{"id":"W3134325730","doi":"10.1049/iet-smt.2020.0083","title":"Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique","year":2020,"lang":"en","type":"article","venue":"IET Science Measurement & Technology","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Wavelet; Autoregressive model; Feature extraction; Artificial intelligence; Detector; Artificial neural network; Insulator (electricity); Deep learning; Wavelet transform; Mean squared error; Fault (geology); Time series; Pattern recognition (psychology); Algorithm; Machine learning; Engineering; Mathematics; Statistics","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":[0.0002531576,0.0005324142,0.000303189,0.0005063759,0.0001328686,0.0003733677,0.0004285311,0.0005573544,0.0009233358],"category_scores_gemma":[0.0005131637,0.0001821676,0.0004057875,0.0003916137,0.0001851219,0.0005926303,0.0003816772,0.0004897492,0.0002558425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003318033,"about_ca_system_score_gemma":0.000272261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002402218,"about_ca_topic_score_gemma":0.002884391,"domain_scores_codex":[0.9999008,0.00001438935,0.000006303531,0.00002461496,0.00003320966,0.00002059704],"domain_scores_gemma":[0.9998461,0.00005679678,0.00001989986,0.00001671074,0.00005120138,0.000009208356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002895501,0.0002241933,0.004221292,0.0001475035,0.0001042622,0.0002143299,0.00009880631,0.4053642,0.08174479,0.001869343,0.001298323,0.5044234],"study_design_scores_gemma":[0.000002500193,0.00005394356,0.0006977291,0.000004119108,0.000008658439,0.00002465831,0.000007831355,0.9916303,0.006978767,0.0003908818,0.0001966323,0.000003876535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1506158,0.000335652,0.8457168,0.0001654797,0.0000629957,0.00003248656,0.0001025158,0.001266166,0.001702051],"genre_scores_gemma":[0.9105819,0.0001704471,0.08712193,0.00005923879,0.00002252498,0.00003143965,0.0001385508,0.00002465237,0.001849165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002402218,"threshold_uncertainty_score":0.004776537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962139749786212,"score_gpt":0.2533223761502795,"score_spread":0.2237009786524173,"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."}}