Aiming at complex features of the fault rotating machinery such as nonstationary and nonlinearity, a new method for fault diagnosis based on multi-fractal was introduced. The vibration signals firstly are analyzed by multi-fractal theory and have multi-fractal characteristics. Then the area of multi-fractal spectrum S and the entropy of multi-fractal spectrum Hm were extracted as new criterions to diagnose machinery faults. Results of experimental analysis indicate that the method is effective and it provides a new way in fault diagnosis of rotating machinery.
Aiming at the computer vision image, firstly, image edge feature is obtained using the edge detection algorithm based on wavelet analysis, selecting some seed points, comparing the gray similarity of seeds, the result of the initial match is based on the SIFT matching algorithm. The weighted SSD (Sum of Squared Difference) series, as the objective function, spread the seeds to the rest of the image matching area with the original growing strategy. Experimental results show that the algorithm was effective to large parallax pictures, images without calibration, and images which textures are sparse.
A new document image retrieval algorithm is proposed in view of the inefficient retrieval of information resources in a digital library. First of all, in order to accurately characterize the texture and enhance the ability of image differentiation, this paper proposes the statistical feature method of the double-tree complex wavelet. Secondly, according to the statistical characteristic method, combined with the visual characteristics of the human eye, the edge information in the document image is extracted. On this basis, we construct the meaningful texture features and use texture features to define the characteristic descriptors of document images. Taking the descriptor as the clue, the content characteristics of the document image are combined organically, and appropriate similarity measurement criteria are used for efficient retrieval. Experimental results show that the algorithm not only has high retrieval efficiency but also reduces the complexity of the traditional document image retrieval algorithm.
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