H
He-Xuan Hu
Researcher at Hohai University
Publications - 5
Citations - 248
He-Xuan Hu is an academic researcher from Hohai University. The author has contributed to research in topics: Image segmentation & Fault (power engineering). The author has an hindex of 3, co-authored 4 publications receiving 135 citations.
Papers
More filters
Journal ArticleDOI
Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks
TL;DR: The deep neural network is adopted to recognize faults in bogies and provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.
Journal ArticleDOI
Parallel Deep Learning Algorithms With Hybrid Attention Mechanism for Image Segmentation of Lung Tumors
TL;DR: The experimental results prove the parallel deep learning algorithm with hybrid attention mechanism performed well in image segmentation of lung tumors, and its accuracy can reach 94.61%.
Journal ArticleDOI
Vehicular Ad Hoc Network Representation Learning for Recommendations in Internet of Things
TL;DR: This work proposes to construct a vehicular ad hoc network based on co-occurrence phenomenon, which has the best performance on friend recommendation compared with several state-of-the-art methods.
Journal ArticleDOI
Network Representation Learning-Enhanced Multisource Information Fusion Model for POI Recommendation in Smart City
TL;DR: This article proposes a network representation learning-enhanced multisource information (MSI) fusion model for POI recommendation in the context of LBSNs and demonstrates that the proposed MSI fusion model outperforms several state-of-the-art algorithms for POi recommendation in terms of precision, recall, and F1.
Journal ArticleDOI
Reservoirs Optimal Operation Based on Reinforcement Learning
TL;DR: In this article , Q-learning combined with a feasible direction method is used to establish time feasible state table and state-feasible action hash table according to the constraints of the discrete four-reservoirs problem, so that the Qlearning algorithm can shorten the optimization time and obtain the optimal solution.