一种传感器网络应用的面向服务体系结构*

为了解决传感器网络和Internet之间的互联以及应用问题,提出了面向服务的体系结构。该体系结构将传感器网络应用的接口和交互作为一种网页服务进行封装。这样用户可以通过HTTP来查找、定位和唤醒这些服务。将传感器网络按功能分为核心服务集合和辅助服务集合。通过应用层的接口来调用传感器网络的功能,为应用开发者提供一个统一的标准,不需要考虑底层的实现,加强了应用开发的通用性和开发速度。描述了该体系结构的实验环境,并与典型应用场景相结合,融合核心服务和辅助服务。研究显示,该体系结构可以促进传感器网络应用的快速发展,有着广阔的发展前景。     

Simulation of heterogeneous neural networks on serial and parallel machines

There has recently been a tremendous rebirth of interest in neural networks, ranging from distributed and localist spreading-activation networks to semantic networks with symbolic marker-passing. Ideally these networks would be encoded in dedicated massively-parallel hardware that directly implements their functionality. Cost and flexibility concerns, however, necessitate the use of general-purpose machines the simulate neural networks, especially in the research stages in which various models are being explored and tested. Issues of a simulation's timing and control become more critical when models are made up of heterogeneous networks in which nodes have different processing characteristics and cycling rates or which are made up of modular, interacting sub-networks. We have developed a simulation environment to create, operate, and control these types of connectionist networks. This paper describes how massively-parallel heterogeneous networks are simulated on serial machines as efficiently as possible, how large-scale simulations could be handled on current SIMD parallel machines, and outlines how the simulator could be implemented on its ideal hardware, a large-scale MIMD parallel machine.     

无线自组织网中的入侵检测技术

主要对无线自组织网络中的入侵检测技术做一综述,并介绍了多种不同的适用无线自组织网络的入侵检测技术,最后为无线自组织网络提出一种混合式的入侵检测系统模型,对无线自组网入侵检测技术研究有所帮助。     

Innovation and regional development

This paper is based on the knowledge gained from working with two industrial networks from the county of Østfold in Norway. There is a particular focus on the creation of networking arenas and on the development of these arenas for better co-operation between the companies in the network. Much of the empirical knowledge has been achieved by interviewing participants from the networks and by active participation in co-operation projects. The theme is innovation processes in industrial networks, with special attention on how it is possible to enhance the innovation capacity of the networks. We discuss innovation processes in industrial networks as a part of regional innovation systems, because innovation processes in industrial networks cannot be fully understood without understanding how this also relates to the surroundings. We discuss how good relationships, continuous change and strategies for knowledge creation are important factors for innovation in networks. This could be achieved by following the principles from co-generated learning processes (such as a search conference). The principles could be adapted and specially designed for each network in a way that increases the quality and efficiency of the co-operation processes.     

Improved gradient-based neural networks for online solution of Lyapunov matrix equation

By adding different activation functions, a type of gradient-based neural networks is developed and presented for the online solution of Lyapunov matrix equation. Theoretical analysis shows that any monotonically-increasing odd activation function could be used for the construction of neural networks, and the improved neural models have the global convergence performance. For the convenience of hardware realization, the schematic circuit is given for the improved neural solvers. Computer simulation results further substantiate that the improved neural networks could solve the Lyapunov matrix equation with accuracy and effectiveness. Moreover, when using the power-sigmoid activation functions, the improved neural networks have superior convergence when compared to linear models.     

梯度神经网络实时求解线性方程组之收敛性分析

探讨归纳了一类用于在线求解线性方程组的梯度神经网络, 并且证明了该类梯度网络具有全局指数收敛特性, 而非以往提出的渐进收敛特性. 此外, 相对于使用线性激励函数的情况, 当使用幂S形激励函数时网络具有更好的收敛效果. 计算机仿真结果进一步验证了上述分析的准确性和该网络求解线性方程组问题的有效性.     

A new clustering algorithm based on data field in complex networks

The evaluation of node importance in complex networks has been an increasing widespread concern in recent years. Seeking and protecting vital nodes is important to ensure the security and stability of the whole network. Existing clustering algorithms of complex networks all have certain drawbacks, which could not cover everything in calculation accuracy and time complexity, and need external supervision. To design a fast complex networks clustering method is a problem which requires to be solved immediately. This paper proposes a clustering algorithm of complex networks based on data field using physical data field theory, which excavates key nodes in complex networks by evaluating the importance of nodes based on a mutual information algorithm, and then uses it to classify the clusters. To verify the validity of the algorithm, a simulation experiment was conducted. The results indicated that the algorithm could analyze the cluster exactly and calculate with high-speed, it could also determine the granularity of a partition according to the actual demand.     

Neural networks-then and now

The emergence of neural networks as a significant subdiscipline with corresponding attempts at application to engineering problems is traced back to the 1960s, when Frank Rosenblatt, a Cornell University psychologist, showed by mathematical analysis, digital computer simulation, and experiments with special-purpose parallel analog systems that neural networks with variable-weight connections could be trained to classify spatial patterns into prespecified categories. In his attempts to provide biologically plausible explanations of the function of the central nervous system, he investigated relatively simple networks that were amenable to analysis and more complex networks whose behavior could be predicted only in terms of gross characteristics. He assembled a sizable group involving theoreticians, experimentalists, technologists, and, later, biologists. His work caught the imagination of the press and led to the wave of febrile activity that subsided at the end of that decade.     

基于神经网络的蜂窝纸板缓冲特性建模

以蜂窝纸板的静态压缩实验数据为训练样本，建立了蜂窝纸板静态缓冲特性的神经网络模型，并对网络训练算法进行了一些改进，仿真结果表明蜂窝纸板的神经网络特性模型较好地模拟了实际的蜂窝纸板缓冲特性，训练误差均控制在5％以内，可满足工程设计的要求。     

基于子图显著性剖面的软件超家族

研究了开源软件的网络结构中3节点子图的显著性,发现软件规模越大局部结构的网络化趋势越显著.树型的3节点子图呈现下降趋势,在封闭的3节点子图中,除部分趋势不显著外,大都呈现上升趋势.根据3节点子图的显著性剖面,软件网络大致可以分为3类,与已发现的4个有向复杂网络超级族中的3个基本一致,大部分软件网络的局部结构与生物网络相...     

宽带自组织网络信道分配和接入技术研究与仿真

本文研究了当前多种有效的多信道自组织网络信道分配和接入技术,提出一种结合CSMA和TDMA两种接入方式,能适用于宽带无线自组织网络的信道共享机制,并利用OPNET仿真工具构建模拟的自组织网络场景,对提出的网络协议进行了性能分析,为下一步研究工作打下了基础.     

Multiple network fusion using fuzzy logic

Multiplayer feedforward networks trained by minimizing the mean squared error and by using a one of c teaching function yield network outputs that estimate posterior class probabilities. This provides a sound basis for combining the results from multiple networks to get more accurate classification. This paper presents a method for combining multiple networks based on fuzzy logic, especially the fuzzy integral. This method non-linearly combines objective evidence, in the form of a network output, with subjective evaluation of the importance of the individual neural networks. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.     

智能微尘传感网的轻量协作式认证算法*

提出一种轻量协作式认证算法（LCAA）,该算法使用简单的对称密钥及其生成函数和节点唤醒机制为智能微尘传感网通信提供了基本的安全,通过与其他认证算法协作,可以进一步提高网络的安全性,并可应用于普通无线传感网络中。通过仿真实验证明,该算法具有良好的安全性和能效,适用于智能微尘传感网。     

A robust ant colony optimization-based algorithm for community mining in large scale oriented social graphs

Community detection plays a key role in such important fields as biology, sociology and computer science. For example, detecting the communities in protein–protein interactions networks helps in understanding their functionalities. Most existing approaches were devoted to community mining in undirected social networks (either weighted or not). In fact, despite their ubiquity, few proposals were interested in community detection in oriented social networks. For example, in a friendship network, the influence between individuals could be asymmetric; in a networked environment, the flow of information could be unidirectional. In this paper, we propose an algorithm, called ACODIG, for community detection in oriented social networks. ACODIG uses an objective function based on measures of density and purity and incorporates the information about edge orientations in the social graph. ACODIG uses ant colony for its optimization. Simulation results on real-world as well as power law artificial benchmark networks reveal a good robustness of ACODIG and an efficiency in computing the real structure of the network.     

遗传BP神经网络在大宗商品市场风险预警中的应用

研究利用遗传BP神经网络预警大宗商品电子交易市场风险的应用方法,将定量分析的思维方式引入大宗商品市场风险评价管理中.为此目的,建构了一个基于遗传BP神经网络的预警模型（GA-BPNNM）,在市场调研的基础上建立了大宗商品电子交易市场风险评价指标体系,并通过实验确定了预警模型的最佳训练函数和隐层的最佳节点数.GA-BPNNM借助BP神经网络强大的自学习能力和非线性映射能力,克服传统手段在分析大宗商品电子交易市场风险时因其定义的模糊性和诱发因素的多样性所带来的困难;同时通过遗传算法与BP网络两者相互融合优化,解决BP神经网络易落入局部最优、收敛速度慢以及遗传算法易早熟等问题.仿真测试实验表明,GA-BPNNM预测结果优于标准BP神经网络预测方法,用于大宗商品电子交易市场风险损失程度预警是有效可行的.     

Applications of neural network prediction of conformational states for small peptides from spectra and of fold classes.

Electronic structures of small peptides were calculated 'ab initio' with the help of Density Functional Theory (DFT) and molecular dynamics that rendered a set of conformational states of the peptides. For the structures of these states it was possible to derive atomic polar tensors that allowed us to construct vibrational spectra for each of the conformational states with low energy. From the spectra, neural networks could be trained to distinguish between the various states and thus be able to generate a larger set of relevant structures and their relation to secondary structures of the peptides. The calculations were done both with solvent atoms (up to ten water molecules) and without, and hence the neural networks could be used to monitor the influence of the solvent on hydrogen bond formation. The calculations at this stage only involved very short peptide fragments of a few alanine amino acids but already at this stage they could be compared with reasonable agreements to experiments. The neural networks are shown to be good in distinguishing the different conformers of the small alanine peptides, especially when in the gas phase. Also the task of predicting protein fold-classes, defined from line-geometry, seems promising.     

具有时滞的双向联想记忆神经网络的定性分析

文中通过经入一个关键性条件研究了具有时滞的双向联想记忆神经的定性性质。这一关键性条件关联了网络双向的联结权系数。在这一条件下,证明了网络存在唯一的平衡态,并且这一平衡态是全局指数稳定的。当网络的外部输入具有周期性时,利用上述条件证明了网络存在全局平稳振荡。     

Ad hoc网络中多媒体会议切换机制的研究

提出了一种适用于Adhoc网络中的多媒体会议系统模型,该模型结合了移动IPv6技术和SIP(session initiation protocol)技术,并对传统的AODV(Ad hoc on-demand distance vector)路由算法路由进行了修改,使其可以很好地匹配系统模型.将传统的AODV算法和修改后的算法应用到系统模型中,对会话中移动节点在不同Ad hoc网络间的切换进行了研究.仿真结果显示,修改后的AODV路由算法性能可以将切换时的时延抖动和分组丢失率改善50%左右.     

基于随机网络集成模型的广义网络社区挖掘算法

根据结点的属性和链接关系,现实世界中的复杂网络大多可分为同配网络和异配网络,社区结构在这两类网络中均普遍存在. 准确地挖掘出两种不同类型网络的社区结构具有重要的理论意义和广泛的应用领域.由于待处理的网络类型通常未知, 因而难以事先确定应当选择何种类型的网络社区挖掘算法才能获得有意义的社区结构. 针对该问题, 本文提出了广义网络社区概念,力图将同配和异配网络社区结构统一起来. 本文提出了随机网络集成模型, 进而提出了广义网络社区挖掘算法G-NCMA. 实验结果表明: 该算法能够在网络类型未知的前提下准确地挖掘出有意义的社区结构, 并能分析出所得社区的类型特征.