A message distribution technique and its application to network control

The patterns of message exchange in distributed computer systems can become sufficiently complex to justify the construction of communication services that extend the basic message transmission mechanism. A simple method for implementing a copy distribution, or broadcast, service is described. It is shown how the method can support command and monitoring functions in a computer communication network.     

网络安全技术的应用与分析

本文讨论了网络安全现状,分析了网络安全文化的作用机制及网络安全防护。     

A distributed solution to the network reconstruction problem

It has been recently shown in Ren et al. (2010) that by collecting noise-contaminated time series generated by a coupled-oscillator system at each node of a network, it is possible to robustly reconstruct its topology, i.e. determine the graph Laplacian. Restricting ourselves to linear consensus dynamics over undirected communication networks, in this paper we introduce a new dynamic average consensus least-squares algorithm to locally estimate these time series at each node, thus making the reconstruction process fully distributed and more easily applicable in the real world. We also propose a novel efficient method for separating the off-diagonal entries of the reconstructed Laplacian, and examine several concepts related to the trace of the dynamic correlation matrix of the coupled single integrators, which is a distinctive element of our network reconstruction method. The theory is illustrated with examples from computer, power and transportation systems.     

A neural network application to consumer classification to improve the timing of direct marketing activities

This article develops an alternative estimation approach for classifying new prospective consumers as “good'' or “bad'' prospects for direct marketing purposes. We show that the traditional approach of using demographics alone to profile non-active consumers (those who have yet to buy in the category) can be improved by waiting to observe their initial and limited number of sequential purchases in the category. We call this method the early purchase classification (EPC) approach. We make use of two established classification models, a multinomial logit model (MNL) and a neural network model (NN), and show that the classification accuracy of both models using our EPC approach outperforms the traditional approach of classifying non-active prospects using demographics only. Furthermore, we find that the NN model consistently outperforms the MNL model at this task. This research uses the best aspects of each model by utilizing the MNL model to determine which variables are most relevant to the classification and then using those variables for classification in the NN model. Using the complementary features of the MNL and NN models, managers can use the EPC approach to determine the most profitable time in a purchasing history to classify and target prospective consumers new to their categories.     

A network dynamics approach to chemical reaction networks

A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a ‘zero’ complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.     

浅谈教育城域网网络安全建设

为实现教育资源的广泛互联和共享,各地以教育局为核心,规划并组建各地的教育城域网,实现“三通两平台”信息化建设.信息网络的联通同时也带来了很多安全问题,教育城域网中存在网络威胁、大量带宽占用、远程传输安全等风险,为此需要采取必要的安全防护措施,对各个接入节点信息网络实施严密的防护与控制,同时确保通信链路的安全性和机密性.     

一种网络流量预测的小波神经网络模型

结合小波变换和人工神经网络的优势,建立一种网络流量预测的小波神经网络模型。首先对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,以系数序列和原来的流量时间序列分别作为模型的输入和输出,构造人工神经网络并且加以训练。用实际网络流量对该模型进行验证,结果表明,该模型具有较高的预测效果。     

1Traceroute及其在网络拓扑发现中的应用

本文分析了Traceroute正确性和完整性两方面存在的问题．设计了相应的改进方案，结合其网络拓扑发现中的应用，详述了防火墙定位的方法．并提出了一种子网发现算法。     

提高计算机网络可靠性的方法分析

在信息化时代,随着科技的不断发展,网络更以惊人的速度向前发展,不断更新技术,便捷的网络工具在通讯上逐渐主流化,方便快捷的优势使人们逐渐接受了快速化的网络信息生活.网络通讯的确给我们带来了太多的便捷,但是也存在大量的问题,网络也存在大量的问题,网络安全得不到保障的话,会给我们的生活带来很多麻烦,网络黑客往往会攻击我们的程序,会使我们造成很大的损失,甚至损失无法估量.随着信息的不断发展,我们在利用网络的同时,也应该加强网络的防范,在自己充分利用网络的同时,也要防止网络的侵害,让网络更好的为其服务.因此,提高计算机网络可靠性就显得尤为必要.笔者从计算机网络可靠性的概念和设计原则出发就如何提高计算机网络可靠性提出了自己的见解.     

利用PCNN神经元点火位置信息的语音特征研究

语谱图是语音信号的时频表示,含有丰富的信息。把语谱图输入到脉冲耦合神经网络（PCNN）可以获得语音的特征矢量。传统的语音特征采用PCNN50次迭代的点火次数。提出了一种新的语音特征参数,该参数基于PCNN神经元点火位置的信息。说话人识别的实验表明,新语音特征比传统的特征更能反映话者语音信号的特点,获得更好的识别结果。     

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features.     

降噪自编码器深度卷积过程神经网络及在时变信号分类中的应用

针对非线性时变信号分类问题,将过程神经网络（PNN）的信息处理机制与卷积运算相结合,提出了一种降噪自编码器深度卷积过程神经网络（DAE-DCPNN）。该模型由时变信号输入层、卷积过程神经元（CPN）隐层、深度降噪自动编码器（DAE）网络结构和softmax分类器构成。CPN的输入为时序信号,卷积核取为具有梯度性质的5阶数组,基于滑动窗口进行卷积运算,实现时序信号的时空聚合和过程特征提取。在CPN隐层之后,栈式叠加DAE深度网络和softmax分类器,实现对时变信号特征高层次的提取和分类。分析了DAE-DCPNN的性质,给出了按各信息单元分别进行赋初值训练、模型参数整体调优的综合训练算法。以基于12导联心电图（ECG）信号对7种心血管疾病分类诊断为例,实验结果验证了所提模型和算法的有效性。     

A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network

Energy is one of the scarcest resources in a wireless sensor network (WSN). Therefore, the need to conserve energy is of utmost importance in WSN. There are many ways to conserve energy in such a network. One fundamental way of conserving energy is judicious deployment of sensor nodes within the network area so that the energy flow remains balanced throughout the network. This avoids the problem of occurrence of ‘energy holes’ and ensures prolonged network lifetime. We have investigated the problem of uniform node distribution and shown its incapability to deal with the energy hole problem. Next we have derived the principle of non-uniform node distribution that ensures energy balancing. Further, we have developed a non-uniform, location-wise pre-determined node deployment strategy based on this principle leading to an increase in network lifetime. Finally exhaustive simulation is performed to substantiate our claim of energy balancing and subsequent enhancement of network lifetime. The results are compared with an existing work to prove the efficacy of our scheme.     

浅析网络安全及防火墙技术在网络安全中的应用

随着时代的发展,网络的普及,给人们带来极大的方便。但是由于网络是一个开放的无控制机构的特性,经常会受到计算机病毒、黑客的侵袭。因此网络安全变得越发重要,作为保护局域子网的一种有效手段,防火墙技术备受关注。随着计算机网络技术的飞速发展,尤其是互联网的应用变得越来越广泛,在为用户带来前所未有的海量信息的同时,计算机网络的安全性变得日益重要起来。为确保信息安全与网络畅通,研究计算机网络的安全与防护措施已经迫在眉睫。大多数的黑客入侵事件都是由于未能正确安装防火墙而引发,所以防火墙技术应该引起我们的注意和重视。本文主要阐述网络安全技术概述及防火墙技术对网络安全起到不可忽视的影响。     

Incorporating state estimation into model predictive control and its application to network traffic control

Model predictive control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. This paper focuses on exploring the inclusion of state estimates and their interaction with constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. Using a gaussian assumption, the original problem is approximated by a standard deterministically-constrained MPC problem for the conditional mean process of the state. The state estimates’ conditional covariances appear in tightening the constraints. ‘Closed-loop covariance’ is introduced to reduce the infeasibility and the conservativeness caused by using long-horizon, open-loop prediction covariances. The resulting control law is applied to a telecommunications network traffic control problem as an example.     

浅析互联网络管理中存在的问题及解决对策

互联网络在给人们提供便捷的同时,也给网络犯罪分子提供了很大的“施展”空间.在此情况下,互联网络面临严峻的监控与管理问题.为了解决这个问题,我们将首先对互联网络管理中存在的问题进行描述.然后,对其问题形成的原因进行分析.在此基础上,结合新时期互联网络发展的特点,提出解决问题的对策和措施.     

论信息系统网络改造规划

中国电子口岸数据中心建设十余年来,随着业务不断增长,已经发展成为以口岸通关管理向物流商务延伸的统一网络信息平台。作为直属关的xx分中心,急需对现有的网络进行安全等相关方面扩容和改造及相关项目的跨平台对接,实现业务延伸。     

如何对校园网络的性能进行监视与维护

本文主要分析如何定期收集各种信息,分析网络中各种资源的使用率,及时发现影响网络性能的"瓶颈",并做出相应的优化或者增加网络设备从而提高网络性能.     

A knowledge and collaboration-based CBR process to improve network performance-related support activities

In a context characterized by a growing demand for networked services, users of advanced applications sometimes face network performance troubles that may actually prevent them from completing their tasks. Therefore, providing assistance for user communities that have difficulties using the network has been identified as one of the major issues of performance-related support activities. Despite the advances network management has made over the last years, there is a lack of guidance services to provide users with information that goes beyond merely presenting network properties. In this light, the research community has been highlighting the importance of User-Perceived Quality (UPQ) scores during the evaluation of network services for network applications, such as Quality of Experience (QoE) and Mean Opinion Score (MOS). However, despite their potential to assist end-users to deal with network performance troubles, only few types of network applications have well established UPQ scores. Besides that, they are defined through experiments essentially conducted in laboratory, rather than actual usage. This paper thus presents a knowledge and Collaboration-based Network Users’ Support (CNUS) Case-Based Reasoning (CBR) Process that predicts UPQ scores to assist users by focusing on the collaboration among them through the sharing of their experiences in using network applications. It builds (i) a knowledge base that includes not only information about network performance problems, but also applications’ characteristics, (ii) a case base that contains users’ opinions, and (iii) a user database that stores users’ profiles. By processing them, CNUS benefits users through the indication of the degree of satisfaction they may achieve based on the general opinion from members of their communities in similar contexts. In order to evaluate the suitability of CNUS, a CBR system was built and validated through an experimental study conducted in laboratory with a multi-agent system that simulated scenarios where users request for assistance. The simulation was supported by an ontology of network services and applications and reputation scheme implemented through the PageRank algorithm. The results of the study pointed to the effectiveness of CNUS, and its resilience to users’ collusive and incoherent behaviors. Besides that, they showed the influence of the knowledge about network characteristics, users’ profiles and application features on computer-based support activities.