Dear editor,Primal-dual dynamics(PDD)and its variants are prominent first-order continuous-time algorithms to determine the primal and dual solutions of a constrained optimization problem(COP).Due to the simple struct...Dear editor,Primal-dual dynamics(PDD)and its variants are prominent first-order continuous-time algorithms to determine the primal and dual solutions of a constrained optimization problem(COP).Due to the simple structure,they have received widespread attention in various fields,such as distributed optimization[1],power systems[2],and wireless communication[3].In view of their wide applications,there are numerous theoretic studies on the convergence properties of PDD and its variants,including the exponential stability analysis[4]-[9].展开更多
Delay and throughput are the two network indicators that users most care about.Traditional congestion control methods try to occupy buffer aggressively until packet loss being detected,causing high delay and variation...Delay and throughput are the two network indicators that users most care about.Traditional congestion control methods try to occupy buffer aggressively until packet loss being detected,causing high delay and variation.Using AQM and ECN can highly reduce packet drop rate and delay,however they may also lead to low utilization.Managing queue size of routers properly means a lot to congestion control method.Keeping traffic size varying around bottleneck bandwidth creates some degree of persistent queue in the router,which brings in additional delay into network unwillingly,but a corporation between sender and router can keep it under control.Proper persistent queue not only keeps routers being fully utilized all the time,but also lower the variation of throughput and delay,achieving the balance between delay and utilization.In this paper,we present BCTCP(Buffer Controllable TCP),a congestion control protocol based on explicit feedback from routers.It requires sender,receiver and routers cooperating with each other,in which senders adjust their sending rate according to the multiple bit load factor information from routers.It keeps queue length of bottleneck under control,leading to very good delay and utilization result,making it more applicable to complex network environments.展开更多
The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testi...The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59.The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.展开更多
文摘Dear editor,Primal-dual dynamics(PDD)and its variants are prominent first-order continuous-time algorithms to determine the primal and dual solutions of a constrained optimization problem(COP).Due to the simple structure,they have received widespread attention in various fields,such as distributed optimization[1],power systems[2],and wireless communication[3].In view of their wide applications,there are numerous theoretic studies on the convergence properties of PDD and its variants,including the exponential stability analysis[4]-[9].
基金supported in part by the National Key R&D Program of China(2018YFB1800602)the Ministry of Education-China Mobile Research Fund Project(MCM20180506)the CERNET Innovation Project(NGIICS20190101)and(NGII20170406)。
文摘Delay and throughput are the two network indicators that users most care about.Traditional congestion control methods try to occupy buffer aggressively until packet loss being detected,causing high delay and variation.Using AQM and ECN can highly reduce packet drop rate and delay,however they may also lead to low utilization.Managing queue size of routers properly means a lot to congestion control method.Keeping traffic size varying around bottleneck bandwidth creates some degree of persistent queue in the router,which brings in additional delay into network unwillingly,but a corporation between sender and router can keep it under control.Proper persistent queue not only keeps routers being fully utilized all the time,but also lower the variation of throughput and delay,achieving the balance between delay and utilization.In this paper,we present BCTCP(Buffer Controllable TCP),a congestion control protocol based on explicit feedback from routers.It requires sender,receiver and routers cooperating with each other,in which senders adjust their sending rate according to the multiple bit load factor information from routers.It keeps queue length of bottleneck under control,leading to very good delay and utilization result,making it more applicable to complex network environments.
基金supported by the National Key Research and Development Program of China (Grant No. 2020YFA0714300)the National Natural Science Foundation of China (Grant Nos. 61833005 and 62003084)the Natural Science Foundation of Jiangsu Province of China (Grant No.BK20200355)。
文摘The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59.The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.
