Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Jiaao LIU"'
Autor:
Jiaao Liu, Weihong Zhang, Feiqiang Mei, Xin Xin, Yichao Cao, Chongwei Zhu, Qingao Liu, Xuhui Zhu, Wenru Sun
Publikováno v:
Journal of Materials Research and Technology, Vol 24, Iss , Pp 5792-5804 (2023)
Using uniaxial compression tests, the microstructure evolution and work hardening behaviour of Haynes 214 superalloy were investigated in the strain rate range of 0.01–5 s−1. The true strain-stress relationship was formulated based on a modified
Externí odkaz:
https://doaj.org/article/decf7b6fefff4a308ed111fc60db1660
Publikováno v:
Shipin gongye ke-ji, Vol 43, Iss 11, Pp 58-64 (2022)
In order to improve the drying efficiency and quality of potato chips, and to control the shrinkage deformation during hot air drying, the effects of temperature (45, 55, 65, 75 ℃) and slice thickness (3, 5, 7, 9 mm) on the drying characteristic cu
Externí odkaz:
https://doaj.org/article/61a310da42a74709aaccc7e956620b85
Autor:
Jiaao Liu
Publikováno v:
Proceedings of the 2nd International Conference on Information, Control and Automation, ICICA 2022, December 2-4, 2022, Chongqing, China.
Publikováno v:
Science China Earth Sciences. 64:1893-1908
The Red River Fault, which originated from the southeastern margin of the Tibetan Plateau, has a great significance for obtaining a further understanding of the regional tectonics, topography and river catchment evolution, as well as the petroliferou
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
Journal of Alloys and Compounds. 919:165755
Publikováno v:
Journal of Biobased Materials and Bioenergy. 13:464-474
The innocuous utilization of diseased swine carcasses is a key issue in reducing environmental pollution and ensuring safety in animal husbandry. In this study, by using fat from diseased swine carcasses as raw materials, response surface experiments
Publikováno v:
Materials Science and Engineering: A. 850:143590
Publikováno v:
Materials Science and Engineering: A. 832:142464
Publikováno v:
2019 IEEE 5th International Conference on Computer and Communications (ICCC).
Aiming at the problem that feature extraction in traditional modulation recognition relies on manual experience and the poor performance of traditional methods in low signal-to-noise ratio (SNR), a deep learning intelligent modulation recognition alg