基于稀疏度特征的短时电能质量扰动信号重构方法

现有基于压缩感知的短时电能质量扰动信号重构方法尚未考虑信号稀疏度特征,重构性能有待进一步提高。为此,提出一种基于稀疏度特征的信号重构方法。首先,根据压缩感知理论对信号进行采样。随后,开发出短时电能质量扰动信号的稀疏度特征—稀疏度在频域为偶数。基于该特征,提出"双步长稀疏度自适应匹配追踪"重构方法。分析与仿真结果表明,相对于传统的稀疏度自适应匹配追踪算法,提出方法降低了计算复杂度和均方误差,提高了重构信噪比和信号的正确重构概率。

Sparseness-controlled non-negative tensor factorization and its application in machinery fault diagnosis

Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization （SNTF） is proposed. First, a non-negative tensor factorization（NTF） algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images＇ weights in constituting original bispectral images are calculated for fault classification. Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies. These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.

基于稀疏表示的立体图像客观质量评价方法

提出了一种基于稀疏表示的立体图像质量评价方法,分为训练和测试两个部分。在训练部分,通过训练不同频带的立体图像获得立体图像的稀疏字典;在测试部分,根据稀疏字典计算得到立体图像的稀疏特征,定义了稀疏特征相似度衡量原始和失真图像信息的差异,并根据稀疏字典计算了频带增益和左右视点的融合权值,最后融合稀疏特征相似度作为立体图像质量的客观评价值。在立体图像测试库上的实验结果表明,本文方法的评价结果与主观评价结果有较好的相关性,符合人类视觉系统的感知。