{"id":"W2188495923","doi":"10.1016/j.ifacol.2015.09.580","title":"Multiple oscillations detection in control loops by using the DFT and Raleigh distribution ★ ★This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada; the National Natural Science Foundation of China [61174161, 61304141, 61375077]; the specialized Research Fund for the Doctoral Program of Higher Education of China [20130121130004]; and the Fundamental Research Funds for the Central Universities in China [201212G005].","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Control Systems and Identification","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Work (physics); Distribution (mathematics); Engineering research; Computer science; SIGNAL (programming language); Oscillation (cell signaling); Fast Fourier transform; Engineering; Algorithm; Telecommunications; Mathematics; Mathematical analysis; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01308766,0.000146067,0.0002164695,0.0001400468,0.001401193,0.0002185615,0.0005044975,0.00006077544,0.000005426124],"category_scores_gemma":[0.001231526,0.00006611407,0.00004954835,0.00144718,0.002639704,0.0003504972,0.00009535821,0.0004453579,3.523931e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013763,"about_ca_system_score_gemma":0.001455736,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07276137,"about_ca_topic_score_gemma":0.05059777,"domain_scores_codex":[0.9966251,0.0004682853,0.000447716,0.0002462162,0.001744983,0.0004676624],"domain_scores_gemma":[0.9953564,0.002927556,0.0001743323,0.0002384612,0.001257952,0.00004526012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01295199,0.00126312,0.05434297,0.001601068,0.00130685,8.161505e-7,0.1174928,0.07678207,0.6092496,0.05079068,0.009713962,0.06450411],"study_design_scores_gemma":[0.002843086,0.0001363708,0.3177894,0.0001097755,0.00004736292,0.000005580168,0.01389686,0.659879,0.001063818,0.0002119436,0.003885307,0.0001314227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859659,0.004697077,0.0001508754,0.005414587,0.0004493206,0.003047101,0.0002191171,0.000007944832,0.0000480928],"genre_scores_gemma":[0.9992465,0.00009665608,0.00008985921,0.000006372014,0.0001465592,0.0001595483,0.00003476532,0.00001125875,0.0002084263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6081858,"threshold_uncertainty_score":0.9998989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08353592221469229,"score_gpt":0.343687302684646,"score_spread":0.2601513804699537,"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."}}