{"id":"W2224373830","doi":"10.1088/0964-1726/25/1/015013","title":"Towards damage detection using blind source separation integrated with time-varying auto-regressive modeling","year":2015,"lang":"en","type":"article","venue":"Smart Materials and Structures","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blind signal separation; Structural health monitoring; Identification (biology); System identification; Vibration; Signal processing; SIGNAL (programming language); Suite; Scale (ratio); Computer science; Engineering; Autoregressive model; Field (mathematics); Control engineering; Data mining; Structural engineering; Electronic engineering; Digital signal processing; Acoustics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005884545,0.0008522194,0.000552633,0.0006402904,0.0001766196,0.0004298935,0.0006469341,0.0009674481,0.0005736502],"category_scores_gemma":[0.001266945,0.0003170641,0.0007141723,0.0004174178,0.0005673544,0.0009235466,0.0005566475,0.0008273078,0.0004722821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002670122,"about_ca_system_score_gemma":0.0003995706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001645806,"about_ca_topic_score_gemma":0.001210625,"domain_scores_codex":[0.9996936,0.00008460355,0.00001487075,0.00005840833,0.0001321696,0.00001629072],"domain_scores_gemma":[0.9995104,0.0002093331,0.00008246758,0.00006252139,0.0001181808,0.00001706396],"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":[0.0001747842,0.0001356129,0.001043447,0.0002110752,0.0001119432,0.0001363973,0.0001454065,0.6179206,0.1197194,0.01307521,0.0009203678,0.2464058],"study_design_scores_gemma":[0.00000354498,0.00002982403,0.0001973909,0.000004464583,0.000008270933,0.00002690453,0.000005178286,0.9913907,0.006592704,0.001244074,0.0004890662,0.00000784655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005616768,0.0001644511,0.9936979,0.00003802266,0.00001289659,0.000008862045,0.000006419488,0.0002160568,0.0002385918],"genre_scores_gemma":[0.2933594,0.0007854127,0.7030723,0.00007329685,0.00004971977,0.00007278543,0.0001131163,0.00006860818,0.002405402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001645806,"threshold_uncertainty_score":0.003272474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03161877720483405,"score_gpt":0.2954875263713747,"score_spread":0.2638687491665406,"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."}}