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International Conference on Innovative Computing Technology (INTECH)

作者： Sen Qiu Yu Yang Jijian Hou Ran Ji Huosheng Hu Zhelong Wang Department of Computer Science and Electronic Engineering University of Essex Colchester UK School of Control Science and Engineering Dalian University of Technology Dalian China Dalian University of Technology Dalian Liaoning CN

This paper presents a MARG Sensors based walking motion analysis method that can be used for rehabilitation treatment evaluation under ambulatory conditions in daily lives. A commercial inertial measure unit MTx is fixed on human shank segment. Then digital data box worn on the waist transmits the raw data to laptop with Blue-tooth for post processing. Subjects are required to perform level walking and upstairs walking respectively. After the initial alignment and quaternion convergence, the proposed method fuses the inertial data using Kalman filter based on quaternion. Walking trajectory and knee joint angle are estimated and validated by available ground truth. Experiment results demonstrate that the proposed method has a good performance at both motion patterns. It also shows that no significant drifts exist in the overall results presented in the paper. The method can also be adapted to measure other sites of articulation such as ankle and elbow.

关键词： Legged locomotion Sensors Quaternions Gyroscopes Kalman filters Estimation Accelerometers

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ANN-based DTC scheme to improve the dynamic performance of an IM drive

ANN-based DTC scheme to improve the dynamic performance of a...

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7th IET International Conference on Power Electronics, Machines and Drives, PEMD 2014

作者： Bouhoune, K. Yazid, K. Boucherit, M.S. Power and Industrial Systems Laboratory Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene PO Box 32 El Alia Bab Ezzouar 16111 Algiers Algeria Process Control Laboratory Department of Electrical Engineering Polytechnic National School PO Box 182 10 Hassan Badi El-Harrach 16200 Algiers Algeria

ISBN: (纸本)9781849198158

In addition to simplicity, the DTC of the IM allows a good torque control in steady-state and transient operating conditions. However, high torque ripple is produced, which is reflected in speed estimation responses. It also increases acoustical noise and makes the voltage source inverter operated in high and variable switching frequency, requiring a high sampling frequency. In this paper, a simple effective ANN-based DTC of the induction machine is proposed. Neural networks with a simple architecture are designed and implemented in influential points of the direct torque control block of the three phase induction machine in order to improve its dynamic performance while preserving the DTC structure simplicity as much as possible. In particular, this paper proposes to reduce, on the one hand, the torque ripple and commutation frequency. Neural comparators are used to select the appropriate bandwidth for the torque and flux hysteresis controllers. Their aim is to optimize the ripple level in the developed torque and flux. On the other hand, a neural speed controller is designed in order to improve the system ability to respond rapidly to changes in process variables and mitigate the effects of external perturbations. The performance of the proposed control are tested in simulation and highlighted by comparing to the conventional DTC control based on conventional hysteresis comparators and conventional PI speed controller. The obtained results show the feasibility and good performances of the proposed control.

关键词： Neural networks

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Efficient control synthesis for augmented finite transition systems with an application to switching protocols

Efficient control synthesis for augmented finite transition ...

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American control Conference

作者： Fei Sun Necmiye Ozay Eric M. Wolff Jun Liu Richard M. Murray Information Security Center Beijing University of Posts and Telecommunications China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA Department of Control and Dynamical Systems California Institute of Technology Pasadena CA 91125 USA Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom

ISBN: (纸本)9781479932757

Augmented finite transition systems generalize nondeterministic transition systems with additional liveness conditions. We propose efficient algorithms for synthesizing control protocols for augmented finite transition systems to satisfy high-level specifications expressed in a fragment of linear temporal logic (LTL). We then use these algorithms within a framework for switching protocol synthesis for discrete-time dynamical systems, where augmented finite transition systems are used for abstracting the underlying dynamics. We introduce a notion of minimality for abstractions of certain fidelity and show that such minimal abstractions can be exactly computed for switched affine systems. Additionally, based on this framework, we present a procedure for computing digitally implementable switching protocols for continuous-time systems. The effectiveness of the proposed framework is illustrated through two examples of temperature control for buildings.

关键词： Switched systems Switches Protocols Heuristic algorithms Transient analysis Complexity theory

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On infinite time performance of nonlinear model predictive controllers 19

On infinite time performance of nonlinear model predictive c...

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19th IFAC World Congress on International Federation of automatic control, IFAC 2014

作者： Cai, Xin Li, Shaoyuan Li, Ning Li, Kang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast BelfastBT7 1NN United Kingdom

ISBN: (纸本)9783902823625

This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal performance limits of closed-loop model predictive control systems. Based on the Principle of Optimality, an upper and a lower bound of the ratio between the total performance and the finite horizon optimal cost are obtained explicitly expressed by the optimization horizon. The results also illustrate, from viewpoint of performance, how model predictive controllers approaches to infinite optimal controllers as the optimization horizon increases. © IFAC.

关键词： controllers

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Fault Tolerant Shape control for Output PDFs Tracking of Stochastic Distribution Systems

Fault Tolerant Shape Control for Output PDFs Tracking of Sto...

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American control Conference

作者： Tao Li Gang Li Qing Zhao School of Information and Control Nanjing University of Information Science and Technology Nanjing Jiangsu 210044 China Department of Electrical and Computer Engineering University of Alberta

ISBN: (纸本)9781479932757

This paper investigates the fault-tolerant shape control (FTSC) problem for stochastic distribution systems. The available information for the addressed problem is the input and the measurable output Probability Density Function (PDF) of the system. The system is subject to actuator faults. In this case, the main objective is to achieve fault-tolerant shape control so that the output PDF can track a given target PDF shape even in the presence of faults. In this framework, an effective novel FTSC strategy is proposed based on the online estimation of the actuator faults, which includes a normal control law and an adaptive compensation control law simultaneously. The former can track the given output PDF with optimized performance index in the fault-free case, while the latter can automatically reduce (or even eliminate) the impact of faults for the given PDF shape. Finally, the effectiveness of the proposed design method is illustrated via a numerical example.

关键词： Shape control Fault tolerance Stochastic theory dispensing system control law design approach fault-free performance indicators

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On YASS’s Non-monotonic Security Performance

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Circuits, Systems, and Signal Processing 2014年第11期33卷 3527-3537页

作者： Yu, Lifang Zhao, Yao Ni, Rongrong Shi, Yun Qing Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipments Beijing Institute of Graphic Communication Beijing102600 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Recently, researchers have discovered unexpected bumps in the detection rate curve of yet another steganographic scheme (YASS). We refer to this abnormal phenomenon as non-monotonic security performance. This paper first analyzes this abnormality and points out that it is caused by the non-uniformity in probability of coincidence of (Formula presented.) embedding blocks and (Formula presented.) JPEG blocks. Based on this observation, we propose that further randomizing data embedding positions can overcome the non-monotonic security performance. Experimental results have demonstrated a complete removal of bumps in the detection rate curve of YASS after further randomization. © 2014, Springer science+Business Media New York.

关键词： Steganography

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TISVM: LARGE MARGIN CLASSIFIER FOR MISALIGNED IMAGE CLASSIFICATION

TISVM: LARGE MARGIN CLASSIFIER FOR MISALIGNED IMAGE CLASSIFI...

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IEEE International Conference on Image Processing

作者： Bin Shen Bao-Di Liu Jan P. Allebach Department of Computer Science Purdue University West Lafayette IN USA College of Information and Control Engineering China University of Petroleum Qingdao China School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Support vector machine is one of the most successful machine learning methods in image processing and computer vision in the past decades. However, its performance strongly depends on the training data, which are sometimes expensive and of low quality. Specifically, in many real applications, such as face recognition, the images are rarely perfectly aligned, thus the misalignment between training and testing data impairs the performance. In this paper, we propose a strategy to compensate the misalignment between images while learning the classifier without looking at the testing samples. Specifically, some certain critical transformations are inferred and applied to training samples to alleviate the effect of the worst case of possible misalignment. The resulted large margin classifier generalizes better than traditional SVM, especially when there is misalignment. Experimental results on real image data sets show the efficacy of the proposed algorithm.

关键词： Support vector machines Training Testing computer vision Face Optimization Face recognition

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Conceptual Ship Hull Design with a Genetic Algorithm

Conceptual Ship Hull Design with a Genetic Algorithm

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2013 International Conference on Frontiers of Energy,Environmental Materials and Civil engineering(FEEMCE 2013)

作者： YILING CAO YOUHONG TANG WEI LIN CHENGBI ZHAO Department of Naval Architecture and Ocean Engineering School of Civil Engineering and Transportation South China University of Technology Centre for Maritime Engineering Control and Imaging School of Computer Science Engineering and Mathematics Flinders University

A direct fairing ship hull generation method is proposed in this research with three kinds of characteristic parameterized curves confined hull shape and using genetic algorithm optimized for design requirements. The proposed method provides the basis of rapid prototype conceptual design and benefit designers creation and modification in a large range of parameters.

关键词： Ship hull Constraint design B-spline surface Genetic algorithm

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Team of Bayesian optimization algorithms to solve task assignment problems in heterogeneous computing systems

Team of Bayesian optimization algorithms to solve task assig...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jie Li JunQi Zhang Qi Kang ChangJun Jiang Ministry of Education China Key Laboratory of Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai Department of Control Science and Engineering Tongji University Shanghai China

ISBN: (纸本)9781479938414

A Bayesian optimization algorithm (BOA) belongs to estimation of distribution algorithms (EDAs). It is characterized by combining a Bayesian network and evolutionary algorithms to solve nearly decomposable optimization problems. BOA is less popularly applied to solve high dimensionality complex optimization problems. A key reason is that the cost of training all dimensions by BOA becomes expensive with the increase of problem dimensionality. Since data are relatively sparse in a high dimensional space, even though BOA can train all dimensions simultaneously, the interdependent relations between different dimensions are difficult to learn. Its search ability is thus significantly reduced. In this paper, we propose a team of Bayesian optimization algorithms (TBOA) to search and learn dimensionality. TBOA consists of multiple BOAs, in which each BOA corresponds to a dimension of the solution domain and it is responsible for the search of this dimension's value region. The proposed TBOA is used to solve the real problem of task assignment in heterogeneous computing systems. Extensive experiments demonstrate that the computational cost of the overall training in TBOA is decreased very significantly while keeping high solution accuracy.

关键词： Bayes methods Sociology Statistics Optimization Computational efficiency Joints Probability distribution

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A data clustering algorithm based on mussels wandering optimization

A data clustering algorithm based on mussels wandering optim...

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IEEE International Conference on Networking, Sensing and control

作者： Peng Yan ShiYao Liu Qi Kang BingYao Huang MengChu Zhou Dept. of Control Science and Eng Tongji University Shanghai China Department of Electrical & Computer Engineering Rowan University Glassboro NJ USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

As an unsupervised learning method, clustering methods plays an important role in quality data mining and various other applications. This work investigates them based on swarm intelligence, introduces a new intelligence algorithm called mussels wandering optimization (MWO) to the data clustering field, and proposes a new clustering algorithm by combining K-means clustering method and MWO. Tests on six standard data sets are performed. The results demonstrate the validity and superiority of the proposed method over some representative clustering ones.

关键词： Reactive power Iris Particle swarm optimization Standards Sociology Statistics Vehicles

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