Long Yu

Orcid: 0000-0002-5481-8030

Affiliations:
  • Southwest Jiaotong University, School of Electrical Engineering, Chengdu, China (PhD 2008)


According to our database1, Long Yu authored at least 20 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2024
Robust deep Gaussian process-based trustworthy fog-haze-caused pollution flashover prediction approach for overhead contact lines.
Reliab. Eng. Syst. Saf., March, 2024

Uncertainty-aware trustworthy weather-driven failure risk predictor for overhead contact lines.
Reliab. Eng. Syst. Saf., February, 2024

A Real-Time Siamese Network Based on Knowledge Distillation for Insulator Defect Detection of Overhead Contact Lines.
IEEE Trans. Instrum. Meas., 2024

2023
A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network.
Reliab. Eng. Syst. Saf., July, 2023

ArcMask: a robust and fast image-based method for high-speed railway pantograph-catenary arcing instance segmentation.
Neural Comput. Appl., March, 2023

A Novel Arcing Detection Model of Pantograph-Catenary for High-Speed Train in Complex Scenes.
IEEE Trans. Instrum. Meas., 2023

Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model.
Reliab. Eng. Syst. Saf., 2023

Weather-Related Failure Risk Prediction of Overhead Contact Lines Based on Deep Gaussian Process.
Proceedings of the 7th International Conference on High Performance Compilation, 2023

2022
A Survey on Automatic Inspections of Overhead Contact Lines by Computer Vision.
IEEE Trans. Intell. Transp. Syst., 2022

Adaptive Deep Learning for High-Speed Railway Catenary Swivel Clevis Defects Detection.
IEEE Trans. Intell. Transp. Syst., 2022

Defect Severity Identification for a Catenary System Based on Deep Semantic Learning.
Sensors, 2022

Predicting wind-caused floater intrusion risk for overhead contact lines based on Bayesian neural network with spatiotemporal correlation analysis.
Reliab. Eng. Syst. Saf., 2022

2021
DefGAN: Defect Detection GANs With Latent Space Pitting for High-Speed Railway Insulator.
IEEE Trans. Instrum. Meas., 2021

Fault Tree Construction Model Based on Association Analysis for Railway Overhead Contact System.
Int. J. Comput. Intell. Syst., 2021

2020
A Robust Pantograph-Catenary Interaction Condition Monitoring Method Based on Deep Convolutional Network.
IEEE Trans. Instrum. Meas., 2020

2019
Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning.
IEEE Trans. Instrum. Meas., 2019

An Efficient Method for High-Speed Railway Dropper Fault Detection Based on Depthwise Separable Convolution.
IEEE Access, 2019

A Novel Fault Prevention Model for Metro Overhead Contact System.
IEEE Access, 2019

Contact Wire Support Defect Detection Using Deep Bayesian Segmentation Neural Networks and Prior Geometric Knowledge.
IEEE Access, 2019

2018
An Accurate and Efficient Vision Measurement Approach for Railway Catenary Geometry Parameters.
IEEE Trans. Instrum. Meas., 2018


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