A method to evaluate the a switching unit spatial extensibility of and network

Switching units and networks have been analyzed as extensible fabrics, mostly in terms of their scheduling algorithms. The traditional literature on switching extensibility has provided complexity theory only relating to the total numbers of inputs （or outputs） and exchange lines. This paper analyzes switching extensibility in terms of not only the scheduling algorithm and also the fabric itself. It is found that determining extensibility from soft complexity related to the number of inputs （or outputs） of the scheduling algorithm and the fabric extensibility in previous studies without quantization is a flawed conception. A method is thus proposed to express the spatial extensibility of a switching unit or network in terms of the connections of a switching resource and capacity. The method calculates parameter ES （the efficiency of switching） of an m x n switching unit and obtains two functions of the switching unit to describe spatial extensibility along with the number of unilateral inputs or outputs. It is found that the range of ES is （0, 1] and three types of switching unit and two types of crosspoint networks have ES = 1. ES is calculated for banyan, Clos, parallel packet, fully interconnected and recirculation switching networks. The ES value for the banyan switching network is larger than that for other networks, and switching networks are classified into three types that have absolute/linear/denied spatial extensibility according to the limES value. It is demonstrated that a switching network has the largest ES value when it contains only the five types of switching unit for which ES ---- 1. Finally, a group-switching-first self-routing banyan switching network with lower blocking probability and time delay is deduced, and the ES method is contrasted with two other methods of evaluating spatial extensibility in terms of their mathematical expressions and intuitive graphics, for the five types of switching network listed above.     

基于小波网络的配电网空间数据库故障诊断方法

在进行配电网空间数据库建模中,需要对空间数据库进行优化检测和诊断,提高配电网的稳定运行能力,提出基于小波网络的配电网空间数据库故障诊断方法。构建配电网空间数据库的分布模型,采用高维特征分解方法进行配电网空间数据库的特征量化分解,结合小波网络学习方法进行配电网空间数据库故障状态特征提取,根据故障特征的聚类性进行配电网络空间数据库的结构重组和故障特性辨识,结合模糊统计分析方法,实现对配电网空间数据库的故障智能诊断。仿真结果表明,采用该方法进行配电网空间数据库故障诊断的准确性较好,分辨能力较强,提高了配电网空间数据库的稳定性。     

基于ZigBee嵌入式双网路由交换节点的设计

针对油井位置偏远,线路架设不便,不能实时、有效地对其进行日常数据采集与监管的问题,采用物联网技术,通过Zig Bee的自组网与Internet相结合,有效地将油田采集的数据通过所设计的交换节点进行传输。通过上位机与下位机的系统测试表明,该系统具有数据处理能力高、网络信号强、系统维护方便等优点,该系统在油田数据监测中具有较好的应用前景。     

GNet: A generalized network model and its applications in qualitative spatial reasoning

A data model, named generalized network (GNet), is proposed to perform various network-tracing tasks, especially tracing conceptual proposition networks in qualitative spatial reasoning (QSR). The GNet model can be defined as a 6-tuple: (V, A, q, ⊕, ∼, L). By specifying each element in the 6-tuple, a GNet can function as a conventional network, or an activity on edge (AOE) network, etc. The algorithm for searching for the generalized optimum path weight (GOPW) between two vertices in a GNet is developed by extending the Bellman-Ford algorithm (EBFA). Based on the GNet model, this paper focuses on representing spatial knowledge, which consists of a set of binary relations. We present two applications of GNets, namely the RCC8 network and the hybrid RCC8 network involving cardinal direction relations. Both can be traced to infer new spatial knowledge using EBFA. The applications demonstrate that the GNet model provides a promising approach to dealing with proposition-based geospatial knowledge based on weak composition. We also point out that EBFA can check whether a network is algebraically closed, or path-consistent when the corresponding composition table is extensional.     

用熵权法估计地域通信网络节点

评估网络节点的重要性,是研究地域通信网网络可靠性的重要内容。本文将节点拓扑权重、节点运行情况、交换能力、抗毁能力、设施费用和节点通信能力指标通过熵权法综合的反映到节点重要性权重中,使得节点重要性的评价更全面。结合实例,对用熵权法估计节点重要性权重进行了说明,为地域通信网的可靠性研究和实战应用提供重要参考。     

乙基橙偶氮苯聚合物的全光开关特性曲线拟合中的应用

BP神经网络通过调节连接权重可以实现以任意精度逼近非线性函数,利用这一特点可以对非线性函数关系进行拟合。偶氮苯聚合物的全开关特性曲线是非线性,很难用数学函数表达式来描述。因而本文首先介绍神经网络的基本原理和BP算法神经网络,然后BP神经网络应用于的全光开关特性曲线拟合,在MATLAB环境下,利用实验数据进行了实验测试,结果表明该方法处理数据精度高,拟合效果好。     

网络安全风险评估方法分析与比较

本文首先对目前较有代表性的几种网络安全风险评估方法进行简介,然后逐一分析各自优、缺点,最后对各评估方法进行综合对比,指出网络安全风险评估的发展方向,为研究网络安全风险评估提供参考。     

Address switching: Reforming the architecture and traffic of Internet

The success of the Internet is largely ascribable to the packet-switching scheme, which, however, also presents major challenges. Having identified three missing links in the current Internet architecture based on our long-term experiences of designing and operating large-scale backbones, we put forward a new, but incrementally deployable, network scheme—address switching. The address switching has both the advantages of packet switching and circuit switching; it supplies the missing links in the current Internet architecture and can reform the Internet traffic. Our analysis, protocol design and experiments indicate that the address switching can greatly improve the quality of service (QoS), security and routing scalability of today’s Internet. So it can provide flexible, high-performance and “per-service” networking for the scientific research communities. Moreover, it can provide a fairer and more sustainable business model for the commodity Internet. Supported by the China Next Generation Internet Project (Grant No. CNGI-04-13-2T), and the National Basic Research Program of China (Grant No. 041710001)     

A bifurcation method for solving multiple positive solutions to the boundary value problem of the Henon equation on a unit disk

Three algorithms based on the bifurcation theory are proposed to compute the O(2) symmetric positive solutions to the boundary value problem of the Henon equation on the unit disk. Taking l in the Henon equation as a bifurcation parameter, the symmetry-breaking bifurcation point on the branch of the O(2) symmetric positive solutions is found via the extended systems. Finally, other symmetric positive solutions are computed by the branch switching method based on the Lyapunov-Schmidt reduction.     

A note on the tool switching problem of a flexible machine

The problem of minimizing the total number of tool switches for a numerically controlled flexible machine is considered. A set of parts is to be processed with the machine. Each part needs a set of tools which should reside in the magazine of the machine at the moment of processing. Because of the limited capacity of the magazine, tools must be switched and the objective is to minimize the amount of this work. We propose an algorithm which tries to avoid sticking to a local minimum by repeated searches from different initial starting points which are created by repeated construction of super parts from parts with similar tools. The proposed algorithm and a number of efficient heuristics presented in the literature are empirically tested by both random test problems and real production data. The new algorithm performs well when considering the tradeoff between solution quality and running time.     

A front-fixing finite element method for the valuation of American options with regime switching

American option problems under regime-switching model are considered in this paper. The conjectures in [H. Yang, A numerical analysis of American options with regime switching, J. Sci. Comput. 44 (2010), pp. 69–91] about the position of early exercise prices are proved, which generalize the results in [F. Yi, American put option with regime-switching volatility (finite time horizon) – Variational inequality approach, Math. Methods. Appl. Sci. 31 (2008), pp. 1461–1477] by allowing the interest rates to be different in two states. A front-fixing finite element method for the free boundary problems is proposed and implemented. Its stability is established under reasonable assumptions. Numerical results are given to examine the rate of convergence of our method and compare it with the usual finite element method.     

一种应用PSO优化RBF神经网络的方法

RBF神经网络算法是一种常用的数据训练方法,在该训练过程中,如何选取更合理的个体作为RBF神经网络的神经元,直接关系到该数据训练方法的性能.利用传统的RBF神经网络模型进行数据训练,由于不同的神经元之间的差异性较小,造成建立的RBF神经网络集成模型的精确度过低.为此,提出应用PSO优化RBF神经网络的方法.动态构造PSO优化RBF神经网络结构,针对不同的动态构造方法进行分类,得到网格删除法、网络构造法和综合法等不同的动态构造方法,在动态构造的基础上,建立引用PSO优化RBF神经网络模型,计算RBF神经网络中的粒子变量,获取对应的适应性值,得到RBF神经网络的输出结果,实现应用PSO优化的RBF神经网络建模.实验结果表明,利用改进算法进行RBF神经网络构建,能够降低RBF神经网络的数据训练误差,满足实际需求.     

A method to evaluate the routing policy with two minimal paths within time threshold

Two attributes, the capacity and the lead time, are involved in the quickest path problem which finds a path with the minimum transmission time. The capacity of each edge is assumed to be deterministic in this problem. However, in many real-life networks such as computer, telecommunication, logistics networks, etc., each edge should be multistate due to failure, maintenance, etc. Such a network is named a multistate network. Hence, the minimum transmission time through a multistate network is not fixed. We evaluate the system reliability that a specified amount of data can be sent through a pair of minimal paths simultaneously within the time threshold. A solution procedure is first proposed to calculate it. In order to boost the system reliability, the network administrator decides the routing policy in advance to indicate the first and the second priority pairs of minimal paths. The second one will be responsible for the transmission duty if the first one fails. According to the routing policy, the system reliability can be subsequently computed. The case to transmit data through more than two minimal paths can be extended easily.     

无线传感网络节点锂电池剩余电量估算方法

针对采用锂电池电量检测芯片DS2786进行剩余电量估算存在误差较大的问题,结合DS2786芯片和卡尔曼滤算法,提出了一种面向无线传感网络节点锂电池的剩余电量估算方法。该方法通过建立能表征锂电池特性的等效电路模型,将芯片DS2786所获得的锂电池两端开路电压和放电电流作为卡尔曼滤波算法的输入参数,进行精确的剩余电量估算。实验结果表明,该方法对于锂电池剩余电量的估计误差可维持在3%以内,且算法复杂度低,能满足嵌入式设备的应用要求。     

蓝牙在微微网间的一种数据通讯方法

采用蓝牙技术，结合1至14点蓝牙无线数据传输单元的具体应用特点，文章提出了一种新型的蓝牙在微微网间通过主机控制器进行数据交换的方法，给出了硬件的构成及通讯时序图。     

3G与校园网整合的一种方法

高校校园3G网络建设已经成为各运行商争夺的焦点。通过合路技术进行多系统的室内覆盖、通过双SSID实现运行商WLAN与无线校园网集成,实现了3G与校园网的有机整合。实践表明,该方法实现了节省投资、使用维护方便、双方共赢的目标。     

Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes

In this paper, a customized network data envelopment analysis model is developed to evaluate the efficiency of electric power production and distribution processes. In the production phase, power plants consume fuels such as oil and gas to generate the electricity. In the distribution phase, regional electricity companies transmit and distribute the electricity to the customers in houses, industries, and agriculture. Although, the electricity is assumed to be a clean type of energy, several types of emissions and pollutions are produced during electricity generation. The generated emissions are considered as an undesirable output. A customized network data envelopment analysis (NDEA) approach is proposed to evaluate the efficiency of these processes Each decision making unit (DMU) includes two serially connected sub-DMUs, i.e., production and distribution stages. The models are extended using interval data to address the considerable uncertainty in the problem. The efficiency scores of main process, and each sub-process are determined. The final ranking of DMUs and sub-DMUs are achieved using a multi-attribute decision making (MADM) method. The whole approach is applied in a real case study in electrical power production and distribution network with 14 DMUs. The proposed approach has the following innovations in comparison with existing methods: (1) Both production and distribution process are evaluated in a unique model; (2) Undesirable outputs and uncertainty of data are considered in proposed approach; (3) Properties of proposed models are discussed through several theorems; (4) The efficiencies of production and distribution phases are determined distinctively; (5) A full ranking approach is proposed; (6) A real case study of electrical power production and distribution network is surveyed. The results of proposed approach are adequate and interesting. This approach can be customized for application in similar systems such as water production-supply management, Oil and fuel production–distribution systems, and supply chains.     

A reinforcement learning method based on an immune network adapted to a semi-Markov decision process

The immune system is attracting attention as a new biological information processing-type paradigm. It is a large-scale system equipped with a complicated biological defense function. It has functions of memory and learning that use interactions such as stimulus and suppression between immune cells. In this article, we propose and construct a reinforcement learning method based on an immune network adapted to a semi-Markov decision process (SMDP). We show that the proposed method is capable of dealing with a problem which is modeled as a SMDP environment through computer simulation. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A method of qualitative spatial envisionment with applications to planar mechanism configuration modeling and planning

This paper describes a method for qualitatively representing and reasoning about spatial configurations ofplanar mechanisms. The method has direct relevance to, and implications for,computer-aided mechanism design androbotics. In particular, it can be used to solve spatial configuration problems where exact geometric knowledge is not available, and to provide guidance for the application of quantitative configuration modeling and planning methods. In this paper, two applications of this method are demonstrated. The first application is concerned with inferring the instantaneous configurations and coupler curves inone-degree-of-freedom planar linkages. The second application deals with planning collision-free paths foropen-chain planar mechanisms moving among static obstacles.     

Investigation of a one-channel queueing system with an unlimited queue and the markovian anticipatory switching

Acondition of stability on the second approximation is obtained for a pulse system with anticipatory switching based on the diffusive approximation of normalized deviations of solutions of the system from the solution of an averaged system. The theoretical results obtained are used for investigating a one-channel queueing system with an unlimited queue. Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 168–173, July–August, 2000.