{"id":"W2916182456","doi":"10.1109/access.2019.2894764","title":"Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Honeywell (Canada)","funders":"China Scholarship Council; Ministry of Science and Technology, Taiwan; National Natural Science Foundation of China","keywords":"Autoencoder; Nonlinear system; Computer science; Data modeling; Nonlinear model; Data mining; Artificial intelligence; Algorithm; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002094934,0.0001192508,0.0002432096,0.00008575049,0.00003254365,0.00006060098,0.0004588148,0.00005319592,0.00000659873],"category_scores_gemma":[0.00007355639,0.0001050284,0.00002422215,0.0001520147,0.000002629316,0.0002566461,0.00001800353,0.00005028924,0.00001344482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006594321,"about_ca_system_score_gemma":0.0002897294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001480723,"about_ca_topic_score_gemma":0.00001303856,"domain_scores_codex":[0.9990535,0.00001021518,0.0003729801,0.0001800788,0.0002482019,0.0001350397],"domain_scores_gemma":[0.9992797,0.0001361786,0.00008379538,0.0003672386,0.00008863777,0.00004450113],"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":[0.00001927938,0.00001802808,0.00008546443,0.01180677,0.00005156864,1.160919e-7,0.0001342166,0.9868562,0.0006880303,0.00000650525,0.0001263011,0.0002075658],"study_design_scores_gemma":[0.0005739255,0.00001157327,0.00003310544,0.001474338,0.000017307,3.614169e-7,0.00002677648,0.9940597,0.003542446,0.00001902406,0.0001225548,0.0001189067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02393006,0.00007679005,0.9736434,0.00001888146,0.0009177324,0.0009784992,0.00005151859,0.0001503467,0.0002327835],"genre_scores_gemma":[0.9739984,0.000001098592,0.02536093,0.00001935373,0.0001598774,0.0001553602,0.00002871794,0.00002961798,0.0002466245],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9500684,"threshold_uncertainty_score":0.4282936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05613451510337041,"score_gpt":0.3132009520808868,"score_spread":0.2570664369775164,"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."}}