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ieee symposium Series on computational intelligence (SSCI)

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781728124858

In this paper, we propose Simplified Space based Neural Architecture Search (SSNAS), an efficient approach for automatic architecture search. Inspired by the popular convolutional neural networks which have strong capability for feature extraction with small convolutional kernels, we design a simplified search space of convolutional operations with small kernel and construct a large model based on it. Furthermore, we use an Long Short-Term Memory (LSTM) to sample child model from the large model in a way of selective activation. The probability distribution of selective activation is obtained by training the LSTM with reinforcement learning for maximizing the expected reward of selected child model on the validation set. Moreover, the trained weights are saved in a large model which concludes all child models. Each weight of new sampled child model will be restored when being selected by another one again instead of training from scratch so that SSNAS can greatly reduce the expensive computation than traditional approaches for automatic model design. Extensive experiments on CIFAR-10, ImageNet and VOC show that the proposed approach excels in discovering transferable architectures with high performance.

关键词： neural architecture search simplified space image classification object detection

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Robust Estimator based Adaptive Multi-Task Learning

Robust Estimator based Adaptive Multi-Task Learning

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ieee symposium Series on computational intelligence (SSCI)

作者： Zhu, Peiyuan Chen, Cailian He, Jianping Zhu, Shanying Shanghai Jiao Tong Univ Key Lab Syst Control & Signal Proc Shanghai Peoples R China

ISBN: (纸本)9781728124858

Multi-Task Learning(MTL) algorithm leverages valuable information across similar tasks to improve regression accuracy. However, applications in practice often encounter scenes that cannot give an accurate relationship between the large-scale number of multiple tasks. When task clustering based method is employed in those cases, excessive connections between dissimilar tasks and meaningless dimensions would cause a poorly predictive performance, which cannot be perfectly interpreted by existing MTL methods. In this paper, a novel MTL method is proposed to make more accurate use of valuable shared information between multiple tasks, where a redescending robust estimator is utilized to adaptively unify the continuous clustering of a large-scale number of tasks and dynamically selecting valuable features of few-shot tasks. To enable a better description of relationships between multiple tasks, we formulate a multi-convex objective function that can be optimized alternatively. After analyzing the complexity and convexity of the problem, we provide a scalable solving approach which can converge to the optimum with approximately linear time complexity. Compared with state-of-the-art models, the proposed approach performs better RMSE score and time efficiency both in synthetic and realistic datasets. Meanwhile, with similar computational overhead, the experiment demonstrates that our method has better regression accuracy than clustering tasks alone or selecting valuable features individually.

关键词： multi-task learning robust estimator continuous clustering group sparsity

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Artificial Identification, Blockchain, Cyberphysical Social Systems, Digital Twins, and Parallel intelligence Opportunities and Synergies Between the ieee Council on Radio-Frequency Identification and Systems, Man, and Cybernetics Society

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ieee SYSTEMS MAN AND CYBERNETICS MAGAZINE 2021年第2期7卷 61-66页

作者： Wang, Fei-Yue Rudas, Imre J. Wu, Dongrui Wang, Xiao Yuan, Yong Zhang, Jun Jason Li, Yidong Bennett, Gisele Bassiri-Gharb, Nazanin Univ Arizona Tucson AZ 85721 USA Univ Arizona Robot & Automat Lab Tucson AZ 85721 USA Univ Arizona Program Adv Res Complex Syst Tucson AZ 85721 USA Chinese Acad Sci Intelligent Control & Syst Engn Ctr Inst Automat Beijing Peoples R China Chinese Acad Sci Key Lab Complex Syst & Intelligence Sci Beijing Peoples R China State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China CAAs Supervis Council Beijing Peoples R China IEEE Council Radiofrequency Identificat Beijing Peoples R China IEEE Syst Man & Cybernet Soc Beijing Peoples R China Obuda Univ H-1034 Budapest Hungary Huazhong Univ Sci & Technol Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao Qingdao 266109 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Minist Educ Engn Res Ctr Finance Computat & Digital Engn Beijing Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China MEPSS LLC Indian Harbor Beach FL 32937 USA Georgia Tech Atlanta GA 30332 USA Georgia Inst Technol GW Woodruff Sch Mech Engn Atlanta GA 30332 USA

This article describes several opportunities and synergies between the ieee Council on Radio-Frequency Identification (CRFID) and ieee Systems, Man, and Cybernetics Society (SMCS) to initiate a roadmap study and working plan for a new model of support and collaboration among ieee Societies and Councils in the future. We hope this will stimulate more communication and discussion for deep and effective coordination and collaboration among ieee Councils and Societies.

关键词： Digital twin Collaboration Blockchain Cybernetics Radiofrequency identification

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Event-trigger-based robust control for nonlinear constrained-input systems using reinforcement learning method

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NEUROCOMPUTING 2019年 340卷 158-170页

作者： Yang, Dongsheng Li, Ting Zhang, Huaguang Xie, Xiangpeng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Jiangsu Peoples R China

In this paper, an online integral reinforcement learning strategy is proposed to deal with robust constrained control problems using event-triggered mechanism for nonlinear Continuous-Time (C-T) systems with external disturbances. The novel design of constrained control law is addressed together with the adaptive event-triggered condition by guaranteeing the optimal performance and system stability. An adaptive online actor-critic Neural Network (NN) reinforcement learning scheme is developed to approximate the optimal solution of the complicated Hamilton-Jacobi-Isaacs equation. Meanwhile, the convergence of NN weight errors and the event-triggered closed-loop system stability are demonstrated to be uniform ultimate bounded by Lyapunov analysis under the proposed triggering condition. Moreover, event-triggered H-infinity tracking control with input constrains and limited network communication is also presented by establishing an augmented system. Finally, simulation results are provided to show the algorithm validity. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Event-triggered control Robust H-infinity control Hamilton-Jacobi-Isaacs (HJI) equation Neural networks Input constrains

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Voltage-Violation Mitigation in Power System Networks With Photo-Voltaic Penetration

Voltage-Violation Mitigation in Power System Networks With P...

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ieee symposium Series on computational intelligence (SSCI)

作者： Mubeenah Titilola Sanni Hemanshu Pota Huadong Mo Daoyi Dong School of Engineering and Information Technology The University of New South Wales Canberra Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

This study explores the influence and control strategies of rising photo-voltaic (PV) penetration in power system networks. The power system network is simulated for varying levels of PV penetration and a non-linear pattern of voltage increase is observed with increasing PV penetration. Popular local control strategies to tackle the overvoltage problem: reactive power control; active power curtailment; and power factor control are then analysed. Simulations of these control techniques are carried out under different weather conditions to analyse the suitablility of these techniques. The combination of active power curtailment and reactive power control strategies is explored to provide higher efficiency and cater for the deficiency of both techniques. The analysis and simulation of these control techniques would be an aid for power system operators to evaluate penetration levels of PV in the network as well as provide technical support for local control techniques to alleviate overvoltages.

关键词： Voltage control Reactive power Power system stability Inverters Voltage measurement Reactive power control Australia

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Grid-Connected Renewable Energy Microgrids: A Systematic Review

Grid-Connected Renewable Energy Microgrids: A Systematic Rev...

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ieee symposium Series on computational intelligence (SSCI)

作者： Mohammed Abdullah Al-Shehri Youguang Guo Gang Lei School of Electrical and Data Engineering University of Technology Sydney(UTS) Sydney Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

A comprehensive review of the literature for the optimum design of microgrid is presented in this paper. This is aim at realistic evaluation of the current status, some existing research problems, and developed a future research topic in the area. Presently, the penetration of microgrid is increasing, ranging from developed to underdeveloped nations. Depending on the application, microgrids could be installed for specific applications, such as community-based and experimental microgrids. Examples of these situations are also highlighted in the paper.

关键词： Microgrids Batteries Renewable energy sources Reliability Voltage control Optimization Load modeling

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Evaluation of air quality variability in Timișoara, Romania

Evaluation of air quality variability in Timișoara, Romania

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Iasmina Gruicin Marian-Emanuel Ionașcu Mircea Popa Faculty of Automation and Computers University Politehnica of Timișoara Timișoara Romania

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

This study presents the evaluation and analysis of air pollution levels in Timisoara, Romania, regarding the main air pollutants. We examined diurnal, monthly and seasonal variations, using datasets of measurements from the four existing fixed air quality monitoring stations in Timisoara. The results show that the station TM-1, located in the south of the city, has overall much higher values that the other stations. All pollutants except for ozone show peaks in the diurnal variation in the morning and late in the afternoon, which can be correlated to the traffic rush hours, and fall and winter dominance in the seasonal variation. Ozone variation follows the chemistry of its formation, showing proportional increases with the increase of sunlight and ultraviolet radiation.

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Sampling-based Polytopic Trees for Approximate Optimal control of Piecewise Affine Systems

Sampling-based Polytopic Trees for Approximate Optimal Contr...

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ieee International Conference on Robotics and automation (ICRA)

作者： Sadraddini, Sadra Tedrake, Russ MIT Comp Sci & Artificial Intelligence Lab 32 Vassar St Cambridge MA 02139 USA

ISBN: (纸本)9781538660263

Piecewise affine (PWA) systems are widely used to model highly nonlinear behaviors such as contact dynamics in robot locomotion and manipulation. Existing control techniques for PWA systems have computational drawbacks, both in offline design and online implementation. In this paper, we introduce a method to obtain feedback control policies and a corresponding set of admissible initial conditions for discrete-time PWA systems such that all the closed-loop trajectories reach a goal polytope, while a cost function is optimized. The idea is conceptually similar to LQR-trees [1], which consists of 3 steps: (1) open-loop trajectory optimization, (2) feedback control for computation of "funnels" of states around trajectories, and (3) repeating (1) and (2) in a way that the funnels are grown backward from the goal in a tree fashion and fill the state-space as much as possible. We show PWA dynamics can be exploited to combine step (1) and (2) into a single step that is tackled using mixed-integer convex programming, which makes the method suitable for dealing with hard constraints. Illustrative examples on contact-based dynamics are presented.

关键词： Trajectory Robots Programming Integrated circuit modeling Optimization Optimal control computational modeling

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Autonomous Vehicle control Using Particle Swarm optimization in a Mixed control Environment

Autonomous Vehicle Control Using Particle Swarm optimization...

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ieee symposium Series on computational intelligence (SSCI)

作者： Na’Shea Wiesner John Sheppard Brian Haberman Gianforte School of Computing Montana State University Bozeman MT Applied Physics Laboratory Johns Hopkins University Laurel MD

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

This work examines effective ways of controlling autonomous vehicles on the roadway while human-operated vehicles remain in use. Particle Swarm optimization is used to control speed, gap, and braking of autonomous vehicles on a merge lane where human-operated vehicles are simulated using the Krauss car-following model. Experiments performed in a simulated environment tested various vehicle densities, ratios of autonomous versus Krauss-operated vehicles, and scenarios where the type of vehicle merging was adjusted. Metrics collected from the simulation include number of merges, collisions, the average merge lane speed, and the average highway or “nonmerging” speed. Results show that the autonomous vehicles are able to learn vehicle following and merging techniques to keep merges and speeds maximal, while keeping collisions minimal.

关键词： Autonomous vehicles Merging Vehicles Roads computational modeling Adaptation models Particle swarm optimization

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Ontology Based Security Risk Model for Power Terminal Equipment 12

Ontology Based Security Risk Model for Power Terminal Equipm...

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12th International symposium on computational intelligence and Design (ISCID)

作者： Liang, Xiao Ma, Lixin An, Ningyu Jiang, Dongxiao Li, Chenggang Chen, Xiaona Zhao, Lijiao Global Energy Interconnect Res Inst Co Ltd Artificial Intelligence Elect Power Syst State Gr Beijing 102209 Peoples R China State Grid Jilin Elect Power Co Ltd Changchun 130000 Jilin Peoples R China State Grid Jilin Prov Elect Power Res Inst Changchun 130021 Jilin Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

ISBN: (数字)9781728146539

ISBN: (纸本)9781728146522

IoT based technology are drastically accelerating the informationization development of the power grid system of China that consists of a huge number of power terminal devices interconnected by the network of electric power IoT. However, the networked power terminal equipment oriented cyberspace security has continually become a challenging problem as network attack is continually varying and evolving. In this paper, we concentrate on the security risk of power terminal equipment and their vulnerability based on ATP attack detection and defense. We first analyze the attack mechanism of APT security attack based on power terminal equipment. Based on the analysis of the security and attack of power IoT terminal device, an ontology-based knowledge representation method of power terminal device and its vulnerability is proposed.

关键词： ontology APT attack power terminal device vulnerability security risk model

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