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임장수,이양민,이재기 동아대학교 정보기술연구소 2003 情報技術硏究所論文誌 Vol.10 No.2
Variety of Collaborative Virtual Environment systems were developed until now and many researches are under the progress at next generation Internet. In this paper, we propose a Virtual Collaborative Performance System design by wireless control that is a type of CVE. Previous Collaborative system can not satisfy special condition of a performance system. The systems are need to be synchronized but it is work hard. The system which we propose is satisfied with the synchronization among players.
임장수,전인동,이유빈,김은미,신동석,전호정,박한수,신흥수 한국고분자학회 2016 Macromolecular Research Vol.24 No.6
Although many efforts have been made to engineer cell sheets with anisotropic patterns, current techniques still exhibit several shortcomings including uncontrolled harvest time and deformation of pattern by contraction. In this study, we report a simple method to harvest a cell sheet with a striated structure of extracellular matrix (ECM) and myoblasts using a thermosensitive hydrogel micropatterned with different sizes (25 and 80 μm). The hydrogel supported the formation of a confluent monolayer of myoblasts with aligned morphology. When the temperature was reduced from 37 to 4 oC, the size of the hydrogel increased by approximately 1.2-fold within 10 min. In response to this change, a cell sheet was harvested and could be transferred to the desirable substrate through conformal contact between the hydrogel and target. We further examined the effect of pattern size on alignment of ECM/ cell assembly by fast Fourier transform (FFT) analysis of fluorescently stained stress fibers and ECM proteins within the harvested cell sheet. The cell sheet maintained alignment of confluent myoblasts after being harvested to the substrate. In addition, the topologic patterns also promoted formation of aligned myotube on the harvested cell sheet, which was important for muscle tissue regeneration. However, the pattern size appeared to have no influence on alignment of cells on the cell sheet. Finally, a bi-layered structure was fabricated in which each layer was stacked with aligned direction on the cell sheet being perpendicularly assembled. Collectively, our platform could be used for rapid harvest of cell sheets with aligned architecture of ECM/cell, which can be applied to fabrication of welldefined 3D tissue for tissue engineering.
임다예,문용재,박종엽,박은수,이강진,이진이,장수정 한국천문학회 2019 Journal of The Korean Astronomical Society Vol.52 No.4
We develop forecast models of daily probabilities of major flares (M- and X-class) based on empirical relationships between photospheric magnetic parameters and daily flaring rates from May 2010 to April 2018. In this study, we consider ten magnetic parameters characterizing size, distribution, and non-potentiality of vector magnetic fields from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are classified into three types: the total unsigned parameters, the total signed parameters, and the mean parameters. We divide the data into two sets chronologically: 70% for training and 30% for testing. The empirical relationships between the parameters and flaring rates are used to predict flare occurrence probabilities for a given magnetic parameter value. Major results of this study are as follows. First, major flare occurrence rates are well correlated with ten parameters having correlation coefficients above 0.85. Second, logarithmic values of flaring rates are well approximated by linear equations. Third, using total unsigned and signed parameters achieved better performance for predicting flares than the mean parameters in terms of verification measures of probabilistic and converted binary forecasts. We conclude that the total quantity of non-potentiality of magnetic fields is crucial for flare forecasting among the magnetic parameters considered in this study. When this model is applied for operational use, it can be used using the data of 21:00 TAI with a slight underestimation of 2–6.3%.