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pro vyhledávání: '"Jia, Shuyi"'
Generative models hold the promise of significantly expediting the materials design process when compared to traditional human-guided or rule-based methodologies. However, effectively generating high-quality periodic structures of materials on limite
Externí odkaz:
http://arxiv.org/abs/2408.07213
Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods to rapidl
Externí odkaz:
http://arxiv.org/abs/2406.13163
Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help identify or, in the case of generative models, even create novel cryst
Externí odkaz:
http://arxiv.org/abs/2207.13227
Autor:
Jia, Shuyi, Zhou, Liqun
Publikováno v:
In ISA Transactions January 2024 144:167-175
Akademický článek
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Publikováno v:
In Physica A: Statistical Mechanics and its Applications 15 October 2020 556
Akademický článek
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Autor:
Hualin Liu, Wang Haipeng, Pan Xinlong, Xiangqi Gu, Xueteng Li, Huang Youpeng, Jia Shuyi, Liu Hao
Publikováno v:
DEStech Transactions on Computer Science and Engineering.
In the complex marine environment, with the continuous enrichment and development of target detection methods, massive target track data is stored and accumulated in the database, which contains a large amount of information and knowledge. It is poss
Publikováno v:
Journal of Systems Engineering and Electronics. 25:183-193
The acceleration of a high maneuvering target in signal processing is helpful to enhance the performance of the tracker and facilitate the classification of targets. At present, most of the research on acceleration estimation is carried out in cases
Publikováno v:
2016 CIE International Conference on Radar (RADAR).
Aiming to solve the track refined correlation problem of the group targets with systematic errors, based on the characteristics of the group tracks, an algorithm of track refined correlation with in group targets based on systematic error compensatio