{"id":"W2972363742","doi":"10.3390/en12183485","title":"Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Noise reduction; Partial discharge; Radio frequency; Artificial neural network; Thresholding; Computer science; Artificial intelligence; Noise (video); Wavelet; Energy (signal processing); Pattern recognition (psychology); Electronic engineering; Voltage; Engineering; Mathematics; Electrical engineering; Telecommunications","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":[0.0006632689,0.0004266945,0.0004613543,0.0004671864,0.0001401602,0.0004360884,0.0003828461,0.0007545513,0.0004501617],"category_scores_gemma":[0.001113463,0.0001773603,0.0004494418,0.000423832,0.0002342738,0.0004896554,0.0002477081,0.0005186141,0.0001645309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002839045,"about_ca_system_score_gemma":0.0001923181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001441278,"about_ca_topic_score_gemma":0.001159344,"domain_scores_codex":[0.9997805,0.00003815695,0.00001645531,0.00005493506,0.00008823097,0.00002175544],"domain_scores_gemma":[0.9997227,0.0001143554,0.0000333398,0.00001923614,0.0001039947,0.000006307621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003459926,0.0001247929,0.001779,0.0001876308,0.00008705958,0.0001549831,0.0001331968,0.4840032,0.09670851,0.002353679,0.0008777886,0.4132442],"study_design_scores_gemma":[0.000002157776,0.00002276653,0.0003980734,0.00000392734,0.000006547481,0.00001621975,0.000004915551,0.9927688,0.00632505,0.0002089963,0.0002389862,0.000003657697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1022471,0.0004275028,0.8949274,0.000106647,0.00006580173,0.00002705284,0.00003555228,0.0005007142,0.001662124],"genre_scores_gemma":[0.7872567,0.0005946047,0.2082853,0.00007182093,0.00004294689,0.00006441837,0.0001820102,0.00004538731,0.003456917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001441278,"threshold_uncertainty_score":0.003507733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02387679676802997,"score_gpt":0.254519164083269,"score_spread":0.230642367315239,"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."}}