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Ying Sun
Researcher at King Abdullah University of Science and Technology
Publications - 255
Citations - 5822
Ying Sun is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Anomaly detection & Fault detection and isolation. The author has an hindex of 29, co-authored 233 publications receiving 3256 citations. Previous affiliations of Ying Sun include University of Oran & Texas A&M University.
Papers
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Journal ArticleDOI
Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study
TL;DR: Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms.
Journal ArticleDOI
Statistical fault detection in photovoltaic systems
TL;DR: This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system and shows that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.
Book ChapterDOI
Geostatistics for Large Datasets
Ying Sun,Bo Li,Marc G. Genton +2 more
TL;DR: The authors thank Reinhard Furrer for valuable comments on themanuscript.
Journal ArticleDOI
Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches
TL;DR: In this paper, the authors proposed a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults.
Journal ArticleDOI
Titanium-Phosphonate-Based Metal-Organic Frameworks with Hierarchical Porosity for Enhanced Photocatalytic Hydrogen Evolution
TL;DR: This work's rationally designed photocatalyst of hierarchically mesoporous titanium phosphonate based metal-organic frameworks, featuring well-structured spheres, a periodic mesostructure, and large secondary mesoporosity, can provide more insights into designing advanced photocatalysts for energy conversion and render a tunable platform in photoelectrochemistry.