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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Wan, Feiya Li, Zhen Liu, Bin Li, Guanghui Dong, Xiaoliang Chen, Xi Yu, Samson S. Oo, Amanullah M. T. Shan, Zhenyu Liu, Xiangdong Beijing Institute of Technology School of Automation and Key Laboratory for Intelligent Control Decision on Complex Systems Beijing China China Electric Power Research Institute Beijing China State Grid Corporation of China and North China Branch of State Grid Corporation of China Beijing China GEIRI North America United States Deakin University School of Engineering 75 Pigdon Rd Waurn Ponds Victoria Australia North China University of Technology School of Electrical and Control Engineering Beijing China

ISBN: (纸本)9781728155081

With the booming installed capacity of permanent magnet synchronous generator (PMSG) for wind energy generation, the grid becomes weaker and the oscillation events are frequently observed in weak-grid-tied large-scale grid-connected PMSG wind farms. Although the impedance-based method is effective to analyze the stability of grid-connected systems, the mathematical relationship between external impedance characteristics and the internal controllers has not been quantitatively analyzed in depth, which is beneficial to the controller design. This paper studies the influence of phase-locked loop (PLL) on the impedance characteristics based on the analytical sequence impedance model of PMSG with typical topology and control structure. The shaping effect of PLL on the impedance characteristics is described so as to clearly describe the relationship between the external impedance characteristics of the wind turbines and the internal controller. Finally, the stability analysis of the grid-connected PMSG wind turbine system is provided in Matlab and simulation results are given to verify the oscillating mechanism caused by PLL. © 2020 IEEE.

关键词： Phase locked loops

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A Parallel Education Based intelligent Tutoring systems Framework

A Parallel Education Based Intelligent Tutoring Systems Fram...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Jing, Sifeng Tang, Ying Liu, Xiwei Gong, Xiaoyan Cui, Wei Liang, Joleen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

although online education has improved efficiency of learners' access to high-quality educational resources, realtime interaction between instructors and learners have not yet been achieved in online personalized learning. intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed. © 2020 IEEE.

关键词： Computer aided instruction

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A Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds

arXiv

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arXiv 2021年

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong School of Mechatronical Engineering Advanced Innovation Center for Intelligent Robots and Systems Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of’Hand-Eye-Brain’ Interaction Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

—Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making. © 2021, CC BY.

关键词： Mammals

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 IEEE.

关键词： Curricula

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Temporal-Difference Spatial Sampling and Aggregating Graph Neural Network for Crowd Flow Forecasting

Temporal-Difference Spatial Sampling and Aggregating Graph N...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Xiaojie Bu Zebing Wei Zhishuai Li Xiao Wang Yuanyuan Chen Yunquan Song Yisheng Lv College of Science China University of Petroleum Qingdao China Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

With the development of traffic demand management, crowd flow forecasting arouse increasing interest. In order to tackle this spatial-temporal prediction problem, GNN (Graph Neural Networks) have been recently employed to model spatial dependencies, and usually spatial graphs are constructed based on geodetic distances or direct connections between nodes. However, it might be insufficient for spatial graphs to model real dependencies because they neglect spatial-temporal correlations. In this paper, a novel Temporal-Difference Spatial Sampling and Aggregating graph neural network (TDSSA) is proposed to model spatial-temporal dependencies. Firstly, a new sub-sparse spatial-temporal graph is constructed to represent spatial-temporal relationships among different nodes, then a TDSSA block is designed to extract features by spatial sampling and aggregating, and difference is utilized in TDSSA blocks to exploit temporal trend and increase robustness. Experiments show that the proposed method outperforms baseline methods.

关键词： Correlation Digital twin Conferences Aggregates Predictive models Feature extraction Market research

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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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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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Chinese Automation Congress (CAC)

作者： Xiaoxuan Zhao Haoyu Wang Xiujuan Wang Udom Lewlomphaisarl Dong Li Jing Hua Mengzhen Kang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.

关键词： Ventilators Green buildings Buildings Production Ventilation Data models Data mining

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A novel hybrid neural network of fluid-structure interaction prediction for two cylinders in tandem arrangement

arXiv

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arXiv 2025年

作者： Lyu, Yanfang Zhang, Yunyang Gong, Zhiqiang Kang, Xiao Yao, Wen Pei, Yongmao State Key Laboratory for Turbulence and Complex Systems Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China Defense Innovation Institute Chinese Academy of Military Science Beijing100071 China Intelligent Game and Decision Laboratory Beijing100071 China Institute of Advanced Structure Technology Beijing Institute of Technology Beijing100081 China

Deep learning has shown promise in improving computing efficiency while ensuring modeling accuracy in fluid-structure interaction (FSI) analysis. However, its current capabilities are limited when it comes to constructing multi-object coupling systems with dynamic boundaries. To address such limitation, a novel FSI neural solver integrated by a fluid deep learning model with multi-time steps and a structural dynamic solver is proposed to accurately and reliably predict the vortex-induced vibration (VIV) evolution for two cylinders in tandem. This well-designed model in the form of end-to-end can precisely predict the instantaneous flow field state at the subsequent time by coupling the temporal flow fields of historical multi-time sequences and the current structural responses, moreover, derives the structural state at the next time. Furthermore, the novel fluid deep learning model consists of a wall shear model utilizing a multilayer perception network and flow field model with U-shaped architecture jointing the Fourier neural operator and modified convolution long-short term memory model. Both models effectively capture coupling transfer forces and predict instantaneous flow fields, with the latter demonstrating superior accuracy compared to Convolutional Neural Network- or Unet-based models with similar parameters. The prediction speed of the proposed models realizes an improvement of over 1000 times compared with the numerical simulation. Significantly, the proposed FSI neural model demonstrates exceptional capability in constructing the nonlinear complex multi-vibration systems and has substantial potential for advancing FSI modeling of flexible structures featuring pronounced nonlinear deformation boundaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Hydraulics

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Cyber-Physical -Social systems for Smart Cities: An Overview

Cyber-Physical -Social Systems for Smart Cities: An Overview

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Noreen Anwar Gang Xiong Wanze Lu Peijun Ye Hongxia Zhao Qinglai Wei The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation and with The Cloud Computing Center Chinese Academy of Sciences China School of Artificial Intelligence and Automation Beijing University of Technology Beijing China

Cyber-physical social systems (CPSS) include cyberspace, physical space, and social space. Since the introduction of CPSS, human relationships with machinery and the physical world have altered as a new paradigm. Recent estimates indicate that approximately 60 percent of the world's population will live in cities by 2030. Large, dense cities can be highly productive, innovative, and very green per capita, hence ideal for our future. However, the huge influx of new people poses enormous challenges to their administrations. Smart cities are becoming a reality, therefore, to achieve higher efficiency, optimum use of resources, management and to strengthen several features of modern towns, several elements of modern cities are automated and integrated into Information and Communications Technology (ICT). Cities and infrastructure are growing all over the world, in providing end-users with increased performance, comfort, visibility and convenience, the Smart Cities concept is critical. This paper will discuss smart cities and the CPSS concept intensively with some of its key technologies, challenges, and further development by using CPSS.

关键词： Smart cities Digital twin Sociology Information and communication technology Security Resource management Statistics

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Graph Optimization based Visual SLAM fusing keyPoints and Markers

Graph Optimization based Visual SLAM fusing KeyPoints and Ma...

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Chinese control Conference (CCC)

作者： Zhen Chen Yang Zhou Fengdi Zhang Min Xu Xiangdong Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing P. R. China Beijing Aerospace Automatic Control Institute P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Simultaneous localization and mapping (SLAM) plays an important role in autonomous navigation for mobile robots. Most of the visual SLAM approaches use keypoints for tracking, whose performance however suffers from the unstable landmarks during task due to uncertain light condition and frequently changeable viewpoint. The situation even becomes worse for visual SLAM in low texture environment especially in indoor buildings, where the supplementary artificial markers can be used to improve the robust detection under a wider range of circumstances. Inspired by this thought, this paper developed a visual SLAM system integrated with keypoints and artificial markers. A graph optimization problem is constructed to optimize the trajectory by taking both the reprojection error of keypoints and the influence of markers into consideration. The experimental results on SPM dataset demonstrate the superior accuracy of the proposed graph optimization algorithm compared with the start-of-the-art ORB-SLAM2.

关键词： Cameras Jacobian matrices Simultaneous localization and mapping Optimization Visualization Trajectory

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