半透膜仪采集空气中多氯联苯采样速度与其结构和性质定量构效关系研究

根据MOPAC2009软件包中PM6算法得到的分子描述符研究半透膜仪(SPMDs)采集大气中多氯联苯(PCBs)采样速度(R_(air))的定量构效关系(QSPR)模型,并分析影响R_(air)的关键因素。以半经验PM6算法得到的分子量子化学描述符作为预测变量,采用偏最小二乘算法(PLS)构建了R_(air)的QSPR模型。根据交叉验证,所得到的最佳模型中PLS成分解释的因变量的累积变异(Q~2_(cum))为0.683,这表明该模型具有良好的预测能力和稳健性。通过外部验证和将实验测得的R_(air)与预测得到的R_(air)进行比较,对所构建模型的稳定性和可靠性进行了验证,结果表明无论是训练组还是预测组,其预测值与实测值间均具有较好的线性关系,线性相关系数均大于0.8376。对PCBs采样速度R_(air)的主要影响因素为PCBs与SPMDs中甘油三油酸酯分子间的相互作用大小和为将PCBs溶解在甘油三油酸酯中形成洞穴所需能量要求。     

基于星地通信系统的变速率通信质量控制方法

为有效地提高星地通信系统的通信质量,提出了一种改变信息传输速率的方法VCTRM。该方法根据当前星地信道状况自适应地改变调制方式和码元传输速率,克服了采用变调制方式传输时在低信噪比时不能满足系统误比特率的缺陷,同时又解决了采用变码元传输速率在高信噪比时不能提高系统吞吐量的问题。通过Matlab对变调制方式、变码率及VCTRM三种传输方式进行了仿真,结果表明,VCTRM在不同信道状况下均能满足系统对误比特率(BER)的要求,且获得的系统吞吐量明显高于其他方法。     

基于各向异性质心Voronoi图的网络异常检测技术

网络异常检测技术是入侵检测研究领域中的重要内容,但在检测率和误报率上存在相互制约的问题,导致实际应用中性能不高。基于各向异性质心Voronoi图,提出一种新的网络异常检测算法。在新算法中,首先对数据集用各向异性质心Voronoi图进行聚类,然后计算每个数据点的点密度,判断数据点是否正常。通过KDD Cup1999数据集的实验测试表明,新算法具有较高的检测率和较低的误报率。     

卫星初始速度阻尼智能控制器设计

针对卫星入轨后的初始速率阻尼问题,提出一种基于人脑中情绪学习模型的在线自主自适应控制器。该控制器模拟人脑对感官输入和情绪刺激的处理过程,自主选择适当的控制信号,完成控制任务。设计了基于PID控制和PWPF调制的人脑情绪学习模型智能控制器用以完成卫星初始速率阻尼控制。仿真结果表明,该智能控制器对于卫星转动惯量的不确定性具有较强的鲁棒性,在线学习能力使得智能控制器的性能明显优于PID控制器。     

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.