Q
Qiang Yang
Researcher at Hong Kong University of Science and Technology
Publications - 1795
Citations - 96705
Qiang Yang is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 112, co-authored 1117 publications receiving 71540 citations. Previous affiliations of Qiang Yang include University of London & Zhejiang University of Technology.
Papers
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Proceedings ArticleDOI
Robust dynamic bandwidth allocation method for virtual networks
TL;DR: A robust dynamic approach is presented which periodically identifies bandwidth allocation to VNs to work reasonable well for a range of traffic patterns over a period of time, rather than certain traffic pattern instance.
Proceedings ArticleDOI
Avoiding the evolved node B buffer overflow by using advertisement window control
TL;DR: A novel approach is proposed to protect the buffer from overflow by appropriately controlling the advertisement window to enhance the Transmission Control Protocol (TCP) when the buffer becomes congested.
Journal ArticleDOI
Transfer Learning by Reusing Structured Knowledge
TL;DR: It is shown that optimization methods, and techniques inspired by the concerns of data reuse can be applied to extract and transfer deep structural knowledge between a variety of source and target problems.
Proceedings Article
SMART: Semi-Supervised Music Emotion Recognition with Social Tagging.
TL;DR: In this article, a semi-supervised music affective emotion recognition with social tagging (SMART) model is proposed, which combines a graph-based semisupervised learning algorithm with a novel tag refinement method.
Proceedings ArticleDOI
On the use of EEMD and SVM based approach for bearing fault diagnosis of wind turbine gearbox
TL;DR: An algorithmic solution to carry out the analysis of vibration signals of bearings by the use of ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) and the numerical result validates that the solution can efficiently identify the early faults accurately with sufficient robustness.