Lingyu Yang
Tongji University
13 Papers
1 Citations
Lingyu Yang is an academic researcher from Tongji University. The author has contributed to research in topics: Computer science & Fault (power engineering). The author has an hindex of 1, co-authored 7 publications.
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Papers
Delay-sensitive tasks offloading in multi-access edge computing
TL;DR: In this paper , an algorithm based on ant colony optimization is proposed in order to find the best solution for task offloading in multi-access edge computing system, where edge devices are divided into different cooperation spaces.
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Turnout Fault Diagnosis Based on CNNs with Self-Generated Samples
Shize Huang,Lingyu Yang,Fan Zhang,Wei Chen,Zaixin Wu +4 more
- 01 Sep 2020
TL;DR: China’s rapid development of high-speed railways has imposed increasing requirements for safety and reliability of signal systems, especially the critical part: turnouts, which is studied in detail in this paper.
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Few-Shot Hyperspectral Image Classification Based on Convolutional Residuals and SAM Siamese Networks
Mengen Xia,Guowu Yuan,Lingyu Yang,Kunming Xia,Ying Ren,Zhiliang Shi,Hao Zhou +6 more
TL;DR: This work proposes a model called CRSSNet (Convolutional Residuals and SAM Siamese Networks) for few-shot hyperspectral image classification and introduces the Spatial Attention Mechanism (SAM) to effectively leverage spatial information features in hyperspectrals.
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Arc Detection and Recognition in the Pantograph-Catenary System Based on Multi-Information Fusion:
TL;DR: A fusion method for the pantograph-catenary arc detection based on multi-type videos that can avoid misjudgments of the two individual detection methods in certain scenarios, and perform better than each of them.
11
Object Detection-Based License Plate Localization and Recognition in Complex Environments:
TL;DR: This method uses a cascade of object detection algorithms to accurately and speedily recognize plates’ contents and exhibits a better performance on challenging images that contain blurred plates, skewed angles, or accidental occlusion, or have been captured in bad weather or poor light, which implies its potential in more diversified practice scenarios.
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