{"id":"W2804739928","doi":"10.1007/s12555-017-0087-1","title":"Inertial Parameter Estimation of an Excavator with Adaptive Updating Rule Using Performance Analysis of Kalman Filter","year":2018,"lang":"en","type":"article","venue":"International Journal of Control Automation and Systems","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Excavator; Control theory (sociology); Inertia; Extended Kalman filter; Kalman filter; Recursive least squares filter; Swing; Filter (signal processing); Computer science; Engineering; Algorithm; Adaptive filter; Artificial intelligence; Computer vision","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":[0.0004604798,0.00009504694,0.0003811126,0.0004053134,0.00002282204,0.00004017421,0.000120465,0.00004599373,0.00002276999],"category_scores_gemma":[0.00004086027,0.00007212161,0.00005988749,0.0001622765,0.00004442715,0.0004474367,0.000005611708,0.00005299607,7.018099e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004469458,"about_ca_system_score_gemma":0.00002673044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005556876,"about_ca_topic_score_gemma":0.000007805087,"domain_scores_codex":[0.998564,0.00007547162,0.0008038903,0.00006312253,0.0004196224,0.00007388151],"domain_scores_gemma":[0.9985226,0.00009762143,0.0006625379,0.00008094411,0.0005901518,0.00004609243],"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":[0.0001489977,0.00002871502,0.00737753,0.00008207733,0.002089275,0.000002495909,0.00173093,0.9746969,0.006122036,0.0001446211,0.0000125134,0.007563892],"study_design_scores_gemma":[0.0007828078,0.0001826588,0.01700455,0.0002796174,0.000190709,0.00006182581,0.0003010698,0.9802474,0.000860285,0.000004966071,0.00001410213,0.00006998088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7850558,0.00004339301,0.2143291,0.000006846541,0.000318734,0.0000768998,0.00001354191,0.00001210761,0.0001435928],"genre_scores_gemma":[0.9975154,0.000002782279,0.002278601,0.000008780801,0.0001722568,0.000002537873,0.0000062661,0.00000903452,0.000004276098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2124597,"threshold_uncertainty_score":0.2941034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339356041464898,"score_gpt":0.2507550661806203,"score_spread":0.2373615057659713,"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."}}