{"id":"W170046012","doi":"10.1007/978-3-540-72990-7_72","title":"Self-tuning Optimal PI Rate Controller for End-to-End Congestion With LQR Approach","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Active queue management; Control theory (sociology); Network congestion; Router; Controller (irrigation); Transient (computer programming); Computer science; Queue; PID controller; Engineering; Computer network; Control engineering; Control (management); Network packet","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002336718,0.000774976,0.0008598737,0.0008922691,0.0004648798,0.0007926319,0.002599428,0.0004145183,0.000009993575],"category_scores_gemma":[0.0000759836,0.0006504746,0.0001724451,0.0007458924,0.0004685075,0.0005770253,0.0004928153,0.0008497764,0.00002200501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000320305,"about_ca_system_score_gemma":0.0006874597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007966951,"about_ca_topic_score_gemma":0.00003251561,"domain_scores_codex":[0.9950196,0.00006641909,0.0006415171,0.002129799,0.0009957084,0.001146937],"domain_scores_gemma":[0.9962705,0.00130122,0.0003788226,0.001014491,0.0006300969,0.0004048871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001036373,0.00004039237,0.000008407771,0.00003110403,0.00004395017,0.00003218877,0.0003939089,0.2932928,0.000018907,0.04249731,0.00002906415,0.6635084],"study_design_scores_gemma":[0.001648275,0.0006151552,0.00003463332,0.0002224598,0.00004089611,0.00009203891,4.891247e-7,0.9856839,0.00006948455,0.003506917,0.007227238,0.0008585059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005834673,0.0002874644,0.9927512,0.0008598833,0.001105309,0.001744202,0.000007949709,0.0004291452,0.002756543],"genre_scores_gemma":[0.1884766,0.0000125525,0.8073033,0.002662105,0.0009129204,0.00009681235,0.00001122857,0.00005865425,0.0004658432],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6923912,"threshold_uncertainty_score":0.9995946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769039706209375,"score_gpt":0.2337911100705686,"score_spread":0.2161007130084749,"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."}}