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1·LU decomposition is a triangular decomposition approach of non-singular matrix, and the digital image can be seen as a matrix.

LU分解是一种将非奇异矩阵进行三角分解的方法，而数字图像也可以看作矩阵。

2·Based on the original precise integration method, the problem that singular matrix appears in non-homogeneous equation was discussed.

在原有精细积分法的基础上，对非齐次方程出现奇异矩阵的问题进行探讨。

3·Necessary cycle detection is added to avoid the singular matrix appearing after gross error compensation,. Simulation results verify the effectiveness of modified algorithm.

通过加入必要的回路检测，避免了幅度补偿后矩阵奇异性的产生，仿真结果表明了改进算法的有效性。

4·Furthermore, each element in the singular stiffness matrix can be expressed by some more simple analytic formulas.

而且奇异单元刚度矩阵中的各元素可以用较简单的分析式表出。

5·Using matrix iteration methods, the eigenvector derivatives can be iterated directly, solving the singular sensitivity equation can be avoided.

采用矩阵迭代法可以直接迭代计算特征向量导数，避免了对奇异灵敏度方程的求解。

6·The complex modal analysis method is generalized to the cases in which stiffness matrix may be singular and mass matrix, damping matrix and stiffness may be nonsymmetric.

把复模态分析法推广到刚度矩阵可以奇导且质量矩阵、阻尼矩阵和刚度矩阵均不必为对称的情形。

7·Singular value is a good characteristic of matrix.

奇异值是矩阵的一个良好特征。

8·A robust stability boundary of uncertain singular systems is proposed by utilizing singular value decomposition and the character of mode matrix.

并利用奇异值分解方法和模矩阵的性质，给出了使不确定广义系统鲁棒稳定的一个鲁棒界。

9·The proposed method uses the maximum singular value of an cumulant matrix to distinguish between voiced parts of the speech signal and noise.

提出一种采用累积量矩阵的最大奇异值来实现语音端点检测的方法，并引入一种自适应的实现方法。

10·In training phase, singular value vector of a lot of face image information matrix from face image sets constitutes face image sets characteristic matrix as a column.

在训练阶段，从人脸图像集中选取大量人脸图像的信息矩阵的奇异值向量作为矩阵中的一列而构成的人脸图像集特征矩阵。