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第43届中国控制会议

作者： Junyu Shi Zhaosen Chen Jiateng Yin Andrea D'Ariano Jun Xu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Department of Engineering Roma Tre University Nanchang Rail Transit Group Co. Ltd.

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

With the promotion of "double carbon" plan in China,the energy-saving problem of urban rail transit,as a major energy user of the government,has garnered significant *** urban rail train operations,the energy storage devices(ESDs)can temporarily store the regenerative energy from braking trains and feed it back to other accelerating ***,the ESDs comes with a high price tag and limited *** minimize the overall energy consumption of urban rail lines with limited ESDs,this paper addresses the collaborative optimization of ESD allocation and train speed profiles,in order to coordinate the usage of energy among ESDs and accelerating/braking trains,such that the overall energy consumption,i.e.,the consumed energy for train acceleration subtracting the used regenerated energy,can be ***,we construct a mixed-integer programming(MIP) model to collaboratively optimize the train running speed curve(with accelerating,coasting and braking) and ESD locations.A series of constraints are developed to limit the flow of energy among ESDs and the state transition of running ***,we verify the feasibility and effectiveness of our integrated approach using real-world data from Beijing Metro Yizhuang Line.

关键词： Subway system ESD energy-efficient train control regenerative energy

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Mechanochemical reactions of GaN-Al_(2)O_(3) interface at the nanoasperity contact:Roles of crystallographic polarity and ambient humidity

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Friction 2022年第7期10卷 1005-1018页

作者： Jian GUO Jian GAO Chen XIAO Lei CHEN Linmao QIAN School of Mechanical Engineering University of South ChinaHengyang 421001China Tribology Research Institute State Key Laboratory of Traction PowerSouthwest Jiaotong UniversityChengdu 610031China Advanced Research Center for Nanolithography(ARCNL) Science Park 106Amsterdam 1098XGthe Netherlands Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Southwest Jiaotong UniversityChengdu 610031China

Mechanochemical reactions of the GaN-Al_(2)O_(3) interface offer a novel principle for scientific and technological merits in the micro-/nano-scale ultra-precision surface *** this work,the mechanochemical reactions on Ga-and N-faced GaN surfaces rubbed by the Al_(2)O_(3) nanoasperity as a function of the environmental humidity were *** results indicate that the N-face exhibits much stronger mechanochemical removal over the relative humidity range of 20%-80%than the *** water molecules in environmental conditions significantly promotes the interfacial mechanochemical reactions and hence accelerates the atomic attrition on *** hypothesized mechanism of the selective water-involved mechanochemical removal is associated with the dangling bond configuration,which affects the mechanically-stimulated chemical reactions via altering the activation energy barrier to form the bonding bridge across the sliding *** findings can enrich the understanding of the underlying mechanism of mechanochemical reactions at GaN-Al_(2)O_(3) interface and a broad cognition for regulating the mechanochemical reactions widely existing in scientific and engineering applications.

关键词： crystallographic polarity ambient humidity mechanochemical removal GaN-Al_(2)O_(3)interface

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Automatic Modulation Classification Based on CNN, LSTM and Attention Mechanism

Automatic Modulation Classification Based on CNN, LSTM and A...

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International Conference on Computer and Communications (ICCC)

作者： Zewen Wang Ping Wang Pingying Lan School of information science and technology Southwest Jiaotong University Chengdu China State Key Laboratory of Rail Transit Engineering Informatization FSDI Xi'an China

Automatic modulation recognition (AMR) is a key technology in the field of cognitive radio. To improve the performance, a new model for AMR, which incorporates an attention mechanism to extract the important features of the signal more effectively, is proposed in this paper. The simulation results show that our proposed method has a good recognition performance. In addition, our method can still achieve high accuracy, even if only a few samples are provided as the training set for training. We also visualize the output features of the model and show the positive effect of the attention mechanism on signal modulation category identification.

关键词： Training Simulation Computational modeling Modulation Data visualization Feature extraction Data models

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Modeling and Validation of Typical Scenarios of High-Speed railway Automatic Train Operation Based on Alvis

Modeling and Validation of Typical Scenarios of High-Speed R...

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2022 International Conference on Frontiers of Traffic and Transportation engineering, FTTE 2022

作者： Zhang, Yi Li, Kaicheng Wei, Guodong Yuan, Lei Wang, Fei National Engineering Laboratory for Urban Rail Transit Communication and Operation Control Beijing Jiaotong University China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University China Signal& Communication Research Institute of CARS

ISBN: (纸本)9781510657526

Formal methods are used to improve the credibility and correctness of scenarios in Chinese train control systems (CTCS). At present, there are few formal studies on the high-speed Automatic Train Operation system that developed from CTCS-3 level high-speed railway technology. In this paper, we adopt a new formal method, Alvis, which combines the advantages of high-level programming language and graphical modeling, and select the interzone TSRS handover scene in high-speed railway ATO system as the research object. The messaging behaviours of the scene are analyzed using UML sequence diagrams, and an Alvis model of the system is built, which is converted into an smv model supporting the formal validation of the model checking tool nuXmv. The real-time performance of the extracted messaging was verified on demand, inconsistency between technical specifications and actual operational scenarios was identified and solution was provided. The results of this method can provide some reference for the technical promotion of ATO train control system in China. © 2022 SPIE.

关键词： Formal methods

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高雷诺数下覆羽控制直翼型流动分离的位置影响实验

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Acta Mechanica Sinica 2024年第4期40卷 125-135页

作者：巩绪安马兴宇姜楠 Department of Mechanics School of Mechanical EngineeringTianjin UniversityTianjin300354China National Key Laboratory of Aerodynamic Noise Control China Aerodynamics Research and Development CenterSichuan621000China National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi’an710072China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu610031China

本文在二维机翼上安装了仿生的覆羽结构,在5.0×105的雷诺数下研究它们控制前缘流动分离的效果.覆羽是指覆盖在鸟类翅膀上的多排短而柔软的带有锯齿状尖端的羽毛.它们覆盖在长而硬的飞羽边缘,使鸟的翅膀保持流线型.当流动分离发生时... 详细信息

本文在二维机翼上安装了仿生的覆羽结构,在5.0×105的雷诺数下研究它们控制前缘流动分离的效果.覆羽是指覆盖在鸟类翅膀上的多排短而柔软的带有锯齿状尖端的羽毛.它们覆盖在长而硬的飞羽边缘,使鸟的翅膀保持流线型.当流动分离发生时,这些柔性锯齿形的人工覆羽在来流的激励下能够自适应地变形和颤振.在风洞实验中,本文使用高时间分辨率的粒子图像测速仪(PIV)来测量机翼上表面的速度场,并使用热线风速仪来测量尾流中的高频湍流脉动.安装在20%弦长处的人工覆羽在前缘分离流的激励下会自适应地大幅翻转.安装在80%弦长处的覆羽则会被尾缘卷起的脱落涡向上抬起,并在准平衡状态下轻微摆动.这两类人工覆羽均能够有效地控制流动分离,并减小尾流的湍流脉动强度.

关键词：流动分离风洞实验分离流湍流脉动高时间分辨率热线风速仪高雷诺数锯齿形

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Adaptive frequency attention-based interpretable Transformer network for few-shot fault diagnosis of rolling bearings

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Reliability engineering and System Safety 2025年 263卷

作者： Liu, keying Li, Yifan Cui, Zhaoyang Qi, Guangdong Wang, Biao School of Mechanical Engineering Southwest Jiaotong University Chengdu610031 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu610031 China

In recent years, deep learning-based approaches have demonstrated superior performance in few-shot fault diagnosis. Nevertheless, many of these methods lack explicit interpretability, making it difficult to intuitively understand their diagnostic logic. To tackle this issue, an interpretable deep learning model called the adaptive frequency attention-based interpretable Transformer network is proposed for few-shot fault diagnosis of rolling bearings. From a frequency interpretability perspective, the standard Transformer network architecture has been innovatively improved. First, an adaptive frequency attention mechanism is developed that quantifies the importance of various frequency components during the diagnostic process, adaptively identifying and emphasizing key frequency components closely associated with fault modes. This boosts both diagnostic performance and model interpretability. Second, to enhance the diversity of fault features under limited sample conditions, a multiscale convolutional architecture is developed to replace the linear projection layer in input embedding. This architecture employs parallel multiscale convolution kernels to extract both local and global fault features, enabling a comprehensive capture of fault information and further supporting the interpretability of the diagnostic model. Finally, Experiments on interpretable few-shot fault diagnosis are carried out on three rolling bearing datasets, and the diagnostic results further validate the effectiveness and interpretability of the proposed method. © 2025 Elsevier Ltd

关键词： Roller bearings

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Bridge crack image classification based on improved transformer 8

Bridge crack image classification based on improved transfor...

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8th International Conference on Traffic engineering and Transportation System, ICTETS 2024

作者： Taob, Dan Guo, Shouhong Qiu, Guangying You, Dequan Wu, Linming State Key Laboratory of Rail Transit Infrastructure Performance Monitoring and Guarantee East China Jiaotong University Nanchang330013 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang330013 China Fujian Communications Research Institute Co. Ltd. Fuzhou350000 China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

As a major bridge country. China's facilities are vital to the transportation network and regional economic development. However, bridges exposed to the natural environment and traffic pressure for a long time are prone to problems such as cracks and corrosion, which, if not detected and repaired promptly, may lead to structural failures, affecting safety and economic benefits. Traditional image classification methods are susceptible to interference from shadows and dirt in practical applications, leading to reduced classification accuracy and limitations in dealing with fine-grained features and small sample datasets. In this paper, based on the Transformer model, combined with AlignMixup and Image2 Token data enhancement methods, the accuracy and recognition ability of the model on fine-grained and small-sample image classification is improved. ©2024 SPIE.

关键词： Cracks

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Spatial Variation Motion Blur Bridge Image Quality Improvement 6

Spatial Variation Motion Blur Bridge Image Quality Improveme...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Tao, Dan Deng, Suyuan Qiu, Guangying You, Dequan Wu, Linming East China Jiaotong University State Key Laboratory of Rail Transit Infrastructure Performance Monitoring and Guarantee Nanchang330013 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang330013 China Fujian Communications Research Institute Co. Ltd. Fuzhou350000 China

ISBN: (纸本)9798331541750

China is a leading nation in bridge construction, with bridge infrastructure not only forming a critical part of the transportation network but also driving regional economic development. To ensure the safety and reliability of these structures, regular inspection and maintenance are required, with crack detection being particularly crucial. With technological advancements, machine vision has started to be applied in bridge health monitoring. However, due to issues such as low light, noise, and camera shake, images often exhibit motion blur, leading to the loss of key details and structural deformation, which affects the accuracy of crack detection. To address this, this paper proposes an image restoration algorithm that incorporates a strip attention mechanism, leveraging intra- and inter-strip attention blocks within the Transformer framework to handle blurs of different directions and magnitudes. Additionally, an operator is used for sharpening to reduce artifacts and noise, thereby improving the deblurring effect. Experimental results demonstrate that this algorithm effectively enhances image quality, improving the reliability of data used for bridge health management. © 2024 IEEE.

关键词： Restoration

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Degradation Prognostics of Lithium-ion Batteries Based on Partial Features and Long Short-term Memory Network 13

Degradation Prognostics of Lithium-ion Batteries Based on Pa...

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13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Geng, Mengyao Meng, Huixing An, Xu Xing, Jinduo State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles School of Mechanical-Electronic and Vehicle Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

The accurate degradation prediction of Lithium-ion batteries is beneficial to the reliability and safety of battery-driven systems. In this paper, a long short-term memory network (LSTM) model is utilized to predict the capacity degradation trend using partial charge and discharge features of Lithium-ion batteries. Firstly, significant features are extracted from the original charge and discharge data. Then the Pearson correlation coefficient is adopted to filter the features with high correlation coefficients. Selected features are subsequently treated as the input of the prediction model. Finally, a LSTM model is developed and associated hyperparameters are established by Adam algorithm. The proposed method is validated by experimental results on the NASA battery dataset. © 2022 IEEE.

关键词： Long short-term memory

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An Analysis of Abnormal Vibration and Noise Caused by Misalignment of High-Speed Train Shaft Coupling

SSRN

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SSRN 2024年

作者： Liang, Zhaolong Cai, Wubin Liang, Zhao long Zhou, Yabo Chi, Maoru State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu China School of Mechanical Engineering Southwest Jiaotong University Chengdu China

This study conducts on-site testing and simulation analysis of the abnormal noise phenomenon generated during the operation of high-speed trains. The sound pressure characteristics in the cabin and the vibration of different vehicle components are recorded and subjected to comprehensive analysis. Test results indicate that significant abnormal noise occurs inside the cabin at the speed of 250km/h, with frequencies of 87Hz, 173Hz, 259Hz, and 346Hz. Through sound source identification, vibration transmission analysis and theoretical derivation, it has been determined that the abnormal noise primarily originates from the transmission system, and it is further speculated that these abnormal vibrations and noises may be correlated with the misalignment of the shaft coupling within the transmission system. To verify the conjecture, a high-speed vehicle electromechanical coupling simulation model was established, incorporating the misalignment of the shaft coupling in the transmission system. The vibration characteristics of vehicle components were analyzed, confirming that shaft coupling misalignment leads to uneven meshing forces in gears, resulting in significant vibration and noise that affect the riding comfort of the vehicle. We propose replacing the shaft coupling to solve this problem, the test results showed that the abnormal vibration amplitudes of the transmission system at 1fr, 2fr, 3fr, and 4fr were significantly reduced after replacing the shaft coupling, and the amplitude of the corresponding frequencies on the cabin floor decreased by 91%, 78%, 98%, and 66%, respectively. The issue of abnormal noise has been effectively resolved. © 2024, The Authors. All rights reserved.

关键词： Electromechanical coupling

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