摘要：
Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.
摘要：
This paper presents the development of wearable sensing system that can be used to study the gait dynamics of human. A tester wearing sensing shoes participates in this study. Human gait information about standing, jumping and walking is obtained as prior probability based on the train movement model setup theory. For feature extraction of gait, five kinds of features are extracted from foot pressure signals, which are subsequently used for motion analysis. We employ support vector machine and fractal analysis for gait recognition and test the identification performance. Testing outcomes indicate an overall accuracy of 93.57% via radial basis function kernel function. These results demonstrate considerable potential in gait identification.
期刊：
Multimedia Tools and Applications,2016年75(23):15601-15617 ISSN：1380-7501
通讯作者：
Zhang, Yudong
作者机构：
[Yang, Gelan] Hunan City Univ, Sch Informat Sci & Engn, Yiyang 413000, Peoples R China.;[Zhang, Yudong; Wang, Shuihua; Ji, Genlin] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China.;[Zhang, Yudong; Feng, Chunmei; Wang, Qiong; Yang, Jiquan] Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China.;[Dong, Zhengchao] Columbia Univ, Translat Imaging Div, New York, NY 10032 USA.;[Dong, Zhengchao] Columbia Univ, MRI Unit, New York, NY 10032 USA.
通讯机构：
[Zhang, Yudong] N;[Zhang, Yudong] J;Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China.;Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China.
摘要：
It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5 x 5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.
作者机构：
[刘琮; 杨格兰] School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China;[邓晓军] College of Computer and Communication, Hunan University of Technology, Zhuzhou, 412007, China;[杨格兰] School of Information Science and Engineering, Hunan City University, Yiang, 413000, China
通讯机构：
College of Computer and Communication, Hunan University of Technology, Zhuzhou, China
摘要：
A variety of recognizing architectures based on deep convolutional neural networks have been devised for labeling videos containing human motion with action labels. However, so far, most works cannot properly deal with the temporal dynamics encoded in multiple contiguous frames, which distinguishes action recognition from other recognition tasks. This paper develops a temporal extension of convolutional neural networks to exploit motion-dependent features for recognizing human action in video. Our approach differs from other recent attempts in that it uses multiplicative interactions between convolutional outputs to describe motion information across contiguous frames. Interestingly, the representation of image content arises when we are at work on extracting motion pattern, which makes our model effectively incorporate both of them to analysis video. Additional theoretical analysis proves that motion and content-dependent features arise simultaneously from the developed architecture, whereas previous works mostly deal with the two separately. Our architecture is trained and evaluated on the standard video actions benchmarks of KTH and UCF101, where it matches the state of the art and has distinct advantages over previous attempts to use deep convolutional architectures for action recognition.
期刊：
Mathematical Problems in Engineering,2014年2014 ISSN：1024-123X
通讯作者：
Yang, G.(glyang@mail.ustc.edu.cn)
作者机构：
[Yang, Gelan] College of Information Science and Engineering, Hunan City University, Yiyang 413000, China;[Wu, Yue] Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, United States;[Cao, Su-Qun] Faculty of Mechanical Engineering, Huaiyin Institute of Technology, Huai'an 223003, China
通讯机构：
College of Information Science and Engineering, Hunan City University, China
期刊：
International Journal of Distributed Sensor Networks,2014年10(5) ISSN：1550-1477
通讯作者：
Yang, Gelan(glyang@mail.ustc.edu.cn)
作者机构：
[Yang, Gelan] College of Information Science and Engineering, Hunan City University, Yiyang, 413000, China;[Cao, Su-Qun] Faculty of Mechanical Engineering, Huaiyin Institute of Technology, Huai'an, 223003, China;[Jin, Yong; Le, Deguang] School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, 215500, China
通讯机构：
[Gelan Yang] C;College of Information Science and Engineering, Hunan City University, Yiyang 413000, China
期刊：
Advances in Information Sciences and Services,2012年4(16):503-509 ISSN：1976-3700
通讯作者：
Yang, G.(glyang@mail.ustc.edu.cn)
作者机构：
[Zuo, Weiming; Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang, 413000, China;[Deng, Xiaojun] College of Computer and Communication, Hunan University of Technology, Zhuzhou, 412008, China
通讯机构：
[Yang, G.] D;Department of Computer Science, , Yiyang, 413000, China
关键词：
Blog;Network learning;Research learning
摘要：
Weblogs are becoming the grassroots publishing medium of the current electronic publishing era. This paper discussed the request of blog in the web-based learning for teachers and students. We analysed the major role of networking in research learning, and it designed research learning model which is in the blog-based network environment, it aims at predicting the trends and other inherent latent information present in the blogs with various techniques. The method achieved well effective on aspects of the application in the implementation Blog environment in research learning.
期刊：
International Review on Computers and Software,2012年7(6):3106-3112 ISSN：1828-6003
通讯作者：
Yang, G.
作者机构：
[Jin, Huixia; Yang, Gelan] School of Information Science and Engineering, Hunan City University, Yiyang, 413000, China;[Du, Hui] School of Mechanical and Electronic Engineering, Zaozhuang University, ZaoZhuang, 277160, China;[Na, Bai] National Mobile Communication Research Laboratory, Southeast University, Nanjing, 210096, China
通讯机构：
School of Information Science and Engineering, Hunan City University, China
关键词：
Community structure;Disassembly sequence planning;Modularization
期刊：
Journal of Computational Information Systems,2012年8(10):4315-4322 ISSN：1553-9105
通讯作者：
Yang, G.(glyang@mail.ustc.edu.cn)
作者机构：
[Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang 413000, China;[Jin, Huixia] Department of Physics and Telecom Engineering, Hunan City University, Yiyang 413000, China;[Wu, Yue] Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, United States
通讯机构：
Department of Computer Science, Hunan City University, China
期刊：
Lecture Notes in Electrical Engineering,2012年107:271-281 ISSN：1876-1100
通讯作者：
Liao, Z.
作者机构：
[Zhou, Jiancun; Liao, Zhiping; Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang 413000, China;[Liu, Song] Department of Economy Management, Hunan City University, Yiyang 413000, China
期刊：
International Journal of Digital Content Technology and its Applications,2012年6(19):253-261 ISSN：1975-9339
通讯作者：
Yang, G.(glyang@mail.ustc.edu.cn)
作者机构：
[Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang, 413000, China;[Deng, Xiaojun] College of Computer and Communication, Hunan University of Technology, Zhuzhou, 412008, China;[Deng, Chuanchou] Department of Economic and Information, Hunan urban professional college Hunan, Changsha, 410137, China
摘要：
Manifold alignment is very useful in seeking the correspondence between high dimension data sets. In this paper, we proposed semi supervised manifold alignment based on improved local tangent space to solve this problem. LTSA is used here as a method to find the inner manifold constraint of each dataset, which exists a linear mapping from a high-dimensional data point to its local tangent space. We improved linear mapping of local tangent space, then additional knowledge about the intrinsic embedding coordinates of some of the samples were used to constrain the alignment. Experiments on aligning various high dimension data set data demonstrate the effectiveness.
