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Chinese Automation Congress (CAC)

作者： Jing Yang Xiuqin Shang Bin Hu Zhen Shen Gang Xiong Hui Wang College of Information Sciences and Technology Beijing University of Chemical Technology Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China

In this paper, several methods are combined together and applied to a Particle Filter based on Particle Swarm Optimization acoustic source tracking framework in order to solve some problems of sound source tracking. The Time Difference of Arrival (TDOA), which is extracted from the audio signal received by the microphone pair, makes observations. The Langevin Model is used to describe the speaker's motion characteristics. The pseudo-likelihood function is constructed by using the Global Coherence Field (GCF) function to update the particle weight, in order to solve problem of the phenomenon of spurious source due to noise and reverberation. According to the iterative process and particle flight conditions, the inertial weights of the particles are adjusted nonlinearly and dynamically. The concepts of superior flight speed and inferior flight speed are introduced to Particle Swarm Optimization, which helps jump out of local optimum and solves the problem of lack of particle diversity. Thus, the faster tracking of sound source is realized. In the framework, with fewer particles, the robustness of tracking is enhanced and accuracy of tracking is improved. Finally, the effectiveness of this framework for acoustic source tracking is verified through several Monte Carlo simulation experiments.

关键词： Microphones Particle filters Particle swarm optimization Tracking Coherence Reverberation

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Joint Face Alignment and 3D Face Reconstruction with Efficient Convolution Neural Networks

Joint Face Alignment and 3D Face Reconstruction with Efficie...

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International Conference on Pattern Recognition

作者： Keqiang Li Huaiyu Wu Xiuqin Shang Zhen Shen Gang Xiong Xisong Dong Bin Hu Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China 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 Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

3D face reconstruction from a single 2D facial image is a challenging and concerned problem. Recent methods based on CNN typically aim to learn parameters of 3D Morphable Model (3DMM) from 2D images to render face alignment and 3D face reconstruction. Most algorithms are designed for faces with small, medium yaw angles, which is extremely challenging to align faces in large poses. At the same time, they are not efficient usually. The main challenge is that it takes time to determine the parameters accurately. In order to address this challenge with the goal of improving performance, this paper proposes a novel and efficient end-to-end framework. We design an efficient and lightweight network model combined with Depthwise Separable Convolution and Muti-scale Representation, Lightweight Attention Mechanism, named Mobile-FRNet. Simultaneously, different loss functions are used to constrain and optimize 3DMM parameters and 3D vertices during training to improve the performance of the network. Meanwhile, extensive experiments on the challenging datasets show that our method significantly improves the accuracy of face alignment and 3D face reconstruction. Model parameters and complexity of our method are also improved greatly.

关键词： Training Solid modeling Three-dimensional displays Convolution Face recognition Neural networks complexity theory

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Fast Calibration of Agent-Based Model using Mean-Field Approach

Fast Calibration of Agent-Based Model using Mean-Field Appro...

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

作者： Wanze Lu Gang Xiong Xudong Liu Sheng Liu Peijun Ye Liguo Zhang Faculty of Information and Technology Beijing University of Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

Calibration of simulation system is an important step to establish a reliable simulation system. In this paper, based on the existing mean field calibration theory, a new fast calibration method for complex urban traffic system based on mean field method is discussed by establishing the relationship between the macroscopic observation quantity and the proxy microscopic parameters. This method makes use of the machine learning method, and reduces the dimension of the state vector in the discrete time period, so that the dimension of the macroscopic state transition matrix is lower, and then reduces the computational complexity of the system calibration. Finally, the calibration results of the simulation system were verified by SUMO traffic simulation software.

关键词： Visualization Computational modeling Microscopy Digital twin Machine learning Traffic control Reliability theory

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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Chinese Automation Congress (CAC)

作者： Weixin Hong Huaiyu Wu Zhen Shen Gang Xiong Bin Hu Xisong Dong 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 Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available.

关键词： Automation Reinforcement learning Predictive models Prediction algorithms Planning Task analysis

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Generalized Zeroing Neural Network Model for Online Solving Time-Varying Cube Roots with Various External Disturbances in Different Domain

SSRN

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SSRN 2022年

作者： Wang, Gang Liu, Yongbai Sun, Yingyi Yu, Junzhi Sun, Zhongbo Department of Mechatronic Engineering Changchun University of Technology Changchun130012 China College of Economics and Management Jilin Agricultural University Changchun130118 China State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China Department of Control Engineering Changchun University of Technology Changchun130012 China

As a basic problem of the nonlinear dynamic model, the online solution of time-varying cube root problem (TVCRP) is widely used in science and engineering. However, the practical system is frequently disturbed by the external factors, which inevitably leads to unknown disturbances in the solution process. Therefore, addressing the TVCRP with high accuracy in non-ideal environment (unknown disturbances) is the basis of solving the nonlinear dynamic model. In this work, a noise-suppression zeroing neural dynamics (NSZND) model is investigated and developed from the control perspective to resolve the time-varying or time-invariant cube root problem (TV/TICRP) under the different disturbances (bounded and unbounded disturbances) in © 2022, The Authors. All rights reserved.

关键词： Dynamic models

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Public Service management system of Urban Placenames for Sanya Case

Public Service Management System of Urban Placenames for San...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Wei Liu Fenghua Zhu Xisong Dong Gang Xiong Yongqiang Zhu Huazhou Wu Taozhong Liu Yuexiang Hu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences Dongguan Research Institute of CASIA Cloud Computing CenterChinese Academy of Sciences Instituteof Smart Education Systems Qingdao Academy of Intelligent Industries Xiamen Kingtopinfo Technology Co.Ltd

The rapidly growing amount of social interaction makes geographical names an important and indispensable medium for people in social and economic exchanges. Especially in today's information age, the frequency of usage of name information has increased greatly. In this paper, a method for the construction of a cities' names database is proposed. This will include the processing of the outcome data from the names pilot survey, the consolidation into the database, the development of the cloud service and management system based on the geographical names and zoning database, and the construction of a relatively complete set of the public service system of geographical names based on a website and touch screen interface. The method has been applied in San Ya, China successfully, and very good results have been obtained. The platform can provide not only accurate and convenient services of geographical names, but also promote its economic development and social progress.

关键词： Public service system geographical names geographical names database cloud service platform

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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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Calibration of Agent-Based Model Using Reinforcement Learning

Calibration of Agent-Based Model Using Reinforcement Learnin...

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

作者： Bing Song Gang Xiong Songmin Yu Peijun Ye Xisong Dong Yisheng Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China Fraunhofer Institute for Systems and Innovation Research Breslauer Germany

In the research and application of Agent-Based Models (ABM), parameter calibration is an important content. Based on the existing state transfer equations that link the micro-parameters and macro-states of the multi-agent system, this paper further proposes to introduce Reinforcement Learning when calibrating the parameters. The state transfer of the agent after learning is used to calibrate the micro-parameters of ABM, and the interaction between each agent and multiple other agents is expressed as the parameters of the agent. The application case study of population migration demonstrates that our method can achieve high accuracy and low computational complexity.

关键词： Learning systems Microscopy Digital twin Sociology Reinforcement learning Mathematical models Calibration

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Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification

Learning Network-Invariant and Label-Discriminative Represen...

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Chinese Automation Congress (CAC)

作者： Linyao Yang Yancai Xu Jiachen Hou Yuxin Dai Chen Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Systems Engineering Macau University of Science and Technology Macau China School of Electrical Engineering and Automation Wuhan University Wuhan China China Electric Power Research Institute Beijing China

ISBN: (纸本)9781665426480

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of labeled data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the labeled source network, this paper proposes to learn network-invariant and label-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain label-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into label-discriminative subspaces. In the end, a support vector machine model trained on the labeled source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules.

关键词： Support vector machines Knowledge engineering Analytical models Automation Transfer learning Network analyzers Data structures

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Design and Implementation of RFID based Smart Shopping Booth

Design and Implementation of RFID based Smart Shopping Booth

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International Conference on Information Science and control Engineering (ICISCE)

作者： Ehtisham Lodhi Fenghua Zhu Zeeshan Lodhi Qasim Saleem Gang Xiong FeiYue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

In this modern era, technology has advanced to such an extent to make our daily life more proficient and convenient. This paper presents the design and implementation of vending machine (VM) for students, which operates on radio frequency identification technology (RFID). Hence, it turns the VM into smart shopping booth (SSB). The SSB gives the access only through RFID card and vend different products according to the desire of student. The SSB is equipped with RFID reader and student identity card that holds the RFID tag. When the student identity card is scanned for verification and then after selecting the required products, will be dispensed off if a student has available balance in his/her identity card and SMS will be sent to students about remaining balance in their cards by GSM technology. The programming for controller is done by using proton IDE language. The SSB is the customized idea of vending machine and it attempts to resolve various issues that appeared in coin/note based VM. The student identity card RFID based technology is a breakthrough towards a paperless and cashless environment. Moreover, it is very beneficial in saving the precious time of students due to fast paced life and also user friendly. RFID based SSB also improved the communication and sensing capabilities of VM. Hence, our suggested approach outperforms in all aspects.

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