Time Series Prediction Based on Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

精品光网络建设策略--智能光网络的引入

简要介绍了ASON技术的特点、ASON的引入为新一代光网络带来的好处及衡量网络质量的一些参数和注意事项。     

基于ANN的声目标识别系统及其DSP实时实现

介绍了一种基于人工神经网络(ANN)的声目标识别系统,概述了用MATLAB专用工具箱对神经网络权值进行训练及仿真的过程,叙述了ANN目标识别系统的数字信号处理器(DSP)实时实现过程,并着重分析定点实现过程中程序变量的定标、非线性运算的实现、溢出的处理等关键步骤.对不同字长的识别结果进行比较表明,基于定点实时实现的系统数据保持很高的精度,可以得到与浮点处理相同的识别率.     

关于本地传输网网络优化的探讨

根据长期工作经验,对现有网络存在的问题进行分析,提出对本地传输网网络优化的有关建议。     

一种具有弹性服务质量要求的多业务最优接纳控制策略

随着无线网络所支持的业务种类的增加和具有弹性服务质量要求的业务的大量出现，与服务质量保证密切相关的呼叫接纳控制问题成了近年来无线网络研究的热点之一。本文研究了基于准马尔可夫决策过程方法的多业务最优呼叫接纳控制问题。根据业务的特点，首先引入了带宽分配满意度函数和收益率函数，在此基础上，提出了基于带宽分配满意度的最优带宽分配算法和基于准马尔可夫决策过程方法的最优呼叫接纳控制策略。计算结果表明，本文方案能够在对各类业务的呼叫阻塞率进行适当权衡的前提下，进一步提高网络的期望收益率和期望带宽利用率，同时满足了各类业务的最低服务质量要求。     

Mesh结构P/G布线网络层次化快速分析方法

本文提出了一个基于网络划分的P／G布线网络层次化快速分析方法。其中，对于子网运算，通过对Cholesky分解法三角化对称正定阵的图模型分析，并基于Mesh结构网络的自身特点，提出了一个基于图顶点排序的加速子网分析运算策略；并用基于MPI的并行结构实现了P／G布线网络分析的并行运算。     

无源性理论在永磁同步电动机混沌控制中的应用

永磁同步电动机在一定的工作条件下呈现出混沌运动，根据无源性网络理论，设计电动机混沌动力学模型的控制器，将混沌系统等效为无源系统，消除系统中的混沌运动，降低系统自激振动的危害，实现混沌系统的快速稳定。     

动态多目标决策问题的灰色分析方法

将用于固定时间截面下静态多目标决策的灰色关联理论推广到动态情形，引入局部理想最优效果和整体理想最优效果的概念，提出一种新型的动态多目标决策问题的灰色关联模型，并通过算例说明该方法的合理可行性。     

Mesh-Tree两级层次结构P/G网布线优化方法

本文采用Mesh-Tree两级层次结构作为P／G网布线拓扑结构，并提出了一个对于该结构的线宽优化方法，使超大型P／G网的布线优化速度大大提高。