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arXiv 2017年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center and the Electrical and Computer Engineering Department University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200072 China

—In today’s cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow, hence inadequate for real-time system monitoring. This paper develops two novel algorithms for efficient LAV estimation, which draw from recent advances in composite optimization. The first is a deterministic linear proximal scheme that handles a sequence of (5 ∼ 10 in general) convex quadratic problems, each efficiently solvable either via off-the-shelf toolboxes or through the alternating direction method of multipliers. Leveraging the sparse connectivity inherent to power networks, the second scheme is stochastic, and updates only a few entries of the complex voltage state vector per iteration. In particular, when voltage magnitude and (re)active power flow measurements are used only, this number reduces to one or two, regardless of the number of buses in the network. This computational complexity evidently scales well to large-size power systems. Furthermore, by carefully mini-batching the voltage and power flow measurements, accelerated implementation of the stochastic iterations becomes possible. The developed algorithms are numerically evaluated using a variety of benchmark power networks. Simulated tests corroborate that improved robustness can be attained at comparable or markedly reduced computation times for medium- or large-size networks relative to existing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Copy and Paste GAN: Face Hallucination From Shaded Thumbnails

Copy and Paste GAN: Face Hallucination From Shaded Thumbnail...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang Zhang Ivor W. Tsang Yawei Luo Chang-Hui Hu Xiaobo Lu Xin Yu School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China Centre for Artificial Intelligence University of Technology Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology China Southeast University Nanjing China Australian Centre for Robotic Vision Australian National University Australia

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details; while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternatively in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively.

关键词： Face Lighting Gallium nitride Image resolution Feature extraction Generative adversarial networks automation

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Learning ReLU networks on linearly separable data: Algorithm, optimality, and generalization

arXiv

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arXiv 2018年

作者： Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

Neural networks with REctified Linear Unit (ReLU) activation functions (a.k.a. ReLU networks) have achieved great empirical success in various domains. Nonetheless, existing results for learning ReLU networks either pose assumptions on the underlying data distribution being e.g. Gaussian, or require the network size and/or training size to be sufficiently large. In this context, the problem of learning a two-layer ReLU network is approached in a binary classification setting, where the data are linearly separable and a hinge loss criterion is adopted. Leveraging the power of random noise perturbation, this paper presents a novel stochastic gradient descent (SGD) algorithm, which can provably train any single-hidden-layer ReLU network to attain global optimality, despite the presence of infinitely many bad local minima, maxima, and saddle points in general. This result is the first of its kind, requiring no assumptions on the data distribution, training/network size, or initialization. Convergence of the resultant iterative algorithm to a global minimum is analyzed by establishing both an upper bound and a lower bound on the number of non-zero updates to be performed. Moreover, generalization guarantees are developed for ReLU networks trained with the novel SGD leveraging classic compression bounds. These guarantees highlight a key difference (at least in the worst case) between reliably learning a ReLU network as well as a leaky ReLU network in terms of sample complexity. Numerical tests using both synthetic data and real images validate the effectiveness of the algorithm and the practical merits of the theory. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Adaptive fuzzy control of output-constrained switched nonlinear systems

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ICIC Express Letters 2015年第12期9卷 3355-3362页

作者： Niu, Ben Liu, Yanli Shen, Mouquan School of Control Science and Engineering Dalian University of Technology No. 2 Linggong Road Ganjingzi District Dalian China College of Mathematics and Physics Bohai University No. 19 Keji Road New Songshan District Jinzhou China Automation Research Institute Bohai University No. 19 Keji Road New Songshan District Jinzhou China College of Electrical Engineering and Control Science Nanjing Technology University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

In this paper, the problem of adaptive fuzzy tracking control is considered of uncertain switched nonlinear systems with an output constraint. First, a barrier Lyapunov function (BLF) is employed to deal with the output constraint. Then fuzzy logic systems are used to approximate the unknown nonlinearities and backstepping technique is employed to construct an adaptive fuzzy controller. The designed controller guarantees all the closed-loop signals remain bounded while the system output converges to a small neighborhood of the desired reference signal without violating the constraint. An example is given to demonstrate the validity of the developed result. © 2015 ICIC International.

关键词： Lyapunov functions

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A Projection-Based Algorithm for Constrained L1- Minimization Optimization with Application to Sparse Signal Reconstruction

A Projection-Based Algorithm for Constrained L1- Minimizatio...

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International Conference on Information Science and Technology (ICIST)

作者： Qingshan Liu Wei Zhang Jiang Xiong Bingrong Xu Long Cheng School of Mathematics Southeast University Nanjing China College of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Automation Huazhong University of Science and Technolog< Wuhan China State Key Lab. of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China

In this paper, a projection-base algorithm is proposed for solving the constrained L 1 -minimization problem. Furthermore, the algorithm is utilized to sparse signal reconstruction. The L 1 -minimization is first converted into some equations which are described by the projections onto a hyper box set and the nonnegative quadrant. Then a iterative algorithm is proposed for solving the L 1 -minimization problem. Next, the algorithm is applied to sparse signal reconstruction described as an L 1 -minimization problem subject to L ∞ -norm noise constraint, or equivalently bound constraint. Finally, several experiments are presented to show the performance of the proposed algorithm.

关键词： Signal processing algorithms Signal reconstruction Optimization Convergence Electronic mail Recurrent neural networks Lyapunov methods

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Contraction analysis on primal-dual gradient optimization

arXiv

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arXiv 2019年

作者： Su, Yanxu Shi, Yang Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 3P6 Canada

This paper analyzes the contraction of the primaldual gradient optimization via contraction theory in the context of discrete-time updating dynamics. The contraction theory based on Riemannian manifolds is first established for convergence analysis of a convex optimization algorithm. The equality and inequality constrained optimization cases are studied, respectively. Under some reasonable assumptions, we construct the Riemannian metric to characterize a contraction region. It is shown that if the step-sizes of the updating dynamics are properly designed, the convergence rates for both cases can be obtained according to the contraction region in which the convergence can be ***, the augmented Lagrangian function which is projection free is adopted to tackle the inequality constraints. Some numerical experiments are simulated to demonstrate the effectiveness of the presented contraction analysis results on primal-dual gradient optimization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Hierarchical control Scheme for VSC-MTDC System with multiple Renewable Energy Sites Based on Optimal Power Flow and Droop Scheme

Hierarchical Control Scheme for VSC-MTDC System with multipl...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Asif Raza Qi Huang Jian Li Olusola Bamisile Cai Dongsheng Ghalib Raza Sichuan Provincial Key Lab of Power System Wide –Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science & Technology of China College of Nuclear and Automation Engineering Chengdu University of Technology Sichuan Provincial Key Lab of Power System Wide–Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science & Technology of China University of Electronic Science & Technology of China College of Nuclear and Automation Engineering Chengdu University of Technology Sichuan P.R. China Benazir Bhutto Shaheed University of Technology & Skill Development Khairpur Pakistan

This paper designs a hierarchical control system to improve control performance in a multi-terminal high voltage direct current based on the voltage source converter (VSC-MTDC) transmission system. In this control scheme, the primary control layer employs droop control, while the secondary control layer has developed optimal power flow control (OPF) for maintaining the variation of DC voltage and regulating the active power, and simultaneously minimizing power loss. In the event of a power disturbance or a change in power generation or consumption in the DC system, based on OPF results a new stable droop operating mode is established to stabilize DC voltage variation and regulate active power in the network. A ±400 kV four-terminal VSC-MTDC test network has been developed by using PSCAD and MATlab simulation tools to assess the proposed control scheme. The primary controller is designed in PSCAD, and the secondary optimum control technique is applied in MATlab. The results of steady-state and transient operating modes demonstrate that the planned control technique can achieve DC voltage regulation and keep active power, and minimize the power losses in the MTDC system.

关键词： Renewable energy sources System integration control systems Steady-state Power conversion Voltage control PSCAD

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Distributed model predictive control under inexact primal-dual gradient optimization based on contraction analysis

arXiv

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arXiv 2019年

This paper develops a distributed model predictive control (DMPC) strategy for a class of discrete-time linear systems with consideration of globally coupled constraints. The DMPC under study is based on the dual problem concerning all subsystems, which is solved by means of the primal-dual gradient optimization in a distributed manner using Laplacian consensus. To reduce the computational burden, the constraint tightening method is utilized to provide a capability of premature termination with guaranteeing the convergence of the DMPC optimization. The contraction theory is first adopted in the convergence analysis of the primal-dual gradient optimization under discrete-time updating dynamics towards a nonlinear objective function. Under some reasonable assumptions, the recursive feasibility and stability of the closed-loop system can be established under the inexact solution. A numerical simulation is given to verify the performance of the proposed strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Closed loop systems

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An Enhanced Fault Diagnosis Method with Uncertainty Quantification Using Bayesian Convolutional Neural Network

An Enhanced Fault Diagnosis Method with Uncertainty Quantifi...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Qihang Fang Gang Xiong Xiuqin Shang Sheng Liu Bin Hu Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Fault diagnosis is a vital technique to pinpoint the machine malfunctions in manufacturing systems. In recent years, the deep learning techniques greatly improve the fault detection accuracy, but there still remain some problems. If one fault is absent in the training data or the fault signal is disturbed by severe noise interference, the fault classifier may misjudge the health state. This problem limits the reliability of the fault diagnosis in real applications. In this paper, we enhance the fault diagnosis method by using Bayesian Convolutional Neural Network (BCNN). A Shannon entropy-based method is presented to quantify the prediction uncertainty. The BCNN turns the deterministic predictions to probabilistic distributions and enhances the robustness of the fault diagnosis. The uncertainty quantification method helps to indicate the wrong predictions, detect unknown faults, and discover the strong disturbances. Then, a fine-tuning strategy is applied to enhance the model performance further. The potential usability of the proposed method in monitoring the motors of 3D printers is studied. And the experiment is conducted on a motor bearing dataset provided by Case Western Reserve University. The proposed BCNN achieves 99.82% fault classification accuracy over nine health conditions. Its robustness is verified by comparing the testing accuracy with three other methods on the noisy datasets. And the uncertainty quantification method successfully detects the outlier inputs.

关键词： Fault diagnosis Uncertainty Bayes methods Circuit faults Robustness Predictive models Noise measurement

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Temperature Compensation Method of Fluxgate Sensor Based on Polynomial Fitting

arXiv

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arXiv 2024年

作者： Yang, Ruiping Liu, Huan Ge, Jian Chugo, Daisuke Dong, Haobin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan

Fluxgate sensors are widely used in the field of low frequency and weak vector magnetic field measurement because of their good performance, such as high resolution and low power consumption. However, during the long-term continuous observation, the drift errors of the fluxgate sensor will occur due to the variable ambient temperature. This paper proposes a temperature compensation method for fluxgate sensors based on polynomial fitting. First, a physical model of the temperature & fluxgate sensor was established on the COMSOL Multiphysics simulation platform, and the influence of temperature on the measurement performance of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature compensation model of the fluxgate sensor was constructed. And compared it with other temperature compensation method, the result shows that the proposed temperature compensation method is relatively simple and can better achieve real-time compensation for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were *** temperature drift is reduced from more than 500 nT before compensation to about 1 nT. The results show that the proposed method has a good temperature compensation effect on the data measured by the fluxgate sensor within a variable temperature background. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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