On fault tolerance of 3-dimensional mesh networks

In this paper, the concept of k-submesh and k-submesh connectivity fault tolerance model is proposed. And the fault tolerance of 3-D mesh networks is studied under a more realistic model in which each network node has an independent failure probability. It is first observed that if the node failure probability is fixed, then the connectivity probability of 3-D mesh networks can be arbitrarily small when the network size is sufficiently large. Thus, it is practically important for multicomputer system manufacturer to determine the upper bound for node failure probability when the probability of network connectivity and the network size are given. A novel technique is developed to formally derive lower bounds on the connectivity probability for 3-D mesh networks. The study shows that 3-D mesh networks of practical size can tolerate a large number of faulty nodes thus are reliable enough for multicomputer systems. A number of advantages of 3-D mesh networks over other popular network topologies are given.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

Max-flow problem in undirected planar networks with node capacities being in NC

The max-flow problem in planar networks with only edge capacities has been proved to be in NC (Nickle's Class). This paper considers a more general version of the problem when the nodes as well as the edges have capacities. In a general network, the node-edge-capacity problem can be easily reduced to the edge-capacity problem. But in the case of planar network this reduction may destroy the planarity, and reduces the problem to the edge-capacity problem in a general network, which is P-complete. A recent contribution presents a new reduction for planar networks, that maintains the planarity. In this paper, it is proved that this reduction is in NC and thus the node-edge-capacity problem in undirected planar networks is in NC.     

Providing personalized converged services based on flexible network reconfiguration

Recent developments show a progressing convergence of networks, services, and applications, which results in the necessity for new approaches to provide a ubiquitous experience on converged networks for specialized high data-rate services. A fundamental diversifying attribute for the future physical network convergence paradigm is pressing. In this paper, we make researches on converged resource reconfiguration model of substrate physical network based on hardware virtualization and provide a serv...     

The Global Crisis and Academic Communication： The Challenge of Social Networks in Research

The global economic crisis is seriously affecting academic research. The situation is provoking some big changes and an urgent need to seek alternatives to traditional models. It is as if the academic community was reinventing itself; and this reinvention is happening online. Faced with a lack of funding, researchers have determined to help each other develop their projects and they are doing so on social knowledge networks that they have created for this mission. The purpose of this paper is to analyze different social networks designed for academic online research. To this end, we have made a selection of these networks and established the parameters for their study in order to determine what they consist of, what tools they make use of, what advantages they offer and the degree to which they are bringing about a revolution in how research is carried out. This analysis is conducted from both a qualitative and a quantitative perspective, allowing us to identify the percentage of these networks that approach what would be the ideal social knowledge network. As we will be able to confirm, the closer they are to this ideal, the more effective they will be and the better future they will have, which will also depend on the commitment of users to participation and the quality of their contributions.     

Communication network designing:Transmission capacity,cost and scalability

Despite the growing number of works centering around the traffic dynamics on complex networks,these researches still have some common shortcomings,e.g.,too simple traffic flow model and lack of considerations for the designing cost and scalability issues.This paper builds on a more realistic traffic flow model,and offers a holistic view on the network designing problem.In addition to the extensively studied transmission capacity,this paper takes designing cost and scalability as two other designing objectives,and presents a quantitative study of how different designing choices independently and collectively influence these objectives by the introduction of a cartesian coordinate system.It is shown that different kinds of network topologies display different shapes of achievable solution spaces and exhibit different abilities to achieve cost-effective and scalable designing.In particular,we find the philosophy underlying empirical network designing and engineering today fails to meet the cost-effective and scalable designing requirements,and propose a cost-effective and scalable designing scheme for BA-like networks,i.e.,the efficient routing combined with effective betweenness based link bandwidth allocation.In addition,when designing a thoroughly new network from the beginning,we find that ER network is a good candidate to achieve cost-effective and scalable designing in most settings.     

Performance improvement for applying network virtualization in fiber-wireless （FiWi） access networks

Fiber-wireless （FiWi） access networks, which are a combination of fiber networks and wireless networks, have the advantages of both networks, such as high bandwidth, high security, low cost, and flexible access. However, with the increasing need for bandwidth and types of service from users~ FiWi networks are still relatively incapable and ossified. To alleviate bandwidth tension and facilitate new service deployment, we attempt to apply network virtualization in FiWi networks, in which the network＇s control plane and data plane are separated from each other. Based on a previously proposed hierarchical model and service model for FiWi network virtualization, the process of service implementation is described. The performances of the FiWi access networks applying network virtualization are analyzed in detail, including bandwidth for links, throughput for nodes, and multipath flow transmission. Simulation results show that the FiWi network with virtualization is superior to that without.     

A clique-superposition model for social networks

How does a social network evolve? Sociologists have studied this question for many years.According to some famous sociologists,social links are driven by social intersections.Actors who affiliate with the shared intersections tend to become interpersonally linked and form a cluster.In the social network,an actor cluster could be a clique or a group of several smaller-sized cliques.Thus we can conclude that a social network is composed of superposed cliques of different sizes.However,sociologists did not verify the theory in large scale data due to lack of computing ability.Motivated by this challenge,incorporated with the theory,we utilize data mining technologies to study the evolution patterns of large scale social networks in real world.Then,we propose a novel Clique-superposition generative model,which generates undirected weighted networks.By extensive experiments,we demonstrate that our model can generate networks with static and time evolving patterns observed not only in earlier literature but also in our work.     

Finding and evaluating the community structure in semantic peer-to-peer overlay networks

Recently much attention has been paid to semantic overlay networks for information retrieval in large scale peer-to-peer networks,and much research work on semantic overlay protocols and searching algorithms has been done and the results indicate that semantic overlay is efficient for content searching in peer-to-peer networks.However,very limited work has been done to analyze and evaluate the characteristics of semantic overlay networks.In this paper we identify a natural property of semantic overlay networks,the community structure.We propose a mathematical model to evaluate the property of community structure of semantic P2P overlay networks.A heuristic algorithm is designed to optimize the community structure.Using the evaluation model we compare the SemreX semantic overlay with the Gnutella network.Results demonstrate that a SemreX overlay network has the distinctive community structure feature,while a Gnutella-like network does not.We also simulate a simple flooding protocol in both overlays to show that the overlay with community structure is more efficient for content searching.     

考虑不同节点行为要素特征的供应链演化规律研究

为研究供应链的实际演化规律,以复杂网络理论中的多局域世界模型为原型,提出了一种包含局域性节点与全局性节点且反映多种供应链行为要素的供应链网络演化模型。该模型验证了在自然演化情况下,现实供应链网络均具有一定的幂律性;通过算例对比分析说明,该模型相较现有文献中的演化网络能更好地模拟现实的供应链网络。研究表明,在供应链的自然演化过程中,网络的连通性和传输效率不断增强,由于新节点企业多与较大规模的企业合作而忽略了其他企业,其节点间紧密程度降低;企业与供应链网络的紧密度逐步发展到一定的程度将趋于稳定,大规模企业的发展速度先增加后放缓,各规模节点发展速度在中后期保持稳定。     

选择性神经网络二次集成方法在定量构效关系建模中的应用研究

针对在建立定量构效关系(QSAR)模型中,单个人工神经网络模型难以确定参数,容易产生“过拟合”;一般神经网络集成模型虽然建立过程简单,但由于个体差异度小而导致泛化能力相对单个神经网络没有明显改善等问题,提出了一种基于随机梯度法的选择性神经网络二次集成方法。在建立除草剂(苯乙酰胺类化合物)的QSAR模型的实验研究中表明,该方法设计过程简单,能够以较小的运算代价明显地提高了模型的泛化能力,是建立QSAR模型的一个有效方法。     

基于Web的网络教学平台的设计与实现

随着计算机网络技术的高速发展,使人类有了新的教学途径:网上教学。这种教学方式如今已经成为部分高校和教育体系的重要组成部分,而完成网上教学任务的网络教学平台环境也成为现今热门的技术。文中正是在这个前提下研究了基于Web网络教学平台的设计与开发。首先分析了网络教学平台的三种用户:学生、教师、管理员对网络教学平台的主要需求。其次,在网络教学平台实用技术方面,着重介绍了基于Web网络教学平台经常使用的"三层B/S"模型。最后,重点分析了"三层B/S"结构模型在网络教学平台中的应用。     

基于粒子群优化神经网络的“煤改电”地区短期负荷预测研究

为提高“煤改电”地区短期负荷预测水平,本文基于北京市大兴区“煤改电”工程,探索利用神经网络算法对“煤改电”地区短期负荷进行预测。本文首先研究了“煤改电”地区负荷的年周期、周周期以及日周期负荷特性,并对负荷预测进行分类,分析得出了负荷预测的主要影响因素,明确了负荷预测的步骤及误差分析方法。其次,本文研究了BP神经网络的构成和运算过程,分析了历史数据处理方法,建立了基于BP神经网络的“煤改电”地区短期负荷预测模型,并对短期负荷预测模型进行检验。最后,为进一步提高预测效果,本文研究利用粒子群算法和列文伯格-马夸尔特算法对神经网络进行优化改进,建立了基于粒子群算法优化的BP神经网络负荷预测模型,满足了预测目标精度要求。     

Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.     

Dynamic rate adaptation

We describe a technique for automatically adapting to the rate of an incoming signal. We first build a model of the signal using a recurrent network trained to predict the input at some delay, for a typical rate of the signal. Then, fixing the weights of this network, we adapt the time constant of the network using gradient descent, adapting the delay appropriately as well. We have found that on simple signals, the network adapts rapidly to new inputs varying in rate from being twice as fast as the original signal, down to ten times as slow. So far our results are based on linear rate changes. We discuss the possibilities of the application of this idea to speech.     

Small-World在确定性Ad hoc网络信任模型中的应用

该文首先介绍了小世界（Small-World）现象的起源、发展现状及在现实中的应用.随后介绍了一种基于证书链的Ad hoc网络的信任模型.然后根据Ad hoc网络拓扑结构确定性理论,为了在Ad hoc网络中实现“Small-World”现象,使得网络中任意节点之间的信息传输中所经历的中间节点尽可能少,从而节约Ad hoc网络中存在的信息传输对节点计算和电力等资源的不必要的消耗.基于上述理论及对现实应用的考虑,该文建立了一个模型,并在NS-2上进行了仿真实验,最后对该模型的安全性方面进行了一些讨论.     

基于模仿创造的网络流行语传播模型及仿真研究

随着互联网和智能移动终端的发展,研究网络流行语的传播过程和发展趋势对于网络营销和广告文案的创作具有重要的意义。基于SIR传染病模型,综合考虑网民对网络流行语模仿再创造的行为特点,构建新型的网络流行语传播模型,利用神经网络技术结合流行语时序数据对模型进行参数反演,并分别以流行语“佛系”和“确认过眼神”为例进行验证。结果表明,用户的模仿再创造行为是网络流行语传播中后期的主要驱动力;相较SIR模型,本模型着重考虑了网民对流行语的创新行为并运用参数反演方法,其预测准确度更高,模型拟合值与真实数据相比误差更小。进而可以为营销和广告创意人员提供有益的借鉴,并通过预测其发展趋势可对舆论进行及时分析和引导。     

Scalability and robustness of parallel hyperheuristics applied to a multiobjectivised frequency assignment problem

The Frequency Assignment Problem (fap) is one of the key issues in the design of Global System for Mobile Communications (gsm) networks. The formulation of the fap used here focuses on aspects that are relevant to real gsm networks. In this paper, we adapt a parallel model to tackle a multiobjectivised version of the fap. It is a hybrid model which combines an island-based model and a hyperheuristic. The main aim of this paper is to design a strategy that facilitates the application of the current best-behaved algorithm. Specifically, our goal is to decrease the user effort required to set its parameters. At the same time, the usage of such an algorithm in parallel environments was enabled. As a result, the time required to attain high-quality solutions was decreased. We also conduct a robustness analysis of this parallel model. In this analysis we study the relationship between the migration stage of the parallel model and the quality of the resulting solutions. In addition, we also carry out a scalability study of the parallel model. In this case, we analyse the impact that the migration stage has on the scalability of the entire parallel model. Computational results with several real network instances have validated our proposed approach. The best-known frequency plans for two real-world network instances are improved with this strategy.