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arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Differentiator-based disturbance observer 8

Differentiator-based disturbance observer

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8th IEEE Data Driven control and Learning Systems Conference, DDCLS 2019

作者： Li, Xiangyang Gao, Zhiqiang Ai, Wei Tian, Senping School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Center for Advanced Control Technologies Cleveland State University Department of Electrical and Computer Engineering ClevelandOH44115 United States

ISBN: (纸本)9781728114545

The analysis of nonlinear disturbance observer (NDO) is first given in detail and an equivalent differentiator structure is used to explain the NDO. The design procedure of NDO is given according to this equivalent structure. Then the structure and principle of the generalized differentiator-based disturbance observer (DBDO) is given and compared with NDO in their structure and algorithm. As one of DBDO, tracking differentiator (TD) based disturbance observer is applied to compare NDO in simulations. Simulations for disturbance estimation show that both NDO and TD based disturbance observer have very effective disturbance estimation performance but the later has less design restriction. © 2019 IEEE.

关键词： Nonlinear disturbance observer (NDO) Differentiator-based disturbance observer (DBDO) Tracking differentiator (TD) Disturbance observer-based control (DOBC) NDO disturbance observer design process DIFFERENTIATORS

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Smart System of a Real-Time Pedestrian Detection for Smart City

Smart System of a Real-Time Pedestrian Detection for Smart C...

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Mohammed Saleh Ali Muthanna Yuliy T. Lyachek Abdulfattah Mohammed Obadi Musaeed Yaqoob Ahmed Hazzaa Esmail Abuzar B. M. Adam Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Information and Communication Engineering Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen China

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

This paper proposes the possibility of recognizing pedestrians in real time on the end devices using a microcomputer to automate the process of signaling traffic lights and improve system mobility. The paper is devoted to a brief analysis of existing systems for the detection of pedestrians, with the subsequent development of its own system based on those studied, and its further implementation on the Raspberry Pi microcomputer. A natural experiment was conducted to detect pedestrians in an image with the subsequent determination of their location at a pedestrian crossing. A number of popular pedestrian detection systems were analyzed in terms of speed, accuracy of determination, and the possibility of implementation on microcomputers. Recommendations on the choice of the system, conditions of use and improvement of characteristics. The developed system automates the work of the traffic light through the introduction of additional functions (tracking a pedestrian's posture, moving pedestrians along the roadway), ensuring the safety of road users, which allows to improve the level of the urban environment as a whole.

关键词： Streaming media Libraries Roads Real-time systems Object detection Smart cities

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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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K-Means Clustering-based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10250-10254页

作者： Luefeng Chen Kuanlin Wang Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada Tokyo Institute of Technology Yokohama 226-8502 Japan

Emotion is an important part of human interaction. Emotional recognition can greatly promote human-centered interaction techniques. On this basis, multimodal feature fusion can effectively improve the emotion recognition rate. However, in the multimodal feature fusion at the feature level, most of the methods do not consider the intrinsic relationship between different modes. Only the fusion of analysis and transformation of the feature matrices of different modes does not make better use of modal differences to improve the recognition rate. This problem led us to propose feature fusion method based on K-Means clustering and kernel canonical correlation analysis (KCCA). Clustering makes the classification of features not classified by mode, but by the degree of influence on emotional labels, thus positively affecting the results of KCCA. The experimental results obtained on the Savee database show that the proposed K-Means based KCCA improves overall classification performance and produces higher recognition rate than that of the state of art methods, such as the Informed Segmentation and Labeling Approach.

关键词： Emotion Recognition K-Means Clustering Kernel Canonical Correlation Analysis Feature Fusion

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Electricity price-based microgrid energy management system in grid-connected mode

Electricity price-based microgrid energy management system i...

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第三十八届中国控制会议

作者： Xiaoyun Xiong Chen Peng Deliang Zeng Department of Automation School of Mechatronic Engineering and Automation Shanghai University School of Control and Computer Engineering North China Electric Power University

The high permeability of distributed renewable energy resources makes the research on microgrid(MG) urgent. From the perspective of the economic and environmental protection of the MG system, this paper considers the optimal distribution of the outputs of distributed generations(DGs) in the MG on the basis of real-time electricity prices. A general model of microgrid energy management in grid-connected mode is proposed. The objective function and constraints are accurately described, with a novel method of weights of the function. And an innovative optimization algorithm is proposed that combines the Yalmip toolbox with particle swarm optimization(PSO) algorithm to achieve the optimal management. Finally simulation results show that the model and algorithm are effective.

关键词： Microgrid energy management particle swarm optimization electricity prices

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Energy-aware algorithm for lora technology: Prototype implementation 20th

Energy-aware algorithm for lora technology: Prototype implem...

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20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020 and 13th Conference on the Internet of Things and Smart Spaces, ruSMART 2020

作者： Khakimov, Abdukodir Ali Muthanna, Mohammed Saleh Mikhail, Polovov Ibodullokhodzha, Ibodulaev Muthanna, Ammar Samouylov, Konstantin 6 Miklukho-Maklaya St Moscow117198 Russia School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia St. Petersburg State University of Telecommunication 22 Prospekt Bolshevikov St. Petersburg Russia National Research University Higher School of Economics 20 Myasnitskaya St Moscow Russia

ISBN: (纸本)9783030657253

Internet of things (IoT) development has already become one of the main directions in the telecommunications and the information and communication system development as a whole. Promising solutions in the communication networks evolution, such as the latest LTE standards, concepts, and solutions for building 5G networks critical, include this component as an integral part of a promising communication network. There are various modern solutions for building IoT networks, and Long-Range Wide Area Network (LoRaWAN) is one of them. LoRaWAN is a technical solution for the physical and partially link network layers. The paper proposes an algorithm for ensuring traffic quality of service, latency, and data loss, as well as to provide an effectiveway of energy consumption. We impalement a prototype for Long Range (LoRa) based edge computing. © Springer Nature Switzerland AG 2020.

关键词： Wide area networks

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Possibilities of vehicle state estimation using big data approaches 16

Possibilities of vehicle state estimation using big data app...

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16th Mini Conference on Vehicle System Dynamics, Identification and Anomalies, VSDIA 2018

作者： Fényes, Dániel Németh, Balázs Gáspár, Péter Asszonyi, Máté Systems and Control Laboratory Computer and Automation Research Institute Kende u. 13-17 Budapest Hungary Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Stoczek u. 2 BudapestH-1111 Hungary

Nowadays, the development of the autonomous vehicles is one of the most important challenges for the automotive industry. In general, these vehicles are equipped with numerous sensors, such as onboard-camera, lidar, radar, ultrasonic sensor, accelerometer and gyroscope. These sensors provide information about the environment and other vehicles. This information is not only processed and used directly on the car but also can be stored on internal or cloud-based memory. This collected data might contain hidden information about the motion and the dynamical behaviour of the car. This hidden information can be brought out of the datasets by using big data-based data mining approaches. The aim of the paper is to create a vehicle state estimation model based on the collected sensor data and using data mining tools. In the paper, the sensor data is collected from a high-fidelity simulation software, CarSim. The estimation model is created by the machine-learning software Weka. Finally, the created model is validated through several CarSim simulations. © 2019 Budapest University of Technology and Economics. All rights reserved.

关键词： computer software

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Formation control for Mobile Robot using Fuzzy - PI controller

Formation Control for Mobile Robot using Fuzzy - PI Controll...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Min-Fan Ricky Lee H.P.M Willybrordus Sukamto Sharfiden Hassen Asep Nugroho National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan College of Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9781728198231

ISBN: (纸本)9781728198248

Protecting soldiers in the long march of platoon formation is crucial mission in the military operation. Autonomous ground mobile robots can be deployed to carry out such kind of the mission. The primary task is maintaining robot position automatically based on the movement of soldier in a platoon formation. The GPS is employed to determine the soldier current pose. This information is used as an input to create a dynamic convex hull around a platoon. The Proportional-Integral (PI) controller is applied to control each robot so it can move in a desired trajectory. The fuzzy logic control (FLC) is involved to tune the gain of PI controller to optimize the performance. Three protective robots and nine soldiers are used to evaluate the algorithm in simulation. The proposed algorithm will provide a platoon soldiers with optimal protection encirclement and enhance their safety. The simulation results show good performance using the proposed controller.

关键词： Robot kinematics Mobile robots Wheels Task analysis Fuzzy logic Kinematics

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A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

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IFAC-PapersOnLine 2020年第5期53卷 644-649页

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266044 China Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 USA

learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.

关键词： learner model cognitive state metacognitive ability individualized teaching

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