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Performance analysis for remote state estimation via measurement combination *

IFAC Proceedings Volumes 2013年第13期46卷 296-301页

作者： Lidong He Xiaofan Wang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

We consider the discrete linear state estimation problem over a packet-dropping network. Before transmitting the data, the linear measurement combination is designed at the local sensor side. In order to improve the remote estimation performance, whether transmitting the current data or the combination depends on whether the previous packet is successfully received or not. A linear minimum mean square error estimation algorithm is proposed for the novel scheme. Moreover, the recursive expression between estimate and smooth error covariance of the last step, as well as the current estimation error covariance is explicitly established via properly constructing a state observer. The equivalency of the two methods is shown via some basic transformations in algebraic Riccati equation theory. The optimal weight for minimizing the estimation error covariance is derived for scalar systems.

关键词： Remote state estimation packet-dropping network performance optimization

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Multi-cell multi-user downlink beam forming design considering transceiver impairment

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Tongxin Xuebao/Journal on Communications 2013年第12期34卷 104-112页

作者： Xia, Can-Feng He, Shi-Wen Huang, Yong-Ming Wang, Hai-Ming Yang, Lv-Xi School of Information Science and Engineering Southeast University Nanjing 210096 China State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiaotong University Shanghai 200240 China

Multi-cell multi-user downlink beamforming schemes considering transceiver impairments were developed, with the objective of minimizing the maximum mean square error (MSE) and the sum of MSE of all users. The optimization problem was transformed into a second-order cone programming (SOCP) standard form, and a hierarchical iterative algorithm was developed to solve the original problem. Compared with the traditional beamforming methods developed for ideal transceivers, numerical results show that our proposed optimization algorithms greatly reduce the impact of impairments and improve the system performance.

关键词： Mean square error

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AN ACTIVE CONTOUR MODEL BASED ON MULTIPLE BOUNDARY MEASURES

AN ACTIVE CONTOUR MODEL BASED ON MULTIPLE BOUNDARY MEASURES

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IEEE International Conference on Image processing

作者： Yu Qiao Lei Zhou Jie Yang Yonghui Gao The Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781479923427

Weak boundary contrast, inhomogeneous background and overlapped intensity distributions of the object and background are main causes that may lead to failure of boundary detection for many traditional active contour methods. In this paper, we propose a region-based active contour model to address these problems in both local and global ways. A localized active contour framework is developed, in which two local boundary measures are introduced for the evolution of the level set function. These measures are used to select the boundary candidates for boundary preservation such that the evolution of the contour is guided in a reasonable way. The object boundary is determined by a global boundary measure which evaluates the boundary completeness during the entire evolution process. The experiments demonstrate that our method works well against weak boundary contrast, inhomogeneous background and overlapped intensity distributions.

关键词： Region-based active contour model Level set method Local boundary measures Boundary preservation Global boundary measure contour Boundary boundary layer set method boundary contrast Comet Nucleus Tour Edge detection evolution evolution evolution intensity distribution

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A spatio-temporal multivariate adaptive regression splines approach for short-term freeway traffic volume prediction

A spatio-temporal multivariate adaptive regression splines a...

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International Conference on Intelligent Transportation

作者： Yanyan Xu Qing-Jie Kong Yuncai Liu Department of Automation Key Laboratory of System Control and Information Processing Shanghai China State Key Laboratory for Management Institute of Automation Beijing China

ISBN: (纸本)9781479929153

Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments on the freeway are considered as candidates of the independent variables fed into the prediction model. A spatio-temporal multivariate adaptive regression splines (MARS) approach is proposed for the road network analysis and to predict the short-term traffic volume at the observation stations on the freeway. The actual traffic data are collected from a series of observation stations along a freeway in Portland every 15 minutes. In the first phase, the macroscopic dependency relationships of the stations on the freeway are investigated via MARS method. Subsequently the stations most related to the object station are selected and fed into the MARS prediction model to generate the short-term volume. The experiments are carried out on the actual traffic data and the results indicate that the proposed spatio-temporal MARS model can generate superior prediction accuracy in contrast with the historical data based MARS model, the parametric ARIMA, and the nonparametric PPR methods.

关键词： Predictive models Mars Traffic control Data models Roads Solid modeling

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A Heuristic Based on PSO for Irregular Cutting Stock Problem

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IFAC Proceedings Volumes 2013年第13期46卷 473-477页

作者： Yanxin Xu Genke Yang Changchun Pan Department of Automation Shanghai JiaoTong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 CHINA

Cutting Stock Problems (CSP) arise in many production industries where large stock sheets must be cut into smaller pieces. An irregular-shaped nesting approach for two-dimensional cutting stock problem is constructed in this research. We present a heuristic based on Particle Swarm Optimization Algorithm (PSO) for irregular-shaped two-dimensional cutting stock problem, where PSO is utilized to search optimal solution. Furthermore, the proposed approach combines a grid approximation method with Bottom-Left-Fill heuristic placement strategy to allocate irregular items. We evaluate the proposed approach using 15 revised benchmark problems available from the EURO Special Interest Group on Cutting and Packing. The performance illustrates the effectiveness and efficiency of our approach in solving irregular cutting stock problems.

关键词： PSO Cutting Stock Problem Bottom-Left-Fill Grid Approximation

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Mixed H2/H∞ model predictive control for systems with energy-bounded disturbance

Mixed H2/H∞ model predictive control for systems with energ...

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Chinese control Conference (CCC)

作者： Jiwei Li Dewei Li Yugeng Xi Jianbo Lu Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai CN

This paper designs a mixed H 2 /H ∞ model predictive algorithm for systems with energy-bounded disturbance. The formulation of H 2 /H ∞ performance in previous work is improved by introducing a slack variable. Furthermore, we decouple the formulation of H 2 /H ∞ performance from the formulation of input and state constraints by dilated LMI technique. These two aspects are combined to develop a less conservative control algorithm. Recursive feasibility of the algorithm is able to be proved and stability of the controlled system is ensured. Numerical example shows the effectiveness of the proposed algorithm.

关键词： Predictive control Prediction algorithms Equations Robustness Upper bound Linear matrix inequalities Trajectory

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MOTION PATTERN ANALYSIS IN CROWDED SCENES BASED ON HYBRID GENERATIVE-DISCRIMINATIVE FEATURE MAPS

MOTION PATTERN ANALYSIS IN CROWDED SCENES BASED ON HYBRID GE...

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IEEE International Conference on Image processing

作者： Chongjing Wang Xu Zhao Zhe Wu Yuncai Liu Department of Automation and Key Laboratory of China MOE for System Control and Information Processing Shanghai Jiao Tong University

ISBN: (纸本)9781479923427

Crowded scene analysis is becoming increasingly popular in computer vision field. In this paper, we propose a novel approach to analyze motion patterns by clustering the hybrid generative-discriminative feature maps using unsupervised hierarchical clustering algorithm. The hybrid generative-discriminative feature maps are derived by posterior divergence based on the tracklets which are captured by tracking dense points with three effective rules. The feature maps effectively associate low-level features with the semantical motion patterns by exploiting the hidden information in crowded scenes. Motion pattern analyzing is implemented in a completely unsupervised way and the feature maps are clustered automatically through hierarchical clustering algorithm building on the basis of graphic model. The experiment results precisely reveal the distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.

关键词： crowded scene analysis motion pattern tracklet the hybrid generative-discriminative feature maps automatic clustering

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Detection of insects-contaminated wheat based on PSO-BP network

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information Technology Journal 2013年第16期12卷 3596-3599页

作者： Shi, Weiya Keke, Jiao Yitao, Liang Feng, Wang Zhang, Dexian School of Information Science and Technology Henan University of Technology Zhengzhou 450001 China Grain Information Processing and Control Key Laboratory of Ministry of Education Zhengzhou 450001 China

Based on biophoton analytical technology, the ultraweak photons emitted from the normal and insects-contaminated wheat are measured separately. Nine parameters of wheat self-illuminating characteristic are used as wheat feature vector. The study proposed using the PSO-BP algorithm (Particle Swarm optimization-BP neural network) as the classification algorithm. The algorithm is trained to distinguish the normal and insects-contaminated wheat. The experimental results show that the recognition precision can reach to 90%. The model can provide a new thought for the detecting the wheat pests. © 2013 Asian Network for Scientific information.

关键词： Particle swarm optimization (PSO)

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Maximum power point tracking for photovoltaic system using model predictive control

Communications in Computer and Information Science

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Communications in Computer and information Science 2013年 355卷 515-523页

作者： Ma, Chao Li, Ning Li, Shaoyuan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

In this paper, T-G-P model is built to find maximum power point according to light intensity and temperature, making it easier and more clearly for photovoltaic system to track the MPP. A predictive controller considering constraints for safe operation is designed. The simulation results show that the system can track MPP quickly, accurately and effectively. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Maximum power point trackers

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Adaptive visual servoing using common image features with unknown geometric parameters

Adaptive visual servoing using common image features with un...

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作者： Liu, Yun-Hui Wang, Hesheng Chen, Weidong Zhou, Dongxiang Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Hong Kong Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China State Key Laboratory of Robotics Technology and System Harbin Institute of Technology Harbin 150001 China Joint Center for Intelligent Sensing and Systems National University of Defense Technology China

This paper generalizes the concept of the depth-independent interaction matrix, developed for point and line features in our early work, to generalized image features. We derive the conditions under which the depth-independent interaction matrix can be linearly parameterized by the geometric parameters of the generalized image features, and propose an adaptive visual servo controller for robot manipulators using the generalized image features whose geometric parameters are unknown. To estimate the unknown parameters on-line, we propose new error functions that are linear to estimation errors of the parameters and an algorithm that minimizes the error functions using multiple images. The Lyapunov theory is used to prove asymptotic stability of the proposed controller based on the nonlinear dynamics of the manipulator. It is also shown that in addition to points and lines, other common image features like distances, angles, areas, and centroids all satisfy the conditions for the linear parameterization. Experiments have been conducted to validate the proposed control method. © 2013 Elsevier Ltd. All rights reserved.

关键词： controllers

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