Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learn...

A novel image super-resolution reconstruction framework based on multi-groups of coupled dictionary and alternative learning is investigated in this paper. In dictionary learning phase, each image of a training image set is taken as high resolution image (HRI), the reduced and re-enlarged result of HRI by interpolation is taken as low resolution image (LRI), and the difference between them is r...

Motivated by image reconstruction, sparse representation based classification (SRC) has been shown to be an effective method for applications like face recognition. In this paper, we propose a localitysensitive dictionary learning algorithm for SRC, in which the designed dictionary is able to preserve local data structure, resulting in improved image classification. During the dictionary update...

Learning linear subspaces for high-dimensional data is an important task in pattern recognition. A modern approach for linear subspace learning decomposes every training image into a more discriminative part (MDP) and a less discriminative part (LDP) via sparse coding before learning the projection matrix. In this paper, we present a new linear subspace learning algorithm through discriminative...

In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised ma...

The objective of audio inpainting is to fill a gap in an signal. This ideally done by reconstructing the original signal or, at least, inferring meaningful surrogate We propose novel approach applying sparse modeling time-frequency (TF) domain. In particular, we devise dictionary learning technique which learns from reliable parts around with goal obtain representation increased TF sparsity. ba...