基于密文随机性度量值分布特征的分组密码算法识别方案

在研究现有加密算法识别方案局限性的基础上,提出了基于密文随机性度量值分布特征的分组密码算法识别方案。首先,基于码元频数检测、块内频数检测及游程检测对AES、Camellia、DES、3DES及SMS4密文的随机性度量值取值个数进行了统计分析,采用k-means算法对其进行了初始聚类划分。其次,针对相同聚类中的分组密码算法识别问题,基于降低特征向量间相似度的原则,求解了码元频数检测、块内频数检测及游程检测对应的密文随机性度量值特征向量维数。最后,对AES、Camellia、DES、3DES及SMS4算法的实验结果表明,提出方案在已知密文条件下,实现了对以上典型分组密码算法的识别,相关成果可为进一步探索基于密文随机性度量值分布特征的加密算法识别提供参考。     

求半正定Hermitian矩阵特征向量的改进乘幂法

经典乘幂法结合压缩法是计算半正定Hermitian矩阵最大前n个特征值对应特征向量的重要方法,但其固定的迭代次数使得待分解矩阵的随机变化和初始向量的不同选择导致计算精度波动较大,同时,较大特征值对应特征向量的计算误差也会影响较小特征值对应特征向量的计算。为克服这些缺点,提出了一种将前后两次迭代所求向量的距离作为迭代终止条件的改进乘幂法,并证明了它在有误差传播时的收敛性。理论计算结果表明,对4阶半正定Hermitian随机矩阵,在相同计算精度前提下,所提方法比经典方法可至少降低一半计算复杂度。     

Calculation of eigenpair derivatives for asymmetric damped systems with distinct and repeated eigenvalues

An algorithm is derived for the computation of eigenpair derivatives of asymmetric quadratic eigenvalue problem with distinct and repeated eigenvalues. In the proposed method, the eigenvector derivatives of the damped systems are divided into a particular solution and a homogeneous solution. By introducing an additional normalization condition, we construct two extended systems of linear equations with nonsingular coefficient matrices to calculate the particular solution. The method is numerically stable, and the homogeneous solutions are computed by the second‐order derivatives of the eigenequations. Two numerical examples are used to illustrate the validity of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.     

A blocked QR-decomposition for the parallel symmetric eigenvalue problem

In this paper we present a new stable algorithm for the parallel QR-decomposition of “tall and skinny” matrices. The algorithm has been developed for the dense symmetric eigensolver ELPA, where the QR-decomposition of tall and skinny matrices represents an important substep. Our new approach is based on the fast but unstable CholeskyQR algorithm (Stathopoulos and Wu, 2002) [1]. We show the stability of our new algorithm and provide promising results of our MPI-based implementation on a BlueGene/P and a Power6 system.     

Parameterized ABD preconditioning technique for time-periodic convection–diffusion problems

In this paper, a parameterized additive block diagonal (PABD) preconditioning technique is present for solving the nine-point approximations of the time-periodic convection–diffusion problems. The explicit expressions for the eigenvalues and eigenvectors of the corresponding preconditioned matrices are presented. The range of the optimal parameters is derived. Numerical experiments show that the generalized minimal residual method preconditioned by the PABD preconditioner with the experimental optimal parameters or some special values is effective for a wide range of problem sizes.     

刚弹耦合多体系统增广特征矢量及其正交性

特征矢量的正交性是用模态方法求解多体系统动力响应的前提条件。对于刚弹耦合多体系统,振型函数不具有通常意义下的正交性。本文引入了多体系统增广特征矢量的概念,解决了多体系统特征矢量的正交性问题。系统地阐述了多体系统增广特征矢量的构造方法、正交性条件、正交性证明以及增广特征矢量的重要作用。利用增广特征矢量的正交性,可实现对多体系统动力学方程的坐标解耦,使得用模态方法精确分析动力响应成为可能。     

ARD算法收敛性分析

对交替行对角化（ARD）算法收敛性进行分析.基于收敛性分析,给出了一些关于如何选取初始矩阵的策略,提出了算法终止的新准则.通过3个数值例子来说明所提出的分析策略的有效性.     

颜色度量因子耦合局部特征聚类的 图像复制-粘贴篡改检测算法

针对当前较多图像复制-粘贴篡改检测算法主要依靠图像的灰度信息来检测图像特征,没有考虑图像的色彩特征,使其存在误检与漏检的不足,引入余弦调制高斯滤波(cosine modulated Gaussian,CMG),设计了基于颜色度量因子与局部特征聚类的图像复制-粘贴篡改检测算法。利用CMG来求取图像的尺度响应值,并通过极值计算来提取图像的候选特征点;再利用像素点的光谱反射模型来建立颜色度量因子,从候选特征点中确定图像的鲁棒特征点。构建特征点的邻域圆,并求取该圆域中的四元数指数矩(quaternion exponent moments,QEM),从而得到相应的特征向量;利用特征向量来计算特征点间的欧氏距离,完成图像的特征匹配。最后,利用匹配点对的R、G、B值,形成特征点的局部特征,实现图像特征的聚类,准确定位复制-粘贴伪造内容。仿真结果显示,较当前的复制-粘贴伪造检测方法而言,对于简单的复制-粘贴篡改和复杂组合篡改,所提方法都具备更高的检测精度与鲁棒性。     

基于相对小波包能量特征向量的故障诊断

在小波及小波包分析的基础上引进相对小波包能量特征向量的概念,提出了一种建立旋转机械故障库的新方法,并结合模式判别的知识,引进欧几里德距离公式作为判据,使故障诊断公式化、定量化。实验结果表明这种方法能够很快地诊断出转子台故障类型,为旋转机械的故障诊断提供了新方向。     

Detecting community structure in networks by representative energy

Network community has attractedmuch attention recently, but the accuracy and efficiency in finding a community structure is limited by the lower resolution of modularity. This paper presents a new method of detecting community based on representative energy. The method can divide the communities and find the representative of community simultaneously. The communities of network emerges during competing for the representative among nodes in network, thus we can sketch structure of the whole network. Without the optimizing by modularity, the community of network emerges with competing for representative among those nodes. To obtain the proximate relationships among nodes, we map the nodes into a spectral matrix. Then the top eigenvectors are weighted according to their contributions to find the representative node of a community. Experimental results show that the method is effective in detecting communities of networks.