{"id":"W4205816441","doi":"10.1109/tim.2021.3135537","title":"Multirate Sensor Fusion in the Presence of Irregular Measurements and Time-Varying Time Delays Using Synchronized, Neural, Extended Kalman Filters","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Control theory (sociology); Artificial neural network; Extended Kalman filter; State vector; Sensor fusion; Computer science; Soft sensor; Compensation (psychology); Filter (signal processing); State variable; Fusion; Mean squared error; Process (computing); Algorithm; Artificial intelligence; Mathematics; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003978911,0.0001594215,0.0001794745,0.000123438,0.000147686,0.00005280293,0.0000528057,0.00005350904,0.00004865244],"category_scores_gemma":[0.000006904932,0.0001427659,0.00004787737,0.0002019285,0.00003511444,0.0001659707,0.000001265372,0.0001256654,0.00000458032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000129816,"about_ca_system_score_gemma":0.00002390562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006585606,"about_ca_topic_score_gemma":0.00005111129,"domain_scores_codex":[0.9986094,0.0001943894,0.0003366045,0.0002051505,0.0004902205,0.0001642579],"domain_scores_gemma":[0.9996153,0.00002934497,0.00005632637,0.0001551253,0.000089138,0.00005477487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006335436,0.00007602405,0.00003194989,0.00008950147,0.00007326088,0.000004167834,0.0008105609,0.08107027,0.8852623,9.706229e-7,0.00001188003,0.03250572],"study_design_scores_gemma":[0.002588379,0.0000867843,0.0004890182,0.0002240701,0.0000814469,0.00005582554,0.0008983032,0.7489266,0.2463803,0.000005276965,0.00006859371,0.0001952841],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664954,0.0003398494,0.03179199,0.0001015101,0.0004020647,0.0005842821,0.00001328763,0.00006291623,0.0002087294],"genre_scores_gemma":[0.999389,0.00007111958,0.0003822401,0.00006003184,0.00001291784,0.00004062468,0.000002244382,0.00001580191,0.00002600265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6678564,"threshold_uncertainty_score":0.5821826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03489569919199052,"score_gpt":0.2424171462555412,"score_spread":0.2075214470635507,"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."}}