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서동구(Seo, Dong-Goo),이종호(Lee, Jong-Ho),김수암(Kim, Soo-Am),신윤호(Shin, Yun-Ho),황은경(Hwang, Eun-Kyoung) 대한건축학회 2019 大韓建築學會論文集 : 構造系 Vol.35 No.5
Various problems regarding the wet floor method such as its complicated process and waste of thermal storage have been raised, but the usage of dry floor recommended for long-life housing has declined due to lack of confidence on the performance of dry floor. The purpose of this study is to secure the credibility of dry floor. Under this purpose, this study considered precedent studies and established directions to secure the performance of long-life housing infill, and thus, 9 performance items (Impact sound, Smoothness, thermal comfort, sensation hardness while walking, falling safety, impact resistance, local compression load, local strength and strain at heating) were drawn. In addition, the experiment was carried out for 5 performances except for legal performance, some dry floor performances and whole spatial performance. As a result, an appropriate result from all performances except was obtained. The performance of dry floor was verified for each item from these results and it is expected to use such results as basic data on dry floor in the future.
Use of beta-P distribution for modeling hydrologic events
Murshed, Md. Sharwar,Seo, Yun Am,Park, Jeong-Soo,Lee, Youngsaeng The Korean Statistical Society 2018 Communications for statistical applications and me Vol.25 No.1
Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.
박준상 ( Jun Sang Park ),서윤암 ( Yun Am Seo ),김규랑 ( Kyu Rang Kim ),하종철 ( Jong-chul Ha ) 한국농림기상학회 2018 한국농림기상학회지 Vol.20 No.3
Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.
김영아(Young A Kim),서윤암(Yun Am Seo),윤상후(Sanghoo Yoon) 한국데이터정보과학회 2018 한국데이터정보과학회지 Vol.29 No.3
본 연구의 목적은 청소년 대상 비만 중재프로그램의 현황을 파악하고 그 효과를 알아보기 위함이다. 검색어는 ‘청소년비만과 효과’ 또는 ‘학생비만과 효과’를 사용하였고, 1993년부터 2016년까지 보고된 국내 논문 총 39편을 분석하였다. 가장 많이 측정된 변수 5가지는 체중, 체지방률, 중성지방, 고밀도지단백 콜레스테롤, 체질량지수 순이었다. 대부분의 연구에서 중재 전과 비교하여 중재 후에 긍정적이고 유의한 효과가 확인되었고, 교정된 표준화된 평균효과크기는 모두 중간 수준이었다. 추가로 메타회귀분석을 수행하여 통계적 이질성을 확인하고, 주요 종속변수들의 사전 효과크기가 사후 효과크기에 영향을 미치고 있음을 확인하였다. 본 연구는 청소년기 비만 중재 프로그램의 효과를 높이기 위해 프로그램들의 전반적인 구성요소와 형태를 고찰하였다. 이는 청소년기 비만을 예방하고 관리할 수 있는 중재 프로그램의 효과를 최적화시키기 위해 필요한 근거기반의 지침을 제공할 것이다. The purpose of this study was to investigate the current state of the experimental obesity intervention study in Korea and to examine the effect of the intervention program. The key words used ’adolescent obesity and effect’ or ’student obesity and effect’, and in this study 39 articles from 1993 to 2016 were used. The five most frequently measured variables were body weight, body fat percentage, triglyceride, HDL cholesterol, and body mass index. The positive and significant effects were confirmed after the intervention compared with before the intervention, and the corrected standardized mean effect sizes were medium. In addition, meta-regression analysis was performed, and pre-effect size was found to influence post-effect size of the major dependent variables. The results of this study specifically examined the overall components and forms of the programs to enhance the effectiveness of the adolescent obesity intervention program.