对偶导集及其应用

借助对偶范畴的思想和方法在点集拓扑学中引入了对偶导集概念,讨论了对偶导集和对偶导集运算的相关性质.并以此为基础引入了对偶连通空间的概念,讨论了对偶连通空间和连通空间之间的关系,借此给出了不连通空间的几个等价刻画.为拓扑空间的研究提供了新的途径.     

交通网络连通性表达法的研究

通过对增设虚拟边网络连通性表达法和对偶图网络连通性表达法的描述和它们所面临问题的分析研究,说明这两种方法在交通网络连通性表达上,尤其是在引进交通转弯限制时所显示出来的需要大量处理工作的问题,提出了一种新的网络连通性表达法,作者称为“边标号法”,此法避免了对交通网络增设虚拟边或进行点边转化所带来的大量工作量问题,在对交通网络图不作任何修改的情况下,清楚而有效地表达出网络的连通特性。并用一个具体实例通过程序实现该方法,体现出了边标号法的优越性     

射影平面上的对偶无环不可分近三角剖分地图

本文研究了球面和射影平面上对偶无环不可分近三角剖分带根地图的以根面次和内面数为参数的计数问题,得到了这类地图在球面和射影平面上的计数函数满足的方程.还得到了射影平面上2连通地图一个参数的显示表达式和渐近估计式.     

具有白噪声的随机格点系统的随机吸引子的Kolmogorov熵

基于耗散的随机格点系统解的渐近行为理论,主要运用元素分解法与有限维空间中多面体球覆盖的拓扑性质,研究了具有白噪声的随机Klein-Gordon-Schrdinger格点动力系统的随机吸引子的Kolmogorov熵,并得到它的一个上界.     

道路网络规划方案产生的方法研究

本文利用线性规划的对偶理论，研究了交通网络通路影子价格，并据此提出了道路网络规划方案产生的方法．     

持续干扰下马尔科夫切换多智能体系统的均方一致性

文章针对带有持续干扰的连续线性多智能体系统,研究了其拓扑结构马尔科夫切换下的均方一致性问题.假设系统在每一时刻的拓扑结构是不连通的,但他们的联合拓扑是连通的.首先设计了一个控制协议,其由两部分组成,一部分是传统的控制协议,另一部分是对干扰的估计.然后,利用矩阵分析理论,随机理论和系统稳定性理论,得到了闭环系统实现一致的充分条件.最后,仿真结果验证了结论的有效性.     

折叠交叉立方体的分支边连通度

r-分支连通度(边连通度)是衡量大型互连网络可靠性和容错性的一个重要参数.设G是连通图且r是非负整数,如果G中存在某种点子集(边子集)使得G删除这种点子集(边子集)后得到的图至少有r个连通分支.则所有这种点子集(边子集)中基数最小的点子集(边子集)的基数称为图G的r-分支连通度(边连通度).n-维折叠交叉立方体FCQn是由交叉立方体CQn增加2n-1条边后所得.该文利用r-分支边连通度作为可靠性的重要度量,对折叠交叉立方体网络的可靠性进行分析,得到了折叠交叉立方体网络的2-分支边连通度,3-分支边连通度,4分支边连通度.确定了折叠交叉立方体FCQn的r-分支边连通度.     

图的超级限制边连通性

在Moor-Shannon网络模型中,边连通度和限制边连通度较大的网络一般有较好的可靠性和容错性.本文证明:除两种平凡情形外,无向Kautz网络的拓扑结构,无向Kautz图UK(2,n)是超级限制边连通的.因此,它们比de Bruijn网络有更好的限制边连通性.     

广义I型连通级小极大分式问题的对偶

在I型弧连通和广义I型弧连通假设下,建立了极大极小分式优化问题的对偶模型,并提出了弱对偶定理、强对偶定理和严格逆对偶定理.     

协方差结构分析中拟似然估计的对偶几何方法

本文应用几何方法研究了协方差结构分析中的拟似然估计.对于该模型引进了对偶几何,在此基础上得到了拟似然估计的二阶渐近性质.通过对偶曲率给出了拟似然估计的偏差、方差和信息损失,并且给出了反映拟观察信息和拟期望信息之间关系的一个极限定理     

Topological properties of the one dimensional exponential random geometric graph

In this article we study the one‐dimensional random geometric (random interval) graph when the location of the nodes are independent and exponentially distributed. We derive exact results and limit theorems for the connectivity and other properties associated with this random graph. We show that the asymptotic properties of a graph with a truncated exponential distribution can be obtained using the exponential random geometric graph. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2008     

Labeling algorithm for the shortest path problem with turn prohibitions with application to large-scale road networks

In real road networks, the presence of no-left, no-right or no U-turn signs, restricts the movement of vehicles at intersections. These turn prohibitions must be considered when calculating the shortest path between a starting and an ending point in a road network. We propose an extension of Dijkstra’s algorithm to solve the shortest path problem with turn prohibitions. The method uses arc labeling and a network structure with low memory requirements. We compare the proposed method with the dual graph approach in a set of randomly generated networks and Bogotá’s large-scale road network. Our computational experiments show that the performance of the proposed method is better than that of the dual graph approach, both in terms of computing time and memory requirements. We co-developed a Web-based decision support system for computing shortest paths with turn prohibitions that uses the proposed method as the core engine.     

Diameter, connectivity, and phase transition of the uniform random intersection graph

We study properties of the uniform random intersection graph model G(n,m,d). We find asymptotic estimates on the diameter of the largest connected component of the graph near the phase transition and connectivity thresholds. Moreover we manage to prove an asymptotically tight bound for the connectivity and phase transition thresholds for all possible ranges of d, which has not been obtained before. The main motivation of our research is the usage of the random intersection graph model in the studies of wireless sensor networks.     

Hybrid port–Hamiltonian systems: From parameterized incidence matrices to hybrid automata

This paper shows how to formally design a hybrid automaton model for a wide class of dissipative physical systems with sources and switching topology. This method is based on a mathematical representation of the dynamic network graph and of its dual graph, using the hybrid incidence matrix, and on a constructive method for analyzing admissible and constrained configurations. The port–Hamiltonian representation associated with the set of hybrid system configurations, parameterized by the discrete state of the switches, is synthesized to be part of the hybrid automaton of the system. This is a further step towards a generic control synthesis for physical switching systems.     

Frequency transitions in synchronized neural networks

Temporal organization of events can emerge in complex systems, like neural networks. Here, random graph and cellular automaton are used to represent coupled neural structures, in order to investigate the occurrence of synchronization. The connectivity pattern of this toy model of neural system is of Newman–Watts type, formed from a regular lattice with additional random connections. Two networks with this coupling topology are connected by extra random links and an impulse stimulus is either constantly or periodically applied to a unique neuron. Numerical simulations reveal that this model can exhibit a variety of dynamic behaviors. Usually, the whole system achieves synchronization; however, the oscillation frequencies of the stimulus and of each network can be different. The dynamics is evaluated in function of the network size, the amount of the randomly added edges and the number of time steps in which a neuron can remain firing. The biological relevance of these results is discussed.     

基于不确定理论的原油海运网络连通可靠性研究

原油海运网络是原油进口国的海上生命线,为科学衡量网络中节点受到突发事件影响后的原油海运网络的连通可靠性,本文采用不确定变量来描述突发事件发生后各节点的连通性,引入不确定理论对原油海运网络连通可靠性进行评估,并建立了不确定原油海运网络的最可靠路径选择模型。本文不确定变量的引入不再依赖较多的历史数据去描绘节点失效的概率分布,而且提出的最可靠路径选择模型可以确保突发事件发生后原油的及时运输。本文还提出了α-最可靠路径和最大测度最可靠路径选择问题,给出不确定原油海运网络最可靠路径风险值的不确定分布,为突发事件发生后决策者的路径选择提供依据。本文以中国进口原油海运网络为例作案例分析。     

Role Analysis in Networks Using Mixtures of Exponential Random Graph Models

This article introduces a novel and flexible framework for investigating the roles of actors within a network. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of exponential-family random graph models (ERGM) is developed for these ego-networks to cluster the nodes into roles. We refer to this model as the ego-ERGM. An expectation-maximization algorithm is developed to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudo-likelihood approximation. We demonstrate the flexibility and utility of the method using examples of simulated and real networks.     

Intermodal shipments as recourse in logistics disruptions

This research deals with the flexibility of transportation systems when faced with disruptions. A network optimization model is used to investigate the feasibility of using intermodal shipments as recourse to disruptions in a transportation network. In a study of US interstate highways and intermodal rail networks, performance of over-the-road and intermodal shipments is compared under different disruption scenarios. The results show that the topology of US transportation system and locations of existing intermodal terminals provide required path redundancies and a strategic benefit for intermodal shipments to bypass disrupted regions with lower costs and competitive shipment times compared to rerouted road shipments.     

Asymptotic connectivity of infinite graphs

We introduce the notion of the asymptotic connectivity of a graph by generalizing to infinite graphs average connectivity as defined by Beineke, Oellermann, and Pippert. Combinatorial and geometric properties of asymptotic connectivity are then explored. In particular, we compute the asymptotic connectivity of a number of planar graphs in order to determine the extent to which this measure correlates with the large-scale geometry of the graph.     

Novel method of identifying time series based on network graphs

In this article, we propose a novel method for transforming a time series into a complex network graph. The proposed algorithm is based on the spatial distribution of a time series. The characteristics of geometric parameters of a network represent the dynamic characteristics of a time series. Our algorithm transforms, respectively, a constant series into a fully connected graph, periodic time series into a regular graph, linear divergent time series into a tree, and chaotic time series into an approximately power law distribution network graph. We find that when the dimension of reconstructed phase space increases, the corresponding graph for a random time series quickly turns into a completely unconnected graph, while that for a chaotic time series maintains a certain level of connectivity. The characteristics of the generated network, including the total edges, the degree distribution, and the clustering coefficient, reflect the characteristics of the time series, including diverging speed, level of certainty, and level of randomness. This observation allows a chaotic time series to be easily identified from a random time series. The method may be useful for analysis of complex nonlinear systems such as chaos and random systems, by perceiving the differences in the outcomes of the systems—the time series—in the identification of the systemic levels of certainty or randomness. © 2011 Wiley Periodicals, Inc. Complexity, 2011