{"id":"W1673058104","doi":"10.1115/ncad2015-5907","title":"Mixed-Mode Signal Detection of Road Vehicle Vibration Using Hilbert-Huang Transform","year":2015,"lang":"en","type":"article","venue":"","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Victoria University","keywords":"Hilbert–Huang transform; Vibration; Acoustics; Transient (computer programming); SIGNAL (programming language); Mode (computer interface); Range (aeronautics); Computer science; Harmonic; Signal processing; Nonlinear system; Engineering; Electronic engineering; Aerospace engineering; Physics; Telecommunications; White noise","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008851948,0.00009351656,0.0001132479,0.00007955297,0.0000288489,0.000009705257,0.00006108865,0.00008988253,0.000007786901],"category_scores_gemma":[0.000006642823,0.00009082177,0.00003062964,0.0001373431,0.00001282979,0.0002463498,0.000006742708,0.000091615,0.000002409283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001945032,"about_ca_system_score_gemma":0.00001887648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009377547,"about_ca_topic_score_gemma":0.0000834884,"domain_scores_codex":[0.9993793,0.00001346661,0.0002156394,0.00008535327,0.0001506086,0.0001556129],"domain_scores_gemma":[0.9997305,0.00001371635,0.00002425778,0.0001016879,0.00005469477,0.00007519421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003129014,0.000007274679,0.0002244559,0.0001227961,0.00001348469,7.52015e-7,0.0005129924,0.02002842,0.606401,0.0001077961,0.00005386193,0.3724959],"study_design_scores_gemma":[0.0001152656,0.00004667604,0.001458625,0.00001654089,0.000005294773,0.00000379935,0.00004449195,0.3397091,0.6579066,0.0005653118,0.00005247826,0.00007587181],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8878782,0.00006019794,0.1103187,0.00001554386,0.00037454,0.000143181,0.000001692199,0.0005131066,0.0006948825],"genre_scores_gemma":[0.98908,0.00000543325,0.01075335,0.00000574341,0.0001145803,0.000007194679,0.000001884922,0.00002162538,0.000010182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.37242,"threshold_uncertainty_score":0.3703604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04075452931056284,"score_gpt":0.2916572410106442,"score_spread":0.2509027117000814,"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."}}