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.
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Easy Transfer Learning By Exploiting Intra-domain Structures
TL;DR: This paper proposes a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance and satisfies the Occam's Razor principle.
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Autonomous Voltage Regulation and Current Sharing in Islanded Multi-Inverter DC Microgrid
TL;DR: This paper presents a state-of-charge (SoC)-based current sharing method through the incorporation of SoC and capacity information into the double closed-loop control for ESUs, which effectively eliminates the dc bus voltage deviation, and simultaneously guarantees the SoC balance among individual ESUs.
Proceedings ArticleDOI
Crowdsourced time-sync video tagging using temporal and personalized topic modeling
TL;DR: A novel temporal and personalized topic model that jointly considers temporal dependencies between video semantics, users' interaction in commenting, and users' preferences as prior knowledge is proposed that outperforms several state-of-the-art baselines in video tagging quality.
Journal ArticleDOI
Coordinated Investment Planning of Distributed Multi-Type Stochastic Generation and Battery Storage in Active Distribution Networks
Ali Ehsan,Qiang Yang +1 more
TL;DR: The proposed solution aims to identify the optimal mix, siting, and sizing of wind turbine, photovoltaic, and BES units to maximize the net present value of distribution network operator (DNO) while fully exploiting the BES arbitrage benefit.
Proceedings ArticleDOI
Adaptive p-posterior mixture-model kernels for multiple instance learning
TL;DR: This paper proposes an adaptive framework for MIL that adapts to different application domains by learning the domain-specific mechanisms merely from labeled bags, especially attractive when the instances are encountered with novel application domains, for which the mechanisms may be different and unknown.