Zobrazeno 1 - 10 of 38 pro vyhledávání: '"JunHyuk Kim"'
1
Akademický článek
Acute Effect of Active Tibial Rotation Exercise on Tibial Torsion Angle, Ankle Range of Motion, and Neuromuscular Control
Autor: JunHyuk Kim, Kwang-Jin Lee, Keun-Ok An
Publikováno v: 운동과학, Vol 32, Iss 2, Pp 207-214 (2023)
PURPOSE This study aimed to analyze the acute effect of active tibial rotation exercise on tibial torsion angle, ankle range of motion, and neuromuscular control. METHODS This study was based on AB/BA Cross-over design, and participants were divided
Externí odkaz: https://doaj.org/article/6b5379f033de4b00aa8e1b996819cc44
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2
Akademický článek
Miscibility and ternary diagram of aqueous polyvinyl alcohols with different degrees of saponification
Autor: Junhyuk Kim, Shohei Ishikawa, Mitsuru Naito, Xiang Li, Ung-il Chung, Takamasa Sakai
Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Liquid–liquid phase separation (LLPS), an important phenomenon in the field of polymer science and material design, plays an essential role in cells and living bodies. Poly(vinyl alcohol) (PVA) is a popular semicrystalline polymer utilized
Externí odkaz: https://doaj.org/article/dcb1e305d6094e049e869e2e3ac22d8a
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3
Akademický článek
Pulse pileup correction method for gamma-ray spectroscopy in high radiation fields
Autor: Minju Lee, Daehee Lee, Eunbie Ko, Kyeongjin Park, Junhyuk Kim, Kilyoung Ko, Manish Sharma, Gyuseong Cho
Publikováno v: Nuclear Engineering and Technology, Vol 52, Iss 5, Pp 1029-1035 (2020)
The detector suffers from pulse pileup by overlapping of the signals when it was used in high radiation fields. The pulse pileup deteriorates the energy spectrum and causes count losses due to random coincidences, which might not resolve within the r
Externí odkaz: https://doaj.org/article/afbf3f28cbe94e11a7da35828e42745c
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4
Akademický článek
Prediction and control of two-dimensional decaying turbulence using generative adversarial networks.
Autor: Jiyeon Kim, Junhyuk Kim, Changhoon Lee
Publikováno v: Journal of Fluid Mechanics; 2/25/2024, Vol. 981, p1-44, 44p
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6
Interpretable deep learning for prediction of Prandtl number effect in turbulent heat transfer
Autor: Junhyuk Kim, Hyojin Kim, Changhoon Lee
Publikováno v: Journal of Fluid Mechanics. 955
We propose an interpretable deep learning (DL) model that extracts physical features from turbulence data. Based on a conditional generative adversarial network combined with a new decomposition algorithm for the Prandtl number effect, we developed a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::753ecfd196cd8037f5500f48d495f9e5
https://doi.org/10.1017/jfm.2022.1069
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