一类新的互连网络:三角塔网络

本文提出并分析了一种新的互连网络—三角塔网络.当n4或n=4时,它是极大连通的,紧超连通的,即三角塔网络的连通度κ(TTn)是2n-3.星网络是三角塔网络的子网络,故而三角塔网络除了继承星网络的很多优良性质(例如:点对称性、连通性、点可迁性等),还说明Sn能以膨胀数1嵌入TTn.当三角塔网络和超立方体与冒泡排序网络有近乎相同的顶点数时,三角塔网络的直径和连通度与超立方体与冒泡排序网络的直径和连通度相比直径更小、连通度更大.本文给出了三角塔网络的直径和平均距离,并提出了关于三角塔网络Hamilton性的一簇猜想,并且证明这个猜想对于n=3,4以及n=5,6,k=1,2时是正确的.     

论二叉树E=I+2n公式的另一种证明方法

二叉树中E=I+2n公式在查询等领域有着广泛的应用.一般对它采用数学归纳法来证明,且仅限于公式的直接应用.直接证明方法在充分利用二叉树定义的迭代关系基础之上,分析二叉树中E与I之间的密切关系,据此提出该公式的另一种证明方法并进行严格的证明.该方法能更多地了解每一边、每一结点上的具体情况,因而具有广泛意义.     

3G网络在远程医疗中的应用与展望

随着3G技术的日趋成熟,3G网络也时刻围绕在我们身边,虽然我国的网络运营商所采用的技术标准不尽相同,但无论是使用哪种标准的3G设备它所提供的上网速度都已经具备通过无线网络实现远程实时音视频和数据传输的能力。这为我们实现3G网络的远程医疗活动提供前提,也是我们开展基于3G网络远程医疗的一个契机,以3G网络为依托,将当前的远程会诊软件加以调整和优化应用到3G网络必将成为现代化远程医疗中的一个亮点,也将是3G业务的一个应用方向。     

通信4G建设网络无线接通率的分析与指标提升

网络技术在这个愈来愈快速发展的经济社会中发挥着重要的作用,而网络建设的好坏更是直接影响着网络的使用情况。未来网络的发展更是朝着更全面更快速的方向进行发展,那么新兴起来的通信4G网络无线技术更适应现代生活对网络的高层次的追求。4G网络是基于3G网络开发而来,而就目前而言3G网络还没有完全退出市场,那么这就为4G网络的推广带来重要的挑战。本文首先介绍了4G网络的技术现状,基于上述特点介绍了基于3G技术建设4G无线网络的结构建设措施,最后基于技术措施分析4G网络无线接通率的情况及其指标提升。     

三维光子晶体及其制备技术研究进展

光子晶体是一种新概念人工结构功能材料,通过设计可以人为调控经典波的传榆.而三维光子晶体能产生全方向的完全禁带,具有更普遍的实用性.从结构、材料及应用探索3方面介绍了近几年来光学波段三维光子晶体的最新发展动向:以器件化为指导,逐步由简单媒质简单周期向复杂媒质复合周期结构方向发展,由胶体模板自组装等纯化学制备手段向物理化学方法相融合的多元技术扩展,其应用领域也由光电子器件、集成光路进一步拓展到光电对抗、光学探测、传感等.     

一种高效的二叉树存储结构

二叉树顺序存储和链式存储结构都有一定缺陷,本文提出了一种简单高效的二叉树顺序存储结构,并通过一些实例验证了其时间和空间上的高效性。     

分数布朗运动时间序列的基于递归图的网络分析

最近发展了一些将时间序列转化为复杂网络的方法,从而可以通过研究网络的拓扑性质来分析原始时间序列的性质.本文用递归图方法将分数布朗运动(FBM)时间序列转化为复杂网络,并研究其对应递归网络的拓扑性质.我们发现,对固定的Hurst指数H,在网络连通率首次增长到1之前,随着递归图的参数阈值的增大,网络的平均路径长度L也随之递增,之后反而递减.我们也发现由FBM时间序列转化得到的网络是无标度网络.我们采用节点覆盖盒计数法分析发现FBM的递归网络为分形网络,具有自相似特性,其分形维数dB随Hurst指数H的增大而减小,特别当H≥0.4时,有近似关系dB=H2 - 2.1×H+2.     

基于FDMA系统的两跳中继网络的资源分配

首先介绍了一种可用于两跳中继网络的时分模式帧结构,然后基于该帧结构提出了一种资源分配算法.该算法包括两个步骤:第一个步骤是第一个时隙上的动态资源分配,第二个步骤是基站与中继站之间的最优化功率分配.两跳中继网络中下行存在三种链路:"基站-中继"链路、"基站-用户"链路和"中继-用户"链路,在系统资源有限的条件下,通过所提算法为这三种链路分配相应的带宽和功率,可以使得系统的吞吐量最大.仿真结果表明所提算法能够有效地提高系统的吞吐量.     

无线传感器网络概述

无线传感器网络技术是信息科学领域中一种全新的尖端技术。它将网络技术引人到无线智能传感器中,弥补了传统传感器的局限性,具有广阔的发展前景。本文介绍了无线传感器网络的体系结构、发展历程和特征,详细分析了无线传感器网络的支撑技术,并综述了无线传感器网络在军事、工业等领域的应用。     

一种面向复杂嵌入式系统的互连网络研究

提出了一种结构简单,具有分形特征的基三分层互连网络.讨论了适合于基三分层网络的节点编码方案,给出了网络中任意两个节点之间最短路径的求解算法和网络平均最短距离的估计值.针对复杂嵌入式系统的特点,分析了基三分层网络的成本、可靠性和实时性.将基三分层网络与常见的嵌入式实时互连拓扑结构进行了比较,结果表明基三分层网络可靠性较高,具有较高的性价比,适合作为通信局部性较高的嵌入式实时互连网络.     

Optical binary de Bruijn networks for massively parallel computing: design methodology and feasibility study

The interconnection network structure can be the deciding and limiting factor in the cost and the performance of parallel computers. One of the most popular point-to-point interconnection networks for parallel computers today is the hypercube. The regularity, logarithmic diameter, symmetry, high connectivity, fault tolerance, simple routing, and reconfigurability (easy embedding of other network topologies) of the hypercube make it a very attractive choice for parallel computers. Unfortunately the hypercube possesses a major drawback, which is the complexity of its node structure: the number of links per node increases as the network grows in size. As an alternative to the hypercube, the binary de Bruijn (BdB) network has recently received much attention. The BdB not only provides a logarithmic diameter, fault tolerance, and simple routing but also requires fewer links than the hypercube for the same network size. Additionally, a major advantage of the BdB network is a constant node degree: the number of edges per node is independent of the network size. This makes it very desirable for large-scale parallel systems. However, because of its asymmetrical nature and global connectivity, it poses a major challenge for VLSI technology. Optics, owing to its three-dimensional and globalconnectivity nature, seems to be very suitable for implementing BdB networks. We present an implementation methodology for optical BdB networks. The distinctive feature of the proposed implementation methodology is partitionability of the network into a few primitive operations that can be implemented efficiently. We further show feasibility of the presented design methodology by proposing an optical implementation of the BdB network.     

Road Distance Computation Using Homomorphic Encryption in Road Networks

Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services. The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation. To alleviate privacy concerns about location privacy leaks during road distance computation, it is desirable to have a secure and efficient road distance computation approach. In this paper, we propose two secure road distance computation approaches, which can compute road distance over encrypted data efficiently. An approximate road distance computation approach is designed by using Partially Homomorphic Encryption and road network set embedding. An exact road distance computation is built by using Somewhat Homomorphic Encryption and road network hypercube embedding. We implement our two road distance computation approaches, and evaluate them on the real city-scale road network. Evaluation results show that our approaches are accurate and efficient.     

Bayesian networks for multilevel system reliability

Bayesian networks have recently found many applications in systems reliability; however, the focus has been on binary outcomes. In this paper we extend their use to multilevel discrete data and discuss how to make joint inference about all of the nodes in the network. These methods are applicable when system structures are too complex to be represented by fault trees. The methods are illustrated through four examples that are structured to clarify the scope of the problem.     

Exact algorithm for delay-constrained capacitated minimum spanning tree network

The delay-constrained capacitated minimum spanning tree (DC-CMST) problem of finding several broadcast trees from a source node is discussed. While the traditional CMST problem deals with only the traffic capacity constraint served by a port of the source node, and delay-constrained minimum spanning tree (DCMST) considers only the maximum end-end delay constraint, the DC-CMST problem deals with both the mean network delay and traffic capacity constraints. The DC-CMST problem consists of finding a set of minimum cost spanning trees to link end-nodes to a source node satisfying the traffic requirements at end-nodes and the required mean delay of the network. In the DC-CMST problem, the objective function is to minimise the total link cost. A dynamic programming-based three-phase algorithm that solves the DC-CMST problem is proposed. In the first phase, the algorithm generates feasible solutions to satisfy the traffic capacity constraint. It finds the CMSTs in the second phase, and allocates the optimal link capacities to satisfy the mean delay constraint in the third phase. Performance evaluation shows that the proposed algorithm has good efficiency for any network with less than 30 nodes and light traffic. The proposed algorithm can be applied to any network regardless of its configuration, and used for the topological design of local networks and for efficient routing algorithms capable of constructing least cost broadcast trees.     

On Network Designs with Coding Error Detection and Correction Application

The detection of error and its correction is an important area of mathematics that is vastly constructed in all communication systems. Furthermore, combinatorial design theory has several applications like detecting or correcting errors in communication systems. Network (graph) designs (GDs) are introduced as a generalization of the symmetric balanced incomplete block designs (BIBDs) that are utilized directly in the above mentioned application. The networks (graphs) have been represented by vectors whose entries are the labels of the vertices related to the lengths of edges linked to it. Here, a general method is proposed and applied to construct new networks designs. This method of networks representation has simplified the method of constructing the network designs. In this paper, a novel representation of networks is introduced and used as a technique of constructing the group generated network designs of the complete bipartite networks and certain circulants. A technique of constructing the group generated network designs of the circulants is given with group generated graph designs (GDs) of certain circulants. In addition, the GDs are transformed into an incidence matrices, the rows and the columns of these matrices can be both viewed as a binary nonlinear code. A novel coding error detection and correction application is proposed and examined.     

A neuroevolutionary approach to stochastic inventory control in multi-echelon systems

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form.     

A TWO-PHASE NETWORK DESIGN HEURISTIC FOR MINIMUM COST WATER DISTRIBUTION SYSTEMS UNDER A RELIABILITY CONSTRAINT

This paper presents a two-phase design and optimization procedure for constructing a pipe network water distribution system having a built-in degree of reliability. The first phase is comprised of an algorithm called TREESEARCH which iteratively constructs a tree pipe network. Starting with a shortest-path based tree, the procedure employs a linear programming subproblem to systematically modify this tree by adding and deleting one link at a time, with the aim of reducing the cost of the network while satisfying the flow continuity, energy balance, and pressure head requirement constraints. The second phase of the algorithm, called REDUNDANCY, is concerned with the issue of reliability. In this phase, the tree network constructed by the algorithm TREESEARCH is augmented through the addition of links so that there are at least two arc-disjoint paths from each source node to every demand node it serves. This augmentation is performed through the use of a set covering problem which recommends the links to be added, and a Hardy-Cross solver for redesigning the perturbed network to ensure feasibility, while attempting to minimize the overall design cost. The two phases are coordinated by applying algorithm REDUNDANCY to several candidate solutions presented by TREESEARCH. Two example problems from the literature, one involving a single source and the other a multiple source network, are solved using the proposed procedure. The solutions obtained have a smaller cost than those previously obtained by other researchers.     

Quantized hopfield networks for reliability optimization

The use of neural networks in the reliability optimization field is rare. This paper presents an application of a recent kind of neural networks in a reliability optimization problem for a series system with multiple-choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget. The problem is formulated as a nonlinear binary integer programming problem and characterized as an NP-hard problem. Our design of neural network to solve efficiently this problem is based on a quantized Hopfield network. This network allows us to obtain optimal design solutions very frequently and much more quickly than others Hopfield networks.     

Modelling and analysis of big data platform group adoption behaviour based on social network analysis

Due to the importance of big data technology in decision-making, production and service provision, enterprises have adopted various big data technologies and platforms to improve their operational efficiency. However, the number of enterprises that have adopted big data is not promising. The purpose of this study is to explore the current status of big data adoption by Chinese enterprises and to reveal the possible factors that hinder big data adoption from the group behaviour network perspective. Based on a real case survey of 54 big data platforms (BDPs), four types of networks—i.e., the enterprise-platform network, enterprise network, platform network and industry similarity and difference (ISD) network—are constructed and analysed on the basis of social network analysis (SNA). This study finds that among Chinese enterprises, the level and scope of big data adoption are generally low and are imbalanced among industries; the cognitive level and adoption behaviour of enterprises on BDPs are inconsistent, the compatibility of BDPs is different, and the density and distance-based cohesion of networks are weak; although the current big data adoption behaviours of Chinese enterprises have formed some structural features, core-periphery structures and maximal complete cliques are found, and the current network structure has little impact on individual enterprises and platforms; enterprises in the same industry prefer to adopt the same kind of big data technology or platform. Based on these findings, several strategies and suggestions to improve big data adoption are provided.     

半导体精密控温技术在航天器包装容器的应用

目的 对某型号航天器包装容器进行温控系统设计,以达到航天器高精度控温的要求.方法 对总体保温布局进行设计,优化被动保温结构,采用半导体控温方式,利用Ansys Workbench进行稳态和瞬态热力学分析.通过试验测试,验证保温结构设计和热力学分析结果的合理性.结果 在外部施加热源温度36℃和0℃情况下,随着热源区域的远离,包装容器内部的温度也趋于平稳,内部装载产品区域温度基本能维持在20.99～22.662℃.实际试验结果显示箱内温度变化不大于±1℃,比传统的空调控温精度高出70％左右.结论 通过优化箱体的被动保温结构,采用半导体精密控温,可以满足未来航天器小型化、高精度运输要求.