{"id":"W2006219759","doi":"10.1109/tpwrd.2008.2002660","title":"A New Perspective for the IEEE Standard 1459-2000 Via Stationary Wavelet Transform in the Presence of Nonstationary Power Quality Disturbance","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Fast Fourier transform; Discrete wavelet transform; Stationary wavelet transform; Harmonic wavelet transform; Second-generation wavelet transform; Wavelet transform; Mathematics; Spectral leakage; Wavelet; S transform; Discrete Fourier transform (general); Prime-factor FFT algorithm; Algorithm; Electronic engineering; Computer science; Fourier transform; Engineering; Short-time Fourier transform; Fourier analysis; Artificial intelligence; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.000443407,0.0002378616,0.0002673086,0.0001226241,0.0002809958,0.00001938894,0.0003558672,0.0001059404,0.0001408163],"category_scores_gemma":[0.00001091489,0.0001814904,0.0002319141,0.0003950839,0.0001896661,0.0003758873,8.580705e-7,0.0004303856,0.000010221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002868569,"about_ca_system_score_gemma":0.0002945703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003369513,"about_ca_topic_score_gemma":0.0002537833,"domain_scores_codex":[0.9982957,0.0001113267,0.0005024347,0.0002656429,0.0005125133,0.0003123962],"domain_scores_gemma":[0.9979717,0.001320912,0.00006500442,0.0003957163,0.0001805962,0.00006614052],"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":[0.006231222,0.001801153,0.00007068103,0.0003944752,0.001379423,0.00008181735,0.3733353,0.5453293,0.004556014,0.009900963,0.02988407,0.02703559],"study_design_scores_gemma":[0.0444767,0.00972931,0.08962164,0.001657942,0.001824349,0.0009795477,0.1974411,0.2284416,0.1886351,0.07338072,0.1523593,0.0114527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03796769,0.0008647235,0.9558948,0.001129832,0.0006759627,0.0008902283,0.001172645,0.00008572853,0.001318417],"genre_scores_gemma":[0.9972249,0.0006170932,0.001481514,0.000240938,0.00002203458,0.0001274541,0.000007726164,0.00003061669,0.000247768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9592572,"threshold_uncertainty_score":0.7400962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02757147450737429,"score_gpt":0.2687025835074018,"score_spread":0.2411311090000275,"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."}}