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IEEE Chinese Guidance, Navigation and control Conference (CGNCC)

作者： Yuehan Jiang Qing Wang Chaoyang Dong Min Zhou Kewei Zhong School of Automation Science and Electrical Engineering Beihang University Beijing People’s Republic of China School of Aeronautic Science and Engineering Beihang University Beijing People’s Republic of China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing People’s Republic of China Shanghai Aerospace Control Technology Institute Shanghai People’s Republic of China

ISBN: (数字)9781538611715

ISBN: (纸本)9781538611722

For robots to work effectively, the availability of a map with detailed information surrounding the workspace is an important requirement for indoor and outdoor tasks. This is usually achieved with using visual odometry techniques with feature-based methods. In this paper, we compare the performance of three different feature extraction methods: Scale Invariant Feature Transform (SIFT), Speed Up Robust Features (SURF) and Oriented FAST Rotated BRIEF (ORB). This paper presents experimental results on standard evaluation datasets and all experiments use measurement of the number of image correspondences as well as the ratio of good matched for the evaluation purpose. The results of experiments demonstrate that the performances of three methods in processing time, matching capability and accuracy. SIFT presents its stability in most scenarios although it is very slow. SURF is faster than SIFT and outperform SIFT on some scenarios. ORB is the most efficient feature and shows strong performance.

关键词： Feature extraction Detectors Robustness Visualization Transform coding Real-time systems Lighting

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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Chinese control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines Electronic mail Principal component analysis

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Finite-Time decoupling direct control for hypersonic reentry vehicle with multiple disturbances via second-order ADRC

Finite-Time decoupling direct control for hypersonic reentry...

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2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017

作者： Yu, Yue Wang, Honglun Li, Na Zhang, Huiping Liu, Chang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Unmanned System Research Institute Beihang University Beijing 100191 China School of Automation Beijing Institute of Technology and Beijing Aerospace Automatic Control Institute Beijing 100081 China

ISBN: (纸本)9781538637432;9781538637425

In this paper, a novel finite-Time decoupling direct control (FDDC) scheme is proposed for attitude tracking problem of hypersonic reentry vehicle (HRV) based on second-order active disturbance rejection control (SADRC). Unlike traditional control method which separates control system into outer-loop attitude control and inner-loop angular rate control, attitude is controlled by control actuator straightforward with single loop. Firstly, the attitude tracking problem is converted to the decoupling control problem of a three-input and three-output cross coupling system. In each channel, a SADRC controller, which is composed of tracking differentiator (TD), extended state observer (ESO) and nonlinear state error feedback (NLSEF), is designed to track attitude command of the corresponding channel in a finite-Time sense. Finally, the obtained virtual control input is transformed into actual control input, which acts on the HRV model. Simulations are conducted to verify the effectiveness of the proposed control scheme. © 2017 IEEE.

关键词： decoupling control extended state observer (ESO) finite-Time second-order active disturbance rejection control (SADRC)

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Cascaded PI controller Tuning for Power Plant Superheated Steam Temperature based on Multi-Objective Optimization

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IFAC-PapersOnLine 2017年第1期50卷 3227-3231页

作者： Fu H. Pan L. Xue Y.L. Sun L. Li D.H. Lee K.Y. Wu Z.L. He T. Zheng S. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing 210096 China State Key Lab of Power System Department of Thermal Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Baylor University Waco TX 76798 United States Department of Automation Fuzhou University Fuzhou 350000 China

To improve the superheated steam temperature (SST) control performance, this paper considers the conflicting objectives in SST control, multi-objective optimization (MOO) algorithm is employed in this paper. Firstly, the inner-loop PI controller is optimized by considering the objectives of set-point tracking and disturbance rejection, with and without constraints on robustness level. It is found that robustness constraint is essential in the optimization. Secondly, the regulation performance of the outer-loop PI controller is addressed by optimizing the disturbance rejection and robustness simultaneously. The parameter are determined based on the simulation comparison between the non-dominated solutions. The simulation results demonstrate a promising prospect of promoting the proposed strategy to more industrial practices. © 2017

关键词： Disturbance rejection Pareto optimization Power plant control Robustness Superheated steam temperature

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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第37届中国控制会议

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of Education The Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia School of Biomedical Engineering Shanghai Jiao Tong University

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and highdimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment ***, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model（SAE-SVM）, for breast cancer diagnosis. The stacked auto-encoders with the greedy layerwise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Deep learning auto-encoder feature extraction breast cancer diagnosis unlabeled data

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Research on Weld Quality Detection Method Based on Machine Vision and Computer Image Processing

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IOP Conference Series: Materials Science and engineering 2019年第5期631卷

作者： Bin Xue Shufang Ma Huihui Chu Kang Liu Shuangben Jiao Qingsen Meng Engineering and Technology R&D center of Mechanical and Electrical in Colleges of Shandong Qingdao Binhai University Qingdao Shandong 266555 PR China School of Mechanical and Electrical Engineering Qingdao Binhai University Qingdao Shandong 266555 PR China School of Material Science and Engineering Taiyuan University of Science and Technology Taiyuan Shanxi 030024 PR China Qingdao Zhongzhuang Vision Automation System Control Co. Ltd. Shandong 266555 PR China

In the weld quality inspection system based on machine vision and computer image processing, according to the principle of laser triangulation, a line laser is used to project structured light on the weld surface, and the weldment moves uniformly in a straight line on the moving platform. At the same time, the CMOS camera captures the structured light image and transmits it to the industrial computer through Gigabit Ethernet. The image is denoised by the median filtering algorithm on the industrial computer, then the image is segmented by the maximum inter-class error (Otus) method, and the image is binarized according to the threshold value. Then the center of the stripe is extracted by constructing four direction templates, and the 3D model is reconstructed by curve fitting. After the completion of the image processing, the geometric size of the weld is measured by mathematical formula. After the weld seam measurement is completed, the weld seam is transversely cut off at the same position, measured by manual method, and then the data measured by the two methods are compared. Through repeated measurements and comparisons of weld width, residual height and back forming, the precision of the system meets the practical requirements. The weld forming varies with the welding parameters within a certain range, but changes little and has good stability.

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The Ellipsoidal Invariant Set of Fractional Order systems Subject to Actuator Saturation:The Convex Combination Form

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IEEE/CAA Journal of Automatica Sinica 2016年第3期3卷 311-319页

作者： Kai Chen Junguo Lu Chuang Li School of Mechanical and Electrical Engineering Hainan University Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

The domain of attraction of a class of fractional order systems subject to saturating actuators is investigated in this paper. We show the domain of attraction is the convex hull of a set of ellipsoids. In this paper, the Lyapunov direct approach and fractional order inequality are applied to estimating the domain of attraction for fractional order systems subject to actuator saturation. We demonstrate that the convex hull of ellipsoids can be made invariant for saturating actuators if each ellipsoid with a bounded control of the saturating actuators is invariant. The estimation on the contractively invariant ellipsoid and construction of the continuous feedback law are derived in terms of linear matrix inequalities (LMIs). Two numerical examples illustrate the effectiveness of the developed method. © 2014 Chinese Association of automation.

关键词： Algebra Computational geometry Linear matrix inequalities Numerical methods Saturation (materials composition)

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Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer

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Aerospace Science and Technology 2017年 69卷 149-160页

作者： Yue Yu Honglun Wang Na Li Zikang Su Jianfa Wu School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Unmanned System Research Institute Beihang University Beijing 100191 China

This paper proposes an active disturbance rejection control (ADRC) scheme for automatic carrier landing system (ACLS) in final approach. To track desired touchdown point (DTP) altitude in the presence of carrier air wake turbulence and deck motion, based on the estimation and compensation of internal dynamics and external disturbances by extended state observer (ESO), outer-loop altitude controller and inner-loop attitude controllers are designed via ADRC. Moreover, tracking differentiator-based deck motion compensation (TD-DMC) is designed to eliminate the inherent phase lag in ACLS. In addition, controller parameters are optimized via a novel Levy flight based whale optimization algorithm (LWOA). Finally, comparative simulations demonstrate that the proposed ADRC-based ACLS has better tracking performance and robustness compared to conventional ACLS.

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Research on model based design method of permanent magnet synchronous motor servo system based on FPGA

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Journal of Physics: Conference Series 2018年第1期1074卷

作者： Yu, Shimao Zhang, Hualiang Zeng, Peng Zhang, Tao Chen, Xiyou Key Laboratory of Industrial Control Network and System Shenyang Institute of Automation Chinese Academy of Sciences. Shenyang China. Key Laboratory of Industrial Control Network and System Shenyang Institute of Automation Chinese Academy of Sciences. Shenyang China. School of electrical engineering Dalian University of Technology. Dalian China. School of electrical engineering Dalian University of Technology. Dalian China.

The ac servo system plays an increasingly important role in modern society. The ac servo drive technology based on FPGA becomes important research direction for its advantages of fast processing speed and flexible designing. A permanent magnet synchronous motor servo control system based on FPGA was model-based designed in this paper. It built three-closed-loop servo control model in Simulink which can simulate the performance of the algorithm, and it completed automatic code generation and configuration in Xilinx Zynq–7000. In addition, it built a complete set of permanent magnet synchronous motor servo control hardware platform. This paper proposes an integrated design and simulation method based on FPGA servo control to improve the efficiency of system design.

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Assessment of energy storage viability for a PV power plant injecting during peak load time

Assessment of energy storage viability for a PV power plant ...

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IEEE International Symposium on Power Electronics for Distributed Generation systems (PEDG)

作者： Reginaldo V. Ferreira Sidelmo M. Silva Danilo I. Brandao Hélio M. A. Antunes Nicolas T. D. Fernandes Dept. of Control and Automation Federal Institute of Minas Gerais UFMG Betim Brazil Graduate Program in Electrical Engineering Federal University of Minas Gerais UFMG Belo Horizonte Brazil

ISBN: (纸本)9781509053407

Photovoltaic (PV) power plants are a form of renewable energy and their importance is increasing all around the world. In Brazil, the PV power plants connected to the grid are regulated through a credit compensation, in which the prosumer pays the difference between consumed and generated energy. Medium voltage consumers pay different prices for the energy consumed in peak load time and off-peak load time. This paper compares the return of investments of a PV plant without and with energy storage systems. This latter injects active power during peak load time. Results show that although the plant without storage presents lowest costs, the solution with batteries can be more profitable depending on the energy tariff and the equipment cost.

关键词： Batteries Inverters Power generation Discharges (electric) Lead compounds Lead

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