Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mine...Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mined to verify that electrical energy flowing in its railway power feeding system is appropriate or not. Gauss-Seidel, conventional Newton-Raphson, and current injection methods are well-known and widely accepted as a tool for electrical power network solver in DC railway power supply study. In this paper, a simplified Newton-Raphson method has been proposed. The proposed method employs a set of current-balance equations at each electrical node instead of the conventional power-balance equation used in the conventional Newton-Raphson method. This concept can remarkably reduce execution time and computing complexity for multi-train simulation. To evaluate its use, Sukhumvit line of Bangkok transit system （BTS） of Thai- land with 21.6-km line length and 22 passenger stopping stations is set as a test system. The multi-train simulation integrated with the proposed power network solver is developed to simulate 1-h operation service of selected 5-min headway. From the obtained results, the proposed method is more efficient with approximately 18 % faster than the conventional Newton-Raphson method and just over 6 % faster than the current injection method.展开更多
Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signal...Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approach–Firstly,a single-train trajectory optimization(STTO)model is constructed based on train dynamics and operating conditions.The train kinematics parameters,including acceleration,speed and time at each position,are calculated to predict the arrival times in the train timetable.A STTO algorithm is developed to optimize a single-train time-efficient driving strategy.Then,a TTR approach based on multi-train tracking optimization(TTR-MTTO)is proposed with mutual information.The constraints of temporary speed restriction(TSR)and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train.The multi-train trajectories at each position are optimized to generate a timeefficient train timetable.Findings–The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF.The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay(TTD).As for the TSR scenario,the proposed TTR-MTTO can reduce TTD by 60.60%compared with the traditional TTR approach with dispatchers’experience.Moreover,TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/value–With the cooperative relationship and mutual information between train rescheduling and control,the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.展开更多
输电线路巡检中采集的螺栓图像有分辨率低、视觉信息不足的特点。针对传统图像分类模型难以从螺栓图像中学习到语义丰富的视觉表征问题,提出了一种基于多模态对比学习的输电线路螺栓缺陷分类方法。首先,为了将文本中螺栓相关的语义信息...输电线路巡检中采集的螺栓图像有分辨率低、视觉信息不足的特点。针对传统图像分类模型难以从螺栓图像中学习到语义丰富的视觉表征问题,提出了一种基于多模态对比学习的输电线路螺栓缺陷分类方法。首先,为了将文本中螺栓相关的语义信息和先验知识以跨模态的方式注入视觉表征,提出了一种结合多模态对比预训练和监督式微调的二阶段训练算法;其次,为了缓解多模态对比预训练中的过拟合问题,提出了标签平滑的信息噪声对比估计损失(info noise contrastive estimation loss with label smoothing,infoNCE-LS),以提高预训练视觉表征的泛化性能;最后,针对上下游任务的不匹配问题,设计了3种基于文本提示的分类头,以改善预训练视觉表征在监督式微调阶段的迁移学习效果。实验结果表明:该文基于Res Net50和ViT构建的两种模型在螺栓缺陷分类数据集上的准确率分别为92.3%和97.4%,相比基线分别提高了2.4%和5.8%。研究实现了从文本到图像的语义信息跨模态补充,为螺栓缺陷识别的研究提供了新的思路。展开更多
文摘Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mined to verify that electrical energy flowing in its railway power feeding system is appropriate or not. Gauss-Seidel, conventional Newton-Raphson, and current injection methods are well-known and widely accepted as a tool for electrical power network solver in DC railway power supply study. In this paper, a simplified Newton-Raphson method has been proposed. The proposed method employs a set of current-balance equations at each electrical node instead of the conventional power-balance equation used in the conventional Newton-Raphson method. This concept can remarkably reduce execution time and computing complexity for multi-train simulation. To evaluate its use, Sukhumvit line of Bangkok transit system （BTS） of Thai- land with 21.6-km line length and 22 passenger stopping stations is set as a test system. The multi-train simulation integrated with the proposed power network solver is developed to simulate 1-h operation service of selected 5-min headway. From the obtained results, the proposed method is more efficient with approximately 18 % faster than the conventional Newton-Raphson method and just over 6 % faster than the current injection method.
基金This research was jointly supported by the National Natural Science Foundation of China[Grant 62203468]the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(CAST)[Grant 2022QNRC001]+1 种基金the Technological Research and Development Program of China Railway Corporation Limited[Grant K2021X001]by the Foundation of China Academy of Railway Sciences Corporation Limited[Grant 2021YJ043].On behalf all authors,the corresponding author states that there is no conflict of interest.
文摘Purpose–This paper aims to propose a train timetable rescheduling(TTR)approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approach–Firstly,a single-train trajectory optimization(STTO)model is constructed based on train dynamics and operating conditions.The train kinematics parameters,including acceleration,speed and time at each position,are calculated to predict the arrival times in the train timetable.A STTO algorithm is developed to optimize a single-train time-efficient driving strategy.Then,a TTR approach based on multi-train tracking optimization(TTR-MTTO)is proposed with mutual information.The constraints of temporary speed restriction(TSR)and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train.The multi-train trajectories at each position are optimized to generate a timeefficient train timetable.Findings–The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF.The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay(TTD).As for the TSR scenario,the proposed TTR-MTTO can reduce TTD by 60.60%compared with the traditional TTR approach with dispatchers’experience.Moreover,TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/value–With the cooperative relationship and mutual information between train rescheduling and control,the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.
文摘输电线路巡检中采集的螺栓图像有分辨率低、视觉信息不足的特点。针对传统图像分类模型难以从螺栓图像中学习到语义丰富的视觉表征问题,提出了一种基于多模态对比学习的输电线路螺栓缺陷分类方法。首先,为了将文本中螺栓相关的语义信息和先验知识以跨模态的方式注入视觉表征,提出了一种结合多模态对比预训练和监督式微调的二阶段训练算法;其次,为了缓解多模态对比预训练中的过拟合问题,提出了标签平滑的信息噪声对比估计损失(info noise contrastive estimation loss with label smoothing,infoNCE-LS),以提高预训练视觉表征的泛化性能;最后,针对上下游任务的不匹配问题,设计了3种基于文本提示的分类头,以改善预训练视觉表征在监督式微调阶段的迁移学习效果。实验结果表明:该文基于Res Net50和ViT构建的两种模型在螺栓缺陷分类数据集上的准确率分别为92.3%和97.4%,相比基线分别提高了2.4%和5.8%。研究实现了从文本到图像的语义信息跨模态补充,为螺栓缺陷识别的研究提供了新的思路。
