{"id":"W4200117312","doi":"10.3390/app112411796","title":"Online Monitoring of Power Converter Degradation Using Deep Neural Network","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Korea Institute of Radiological and Medical Sciences","keywords":"Snubber; Insulated-gate bipolar transistor; Computer science; Spectrogram; Resistor; Grayscale; Convolutional neural network; Waveform; Converters; Voltage; Electronic engineering; Artificial intelligence; Electrical engineering; Engineering; Capacitor; Image (mathematics)","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":[0.0002288854,0.000726815,0.0003871846,0.0004551655,0.0001486738,0.0003439462,0.0005280305,0.0003983669,0.0005429217],"category_scores_gemma":[0.0007123958,0.000189131,0.0001937108,0.0002746476,0.0001694468,0.0005187839,0.0003794077,0.0004957311,0.0001824562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000508708,"about_ca_system_score_gemma":0.0002633171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003274649,"about_ca_topic_score_gemma":0.005232032,"domain_scores_codex":[0.9998248,0.00001671568,0.00001053024,0.00005030729,0.00006812031,0.00002956589],"domain_scores_gemma":[0.9997626,0.00005224393,0.00004984134,0.00002358518,0.00009523919,0.00001652642],"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":[0.0007607563,0.0008047596,0.03108726,0.0002636493,0.0001853409,0.0005091694,0.0001344878,0.236593,0.1208827,0.0004336221,0.004039306,0.6043059],"study_design_scores_gemma":[0.000007030465,0.00009763939,0.007638995,0.000009254187,0.00001386263,0.00005436851,0.00001928273,0.9739789,0.01758986,0.0002839741,0.0002975581,0.00000917354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7903898,0.001363765,0.2008445,0.0002874142,0.0001234489,0.00006825817,0.0005073848,0.002979233,0.003436308],"genre_scores_gemma":[0.9900412,0.0001253332,0.00844667,0.00005556138,0.000008966817,0.00001967594,0.000290576,0.00001309664,0.000998929],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003274649,"threshold_uncertainty_score":0.006511152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03801575006552831,"score_gpt":0.2619042129776043,"score_spread":0.223888462912076,"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."}}