检索结果-内蒙古大学图书馆

Data-Based Nonaffine Optimal Tracking Control Using Iterative DHP Approach ⁎

IFAC-PapersOnLine 2020年第2期53卷 4246-4251页

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing 100124 China School of Automation Guangdong University of Technology Guangzhou 510006 China

In this paper, a data-based optimal tracking control approach is developed by involving the iterative dual heuristic dynamic programming algorithm for nonaffine systems. In order to gain the steady control corresponding to the desired trajectory, a novel strategy is established with regard to the unknown system function. Then, according to the iterative adaptive dynamic programming algorithm, the updating formula of the costate function and the new optimal control policy for unknown nonaffine systems are provided to solve the optimal tracking control problem. Moreover, three neural networks are used to facilitate the implementation of the proposed algorithm. In order to improve the accuracy of the steady control corresponding to the desired trajectory, we employ a model network to directly approximate the unknown system function instead of the error dynamics. Finally, the effectiveness of the proposed method is demonstrated through a simulation example.

关键词： Adaptive dynamic programming data-based optimal tracking control iterative dual heuristic dynamic programming neural network

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Robotic Micromanipulation for Active Pin Alignment in Electronic Soldering Industry

Robotic Micromanipulation for Active Pin Alignment in Electr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hao Ren Xinyu Wu Wanfeng Shang Chinese Academy of Sciences Institute of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Beijing China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

In the context of robotic high-precision soldering, we propose an image-based pin alignment control method based on active plastic deformation. The plastic deformation is a well-known failure mechanism in most situations, which includes a phenomenon that the objects do not return original state. Here, in contrast to this convention, we utilize the plastic deformation of the metal pin to do pin alignment for improving the quality of the solder joint. To address this, we embed the springback compensation into the image-based pin alignment controller. Lastly, the proposed strategy is successfully demonstrated and evaluated in a practical modified robotic manipulation system. The result shows that the alignment error is less than 20µm, which is far less than pin alignment without considering plastic deformation and elastic recovery. This work considers active plastic deformation and spontaneous elastic recovery of soft object, which would greatly promote the use of robotics in micromanufacturing and microfabrication in lad and industry, especially for soft objects.

关键词： Industries Service robots Microfabrication Elastic recovery Metals Failure analysis Pins

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CFDIL: A context-aware feature deep interaction learning for app recommendation

Research Square

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Research Square 2021年

作者： Hao, Qingbo Zhu, Ke Wang, Chundong Wang, Peng Mo, Xiuliang Liu, Zhen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki Japan

The rapid development of Mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users’ preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method. © 2021, CC BY.

关键词： User profile

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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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Dynamic Output Feedback Control of a Sampled-Data system Under Stochastic Sampling

Dynamic Output Feedback Control of a Sampled-Data System Und...

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IEEE Conference on Decision and Control

作者： Haoyuan Sun Honggui Han Jian Sun Jie Chen Faculty of Information Technology and the Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Beijing Institute of Technology Beijing PR China School of Electronic and Information Engineering Tongji University Shanghai PR China

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this paper, we study the problem of stability and stabilization of a sampled-data system under stochastic sampling. A dynamic output feedback control method is proposed in which it is assumed that the sampling interval is disturbed by noisy perturbations obeying a certain probability density. A stochastic stability condition is derived and presented in terms of a linear matrix inequality (LMI) using the Kronecker product operation and the Vandermonde matrix. On the basis of the obtained stochastic stability condition, a new approach to obtain the controller gain matrices is developed. The effectiveness of the proposed method is demonstrated by two examples.

关键词： Output feedback Stability criteria Sampled data systems Stochastic processes Control systems Power system dynamics Linear matrix inequalities

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Value-Iteration-Based Neuro-Optimal Tracking Control for Affine systems with Completely Unknown Dynamics

Value-Iteration-Based Neuro-Optimal Tracking Control for Aff...

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第三十九届中国控制会议

作者： Mingming Ha Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology School of Automation Guangdong University of Technology

In this paper, the versatile value-iteration-based control method, aimed at affine systems with unknown dynamics, is proposed to deal with the optimal tracking control problem. Neural networks are adopted to approximate system dynamics and a novel approach is presented to estimate the steady state control input based on the established identifier. Additionally, two other neural networks, called the critic network and the action network, are used to implement the optimal tracking control ***, based on the proposed method, the tracking controller is designed to control a specific simulation example. It is shown that, for any randomly given initial state vector, the controller is able to make the affine system track the reference trajectory without knowing the system dynamics.

关键词： Adaptive Dynamic Programming Neuro-Optimal Tracking Control Value Iteration Affine systems with Unknown Dynamics

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Review of Research on Degaussing Technology of Magnetic Storage Media

Review of Research on Degaussing Technology of Magnetic Stor...

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Chinese Automation Congress (CAC)

作者： Zhe Xu Ziying Zhang Pengsheng Li Xiaoge Liu Jian Tang Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Information Technology line Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Magnetic storage medium such as hard disk is the main carrier of electronic information at present. How to completely remove the data information on storage medium such as used hard disk and prevent information leakage is a very important problem in the field of information security research. Based on the current research status of magnetic storage media degaussing technology, this article analyzes and summarizes the composition of degaussing equipment, degaussing process and each module of degaussing equipment. Then the article summarizes the research difficulties of magnetic storage media degaussing technology. Finally the article points out the possible future of magnetic storage media degaussing technology research direction.

关键词： Magnetic memory Magnetic fields Media Magnetic devices Hard disks Magnetic flux Demagnetization

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SG-PBFT: A secure and highly efficient blockchain PBFT Consensus Algorithm for internet of vehicles

arXiv

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arXiv 2021年

作者： Xu, Guangquan Liu, Yihua Xing, Jun Luo, Tao Gu, Yonghao Liu, Shaoying Zheng, Xi Vasilakos, Athanasios V. The Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China The International Engineering Institute Tianjin University Tianjin300350 China The College of Intelligence and Computing Tianjin University Tianjin Tianjin300350 China The school of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Informatics and Data Science Hiroshima University Japan The Department of Computing Macquarie University SydneyNSW2109 Australia The University of Technology Sydney Australia The Fuzhou University Fuzhou China Lulea University of Technology Lulea Sweden

The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To solve these problems, this paper proposes SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of Vehicles, which is based on a distributed blockchain structure. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional PBFT consensus algorithm by using a score grouping mechanism to achieve a higher consensus efficiency. The experimental result shows that our method can greatly improve the consensus efficiency and prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile and can be used in other application scenarios which require high consensus efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Train Speed Trajectory Optimization using Dynamic Programming with speed modes decomposition 2

Train Speed Trajectory Optimization using Dynamic Programmin...

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2019 2nd International Conference on Advanced Materials, intelligent Manufacturing and Automation, AMIMA 2019

作者： Wang, Pu Peng, Yi Gao, Xue-Jin Gao, Hui-Hui Faculty of Information Technology Beijing University of Technology Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China Beijing Laboratory for Urban Mass Transit Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

When applying a dynamic programming algorithm to train speed trajectory optimization, there is a problem of too many discrete points which leading to dimension disaster. Different from using uniform discretization of the time and space by previous research, In this paper, a train operation model is proposed based on conditions (speed limit and slope change) and described in the network diagram. Armed with this model, through the pre-planning of the line, the optimization model of train operation is established, and the dynamic programming algorithm is utilized to find the global optimal value of train traction energy consumption. Taking beijing Yizhuang line as a simulation case, the validity of the proposed model is verified by comparing common optimization algorithms. The results demonstrate that the proposed model can effectively reduce the calculation time of the dynamic programming algorithm by 95.06%, and has better optimization effect. The energy-saving effect reaches 6.0806%. © Published under licence by IOP Publishing Ltd.

关键词： Mode decomposition

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Quadratic Mutual Information based Regression for Prediction of Quality Variable in Batch Process

Quadratic Mutual Information based Regression for Prediction...

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作者： Zheng Li Pu Wang Xuejin Gao Yongsheng Qi Huihui Gao Faculty of Information Technology Beijing University of Technology Engineering Research Centre of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Electric Power Inner Mongolia University of Technology

Quality prediction is of great importance for batch processes. Predicting quality variable is a challenging task because of various factors such as strong nonlinearity and nonGaussian exist in batch data. A quadratic mutual information based regression method is proposed to handle the problem. The proposed method takes into account higher order statistics that reveal the non-linear dependencies between the process variables and important quality variables. Furthermore, the proposed method is implemented without the hypothesis of Gaussian distribution of the dataset as in MPLS. The effectiveness of the QMIR method is illustrated by a dataset of industrial Escherichia coli fermentation process, compared with MPLS.

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