{"id":"W2955584250","doi":"10.1002/cjce.23576","title":"Tensor sequence component analysis for fault detection in dynamic process","year":2019,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Dimensionality reduction; Dimension (graph theory); Curse of dimensionality; Tensor (intrinsic definition); Process (computing); Sequence (biology); Computer science; Principal component analysis; Feature extraction; Fault detection and isolation; Fault (geology); Algorithm; Reduction (mathematics); Pattern recognition (psychology); Intrinsic dimension; Data mining; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0001943086,0.00009908457,0.00022897,0.0003014847,0.00001883823,0.00003341552,0.0001652915,0.00006729928,0.000009433388],"category_scores_gemma":[0.00004854508,0.00008123085,0.0001235154,0.0004133911,0.00001040813,0.00007132583,0.000001757558,0.0002505473,0.000003353486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003801431,"about_ca_system_score_gemma":0.00004981267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003796971,"about_ca_topic_score_gemma":0.001816491,"domain_scores_codex":[0.9993193,0.000006400172,0.0002844963,0.00006570519,0.0001033428,0.0002208254],"domain_scores_gemma":[0.9995923,0.00005084967,0.00004541064,0.0000956623,0.00006240623,0.0001533787],"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":[0.000006262134,0.000001067117,0.0001444025,0.00004567243,0.00009961225,0.000002995224,0.0001056351,0.8051684,0.1939427,0.000006250363,0.000001664605,0.0004752732],"study_design_scores_gemma":[0.0003131757,0.00001404412,0.0003505317,0.00003816011,0.00004698532,0.00004295398,0.00003187315,0.9724226,0.02634643,0.00001544197,0.0002755931,0.0001021948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934137,0.0001643045,0.00581504,0.00007263956,0.0003306853,0.0001461092,0.000005616325,0.00002302018,0.00002882728],"genre_scores_gemma":[0.999849,9.055875e-7,0.00004730267,0.00001295474,0.00004924343,0.0000109411,0.000001198006,0.00001754869,0.00001093545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1675963,"threshold_uncertainty_score":0.3312498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006227484419756176,"score_gpt":0.20238814502787,"score_spread":0.1961606606081139,"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."}}