Sensor selection for random field estimation in wireless sensor networks

This paper studies the sensor selection problem for random field estimation in wireless sensor networks.The authors first prove that selecting a set of l sensors that minimize the estimation error under the D-optimal criterion is NP-complete.The authors propose an iterative algorithm to pursue a suboptimal solution.Furthermore,in order to improve the bandwidth and energy efficiency of the wireless sensor networks,the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem.In the case of unknown covariance matrix,the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator.Simulation results show the good performance of the proposed algorithms.     

最优双环网的构造

双环网络已广泛应用于设计和实现计算机通信网、局域网以及各种大规模并行处理系统 .如何设计一个最优的双环网络是人们非常关心的一个问题 .本文给出了一个构造最优双环网络的快速有效的算法 ,从而针对有向双环网的情形解决了文献 [1 ]提出的一个问题.     

Distributed Nonsmooth Convex Optimization over Markovian Switching Random Networks with Two Step-Sizes

This paper investigates the distributed convex optimization problem over a multi-agent system with Markovian switching communication networks. The objective function is the sum of each agent's local nonsmooth objective function, which cannot be known by other agents. The communication network is assumed to switch over a set of weight-balanced directed graphs with a Markovian property. The authors propose a consensus sub-gradient algorithm with two time-scale step-sizes to handle the Markovian switching topologies and the absence of global gradient information. With proper selection of step-sizes, the authors prove the almost sure convergence of all agents' local estimates to the same optimal solution when the union graph of the Markovian network' states is strongly connected and the Markovian chain is irreducible. The convergence rate analysis is also given for specific cases.Simulations are given to demonstrate the results.     

基于改进OMP算法的稀疏目标微波关联成像方法

利用稀疏重构类方法进行雷达微波关联成像时,传统的正交匹配追踪(orthogonal matching pursuit,OMP)算法在每一次迭代过程中均需要求解目标函数的最小二乘解,导致成像算法计算复杂度随矩阵规模和迭代次数增加而急剧攀升.针对此问题,结合频率捷变思想,提出了一种改进OMP算法的稀疏目标微波关联成像方法....     

Inverse Center Location Problem on a Tree

This paper discusses the inverse center location problem restricted on a tree with different costs and bound constraints. The authors first show that the problem can be formulated as a series of combinatorial linear programs, then an O（｜V｜^2 log ｜V｜） time algorithm to solve the problem is presented. For the equal cost case, the authors further give an O（｜V｜） time algorithm.     

变结构离散动态贝叶斯网络及其推理算法

目前的动态贝叶斯网络的研究,是定义在每一个时间片的静态贝叶斯网络结构和参数都一致的基础上,对于过程突变,参数变化等情况就难以适应.为了解决这个问题,提出变结构离散动态贝叶斯网络的概念,并根据概率和动态贝叶斯网络的理论,推导出变结构离散动态贝叶斯网络的推理方法,对算法进行了验证并结合环境变化时的路径选择问题,进行了计算仿真.计算和仿真结果证明了文章提出的变结构离散动态贝叶斯网络的概念和推理算法的正确性.     

不完全信息下交通网络最短路径关键边问题

因各种突发事件（交通事故、自然灾害等）造成道路中断的现象普遍存在，车辆在行驶的过程中并不具有道路中断的完全信息，只有行进到中断处时才获得道路中断的信息。本文就不完全信息（道路中断信息）下的变通网络最短路径关键边问题进行研究，首先定义了不完全信息下最短路径关键边的概念．其次给出了求解不完全信息下最短路径关键边的有效算法厦其时间复杂性分析，然后结合城市道路网络给出了实际算例，比较分析了最短路径关键边、最长绕行路关键边和不完全信息下的最短路径关键边问题，指出了不完全信息下的最短路径关键边问题更具有实际意义。     

An improved algorithm for solving all <Emphasis Type="Italic">d</Emphasis>-MPs in multi-state networks

Reliability is a desirable performance indicator of many real-world systems to measure the quality level. One general method for evaluating multi-state reliability is using d-minimal paths (d-MPs). However, being an NP-hard problem, searching for all d-MPs is a rather challenging task. This paper proposes an improved algorithm to solve the d-MP problem. To reduce the search space of d-MPs, a concept of lower capacity bound is introduced into the d-MP problem, and an effective technique is developed to find lower capacity bounds. Meanwhile, the fast enumeration method which is a recent improvement to the traditional enumeration method is employed to solve d-MPs. In addition, by introducing the operation of transforming undirected edges into directed edges, the proposed algorithm is applicable to solving both directed networks and undirected networks. Through numerical experiments, it is found that the proposed algorithm holds a distinct advantage over the existing methods in solving all d-MPs.     

Community Detection with the Weighted Parsimony Criterion

Community detection in networks has been studied extensively in the last decade. Many criteria, expressing the quality of the partitions obtained, as well as a few exact algorithms and a large number of heuristics have been proposed. The parsimony criterion consists in minimizing the number of edges added or removed from the given network in order to transform it into a set of disjoint cliques.Recently Zhang, Qiu and Zhang have proposed a weighted parsimony model in which a weight coefficient is introduced to balance the numbers of inserted and deleted edges. These authors propose rules to select a good value of the coefficient, use simulated annealing to find optimal or near-optimal solutions and solve a series of real and artificial instances. In the present paper, an algorithm is proposed for solving exactly the weighted parsimony problem for all values of the parameter. This algorithm is based on iteratively solving the problem for a set of given values of the parameter using a row generation algorithm. This procedure is combined with a search procedure to find all lowest breakpoints of the value curve(i.e., the weighted sum of inserted and deleted edges). Computational results on a series of artificial and real world networks from the literature are reported. It appears that several partitions for the same network may be informative and that the set of solutions usually contains at least one intuitively appealing partition.     

基于DCS的发射分集MIMO雷达参数估计

针对目标在多角度观测下的散射系数估计问题,研究了基于分布式压缩感知（distributed compressed sensing, DCS）的发射分集多输入多输出（multiple-input multiple-output, MIMO）雷达参数估计方法。在分析发射分集MIMO雷达信号模型的基础上,构建了其联合稀疏表示模型;在分析正交匹配追踪（orthogonal matching pursuit, OMP）算法实现结构的基础上,提出了一种新的基于迭代式正交匹配追踪的DCS算法。仿真结果表明该方法的估计精度高于DCS SOMP和幅度相位估计+Capon的算法,重构概率也高于DCS-SOMP算法。     

An on-line scheduling problem of parallel machines with common maintenance time

In this paper, the authors consider an on-line scheduling problem of m (m ≥ 3) identical machines with common maintenance time interval and nonresumable availability. For the case that the length of maintenance time interval is larger than the largest processing time of jobs, the authors prove that any on-line algorithm has not a constant competitive ratio. For the case that the length of maintenance time interval is less than or equal to the largest processing time of jobs, the authors prove a lower bound of 3 on the competitive ratio. The authors give an on-line algorithm with competitive ratio $4 - \tfrac{1} {m} $ . In particular, for the case of m = 3, the authors prove the competitive ratio of the on-line algorithm is $\tfrac{{10}} {3} $ .     

不确定情形下通信网络最短路径关键点问题

在通信网络中，因突发事件造成通信路由节点毁坏或者中断的现象时有发生，传输的数据包不得不从中断处沿着最短的替代路径行进到数据包的接收节点，在这种情形下，哪个路由节点中断使得数据包实际行进的总路程最长呢？从通信网络管理的角度来看这是一个非常重要的问题。对该问题．以前的文献都是从确定情形（事先具有节点中断的完全信息）下进行研究的，本文从不确定情形（只有数据包行进到中断节点的邻接点时才获得该节点中断的信息）的角度重新考虑这个问题。本文首先定义了不确定情形下的最短路径关键点概念，给出了计算不确定情形下最短路径关键点的算法及其时间复杂性分析。结合实际通信网络的算例分析，比较了确定情形下最短路径关键点和不确定情形下最短路径关键点问题，指出了不确定情形下最短路径关键点问题更具有实际意义。     

Formalising and Detecting Community Structures in Real World Complex Networks

Community structure is an integral characteristic of real world networks whichever processes or areas they emerge from. This paper addresses the problem of community structure detection theoretically as well as computationally. The authors introduce a number of concepts such as the neighbourhood and strength of a subgraph, p-community, local maximal p-community, hubs, and outliers that play elemental role in formalising the concept of community structure in complex networks. A few preliminary results have been derived that lead to the development of an algorithm for community structure detection in undirected unweighted networks. The algorithm is based on a local seed expansion strategy that uses the concept of interaction coefficient. The authors have analysed the algorithm on a number of parameters such as accuracy, stability, and quality on synthetic and real world networks from different areas.     

基于集合覆盖理论的Agent协作问题研究

多智能Agent系统中的协作体现多Agent系统(MAS)的灵活性、整体性,通过协作提高Agent群体完成任务的效率.将集合覆盖理论(SCP)引入MAS系统协作行为中的任务分配问题求解,使用改进的低logarithmic ratio bound集合覆盖理论求解方法,详细阐述了利用SCP理论求解Agent任务分配问题的算法,并根据一个战场作战Agent任务分配实例进行了计算,有效地解决战场作战Agent的任务分配问题.     

Finding Key Node Sets in Complex Networks Based on Improved Discrete Fireworks Algorithm

Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration. In this paper, the problem of finding key node sets in complex networks is defined firstly. Because it is an NP-hard combinatorial optimization problem,discrete fireworks algorithm is introduced to search the optimal solution, which is a swarm intelligence algorithm and is improved by the prior information of networks. To verify the effect of improved discrete fireworks algorithm(IDFA), experiments are carried out on various model networks and real power grid.Results show that the proposed IDFA is obviously superior to the benchmark algorithms, and networks suffer more damage when the key node sets obtained by IDFA are removed from the networks. The key node sets found by IDFA contain a large number of non-central nodes, which provides the authors a new perspective that the seemingly insignificant nodes may also have an important impact on the robustness of the network.     

Distributed quantized consensus for agents on directed networks

Communication bandwidth and network topology are two important factors that affect performance of distributed consensus in multi-agent systems. The available works about quantized average consensus assume that the adjacency matrices associated with the digraphs are doubly stochastic, which amounts to that the digital networks are balanced. However, this assumption may be unrealistic in practice. In this paper, without assuming double stochasticity, the authors revisit an existing quantized average consensus protocol with the logarithmic quantization scheme, and investigate the quantized consensus problem in general directed digital networks that are strongly connected but not necessarily balanced. The authors first derive an achievable upper bound of the quantization precision parameter to design suitable logarithmic quantizer, and this bound explicitly depends on network topology. Subsequently, by means of the matrix transformation and the Lyapunov techniques, the authors provide a testable condition under which the weighted average consensus can be achieved with the proposed quantized protocol.     

A NEW FAMILY OF TRIVALENT CAYLEY NETWORKS ON WREATH PRODUCT Zm ～ Sn

We propose a new family of interconnection networks （WGn^m） with regular degree three. When the generator set is chosen properly, they are isomorphic to Cayley graphs on the wreath product Zm ～ Sn. In the case of m ≥ 3 and n ≥3, we investigate their different algebraic properties and give a routing algorithm with the diameter upper bounded by [m/2]（3n^2- 8n ＋ 4） - 2n ＋ 1. The connectivity and the optimal fault tolerance of the proposed networks are also derived. In conclusion, we present comparisons of some familiar networks with constant degree 3.     

基于改进正交匹配追踪算法的属性散射中心提取

属性散射中心模型是描述目标后向电磁散射特性的典型模型, 但其中传统的正交匹配追踪(orthogonal matching pursuit, OMP)算法提取模型时具有参数复杂度高、计算时间长等问题。对此提出一种基于稀疏字典的广义正交性的改进OMP算法, 快速定位模型位置参数值, 避免了正交匹配中的寻优过程, 从而降低算法的运算复杂度。通过对两类算法计算复杂度和计算精度进行多次蒙特卡罗实验比较得出,改进OMP算法提高了模型参数的估计精度与噪声鲁棒性, 且大幅降低了算法的运算复杂度, 相比于传统的OMP算法, 运算时间至少降低30%。     

铁路技术站调机运用模型及算法

研究铁路车站作业计划编制过程中,如何编制调机运用计划的关键问题,通过分析运用调机时区集合上的偏序结构特点,可以知道使用调机问题的实质是偏序集合的全序分解问题。利用偏序集合的传递性构造调机的有向图－图,再将调机运用问题转化有向图的有向路分解问题,对于传递图构造它对应的偶图－无向偶图,将传递图的向路分解问题转化为其对应偶图的匹配问题,最后,利用偶图最大匹配问题的算法解决调机运用问题。     

Distributed Algorithm for Robust Resource Allocation with Polyhedral Uncertain Allocation Parameters

This paper studies a distributed robust resource allocation problem with nonsmooth objective functions under polyhedral uncertain allocation parameters. In the considered distributed robust resource allocation problem, the (nonsmooth) objective function is a sum of local convex objective functions assigned to agents in a multi-agent network. Each agent has a private feasible set and decides a local variable, and all the local variables are coupled with a global affine inequality constraint, which is subject to polyhedral uncertain parameters. With the duality theory of convex optimization, the authors derive a robust counterpart of the robust resource allocation problem. Based on the robust counterpart, the authors propose a novel distributed continuous-time algorithm, in which each agent only knows its local objective function, local uncertainty parameter, local constraint set, and its neighbors’ information. Using the stability theory of differential inclusions, the authors show that the algorithm is able to find the optimal solution under some mild conditions. Finally, the authors give an example to illustrate the efficacy of the proposed algorithm.