{"id":"W4396571564","doi":"10.1088/1361-6501/ad4666","title":"Optimizing dynamic measurement accuracy for machine tools and industrial robots with unscented Kalman filter and particle swarm optimization methods","year":2024,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Particle swarm optimization; Computer science; Extended Kalman filter; Robot; Control theory (sociology); Particle filter; Control engineering; Artificial intelligence; Algorithm; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00316102,0.0002325831,0.0002491818,0.0004374276,0.0002499508,0.0001503647,0.0001549828,0.0001453358,0.000002967222],"category_scores_gemma":[0.0007265063,0.0001742615,0.00001684309,0.00080371,0.0003978139,0.0005516119,0.00007286113,0.0002371339,2.758127e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002927715,"about_ca_system_score_gemma":0.00009918769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002837954,"about_ca_topic_score_gemma":0.00002960292,"domain_scores_codex":[0.9982022,0.00002848469,0.0002494291,0.0004939027,0.0005800108,0.0004459929],"domain_scores_gemma":[0.9991542,0.00005031042,0.00004262797,0.0002025,0.0004530541,0.0000973128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007099179,0.00004178787,0.0007326303,0.0001622173,0.0001176205,0.000005413071,0.0001631571,0.005912809,0.5044137,0.001304913,0.00008455312,0.4869902],"study_design_scores_gemma":[0.002051397,0.0008105673,0.0002756444,0.0004490541,0.0002288319,0.00004648775,0.0002694309,0.4908389,0.4971803,0.001444396,0.005769664,0.0006353264],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02805416,0.00734422,0.9599826,0.002043407,0.0002845803,0.001116526,0.000007496925,0.001065674,0.0001012983],"genre_scores_gemma":[0.8538634,0.0004931079,0.1453473,0.00003575281,0.00002009685,0.0002036342,0.000002452549,0.00002787699,0.00000641596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8258092,"threshold_uncertainty_score":0.710618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0744369668561728,"score_gpt":0.310790110601303,"score_spread":0.2363531437451302,"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."}}