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A Security Defense Method Against Eavesdroppers in the Communication-Based Train control System

Chinese Journal of Electronics 2023年第5期32卷 992-1001页

作者： YANG Li WEI Xiukun WEN Chenglin School of Cyberspace College Hangzhou Dianzi University State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Automation Guangdong University of Petrochemical Technology

The communication-based train control system is the safety guarantee for automatic train *** communication brings network security risks to the communication-based train control system. The eavesdropping of the transmitted information by unauthorized third-party personnel will lead to the leakage of the estimated value of the system state, which will lead to major accidents. This paper focuses on solving the problem of defense against eavesdropping threats and proposes an eavesdropping defense architecture. This defense architecture includes a coding mechanism based on punishing eavesdroppers, an information upload trigger mechanism based on contribution, and a random information transmission strategy, and provides a guarantee for the privacy protection of information. This research makes three contributions. First, it is the first attempt to construct an information encoding mechanism with punishing eavesdroppers as the objective function; Second, for the first time, an information upload trigger mechanism based on contribution is proposed; Third, the strategy of random transmission of information is proposed. The proposed method in this paper is verified by taking the medium and low-speed maglev train as the object. The experimental results show that, compared with Gaussian noise and non-Gaussian noise mechanisms, the coding mechanism proposed in this paper can not only protect the security of information but also make the estimation error of eavesdroppers tend to be infinite. Using the state estimation error as a metric, the average growth rate of the state estimation error of the system using the trigger mechanism in this paper is less than 2% while improving the security of the system. The transmission strategy in this paper does not increase the system state estimation error while improving the security of the system.

关键词： Wireless communication Magnetic levitation vehicles Transmitters Information processing control systems Encoding safety

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Research on Robust Passenger Flow control Model of Metro Under a Short-Turning Operation Pattern

Research on Robust Passenger Flow Control Model of Metro Und...

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International Conference on Artificial intelligence and Autonomous Transportation, AIAT 2024

作者： Xue, Hongjiao Jia, Limin Peng, Fei Sun, Xuan Wang, Hongyan Li, Ying School of Automotive Engineering Beijing Polytechnic Beijing100176 China Beijing Automation Engineering School Beijing100101 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819639564

Passenger flow control is the most direct and effective way to solve the problem of metro line congestion. To solve the problem that the random arrival characteristics of passenger flow affect the reliability of the passenger flow control strategy, this paper provides a robust passenger flow control strategy for a metro line under a short-turning operation pattern in peak hours according to the dynamic, time-varying and random characteristics of passenger flow. Firstly, a short-turning operation pattern environment is constructed, and the arrival rate of historical inbound passenger flow is obtained by statistical analysis. Poisson process is used as a parameter to generate multi-scene random arrival passenger flow data. Secondly, a robust passenger flow control and ride strategy is proposed considering the passenger flow OD of the stations inside and outside the short-turning sections. Combining with the strategy, a robust passenger flow collaborative control model is constructed with multi-scene random arrival passenger flow data as input. Finally, an empirical study was carried out on the Beijing Fangshan metro line. The results show that the robust passenger flow control strategy is applicable to the three passenger flow scenarios, and can ensure the passengers’ safety in the three passenger flow scenarios. Moreover, compared with the separate passenger flow control in a single passenger flow scenario, the passenger waiting time under the robust passenger flow control strategy only increases by 1.19%, 4.47% and 4.42% respectively, which basically ensures the travel efficiency of passengers. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Urban transportation

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Artificial intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

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China Communications 2024年第3期21卷 260-272页

作者： Zhang Mengjiao Liu Yu Huang Jie He Ruisi Zhang Jingfan Yu Chongyang Wang Chengxiang School of Microelectronics Shandong UniversityJinan 250101China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China The National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast UniversityNanjing 210096China The Purple Mountain Laboratories Nanjing 211111China

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big *** corresponding artificial intelligence(AI)based HST channel modeling becomes a *** paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST ***,the ray tracing method verified by measurement data is applied to reconstruct four representative HST *** setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is ***,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario ***,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

关键词： artificial intelligence channel characteristic prediction HST channel millimeter wave scenario classification

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An Evaluation Method for Transportation Equipment Testing Indicators Based on Information Sensitivity

An Evaluation Method for Transportation Equipment Testing In...

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International Conference on Artificial intelligence and Autonomous Transportation, AIAT 2024

作者： Mao, Junyan Wu, Jianfan Xie, Zhengyu School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China

ISBN: (纸本)9789819639687

In order to evaluate the testing indicators of transportation equipment better, this paper proposes an evaluation method for transportation equipment testing indicators addressing advancement, economy, technical maturity, and foresight, which incorporates 20 primary and 74 secondary indicators, providing a comprehensive framework for assessing testing methods. To achieve scientific and reasonable optimization, an improved information sensitivity analysis model was proposed to optimize the indicators, ensuring the retention of the most representative 32 indicators while reducing informational redundancy. This approach enhances the accuracy and reliability of the evaluation, surpassing traditional methods like principal component analysis. The findings emphasize the critical factors influencing testing methods, including test point representativeness, environmental stability, cost-effectiveness, and adaptability. The proposed method not only supports current testing improvements but also offers significant scalability and practical guidance, contributing to the technological advancement and standardization in railway equipment testing. This research provides valuable insights into optimizing evaluation frameworks for complex testing requirements in the railway industry. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Sensitivity analysis

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A Seamless Train Positioning Method Based on Visual Place Recognition 27

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

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

作者： Liu, Yongqiang Jiang, Wei Hu, Xiao Wang, Jian Cai, Baigen School of Automation and Intelligence Beijing Engineering Research Center of Emc Gnss Technology for Rail Transport Beijing Jiaotong University Beijing100044 China School of Automation and Intelligence Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety The Beijing Engineering Research Center of Emc Gnss Technology for Rail Transportation Beijing100044 China Lower Airspace Economy Research Institute International Digital Economy Academy ShenZhen5100085 China

ISBN: (纸本)9798331505929

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. © 2024 IEEE.

关键词： railroads

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Few-Shot Object Detection for High-Speed rail Infrastructure Defects

Few-Shot Object Detection for High-Speed Rail Infrastructure...

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2023 China automation Congress, CAC 2023

作者： Hong, Zhaorui Xiao, Hongbing Li, Shiyun Qin, Yong Yu, Chongchong School of Artificial Intelligence Beijing Technology and Business University Beijing China Beijing Jiaotong University Institute State Key Lab of Rail Traffic Control and Safety Beijing China

ISBN: (纸本)9798350303759

Ensuring the safety of high-speed rail infrastructure relies on effective defect detection. With the advancements in artificial intelligence, deep learning-based detection methods have been increasingly applied for daily inspections of high-speed rail infrastructure. However, detecting defects in high-speed rail infrastructure poses challenges due to limited samples, small targets, and difficulty in detection. To address these issues, this article proposes a few-shot object detection method based on a multi-scale attention mechanism, aiming to enhance the performance of defect detection in high-speed rail infrastructure. While most existing few-shot object detection methods employ two-stage models that require training on a large number of base class samples followed by fine-tuning on a small number of novel samples, we introduce gradient transmission separately for the gradient decoupled layer in Decoupled Faster R-CNN to better detect novel classes. Additionally, we incorporate the feature pyramid structure and Coordinate Attention into the original network architecture to enhance the detection of HSR infrastructure defects across different scales and improve the model's sensitivity to channel and location information. Experimental evaluations using UAV-captured image datasets, with normal class representing the base class and defects as the novel class, demonstrate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Object detection

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Digital Twin based Train Delay Prediction System:Design and Realization 41

Digital Twin based Train Delay Prediction System:Design and ...

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第41届中国控制会议

作者： Shuai Chen Xingtang Wu Min Zhou Haifeng Song Hairong Dong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Automation Science and Electrical Engineering Beihang University School of Electronic Information Engineering Beihang University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper proposes a digital twin-based train delay prediction system for a high-speed railway network to realize real-time train delay ***,a deep learning model is proposed for delay prediction,whose input is the current state of the system,while output,is the predicted *** a system following the digital twin perspective is designed to observe the train operation state and transmit the state information to the delay prediction unit to fulfill the delay prediction *** architecture of the digital twin system is designed,and the actual infrastructure data and *** data of the Beijing railway Bureau are adopted to construct a prototype *** function of the system is verified with numerous experiments.

关键词： High-speed railway train delay prediction digital twin

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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

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IEEE Transactions on Intelligent Transportation Systems 2024年第5期25卷 4552-4564页

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng Wuhan University of Technology Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan430063 China State Key Laboratory of Maritime Technology and Safety Wuhan430063 China National Taiwan University of Science and Technology Graduate Institute of Automation and Control and the Advanced Manufacturing Research Center Taipei10617 Taiwan Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety. © 2000-2011 IEEE.

关键词： Real time systems

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Optimization of Car Flow Management within the Collection System of Heavy-Haul railway System 27

Optimization of Car Flow Management within the Collection Sy...

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

作者： Qiu, Zhian Wang, Hongwei Yang, Xin Ning, Pengfei Li, Yang Wang, Xi School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China

ISBN: (纸本)9798331505929

Heavy-haul railways play a crucial role in transporting large volumes of bulk cargo, particularly coal. The optimization of car flow management within the collection system is pivotal, given that substantial time is spent by both loaded and empty trains in marshaling stations. This optimization can significantly enhance the transport efficiency of heavy-haul railways. In this paper, we propose a mixed-integer linear programming model to minimize the total time spent by loaded trains at marshaling stations and the time taken by empty trains to load and return to the marshaling station. To enhance the model's computational efficiency, we designed two valid inequalities (VI). To evaluate the effectiveness of our proposed model, we construct test cases using real-world data from the Daqin railway and the Hudong marshaling station and various numerical experiments are constructed. The computational analysis reveals that our model can reduce the objective function value by up to 4.7% of X4a2 test case and an average of 2.7% in all test case compared to traditional empirical methods. Furthermore, the VI results in a maximum reduction of 1400 seconds in computation time, showcasing the practical benefits of our approach. These findings highlight the potential of our methodology to significantly reduce the dwell time of heavy-haul trains at collection system, and than improve the operational efficiency of heavy-haul railways. © 2024 IEEE.

关键词： Integer programming

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Fatigue Detection Based on Multiple Facial Features 42

Fatigue Detection Based on Multiple Facial Features

引用

42nd Chinese control Conference, CCC 2023

作者： Luo, Shan Song, Qi School of Electronic and Information Engineering Beijing Jiaotong University State Key Lab of Rail Traffic Control & Safety Beijing100044 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100044 China

ISBN: (纸本)9789887581543

There are certain safety risks when people are in the fatigue state in the operating environment such as driving and scheduling. To avoid risk, fatigue detection is necessary. However, due to individual differences, each person has different fatigue features. Even if the same fatigue feature, the individuals differ, the performance level of fatigue feature is also different. In view of these differences, this paper proposes a fatigue detection method based on multi-feature fusion, in which the adaptive threshold model is designed and the fatigue features are normalized to solve the problem of reduced accuracy caused by individual differences, which enables a more comprehensive assessment of individual fatigue status. The EAR (the eye aspect ratio) and the MAR (the mouth aspect ratio) waveforms are calculated based on facial landmarks, the corresponding adaptive threshold models are designed for obtaining facial fatigue features such as opening speed of eyes, blink frequency, yawning, etc. The features of facial expression are calculated by facial landmarks such as MCCD (the sagging degree of the corners of mouth) and ERD (the brow and eye distance ratio). The Xgboost machine learning model and RLDD dataset are used to implement fatigue detection. The developed method does not require the wearing of invasive devices, and the experimental results show that the method can accurately detect the fatigue state with higher accuracy and strong robustness. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Aspect ratio

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