无标度复杂网络负载传输优化策略

提出了一种能够显著提高无标度复杂网络负载传输性能的优化路由策略.实现了负载在核心节点与边缘节点间的合理分配.分析表明该策略使得网络的负载处理能力正比于网络规模的平方,而与单个节点的度值无关.实验结果显示优化路由策略在保持了最短路由策略小世界效应的同时,成倍地提升了网络的负载传输能力,且随着网络平均节点度的增加其优势越趋显著.此外,与有效路由策略的比较进一步验证了优化路由策略的优异性能. 关键词： 优化路由策略 复杂网络 负载传输 网络阻塞     

基于改进单高斯模型法的交通背景提取

在分析交通背景提取的特点和单高斯模型法的不足的基础上,提出了一种改进的单高斯模型法.该算法利用均值法初始化背景模型,引入判断值进行背景更新,运用邻域判别法实现干扰点抑制.不同天气条件,不同交通场景下的实验表明：与单高斯模型法相比,改进算法在提取背景时可以随车辆的走停及时更新,并且抑制了非静止背景物体的干扰.     

Motility States of Molecular Motors Engaged in a Stochastic Tug-of-War

Intracellular transport is mediated by molecular motors that pull cargos along cytoskeletal filaments. Many cargos move bidirectionally and are transported by two teams of motors which move into opposite directions along the filament. We have recently introduced a stochastic tug-of-war model for this situation. This model describes the motion of the cargo as a Markov process on a two-dimensional state space defined by the numbers of active plus and active minus motors. In spite of its simplicity, this tug-of-war model leads to a complex dependence of the cargo motility on the motor parameters. We present new numerical results for the dependence on the number of involved motors. In addition, we derive a simple and intuitive sharp maxima approximation, from which one obtains the cargo motility state from only four simple inequalities. This approach provides a fast and reliable method to determine the cargo motility.     

Delay times in a cellular traffic flow model for road sections with periodic outflow

A simple cellular automaton to model traffic flow is used to analyze the impact of traffic lights on travel times. The model is investigated on a stretch of road with open boundaries, where the inflow is driven stochastically, but the outflow is controlled by a traffic light with periodically changing outflow rates. Especially in the transition regime from free flow to oversaturated flow, the detailed space-time structure can be used to derive analytical lower and upper boundaries for the travel time function. The analytical results have been verified by a simulation study. The function displays a strong non-trivial dependency on the length of the system, which is at odds with a well-known formula derived almost 50 years ago by Webster. Such a model is interesting both from a theoretical point of view and for applications.     

Heavy-traffic analysis of the sojourn time in a three node Jackson network with overtaking

We consider a Jackson network consisting of three first-in-first-out (FIFO)M/M/1 queues. When customers leave the first queue they can be routed to either the second or third queue. Thus, a customer that traverses the network by going from the first to the second to the third queue, can be overtaken by another customer that is routed from the first queue directly to the third. We study the distribution of the sojourn time of a customer through the three node network, in the heavy traffic limit. A three term heavy traffic asymptotic approximation to the sojourn time density is derived. The leading term shows that the nodes decouple in the heavy traffic limit. The next two terms, however, do show the dependence of the sojourn times at the individual nodes and give quantitative measures of the effects of overtaking.     

停车场规划的分片凸规划-建模、算法及应用

本研究停车场选址问题。本抓住主要矛盾，建立实用停车场选址的分片凸规划模型。设计往复调整、下降迭代相结合的算法。并在常州市交通管理规划应用中取得了良好的效果。     

An empirical extension of the M/G/1 heavy traffic approximation

Numerical evaluation of waiting time distributions for M/G/1 systems is somewhat difficult. This paper examines a simple variation of the heavy traffic formula which may be useful at modest levels of traffic intensity. One can justify the heavy traffic approximation by expressing the Laplace transform of the service time distribution as a Maclaurin series and then truncating to three terms. The spectrum factorization and inversion leads in a straightforward fashion to the heavy traffic approximation. If one carries two additional terms from the Maclaurin series, the characteristic equation is a cubic with exactly one real negative root. This root provides an easy way to extend the heavy traffic formula to cases where the traffic is not so heavy. This paper studies the quality of this approximation and includes some numerical evaluation based on data actually encountered.     

The Finite Element Method for Computing the Stationary Distribution of an SRBM in a Hypercube with Applications to Finite Buffer Queueing Networks

This paper proposes an algorithm, referred to as BNAfm (Brownian network analyzer with finite element method), for computing the stationary distribution of a semimartingale reflecting Brownian motion (SRBM) in a hypercube. The SRBM serves as an approximate model of queueing networks with finite buffers. Our BNAfm algorithm is based on the finite element method and an extension of a generic algorithm developed by Dai and Harrison [14]. It uses piecewise polynomials to form an approximate subspace of an infinite-dimensional functional space. The BNAfm algorithm is shown to produce good estimates for stationary probabilities, in addition to stationary moments. This is in contrast to the BNAsm algorithm (Brownian network analyzer with spectral method) of Dai and Harrison [14], which uses global polynomials to form the approximate subspace and which sometimes fails to produce meaningful estimates of these stationary probabilities. Extensive computational experiences from our implementation are reported, which may be useful for future numerical research on SRBMs. A three-station tandem network with finite buffers is presented to illustrate the effectiveness of the Brownian approximation model and our BNAfm algorithm.     

On–off fluid models in heavy traffic environment

We consider fluid models with infinite buffer size. Let {Z _N (t)} be the net input rate to the buffer, where {{Z _N (t)} is a superposition of N homogeneous alternating on–off flows. Under heavy traffic environment {{Z _N (t)} converges in distribution to a centred Gaussian process with covariance function of a single flow. The aim of this paper is to prove the convergence of the stationary buffer content process {X _N ^* (t)} in the fNth model to the buffer content process {X _N (t)} in the limiting Gaussian model. This revised version was published online in June 2006 with corrections to the Cover Date.