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model reduction for continuous-time Markovian jump systems with incomplete statistics of mode information

International Journal of Systems science 2014年第7期45卷 1496-1507页

作者： Yanling Wei Jianbin Qiu Hamid Reza Karimi Mao Wangpages 1496-1507View full textDownload full textAccess optionsDOI:10.1080/00207721.2013.837545Published online: 20 Sep 2013Citing articles: 0Article Views: 32Article usage statistics combine cumulative total PDF downloads full-text HTML views from publication date (but no earlier than 25 Jun 2011 launch date of this website) to 14 Jun 2014. Article views are only counted from this site. Although these data are updated every 24 hours there may be a 48-hour delay before the most recent numbers are available.Article usage statistics combine cumulative total PDF downloads full-text HTML views from publication date (but no earlier than 25 Jun 2011 launch date of this website) to 14 Jun 2014. Article views are only counted from this site. Although these data are updated every 24 hours there may be a 48-hour delay before the most recent numbers are available.Further InformationAbstractReferencesRelated articles [a] The Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 P. R. China[b] The?Department of Engineering Faculty of Engineering and Science University of Agder N-4898 Grimstad Norway

This paper investigates the problem ofmodel reduction for a class of continuous-time Markovian jump linear systems with incomplete statistics of mode information, which simultaneously considers the exactly known, partially unknown and uncertain transition rates. By fully utilising the properties of transition rate matrices, together with the convexification of uncertain domains, a new sufficient condition forperformance analysis is first derived, and then two approaches, namely, the convex linearisation approach and the iterative approach, are developed to solve the model reduction problem. It is shown that the desired reduced-order models can be obtained by solving a set of strict linear matrix inequalities (LMIs) or a sequential minimisation problem subject to LMI constraints, which are numerically efficient with commercially available software. Finally, an illustrative example is given to show the effectiveness of the proposed design methods.

关键词： Markovian jump systems model reduction incomplete statistics of mode information linear matrix inequality

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Genetic Algorithm based Neural Network for the Displacement of Landslide Forecasting

Genetic Algorithm based Neural Network for the Displacement ...

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第三十三届中国控制会议

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

This paper proposes two hybrid prediction models using for predicting the displacement of landslide, Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFN) and Genetic Algorithm- Back Propagation Neural Network (GA-BPNN). A case study of Yuhuangge landslide in the Three Gorges reservoir in China is used to illustrate the capability and merit of our schemes. In addition, the result shows that GP-BPNN get better accuracy than GA-RBFN in the same measurements.

关键词： Terrain factors Genetic algorithms Neural networks Forecasting Training Transfer functions Educational institutions

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Development of an anthropomorphic flute robot for the application in science and technology museum

Development of an anthropomorphic flute robot for the applic...

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IEEE International Conference on Robotics and Biomimetics

作者： Gao Huang Weimin Zhang Fei Meng Ye Tian Qing Shi Klaus Petersen Atsuo Takanishi Qiang Huang Key Laboratory of Biomimetic Robots and Systems Ministry of Education China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Faculty of Science and Engineering Waseda University Tokyo Japan Key Laboratory of Intelligent Control and Decision of Complex System China

ISBN: (纸本)9781479973989

To resemble the appearance of human beings, humanoid robots should be designed with many DOFs, however, it increases the complexity of the mechanical design and the real-time control, which will make a high cost and have a poor reliability and will not fit for the application in museums or human's daily lives. In this paper, to enable the low cost and high reliability robot to interact with humans naturally and sever humans more friendly, we imported advanced technology of the Waseda's flute robot and retained the most important mechanical parts, such as the lip, lungs and the fingers for the flute playing, on this basis, some other mechanical structures were redesigned; particularly, the robot's appearance was carefully designed; in addition, we also redesigned the hardware structure using our own joint controllers and software system based on CAN bus communication for the cost saving and high reliability. The redesigned robot had just 16 DOFs. Preliminary performance test results showed that this new redesigned robot achieved in producing high quality of flute sound and high sound conversion efficiency, and the period of communication was about 4ms.

关键词： Robot kinematics Robot sensing systems Lungs Joints Legged locomotion control systems

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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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World Congress Integrative Medicine & Health 2017: part two Berlin, Germany. 3-5 May 2017 Abstracts

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BMC COMPLEMENTARY AND ALTERNATIVE MEDICINE 2017年第SUPPL 1期17卷 317-317页

作者： [Anonymous] National Institute of Complementary Medicine Western Sydney University Sydney Australia Department of General Practice University of Melbourne Melbourne Australia School of Rehabilitation Therapy Queens University Ontario Canada School of Health and Biomedical Sciences RMIT University Melbourne Australia School of Public Health and Preventive Medicine Monash University Melbourne Australia NAFKAM UiT The Arctic University of Norway Tromsø 9019 Norway Department of Psychology University of Sheffield Sheffield S1 1HD UK Department of Community Medicine UiT The Arctic University of Norway Tromsø 9019 Norway Institue of General Practice Goethe University Frankfurt Frankfurt 60590 Germany Department of General Practice University of Tübingen Tübingen Germany Med.-pharm. Information/Klinische Forschung WALA Heilmittel GmbH Bad Boll/ Eckwälden 73087 Germany Ressortleitung Wissenschaft WALA Heilmittel GmbH Bad Boll/Eckwälden 73087 Germany University of Minnnesota Minneapolis 55455 MN USA Minneapolis College of Art and Design Minneapolis MN USA Duke University Durham NC USA Caring Bridge Minneapolis MN USA University of Minnesota Minneapolis 55455 MN USA Phytomedicines Supply and Development Center Innovation & Development Bayer Consumer Health Steigerwald Arzneimittelwerk GmbH Darmstadt 68526 Germany University Medicine of Rostock Rostock 18055 Germany Primary Care and Population Sciences University of Southampton Southampton UK Psychology University of Southampton Southampton UK Faculty of Health University of Technology Sydney Sydney 2007 Australia st Department of Pathology and Experimental Cancer Research Semmelweis University Budapest 1085 Hungary University of Alberta Edmonton Canada Parker University Dallas TX USA Canadian Memorial Chiropractic College Toronto Canada Anglo-European College of Chiropractic Bournemouth UK University of Calgary Calgary Canada University of Toronto Toronto Canada University of Hawaii at Manoa Honolulu Hawai’

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Embedding of Nonlinear Systems in a Linear Parameter-Varying Representation

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IFAC Proceedings Volumes 2014年第3期47卷 6907-6913页

作者： Hossam S. Abbas Roland Tóth Mihály Petreczky Nader Meskin Javad Mohammadpour Department of Electrical Engineering College of Engineering Qatar University PO Box 2713 Doha Qatar Electrical Engineering Department Faculty of Engineering Assiut University Assiut Egypt Control Systems Group Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven The Netherlands Department of Computer Science and Automatic Control Ecole des Mines de Douai 59508 Douai France Intelligent Control Systems Lab College of Engineering The University of Georgia Athens GA 30602 USA

This paper introduces a systematic approach to synthesize linear parameter-varying (LPV) representations of nonlinear (NL) systems which are originally defined by control affine state-space representations. The conversion approach results in LPV state-space representations in the observable canonical form. Based on the relative degree concept of NL systems, the states of a given NL representation are transformed to new coordinates that provide its normal form. In the SISO case, all nonlinearities of the original system are embedded in one NL function which is factorized to construct the LPV form. An algorithms is proposed for this purpose. The resulting transformation yields an LPV model where the scheduling parameter depends on the derivatives of the inputs and outputs of the system. In addition, if the states of the NL model can be measured or estimated, then the procedure can be modified to provide LPV models scheduled by these states. Examples are included for illustration.

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Fall detection for elderly by using an intelligent cane robot based on center of pressure (COP) stability theory

Fall detection for elderly by using an intelligent cane robo...

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International Symposium on Micromechatronics and Human science (MHS)

作者： Pei Di Jian Huang Shotaro Nakagawa Kosuke Sekiyama Toshio Fukuda Department of Micro-Nano Systems Engineering Nagoya University Aichi Nagoya Japan Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Institute for Advanced Research Nagoya University Chikusa-ku Nagoya Japan The Intelligent Robotics Institute School of Mechatronic Engineering Beijing Institute of Technology China The Faculty of Science and Engineering Meijo University Japan

An intelligent cane robot was designed for aiding the elderly and handicapped people walking. The robot consists of a stick, a group of sensors and an omni-directional basis driven by three Swedish wheels. Multiple sensors were used to recognize the user's “walking intention”, which is quantitatively described by a new concept called intentional direction (ITD). Based on the guidance of filtered ITD, a novel intention-based admittance motion control (IBAC) scheme was proposed for the cane robot. To detect the fall of user, a detection method based on Dubois possibility theory was proposed using the combined sensor information from force sensors, a laser ranger finder (LRF) and an on-shoe load sensor. The human fall model was represented in a two-dimensional space, where the relative position between the center of pressure (COP) and the center of support triangle was utilized as a significant feature. The effectiveness of proposed fall detection method was also confirmed by experiments.

关键词： Legged locomotion Robot sensing systems Senior citizens Artificial intelligence

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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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Displacement prediction model of landslide based on functional networks

Displacement prediction model of landslide based on function...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Chen, Jiejie Zeng, Zhigang Tang, Huiming 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 Faculty of Engineering China University of Geosciences Wuhan 430074 China

ISBN: (纸本)9783642390678

In this paper, a novel computational intelligence scheme is proposed to forecast landslide based on functional networks. Two types functional networks, general functional networks with two variables basis function (GFN) and separable functional networks (SFN) are applied to predict a real-world example. In addition, the experiments reveal that the landslide prediction using functional networks is reasonable and effective, and GFN are consistently better than SFN in terms of the same measurements. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Landslides

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Real-time fall and overturn prevention control for human-cane robotic system

Real-time fall and overturn prevention control for human-can...

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2013 44th IEEE International Symposium on Robotics, ISR 2013

作者： Di, Pei Huang, Jian Nakagawa, Shotaro Sekiyama, Kosuke Fukuda, Toshio Department of Micro-Nano Systems Engineering Nagoya University 464-0814 Aichi Nagoya Japan Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Faculty of Science and Engineering Meijo University Nagoya Aichi Japan Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781479911738

A intelligent Cane Robot(iCane) has been proposed to assist the elderly walking in daily life. As a nursing-care robot, the safety and dependability are the most important issues that should be investigated. For preventing the human subject from falling, the angle of human body and the acceleration of center of gravity(COG) should be less than some threshold. Although in a human-in-the-loop system, the human subject is regarded a uncontrollable object. However, while the user is falling over, the cane robot can move to a appropriately position and support the user for balance. As the prerequisite condition that the cane robot support the human balance, the stability of cane robot should be ensured firstly. According Newton-Euler Law, a dynamic model is proposed to present the stability of human-cane robotic system. A impedance control are used to achieve position, posture and force control of iCane for fall prevention. The simulation and experimental results show the performance of fall prevention by using iCane. © 2013 IEEE.

关键词： Accident prevention

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