1–20 von 20 Ergebnissen
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    Research on Indoor Location Technology in Metro Station

    Xing, Zongyi / Yang, Hang / Liu, Yuan | Springer Verlag | 2020

    In order to assist blind people to travel independently and solve the problems of jumping of location points and

     ...
    Schlagwörter: Computational Intelligence

    An Improved SSD and Its Application in Train Bolt Detection

    Zhang, Jiabing / Su, Zhouyi / Xing, Zongyi | Springer Verlag | 2020
    not good in detecting small targets. On this basis, a multi-window and multi-scale fusion model is proposed and explains its model and working ...
    Schlagwörter: Computational Intelligence

    Application of Improved PSO Algorithms in Train Energy Consumption Optimization

    Yang, Binhui / Zhang, Jing / Zhang, Yong et al. | Springer Verlag | 2020
    classical PSO algorithm is proposed in this paper. On the premise of determining the operation strategy and operating conditions, the dynamic ...
    Schlagwörter: Computational Intelligence

    Intuitionistic Fuzzy FMEA Approach for Key Component Identification of Rail Bogie

    Zhang, Zhenyu / Xing, Zongyi / Qin, Yong | Springer Verlag | 2022
    Schlagwörter: Computational Intelligence

    Research on Algorithm of Wheelset Profile Extraction Based on Clustering and Huff Transform

    Zhang, Yihui / Xing, Zongyi / Li, Jun et al. | Springer Verlag | 2022
    Schlagwörter: Computational Intelligence

    On Line Inspection System of Wheelset Size Based on Multi-line Structured Light

    Zhang, Juhui / Xing, Zongyi / Yao, Xiaowen et al. | Springer Verlag | 2024
    Schlagwörter: Computational Intelligence

    Reliability Analysis of Metro Traction Substation Based on Bayesian Network

    Cong, Guangtao / Xu, Wen / Xing, Zongyi | Springer Verlag | 2020
    for ensuring the safe and stable operation of the system. In order to evaluate the reliability of the metro traction substation, the ...
    Schlagwörter: Computational Intelligence

    Urban Rail Train Wheel Fault Diagnosis Based on Improved EEMD

    Fu, Ning / Qian, Kaijie / Xing, Zongyi | Springer Verlag | 2020

    In order to accurately identify the types of wheel faults in urban rail trains, a method based on improved

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    Schlagwörter: Computational Intelligence

    Research of Turnout Fault Diagnosis Method Based on Qualitative Trend Analysis

    Zhou, Yuanyuan / Han, Yulin / Xing, Zongyi | Springer Verlag | 2020
    Schlagwörter: Computational Intelligence

    Research on the Wheelset Life Optimization of Urban Rail Transit Trains

    Xu, Xiaoxiao / Ye, Zhengjun / Zhang, Jianyu et al. | Springer Verlag | 2020

    In the case of long-term operation, the wear level of the wheelset is intensified with the increase of the

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    Schlagwörter: Computational Intelligence

    High-Temperature Zone Localization Image Processing Algorithm for Pantograph Infrared Image

    Xu, Wen / Su, Zhaoyi / Cong, Guangtao et al. | Springer Verlag | 2020

    The infrared temperature of the pantograph is one of the important technical parameters in the train operation

     ...
    Schlagwörter: Computational Intelligence

    A Rail Train Number Identification Algorithm Based on Image Processing

    Yang, Shuangyan / Xu, Ruifeng / Zhou, Zhihui et al. | Springer Verlag | 2020
    trains. In this paper, the image processing technology is used to locate, segment, and identify the vehicle number, thus completing the online ...
    Schlagwörter: Computational Intelligence

    Design and Implementation of Online Inspection System for Linear Motor Air Gap

    Zhu, Lingqi / Su, Zhaoyi / Zhu, Jiawei et al. | Springer Verlag | 2020

    In order to solve the problems of low detection efficiency and inaccurate detection data in the manual detection

     ...
    Schlagwörter: Computational Intelligence

    Denoising Method of Train Vibration Signal Based on Improved Wavelet Threshold

    Wang, Zihao / Liu, Xinhai / Xing, Zongyi | Springer Verlag | 2020

    In view of the wheel state monitoring, the wheel vibration signal is often used to reflect the wheel state

     ...
    Schlagwörter: Computational Intelligence

    Method of Wheel Out-of-Roundness Detection Based on POVMD and Multinuclear LS-SVM

    Fang, Lichao / Li, Shibo / Dai, Wang et al. | Springer Verlag | 2020
    -roundness due to the collision and friction between the wheels and track. It has great significance to detect wheel polygon in order to ensure the ...
    Schlagwörter: Computational Intelligence

    Research on Monitoring Technology of Pantograph Sliding Plate Abrasion Based on Sub-pixel Edge Extraction

    Dai, Wang / Su, Zhaoyi / Fang, Lichao et al. | Springer Verlag | 2020
    pantograph sliding plate abrasion monitoring algorithm based on sub-pixel edge extraction is designed in this paper. This paper mainly analyzes the ...
    Schlagwörter: Computational Intelligence

    Research on Service Ability Evaluation of Automatic Fare Collection System Based on DEMATEL and VIKOR-Gray Relational Analysis

    Zhou, Yuanyuan / Zhang, Jiabing / Zhou, Zhihui et al. | Springer Verlag | 2020
    DEMATEL and VIKOR-gray correlation analysis is proposed. Firstly, in order to balance expert knowledge and objective reality, the fuzzy DEMATEL is ...
    Schlagwörter: Computational Intelligence

    Research on Vehicle Number Localization of Urban Rail Vehicle Based on Edge-Enhanced MSER

    Xu, Ruifeng / Bao, Jiandong / Yang, Shuangyan et al. | Springer Verlag | 2020

    In order to reduce the impact of shooting angle, distance and motion blur on vehicle number localization, and

     ...
    Schlagwörter: Computational Intelligence

    Research on Train Energy-Saving Optimization Based on Parallel Immune Particle Swarm Optimization

    Li, Shibo / Dai, Wang / Fang, Lichao et al. | Springer Verlag | 2020
    the train energy saving in two stages: firstly, the running time of each interval is fixed, the algorithm is used to search for the optimal ...
    Schlagwörter: Computational Intelligence

    A Global Non-roundness Detection Algorithm for Urban Rail Vehicle Wheels

    Zhou, Zhihui / Liu, Xinhai / Zhou, Yuanyuan et al. | Springer Verlag | 2020
    Schlagwörter: Computational Intelligence