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Chinese automation Congress (CAC)

作者： Zicong Zhao Jing Xun Hairong Dong Wenxing Wu Wentao Zhang Mulin Wan State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronic and Inoformation Enginerring Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665426480

Punctuality is always one of the goals of high speed railway. At the same time, train delay caused by a variety of reasons is also unavoidable. It is necessary to study a method to alleviate train delay. This paper proposes a two-step train operation adjustment method based on integrating the opening management of home signal to alleviate the typical train delay caused by entering station conflict. Find the adjustment point by minimizing the running time for entering station in the first step, and optimize the speed profile to the adjustment point by an improved Grey Wolf Optimizer (GWO) in the second step. Through analysis and simulation, the effectiveness and availability of the proposed method is verified. In the simulation scenario, the delay time is reduced by more than 30s.

关键词： Analytical models automation Simulation Mathematical models rail transportation Delays

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Fractional-order control of PMSM Servo System with Disturbances and Demagnetization Fault

Fractional-order Control of PMSM Servo System with Disturban...

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Chinese automation Congress (CAC)

作者： Luhan Liu Qi Song Meng Ge State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665426480

This paper focuses on the position and velocity tracking control of permanent magnetic synchronous motor (PMSM) servo system with considering the torque ripple, friction, load disturbance and unknown external interference as well as demagnetization fault. Based on the theory of fractional calculus, a set of fractional-order control methods of the position-velocity loop and current loop are established to improve the tracking control performance of PMSM servo system. It should be pointed out that the developed methods are independent of the system parameters and able to deal with uncertainties and nonlinearities of the system as well as demagnetization fault without any "trail and error" process. The effectiveness and superiority of the proposed control methods are verified by theoretical analysis and the MATLAB-ADAMS co-simulation studies, as well as the contrasts with PI methods.

关键词： Tracking loops Uncertainty Friction Synchronous motors Robustness Steady-state Torque measurement

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Digital Twin-based Automatic Train Regulation for Integration of Dispatching and control

Digital Twin-based Automatic Train Regulation for Integratio...

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

作者： Min Zhou Zhuopu Hou Jun Liu Clive Reborts Hairong Dong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences CRSC Research & Design Institute Group Co. Ltd. School of Electronic Electrical and Systems Engineering University of Birmingham United Kingdom

This paper proposes a framework of digital twin-based integration of train regulation and control for metro systems. We study the integration of train regulation and train control by using deep learning and hybrid search methods. Specifically, a well-trained convolutional neural network is adopted to fit the nonlinear function mapping relationship between the input set (the line conditions and running time of the train in each section) and the output set (the energy consumption of the optimal speed profile and the switching points), which greatly reduces the calculation time of the optimal recommended speed profile in the process of train regulation. An integrated optimization model is proposed with the aim to minimize the total train delay and energy consumption, a hybrid search algorithm based on particle swarm optimization and a genetic algorithm was developed to solve the problem. The effectiveness of the proposed method is verified based on the actual operating data of a Beijing subway line.

关键词： Energy consumption Neural networks Switches Search problems Regulation Delays Particle swarm optimization

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Evaluation and Application of Intelligent Algorithms for Image-based Target Detection

Evaluation and Application of Intelligent Algorithms for Ima...

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第32届中国过程控制会议(CPCC2021)

作者： Xiying Song Haifeng Song Hairong Dong Xianshu Gao State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Department of Radiation Therapy Peking University First Hospital

Computer vision takes the simulated biological vision as the core,trying to establish an artificial intelligence system that can obtain information from images or multidimensional data and perceive the *** an essential part of this field,image detection has always been a research *** paper briefly introduces the research ideas and optimization directions of template matching,support vector machine(SVM),and convolutional neural network(CNN) from the perspective of image *** the characteristics,application scenarios,and possible future research directions of these three types of algorithms are compared and analyzed.

关键词： Image Detection SIFT Support Vector Machine Convolutional Neural Network

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Adaptive Cruise control of Virtual Coupled High-Speed Trains

Adaptive Cruise Control of Virtual Coupled High-Speed Trains

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Chinese automation Congress (CAC)

作者： Di Wang Yuan Cao Shuai Su State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Beijing China School of Electric and Information Engineering Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-Speed Railway System State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665426480

The adaptive cruise formation control problem for multiple high-speed trains (MHSTs) with input, speed and minimum tracking distance constraints under virtual coupling (VC) is proposed in this paper. First, the dynamic equation of MHSTs is created based on the law of kinematics. The communication topology is modeled by graph theory. Next, the speed limit, minimum tracking distance with adjacent trains are dealt with by using the ideas of the potential function. The resistance and unknown disturbances are dealt with by using adaptive control. Then, a cruise control protocol for MHSTs to track the expected trajectory is designed. The Lyapunov theory is used to prove the system stability. Finally, numerical experiments are conducted, which illustrates that the control protocol is effective.

关键词： Adaptation models Protocols Mathematical models Stability analysis Graph theory Trajectory Numerical models

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Train Delay Prediction based on a Multimodal Deep-learning Method

Train Delay Prediction based on a Multimodal Deep-learning M...

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Chinese automation Congress (CAC)

作者： Shuai Chen Xingtang Wu Min Zhou Bo Yang Jinhu Lü Hairong Dong The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China China Railway Beijing Bureau Group Co. Ltd. Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9781665426480

High-speed rail (HSR) trains may suffer delays under emergencies, which will disturb the normal operation of trains and reduce passengers’ travel satisfaction. Accurately predicting the delay time can help the dispatchers make advanced rescheduling strategies and thus improve train operation efficiency. Therefore, to quickly and effectively predict the delay time of trains for the HSR network under emergencies, a delay prediction model based on a multi-modal deep learning framework is proposed. The model considers the temporal and spatial characteristics and time-series characteristics of train operation, and then the Transformer and the three-dimensional convolutional neural network (3D CNN) are adopted to independently extract and merge these features. Specifically, the Transformer with the attention mechanism solves the interference mechanism among trains and stations on the delay of the train of interest. At the same time, 3D CNN is used to extract the temporal and spatial data characteristics of trains. Finally, the real operation data of the Beijing railway Administration HSR network is adopted to verify the effectiveness of the proposed model. Results show that compared with the results obtained by other benchmark models, the predicted delays by the proposed model can lower the average absolute error (MAE), and root mean square error (RMSE) by 10% and 12%, specifically.

关键词： Three-dimensional displays Predictive models Benchmark testing Feature extraction Transformers rail transportation Data models

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A digital-twin-assisted fault diagnosis of railway point machine

A digital-twin-assisted fault diagnosis of railway point mac...

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

作者： Shiyao Zhang Hairong Dong Ulrich Maschek Haifeng Song “Friedrich List” Faculty of Transport and Traffic Sciences Technische Universität Dresden Dresden Germany State Key Laboratory of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China School of Electric and Information Engineering Beijing Jiaotong University Beijing China

railway point is a critical movable track element in railway infrastructure, its failures mostly cause risks of derailment and collision. railway point machine is used for the switching, locking, and supervising railway point, its maintenance is crucial to guarantee the availability of railway point. With the wide deployment of sensors and the rapid development of digital technology, the pure detection methods cannot meet the current demand for the fault detection and diagnosis of railway point machines. As a way to build a closed loop between the physical object and the virtual model, the digital twin can realize the identification of possible root causes of malfunctions. In this paper, we proposed an extended framework for digital-twin-assisted fault diagnosis of RPM, which supports the realization of the accurate and real-time monitoring and diagnosis of the fault of the railway point machine. This proposed framework is developed based on the three-dimension and five-dimension structure of the digital twin model. In the case study, the predefined failures and a method based on the electric current value and shape for digital-twin-assisted fault diagnosis of RPM are presented.

关键词： Fault diagnosis Shape Digital twin railway accidents Switches rail transportation Real-time systems

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Iteratively Weighted MMSE Uplink Precoding for Cell-Free Massive MIMO

arXiv

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arXiv 2022年

作者： Wang, Zhe Zhang, Jiayi Ngo, Hien Quoc Ai, Bo Debbah, Mérouane School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Queen's University Belfast United Kingdom State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Mohamed Ben Zayed University on Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates

In this paper, we investigate a cell-free massive MIMO system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) based on a two-layer decoding structure with maximum ratio (MR) or local minimum mean-square error (MMSE) combining applied in the first layer and optimal large-scale fading decoding method implemented in the second layer, respectively. To maximize the weighted sum SE, an uplink precoding structure based on an Iteratively Weighted sum-MMSE (I-WMMSE) algorithm using only channel statistics is proposed. Furthermore, with MR combining applied in the first layer, we derive novel achievable SE expressions and optimal precoding structures in closed-form. Numerical results validate our proposed results and show that the I-WMMSE precoding can achieve excellent sum SE performance. Copyright © 2022, The Authors. All rights reserved.

关键词： MIMO systems

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Parallel intelligence Method for Timetable Rescheduling of High-speed railways under Disturbances

Parallel Intelligence Method for Timetable Rescheduling of H...

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

作者： Xuan Liu Min Zhou Haifeng Song Shigen Gao Chenkun Jin Hairong Dong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Institute of Computing Technology Academy of Railway Sciences Beijing China

This paper focuses on solving the real-time timetable rescheduling problem for dispatching systems of high-speed railway systems (HSRs) in case of temporary speed restriction by utilizing the parallel intelligence method. We construct a parallel intelligent system framework, which combines the actual and artificial dispatching systems through computational experiments and parallel execution. Then a rescheduling model includes retiming, recording and rerouting, and a solution method based on rolling horizon optimization are both proposed. Finally, a timetable of Beijing-Shanghai HSR within the jurisdiction of the Beijing railway Bureau based case study indicates that the proposed parallel intelligence method can effectively decrease the total delay.

关键词： Digital twin Conferences rail transportation Dispatching Real-time systems Data models Delays

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