引入信息学习和绑定代理的FMIPv6切换方案

针对FMIPv6和F-HMIPv6存在的切换延迟长和丢包率高的问题,提出了一种引入信息学习和绑定代理的FMIPv6改进方案。该方案通过引入信息学习机制,将新转交地址配置、重复地址检测DAD和隧道建立等工作提前完成;并使用隧道定时器,按需设定路由器之间的隧道生存时间;结合代理机制,使得绑定注册过程在二层切换前由路由器代理执行。NS-2仿真结果表明,改进方案在切换延迟和丢包率方面的性能优于FMIPv6和F-HMIPv6。     

Network State Estimation and Prediction for Real-Time Traffic Management

Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) have the potential to contribute to the solution of the traffic congestion problem. DynaMIT is a real-time system that can be used to generate guidance for travelers. The main principle on which DynaMIT is based is that information should be consistent, and user optimal. Consistency implies that the traffic conditions experienced by the travelers are consistent with the condition assumed in generating the guidance. To generate consistent user optimal information, DynaMIT performs two main functions: state estimation and prediction. A demand simulator and a supply simulator interact to perform these tasks. A case study demonstrates the value of the system.     

路由选择的多Agent系统模型

路由选择是曜网的关键技术之一,其优劣直接影响着网络的接通率和负载平衡程度,我国长途电信网的率只有４５％左右,链路的利用率又仅为３０％～４０％,据介绍,若其接通率提高一个百分点,收益可达１０亿元,随着业务量的飞速增长,路由选择问题日益突出,文中在分析目前程控交换机使用的路由选择方法的不足的基础上,提出基于多Ａｇｅｎｔ系统和神经网络预测的智能路由策略,介绍系统模型和选择方法,它因良好的分布特性和智能决     

无线Ad Hoc网络MAC层关键技术研究

无线Ad Hoc网络是一种无中心、自组织的多跳网络,其MAC层的研究是近年来的研究热点。在MAC层的研究中,有一些关键的技术问题有待解决,包括隐藏终端和暴露终端问题,公平性问题,QoS保证,速率自适应等。文章详细论述了每一个问题,并提供了一些解决的思路。     

Convergence analysis of convex incremental neural networks

Recently, a convex incremental algorithm (CI-ELM) has been proposed in Huang and Chen (Neurocomputing 70:3056–3062, 2007), which randomly chooses hidden neurons and then analytically determines the output weights connecting with the hidden layer and the output layer. Though hidden neurons are generated randomly, the network constructed by CI-ELM is still based on the principle of universal approximation. The random approximation theory breaks through the limitation of most conventional theories, eliminating the need for tuning hidden neurons. However, due to the random characteristic, some of the neurons contribute little to decrease the residual error, which eventually increase the complexity and computation of neural networks. Thus, CI-ELM cannot precisely give out its convergence rate. Based on Lee’s results (Lee et al., IEEE Trans Inf Theory 42(6):2118–2132, 1996), we first show the convergence rate of a maximum CI-ELM, and then systematically analyze the convergence rate of an enhanced CI-ELM. Different from CI-ELM, the hidden neurons of the two algorithms are chosen by following the maximum or optimality principle under the same structure as CI-ELM. Further, the proof process also demonstrates that our algorithms achieve smaller residual errors than CI-ELM. Since the proposed neural networks remove these “useless” neurons, they improve the efficiency of neural networks. The experimental results on benchmark regression problems will support our conclusions. The work is under the funding of Singapore MOE AcRF Tier 1 grant WBS No: R 252-000-221-112.     

改进的RBF神经网络在磨矿指标预测中的应用

针对选矿生产过程中磨矿生产率和磨矿浓度、介质填充率和料球比3个指标之间的关系,分析了磨矿生产率对磨矿过程产品质量和工作效率的影响,提出了磨矿过程预测模型,采用了模糊聚类与改进的遗传算法相结合训练RBF神经网络的新方法。仿真结果表明,该模型可以快速准确地预报生产状况,对于企业进行实时生产控制具有较高的实用价值。     

Online face recognition system based on multi-view algorithm and Kernel PCA

An online face recognition system is presented in the paper. To online face recognition system, we should consider the recognition rate, the image compression and image size. In the paper we researched the innovation technologies for face recognition system, including Kernel Principal Component Analysis （Kernel PCA）, Delta low-pass wavelet filter, and face recognition algorithm using multiple images. Kernel PCA is derived to classify the characteristics of training images in the database. Delta low-pass wavelet filter is used to reduce the image size. A face recognition algorithm using multiple images is presented to improve the recognition rate. Simulation experiment shows that in the case of packet loss recognition rate is improved highly.     

MCLP-based methods for improving “Bad” catching rate in credit cardholder behavior analysis

Cardholders’ behavior prediction is an important issue in credit card portfolio management. As a promising data mining approach, multiple criteria programming (MCLP) has been successfully applied to classify credit cardholders’ behavior into two groups. In order to better control credit risk for financial institutes, this paper proposes three methods based on MCLP to improve the “Bad” catching accuracy rate. One is called MCLP with unbalanced training set selection, the second is called fuzzy linear programming (FLP) method with moving boundary, and the third is called penalized multi criteria linear programming (PMCLP). The experimental examples demonstrate the promising performance of these methods.     

Effects of shear rate and small periodic corrugation on the slip velocity in microscopic domain

We obtain analytically the slip velocity (up to the second order) of shear-thinning fluids inside the periodically corrugated microtube by using the verified fluid model and boundary perturbation method. Our results show that, even the slip length being zero, there is a slip velocity which is proportional to the small amplitude of periodic corrugation, the (referenced) shear rate, the applied forcing, and the orientation or the angle. Our results could be applied to the flow control in microfluidics as well as biofluidics.