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Your search keyword '"Xian, Guang-Ming"' showing total 18 results
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18 results on '"Xian, Guang-Ming"'

5. A novel intelligent system based on WPA-SVMs for fault signals recognition

Author

Xian Guang-lin, Liu Lie-gen, Xian Guang-ming, and Zhang Yu

Subjects
Artificial neural network, Computer science, business.industry, Feature extraction, Wavelet transform, Pattern recognition, Support vector machine, Kernel (linear algebra), symbols.namesake, Wavelet, Polynomial kernel, Kernel (statistics), Multilayer perceptron, Gaussian function, symbols, Artificial intelligence, business
Abstract

Conventional wavelet transform (WT) omits some useful details information of fault signals since it only decomposes low frequency band in a higher scale. In this paper, a novel intelligent system is presented for real-time detection and diagnosis of the fault signals. The model consists of wavelet packet analysis (WPA) unit and support vector machines (SVMs) unit. When signals are decomposed in wavelet packet space, different frequency bands are processed from original signals adequately. WPA can improve abilities of feature extraction than conventional WT. We use a large number of samples to compare the accuracy rate of three kinds kernel function of SVMs, the results indicate that accuracy of Gaussian kernel is higher than polynomial kernel and multilayer perceptron (MLP) kernel. No matter whether the data set is small or huge, accurate classification rate of SVMs is better than RBF and BP neural network methods for normal and exceptional subjects.

Published
2005

6. Using combination of lifting wavelet and multiclass SVM based on global optimization class strategy for fault pattern identification

Author

Xian, Guang-Ming, Zeng, Bi-Qing, Pang, Xiong-wen, Yun, Qiao-yun, Xiao, Ying-wang, Huang, Ai-Yi, Ma, Ying-Yi, Wu, Xing-Chun, Xian, Guang-Ming, Zeng, Bi-Qing, Pang, Xiong-wen, Yun, Qiao-yun, Xiao, Ying-wang, Huang, Ai-Yi, Ma, Ying-Yi, and Wu, Xing-Chun

Abstract

This paper presents a new method based on lifting wavelet for obtaining a fast multiclass SVM classification based on global optimization class strategy for fault diagnosis of roller bearing. Decision making was performed in two stages: feature extraction by computing the lifting wavelet coefficients and classification using the multiclass SVM classifiers trained on the extracted features. Experiments demonstrate that in comparison to discrete wavelet transform the lifting wavelet feature extraction can speed up the identification phase as well as achieve higher accuracy of multiclass SVM that is based on global optimization class strategy. Experimental results also reveal that the proposed multiclass SVM of global optimization is better than strategy of one against one and DAGSVM.

Published
2012

18. An Effective On-line Polymer Characterization Technique by Using SALS Image Processing Software and Wavelet Analysis

Author

Xian, Guang-ming, Qu, Jin-ping, and Zeng, Bi-qing

Abstract

This paper describes an effective on-line polymer characterization technique by using small-angle light-scattering (SALS) image processing software and wavelet analysis. The phenomenon of small-angle light scattering has been applied to give information about transparent structures on morphology. Real-time visualization of various scattered light image and light intensity matrices is performed by the optical image real-time processing software for SALS. The software can measure the signal intensity of light scattering images, draw the frequency-intensity curves and the amplitude-intensity curves to indicate the variation of the intensity of scattered light in different processing conditions, and estimate the parameters. The current study utilizes a one-dimensional wavelet to delete noise from the original SALS signal and estimate the variation trend of maximum intensity area of the scattered light. So, the system brought the qualitative analysis of the structural information of transparent film success.

Published
2008
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