• Journal of Korea Water Resources Association
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• v.35 no.6
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• pp.763-777
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• 2002

The objective of this study is to develop computer simulation model that produces precipitation patterns from stochastic model. The hourly precipitation process consists of the precipitation occurrence and precipitation amounts. In this study, an event cluster model developed by Lee and Lee(2002) is used to describe the occurrence process of events, and the hourly precipitation amounts within each event is described by a nonstationary form of a first-order autoregressive process. The complete stochastic model for hourly precipitation is fitted to historical precipitation data by estimating the model parameters. An analysis of historical and simulated hourly precipitation data for Seoul indicates that the stochastic model preserves many of the features of historical precipitation. The autocorrelation coefficients of the historical and simulated data are nearly identical except for lags more than about 3 hours. The precipitation intensity, duration, marginal distributions, and conditional distributions for event characteristics for the historical and simulated data showed in general good agreement with each other.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.64-76
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• 2021

Culture technology (CT) becomes important in the recent environment where digital technology drives content-based innovations. However, technological trends of CT have not been systematically discussed. Especially, the trends of CT should be analyzed from the national perspective, because CT has grown with the help of government-driven innovation. Therefore, this paper aims to analyze CT trends focusing on national research projects. We collected data on CT from the national science and technology information service (NTIS) database, analyzed the keyword co-occurrence network, and identified the patterns of technological innovation using a clustering analysis. As a result, we found that CT has contributed to the digital content and cultural media, and has been actively developed with the help of machine learning technique. Especially, due to the rise of Covid-19, the non-face-to-face online content is rapidly increasing. This study provides important clues for understanding, analyzing CT trends.

• Resources Recycling
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• v.13 no.5
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• pp.45-50
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• 2004

To investigate the characteristics of phases formed in iron carbides, super iron carbide was synthesized from hematite fines with $CO-H_2$ gas mixture after reduction under $H_2$ gas at $600^{\circ}C$. Before carburization, the surface of iron powder reduced was pre-treated in the atmosphere of 0.05 vol% $NH_3$-Ar. The synthesized iron carbides were comprehensively explored by C/S analyzer(Low C/S determinator), M$\"{o}$ssbauer spectroscopy, X-ray diffraction patterns(XRD), scanning electron microscopy(SEM), transmission electron microscopy(TEM), and Raman spectroscopy at various reaction time of 5, 10, 15, 20, 25, 30, and 35 min, respectively. By adding a small amount of $NH_3$ gas, the super iron carbides containing 10 wt% carbon were synthesized, and its addition stabilized iron carbides. It was found that the $NH_3$ treatment played a major role in the formation of iron carbide without decomposition($Fe_3C{\to}$3Fe+C) of iron carbides and precipitation of free carbon. It also succeed to synthesize super iron carbide, $Fe_5C_2$, as a stable single phase without involving Fe and $Fe_3C$ phases.