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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jiajun Wang Xiaozhong Liao Bin Liu Xi Chen Xiangdong Liu Xiaoqin Lian Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China GEIRI North America CA 95134 USA Beijing Technology and Business University Beijing 100048 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Due to the advantages of large unit capacity, high efficiency and high reliability, direct-drive permanent magnet synchronous generators (PMSGs) wind turbines (WTs) have been widely used in the offshore wind power generation. The power loop controller aims to maintain the DC-Bus voltage of each PMSG-WT is equal to the others, guaranteeing that multiple WTs can be connected in DC serial parallel collection. In this paper, the DC-Port sequence impedance model of the PMSG-WT is established based on the harmonic linearization method, which takes the outer power loop into consideration. The impedance model is further verified by point-by-point scanning in MATLAB/Simulink. The proposed DC-Port impedance model of the PMSG-WT facilitates the establishment of offshore direct-drive wind farms, and is of great significance for analyzing and improving the system stability.

关键词： Impedance Mathematical model Harmonic analysis Wind farms Generators Power system stability Topology

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Dynamic constrained multi-objective evolutionary algorithms with a novel selection strategy for constrained optimization 18

Dynamic constrained multi-objective evolutionary algorithms ...

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Jiao, Ruwang Li, Changhe Zeng, Sanyou Jiang, Yuhong China University of Geosciences Wuhan China China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781450357647

The recently proposed dynamic constrained multi-objective evolutionary algorithm (DCMOEA) is effective to handle constrained optimization problems (COPs). However, one drawback of DCMOEA is it mainly searches the global optimum from infeasible regions, which may result in the bias against feasible solutions. Then the useful information about the optimal direction of feasible regions is not fully used. To overcome this defect, this paper proposes a novel selection strategy based on DCMOEA framework, called NSDCMOEA to solve COPs. The performance of NSDCMOEA is evaluated using a set of benchmark suites. Experimental results validate that the proposed method is better than or very competitive to five state-of-the-art algorithms. © 2018 Copyright held by the owner/author(s).

关键词： Constrained optimization

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Learning Hidden Markov Models for Linear Gaussian systems with Applications to Event-based State Estimation

arXiv

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arXiv 2020年

作者： Zheng, Kaikai Shi, Dawei Shi, Ling State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimation problems. An indirect modeling approach is developed, wherein a state space model (SSM) is firstly identified for a Gaussian system and the SSM is then used as an emulator for learning an HMM. In the proposed method, the training data for the HMM are obtained from the data generated by the SSM through building a quantization mapping. Parameter learning algorithms are designed to learn the parameters of the HMM, through exploiting the periodical structural characteristics of the HMM. The convergence and asymptotic properties of the proposed algorithms are analyzed. The HMM learned using the proposed algorithms is applied to event-triggered state estimation, and numerical results on model learning and state estimation demonstrate the validity of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Parallel Ant Colony Optimization for the Electric Vehicle Routing Problem

Parallel Ant Colony Optimization for the Electric Vehicle Ro...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Changhe Li Georgios Ellinas Marios Polycarpou KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Parallelizing metaheuristics has become a common practice considering the computation power and resources available nowadays. The aim of parallelizing a metaheuristic is either to increase the quality of the generated output, given a fixed computation time, or to reduce the required time in generating an output. In this work, we parallelize one of the best-performing ant colony optimization (ACO) algorithms and apply it to the electric vehicle routing problem (EVRP). EVRP is more challenging than the conventional vehicle routing problem, as with the consideration of electric vehicles additional hard constraints arise within the EVRP due to their limited driving range (e.g., the consideration whether electric vehicles need to visit a charging station during their daily operation). The proposed parallel ACO algorithm with several colonies also uses a migration policy to allow communication between the different colonies. From the simulation studies it is shown that parallelizing ACO algorithms, both with and without a migration policy, is highly effective.

关键词： Electric vehicles Routing Batteries Technological innovation Vehicle routing Charging stations Optimization

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Feature Aggregation Attention Network for Single Image Dehazing

Feature Aggregation Attention Network for Single Image Dehaz...

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IEEE International Conference on Image Processing

作者： Lan Yan Wenbo Zheng Chao Gou Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Software Engineering Xi’an Jiaotong University Xi’an China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Due to its ill-posed nature, single image dehazing is a challenging problem. In this paper, we propose an end-to-end feature aggregation attention network (FAAN) for single image dehazing. It incorporates the idea of attention mechanism and residual learning and can adaptively aggregate different level features. In particular, in the proposed FANN, we design a novel block structure consisting of feature attention module, smoothed dilated convolution and local residual learning. The local residual learning allows the less useful information to be bypassed through multiple skip connections. The feature attention module is designed to assign more weight to important features. The smoothed dilated convolution is adopted to enlarge the receptive field without the negative influence of gridding artifacts. The experiments on the RESIDE dataset show that the proposed approach acquires state-of-the-art performance in both qualitative and quantitative measures.

关键词： Convolution Atmospheric modeling Kernel Image color analysis Task analysis Training

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power systems

Data Augment Using Deep Convolutional Generative Adversarial...

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Chinese control Conference (CCC)

作者： Jiamin Li Hongying Yang Liping Yan Zonghan Li Daowei Liu Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China China Electric Power Research Institute Beijing P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Real-time and accurate transient stability assessment (TSA) is essential for planning, operation and control of power systems. As a data-driven technology, deep learning method plays an important role in TSA. Nevertheless, the fact that instability situations rarely occur would lead to a challenging class-imbalanced issue, which brings great difficulties to the deep learning methods. Besides, feature extraction from high dimensional input data and transient stability classification seem extremely difficult for conventional classification methods. To address these problems, this paper develops a class-imbalanced TSA method by combining nonlinear data synthesis method with the deep learning classification model. Firstly, deep convolutional generative adversarial network (DCGAN) is conducted to generate unstable instances based on the existing samples to balance the proportion of different classes. Furthermore, the convolutional neural network (CNN) is utilized to extract the nonlinear mapping relationship between the disturbance features and the stability category and realize TSA. Finally, the IEEE 10-machine, 39-bus New England system is utilized to verify the validity and effectiveness of the proposed method.

关键词： Power system stability Feature extraction Generative adversarial networks Transient analysis Machine learning Training

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Optimal Blood Glucose Prediction based on Intermittent Data from Wearable Glucose Monitoring Sensors

Optimal Blood Glucose Prediction based on Intermittent Data ...

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第三十八届中国控制会议

作者： Lijun Hou Huipeng Zhang Junzheng Wang Dawei Shi State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

Blood glucose prediction is to predict the glucose trend over time based on historical glucose data, and it plays a crucial role in the closed-loop control of artificial pancreas, which can reduce the risk of complications by regulating insulin dose and injection time. This paper proposes a Kalman-filter-based glucose prediction method through minimizing the mean square prediction error, which assumes that the data is sampled every 15 min from a wearable flash glucose monitoring sensor. This method calculates glucose estimates every 5 min and provides glucose predictions for the next 30 min. The method is evaluated on in-silico data generated from the 10-adult cohort of the US FDA-accepted UVA/Padova T1 DM simulator. The predicted results are compared with CGM data with 5-min sample-period through multiple metrics, including the mean square prediction error and the mean absolute relative deviation. The results show that the performance of the proposed approach with slow-rate glucose data(15 min) is close to that obtained based on fast-rate data(5 min).

关键词： Blood Glucose Prediction Wearable Devices Mean Square Prediction Error Sampled Data Kalman Filtering

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A Facial Expression Emotion Recognition Based Human-robot Interaction System

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 668-676页

作者： Zhentao Liu Min Wu Weihua Cao Luefeng Chen Jianping Xu Ri Zhang Mengtian Zhou Junwei Mao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems IEEE

A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.

关键词： Emotion generation facial expression emotion recognition(FEER) human-robot interaction(HRI) system design

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Glucose Regulation for Subjects with Type 1 Diabetes Using Active Disturbance Rejection control

Glucose Regulation for Subjects with Type 1 Diabetes Using A...

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第三十八届中国控制会议

作者： Deheng Cai Jiliang Song Junzheng Wang Dawei Shi State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

In artificial pancreas(AP) systems, control algorithms play an important role in the blood glucose(BG) *** ability of active disturbance rejection control(ADRC), which can deal with various disturbances, enables it to become a promising approach in the control algorithm design for BG regulation when facing many disturbances(e.g. glucose fluctuation caused by meal intakes and exercises). In this paper, an ADRC controller is proposed for the AP systems. To ensure the safety,the insulin on board(IOB) constraints and the insulin delivery constraints are introduced to the ADRC controller. The controller is evaluated on the FDA-accepted UVA/Padova T1 DM simulator with the 10-adult cohort. For both announced and unannounced meals, the controller achieves satisfactory performance in terms of mean glucose(139.9 mg/dl for announced meals, 157.9 mg/dl for unannounced meals) and percentage time in [70, 180] mg/dl(91.5% for announced meal, 68.9% for unannounced meals)without having the risk of hypoglycemia.

关键词： Glucose Regulation Type 1 Diabetes Artificial Pancreas Active Disturbance Rejection control

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