基于最大似然估计的空间谱测向技术

介绍了基于最大似然估计（简称ML）的空间谱测向,ML是一种高分辨算法,常用于空间测向和谱估计等方面。文中首先介绍了ML测向原理,由于ML是一个非线性、多维的极值问题,需要全局极值的多维搜索,其计算量非常的巨大,因此采用了AP算法进行ML算法的优化,最后通过均匀圆阵对非相干信号和相干信号进行了ML测向的计算机仿真,仿真结果表明：一个阵列对空间多个信号的分辨能力除了与信噪比有关外,还与入射信号是否相干以及两相干信号的相位差有关,ML算法可以直接对相干的入射信号进行测向,但是信号的相干性会影响ML算法测向的角度分辨率,在两信号相关的情况下,两信号间的相位差越大则测向精度越高。     

基于词性的文本挖掘算法在IDS日志中的应用

提出一种以词性为参考值的文本挖掘算法,能有效挖掘与种子词有关的关联规则。基于Bootstrapping算法思想,既减少了预处理阶段对于词根还原的依赖,能处理日志中出现的中文词汇。增加了对于日志文本上下的理解,提高了关联规则的有效性,并应用与IDS日志挖掘之中,有效改善挖掘效率,为规则库提供关联规则。     

基于NS-2的视频流仿真方法研究

MPEG标准将视频图像序列编码为I帧、P帧和B帧,具有压缩比高、节省网络带宽的重要特性。随着视频会议等网络多媒体应用在因特网上的部署,视频流的流量特性的仿真研究具有越来越重要的作用。文中对MPEG视频流的流量特性及其仿真方法进行了研究。在分析MPEG编码技术原理的基础上,用C／C＋＋语言扩展了NS-2仿真器内核,添加了MPEG视频流量产生器模块,对NS-2中的流量产生器模块及视频仿真接口进行了扩展。实验结果表明,所提出的NS-2视频流仿真扩展方法是可行的和有效的。     

Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy wavelet support vector classifier machine to diagnosing the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory, wavelet analysis theory and v-support vector classifier machine, fuzzy wavelet v-support vector classifier machine (FWv-SVCM) is proposed. To seek the optimal parameters of FWv-SVCM, genetic algorithm (GA) is also applied to optimize unknown parameters of FWv-SVCM. A diagnosing method based on FWv-SVCM and GA is put forward. The results of the application in car assembly line diagnosis confirm the feasibility and the validity of the diagnosing method. Compared with the traditional model and other SVCM methods, FWv-SVCM method requires fewer samples and has better diagnosing precision.     

A method based on preference degrees for handling hybrid multiple attribute decision making problems

In this paper, we investigate hybrid multiple attribute decision making problems with various forms of attribute values (real numbers, linguistic labels, interval numbers, intuitionistic fuzzy numbers and interval intuitionistic fuzzy numbers). We propose a method based on preference degrees which may take the forms of fuzzy numbers, intuitionistic fuzzy numbers and interval intuitionistic fuzzy numbers. The method first normalizes various forms of attribute values into preference degrees, and then uses a preference degree-based weighted averaging operator to aggregate the normalized preference degrees. Meanwhile, for convenience of calculation, a new linguistic representation model is presented, whose feasibility is verified by comparing it with the traditional 4-tuple linguistic representation model, and from our model, the mapping relationship between interval intuitionistic fuzzy numbers and linguistic labels can be constructed. Finally, we illustrate the rationality and practicality of the proposed method by an application example.     

Car assembly line fault diagnosis based on triangular fuzzy Gaussian support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of Gaussian noises and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating fuzzy theory, Gaussian loss function and v-support vector classifier machine, the fuzzy Gaussian v-support vector regression machine (Fg-SVCM) is proposed. To seek the optimal parameters of Fg-SVCM, the modified genetic algorithm (GA) is also applied to optimize parameters of Fg-SVCM. A diagnosing method based on Fg-SVCM and GA is put forward. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on Fg-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other v-SVCMs.     

A new control method for MIMO first order time delay non-square systems

This paper proposes a new method using internal model control (IMC) to design Smith delay compensation decoupling controller for multivariable non-square systems with transfer function elements consisting of first order + time delay. This proposed method is applied to a shell control problem in multiple-input-multiple-output (MIMO) first order plus dead time non-square systems in which the number of input variables exceeds the number of output variables, with input and output variables being 3 and 2 respectively. This method does not only dynamically compensate for shortcoming caused by static decoupling but also overcomes the impact of model error on system performance caused by model approximation and uncertainty. In other words, the design method proposed in this paper is capable of significantly improving dynamic quality and robustness of the control system as can be seen from the simulation results. Moreover, this new method is simple and easy to implement. Integral of squared error (ISE) performance criterion is employed to quantitatively evaluate the design method.     

一种面向连续数据保护的分布式存储模型研究

一个典型的连续数据保护(CDP,Continuous Data Protection)系统必须具有一个高效的存储系统来支撑.本文提出了一种分布式的存储模型,在此基础之上设计了一个面向CDP应用的分布式存储系统.同传统CDP系统相比,本文提出了分布式存储应用于CDP应用,将文件级和块级的数据存储都归纳到一个存储系统中;通过引入了元数据和数据分离的思想,并且把数据分级存储.根据原型系统的理论论证以及测试,结果表明,论文所提存储模型很好的解决了CDP服务器低效率,高负荷的问题,并且具有低成本的优势.     

视频点播系统的软件老化估计和预测

针对视频点播系统,研究其软件老化模式,对系统资源和视频点播服务器的实时参数,采用Mann-Kendall方法来检测老化趋势以判断系统是否存在软件老化现象,并采用Sen的斜率估计方法来估计老化衰退速率;提出了基于径向基网络的软件老化预测模型,对老化趋势进行预测,并采用主成分分析方法来减少径向基网络的复杂度以提高效率.实验结果表明:视频点播系统中存在软件老化现象;基于径向基网络的软件老化预测模型预测效果优于时间序列模型.基于提出方法以及对视频点播系统的老化分析,可为进一步研究相应的软件再生策略提供理论依据.     

列存储数据仓库查询执行中重用缓冲区调度算法

查询的中间结果重用是提高查询效率的重要手段.现有列存储系统主要关注多查询计划间的中间结果重用,忽略了单一查询计划执行过程中大量可重复访问的中间结果.单一查询中的中间结果具有确定性高、结果大小可估计的特征,非常适合作为重用的对象.为此,针对列存储数据仓库单一查询计划执行过程中的中间结果重用问题,提出了一个重用缓冲区空间的...