ATM网络ABR流量的比例与滞后-超前控制

为了克服异步传输模式(ATM)网络不确定时延和饱和非线性的影响,基于比例控制和滞后超前控制方法研究可用比特率(ABR)服务流量控制器设计问题,给出了控制幅值约束和缓冲区容量约束下系统满足ABR流量控制要求的条件,提出了满足这些条件的比例控制器和滞后超前控制器的设计方法。该方法能使ABR流量控制系统在网络传输时延不确定时,有效控制网络中瓶颈节点缓冲区的队列长度,同时能够及时响应ABR可用带宽的变化,从而避免拥塞和丢包的发生,使链路带宽得以充分利用。     

基于虚拟延迟矢量的核心无状态分组调度框架DVRS

现有提供确定性性能保证的核心无状态分组调度策略中,可扩展性的获得通常以中间节点的速率损失为代价,从而导致数据传输突发性的增强,降低了网络资源的利用率。为了减少速率损失,提出一个核心无状态调度框架DVRS(de lay vector reference system),通过运用虚拟延迟矢量技术,在保证端到端延迟上限与V irtua lC lock算法相同的同时,将核心无状态算法的基本速率保证能力从现在的首尾点精确保证提高到逐点精确保证;并利用局部平均法满足用户对处理开销与带宽保证精度的不同需求;另外,由于核心节点操作的相似性,DVRS类算法具有较好的配置渐进性。     

复杂动态网络模型研究进展

对复杂动态网络模型的研究现状做了综述,总结了复杂网络的演化机制与属性特征,提出了以复杂动态网络为虚拟计算平台和理论基础,开展大型Ad Hoc网络、传感器网络的网络性能、网络规划、信号增强、拓扑优化的一系列研究课题.     

Techniques of Image Processing Based on Artificial Neural Networks

This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.     

基于混合编码框架的H.264/AVC核心新技术

介绍了H.264独有的特点,包括帧内预测、整数余弦变换、二进制自适应算术编码、概念性网络适配、多种块尺寸运动搜索、1/8像素运动估计精度等。     

Adaptive control of system with hysteresis using neural networks

1.INTRODUCTIONThe piezoelectric actuators are well suited for micro-position devices in precision engineering because oftheir fast response,nanometer resolution and biggerdriving force[1].However,hysteresis inherent topiezoelectric actuator severely li mits system’s perfor-mance such as giving rise to undesirable accuracy oroscillations,even leading to instability.Hysteresischaracteristics are generally nondifferentiable andusually unknown.It is a difficult task to mitigate itsharmful ef…     

Multicast routing algorithm of multiple QoS based on widest-bandwidth

1 .INTRODUCTIONA mobile Ad hoc network refers to a temporal multi-hop autonomy system,whichis constituted by a groupof mobile nodes containing wireless send-receive equip-ments[1].It possesses manycharacteristics-fast-deploy-able,self-organized,acentric , movable and of multi-hop.It can be wildly applied to many fields ,such asnational defense,emergency and disaster ,scientificin-vestigation and exploration etc .,and uponthat ,it hasgreat prospects[1 ,3 6]. With the wide spread of mo-bile…     

Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks

In this paper, a method of direct multi-step prediction of chaotic time series is proposed, which is based on Kolmogorov entropy and radial basis functions neural networks. This is done first by reconstructing a phase space using chaotic time series, then using K-entropy as a quantitative parameter to obtain the maximum predictability time of chaotic time series, finally the predicted chaotic time series data can be acquired by using RBFNN. The application considered is Lorenz system. Simulation results for direct multi-step prediction method are compared with recurrence multi-step prediction method. The results indicate that the direct multi-step prediction is more accurate and rapid than the recurrence multi-step prediction within the maximum predictability time of chaotic time series. So, it is convenient to forecast and control with real time using the method of direct multi-step prediction.     

一种基于改进径向基神经网络的人脸图像识别方法

常用于径向基神经网络中心参数学习的K-均值聚类算法,易受初始参数选取的影响而收敛于局部极小值.将自动终止聚类判据的减聚类算法用于径向基网络的学习,可根据样本集确定径向基函数数目,且其计算量与数据点的数目与考虑问题的维数无关,很适合于人脸这种维数较高的模式.实验证明,应用这种算法训练径向基神经网络识别人脸,从识别精度到识别速度上都优于传统算法.     

深基坑降水与地面沉降变形三维耦合数值模拟

以比奥固结理论为基础，考虑土体的非线性特征及土的渗透性随应力状态的动态变化，将地下水渗流场和土体应力场进行耦合，建立了深基坑降水与地面沉降变形的水土全耦合三维数学模型，并采用三维有限元数值分析方法，以上海市环球金融中心深基坑降水为例，模拟预测了基坑中心水位降至标高-23．4m时基坑周围地下水渗流场与地面沉降变形场的分布特征．结果表明，该模型稳定性好，收敛速度快，能模拟复杂三维地质体和整个基坑降水工程的结构。