{"id":"W4229445315","doi":"10.1109/iceeict53079.2022.9768401","title":"Power Quality Disturbance Detection, Classification and Correction","year":2022,"lang":"en","type":"article","venue":"2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Noise (video); Computer science; Energy (signal processing); Morlet wavelet; SIGNAL (programming language); Signal transfer function; Wavelet; Control theory (sociology); Filter (signal processing); Noise reduction; Gaussian noise; Artificial intelligence; Wavelet transform; Pattern recognition (psychology); Mathematics; Telecommunications; Computer vision; Discrete wavelet transform; Analog signal; Statistics; Transmission (telecommunications); Control (management)","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":[0.0006560999,0.0006319637,0.0008276874,0.001753058,0.0003822507,0.001019847,0.0006743968,0.0006065224,0.003402359],"category_scores_gemma":[0.001811133,0.0002030495,0.0003927873,0.001119304,0.0002735975,0.0005461718,0.0004225484,0.0006155904,0.002140376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004414492,"about_ca_system_score_gemma":0.0005781661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006127022,"about_ca_topic_score_gemma":0.003953655,"domain_scores_codex":[0.9992711,0.00003813121,0.00005738023,0.0001693281,0.00037138,0.00009263717],"domain_scores_gemma":[0.9990457,0.000101418,0.0001114688,0.0001111658,0.0005966242,0.00003364409],"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":[0.0002659362,0.0001576057,0.009034044,0.0001967145,0.00002128122,0.000212929,0.0001063532,0.0126045,0.0332205,0.0007138659,0.005790547,0.9376758],"study_design_scores_gemma":[0.00005206147,0.0003173693,0.08590828,0.00008647251,0.00006808498,0.000618584,0.0002305461,0.7931148,0.09647978,0.001951978,0.02109289,0.00007913014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.108202,0.000544719,0.8694752,0.0003027516,0.0002960016,0.0008433962,0.0009159293,0.01034974,0.009070157],"genre_scores_gemma":[0.7570453,0.0007968505,0.2192276,0.0001751811,0.0001389699,0.0004300076,0.001836648,0.0002880384,0.02006155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006127022,"threshold_uncertainty_score":0.01218271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02368118159493341,"score_gpt":0.2543233004035992,"score_spread":0.2306421188086658,"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."}}