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International Symposium on Parallel and Distributed Computing

作者： Zheyi Chen Yuli Liu Weiping Zhu Department of Computer Science National Laboratory for Information Science and Technology Beijing China College of Mathematics and Computer Science Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9781509041534

In the study of social networks, analyzing node influence and identifying influential nodes in social networks is of great theoretical and practical significance. To effectively evaluate node influence, a novel concept of influence label is introduced, which can measure node influence through two label attributes called influence level and node degree. Then, a novel influence model called LDM (Level and Degree Model) is proposed. LDM updates the influence label of each node iteratively by the quality of neighbors and the number of neighbors. A gain function that conforms to power-law distribution is also introduced to further optimize the accuracy of LDM. In the experiment part, to prove the effectiveness of LDM, LDM is compared with other typical methods by employing IC model to simulate the information diffusion process on four real-world social networks. Experimental results show that LDM can rank node influence more accurately than other methods.

关键词： social networking (online) CYP51A1 gene LDM Social Networks Line of nodes Social Capital

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Towards multi-functional light-weight long-term real-time coastal ocean observation system 11

Towards multi-functional light-weight long-term real-time co...

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Jiang, Zhenfeng Ma, Yanming Chen, Jiali Wang, Zigeng Peng, Zheng Liu, Jun Han, Guitao Smart Ocean Industry Research Institute Nantong Jiangsu226000 China Computer Science and Engineering Department University of Connecticut StorrsCT06269 United States Computer Science Deparment City College of New York NY10031 United States Computer Science and Technology Department Jilin University Changchun Jilin130012 China State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

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Robust monocular visual odometry using optical flows for mobile robots

Robust monocular visual odometry using optical flows for mob...

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Chinese Control Conference (CCC)

作者： Haifeng Li Zunhe Hu Xinwei Chen College of Computer Science and Technology Civil Aviation University of China Tianjin Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou Department of Computer Science Minjiang University Fuzhou

A robust optical flow-based visual odometry method using a single onboard camera is proposed in this paper. To improve the quality of the noisy optical flows, a correction method across multiple frames is developed. Furthermore, the optical flows in the plane at infinity are detected and removed as these optical flows have very low signal to noise ratio for robot translation estimation. Finally, a RANSAC approach for robot ego-motion estimation is proposed. Physical experiments are carried out and the results show that the proposed method is able to estimate the camera trajectory robustly with reasonable accuracy.

关键词： Adaptive optics Image motion analysis computer vision Optical imaging Optical sensors Cameras Optical noise

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Fractional-order control of USV course-keeping based on self-adaptive weight PSO algorithm

International Journal of Control and Automation

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International Journal of Control and Automation 2015年第10期8卷 309-320页

作者： Li, Guang-Yu Guo, Chen Li, Yan-Xin Deng, Wu Information Science and Technology College Dalian Maritime University Dalian China Software College Dalian Jiaotong University Dalian China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

In order to improve the stability of underactuated surface vessels (USV) course-keeping, a fractional-order PIλDμcontroller based on self-adaptive weight particle swarm optimization (SAWPSO-FOCPID) was proposed for ship autopilot and had good overall control ability. The integral-order λ and differential-order μ can improve the controller with more robustness and take on stronger disturbance rejection ability. However, the difficulties for parameter verification are increased. A new designing method of PIλDμcontroller based on SAWPSO algorithm was proposed in order to balance the global search ability and local search ability of PSO algorithm. And at the same time, the difficulties of tuning more parameters and designing complexity of fractional-order PIλDμcontroller were solved. The SAWPSO-FOCPID controller was compared with the traditional PSO-PID controller under the same condition. The simulation results showed that the SAWPSO-FOCPID controller has a high dynamic tracking performance, small overshoot and strong immunity. © 2015 SERSC.

关键词： Controllers

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Hybrid discrimination method for samples classification in medicine

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International Journal of Simulation: Systems, Science and technology 2016年第27期17卷 17.1-17.9页

作者： Chen, Xiaoyu Liu, Bo Xia, Xin Yan, Dandan Yan, Wang Ma, Lizhuang Department of Information Center East Hospital Tongji University School of Medicine Jimo Road 150 Shanghai China School of Continuing Education Shanghai Jiao Tong University Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Department of Computer Science Shanghai Jiao Tong University Shanghai200240 China

As indispensable solutions in classification problems, discrimination for samples has been employed in medicine, and it is performed subjectively by physicians at present, which hinders the diagnosis and treatment in medicine. In this paper, a hybrid discrimination method (HDM) in medicine is proposed, which consists of two phases, including attribute selection phase and discriminant phase. In attribute selection phase, critical attributes are selected from the original features by linear correlation and C5.0 decision tree. In discriminant phase, samples are discriminated by discriminant analysis. This discrimination method is evaluated through five datasets of chronic hepatitis B, cardiac Single Proton Emission Computed Tomography (SPECT) images, Lung Cancer, Hepatitis survival and Iris plant for demonstrating its viability and applications. Finally, this proposed method has obtained the critical clinical lab indicators and discriminants related to three syndromes in CHB dataset, and it also performs well than some typical classification methods in the other four datasets for its broader applications. © 2016, UK Simulation Society. All rights reserved.

关键词： Discriminant analysis

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Neighborhood collaborative classifiers

Neighborhood collaborative classifiers

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Suping Xu Xibei Yang Eric C. C. Tsang Eric A. Mantey Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Economics and Management Nanjing University of Science and Technology Nanjing China Faculty of Information Technology Macau University of Science and Technology Macau China

ISBN: (纸本)9781509003914

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC; 2) NCC reduces the size of dictionary when compared with CRC.

关键词： Collaboration Rough sets Training Cybernetics Numerical models Error analysis Measurement

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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An improved image saliency detection approach based on features of space 4th

An improved image saliency detection approach based on featu...

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4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Wang, Yan Yu, Weiyu Qian, Shaohui Tang, Rundong School of Electronic and Information Engineering South China University of Technology Guangzhou China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781138028302

Automatic detection salient region of images is widely used in image applications such as image editor, segmentation, retrieval and object recognition. The AC algorithm is one of the typical algorithms, which uses regional contrast to estimate salience map. It only takes into account the regional information without considering spatial information, so there are some defects in some cases. An improved algorithm is proposed, which increasingly evaluates space information by extracting depth message of the depth map. To confirm the algorithm’s effectiveness, the typical algorithms are compared with the new one. And the experimental results show that the proposed method generates high quality results. © 2015 Taylor & Francis Group, London.

关键词： Object recognition

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Predicting helix boundaries of α-helix transmembrane protein with feedback conditional random fields 11th

Predicting helix boundaries of α-helix transmembrane protei...

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11th International Conference on Intelligent Computing, ICIC 2015

作者： Wang, Kun Wu, Hongjie Lu, Weizhong Fu, Baochuan Lü, Qiang Huang, Xu School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China School of Computer Science and Technology Soochow University Suzhou215006 China Jiangsu Provincial Key Lab for Information Processing Technologies Suzhou215006 China School of Information Engineering Huzhou University Huzhou313000 China

ISBN: (纸本)9783319221793

Transmembrane proteins play an important role in cellular energy production, signal transmission, metabolism. Existing machine learning methods are difficult to model the global correlation of the membrane protein sequence, and they also can not improve the quality of the model from sophisticated sequence features. To address these problems, in this paper we proposed a novel method by a feedback conditional random fields (FCRF) to predict helix boundaries of α-helix transmembrane protein. A feedback mechanism was introduced into multi-level conditional random fields. The results of lower level model were used to calculate new feedback features to enhance the ability of basic conditional random fields. One wide-used dataset DB1 was used to validate the performance of the method. The method achieved 95% on helix location accuracy. Compared with the other predictors, FCRF ranks first on the accuracy of helix location. © Springer International Publishing Switzerland 2015.

关键词： Learning systems

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