局域网网络流量监控与管理方案

网络流量监控是网络管理的重要组成部分,该文通过对局域网使用情况的分析,提出一个单位局域网的网络管理方案,通过带宽的分配和限制屏蔽网络流氓软件,检测网络异常行为,提高网络的使用效率。     

校园网络流量自相似性分析与研究

对于校园网等小规模的局域网,通过计算网络流量自相似值的方法无法有效检测网络异常流量。针对该问题,在分析校园网络流量特点的基础上,将网络流量分解成趋势项和随机成分等其他项,使用经验模式分解消除网络流量中的趋势项,使得网络流量序列的自相似值能直接反映随机成分状态。实验结果表明,该方法能提高异常流量检测的准确性。     

局域网拓扑结构优化的探讨

以局域网拓扑结构为研究对象,对局域网的需求分析、技术选型、设备选择和安全防范的优化方法进行了探讨,并且提出了使用NLB技术在网络负荷、控制网络流量方面的应用解决方案。     

基于Web的网络流量监测系统的设计

网络流量测量是认识网络规律、了解网络行为的前提工作。该文从园区网络流量监测入手，通过对物理网络拓扑自动发现、网络拓扑自动布局以及对RMON2规范的研究，给出了能够分析网络流量成份的流量监测方案，设计并实现了一个基于Web的园区网络流量监测系统。通过对该系统的实施，可以有效地监控网络流量状况。     

自适应伪随机序列混合网络隐蔽通道构建方法

现有网络隐蔽通道研究主要集中于存储型、时间型和序列型等单一模式,单一网络隐蔽通道对网络流量影响较大,隐蔽性差,综合使用多种模式构建混合型隐蔽通道方法鲜见,且混合使用方法无法根据网络流量动态调整。提出了一种新的混合使用多种隐蔽通道的方法,建立可以根据当前网络流量特征自适应变化的隐蔽通道构建框架,通过网络流量在不同隐蔽通道下的隐蔽性强弱生成具有一定分布的伪随机序列,使用该伪随机序列在不同的模式中写入隐蔽信息,使得隐蔽信息在信息传输的过程中在多种通道中随机存在,在传输隐蔽信息的同时降低对原始流量的影响,并使隐蔽通道可以实时根据网络流量动态调整使用模式。真实网络环境中实验结果证明,该类隐蔽通道具有较强的隐蔽性和自适应性,实用价值明显。     

对网络管理中的流量监测方法探讨

随着现代网络技术的飞速发展,通过对网络流量的监测,及时发现企业局域网内流量异常的主机,或者根据系统设置的阈值提前预警,从而更好的保护正常业务对网络带宽的需求,是网络技术发展的必然要求.本文从网络流量的特性,网络流量的测量等方面做了探讨,从而为流量监测技术的优化提出了一些建议.     

一个网络管理软件中基于自适应的流量预测

传统的基于流量的网络报警系统往往根据系统配置的固定阈值来判断当前网络流量是否正常，通过运用统计学和预测学对网络流量预测方法进行研究。提出了一种能根据网络实际流量状态自己调整阈值的新型网络报警机制，能有效地发现网络流量异常，从而为评估和优化网络性能提供有力的依据。     

Linux的网络流量监测

本文旨在介绍基于Linux的网络流量监测系统,本系统是一个基于TCP/IP协议的网络应用程序,它提供了一个能够对局域网内部网络数据流量的详尽统计的有用工具,为网络管理提供了重要手段。其使用对象为各学校、科研单位、政府部门、公司企业的网络管理中心。该系统通过对以太网数据包的获取、分析、处理,即对Arp包、IP包的收集、分析,对IP包协议TCP、UDP、ICMP的分析、处理,从而达到对网络流量的监测和统计。     

互联网流量的监管技术研究

由于互联网规模的不断扩大，网络流量也变得越来越复杂，所面临的网络管理问题日益繁重。本文对传统网络流量管理系统的运行模式进行了探讨，论证了通过浏览器实现网络流量管理，是一项非常有现实意义的工作。     

无线网络教室的监测与控制技术

本文针对无线网络教室的应用场景，分析了无线局域网中音视频点播应用的特点．提出并实现了对网络传输质量的监测和控制。通过对无线网络的监听和网络流量的测量，分析网络负载状况．进行准入控制和分级服务，以实现网络带宽的有效利用．提高无线网络的服务质量。     

On the language equivalence of NE star-patterns

A pattern is a finite string of constant and variable symbols. The language generated by a pattern is the set of all strings of constant symbols which can be obtained from the pattern by substituting (non-empty) strings for variables. The pattern languages are one of language family which is orthogonal to the Chomsky-type languages hierarchy. They have many applications, such as the extended regular expressions, for instance, in languages Perl, awk, etc. They are well applicable in machine learning as well. There are erasing and non-erasing patterns are used. For non-erasing pattern languages the equivalence of languages is decidable but the inclusion problem for them is undecidable. In extended regular expressions one can use union, concatenation and Kleene star to make more complex patterns. It is also known, that the equivalence problem of extended regular expressions is undecidable. However, the problem, whether the equivalence is decidable for patterns using only concatenation and star still open. In this paper there are some interesting results about inclusion properties and equivalences of some kinds of erasing and non-erasing pattern languages. We show that the equivalence problem of non-erasing patterns in some cases can be reduced to the decidability problem of some very special inclusion properties. These results may be useful to decide whether the language equivalence of non-erasing star-patterns is decidable or not.     

Neural net based torque sensor using birefringent materials

Photoelasticity can be used to accurately measure surface strains or stresses in a part or structure. In this paper we describe the use of a photoelastic transducer and neural net image processing to estimate the torque of stationary and rotating shafts. A strain sensitive (photoelastic) plastic cylinder is attached to the shaft and illuminated by polarized light. As the shaft torque varies the photoelastic plastic displays the corresponding shaft strain as a two-dimensional fringe pattern when viewed through an optical polarizer. The strain that causes this observed optical pattern is a complex function of the torque applied to the shaft. In this paper, we describe the use of neural net image processing to estimate this function to realize an optical torque sensor. A CCD camera/image processing system was used to acquire and process the optical patterns. A neural net torque estimator was trained with these fringe patterns and tested against a laboratory strain gauge torque sensor. Our experiments show that the neural net torque estimator can accurately estimate (to within a few percent) the applied torque for both static and slowly rotating (<20 rpm) shafts.     

模糊神经网络两相流流型辨识算法的研究

针对电容测量灵敏度分布易受被测两相流介质分布的影响,对由电容传感器阵列采集的反映两相流介质分布的信息进行模糊处理,经Ｋｏｈｏｎｅｎ 网络的自组织学习,提取输入模式样本的分类特征,通过ＢＰ网络的监督学习,可实现两相流流型的有效判别和分类     

Weight smoothing to improve network generalization

A weight smoothing algorithm is proposed in this paper to improve a neural network's generalization capability. The algorithm can be used when the data patterns to be classified are presented on an n-dimensional grid (n>/=1) and there exists some correlations among neighboring data points within a pattern. For a fully-interconnected feedforward net, no such correlation information is embedded into the architecture. Consequently, the correlations can only be extracted through sufficient amount of network training. With the proposed algorithm, a smoothing constraint is incorporated into the objective function of backpropagation to reflect the neighborhood correlations and to seek those solutions that have smooth connection weights. Experiments were performed on problems of waveform classification, multifont alphanumeric character recognition, and handwritten numeral recognition. The results indicate that (1) networks trained with the algorithm do have smooth connection weights, and (2) they generalize better.     

On Pattern Separating Function in a Two－Layered Random Nerve Net with Feedforward Inhibitory Connections

In this paper,we derive a theory for investigating the pattern separating function in the nerve net ,then,we consider some effects of the stochastic parameters constructing the nerve net,firing rates of the second layer and input patterns on the pattern separating function through the stochastic parameters are selected proerly.we also show that the pattern separating function is enhanced by controlling the firing rate of the second layer to a small value.Furthermore,the input patterns are separated largely when the firing rates of them are large.     

生成最优二划分装盘方案的递归算法

针对物流领域的研究装盘问题,为提高装载效率,在矩形托盘中正交的布置最多数目同尺寸长方体小箱,并且小箱之间不发生重叠。采用二划分装盘方案。所谓"二划分",是指一条划分线总是贯穿被划分的区域,将区域划分为二个较小的矩形子区域。二划分方案可以通过使用水平或竖直的划分线,将装裁区域递归地二划分为若干个子区域,每一子区域有且仅有一个小箱。采用递归算法,通过适当设置成本参数,生成排数最少的最优二划分装盘方案。实验计算结果表明所述算法可以有效地简化装箱方案。     

基于扩展HTML的蒙太奇超文本模式

在人们寻求结构的或有规律的超文本过程中，除了使用形式化模型外，还有一种采用面向应用，简单、直观的方法。通过拓扑的和修辞的观察，总结出一些不同链的模式或结构。这些模式包括环型模式、对偶模式、镜像模式、缠结模式、筛选模式、蒙太奇模式等。而现有浏览器并不支持蒙太奇模式，该文开发了一个超文本系统，扩展HTML加入对蒙太奇模式的支持并获得成功。     

Single Criterion Net Flow Using Concordance and Discordance Relations

This article proposes a flow-based method using the single criterion net flow for pattern classification. Traditional single criterion net flow, generated on the basis of net partial concordance indices, measures the preference intensity for one pattern with respect to all others on this criterion. A concordance index measures the intensity of preference for one pattern over another pattern. Contrary to traditional flow-based methods, such as PROMETHEE, in which only the concordance relation is taken into account, the proposed method proposes a new definition of single criterion net flow using both concordance and discordance relations. This can balance pros and cons for the preference of one pattern over another pattern for a criterion. The final classification decision for a new pattern depends on its net flow. Criteria weights are determined using a genetic-algorithm-based approach. Empirical results involving bankruptcy prediction demonstrate that the proposed method performs well compared to other well-known classification methods.