{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005618287,0.0002194316,0.0002021933,0.0004054689,0.001628316,0.000160255,0.0006692208,0.0001528315,0.0001469624],"category_scores_gemma":[0.000232413,0.000252254,0.00004365333,0.0005200748,0.000136563,0.0007203246,0.0004238089,0.001155021,0.00001561952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007322502,"about_ca_system_score_gemma":0.00006465057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003002984,"about_ca_topic_score_gemma":0.00005498236,"domain_scores_codex":[0.9983384,0.000105416,0.0006116176,0.0002224885,0.0004309251,0.0002911427],"domain_scores_gemma":[0.998803,0.0001563009,0.0002772275,0.000516406,0.0002015371,0.00004551246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000361308,0.000301853,0.001177514,0.00006928335,0.0002517621,5.666718e-7,0.004273025,0.002084623,0.002983817,0.7167574,0.00608739,0.2656514],"study_design_scores_gemma":[0.0008166519,0.0005215352,0.005966175,0.00002185432,0.0000186416,0.00003670122,0.004711234,0.508881,0.002006777,0.01648474,0.4599127,0.0006219447],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8016775,0.0146197,0.04630005,0.04241735,0.003083422,0.002786712,0.0003346404,0.01006568,0.07871499],"genre_scores_gemma":[0.9843909,0.0144879,0.0001049749,0.0002877609,0.000007578803,0.0003506996,0.0002098289,0.00001434252,0.0001460439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7002727,"threshold_uncertainty_score":0.999993,"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."}}