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第42届中国控制会议

作者： Zixuan Zhang Haifeng Song Weiqi Bai Hongwei Wang Hairong Dong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Electronic Information Engineering Beihang University School of Automation Science and Electrical Engineering Beihang University

This paper presents a distributed model predictive control of multiple trains,aiming at overcoming coupled constraints and achieving cooperative operation while reducing the computational *** to the coupled constraints associated with maintaining safety distance between adjacent trains,trains are divided into sub-systems and distributed optimal control problem is then *** solve the problem,an alternating direction method of multipliers(ADMM) is employed with a fully distributed scheme,decomposing the original optimal control problem into smaller sub-problems that can be computed in parallel and meet the real-time control *** proposed method effectively guarantees the cooperative operation of trains under local and coupled safety *** simulations are provided to demonstrate the feasibility and effectiveness of the proposed train control model and methods.

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Optimization of Heavy Haul Train Speed Profiles Under Discrete Notches and Nonlinear Braking Forces 27

Optimization of Heavy Haul Train Speed Profiles Under Discre...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zhang, Zhiyuan Wang, Hongwei Wang, Xi Jin, Jun Ying, Zhipeng Ning, Pengfei School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China China State Railway Group Co. Ltd. Beijing100848 China Signals and Communication Research Institute China Academy of Railway Sciences Beijing100081 China Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798331505929

Due to the large mass of heavy haul trains, operating on downhill sections poses significant challenges on train control. Drivers must perform air braking and releasing at the appropriate times to prevent the movement of heavy hual train from decoupling and derailment. To promote the smooth operation of the heavy haul train, this research investigates the optimization problem of heavy haul train speed profiles under discrete notches and nolinear braking forces. An optimization model is first established considering constraints such as speed and air-refilling time to maximize the running distance and minimize the air braking time of a train on downhill sections. This model takes into account discrete notches of electric braking force, each with a nonlinear braking profile. To linearize the model, piecewise approximation (PWA) function and auxiliary variables are introduced, transforming the original problem into a mixed integer linear programming (MILP) problem. The proposed MILP problem is then solved using a solver. Simulations using actual data from the Shuohuang railway illustrate the effectiveness of the proposed methods. © 2024 IEEE.

关键词： Integer programming

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A Seamless Train Positioning Method Based on Visual Place Recognition

A Seamless Train Positioning Method Based on Visual Place Re...

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International Conference on Intelligent Transportation

作者： Yongqiang Liu Wei Jiang Xiao Hu Jian Wang Baigen Cai School of Automation and Intelligence Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transport Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety and the Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation School of Automation and Intelligence Beijing Jiaotong University Beijing China Lower Airspace Economy Research Institute International Digital Economy Academy ShenZhen China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Accurate and reliable train positioning stands as a cornerstone in railway train control system applications. In this paper, a seamless train positioning method based on visual position identification (VPR) is proposed. Our method uses on-board vision sensors to continuously capture images of the railway environment. Then, these images are processed by using the NetVLAD model to extract visual features. Subsequently, Euclidean distance is employed to conduct similarity retrieval on these features against pre-constructed railway image maps, facilitating precise positioning along the train line. This method has several obvious advantages. First, independence from external infrastructure: Unlike traditional methods reliant on trackside infrastructure such as physical transponders, our approach achieves precise positioning akin to a transponder without external dependencies. Second, decoupled position processes: Each image retrieval process operates independently, devoid of strong coupling relationships between positioning at adjacent frames. Third, robust adaptability to diverse environmental conditions: Experiments show that train location can be achieved in both open environment and tunnel scenes, and the coverage rate of effective train location reaches 98.2%, which proves the potential of this method in train location.

关键词： Training Visualization Accuracy Soft sensors Image retrieval Vision sensors Feature extraction rail transportation Transponders Visual place recognition

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A Space-Time Interval Based Protection Method for Virtual Coupling

A Space-Time Interval Based Protection Method for Virtual Co...

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2022 Chinese automation Congress, CAC 2022

作者： Wang, Jiayu Liu, Hongjie Tang, Tao Luo, Xiaolin Chai, Ming Beijing Jiaotong University School of Electronics and Information Engineering Beijing China Beijing Jiaotong University National Engineering Research Centerof Rail Transportation Operation and Control System Beijing China State Key Laboratory of Rail Traffic Control Safety Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665465335

As a recent hot topic in railways, Virtual Coupling (VC) is aimed at providing more frequent rail services by operating trains closer. Given that current protection methods cannot fulfill the requirements of VC in terms of safety and efficiency, the implementation of VC urgently calls for an advanced protection method that allows successive trains to drive closer on the premise of safety. Therefore, to improve efficiency, after the force analysis of the train, a train emergency braking model that can describe the real process of emergency braking is proposed. The train emergency braking distance calculated from this model is smaller compared with the traditional train emergency braking model. Then, to ensure train safety, the deficiencies of the existing relative braking distance(RBD) based protection methods are analyzed. After adding constraints of time and space to the train emergency braking process and using the train emergency braking model proposed in this paper, a space-time interval based protection method is proposed. Compared with the RBD protection method, this method can ensure that the trains can track closer under the premise of safety, which can fulfill the requirements of VC. Finally, experiments are conducted to verify the safety and efficiency of the proposed methods. When the braking performance of the preceding train is worse than that of the following train, the space-time interval based protection method avoids the train colliding during the braking process, while RBD based protection method cannot avoid train collision. Experimental results numerically show that the minimum safe distance can be reduced by 29%, compared with the RBD based protection method. © 2022 IEEE.

关键词： Efficiency

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Research on the Algorithm of Cross-region Target Tracking in the Perimeter of High Speed railway Based on Deep Learning 1

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5th International Conference on Electrical Engineering and Information Technologies for rail Transportation, EITRT 2021

作者： Guo, Ting Jia, Limin Xie, Zhengyu Qin, Yong Lin, Feng School of Traffic and Transportation Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Key Lab of Proactive Safety and Risk Control in Railway Operation Beijing China National Railway Test Center China Academy of Railway Sciences Co. Ltd Beijing China

ISBN: (数字)9789811699092

ISBN: (纸本)9789811699085

With the continuous development of science and technology, more and more fields are applying emerging video surveillance technology to ensure public safety. Compared with the way of manually processing video, the video monitoring technology based on deep learning also has the advantages of higher efficiency, high accuracy and intelligence when processing more and more massive video image data. In this paper, aiming at the detection requirements of personnel intrusion in the perimeter of high-speed railways, it realizes cross-region tracking of suspicious persons. It studies the application of deep learning cross-region tracking algorithm based on ResNet-50 in high-speed railway perimeter intrusion detection, and analyzes its reliability and optimizes accuracy. The degree reached 88%. Compared with the results of other cross-region tracking algorithms, the detection accuracy is still high. Therefore, it can be seen that the high-speed rail perimeter cross-region target tracking based on deep learning is feasible and efficient. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Target tracking

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Dynamic Passenger Demands Oriented Optimization of Virtually Coupled Train Formation Scheduling

Dynamic Passenger Demands Oriented Optimization of Virtually...

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2022 Chinese automation Congress, CAC 2022

作者： Zhang, Haobei Tang, Tao Liu, Hongjie Lang, Yinghui Beijing Jiaotong University School of Electronics and Information Engineering Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing China

ISBN: (纸本)9781665465335

The research on virtual coupling technology in metros is a hot topic recently. Intelligent automatic train units can flexibly form virtually coupled train formations (VCTFs) when needed, so that the train capacity would be able to adapt to passenger demands, which are changing dynamically with time and space. However, matching the capacity of trains with passenger demands is a difficult decision-making optimization problem. In this work, we propose a collaborative control problem to decide when and how to create a VCTF to meet the dynamic passenger demands in a metro system. A mathematical model is formulated which can simultaneously optimize the number of related train units in a VCTF, the train dwelling time at a station and the running time in a section. Performance indicators such as train load factor and number of stranded passengers are adopted to measure the satisfaction of the operators and passengers. Then, a multi-objective genetic algorithm is adopted to obtain the Pareto optimum solutions. Finally, simulation experiments are conducted, and the relationship between line efficiency and passenger demand in different discrete time periods is analyzed to verify the correctness and feasibility of the model. Compared with the traditional timetabling method, adopting the virtual coupling technology can better adapt the train capacity to meet the time changing passenger demands, which is welcomed by both metro operators and passengers in reality. © 2022 IEEE.

关键词： Genetic algorithms

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Reinforcement Learning for Virtually-Coupled Trains Using Q-Learning with Continuous Actions 42

Reinforcement Learning for Virtually-Coupled Trains Using Q-...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Zhengda Gao, Shigen Zhang, Hang Dong, Hairong Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China Planning and Standard Research Institute National Railway Administration of the People's Republic of China Beijing100055 China School of Civil Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789887581543

As an unsupervised and value-based reinforcement learning (RL) algorithm, Q-learning is an effective tool to determine off-policy strategies for corresponding agents which mimic the behaviors of physical plants. In order to further improve the rail-line efficiency, virtual coupling mode has been recognized as an effective development direction, receiving wide concerns from both theoretical research and practical applications. In this paper, a QCA-learning algorithm control strategy with continuous actions (CA), aliased as QCA-learning, is proposed for virtually-coupled trains, to maintain steady tracking distance, by discretizing the train operation state and adjusting the action change rate, speed error and distance as the reward function. Therein, an ϵ-greedy strategy, with a greedy function decreasing with the number of learning, is designed to reduce the invalid exploration of the algorithm and improve the learning efficiency. Finally, simulation results by collected data from G1# train, running from Beijingnan to Jinanxi railway stations, are presented to demonstrate that the QCA-learning algorithm performs better speed and distance convergences in comparison to traditional strategy and guarantee satisfactory performance regarding to steady desired tracking distance among virtually-coupled trains. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Reinforcement learning

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railway track circuit fault diagnosis using 1DCNN considering locality dependencies and multiscale feature fusion 41

Railway track circuit fault diagnosis using 1DCNN considerin...

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第41届中国控制会议

作者： Yulong Xing Jian Wang Cong Peng Linfu Zhu School of Electronic and Information Engineering Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China Academy of Railway Sciences Corporation Limited Standards & Metrology Research Institute China Railway Test & Certification Center

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

railway track circuit plays a vital part in railway signal *** detection and identification of faults in track circuits are crucial for the safety of rail *** this paper,a novel framework based on one-dimensional convolutional neural network with multiscale feature fusion is presented for detecting the faults of track circuits via condition monitoring *** dependencies among the monitoring variables are innovatively noticed and *** layers in onedimensional convolutional neural network are used to learn these ***,serial and dense—three multiscale feature fusion methods are proposed to further enhance the performance of feature learning and *** feature fusion captures more rich diagnosis information at different *** experimental results and the comprehensive comparison analysis with respect to the traditional classifiers have demonstrated the superiority of the proposed *** dense feature fusion produces the most robust and reliable promotion on fault diagnosis performance among the three proposed multiscale feature fusion methods.

关键词： Track Circuit Fault Diagnosis Convolutional Neural Networks Multiscale Representation

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Adaptive cropping shallow attention network for defect detection of bridge girder steel using unmanned aerial vehicle images

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2023年第3期24卷 243-256页

作者： Zonghan MU Yong QIN Chongchong YU Yunpeng WU Zhipeng WANG Huaizhi YANG Yonghui HUANG State Key Lab of Rail Traffic Control&Safety Beijing Jiaotong UniversityBeijing 100091China School of Traffic and Transportation Beijing Jiaotong UniversityBeijing 100091China School of Artificial Intelligence Beijing Technology and Business UniversityBeijing 100048China School of Safety Engineering and Emergency Management Shijiazhuang Tiedao UniversityShijiazhuang 050043China Beijing-Shanghai High Speed Railway Co. Ltd.Beijing 100038China

Bridges are an important part of railway infrastructure and need regular inspection and *** unmanned aerial vehicle(UAV)technology to inspect railway infrastructure is an active research ***,due to the large size of UAV images,flight distance,and height changes,the object scale changes *** the same time,the elements of interest in railway bridges,such as bolts and corrosion,are small and dense objects,and the sample data set is seriously unbalanced,posing great challenges to the accurate detection of *** this paper,an adaptive cropping shallow attention network(ACSANet)is proposed,which includes an adaptive cropping strategy for large UAV images and a shallow attention network for small object detection in limited *** enhance the accuracy and generalization of the model,the shallow attention network model integrates a coordinate attention(CA)mechanism module and an alpha intersection over union(α-IOU)loss function,and then carries out defect detection on the bolts,steel surfaces,and railings of railway *** test results show that the ACSANet model outperforms the YOLOv5s model using adaptive cropping strategy in terms of the total mAP(an evaluation index)and missing bolt mAP by 5%and 30%,***,compared with the YOLOv5s model that adopts the common cropping strategy,the total mAP and missing bolt mAP are improved by 10%and 60%,*** with the YOLOv5s model without any cropping strategy,the total mAP and missing bolt mAP are improved by 40%and 67%,respectively.

关键词： railway Bridge Unmanned aerial vehicle(UAV)image Small object detection Defect detection

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AI Assisted PHY in Future Wireless Systems: Recent Developments and Challenges

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China Communications 2021年第5期18卷 285-297页

作者： Wei Chen Ruisi He Gongpu Wang Jiayi Zhang Fanggang Wang Ke Xiong Bo Ai Zhangdui Zhong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

Nowadays,the rapid development of artificial intelligence(AI)provides a fresh perspective in designing future wireless communication *** attempts exploiting AI methods have been carried out,which results in the state-of-the-art performance in many different areas of wireless *** this article,we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer(PHY)components and applications including channel characterization,channel coding,intelligent signal identification,channel estimation,new PHY for random access in massive machine-type communication(mMTC),massive multiple-input multiple-output(MIMO)power control and PHY resource *** challenges and potential future directions are identified and discussed along this research line.

关键词： artificial intelligence wireless communications physical layer

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