{"id":"W2006987799","doi":"10.1155/2014/524740","title":"Application of Compressive Sampling in Computer Based Monitoring of Power Systems","year":2014,"lang":"en","type":"article","venue":"Advances in Computer Engineering","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Compressed sensing; Nyquist–Shannon sampling theorem; Nyquist rate; Sampling (signal processing); SIGNAL (programming language); Computer science; Power (physics); Data acquisition; Sampling theory; Electronic engineering; Algorithm; Computer vision; Mathematics; Statistics; Engineering; Physics; Sample size determination","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":[0.0003968846,0.0003087934,0.0003260545,0.0007911824,0.0001758762,0.0006409449,0.0003284446,0.0005815479,0.0009807971],"category_scores_gemma":[0.001663333,0.0001286097,0.0001864534,0.001297016,0.0005043973,0.0006023053,0.0004523111,0.0005322445,0.0002174917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003252739,"about_ca_system_score_gemma":0.0002839871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008328969,"about_ca_topic_score_gemma":0.0006556088,"domain_scores_codex":[0.9996,0.0001308435,0.00002302913,0.00005825189,0.0001735886,0.00001431073],"domain_scores_gemma":[0.9994105,0.0004120654,0.00004752963,0.00003553306,0.0000829838,0.00001134241],"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":[0.00009244755,0.00005576894,0.00209033,0.0008779658,0.00005510444,0.0004411888,0.0002647244,0.215498,0.02333367,0.1115979,0.004121844,0.6415711],"study_design_scores_gemma":[0.00001715106,0.0002618737,0.003161995,0.0003474599,0.00004745637,0.00091388,0.0001870164,0.8032132,0.01907857,0.1088859,0.06382367,0.00006187495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01722471,0.03404934,0.9235537,0.001403138,0.0002349608,0.00007443039,0.0001096939,0.0002692405,0.02308086],"genre_scores_gemma":[0.7026728,0.05873946,0.2325293,0.0004511037,0.001072982,0.00009941636,0.0001581834,0.00003414014,0.004242651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009807971,"threshold_uncertainty_score":0.003281057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007767132484739665,"score_gpt":0.234709401332244,"score_spread":0.2269422688475043,"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."}}