{"id":"W3025978319","doi":"10.1109/tmech.2020.2993573","title":"Distributed Optimization of Visual Sensor Networks for Coverage of a Large-Scale 3-D Scene","year":2020,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Tianjin City; Science Fund for Distinguished Young Scholars of Tianjin; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Polygon mesh; Scalability; Polygon (computer graphics); Software deployment; Partition (number theory); Greedy algorithm; Scale (ratio); Computer vision; Artificial intelligence; Algorithm; Computer graphics (images); Mathematics; Computer network","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":[0.00008407433,0.0001680198,0.0002870225,0.00006905451,0.00006736872,0.00001267159,0.00008332921,0.0001564735,0.0000436129],"category_scores_gemma":[0.000007989447,0.0001883575,0.0001579152,0.0003420997,0.00001909346,0.00007758208,0.000001206631,0.0001539691,0.000001625725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000616935,"about_ca_system_score_gemma":0.00002919315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002555621,"about_ca_topic_score_gemma":0.000006192998,"domain_scores_codex":[0.99899,0.00002674077,0.000397674,0.0001776469,0.0001661703,0.0002417912],"domain_scores_gemma":[0.9994813,0.00006913568,0.0000799565,0.0001498804,0.0001271292,0.00009258225],"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":[0.0001006018,0.0001213579,0.000003917698,0.0001459388,0.00006735591,3.357115e-7,0.00009813177,0.9961307,0.002259156,0.0001419232,0.00005553062,0.0008750604],"study_design_scores_gemma":[0.0010198,0.000283868,0.000002892991,0.00003127357,0.00008690082,5.467012e-7,0.00006747904,0.9684137,0.02966688,0.00001404034,0.0002571749,0.0001554775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006533802,0.00005881118,0.9918964,0.0001034729,0.0002677069,0.0003705785,0.0006186902,0.0001325089,0.00001804325],"genre_scores_gemma":[0.9755511,0.0002904714,0.02372311,0.00005291198,0.00005008183,0.00001788776,0.0002489614,0.00005643193,0.000009033135],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9690173,"threshold_uncertainty_score":0.7680994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009531359772128342,"score_gpt":0.2236413869490546,"score_spread":0.2141100271769262,"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."}}