文章将动态主元分析(Dynamic Principal Component Analysis,DPCA)和稀疏主元分析(Sparse Principal Component Analysis,SPCA)两种方法结合起来,提出一种新的稀疏动态主元分析方法,并将其用于工业过程的故障检测;所提出的稀疏动态主元...文章将动态主元分析(Dynamic Principal Component Analysis,DPCA)和稀疏主元分析(Sparse Principal Component Analysis,SPCA)两种方法结合起来,提出一种新的稀疏动态主元分析方法,并将其用于工业过程的故障检测;所提出的稀疏动态主元分析方法通过对过程数据的动态增广矩阵进行稀疏主元的求解,获取稀疏的负荷向量,该方法既考虑到了过程数据的动态特性,又降低了过程数据的冗余度,同时降低了计算负荷,非常适合工业过程的实时故障检测;此外,还提出了一种前向选择算法,用于确定稀疏主元中的非零负荷数目;最后,将所提出方法应用于数值例子和田纳西-伊斯曼过程,并将与主元分析、动态主元分析和稀疏主元分析等3种方法相比较,表明所提方法可以获得更好的故障检测效果。展开更多
In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation...In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.展开更多
文摘文章将动态主元分析(Dynamic Principal Component Analysis,DPCA)和稀疏主元分析(Sparse Principal Component Analysis,SPCA)两种方法结合起来,提出一种新的稀疏动态主元分析方法,并将其用于工业过程的故障检测;所提出的稀疏动态主元分析方法通过对过程数据的动态增广矩阵进行稀疏主元的求解,获取稀疏的负荷向量,该方法既考虑到了过程数据的动态特性,又降低了过程数据的冗余度,同时降低了计算负荷,非常适合工业过程的实时故障检测;此外,还提出了一种前向选择算法,用于确定稀疏主元中的非零负荷数目;最后,将所提出方法应用于数值例子和田纳西-伊斯曼过程,并将与主元分析、动态主元分析和稀疏主元分析等3种方法相比较,表明所提方法可以获得更好的故障检测效果。
基金The National Natural Science Foundation of China(No.61374194,No.61403081)the National Key Science&Technology Pillar Program of China(No.2014BAG01B03)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20140638)Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.
