{"id":"W4390861658","doi":"10.1016/j.ymssp.2023.111075","title":"A novel data-driven sensor placement optimization method for unsupervised damage detection using noise-assisted neural networks with attention mechanism","year":2024,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"University of California, San Diego; University of Colorado Boulder; China Scholarship Council; Stanford University","keywords":"Benchmark (surveying); Noise (video); Computer science; Structural health monitoring; Artificial neural network; Process (computing); Modal; Independence (probability theory); Sensitivity (control systems); Artificial intelligence; Pattern recognition (psychology); Engineering; Electronic engineering; Mathematics; Structural engineering","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":[0.0004696124,0.0008323832,0.0008452277,0.0004239757,0.0003222057,0.0004511399,0.001386304,0.0009963831,0.001529632],"category_scores_gemma":[0.001185753,0.0005118276,0.0005778549,0.0004737827,0.0003922061,0.0007814796,0.0009995411,0.0008196887,0.0003443898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000490232,"about_ca_system_score_gemma":0.0008796326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005093,"about_ca_topic_score_gemma":0.007447872,"domain_scores_codex":[0.9997225,0.00004284239,0.00001591426,0.00009385443,0.0000957101,0.00002905825],"domain_scores_gemma":[0.9996536,0.0001400633,0.00003860979,0.00002620936,0.0001230582,0.00001838078],"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":[0.0001535696,0.0001165889,0.0005528702,0.0001224307,0.00009768851,0.0001112915,0.00008390292,0.627553,0.02915132,0.005902056,0.002585885,0.3335694],"study_design_scores_gemma":[0.000002675579,0.00001387686,0.00004826058,0.00000189581,0.000004140182,0.00001030453,0.000001685715,0.998278,0.001074325,0.0003789966,0.0001833356,0.000002547541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003828666,0.000109287,0.9952683,0.00004413091,0.00002431661,0.00001504836,0.00001293822,0.0002391918,0.0004581686],"genre_scores_gemma":[0.4019653,0.0002045974,0.5916815,0.0002267418,0.0001051366,0.0001944376,0.0001639235,0.0001779593,0.005280388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005093,"threshold_uncertainty_score":0.01012671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0533966323432735,"score_gpt":0.3108253451477462,"score_spread":0.2574287128044727,"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."}}