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Energy-Efficient Driving Strategy for High-Speed Trains with Considering the Checkpoints

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Chinese Journal of Electronics 2024年第3期33卷 847-858页

作者： Zixuan ZHANG yuan cao Shuai SU State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

With rising energy prices and concerns about environmental issues,energy-efficient driving strategies(EEDS) for high-speed trains have received a substantial amount of attention.In particular,energy-saving schemes play a huge role in reducing the energy and operating costs of trains.This article studies the EEDS of high-speed trains at a given time.A well-posed model is formulated,in which the constraints of the checkpoints,in addition to the speed limits,vehicle dynamics,and discrete control throttle,are first considered.For a given control sequence,the Karush-Kuhn-Tucker(KKT) conditions are used to obtain the necessary conditions for an EEDS.According to several key equations of the necessary conditions,the checkpoint constraints are satisfied.Some case studies are conducted based on the data of the Beijing-Shanghai high-speed line to illustrate the effectiveness of the proposed approach.

关键词： Energy consumption Costs Simulation Mathematical models Energy efficiency Vehicle dynamics Optimization

Prompt learning in computer vision: a survey

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 42-63页

作者： Yiming LEI Jingqi LI Zilong LI yuan cao Hongming SHAN Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200438China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 201210China

Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.

关键词： Prompt learning Visual prompt tuning(VPT) Image generation Image classification Artificial intelligence generated content(AIGC)

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE yuan cao Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and researchers.Seldom have studies considered diverse noises along the track.To fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises.MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees.In this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale.In the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the approach.The adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Fault diagnosis Condition monitoring Noise Signal processing algorithms Aerospace electronics Rail transportation Robustness

Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN yuan cao Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation safety.Thus fault diagnosis for railway point machines becomes a hot topic.Considering the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature selection.Then,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection methods.Finally,support vector machine is utilized for fault diagnosis.Experiment comparisons show that the proposed method performs best.The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively.Especially,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Vibrations Support vector machines Diversity reception Switches Feature extraction Rail transportation

Indirubin alleviates retinal neurodegeneration through the regulation of PI3K/AKT signaling

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Journal of Biomedical Research 2024年第3期38卷 256-268页

作者： Huan Li Huiying Zhang Lushu Chen Yaming Shen yuan cao Xiumiao Li Jin Yao Department of Ophthalmology the Affiliated Eye Hospital of Nanjing Medical UniversityNanjingJiangsu 210029China Department of Ophthalmology the Fourth School of Clinical MedicineNanjing Medical UniversityNanjingJiangsu 210029China

Retinal neurodegenerative disease is a leading cause of blindness among the elderly in developed countries,including glaucoma,diabetic retinopathy,traumatic optic neuropathy and optic neuritis,etc.The current clinical treatment is not very effective.We investigated indirubin,one of the main bioactive components of the traditional Chinese medicine Danggui Longhui Pill,in the present study for its role in retinal neurodegeneration.Indirubin exhibited no detectable tissue toxicity in vivo or cytotoxicity in vitro.Moreover,indirubin improved visual function and ameliorated retinal neurodegeneration in mice after optic nerve crush injury in vivo.Furthermore,indirubin reduced the apoptosis of retinal ganglion cells induced by oxidative stress in vitro.In addition,indirubin significantly suppressed the increased production of intracellular reactive oxygen species and the decreased activity of superoxide dismutase induced by oxidative stress.Mechanically,indirubin played a neuroprotective role by regulating the PI3K/AKT/BAD/BCL-2 signaling.In conclusion,indirubin protected retinal ganglion cells from oxidative damage and alleviated retinal neurodegeneration induced by optic nerve crush injury.The present study provides a potential therapeutic medicine for retinal neurodegenerative diseases.

关键词： retinal neurodegenerative disease oxidative stress PI3K/AKT retinal ganglion cell apoptosis

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An unusual anterior mitral leaflet perforation in a patient with no infective endocarditis:a case report

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Emergency and Critical Care Medicine 2024年第1期4卷 39-41页

作者： Kaiming Wei Yu Yao Chuanzhen Liu yuan cao Department of Cardiovascular Surgery Qilu Hospital of Shandong UniversityJinanShandongChina Department of Emergency Medicine Qilu Hospital of Shandong UniversityJinanShandongChina National Key Laboratory for Innovation and Transformation of Luobing Theory The Key Laboratory of Cardiovascular Remodeling and Function ResearchChinese Ministry of EducationChinese National Health Commission and Chinese Academy of Medical SciencesDepartment of CardiologyQilu Hospital of Shandong UniversityJinanShandongChina.

Background:Mitral valve perforation refers to the occurrence of cracks or openings in the structure of the mitral valve,allowing blood to escape through these gaps.Typically,this is caused by infective endocarditis and the most common site is the anterior leaflet.However,it is crucial to explore other potential causes of valve damage,particularly when conventional risk factors are not apparent.Case presentation:We present a case of a middle-aged male patient who developed mitral valve perforation because of aortic valve regurgitation in the absence of infective endocarditis.Conclusion:Exploring such rare cases contributes to a deeper understanding of valvular diseases and enhances clinical decision making for effective management.

关键词： Anterior mitral leaflet Aortic valve Case report Perforation Prolapse Regurgitation

Research on Virtual Coupled Train Control Method Based on GPC & VAPF

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Chinese Journal of Electronics 2022年第5期31卷 897-905页

作者： cao yuan YANG Yaran MA Lianchuan WEN Jiakun State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and Information Engineering Beijing Jiaotong University

Improving transportation efficiency is an eternal research hotspot in rail transit system. In recent years, the train operation control method based on virtual coupling has attracted the attention of many scholars.The method of train coordination and anti-collision control is not only the key to realize the virtual coupling of train, but also the key to ensure the safety of train operation. Therefore, based on the existing research, a virtual coupled train dynamics model with nonlinear dynamics is established. Then, the parameters of the operation process model of the nonlinear virtual coupled train are identified by the recursive least squares method based on realtime data, which is applied to the variable parameter artificial potential field(VAPF) for parameter identification.A fusion controller based on feature-based generalized model prediction(GPC) and VAPF is used to control the virtual coupled train and prevent collision. Finally, the validity of the proposed method is verified by using real high-speed railway data.

关键词： Virtual coupling train control system Generalized model predictive control Nonlinear system Artificial potential field (APF)

Fault Diagnosis for Railway Point Machines Using VMD Multi-scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2024年

作者： Yongkui SUN yuan cao Peng LI Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

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Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

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Chinese Journal of Electronics 2023年第5期32卷 972-981页

作者： CHEN Xiaohan HU Xiaoxi WEN Tao cao yuan The School of Electronic and Information Engineering Beijing Jiaotong University The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong University

In the railway transportation industry,fault diagnosis of railway point machines(RPMs) is vital.Because operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signalbased fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently robust.To overcome these shortcomings, we propose a doublescale wide first-layer kernel convolutional neural network(DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for lowsampling-rate signals. Meanwhile, the proposed doublescale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison,were conducted to demonstrate the robustness and accuracy of the proposed algorithm.

关键词： Railway point machines Fault diagnosis Condition monitoring Vibration signals Convolutional neural networks