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第三十四届中国控制会议

作者： LIU QiaoQiao LI JianXun Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467374439

Combining Stacked Contractive Auto-Encoders(SCAE) with Support Vector Regression(SVR) method based on mass of data, a novel state of health estimation method is proposed in this paper. With the development of SCAE-SVR, SCAE could learn features automatically for SVR instead of extracting hand-designed features. SCAE is a deep machine learning method of unsupervised statistical algorithm that makes the learned features more robust and efficient. Then Support Vector Regression machine is used to estimate quantitative values dealing with the new feature representations. The composite structure of network not only remedies not enough features abstracted by a simplex shallow machine learning net, but also effectively avoid over-fitting in data regression. State of health estimation for Fuel cell systems from Prognostics and Health Management(PHM) 2014 Data Challenge demonstrates that the proposed method outperforms than other state of health estimation methods based on data-driven.

关键词： State of health estimate,CAE,SVR,Fuel cell systems

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Road DNA based localization for autonomous vehicles

Road DNA based localization for autonomous vehicles

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IEEE Symposium on Intelligent Vehicle

作者： Liang Li Ming Yang Chunxiang Wang Bing Wang Department of Automation Key Laboratory of System Control and Information Processing Shanghai CN Chunxiang Wang is with Research Institute of Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781509018222

High-precision and reliable localization is current research focus in the area of autonomous vehicles. Previous studies rely on either high-cost sensors or some specific characteristics, which means that the methods are limited to only a bit given situations. In this paper, a road DNA based localization method is proposed. It could afford high-precision result and does not have the shortcomings of previous methods at the same time. The scenery on both sides of the roads are used to generate the prior-map. The map is presented as grid map by the joint probability of occupation and reflectivity. With this type of map, different environments show different properties, which means that this method is not limited to specific environments and is effective in most cases. It costs much less memory than the previous maps. The map and live road scene flatting are both generated by data collected by low-cost LIDAR. Normalized information Distance is utilized to align the live road scene flatting with the road DNA. Experiments show the validation and precision of this method.

关键词： Roads Three-dimensional displays Vehicles DNA Trajectory Simultaneous localization and mapping Autonomous automobiles

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Output Tracking of Boiler-Turbine system by Fuzzy Guaranteed Cost control

Output Tracking of Boiler-Turbine System by Fuzzy Guaranteed...

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第35届中国控制会议

作者： Bo Pang Yadong Zhao Bowen Yi Liyue Chen Weidong Zhang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Electric power dispatching control center State Grid Zhejiang Electric Power Company

ISBN: (纸本)9781509009107

This paper proposes a controller design strategy of output tracking by guaranteed cost control(GCC) for TakagiSugeno(T-S) fuzzy systems and applies it to a boiler-turbine ***,based on the original T-S fuzzy system,an augmented system with integral action is established to eliminate steady tracking ***,a state-feedback parallel distributed compensation(PDC) controller is designed such that the guaranteed cost function has an upper *** condition that guarantees quadratical stability of the resulting closed-loop system is derived by using a relaxed stability condition of T-S fuzzy *** obtain as good performance as possible,the design procedure is transformed into a linear matrix inequalities(LMIs) optimization problem and the upper bound of the guaranteed cost function is ***,the simulation of output tracking control of a boiler-turbine system verifies the effectiveness and feasibility of the proposed approach.

关键词： Guaranteed cost control(GCC) Takagi-Sugeno(T-S) fuzzy system Linear matrix inequalities(LMIs) Boilerturbine unit

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A Data Driven Approach for Resting-state EEG signal Classificition of Schizophrenia with control Participants Using Random Matrix Theory

arXiv

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arXiv 2017年

作者： Liu, Haichun Zhang, TianHong Ye, Yumeng Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Resting state electroencephalogram (EEG) abnormalities in clinically high-risk individuals (CHR), clinically stable first-episode patients with schizophrenia (FES), healthy controls (HC) suggest alterations in neural oscillatory activity. However, few studies directly compare these anomalies among each types. Therefore, this study investigated whether these electrophysiological characteristics differentiate clinical populations from one another, and from non-psychiatric controls. To address this question, resting EEG power and coherence were assessed in 40 clinically high-risk individuals (CHR), 40 first-episode patients with schizophrenia (FES), and 40 healthy controls (HC). These findings suggest that resting EEG can be a sensitive measure for differentiating between clinical *** paper proposes a novel data-driven supervised learning method to obtain identification of the patients mental status in schizophrenia research. According to Marchenko-Pastur Law, the distribution of the eigenvalues of EEG data is divided into signal subspace and noise subspace. A test statistic named LES that embodies the characteristics of all eigenvalues is adopted. different classifier and different feature(LES test function) are selected for experiments, we have shown that using von Neumann Entropy as LES test function combine with SVM classifier could obtain the best average classification accuracy during three classification among HC, FES and CHR of Schizophrenia group with EEG signal. It is worth noting that the result of LES feature extraction with the highest classification accuracy is around 90% in two classification(HC compare with FES) and around 70% in three classification. Where the classification accuracy higher than 70% could be used to assist clinical diagnosis. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Blind Image Quality Assessment via Convolutional Neural Network

Blind Image Quality Assessment via Convolutional Neural Netw...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Meiyin Wu Li Chen College of Computer Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781509035595

This paper proposed a novel blind image quality assessment method that is created by training a convolutional neural network to learn discriminant features of image quality and fitting the features with a support vector regression to get an evaluation score. The pooling procedure is help to reduce the feature dimension and improve computation efficiency. The proposed method does not need any hand-crafted features contrast with most previous BIQA methods. It achieves better performance than previous BIQA methods on LIVE database. The experimental results show that the proposed method has good consistency, robustness and efficiency.

关键词： Feature extraction Image quality Databases Distortion Training Kernel Robustness

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The optimization of location and control of pump stations in urban drainage system

The optimization of location and control of pump stations in...

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Chinese control Conference (CCC)

作者： Yiqun Cui Shufang Hou Dewei Li Yugeng Xi Lihui Cen Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai CN School of Information Science and Engineering Central South University Changsha Hunan CN

ISBN: (纸本)9781509009107

To reduce the overflow of the wastewater and high expense caused by redundant arrangement of the stations of pumps in the urban drainage system, a hybrid logistic optimization model is proposed to choose the locations of pump stations and the corresponding optimal control algorithm is also developed in this paper. The inflow in the conduit from rainfall is estimated based on rainfall-runoff model and meanwhile the performance of the pump is described by the nonlinear pump model. After determining the optimal pump locations by the optimization algorithm, this paper further studies the energy saving optimal control of the system. When taking both the position and the working situation of the pumps into consideration, the energy consumption of the system and the frequency of overflow can be brought down effectively.

关键词： Optimization Mathematical model Wastewater Logistics Optimal control Delays Energy consumption

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Learning robust and discriminative low-rank representations for face recognition with occlusion

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Pattern Recognition 2017年 66卷 129-143页

作者： Guangwei Gao Jian Yang Xiao-Yuan Jing Fumin Shen Wankou Yang Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Laboratory of Big Data Analysis and Control for active distribution network Nanjing University of Posts and Telecommunications Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Southeast University Nanjing China

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where both training and testing images are corrupted due to occlusions. Previous low-rank based methods stacked each error image into a vector and then used L 1 or L 2 norm to measure the error matrix. However, in the stacking step, the structure information of the error image can be lost. Depart from the previous methods, in this paper, we propose a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured. In order to learn more discriminative low-rank representations, we formulate our objective such that the learned representations are optimal for classification with the available supervised information and close to an ideal-code regularization term. With strong structure information preserving and discrimination capabilities, the learned robust and discriminative low-rank representation (RDLRR) works very well on face recognition problems, especially with face images corrupted by continuous occlusions. Together with a simple linear classifier, the proposed approach is shown to outperform several other state-of-the-art face recognition methods on databases with a variety of face variations.

关键词： Face recognition Low-rank matrix recovery Nuclear norm

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Event-triggered Model Predictive control for Constrained Invariant Set Trajectory 34

Event-triggered Model Predictive Control for Constrained Inv...

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第三十四届中国控制会议

作者： LI Zhe LI Shaoyuan WU Jing Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781467374439

This paper proposes a novel event-triggered algorithm based on a contract robust control invariant set trajectory for robust constrained model predictive control system. The trigger condition relies on the relationship between the sets where two continuous state vectors lie in. Feasibility of the algorithm and stability of the system are derived utilizing the invariant set theory. Simulation shows that the proposed algorithm is feasible and able to ultimately drive the states to a bound set related to the desired performance, while reducing the frequency of solving optimization problem and the number of control updates significantly.

关键词： Invariant set trajectory,Event-triggered control,Constrained model predictive control

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Evolution of International Trade and Investment from a Complex Network Perspective 34

Evolution of International Trade and Investment from a Compl...

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第三十四届中国控制会议

作者： ZHANG Shuhong WANG Lin Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781467374439

In this paper, from a perspective of complex network, we investigate the international trade and investment for more than 200 countries from 2001 to 2010. Through analyzing the topological properties, we nd that the average partner numbers for both trade and investment are increasing year by year, and the average volume has a signi cant decrease in 2008 for investment and 2009 for trade. Although the edge density is dramatically lower and the average path length is longer for the international investment network, its clustering coef cient is higher every year. Moreover, we detect community structures and rank each country according to its total trade or investment volume, which demonstrate that the partnerships for the international trade are more stable, and USA always ranks rst in its community for both trade and investment.

关键词： International Trade,International Investment,Complex Network,Evolution,Community,Rank

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A Mixed-integer Quadratic Programming Solver based on GPU 34

A Mixed-integer Quadratic Programming Solver based on GPU

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第三十四届中国控制会议

作者： WANG Xi LI Dewei XI Yugeng Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781467374439

Solving the mixed-integer quadratic programming(MIQP) problem is often required in many practical applications. But the existing solvers always encounter the contradiction between high precision and low time consumption. To solve this problem, this paper designs a new MIQP solver by developing a parallel branch-and-bound algorithm utilizing multi-point radiation based on the multithreading parallel structure of GPU. This solver can obtain the global optimal solution by inheriting the advantages of the classical branch-and-bound algorithm. To increase the efficiency of the MIQP solver, for the quadratic programming(QP) to be solved each time during iteration, we fully use the massive parallelism of GPU and adopt the discrete-time simplified dual neural network. The idea of multi-point radiation is used to simultaneously generate multiple search branches to improve the search efficiency. These strategies enhance the throughput and the degree of parallelization. The high computational efficiency of the proposed MIQP solver is verified by test results with solving time statistics for multiple examples.

关键词： GPU,Mix-integer Quadratic Programming,Branch and Bound Algorithm,Parallel Structure

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