• Geotechnical Engineering
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• v.15 no.10
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• pp.16-26
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• 1999

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.209-210
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• 2013

본 연구의 목적은 공학에 대한 흥미, 태도, 성취가 낮은 여학생에게 공학 경험을 제공함으로써 공학 분야의 여성 인재를 양성할 수 있는 계기를 마련하기 위한 공학 중심 STEAM 프로그램의 틀을 설계하는 데 있다. 이를 위하여 여학생의 특성을 분석하고 여학생 친화적인 환경을 구축하였으며 여학생 친화적 환경에 부합하는 e-textile이라는 도구를 활용하여 STEAM 교육 프로그램을 설계하였다. 교육 프로그램은 학습자가 직접 참여할 수 있도록 Learning by Doing, Learning by Design의 전략을 사용하도록 하였으며 교육 프로그램 전 과정을 학습자 스스로 창의적인 사고를 바탕으로 문제를 해결해나가도록 하고 교사는 조력자, 안내자 역할을 할 수 있도록 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.1-1
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• 2015

최근 연구에 의하면 엘니뇨 패턴의 중심이 열대 동태평양에서 중앙태평양으로 이동하는 양상을 보이고 있는 것으로 보고되고 있으며 태평양 연안 국가를 중심으로 이에 대한 연구가 많이 진행되고 있다. 본 연구에서는 진화하는 엘니뇨패턴과 관련하여 한반도의 영향을 미치는 태풍을 중심으로 태풍의 활동특성과 그에 따른 지역별 태풍강우의 특성을 비교 분석하였다. CT/WP 엘니뇨와 관련하여 북서태평양 지역에서 발생한 태풍이 한반도에 미치는 영향을 분석하기 위하여 태풍에 의해 발생한 여름철 강우와 중호우 사상의 발생특성을 분석하였다. CT 엘니뇨해에는 한반도의 서남부 지역에서 태풍에 의한 여름철 강우가 감소하는 경향이 나타났으며, 동북부 지역에서는 증가하는 특성이 있음을 확인하였다. 또한 WP 엘니뇨 해에는 한반도 대부분 지역에서 태풍에 의한 여름철 강우가 증가하였으며, 중북부지역과 중서부 지역에서 통계적으로 유의한 증가패턴이 있는 것으로 분석되었다. 본 연구의 성과는 태풍을 고려한 지역 맞춤형 기반시설 확충 및 유역대책 수립, 수자원 확보 등에 대한 기초자료로 활용 될 수 있을 것으로 기대된다.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.41 no.4
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• pp.38-46
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• 2012

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.124-132
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• 2022

Recently, due to COVID-19, online remote classes and non-face-to-face exams have made it difficult to manage class attitudes and exam cheating. Therefore, there is a need for a system that automatically recognizes and detects the behavior of students online. Action recognition, which recognizes human action, is one of the most studied technologies in computer vision. In order to develop such a technology, data including human arm movement information and information about surrounding objects, which can be key information in online classes and exams, are needed. It is difficult to apply the existing dataset to this system because it is classified into various fields or consists of daily life action. In this paper, we propose a dataset that can classify attitudes in real-time online tests and classes. In addition, it shows whether the proposed dataset is correctly constructed through comparison with the existing action recognition dataset.

• Korean journal of applied entomology
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• v.60 no.4
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• pp.493-502
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• 2021

We examined American consumers' perceptions and attitudes toward edible insect-based pet food. In this study, 16 Americans in Korea who owned dogs were categorized into two groups, and focus-group discussions were conducted under three conditions. First, we observed the free association perception of edible insect-based pet food, and attitudes were analyzed after providing a newspaper article related to it. Finally, consumer attitude was examined in the context of purchasing. The study found that the participants had high awareness of the eco-friendliness of edible insects and showed a positive attitude toward news articles related to it. However, when it came to purchasing, they considered nutritional and health functional values compared to environmental values. Meanwhile, the rejection of insects was nevertheless the most important negative factor in pet food, as in general food. The results are significant in confirming the possibility of using insect-based pet food by examining the perceptions and attitudes toward the environmental, nutritional, and health functional values of edible insects under three conditions for American consumers.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.20 no.2
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• pp.117-124
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• 1991

• Bulletin of the Society of Naval Architects of Korea
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• v.42 no.3 s.145
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• pp.19-35
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• 2005