• Journal of The Geomorphological Association of Korea
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• v.20 no.3
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• pp.41-53
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• 2013

This research was carried out to classify erosion shapes and sea-cliff erosion rates were estimated through periodic field survey in Dundu-ri, Anmyeondo. Based on the results of field measurements using the datum-point, the annual sea-cliff erosion rate was estimated about 25~102cm/yr by point. The erosion rate gradually increases from spring to summer, but tends to decrease slightly in autumn. Specifically, the erosion rate between June and July indicated a rather decreasing trend, but showed a sharp increase between July and September. This was attributed to erosion that proceeds more rapidly than during other periods due to severe rainstorms in summer that had a direct impact on the study area as well as storm surges caused by hurricanes. Afterwards, the sea-cliff erosion rate gradually decreased in autumn, but reflected an increasing trend again from December to January. This was attributed to the mechanical weathering that actively progresses as bed rocks on the sea-cliff undergo repeated freezing and thawing in winter. The seacliff in Dundu-ri is divided into three types according to the erosion shape. First, Type A is observed in the sea-cliff composed of the same bed rocks and hard rock stratum. Second, Type B is found in the sea-cliff with a relatively gentler slope compared to Type A, since weathering material including soil is formed on the surface of the sea-cliff consisting of the same bed rocks and hard rock stratum. Lastly, Type C is observed in the sea-cliff where hard rock stratum is mixed with soft rock stratum. In this case, the soft rock stratum slumps and erodes first by precipitation and wave energy, followed by additional slumping of the exposed hard rock stratum.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.572-583
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• 2019

In this study, the atmospheric vertical structure (AVS) associated with summertime (June, July, and August) heavy rainfall in Seoul was classified into three patterns (Loaded Gun: L, Inverted V: IV, and Thin Tube: TT) using rawinsonde soundings launched at Osan from 2009 to 2018. The characteristics of classified AVS and precipitation property were analyzed. Occurrence frequencies in each type were 34.7% (TT-type), 20.4% (IV-type), 20.4% (LG-type), and 24.5% (Other-type), respectively. The mean value of Convective Available Potential Energy (1131.1 J kg^-1) for LG-types and Storm Relative Helicity (357.6 ㎡s^-2) for TT-types was about 2 times higher than that of other types, which seems to be the difference in the mechanism of convection at the low level atmosphere. The composited synoptic fields in all cases showed a pattern that warm and humid southwesterly wind flows into the Korean Peninsula. In the cases of TT-type, the low pressure center (at 850 hPa) was followed by the trough in upper-level (at 500 hPa) as the typical pattern of a low pressure deepening. The TT-type was strongly influenced by the low level jet (at 850 hPa), showing a pattern of connecting the upper- and low-level jets. The result of analysis indicated that precipitation was intensified in the first half of all types. IV-type precipitation induced by thermal instability tended to last for a short term period with strong precipitation intensity, while TT-type by mechanical instability showed weak precipitation over a long term period.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.155-174
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• 2022

From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (