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Chinese Automation Congress (CAC)

作者： Jin Qian Jian Huang Songhyok Ri Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China

In this paper, an adaptive fuzzy sliding mode control approach which combines fuzzy control with the sliding mode control, is applied for the tracking control of pneumatic muscle actuator (PMA). This actuator is widely used in the rehabilitation robots while hard to control due to its strong nonlinearity. The switching-type control term in the sliding mode control law is approximated by a fuzzy system. Based on Lyapunov theory, the stability of the closed-loop PMA system can be guaranteed. Both simulation and experimental studies were carried out to verify the proposed methods. The experiments were conducted on a real PMA system, which was connected with the xPC target system. The results demonstrate the validity of the PMA as well as the effectiveness of the control algorithm even there exist system uncertainties and external bounded disturbances.

关键词： Software Real-time systems Fuzzy control Robots Switches Mathematical model Sliding mode control

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Mid-level parts mined by feature selection for action recognition

Mid-level parts mined by feature selection for action recogn...

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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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Design of interval type-2 fuzzy logic controller for mobile wheeled inverted pendulum

Design of interval type-2 fuzzy logic controller for mobile ...

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World Congress on intelligent Control and Automation (WCICA)

作者： MyongHyok Ri Jian Huang Songhyok Ri HyonSu Yun ChangSik Kim Department of Control Science KimChaek University of Technology Pyongyang Wuhan Democratic People's Republic of Korea Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA Department of Computer Science Hamhung University of Chemical Industry Hamhung Wuhan Democratic People's Republic of Korea Department of Computer Science KimChaek University of Technology Pyongyang Democratic People's Republic of Korea

ISBN: (纸本)9781467384155

In this paper, a novel interval type-2 fuzzy logic controller (IT2FLC) is proposed for controlling a mobile wheeled inverted pendulum(MWIP) with model uncertainties and external disturbances. The MWIP is a typical underactuated system which is generally composed of two subsystems, an invert pendulum and a mobile robot system. The IT2FLC proposed in this paper is composed of a balancing controller, a velocity controller and a yaw steering controller. The proposed integrated IT2FLC is linear combination of the three individual IT2FLCs which uses simplified type reduce method. The proposed controller has simple structure and requires less computation, also it is expected to have robustness to uncertainties and external disturbances in the practical implementation. Considering the model uncertainties and external disturbances, simulation results proved the effectiveness and robustness of the proposed IT2FLC compared with type-1 fuzzy logic controller (T1FLC).

关键词： Fuzzy logic Wheels Uncertainty Robustness Mobile communication Control systems Automation

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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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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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RETRACTED: Dual-mode predictive flexible rotor control for active magnetic bearings with input constraints

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Journal of Vibration and Control 2016年第20期23卷 NP2页

作者： Yue Wu Jie Zhao Hai-Tao Zhang Ming-Can Fan Xi Yu Guangdong HUST Industrial Technology Research Institute Guangdong Province Key Lab of Digital Manufacturing Equipment ChinaKey Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology China Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology China Department of Mathematics Huizhou University China School of Electrical and Information Engineering Xi'an Technology University China

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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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A new automatic mass detection method for breast cancer with false positive reduction

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Neurocomputing 2015年 152卷 388-402页

作者： Xiaoming Liu 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 College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430081 China

Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammographic images is proposed. Firstly, suspicious regions are located with an adaptive region growing method, named multiple concentric layers (MCL) approach. Prior knowledge is utilized by tuning parameters with training data set during the MCL step. Then, the initial regions are further refined with narrow band based active contour (NBAC), which can improve the segmentation accuracy of masses. Texture features and geometry features are extracted from the regions of interest (ROI) containing the segmented suspicious regions and the boundaries of the segmentation. The texture features are computed from gray level co-occurrence matrix (GLCM) and completed local binary pattern (CLBP). Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist׳s diagnosis. To deal with the imbalance problem regarding the number of non-masses and masses, supersampling and downsampling are incorporated. The method was evaluated on a dataset with 429 craniocaudal (CC) view images, containing 504 masses. Among them, 219 images containing 260 masses are used to optimize the parameters during MCL step, and are used to train SVM. The remaining 210 images (with 244 masses) are used to test the performance. Masses are detected with 82.4% sensitivity with 5.3 false positives per image (FPsI) with MCL, and after active contour refinement, feature analysis and classification, it obtained 1.48 FPsI at the sensitivity 78.2%. Testing on 164 normal mammographic images showed 5.18 FPsI with MCL and 1.51 FPsI after classification. Experiments on mediolateral oblique (MLO) images have also been performed, the proposed method achieved a sensitivity 75.6% at 1.38 FPsI. The method is also analyzed with free response operating characteristi

关键词： Mass detection Breast cancer Level set Local binary pattern Support vector machine

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Blind source separation with evolution based KICA

Blind source separation with evolution based KICA

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781479979684

Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An optimal kernel model should be rich enough to well map the given samples. However, users usually use a singular kernel based model in their experiments, which leads to a suboptimal kernel model. In order to solve this problem, we propose the evolution based multiple kernel independent component analysis (EMKICA), in which a convex combination of multiple base kernels is used instead of single kernel of KICA. The combination weights are learned by particle swarm optimization algorithm. Firstly, we elaborate the basic theory of KICA and concept of EMKICA, also the combination form of the composition kernel used in EMKICA. Secondly, we describe the presentation of the individuals in the particle swarm optimization algorithm, the settings of the evaluation function and general algorithm. Finally, we evaluate the separation ability of EMKICA on three different data sets including one-dimensional mixed signals, composite images and images with reflection. The experimental results verify the effectiveness of EMKICA.

关键词： Kernel Independent component analysis Algorithm design and analysis Multimedia communication Correlation Optimization Asia

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