{"id":"W2922007470","doi":"10.1109/sdpc.2018.8664949","title":"Cuckoo Search Optimized NN-Based Fault Diagnosis Approach for Power Transformer PHM","year":2018,"lang":"en","type":"article","venue":"2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Cuckoo search; Particle swarm optimization; Artificial neural network; Transformer; Backpropagation; Computer science; Dissolved gas analysis; Reliability engineering; Fault (geology); Engineering; Genetic algorithm; Cuckoo; Machine learning; Data mining; Voltage","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":[0.0004690263,0.0008069955,0.0007271806,0.0008514528,0.0003996001,0.0005117225,0.000833043,0.0009636699,0.001698591],"category_scores_gemma":[0.001330317,0.0003037841,0.0003961716,0.000513944,0.0003414407,0.0004643137,0.0003322845,0.0004261236,0.0001533698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128149,"about_ca_system_score_gemma":0.0009300887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02244201,"about_ca_topic_score_gemma":0.01418394,"domain_scores_codex":[0.9997843,0.00005619674,0.00001559557,0.00004626724,0.00006606682,0.00003158629],"domain_scores_gemma":[0.999613,0.0001978912,0.00004646787,0.00001219934,0.0001149937,0.00001544148],"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":[0.00006056965,0.00002331572,0.0006659381,0.00005677092,0.00002513205,0.00004991312,0.00002487443,0.9604012,0.0009468816,0.001339091,0.0005386049,0.03586771],"study_design_scores_gemma":[0.000002959941,0.00000798496,0.00008787206,0.000002282327,0.00000308458,0.000005702715,0.000002259324,0.9994534,0.000148349,0.0002065759,0.000078173,0.000001430813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0964773,0.001596069,0.8906939,0.0004291981,0.0001321966,0.0001466992,0.00009183243,0.001051034,0.009381816],"genre_scores_gemma":[0.9476449,0.0002288456,0.04891191,0.0001094921,0.00002960243,0.00009697586,0.00008773648,0.00003701211,0.002853411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02244201,"threshold_uncertainty_score":0.04462278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283683752273442,"score_gpt":0.2591330486610381,"score_spread":0.2307646734336939,"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."}}