{"id":"W2054950229","doi":"10.1109/tnsm.2013.043013.120264","title":"Using Fuzzy Logic Control to Provide Intelligent Traffic Management Service for High-Speed Networks","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Network traffic control; Traffic policing; Computer network; Active queue management; Quality of service; Traffic shaping; Traffic generation model; Router; Bottleneck; Network congestion; Queueing theory; Network packet; Fuzzy logic; Network delay; Packet loss; Queue; Distributed computing; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005807486,0.0006303901,0.000397265,0.0006318737,0.0006583127,0.0009511255,0.001053478,0.0007491672,0.001215475],"category_scores_gemma":[0.001137822,0.0001465527,0.0005081299,0.0005352877,0.0005387715,0.0007255061,0.0004062459,0.0008403671,0.000241901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007612765,"about_ca_system_score_gemma":0.0007420276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004194292,"about_ca_topic_score_gemma":0.003593529,"domain_scores_codex":[0.9995691,0.00006561862,0.0000309967,0.00008539808,0.0002038543,0.00004504572],"domain_scores_gemma":[0.9996062,0.0001272235,0.00006766408,0.00003114529,0.0001443672,0.00002334293],"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":[0.0003638101,0.0004901388,0.001747725,0.000483227,0.0001561886,0.0006650555,0.0003316141,0.5339196,0.08750418,0.05632614,0.004833609,0.3131788],"study_design_scores_gemma":[0.00004508766,0.0001199293,0.0002950967,0.00001830265,0.00003802824,0.00007640037,0.00001500581,0.9827477,0.00599803,0.008149159,0.002475924,0.00002125134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0181824,0.0003299367,0.976006,0.0001527778,0.000147364,0.00007707511,0.00003437184,0.0006549105,0.004415134],"genre_scores_gemma":[0.8388476,0.0004809152,0.157443,0.0002193612,0.0001711164,0.000162136,0.00009648696,0.00002842962,0.002550885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004194292,"threshold_uncertainty_score":0.008339763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02080185166822854,"score_gpt":0.2326662117817498,"score_spread":0.2118643601135213,"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."}}