492343473426199,"score_gpt":0.2925585871718765,"score_spread":0.2576351524376145,"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."}}