{"id":"W3110086586","doi":"10.3390/en13236242","title":"Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer","year":2020,"lang":"en","type":"article","venue":"Energies","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Voltage; Control theory (sociology); Computer science; Robustness (evolution); Voltage sag; Power quality; Voltage regulation; Voltage controller; Disturbance voltage; Compensation (psychology); Fuzzy logic; Artificial neural network; Controller (irrigation); Voltage compensation; Wavelet; Voltage optimisation; Voltage regulator; Engineering; Artificial intelligence; Electrical engineering; Control (management); Chemistry; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001157995,0.0001872113,0.0001942907,0.0000423378,0.00009367114,0.00005582938,0.0001435888,0.00009359369,0.00005179194],"category_scores_gemma":[0.00003037098,0.0002020175,0.00008259196,0.0002262764,0.00003772014,0.000164216,0.00003768169,0.0002130195,0.00001855155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009538832,"about_ca_system_score_gemma":0.00002418835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001569728,"about_ca_topic_score_gemma":0.00002688297,"domain_scores_codex":[0.9990005,0.00003445377,0.000256557,0.0002021258,0.0001883948,0.0003179232],"domain_scores_gemma":[0.9995728,0.00004844431,0.00004602262,0.0002084779,0.00002536074,0.00009895384],"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":[0.00002266087,0.000006724251,0.00007891875,0.00007126355,0.00001710726,0.000008494535,0.0003343933,0.9813252,0.01103023,0.0009435332,0.003108032,0.003053423],"study_design_scores_gemma":[0.0002438419,0.00003124699,0.00182589,0.00003614893,0.00002111313,0.000001063131,0.00003490749,0.9763837,0.001624756,0.0003423174,0.01919822,0.000256719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650498,0.001733105,0.03014489,0.0003340101,0.001170774,0.0001222492,0.00002774736,0.0007590799,0.0006583179],"genre_scores_gemma":[0.9975239,0.00003777596,0.001645884,0.0002926696,0.0003188984,0.000005552064,0.00007296045,0.00004645019,0.00005590732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03247408,"threshold_uncertainty_score":0.8238033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03506834005856221,"score_gpt":0.248458877079396,"score_spread":0.2133905370208338,"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."}}