Unified Model for Generation Complex Networks with Utility Preferential Attachment

In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics of this new network are given.     

一种基于最大流的网络结构熵

熵是可用来反映网络结构异质性的指标.针对传统熵指标不能很好反映网络全局异构性的不足,本文引入网络流的概念,综合考虑径向测度和中间测度,提出一种新的网络结构熵.特殊网络(如公用数据集Dolphins网络)的分析结果表明,本文提出的熵指标在一定程度上克服了其他网络熵指标的不足,更能够反映网络的真实拓扑结构;对随机网络、最近邻耦合网络、星型网络、无标度网络、Benchmark网络和小世界网络等典型网络的理论分析和仿真实验,进一步证明本文提出的熵指标在刻画一般复杂网络结构特征上的有效性和适用性.     

Analysis of robustness of urban bus network

In this paper,the invulnerability and cascade failures are discussed for the urban bus network.Firstly,three static models(bus stop network,bus transfer network,and bus line network) are used to analyse the structure and invulnerability of urban bus network in order to understand the features of bus network comprehensively.Secondly,a new way is proposed to study the invulnerability of urban bus network by modelling two layered networks,i.e.,the bus stop-line network and the bus line-transfer network and then the interactions between different models are analysed.Finally,by modelling a new layered network which can reflect the dynamic passenger flows,the cascade failures are discussed.Then a new load redistribution method is proposed to study the robustness of dynamic traffic.In this paper,the bus network of Shenyang City which is one of the biggest cities in China,is taken as a simulation example.In addition,some suggestions are given to improve the urban bus network and provide emergency strategies when traffic congestion occurs according to the numerical simulation results.     

一种新型电力网络局域世界演化模型

现实世界中的许多系统都可以用复杂网络来描述,电力系统是人类创造的最为复杂的网络系统之一.当前经典的网络模型与实际电力网络存在较大差异.从电力网络本身的演化机理入手,提出并研究了一种可以模拟电力网络演化规律的新型局域世界网络演化模型.理论分析表明该模型的度分布具有幂尾特性,且幂律指数在3—∞之间可调.最后通过对中国北方电网和美国西部电网的仿真以及和无标度网络、随机网络的对比,验证了该模型可以很好地反映电力网络的演化规律,并且进一步证实了电力网络既不是无标度网络,也不是完全的随机网络. 关键词： 电力网络 演化模型 局域世界 幂律分布     

Evolving the Optical Transport Network Management to Integrate Multiple Technologies and Services

Abstract

Optical networks are evolving at a fast pace from traditional synchronous digital hierarchy/synchronous optical network (SDH/SONET) and wavelength division multiplexing (WDM) infrastructures, used by client network layers in overlay mode, to a converged multi-service and multi-technology network able to transport traditional time division multiplexing (TDM) traffic and new packet traffic in a flexible way. Alcatel-Lucent is leading the network transformation required by network providers to offer data transport while guaranteeing the same quality and reliability typical of classical transport services. The introduction of new data communication services requires an evolution of the network management platform that needs to integrate new management applications associated with the new technologies and services. The resulting network has to be integrated from service provisioning and management system viewpoints to optimize its use and to reduce the in-field modifications of the transport network. This article describes specificities in the management of multi-service networks, identifying the management architecture able to support the rapid evolution of such environment.     

光学实验室网络教学平台的设计

传统的多媒体课件正在受到网络使用环境挑战，取而代之将是基于网而设计的多媒体教学平台。利用FIASH、Cult3D等技术开发的基于网络环境的虚拟实验室案的提出将为大学物理实验开辟一个崭新的多媒体教学方案。     

一种基于点和边差异性的网络结构熵

熵是反映网络异构性的重要指标. 由于只是关注网络结构中"点"或"边"的单一作用,基于度分布和度相对值的两种传统熵在刻画网络结构特征时均存在缺陷. 文章综合考虑"点"和"边"差异性,定义一种新的网络结构熵,并对规则网络、随机网络和无标度网络等结构熵进行理论分析和仿真实验. 结果表明,这种新网络结构熵可以更有效地反映网络的结构特征,尤其是对于稀疏网络及星型网络的结构差异解释更为合理. 关键词： 均匀网络 无标度网络 熵     

一类高聚类系数的加权无标度网络及其同步能力分析

针对真实世界中大规模网络都具有明显聚类效应的特点, 提出一类具有高聚类系数的加权无标度网络演化模型, 该模型同时考虑了优先连接、三角结构、随机连接和社团结构等四种演化机制. 在模型演化规则中, 以概率p增加单个节点, 以概率1–p增加一个社团. 与以往研究的不同在于新边的建立, 以概率φ在旧节点之间进行三角连接, 以概率1–φ进行随机连接. 仿真分析表明, 所提出的网络度、强度和权值分布都是服从幂律分布的形式, 且具有高聚类系数的特性, 聚类系数的提高与社团结构和随机连接机制有直接的关系. 最后通过数值仿真分析了网络演化机制对同步动态特性的影响, 数值仿真结果表明, 网络的平均聚类系数越小, 网络的同步能力越强. 关键词： 无标度网络 加权网络 聚类系数 同步能力     

Performance of a distributed WR-OBS control architecture

This paper proposes a new distributed wavelength-routed optical burst switching (WR-OBS) network architecture and two corresponding control protocols. By taking advantage of merits from both just enough time (JET) protocol and two-way signaling method, this new control architecture outperforms traditional JET OBS network in points of burst loss probability, system throughput and centralized WROBS network in network scalability confirmed by computer simulations. Further simulation is developed to compare the performance of the two control protocols, which leads to instructive discussion in real WR-OBS network design.     

Prediction based chaos control via a new neural network

In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network.     

一种具有正弦基函数权值的反馈型神经网络模型

提出了一种新的两层反馈型神经网络模型. 该网络采用正弦基函数作为权值, 神经元激活函数为线性函数, 连接形式为两层反馈型结构. 研究并定义了该反馈型神经网络的能量函数, 分析了网络运行的稳定性问题, 并证明了在Liapunov意义下网络运行的稳定性. 网络运行过程中, 其权值不做调整(但随时间按正弦规律变化), 网络状态不断地转换. 随着网络状态变化其能量不断减小, 最终在达到稳定时能量到达极小点. 由于该反馈型神经网络权值为正弦函数, 特别适合于周期信号的自适应逼近和检测, 为实际中周期性信号检测与处理提供了一种新的、有效的网络模型和方法. 作为应用实例把该网络应用于电力系统中电压凹陷特征量实时检测, 仿真结果表明, 网络用于信号检测不仅有很高的静态精度, 而且有非常好的动态响应特性.     

基于网状光网络的智能实时监测保护系统的研究

目前光网络的拓扑结构越来越复杂,逐渐向网状结构光网络发展。提出了一种新的网状光网络智能实时在线监测保护方案,该方案采用下层嵌入式监测设备＋上层网管中心的模式。在嵌入式监测保护设备上加入保护光纤路由计算的功能,由嵌入式监测保护设备事先用路由算法计算出每条工作光纤的备用光纤路由,在工作光纤发生故障时及时进行切换。该方案实现了真正无人值守的光网络智能实时在线监测保护设备,适应网状光网络的发展要求,实现网状光网络的透明无阻断通信。     

异质自适应网络中的核心-边缘结构及其对疾病传播的抑制作用

节点属性异质自适应网络中疾病传播的研究表明节点属性异质性可以很大程度上增大传播阈值, 并且自组织形成一个更鲁棒的度异质网络结构. 本文从数值模拟方面研究鲁棒的度分布异质结构的自组织形成过程, 分析发现核心-边缘结构的形成才是导致传播阈值增大的根本原因. 鉴于此, 提出一种重连策略, 能够促进核心-边缘结构的形成, 从而达到增大传播阈值的目的. 这不仅有助于深入认识节点属性异质自适应网络中的流行病传播过程, 而且为疾病传播控制策略的提出提供了新思路.     

Architectural issues related to the optical implementation of an EGS network based on embedded control

Free-space optical implementations of switching networks have been proposed to circumvent many of the system-level problems that may be encountered in systems that require many high-density, high-bandwidth connections. The details of a new class of switching network (the EGS network), that is well-suited to free-space implementations, is described. The common control injection problem that plagues most free-space photonic networks, i.e. how can control information from an electronic source be injected into the network for applications that require relatively high network reconfiguration rates, is examined. A new technique for control injection, called embedded control, which permits network operation even with relatively high network reconfiguration rates is also proposed.     

Gradient-based PIV using neural networks

This paper proposes a new gradient-based PIV using an artificial neural network for acquiring the characteristics of a two-dimensional flow field. The neural network can effectively realize an accurate approximation of the vector field by introducing some knowledge on the characteristic property. The neural network is trained by using spatial and temporal image gradients so that the basic equation of the gradient-based method is satisfied. Since the neural network itself learns the stream function, the continuity equation of flow is consequently satisfied in the measured velocity vector field. The new gradient-based PIV can be applied to even partly lacking visualized images.     

Characterizing traffic time series based on complex network theory

A complex network is a powerful tool to research complex systems, traffic flow being one of the most complex systems. In this paper, we use complex network theory to study traffic time series, which provide a new insight into traffic flow analysis. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, in order to convert the new time series into a complex network, the critical threshold is estimated by the characteristics of a complex network, which include degree distribution, cumulative degree distribution, and density and clustering coefficients. We find that the degree distribution of associated complex network can be fitted with a Gaussian function, and the cumulative degree distribution can be fitted with an exponential function. Density and clustering coefficients are then researched to reflect the change of connections between nodes in complex network, and the results are in accordance with the observation of the plot of an adjacent matrix. Consequently, based on complex network analysis, the proper range of the critical threshold is determined. Finally, to mine the nodes with the closest relations in a complex network, the modularity is calculated with the increase of critical threshold and the community structure is detected according to the optimal modularity. The work in our paper provides a new way to understand the dynamics of traffic time series.     

Artificial Neural Network That Can Say “Unknown”

A new artificial neural network that can reject strange patterns is presented. The new network is an improved version of the RCE network. Each cell in the last layer of the present network has two thresholds. The new threshold produces the smallest boundary that encloses all examples associated with the cell in the pattern vector space. The present network can reject strange patterns by using this boundary. The rejection of strange patterns in the present and RCE networks was investigated by practical experiments using digits sampled from X-ray films of the human chest.     

一种无线传感器网络簇间拓扑演化模型及其免疫研究

基于复杂网络理论, 研究由于节点失效所导致的无线传感器网络性能下降的问题, 提出一种新的簇间拓扑演化模型, 在此基础上讨论病毒的免疫策略, 并给出一种新的免疫机理. 理论分析表明, 该模型演化生成的网络不仅具有较强的容错性, 而且还可以有效避免节点因能量很快耗尽而过早死亡. 研究还发现, 对于网络全局信息未知的情况, 与随机免疫和熟人免疫策略相比, 本文所提免疫策略能够获得较好的免疫效果. 通过数值仿真对理论分析进行验证.     

Degree and wealth distribution in a network induced by wealth

A network induced by wealth is a social network model in which wealth induces individuals to participate as nodes, and every node in the network produces and accumulates wealth utilizing its links. More specifically, at every time step a new node is added to the network, and a link is created between one of the existing nodes and the new node. Innate wealth-producing ability is randomly assigned to every new node, and the node to be connected to the new node is chosen randomly, with odds proportional to the accumulated wealth of each existing node. Analyzing this network using the mean value and continuous flow approaches, we derive a relation between the conditional expectations of the degree and the accumulated wealth of each node. From this relation, we show that the degree distribution of the network induced by wealth is scale-free. We also show that the wealth distribution has a power-law tail and satisfies the 80/20 rule. We also show that, over the whole range, the cumulative wealth distribution exhibits the same topological characteristics as the wealth distributions of several networks based on the Bouchaud-Mèzard model, even though the mechanism for producing wealth is quite different in our model. Further, we show that the cumulative wealth distribution for the poor and middle class seems likely to follow by a log-normal distribution, while for the richest, the cumulative wealth distribution has a power-law behavior.