The estimation approach of rail thermal stress based on vehicle-track dynamic responses. - Yishi Guo; Zujun Yu; Hongmei Shi; Liqiang Zhu (ITSC, 2014)

An Ultrasonic Guided Wave Mode Excitation Method in Rails. - Xu Xining; Zhuang Lu; Xing Bo; Zujun Yu; Liqiang Zhu (IEEE Access, 2018)

Foreground Detection with Deeply Learned Multi-Scale Spatial-Temporal Features. - Yao Wang 016; Zujun Yu; Liqiang Zhu (Sensors, 2018)

Estimating the Axial Load of In-Service Continuously Welded Rail Under the Influences of Rail Wear and Temperature. - Xiangyu Duan; Liqiang Zhu; Zujun Yu; Xu Xining (IEEE Access, 2019)

Foreground Detection for Infrared Videos With Multiscale 3-D Fully Convolutional Network. - Yao Wang 016; Liqiang Zhu; Zujun Yu (IEEE Geosci. Remote. Sens. Lett., 2019)

High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks. - Baoqing Guo; Gan Geng; Liqiang Zhu; Hongmei Shi; Zujun Yu (Sensors, 2019)

An Adaptive Track Segmentation Algorithm for a Railway Intrusion Detection System. - Yang Wang; Liqiang Zhu; Zujun Yu; Baoqing Guo (Sensors, 2019)

Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning. - Xingxin Li; Liqiang Zhu; Zujun Yu; Baoqing Guo; Yanqin Wan (IEEE Access, 2020)

Action Recognition Based on Two-Stream Convolutional Networks With Long-Short-Term Spatiotemporal Features. - Yanqin Wan; Zujun Yu; Yao Wang; Xingxin Li (IEEE Access, 2020)

On the Identification of Elastic Moduli of In-Service Rail by Ultrasonic Guided Waves. - Liqiang Zhu; Xiangyu Duan; Zujun Yu (Sensors, 2020)

An optimization method for out-of-distribution anomaly detection models. - Ji Qiu; Hongmei Shi; Yu Hen Hu; Zujun Yu (CoRR, 2023)

Intrusion detection for high-speed railways based on unsupervised anomaly detection models. - Yao Wang 016; Zujun Yu; Liqiang Zhu (Appl. Intell., 2023)

ℒ풪2net: Global-Local Semantics Coupled Network for scene-specific video foreground extraction with less supervision. - Tao Ruan; Shikui Wei; Yao Zhao 001; Baoqing Guo; Zujun Yu (Pattern Anal. Appl., 2023)

Unraveling False Positives in Unsupervised Defect Detection Models: A Study on Anomaly-Free Training Datasets. - Ji Qiu; Hongmei Shi; Yuhen Hu; Zujun Yu (Sensors, 2023)

Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations. - Chao He; Hongmei Shi; Ruixin Li; Jianbo Li; Zujun Yu (Adv. Eng. Informatics, 2024)

Spatial Activation Suppression for Unsupervised Anomaly Detectors in Freight Train Fault Detection. - Ji Qiu; Hongmei Shi; Yuhen Hu; Zujun Yu (IEEE Trans. Instrum. Meas., 2024)

Railway Intrusion Detection Based on Machine Vision: A Survey, Challenges, and Perspectives. - Zhiwei Cao; Yong Qin 002; Limin Jia 002; Zhengyu Xie; Yang Gao; Yaguan Wang; Ping Li 038; Zujun Yu (IEEE Trans. Intell. Transp. Syst., 2024)

Tiny network for faults recognition in freight cars. - Yuan Liu; Hongmei Shi; Ji Qiu; Zujun Yu (Eng. Appl. Artif. Intell., 2025)

CMT-TFENet: A Two-Stage Rail Corrugation Detection Algorithm Using On-Board Response. - Shuai Wu; Hongmei Shi; Jianbo Li; Mengfang Ao; Jiaqi Shi; Wenlin Yang; Zujun Yu (IEEE Trans. Instrum. Meas., 2025)

EdgeAD: Unsupervised Learning Model Based on Prior Knowledge Enhanced Image Anomaly Detection of Heavy Railway Freight Cars. - Weiyu Zhang; Hongmei Shi; Ji Qiu; Zujun Yu; Jianbo Li (IEEE Trans. Instrum. Meas., 2025)

Rail-PatchCore: unsupervised learning-based detection of visual anomalies in the railway-turnout environment. - YuanHao Zhang; Zujun Yu; Liqiang Zhu; Baoqing Guo; Yao Wang (Appl. Intell., 2025)