{"id":"W4230776340","doi":"10.1109/.2001.980970","title":"A novel model reduction method for sheet forming processes using wavelet packets","year":2002,"lang":"en","type":"article","venue":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Controllability; Wavelet; Reduction (mathematics); Wavelet packet decomposition; Network packet; Computer science; Actuator; Dimension (graph theory); Scaling; Control theory (sociology); Domain (mathematical analysis); Controller (irrigation); Algorithm; Wavelet transform; Mathematics; Control (management); Artificial intelligence; Applied mathematics","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":[0.0003979524,0.0007566023,0.000665669,0.0003714343,0.0002627677,0.000486182,0.0006898271,0.0005772749,0.001259872],"category_scores_gemma":[0.0007419805,0.0003679609,0.0009024437,0.0004066929,0.0003468547,0.000644523,0.0005524808,0.001178758,0.0004915002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002459576,"about_ca_system_score_gemma":0.0006160673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00124012,"about_ca_topic_score_gemma":0.0009683587,"domain_scores_codex":[0.9997829,0.0000454209,0.00001387981,0.00003821017,0.000104467,0.00001513411],"domain_scores_gemma":[0.9998173,0.0000709339,0.00002253517,0.00002948326,0.00005193225,0.000007824245],"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":[0.0001069472,0.0001066182,0.0003018302,0.0002141918,0.00009621871,0.0001600013,0.000129161,0.479837,0.05778703,0.04166145,0.00276664,0.4168328],"study_design_scores_gemma":[0.000006146723,0.0000287091,0.00004870341,0.000002868547,0.000007305271,0.00002446314,0.00000394377,0.992846,0.00309994,0.002199539,0.001726099,0.000006352525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001298201,0.00003201618,0.9983256,0.00002101686,0.00001775621,0.00001030839,0.000007852445,0.0000745307,0.0002127189],"genre_scores_gemma":[0.1318613,0.0004543614,0.86277,0.00005174434,0.00007374019,0.0002207941,0.0002044687,0.0001327786,0.004230819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001259872,"threshold_uncertainty_score":0.004214644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08378100124528613,"score_gpt":0.3170913461403072,"score_spread":0.2333103448950211,"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."}}