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Shen M.F.,Shantou Polytechnic | Su Z.F.,Shantou UniversityGuangdong
Applied Mechanics and Materials

In this paper we present a compound anisotropic diffusion filter algorithm to apply edge sensitive ICOV operator in NCD model. According to the correlation coefficient of the ICOV operator, we obtain effective nonlinear denoising. The experiment have validated that our algorithm have better effect in smoothing and better ability in edge preservation. © (2014) Trans Tech Publications, Switzerland. Source

Shen M.F.,Shantou Polytechnic | Huang F.,Shantou UniversityGuangdong
Applied Mechanics and Materials

Currently,the ultrasound image has been widely used in diagnosis and treatment of clinical medicine,the results obtained by the diagnostic accuracy and reliability of the image is directly related to the effects of diagnosis and treatment.Because ultrasound images in the imaging process inevitably contaminated noise,thus the research of inhibiting ultrasound image noise is one of the important issues in domestic and international ultrasound imaging techniques.This paper studies the multi-scale analysis and wavelet thresholding two theories,put forwarda denoising algorithm about combining the Nonsubsampling contourlet transform and a new threshold function,experiments show that the new algorithm can not only good at suppressing the noise of ultrasound images,and can better retain image edge and texture details. © (2014) Trans Tech Publications, Switzerland. Source

Sun L.,Shantou UniversityGuangdong | Su Z.,Shantou UniversityGuangdong | Shen M.,Shantou Polytechnic
WIT Transactions on Information and Communication Technologies

Modeling of clinical electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vector machine (SVM) based on the structure risk minimization provides us an effective kind of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training. In this paper, a local-SVM method is proposed for modeling EEG signals. The local method is presented for improving the speed of the prediction of EEG signals. Furthermore, this proposed model is used to detect epilepsy from EEG signals in which dynamic characteristics are different between normal and epilepsy EEG signals. The experimental results show that the training of the local-SVM obtains a good behavior. In addition, the local SVM method significantly improves the prediction and detection precision. © 2014 WIT Press. Source

Lin H.,Shantou UniversityGuangdong | Lin H.,Digital Signal | Fan Z.,Shantou UniversityGuangdong | Fan Z.,Digital Signal | And 9 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

This paper presents a novel multiobjective constraint handling approach, named as MOEA/D-CDP-ID, to tackle constrained optimization problems. In the proposed method, two mechanisms, namely infeasibility driven (ID) and constrained-domination principle (CDP) are embedded into a prominent multiobjective evolutionary algorithm called MOEA/D. Constraineddomination principle defined a domination relation of two solutions in constraint handling problem. Infeasibility driven preserves a proportion of marginally infeasible solutions to join the searching process to evolve offspring. Such a strategy allows the algorithm to approach the constraint boundary from both the feasible and infeasible side of the search space, thus resulting in gaining a Pareto solution set with better distribution and convergence. The efficiency and effectiveness of the proposed approach are tested on several well-known benchmark test functions. In addition, the proposed MOEA/D-CDP-ID is applied to a real world application, namely design optimization of the two-stage planetary gear transmission system. Experimental results suggest that MOEA/D-CDP-ID can outperform other state-of-the-art algorithms for constrained multiobjective evolutionary optimization. © Springer International Publishing Switzerland 2014. Source

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