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Asian Conference on Pattern Recognition (ACPR)

作者： ShiWei Zhang Nong Sang ChangXin Gao FeiFei Chen Jing Hu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology China

ISBN: (纸本)9781479961016

This paper develops a method to learn very few discriminative part detectors from training videos directly, for action recognition. We hold the opinion that being discriminative to action classification is of primary importance in selecting part detectors, not just intuitive. For this purpose, part selection based on feature selection is proposed, employing SVM method. Firstly, large number of candidate detectors are trained using k-means and Exemplar-LDA techniques in whitened feature space. Secondly, each candidate part detector is regarded as a visual feature, so that detector selection can be achieved by feature selection. Detectors with larger weight, indicating more discriminative, will be selected. Meanwhile, to keep space-volume structure information, we use the novel method saliency-driven pooling to form feature primitives which are concatenated into mid-level feature vector. Finally, we conduct experiments on three challenging action datasets (KTH, Olympic Sports, HMDB51) and the results outperform the state-of-the-art.

关键词： Detectors Feature extraction Videos Support vector machines Semantics Pattern recognition image recognition

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Cell resistance slope combined with LVQ neural network for prediction of anode effect 6

Cell resistance slope combined with LVQ neural network for p...

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6th International Conference on intelligent control and Information processing, ICICIP 2015

作者： Zhou, Kaibo Lin, Zhikai Yu, Dengzhi Cao, Bin Wang, Ziqian Guo, Sihai School of Automation Huazhong University of Science and Technology Key Laboratory of Ministry of Education Image Processing and Intelligent Control Wuhan430074 China Guiyang Aluminum Magnesium Design and Research Institute Guiyang550004 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781479917174

It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper combined two methods, the slope of cell resistance and learning vector quantization (LVQ) neural network, to predict anode effect. First of all, the first prediction of anode effect will be conducted based on the slope of cell resistance. Afterwards, the inaccurate data are supposed to be re-predicted. The second prediction consists of two steps, one is to estimate the power spectrum from the signal of cell resistance by means of periodogram, the other is to re-predict the anode effect with the LVQ neural network, since the energy of frequency bands are served as the input feature variables of neural network, so as to raise the accuracy of prediction. It turned out that the success rate of ten-minute in advance prediction for anode effect can be above 85%, though just cell resistance signal is studied. © 2015 IEEE.

关键词： Forecasting

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A high-order disturbance observer based sliding mode velocity control of mobile wheeled inverted pendulum systems

A high-order disturbance observer based sliding mode velocit...

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World Congress on intelligent control and Automation (WCICA)

作者： Songhyok Ri Jian Huang Chunjing Tao Myonghyok Ri Yongsok Ri Daeho Han School of Automation Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Sciences Pyongyang D.P.R. of Korea Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA R&D department National Research Center for Rehabilitation Technical Aids Beijing Automation school Kim Chaek University of Technology Pyongyang D.P.R. of Korea Department of Phisics University of Sciences Pyongyang D.P.R. of Korea

ISBN: (纸本)9781467384155

In this paper, a novel disturbance observer (DO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is proposed. A choice method of optimal gain matrices is also proposed for a given robust gain, which can improve the estimation precision of the DO. Combining the proposed DO and Sliding Mode control (SMC), a new sliding mode velocity control method is designed for the MWIP system. The convergency of the DO is proved by Lyapunov theorem. And the stability of the closed-loop system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by simulation results for the MWIP system.

关键词： Wheels Automation Velocity control Uncertainty Observers Robustness

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Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer＇s Disease

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Chinese Medical Journal 2015年第5期128卷 602-609页

作者： Yuan-Yuan Qin Ya-Peng Li Shun Zhang Ying Xiong Lin-Ying Guo Shi-Qi Yang Yi-Hao Yao Wei Li Wen-Zhen Zhu Department of Radiology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei 430030 China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei 430074 China

Background：Previous studies have indicated that the cognitive deficits in patients with Alzheimer＇s disease （AD） may be due to topological deteriorations of the brain ***,whether the selection of a specific frequency band could impact the topological properties is still not *** hypothesis is that the topological properties of AD patients are also ***：Resting state functional magnetic resonance imaging data from l0 right-handed moderate AD patients （mean age：64.3 years; mean mini mental state examination [MMSE]：18.0） and 10 age and gender-matched healthy controls （mean age：63.6 years; mean MMSE：28.2） were enrolled in this *** global efficiency,the clustering coefficient （CC）,the characteristic path length （CpL）,and ＂small-world＂ property were calculated in a wide range of thresholds and averaged within each group,at three different frequency bands （0.01-0.06 Hz,0.06-0.11 Hz,and 0.11-0.25 Hz）.Results：At lower-frequency bands （0.01-0.06 Hz,0.06-0.11 Hz）,the global efficiency,the CC and the ＂small-world＂ properties of AD patients decreased compared to *** at higher-frequency bands （0.11-0.25 Hz）,the CpL was much longer,and the ＂small-world＂ property was disrupted in AD,particularly at a higher *** topological properties changed with different frequency bands,suggesting the existence of disrupted global and local functional organization associated with ***：This study demonstrates that the topological alterations of large-scale functional brain networks inAD patients are frequency dependent,thus providing fundamental support for optimal frequency selection in future related research.

关键词： Alzheimer＇s Disease Frequency Network

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Thermal management solutions for a traction robot for use in downhole extreme conditions

Thermal management solutions for a traction robot for use in...

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Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITHERM)

作者： Shuang Gao Aihua Tao Jingjing Cheng Ming Zhou Zhiyu Gong School of Automation Huazhong University of Science and Technology Wuhan P. R. China Well-Tech R&D Institutes China Oilfield Service Limited Beijing P. R. China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan P. R. China School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan P. R. China

Accurate techniques for testing production sections are important when developing horizontal wells, but appropriate methods are also needed for transporting loggers through horizontal well segments. The reciprocating grip traction robot is a pipeline robot that has already been tested and broadly accepted for use in horizontal wells. Reciprocating grip traction robots have received a great deal of attention because they are very (more than 40%) efficient, although many other tractors are only 10% to 20% efficient, the remaining energy being converted to waste heat. However, even an efficiency of 40% may constitute a serious thermal problem in high-temperature environments, and cooling methods must be used to remove the heat produced by the waste energy. The research presented here is mainly focused on the development of a thermal management system for the electronics in traction robots. Numerical simulations are used to optimize heat sink and skeleton structure to mitigate the effects of high temperature.

关键词： Heat sinks Robots Heat transfer Resistance Resistance heating Skeleton

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Almost periodic solutions for a memristor-based neural networks with leakage, time-varying and distributed delays

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Neural networks : the official journal of the International Neural Network Society 2015年 68卷 34-45页

作者： Ping Jiang Zhigang Zeng Jiejie Chen School of Automation Huazhong University of Science and Technology Wuhan 430074 China School of Computer Science and Technology Hubei PolyTechnic University Huangshi 435003 China. Electronic address: jiangping20140209@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: hustzgzeng@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China.

In this paper, we study the existence and global exponential stability of almost periodic solution for memristor-based neural networks with leakage, time-varying and distributed delays. Using a new Lyapunov function method, we prove that this delayed neural network has a unique almost periodic solution, which is globally exponentially stable. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for this delayed neural network with periodic coefficients (or constant coefficients).

关键词： Almost periodic solution Global exponential stability Leakage Memristor-based neural networks Time-varying and distributed delays

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Parallel programming in spiking neural P systems with synapses states

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Journal of Computational and Theoretical Nanoscience 2015年第10期12卷 3418-3423页

作者： Yuan, Kong Dongming, Zhao School of Automation Wuhan University of Technology Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan Hubei China

Spiking neural P systems with synapses states characterize the movement of spikes among the neurons. The number of the spikes in neurons can be represented by integers, which provide a way to represent increment, decrement, relational operations and assignment statement using spiking neural P systems with synapses states. According to these basic structures, it is possible to represent programming languages constructs. In this work, a kind of parallel programming language dealing with integer variables is considered, and it is simulated by spiking neural P systems with synapses states. Copyright © 2015 American Scientific Publishers.

关键词： Parallel programming

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Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

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Neural Computing and Applications 2015年第6期27卷 1771-1784页

作者： Chen, Jiejie Zeng, Zhigang Jiang, Ping Tang, Huiming School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearson's cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements.

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New results on anti-synchronization of switched neural networks with time-varying delays and lag signals

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Neural networks : the official journal of the International Neural Network Society 2016年 81卷 52-8页

作者： Yuting Cao Shiping Wen Michael Z Q Chen Tingwen Huang Zhigang Zeng School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: wenshiping226@***. The Department of Mechanical Engineering The University of Hong Kong Hong Kong. Texas A & M University at Qatar Doha 5825 Qatar.

This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper.

关键词： Anti-synchronization Exponential stability Switched neural networks

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The finite languages generated by spiking neural P systems with astrocytes

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Journal of Computational and Theoretical Nanoscience 2015年第10期12卷 3294-3298页

作者： Yuan, Kong Kai, Zhang Key Laboratory of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan Hubei China School of Computer Science Wuhan University of Science and Technology Wuhan Hubei China

Spiking neural P systems with astrocytes (SNPA, for short) are a class of distributed parallel computing devices inspired from the way spikes pass through the synapses between the neurons. In the present work, we discuss the computing power of SNPA systems as language generators, with special emphasis on the finite languages generated by SNPA systems. It is found that the finite languages of special forms can be generated by SNPA systems, while these languages cannot generated by spiking neural P systems without astrocytes. Copyright © 2015 American Scientific Publishers.

关键词： Computing power

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