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Wang R.,Guangxi University | Wang R.,Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology | Dai J.,Kings College
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | Year: 2013

Taking the octahedron for example, a novel plan-space polyhedral metamorphic mechanism is abstracted based on the metamorphic mechanism concepts and the edge forming principle of polyhedral packaging carton by making each edges and each corner of the polyhedron equivalent to the links and hinges of the novel mechanism. The mobility conditions of the novel plan-space polyhedral metamorphic mechanism are analyzed, and the connection and movement relationship between the links of the novel mechanism are analyzed by using the adjacent matrix and the screw theory. A novel plan-space polyhedral metamorphic mechanism with the reconfigurable function is designed based on the reconfigurable theory. The novel mechanism not only can achieve the structural transformation between plane topology and space topology, but also can obtain different sizes and different shapes polyhedron configuration. The novel plan-space polyhedral reconfigurable metamorphic mechanism can be used as skeleton or actuator of machine and used in packaging robots, aerospace and other industrial applications. © 2013 Journal of Mechanical Engineering. Source


Wang S.,Nanjing University | Wang S.,Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology | Yang J.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing | Liu G.,Columbia University | And 2 more authors.
Simulation | Year: 2016

Multi-objective path finding (MOPF) problems are widely applied in both academic and industrial areas. In order to deal with the MOPF problem more effectively, we propose a novel model that can cope with both deterministic and random variables. For the experiment, we compared five intelligence-optimization algorithms: the genetic algorithm, artificial bee colony (ABC), ant colony optimization (ACO), biogeography-based optimization (BBO), and particle swarm optimization (PSO). After a 100-run comparison, we found the BBO is superior to the other four algorithms with regard to success rate. Therefore, the BBO is effective in MOPF problems. © The Author(s) 2016. Source


Zhang Y.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing | Zhang Y.,Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology | Yang J.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing | Liu A.,Arizona State University | Sun P.,City College of New York
International Journal of Biomedical Imaging | Year: 2016

Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches. © 2016 Yudong Zhang et al. Source


Zhang Y.,Nanjing Normal University | Zhang Y.,Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology | Wang S.,Nanjing Normal University | Phillips P.,Shepherd University | And 2 more authors.
Journal of Alzheimer's Disease | Year: 2016

Background: Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal.However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance. Objective: Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images. Methods: We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel. Results: The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2DEB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81%±1.99%, a sensitivity of 92.07%±2.48%, a specificity of 93.02%±2.22%, and a precision of 79.03%±2.37%. Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature. Conclusions: We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD. © 2016 - IOS Press and the authors. All rights reserved. Source


Zhang Y.-D.,Nanjing Normal University | Zhang Y.-D.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing | Zhang Y.-D.,Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology | Wang S.-H.,Nanjing Normal University | And 3 more authors.
Advances in Mechanical Engineering | Year: 2016

Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts. Our dataset contains 200 mammogram images with size of 1024 × 1024. First, we segmented the region of interest from mammogram images. Second, the fractional Fourier transform was employed to obtain the unified time-frequency spectrum. Third, spectrum coefficients were reduced by principal component analysis. Finally, both support vector machine and k-nearest neighbors were used and compared. The proposed "weighted-type fractional Fourier transform+principal component analysis+support vector machine" achieved sensitivity of 92.22% ± 4.16%, specificity of 92.10% ± 2.75%, and accuracy of 92.16% ± 3.60%. It is better than both the proposed "weighted-type fractional Fourier transform+principal component analysis+k-nearest neighbors" and other five state-of-the-art approaches in terms of sensitivity, specificity, and accuracy. The proposed computer-aided diagnosis system is effective in detecting abnormal breasts. © 2016 The Author(s). Source

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