Výsledky vyhledávání - "Wang Xiao-lin"

Akademický článek

Research Progress of Anti-Biofouling Materials for Uranium Extraction From Seawater

Autor: LI Lu-yan, WEN Jun, HU Sheng, WANG Xiao-lin

Publikováno v: He huaxue yu fangshe huaxue, Vol 44, Iss 3, Pp 299-312 (2022)

With the two major development objectives of “emission peak” and “carbon neutrality” proposed, nuclear energy, as a full-fledged clean energy, will play an increasingly important role in China’s energy structure. As one of the most importan

Externí odkaz: https://doaj.org/article/746ff140875d44e49235632d363e5d08

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Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction

Autor: Dong, Pingping, Wang, Xiao-Lin, Bose, Indranil, Ng, Kam K. H., Zhang, Xiaoning, Zhang, Xiaoge

Accurate and reliable prediction has profound implications to a wide range of applications. In this study, we focus on an instance of spatio-temporal learning problem--traffic prediction--to demonstrate an advanced deep learning model developed for m

Externí odkaz: http://arxiv.org/abs/2408.13293

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Akademický článek

The Impact of Large Grain Clay Minerals on X-ray Diffraction Quantitative Analysis of Clay Minerals

Autor: XIE Gu-wei, YE Mei-fang, HUANG Jing, WANG Xiao-lin, NAN Jun-xiang, REN Zhi-peng, SHI Xiao-hu, LIU Na

Publikováno v: Yankuang ceshi, Vol 37, Iss 5, Pp 499-506 (2018)

BACKGROUND Current X-ray Diffraction analysis of clay minerals is used to detect the clay mineral assemblages and contents of suspended solution with a particle size of less than 2 μm, but the content data is sometimes inconsistent with other test d

Externí odkaz: https://doaj.org/article/f646ce6bc091410181e4af2c691a9511

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Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge

Autor: Zhang, Xiaoge, Wang, Xiao-Lin, Fan, Fenglei, Cheung, Yiu-Ming, Bose, Indranil

In this paper, we develop a generic methodology to encode hierarchical causality structure among observed variables into a neural network in order to improve its predictive performance. The proposed methodology, called causality-informed neural netwo

Externí odkaz: http://arxiv.org/abs/2311.17303

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