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arXiv 2019年

作者： Yang, Xiaozhou Chen, Nan Zhai, Chao Department of Industrial Systems Engineering and Management National University of Singapore Singapore117576 Singapore School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Online transmission line outage detection over the entire network enable timely corrective actions to be taken, which prevents a local event from cascading into a large scale blackout. Line outage detection aims to detect an outage as soon as possible after it happened. Traditional methods either do not consider the transient dynamics following an outage or require a full Phasor Measurement Unit (PMU) deployment. Using voltage phase angle data collected from a limited number of PMUs, we propose a real-time dynamic outage detection scheme based on alternating current (AC) power flow model and statistical change detection theory. The proposed method can capture system dynamics since it retains the time-variant and nonlinear nature of the power system. The method is computationally efficient and scales to large and realistic networks. Extensive simulation studies on IEEE 39-bus and 2383-bus systems demonstrated the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Anomaly detection

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Wind farm sub-synchronous oscillation identification based on improved variational mode decomposition 8

Wind farm sub-synchronous oscillation identification based o...

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Ma, Junjie Liu, Fang Wu, Min Ding, Min Li, Danyun School of Information Science and Engineering Central South University Changsha410083 China School of Automation China University of Geoscience Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Sub-synchronous oscillation (SSO), an oscillation at a frequency below the synchronous frequency, is introduced to the generator when it exchanges significant energy with series capacitor compensated system. Identifying mode of SSO from the noise-like signal, as well as small disturbance responses with high amplitude noise, can provide early warning for wind farm, and help to adjust controller parameters. For low signal-to-noise ratio of noise-like signal, normal mode identifying method cannot obtain satisfactory results. In order to solve the above problem, an improved variational mode decomposition based method is proposed in this paper to identify SSO mode information from the wind farm noise-like signal. The mode number of variational mode decomposition is adaptively obtained by fast Fourier transformation and average magnitude calculation. The simulation results show that the proposed method can accurately separate SSO of different frequency, and has high mode identification accuracy for different types of oscillations. The proposed method is also suitable for performing online mode identification. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Variational mode decomposition

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A new modified repetitive control system design based on T-S fuzzy model 8

A new modified repetitive control system design based on T-S...

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作者： Zhang, Manli Wu, Min Chen, Luefeng Yu, Pan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Department of Electrical and Electronic Engineering Chiba University Chiba263-8522 Japan

— A new modified repetitive control strategy based on the Takagi-Sugeno fuzzy model is presented for an affine nonlinear system to track a periodic reference and reject a periodic disturbance. Then, a parallel distributed compensation control strategy is developed for the fuzzy repetitive system. Further, taking advantage of the control action during a period and the learning action between periods, a two-dimensional model is established for the closed-loop control system. A sufficient condition in the form of a set of linear matrix inequalities is derived to guarantee the robust asymptotic stability of the closed-loop control system. Simulation results demonstrate the validity of the method. A comparison with conventional modified repetitive control also shows the superiority of the developed method. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Linear matrix inequalities

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Improving Training-based Reflectance Reconstruction via White-balance and Link Function

Improving Training-based Reflectance Reconstruction via Whit...

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Chinese control Conference

作者： Li-Jun Zhang Jun Jiang Hui Jiang Jing-Jing Zhang Xing Jin School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

An improved spectral reflectance reconstruction method is developed to transform camera RGB to spectral reflectance by inserting white balance and link function during the training-based method. The novelty in our method is the use of white-balancing to normalize the scene illumination and link function to transform the reflectance, we use a radial basis function network to model the mapping between camera-specific RGB values and specific reflectance spectra. Experimental results indicate that the proposed method significantly outperforms currently existing methods in terms of spectral error and shape especially under the illumination not present in the training process.

关键词： Lighting Image reconstruction Training Cameras Testing Principal component analysis Reflectivity

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Comparative Study of Four Speed controllers of Brushless DC Motors for Industrial Applications

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IFAC-PapersOnLine 2020年第5期53卷 59-64页

作者： Tariku Sinshaw Tamir Gang Xiong Zhen Shen Xiaoyan Gong Sheng Liu Ehtisham Lodhi Li Wan Xisong Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan 528225 China

Direct current (DC) motors are one of the most important kind of motors and are widely used in robotic and industrial applications. Recently, there have been significant efforts to develop direct current (DC) motors in an attempt to control speed of motors. However, conventional controlling approaches perform undesirably in terms of stability and quick response. Therefore, this paper presents a hybrid intelligent controller configuration for optimized speed control of brushless direct current (BLDC) motors in a factory supervisory control data acquisition (SCADA) system. We compare this hybrid intelligent controller with a conventional PID controller, fuzzy logic controller (FLC), and artificial neural network model reference controller (ANNMRC) in MATLAB , and the results show that the hybrid (neuro-fuzzy) controller performs superior in terms of stability, speed trajectory tracking capability, fast response, and simplicity for implementation.

关键词： BLDC motor fuzzy logic controller (FLC) artificial neural network model reference controller (ANNMRC) hybrid (neuro-fuzzy) control

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AI education system for primary and secondary schools 126

AI education system for primary and secondary schools

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126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Gong, Xiaoyan Zhao, Liang Tang, RenHao Guo, YaNa Liu, Xiwei He, Jian Wang, Fei-Yue Tang, Ying Shi, Wanruo Niu, Xiaojie Wang, Xinzhu Institute of Smart Education Qingdao Academy of Intelligent Industries China Faculty of Information Technology Beijing University of Technology China Qingdao Academy of Intelligent Industries Institute of Automation Chinese Academy of Sciences China National Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences China Rowan University United States Beijing Normal University China

This paper presents an AI education system and related AI curricula specifically designed for primary and secondary students at different cognitive levels. In this system, complicated AI algorithms are encapsulated and modularized with friendly and easy-to-use interfaces. AI curricula are developed based on constructivism, project-based learning and multidisciplinary integration. Typical teaching cases, such as speech recognition, text recognition, image recognition, intelligent transportation, smart home, intelligent robots, etc., which are used to enhance comprehension of AI concepts and applications, are also discussed. © American Society for Engineering Education, 2019

关键词： Curricula

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An efficient nondominated sorting algorithm 18

An efficient nondominated sorting algorithm

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Wang, Junchen Li, Changhe Diao, Yiya Zeng, Sanyou Wang, Hui School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China Nanchang Institute of Technology Nanchang China

ISBN: (纸本)9781450357647

Nondominated sorting (NS) is commonly needed in multi-objective optimization to distinguish the fitness of solutions. Since it was suggested, several NS algorithms have been proposed to reduce its time complexity. In our study, we found that their performances are closely related to properties of the distribution of a data set, especially the number of fronts. To address this issue, we propose a novel NS algorithm Filter Sort. We also propose a new benchmark data generator for evaluating the performance of a NS algorithm. Experimental results show that our algorithm is superior to several state-of-the-art NS algorithms in most cases. © 2018 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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Extended dissipativity analysis of delayed memristive neural networks based on a parameter-dependent lyapunov functional

Extended dissipativity analysis of delayed memristive neural...

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2018 Australian and New Zealand control Conference, ANZCC 2018

作者： Lu, Chengda Zhang, Xian-Ming Wu, Min Han, Qing-Long He, Yong School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia

ISBN: (纸本)9781538666173

This paper is concerned with extended dissipativity analysis of memristive neural networks with time-varying delays. Using the characteristic function technique, a tractable model of a memristive neural network is obtained. This model is similar to a neural network with polytopic uncertain synaptic weights, enabling us to construct a parameter-dependent Lyapunov functional. By combining this functional and some integral inequalities, a novel extended dissipativity criterion is obtained in terms of linear-matrix-inequalities, where different Lyapunov matrices are used for each form of the memristive neural network. Through a numerical example, this criterion is shown to be less conservative than the one based on a common Lyapunov functional. © 2018 IEEE.

关键词： Linear matrix inequalities

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A Novel Blockchain Oracle Implementation Scheme Based on Application Specific Knowledge Engines

A Novel Blockchain Oracle Implementation Scheme Based on App...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Wang, Shuai Lu, Hao Sun, Xingkai Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781728128535

In blockchain ecosystems, an Oracle is a service tool which provides real-world data for smart contracts and other blockchain applications. At present, there are several Oracle implementation schemes, e.g. centralized Oracles, decentralized Oracles, and hardware Oracles. However, these schemes typically suffer from single source of data and low scalability. Application Specific Knowledge Engine (ASKE) is an integrated topic/application-centered knowledge portal that supports effective information retrieval and analysis. Inspired by ASKE, in this paper, we propose a novel Oracle implementation scheme. The proposed scheme can realize multi-source data extraction and analysis, then working prototypes are demonstrated to show the validity of the scheme. © 2019 IEEE.

关键词： Blockchain

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Speech Emotion Recognition Based on Linear Discriminant Analysis and Support Vector Machine Decision Tree

Speech Emotion Recognition Based on Linear Discriminant Anal...

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第37届中国控制会议

作者： Jun-Wei Mao Yong He Zhen-Tao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

For speech emotion recognition,emotional feature set with high dimension may produce redundant features and influence the recognition *** solve this problem and obtain the optimal emotional feature subset of speech,a feature dimension reduction based on linear discriminant analysis is *** to the confusion degree between different basic emotions,an emotion recognition method based on support vector machine decision tree is *** on speaker-dependent speech emotion recognition using Chinese speech database from institute of automation of Chinese academy of sciences is performed and a speech emotion recognition system is presented,where standard feature sets of the INTERSPEECH and classic classifiers are used in comparative experiments *** results show that the proposal achieves 84.39%recognition accuracy on *** proposal,it would be fast and efficient to discriminate emotional states of diverse speakers from speech,and it would make it possible to realize the interaction between speaker and computer/robot in the future.

关键词： Emotion Recognition Speech Linear Discriminant Analysis Support Vector Machine Decision Tree

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