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第十五届全国氢能会议暨第7届两岸三地氢能研讨会

作者： Lin Zhang Jianhua Jiang Xi Li Huan Cheng Jie Yang Jian Li School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhan430074China School of Materials Science and Engineering State Key Laboratory of Material Processing and Die and Mould TechnologyHuazhong University of Science and TechnologyWuhan 430074China

A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges a... 详细信息

关键词： Solid oxide fuel cell (SOFC) Optimal set-points Load transitions RBF network based direct adaptive controller

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The influencesof different gas flow types on solid oxide fuel cell stack temperature distribution

The influencesof different gas flow types on solid oxide fue...

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第十五届全国氢能会议暨第7届两岸三地氢能研讨会

作者： Huan Cheng Xi Li Jianhua Jiang Yali Liu Jian Li School of automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhan430074China School of Materials Science and Engineering State Key Laboratory of Material Processing and Die and Mould TechnologyHuazhong University of Science and TechnologyWuhan 430074China

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Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

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Stochastic Environmental Research and Risk Assessment 2014年第8期28卷 1957-1972页

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.

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Noise cancellation of memristive neural networks

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Neural networks : the official journal of the International Neural Network Society 2014年 60卷 74-83页

作者： Shiping Wen Zhigang Zeng Tingwen Huang Xinghuo Yu Department of Control Science and Engineering 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. Electronic address: wenshiping226@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng527@***. Texas A & M University at Qatar Doha 5825 Qatar. Electronic address: tingwen.huang@qatar.tamu.edu. Platform Technologies Research Institute RMIT University VIC 3001 Australia. Electronic address: x.yu@rmit.edu.au.

This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.

关键词： Memristor Neural network Stability

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Robust stability clearance of flight control law based on global sensitivity analysis

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Journal of Applied Mathematics 2014年第1期2014卷

作者： Ou, Liuli Liu, Lei Dong, Shuai Wang, Yongji Key Lab of Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology 1037 Luoyu Road Wuhan 430074 China

To validate the robust stability of the flight control system of hypersonic flight vehicle, which suffers from a large number of parametrical uncertainties, a new clearance framework based on structural singular value () theory and global uncertainty sensitivity analysis (SA) is proposed. In this framework, SA serves as the preprocess of uncertain model to be analysed to help engineers to determine which uncertainties affect the stability of the closed loop system more slightly. By ignoring these unimportant uncertainties, the calculation of can be simplified. Instead of analysing the effect of uncertainties on which involves solving optimal problems repeatedly, a simpler stability analysis function which represents the effect of uncertainties on closed loop poles is proposed. Based on this stability analysis function, Sobol's method, the most widely used global SA method, is extended and applied to the new clearance framework due to its suitability for system with strong nonlinearity and input factors varying in large interval, as well as input factors supjecting to random distributions. In this method, the sensitive indices can be estimated via Monte Carlo simulation conveniently. An example is given to illustrate the efficiency of the proposed method. © 2014 Liuli Ou et al.

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An improved character segmentation algorithm based on local adaptive thresholding technique for Chinese NvShu documents

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Journal of Networks 2014年第6期9卷 1496-1501页

作者： Sun, Yangguang Cai, Zhihua College of Computer Science South-Central University for Nationalities Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Software Engineering Wuhan University College of Computer Science South-Central University for Nationalities Wuhan 430072 China

For the structural characteristics of Chinese NvShu character, by combining the basic idea in LLT local threshold algorithm and introducing the maximal betweenclass variance algorithm into local windows, an improved character segmentation algorithm based on local adaptive thresholding technique for Chinese NvShu documents was presented in this paper. Because of designing the corresponding correction parameters for the threshold and using secondary search mechanism, our proposed method could not only automatically obtain local threshold, but also avoid the loss of the character image information and improve the accuracy of the character image segmentation. Experimental results demonstrated its capability to reduce the effect of background noise, especially for Chinese NvShu character images with uneven illumination and low contrast. © 2014 Academy Publisher.

关键词： image segmentation

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Nonlinear Terminal Guidance for Hypervelocity Vehicles Impact in Specific Direction and Speed

Nonlinear Terminal Guidance for Hypervelocity Vehicles Impac...

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第25届中国控制与决策会议

作者： Kun Hu Lei Liu Yongji Wang Zhishen Wang Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Department of Control Science & Engineering Huazhong University of Science & Technology

ISBN: (纸本)9781467355322

The terminal guidance problem of a hypervelocity gliding vehicle to intercept a stationary target in the planar scenario is considered. In addition to impact position accuracy, the guidance law must meet the impact angle and speed demand. This problem is formulated as an infinite-time horizon nonlinear regulator problem, and solved with the state-dependent Riccati equation (SDRE) control technique. We convert the system to a linear-like structure with state-dependent coefficient (SDC) matrices and derive a closed-loop state-feedback control law using the SDRE method. A new state is introduced concerning the impact speed constraint. By rotating the coordinate system, the guidance scheme is extended to satisfy arbitrary impact angle. The state weighting matrix is chosen as the function of time-to-go to include the distance information between the vehicle and target. The numerical simulations are carried out for different impact angles and speeds, the results of which verify the effectiveness of the proposed guidance approach.

关键词： Terminal Guidance, Nonlinear Regulator, State-dependent Riccati Equation

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Event-triggered Consensus Of Nonlinear Multi-Agent Systems With Nonlinear Interconnections

Event-triggered Consensus Of Nonlinear Multi-Agent Systems W...

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第三十二届中国控制会议

作者： YIN Xiuxia YUE Dong Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology

ISBN: (纸本)9781479900305

This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed event-triggered consensus control is designed by taking into account the effect of the nonlinear interconnections. Then, based on the Lyapunov functional method and the Kronecker product technique, sufficient conditions are obtained to guarantee the consensus in the form of linear matrix inequality (LMI). Finally, a simulation example is proposed to illustrate the effectiveness of the developed theory.

关键词： Consensus, Multi-Agent System (MAS), Nonlinear Interconnection, Event-Triggered control, LMI.

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A robust CPD approach based on shape context

A robust CPD approach based on shape context

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Chinese control Conference (CCC)

作者： Yawen Yang Zhang Peng Peng Yu Qiao Jie Yang Sheng Zheng Wang The Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai CN Merchant Marine College Shanghai Maritime University Shanghai CN

Point matching is an important component of image registration. Recent years, Coherent Point Drift (CPD) method becomes a very popular point matching approach. CPD treats point matching as a probability estimation problem and speeds up the process of matching a lot. In this method, one set of points are thought to be sampled from a Gaussian Mixture Model (GMM), which is centered by the other set of points. However, CPD is sensitive to outliers and noises, especially when the noise ratio increased or the number of outliers gets much high. To deal with this problem, we introduce shape context into the step of searching for matching points and then improve the form of prior probabilities of GMM in this paper. The main idea of our method is that if the most points in a data set are likely to be matched to a particular centroid, this Gaussian component should be have a more influence to GMM. Therefore, we set prior probability of GMM with the similarity between GMM components and the data set. And the computation of similarity is based on shape context. The experiments on 2D and 3D images show that when noise ratio is low, our method performs as well as CPD does, but as the ratio increased, our method is more robust and satisfactory than CPD.

关键词： Noise Shape Testing Context Robustness Three-dimensional displays Marine animals

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Performance of combined artificial neural networks for forecasting landslide displacement

Performance of combined artificial neural networks for forec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan Hubei China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geo-sciences Wuhan China

An efficient and accurate method for landslide displacement prediction is very important to reduce the casualties and property losses caused by this type of natural hazard. In recent years, many kinds of artificial neural networks (ANNs) have been widely applied to landslide displacement prediction. But we can't know which type of ANN is the best until we have calculated the prediction error. An improper choice of ANN may result in bad prediction results. In this paper, we use a neural networks combination prediction method based on the discounted MSFE (mean squared forecast error) to reduce the risk of selecting the types of ANNs. Four popular ANNs, radial basis function neural network (RBFNN), support vector regression (SVR), least squares support vector machine (LSSVM) and extreme learning machine (ELM), are selected as candidate neural networks. The performance of our model is verified through two case studies in Baishuihe landslide and Bazimen landslide. Experimental results reveal that the combining neural networks can improve the generalization abilities of ANNs.

关键词： Terrain factors Artificial neural networks Monitoring Predictive models Kernel Training

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