基于FGMRES-PBTG算法的介质粗糙面散射特性的模拟

针对具有大介电常数介质粗糙面的电磁散射问题,提出了灵活的广义最小余量法(FGMRES)和基于物理意义的双网格法(PBTG)的混合算法,并对介质粗糙面的电磁散射系数和发射率进行了仿真.在PBTG算法的基础上,应用了FGMRES迭代算法求解矩阵方程.通过PBTG算法加速矩阵向量积和FGMRES算法加快迭代收敛速度,达到减少计算量和加快计算速度的目的.通过计算实例可知,FGMRES相对其他迭代法具有很好的收敛性,耗时较少,在满足精度要求的前提下,较其他迭代法明显加快了计算速度,并有效地模拟了介质粗糙面的电磁散射特性.     

一种基于块编码的压缩视频通信差控算法

提出了一种压缩视频传输的差错控制机制,联合编码端,信道和解码端对差错进行预防、控制、检测和修复,对压缩视频传输进行控制.并给出了算法的可行性分析.     

NETWORK INTRUSION DETECTION METHOD BASED ON RS-MSVM

A new method called RS-MSVM （Rough Set and Multi-class Support Vector Machine） is proposed for network intrusion detection. This method is based on rough set followed by MSVM for attribute reduction and classification respectively, The number of attributes of the network data used in this paper is reduced from 41 to 30 using rough set theory. The kernel function of HVDM-RBF （Heterogeneous Value Difference Metric Radial Basis Function）, based on the heterogeneous value difference metric of heterogeneous datasets, is constructed for the heterogeneous network data. HVDM-RBF and one-against-one method are applied to build MSVM. DARPA （Defense Advanced Research Projects Agency） intrusion detection evaluating data were used in the experiment. The testing results show that our method outperforms other methods mentioned in this paper on six aspects： detection accuracy, number of support vectors, false positive rate, falsc negative rate, training time and testing time.     

雷达导引头干扰效果的Vague集评估方法

运用Vague集理论,探索解决雷达导引头干扰效果评估问题的方法。首先,建立基于Vague集的干扰效果评价模型,该模型考虑到实践中不同指标的重要性问题,为指标赋予不同的权重,并对原有的符合度判决方法进行改进。然后,结合交叉眼干扰的具体实例,分别运用文中提出的基于Vague集的评价模型和相似度模型进行评估。对比分析试验结果,发现在指标权重相同的情况下,两种方法计算出的评价等级相同,且文中提出的方法计算量更小,验证了该方法的有效性和优越性。     

数据挖掘在芯片生产过程数据分析中的应用

介绍了如何使用基于粗糙集理论数据挖掘技术,对生产线的生产率和成品率进行快速的预测.在出现产量突然降低时,使用统计分析方法迅速找到使其降低的主要原因,然后就可对其采取有效的措施,从而满足高产量的要求.     

Test Set Compaction Using Relaxed Subsequence Removal

Recent work observed that a subset of states are frequently visited during the simulation of a test set for a sequential circuit. Re-visiting a state implies that a cycle has been traversed in the state diagram. Removal of subsequence responsible for the cycle can lead to static compaction. The size of a cycle is the number of vectors in its subsequence. In this work, we extend the subsequence removal technique to provide significantly higher static compaction for sequential circuits. We show that state relaxation techniques can be used to identify more or larger cycles in a test set. State relaxation creates more opportunities for subsequence removal and hence, results in better compaction. Relaxation of a state is possible since not all memory elements in a finite state machine have to be specified for a state transition. The proposed technique has several advantages: (1) test sets that could not be compacted by existing subsequence removal techniques can now be compacted, (2) the size of cycles in a test set can be significantly increased by state relaxation and removal of the larger sized cycles leads to better compaction, (3) only two fault simulation passes are required as compared to trial and re-trial methods that require multiple fault simulation passes, and (4) significantly higher compaction is achieved in short execution times as compared to known subsequence removal methods. Experiments on ISCAS89 sequential benchmark circuits and several synthesized circuits show that the proposed technique consistently results in significantly higher compaction in short execution times.     

一种基于粗糙集理论的双向选择属性约简算法

文章首先对粗糙集的基本理论进行了简要介绍,然后重点讨论粗糙集理论中的属性约简问题,在总结分析现有属性约简算法的基础上,提出了一种双向选择属性约简算法--BSARA,并用一个例子与其它算法进行了对比分析,结果表明该算法是可行和有效的.     

基于直觉模糊集多属性决策的灰色关联分析法

基于直觉模糊集理论,提出了一种新的灰色关联分析方法来研究模糊多属性决策问题.首先,根据直觉模糊集的几何意义,引入了两个直觉模糊集之间的距离,且每个备选方案的评价值用直觉模糊值表示.其次,依据传统灰色关联分析方法的基本思想,通过计算每个方案对直觉模糊正、负理想方案的灰色关联度.然后计算备选方案对直觉模糊正理想方案的相对关联度度.最后,通过一个具体实例说明该方法的有效性和具体应用过程.     

粗糙集理论中一种连续属性离散化算法

连续属性离散化一直是机器学习领域中亟待解决的关键问题之一。提出一种基于断点重要性的离散化算法。首先给出粗糙集理论的几个基本概念:决策表、不可分辨关系、信息熵和条件熵,然后对离散化问题进行介绍,给出断点分类的条件熵定义,在此基础上给出了断点选择的粗糙集连续属性离散化算法。仿真结果表明,算法的综合性能优越于文献报道的同类算法。     

Design for 2-D error feedback networks with identical coefficient sets

In realization of recursive digital filters with fixed point arithmetic, an error caused by roundoff arises. It is known that the level of the roundoff noise of an IIR filter tends to be high when the poles are close to the unit circle. Error feedback (EF) is an effective method to reduce the roundoff noise. It is desirable to design an EF network using as few parameters as possible in order to keep computational costs low. In this paper, we propose a method for designing a 2D EF network with identical coefficient sets. That is, the EF coefficients are divided into several subsets such that all the elements within each set have the same absolute value. In order to optimize the coefficient sets, we propose an algorithm by using the genetic algorithm. In the numerical example, we demonstrate the effectiveness of the proposed method.