Xue Li

Automatic search model of railway shunting route based on improved artificial neural network algorithm

Xue Li, Hui He, Yixuan Yang, Zeyuan Fan

To enhance railway shunting route search accuracy while reducing memory consumption, this study proposes an automatic railway shunting route search model based on an improved artificial neural network algorithm. The method constructs a time-varying railway network topology through dynamic topology modeling and …

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Probabilistic Risk Analysis for Catenary System of Heavy‐Haul Railway Based on Casual Inference

Xue Li, Xiang Yan, Lan Ma, Huawei Wang et autres

ABSTRACT The reliability of the catenary system is crucial for the safety and efficiency of heavy‐haul railways. This study presents a probabilistic risk analysis model for the catenary system, employing causal inference methods to capture the complex relationships among risk factors. Using …

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Adaptive Distributed Training for Multi-Agent Reinforcement Learning in Multi-Objective Traffic Signal Control

Peisong Huang, Puming Wang, Xue Li, Xin Jin et autres

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Ship target detection method based on improved YOLOv8 for SAR images

Xue Li, Zhichao You, H. Gao, Zuomei Lai

Due to the complex background and less effective information in low-resolution and noisy images, the detection of small ships in SAR images suffers from low detection rate and high false alarm rate. In order to solve the above problems, this paper proposes …

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BDIP: An Efficient Big Data-Driven Information Processing Framework and Its Application in DDoS Attack Detection

Qiyuan Fan, Xue Li, Puming Wang, Xin Jin et autres

With the rapid advancement of 5G communication technology in the era of big data, massive terminal devices connected to the Internet have dramatically increased the scale of network, generating a large amount of high-dimensional and heterogeneous information. This not only enhances the …

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IDAD: An improved tensor train based distributed DDoS attack detection framework and its application in complex networks

Qiyuan Fan, Xue Li, Puming Wang, Xin Jin et autres

With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to …

Mutli-focus image fusion based on guided filter and image matting network

Puchao Zhu, Xue Li, Puming Wang, Xin Jin et autres

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Research on intelligent fault diagnosis for railway point machines using deep reinforcement learning

Shuai Xiao, Qingsheng Feng, Xue Li, Hong Li

Abstract The advanced diagnosis of faults in railway point machines is crucial for ensuring the smooth operation of the turnout conversion system and the safe functioning of trains. Signal processing and deep learning-based methods have been extensively explored in the realm of …

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Truncated Lanczos-TSVD: An Effective Dimensionality Reduction Algorithm for Detecting DDoS Attacks in Large-Scale Networks

Sizhang Li, Xu Jing, Peng Liu, Xue Li et autres

With the rapid development of big data, the scale and complexity of network data have significantly increased. Consequently, detecting DDoS attacks has become increasingly difficult. In this context, traditional machine learning has a limited ability to detect DDoS attacks, resulting in lower …

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Multi-focus image fusion based on gradient tensor HOSVD

Puming Wang, Ting Cao, Xue Li, Xin Jin et autres

Multi-focus image fusion is the process of fusing images of the same scene with different focus ranges. However, pixel misclassification and artifacts caused by noise have been unavoidable problems in fusion methods. To overcome this problem, we propose gradient tensor high-order singular …

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Reinforcement learning architecture for cyber–physical–social AI: state-of-the-art and perspectives

Xue Li, Puming Wang, Xin Jin, Qian Jiang et autres

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Multi-Modal Noise-Robust DDoS Attack Detection Architecture in Large-Scale Networks Based on Tensor SVD

Xu Jing, Xue Li, Puming Wang, Xin Jin et autres

Distributed Denial of Service (DDoS) attacks consumes the resources of traditional or cloud computing networks, resulting in the network unable to provide normal services. Therefore, accurate detection of DDoS attacks can avoid greater losses and provide an important guarantee for network space …

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