Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Tangyi, Li"'
Publikováno v:
IEEE Access, Vol 12, Pp 63629-63643 (2024)
High-flow hydraulic servo systems are extensively employed in contemporary industrial applications due to their considerable flow capacity and cost-effectiveness. Nonetheless, hydraulic servo systems frequently encounter unpredictable internal and ex
Externí odkaz:
https://doaj.org/article/563b22adf1cf41669e827c1b439f6b65
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-8 (2021)
Abstract Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex data
Externí odkaz:
https://doaj.org/article/d540163a0eb4481da382526d69a89840
Publikováno v:
IEEE Wireless Communications. :1-9
Autor:
Honghao Chu, Haibiao Chen, Wenju Ren, Zongwei Mei, Kuangda Xu, Shisheng Zheng, Qingshui Hong, Feng Pan, Tangyi Li
Publikováno v:
Journal of Energy Chemistry. 62:610-616
Tungsten carbides have attracted wide attentions as Pt substitute electrocatalysts for hydrogen evolution reaction (HER), due to their good stability in an acid environment and Pt-like behaviour in hydrolysis. However, quantum chemistry calculations
Publikováno v:
AIJ Journal of Technology and Design. 27:1249-1254
Autor:
Zongwei Mei, Wenju Ren, Qingshui Hong, Kuanda Xu, Feng Pan, Honghao Chu, Tangyi Li, Shisheng Zheng, Shunning Li, Yang Li
Publikováno v:
RSC Advances. 11:28908-28911
Effective separation of photoexcited carriers and chemisorption of the N2 molecule are two key issues to efficient nitrogen photofixation. The spatial charge separation of BiVO4 with anisotropic exposed facets, namely the transfer of photoexcited ele
Publikováno v:
Journal of Structural and Construction Engineering (Transactions of AIJ). 86:415-423
Publikováno v:
npj computational materials
npj Computational Materials, Vol 7, Iss 1, Pp 1-8 (2021)
npj Computational Materials, Vol 7, Iss 1, Pp 1-8 (2021)
Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, suc
Autor:
Honghao, Chu, Shisheng, Zheng, Yang, Li, Kuanda, Xu, Qingshui, Hong, Tangyi, Li, Wenju, Ren, Shunning, Li, Zongwei, Mei, Feng, Pan
Publikováno v:
RSC advances. 11(46)
Effective separation of photoexcited carriers and chemisorption of the N
Publikováno v:
2013 International Conference on Computing, Electrical & Electronic Engineering (ICCEEE); 2013, p92-98, 7p