Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Haiyou Huang"'
Publikováno v:
npj Materials Degradation, Vol 8, Iss 1, Pp 1-11 (2024)
Abstract Refractory high-entropy alloys (HEAs) have attracted considerable attention due to their stable phase structure and excellent high-temperature properties. In this work, we performed first-principles calculations, coupled with experiments, to
Externí odkaz:
https://doaj.org/article/cbc20d7819494436914881d84edf70d4
Publikováno v:
Journal of Materials Research and Technology, Vol 28, Iss , Pp 335-346 (2024)
In industrial applications, the mechanical properties of die casting Al alloy parts are influenced by a combination of material composition and forming process. This study establishes a performance data-driven framework that takes into account balanc
Externí odkaz:
https://doaj.org/article/4ea83aeb4d4e4379b582eb2d36b2228d
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-11 (2023)
Abstract Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges. Here we employ a triple-objective optimization algorithm deduced from probabilit
Externí odkaz:
https://doaj.org/article/30cb8eaf152c44ca9cd6af08be17e885
Publikováno v:
Applied Sciences, Vol 13, Iss 11, p 6478 (2023)
The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in microscopic image analysis. However, the accuracy and generalizability of deep learning approaches are still hindere
Externí odkaz:
https://doaj.org/article/72c461474381466881b6c78df30a7d31
Publikováno v:
ACS Omega, Vol 6, Iss 31, Pp 20254-20263 (2021)
Externí odkaz:
https://doaj.org/article/ae596fcd38d44d8680ce2bf93edfb990
Autor:
Shilong Liu, Yanjing Su, Haiqing Yin, Dawei Zhang, Jie He, Haiyou Huang, Xue Jiang, Xuan Wang, Haiyan Gong, Zhuang Li, Hao Xiu, Jiawang Wan, Xiaotong Zhang
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-8 (2021)
Abstract With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative, the need for modern data infrastructures that facilitate the sharing of materials data an
Externí odkaz:
https://doaj.org/article/fbe8a28e521d42e197563ee3d55874d9
Publikováno v:
Materials & Design, Vol 210, Iss , Pp 110037- (2021)
Tuning the martensite transformation temperature through composition design has become an important way to broaden the applicable temperature range of shape memory alloys (SMAs). The empirical formula based on traditional statistics is a key referenc
Externí odkaz:
https://doaj.org/article/ae36dbaa82eb41e888a633cefbc16a68
Publikováno v:
Symmetry, Vol 10, Iss 4, p 107 (2018)
Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using
Externí odkaz:
https://doaj.org/article/a2b39f04e3e045139f837766f77306d1
Publikováno v:
Metals, Vol 7, Iss 12, p 527 (2017)
Solid-state refrigeration technology based on elastocaloric effects (eCEs) is attracting more and more attention from scientists and engineers. The response speed of the elastocaloric materials, which relates to the sensitivity to the strain rate and
Externí odkaz:
https://doaj.org/article/b8d2dbfa826e4ddb98b153c922036c24
Green and Energy-Saving Recycling of LiCoO2 by Synergetic Pyrolysis with Polyvinyl Chloride Plastics
Autor:
Zhe Meng, Weiwei Huang, Lei Gao, Jinchuan Dai, Xiaoying Lu, Jiadong Liu, Haiyou Huang, Kaimin Shih, Yuanyuan Tang
Publikováno v:
ACS Sustainable Chemistry & Engineering. 10:12329-12341