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检索条件"机构=Traffic Control System Engineering"

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Blockchain Based Train-ground Communication of CBTC system

Blockchain Based Train-ground Communication of CBTC System

引用

2021 International Conference on Intelligent Computing, Automation and Applications, ICAA 2021

作者： Zhao, Hongli Zhu, Li Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing100044 China Beijing JiaotongUniversity State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China

ISBN: (纸本)9781665437301

In existing Communication Based Train control (CBTC) system, there are many information security threats especially in train-ground communication, so information security protection methods of CBTC system are designed. For reasons of increasing information security, blockchain technology is used to train-ground communication of CBTC system. Based on blockchain technology, CBTC system information security testing environment is set up. Testing results demonstrate that the train-ground communication information security of CBTC system based on blockchain overcomes the single-point failure of centralization key management, and doesn't influence the real time of CBTC system. © 2021 IEEE.

关键词： Blockchain

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An Optimal control Based Trajectory Planning Method for Cooperative Merging Maneuvers

An Optimal Control Based Trajectory Planning Method for Coop...

引用

Intelligent Robotics and systems (ISoIRS), International Symposium on

作者： Qi Wang Hao Ge Xuting Duan Jianshan Zhou State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control State Key Laboratory of Air Traffic Management System and Technology School of Transportation Science and Engineering Beihang University Beijing China State Key Laboratory of Air Traffic Management System and Technology Nanjing Research Institute of Electronic Engineering Nanjing China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China

Due to the prediction errors and diverse driving styles of surrounding vehicles, traffic congestion and even accidents can occur in the traffic bottleneck area where vehicles merge into the main road. To ensure that connected and automated vehicles (CAVs) can quickly and efficiently merge into the main road, this paper proposes a cooperative trajectory planning method based on optimal control for vehicle cut-in scenarios. A optimal control model is constructed, and a quintic polynomial is used to generate the initial solution of the optimal control problem to accelerate the solution process and improve the success rate. Finally, The obtained merging trajectory is then synchronized with the movement of vehicles on the main road to optimize their actions and ensure a safe merging process. Joint simulation with Robot Operating system (ROS) and RVIZ is utilized, and the results show that the proposed algorithm can quickly obtain cooperative results, the locally successful merging trajectory meets the requirements of smoothness and safety, and if the solution does not allow merging, the merging vehicle is instructed to terminate the execution.

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An Extension of Correlated Kurtosis to Tunable Q-Factor Wavelet Transform in Fault Diagnosis of Rolling Element Bearings

An Extension of Correlated Kurtosis to Tunable Q-Factor Wave...

引用

2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2021

作者： Zhao, Jing Li, Qiang Yang, Shaopu Liu, Yongqiang School of Mechanical Electronic and control Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang China

ISBN: (纸本)9781665449762

In engineering practice, the type of rolling bearing failures are typical, and the fault signals are impulsive, which are influenced by the running status, the frequency component of the vibration signals become extremely complicated. Tunable Q-Factor wavelet transform (TQWT) method based on correlation kurtosis is proposed. This method decomposes the bearing fault signal into two different parts, high and low resonance components (HRC and LRC), basing on the different characteristics of signal oscillation. Then, calculating the correlation kurtosis of each decomposition layer of LRC to achieve signal reconstruction. Envelope demodulation analysis is performed on the reconstructed LRC . This method replaces the kurtosis index used in previous studies with correlated kurtosis index, which can effectively eliminate or weaken the influence of peak value interference. Finally, the simulation signals and wheelset bearing test signals are validated. The results show that this method can extract the features of bearing outer, inner and roller under strong background noise. © 2021 IEEE.

关键词： Wavelet transforms

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Neural Network and Collaborative Computing for Image-based Train Location Data Acquisition 5th

Neural Network and Collaborative Computing for Image-based...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Wu, Xiaoqing Song, Haifeng Wang, Peng Cheng, Jianfeng Zhou, Datian Dong, Hairong Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Beihang University School of Electronic and Information Engineering Beijing100191 China China Academy of Railway Sciences Group Co. Ltd. Beijing100044 China Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789811939976

How to improve the performance and safety of the train has long been a question of great interest. The object of this study is to achieve the recognition of train speed and mileage that are provided by the train Driver Machine Interface (DMI) and reduce the time consumed by model training. For recognition part, a Convolutional Neural Network (CNN) and a Connectionist Temporal Classification (CTC) layer have been combined together to achieve an end-to-end recognition. For the part of time-consuming reduction, a collaborative computing was being utilized to offload the training tasks to each edge device. In addition, the process of offloading was modeled with Stochastic Petri Net (SPN). Overall, the recognition accuracy was significantly improved, compared to the traditional Optical Character Recognition (OCR) and CNNs, what is more, the time consumed on model training was also brought down as expect. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Petri nets

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Federated Learning With Dynamic Staleness Correction for Privacy Protection in Vehicular Networks 23

Federated Learning With Dynamic Staleness Correction for Pri...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Jiajia Wu, Hao Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing China

ISBN: (纸本)9781665494571

Edge intelligence combines mobile edge computing(MEC) with artificial intelligence(AI), which has great potential for improving the security and efficiency of data-driven intelligent transportation systems (ITS) and emerging Internet Of Vehicles (IOV) services. However, when big data and AI empower ITS, the information security issue in vehicular networks needs to be paid more attention. In order to protect the privacy and security of vehicular training data, we propose a privacy protection federated learning scheme with staleness asynchronous update to overcome the differences caused by vehicle heterogeneity. Moreover, unlike the traditional weighted average on the server side only by the number of samples, our solution introduces dynamic time weights according to the calculation and communication capabilities of different vehicles, to make full use of the previously trained local model. The proposed scheme is able to reduce traffic load of the network and improve learning performance while enhancing security and privacy. Experiment results demonstrate that in terms of communication cost and model accuracy, the performance of the proposed asynchronous federated learning is better than the benchmark algorithm, and it also achieves good performance under NIID data. © 2021 IEEE.

关键词： Privacy Data privacy Costs Smart cities Training data Telecommunication traffic Collaborative work

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Distributed Adaptive Cooperative control for Virtually Coupled Train Set with State Constraints and Input Saturation

Distributed Adaptive Cooperative Control for Virtually Coupl...

引用

International Conference on Intelligent Transportation

作者： Ping Dai Di Wang Shuai Su Xubiao Sheng Fuyu Qian Chunjun He State Key Laboratory of Rail Traffic Control and Safety and the Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing P. R. China Traffic Control Technology Co. Ltd Beijing China China Construction Third Bureau Digitalization Engineering CO. LTD

Virtual coupling (VC) is an emerging problem, which could control multiple trains as a virtually coupled train set (VCTS) via train automation and communication. This paper proposes a novel adaptive control method for the VCTS. Firstly, a VCTS dynamics model is constructed by analyzing the dynamic evolution of VCTS in a real-world environment, taking into account input saturation, uncertain parameters and unknown disturbances. Next, we construct a nonlinear state-dependent function (NSDF) to cope with the tracking interval and speed constraints. And a novel adaptive control protocol is designed, which can simultaneously deal with uncertain resistance parameters and unknown disturbances, while enabling the train to track the preceding train stably and efficiently. Based on Lyapunov stability theory, the strategy stability is analyzed. Simulation results are also used to confirm the feasibility and effectiveness of the proposed control method.

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Design of a Quadrotor Platform and Practice Teaching of Basic Theory

Design of a Quadrotor Platform and Practice Teaching of Basi...

引用

International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Qi, Duo Liang, Xiaon-long Zhang, Jia-qiang Zhang, Zhi-hao Li, Yong-li Lei, Peng-fei Aviation Swarm Technology and Operational Application Laboratory Air Traffic Control and Navigation College Air Force Engineering University Xi’an710051 China Shaanxi Province Laboratory of Meta-Synthesis for Electronic and Information System Xi’an710051 China College of Equipment Management and Support Armed Police Engineering University Xi’an710086 China

ISBN: (纸本)9789811581540

In order to improve the engineering practice ability of students and solve the problems of the lack of connection between theory and practice in the teaching of drones, a quadrotor teaching platform is developed. In the early stage of teaching process, the emphasis of the contents is on practical operations, such as assembly and flight, so that students could master the internal composition and flying principles of quadrotor. Especially through outdoor test flights, they can feel the fun of flying, and stimulate the enthusiasm for exploring the unknown world. Then the model and PID control theory of quadrotor are introduced in the second teaching stage, and the flight simulation experiment with MATLAB software is carried out. Practice results show that through this teaching method, the learning initiative of the students is greatly improved, and their ability to explain or solve practical problems with theoretical knowledge is significantly enhanced. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Research on Integrated Navigation Algorithm Based on Radial Basis Function Neural Network

Research on Integrated Navigation Algorithm Based on Radial ...

引用

2021 International Conference on Computer engineering and Innovative Application of VR, ICCEIA VR 2021

作者： Liu, Hang Li, Kaicheng Fu, Qiang Yuan, Lei The National Engineering Research Center for Rail Transportation Operation Control System Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

The Qinghai-Tibet Railway is located on the Qinghai-Tibet Plateau in China. Due to its harsh climatic conditions and geographic environment, the line currently uses the Global Position system (GPS) to achieve train positioning. However, the navigation satellites are easily blocked by obstructions such as tunnels, resulting in a decrease in positioning accuracy. The conventional solution is to use multiple sensors such as an inertial navigation system (INS) for data fusion. But in the INS/GPS integrated navigation system, when the GPS signal is lost, the navigation accuracy will still decrease. In order to solve this problem, this paper proposes a radial basis function (RBF) neural network-assisted integrated navigation filtering algorithm. When the two systems are working normally, the measured raw information is used to train the RBF neural network. When the GPS fails, the measured and calculated values of the inertial navigation system are input to the trained network model to obtain the predicted error value, which is corrected to the inertial navigation system to obtain the final navigation information. The MATLAB simulation results show that when the short-term GPS information is missing, the integrated navigation system assisted by the RBF neural network has better estimation accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Inertial navigation systems

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Application Research of CEEMDAN in Early Fault Diagnosis of Rolling Bearing

Application Research of CEEMDAN in Early Fault Diagnosis of ...

引用

2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2021

作者： Wang, Haiming Li, Qiang Yang, Shaopu Liu, Yongqiang School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China Beijing Jiaotong University Beijing China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang China

ISBN: (纸本)9781665449762

In order to solve the difficulty of extracting early fault features of rolling bearings under strong background noise, a new fault diagnosis method for rolling bearings based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Firstly, in order to enhance the impact characteristics of signals, a set of IMF components were obtained by CEEMDAN. Then kurtosis was used as the evaluation index to select the effective component to obtain recombined signal. Finally, the envelope feature of the recombined signal was analyzed to judge the fault location. The validity and superiority of the proposed method are verified compared with other methods through the experimental analysis of the rolling bearings. © 2021 IEEE.

关键词： Failure analysis

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TICNet: Three-Branch Real-Time Semantic Segmentation Network with Intensive Compensation of Railway Track Scenes

TICNet: Three-Branch Real-Time Semantic Segmentation Network...

引用

Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Yiwen Bai Lu Yang Lei Zhang Yajing Song School of Transportation Science and Engineering Beihang University Beijing China Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China Traffic Control Technology Co. Ltd Beijing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

With the rapid development of railway traffic system, real-time semantic segmentation plays a crucial role in railway track scene monitoring. However, most of the existing methods use lightweight convolution to reduce the computational complexity, which results in reduced accuracy. To solve this problem, this paper proposes a real-time semantic segmentation network called TICNet, which is specifically used for accurate segmentation of railway track scenes. TICNet uses a novel three-branch structure designed for efficient feature extraction and accurate segmentation. Results on the RailSem19 dataset show that TICNet has a segmentation accuracy of 62.36% mIoU with an inference speed of 81.2 FPS, which is significantly better than many existing methods.

关键词： Accuracy Convolution Semantic segmentation Semantics Focusing Machine learning Feature extraction Real-time systems Computational complexity Monitoring

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