• 복식
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• 제52권6호
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• pp.51-69
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• 2002

The purpose of this study was to classify the body shapes. exclusive of size and corpulence factors of more than 60-year old elderly women by distinctions, and to investigate the visual effects of combination of ensemble suit details. For the body shape classification, the factor analysis and cluster analysis were performed : the mean value difference of numeral values for classified types were tested by ANOVA : and the follow-up test was conducted by the Duncan's multiple ranged test. The data analysis for visual effects evaluated by a multiple ranking test was analysed by mean. paired t-test, ANOVA and Duncan's multiple ranged test. The results are summarized as follows : 1. The followings are the types of body shape according to the shape factors of the front line of body for elderly women. The distinctions of the front li e of elderly women's body could be presumed; that was, Body typeⅠ was a comparatively well-balanced body type, Body type Ⅱ was close to an average body type. and Body type In was a severely corpulent body type. 2. The followings are the results on the physical visual effects inducing the constituents of clothing type. 1) The neckline·collar types of a jacket have a great influence on the visual effects of the upper body, and orderly. the tailored collar. soutien collar, and round neckline had positive influence on the visual effects in the upper body. 2) The pleat types of one-piece dress had positive influence on the visual effects in the lower body in the order of gored type, pleats type, and gathered type. Also. the balance in the lower body had more influence on the overall balance of the clothing compared to the constituents of clothing type such as neckline collar type or opening line. 3) It showed that whether there is the front opening line of a jacket influenced on the visual effects of all categories.

• 대기
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• 제25권2호
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• pp.249-260
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• 2015

The changes in thermodynamic and dynamic aspects on near (2025~2049) and long-term (2075~2099) future climate changes between the historical run (1979~2005) and the Representative Concentration Pathway (RCP) 4.5 run with 20 coupled models which employed in the phase five of Coupled Model Inter-comparison Project (CMIP5) over East Asia (EA) and the Korean Peninsula are investigated as an extended study for Moon et al. (2014) study noted that the 20 models' multi-model ensemble (MME) and best five models' multi-model ensemble (B5MME) have a different increasing trend of precipitation during the boreal winter and summer, in spite of a similar increasing trend of surface air temperature, especially over the Korean Peninsula. Comparing the MME and B5MME, the dynamic factor (the convergence of mean moisture by anomalous wind) and the thermodynamic factor (the convergence of anomalous moisture by mean wind) in terms of moisture flux convergence are analyzed. As a result, the dynamic factor causes the lower increasing trend of precipitation in B5MME than the MME during the boreal winter and summer over EA. However, over the Korean Peninsula, the dynamic factor causes the lower increasing trend of precipitation in B5MME than the MME during the boreal winter, whereas the thermodynamic factor causes the higher increasing trend of precipitation in B5MME than the MME during the boreal summer. Therefore, it can be noted that the difference between MME and B5MME on the change in precipitation is affected by dynamic (thermodynamic) factor during the boreal winter (summer) over the Korean Peninsula.

• 대기
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• 제25권2호
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• pp.323-337
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• 2015

This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The model's prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Monsoon (WNPM) index that exhibits a prediction skill up to 4- to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members.

• 응용통계연구
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• 제34권2호
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• pp.153-166
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• 2021

약물유전체학 연구의 주요 목표는 고차원의 유전 변수를 기반으로 개인의 약물 반응성을 예측하는 것이다. 변수의 개수가 많기 때문에 변수의 개수를 줄이기 위해서는 변수 선택이 필요하며, 선택된 변수들은 머신러닝 알고리즘을 사용하여 예측 모델을 구축하는데 사용된다. 본 연구에서는 400명의 뇌전증 환자의 차세대 염기서열 분석 데이터에 로지스틱 회귀, ReliefF, TurF, 랜덤 포레스트, LASSO의 조합과 같은 여러 가지 혼합 변수 선택 방법을 적용하였다. 선택된 변수들에 랜덤포레스트, 그래디언트 부스팅, 서포트벡터머신을 포함한 머신러닝 방법들을 적용했고 스태킹을 통해 앙상블 모형을 구축하였다. 본 연구의 결과는 랜덤포레스트와 ReliefF의 혼합 변수 선택 방법을 이용한 스태킹 모형이 다른 모형보다 더 좋은 성능을 보인다는 것을 보여주었다. 5-폴드 교차 검증을 기반으로 하여 적합한 최적 모형의 평균 검증 정확도는 0.727이고 평균 검증 AUC 값은 0.761로 나타났다. 또한, 동일한 변수를 사용할 때 스태킹 모델이 단일 머신러닝 예측 모델보다 성능이 우수한 것으로 나타났다.

• 대기
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• 제32권1호
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• pp.1-15
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• 2022

Recently, Japan's Meteorological Research Institute presented the d4PDF database (Database for Policy Decision-Making for Future Climate Change, d4PDF) through large-scale climate ensemble simulations to overcome uncertainty arising from variability when the general circulation model represents extreme-scale precipitation. In this study, the change of precipitation characteristics between the historical and future climate conditions in the Yongdam-dam basin was analyzed using the d4PDF data. The result shows that annual mean precipitation and seasonal mean precipitation increased by more than 10% in future climate conditions. This study also performed an analysis on the change of the return period rainfall. The annual maximum daily rainfall was extracted for each climatic condition, and the rainfall with each return period was estimated. In this process, we represent the extreme-scale rainfall corresponding to a very long return period without any statistical model and method as the d4PDF provides rainfall data during 3,000 years for historical climate conditions and during 5,400 years for future climate conditions. The rainfall with a 50-year return period under future climate conditions exceeded the rainfall with a 100-year return period under historical climate conditions. Consequently, in future climate conditions, the magnitude of rainfall increased at the same return period and, the return period decreased at the same magnitude of rainfall. In this study, by using the d4PDF data, it was possible to analyze the change in extreme magnitude of rainfall.

• 한국농림기상학회지
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• 제26권2호
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• pp.115-125
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• 2024

바람은 농업환경에 주요한 영향을 주는 기상요소이며, 강풍은 낙과, 시설물 파괴 등의 피해를 일으킨다. 본 연구는 LENS에 물리모델을 적용해서 농경지에 활용될 수 있는 저고도 풍속예측을 진행하였다. 물리모델은 LOG, POW가 사용되었고 지표 변수에 대해서는 환경부지표와 MODIS 지표를 따로 적용하였다. 농촌진흥청에서 운영하는 2022년도 3 m 고도의 바람 및 강풍 자료를 수집하고 검증을 진행하였고 결과를 산점도, 상관계수, RMSE, NRMSE, TS로 나타내었다. 풍속비교 4가지 방법의 결과에서 모델이 관측보다 더 크게 예측하고 있음을 확인할 수 있었다. 강풍 기준 값이 3 m s^-1 일 때, TS 가 약 0.65 정도로 나타났다. 결과는 RMSE와 NRMSE에서는 LOG_L, LOG_M, POW_L, POW_M 순으로 좋게 나타났고 상관계수와 TS에서는 역순으로 좋게 나타났다. 이러한 결과는 정해진 강풍 기준을 추가하여, 농경지 바람 및 강풍확률예측 연구에 도움이 될 것으로 기대된다.

• 대한지리학회지
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• 제49권5호
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• pp.675-689
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• 2014

본 연구에서는 앙상블 경험적 모드 분해법을 우리나라에 적용하여 봄 시작일을 정의하고, 이에 대한 시 공간적인 변화를 분석하였으며, 봄 시작일의 변동성을 분석하여, 봄 시작일에 영향을 미치는 요인을 파악하였다. 우리나라 평균 봄 시작일은 3월 11일로 나타났고, 연구기간 동안 2.6일/10년으로 빨라졌다. 봄 시작일은 일반적으로 위도와 고도가 높아짐에 따라, 그리고 해안에서 내륙으로 갈수록 늦게 나타났다. 우리나라 봄 시작일에 영향을 미치는 요인을 파악하기 위해 상관분석을 수행하였고, 전구평균기온, 북극진동(Arctic Oscillation, AO), 시베리아 고기압이 우리나라 봄 시작일과 유의한 상관관계를 나타냈다. 봄 시작일에 영향을 미치는 지수들을 대상으로 다중회귀분석을 수행하였고, 세 가지 변수가 모두 입력된 모형은 64.7%의 설명력을 나타냈다. 다중회귀분석의 결과 봄 시작일에 미치는 영향은 전구평균기온이 가장 크고, AO가 그 다음으로 나타났다. 우리나라 봄 시작일에 영향을 미치는 종관적인 요인을 파악하기 위해 기압장 및 바람장을 분석한 결과, 시베리아 고기압, 알류샨 저기압, 상층 기압골의 강도 및 위치에 따른 북풍계열 바람의 강도가 봄 시작일을 결정하는 주요 원인인 것으로 나타났다.

• 상하수도학회지
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• 제35권6호
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• pp.417-424
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• 2021

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

• 한국의류산업학회지
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• 제24권5호
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• pp.655-665
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• 2022

This study evaluated the validity of a newly developed mobility protocol examining the comfort functions and requirements of personal protective equipment (PPE) for COVID-19 healthcare workers. Eight males (age: 24.7 ± 3.0 y, height: 173.4 ± 2.3 cm, and body weight 69.9 ± 3.7 kg) participated in the following three PPE conditions: (1) Plastic gown ensemble, (2) Level D ensemble, and (3) Powered air purifying respirator (PAPR) ensemble. The mobility protocol consisted of 10 different tasks in addition to donning and doffing. The 10 tasks were repeated twice at an air temperature of 25oC with 74% RH. The results showed significant differences among the three PPE conditions in mean skin temperature, local skin temperatures (the forehead, thigh, calf, and foot), clothing microclimate (the chest and back), thermal sensation, thermal comfort, and humidity sensation, while there were no significant differences in heart rate or total sweat rate. At rest, the subjects felt less warm and more comfortable in the PAPR than in the Level D condition (P<0.05). However, subjective perceptions in the PAPR and Level D conditions became similar as the tasks progressed and mean skin and leg temperature became greater for the PAPR than the Level D condition (P<0.05). An interview was conducted just after completing the mobility test protocol, and suggestions for improving each PPE item were obtained. To sum up, the mobility test protocol was valid for evaluating the comfort functions of PPE for healthcare workers and obtaining requirements for improving the mobility of each PPE item.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제6권8호
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• pp.353-358
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• 2017

전신주와 같은 전력 설비에는 스마트한 서비스를 위한 다양한 종류의 센서가 포함되어 있으며, 온도 정보는 전력 설비의 정상 동작 상태를 판단하는 중요한 요소 중 하나이다. 본 연구에서는 칼만 필터(Kalman Filter)와 앙상블 모델(Ensemble Model)을 이용해 스마트 전력 장치의 상태를 판단할 수 있도록 장치의 온도 분석 방법을 제안했다. 제안 된 접근 방식은 서로 다른 위치에 설치된 센서로 부터 수집된 정보 중 온도 데이터를 분류하고 칼만필터 및 앙상블 모델을 사용하여 온도 변화의 특성을 분석했다. 세부적으로 수집된 온도 데이터로부터 기상 온도 데이터와 같은 외부 인자를 제거하고 전력 장치의 각 위치로부터의 실제 장치의 온도값만을 분석했으며, 이 과정에서 칼만필터를 사용하여 오류 데이터를 제거하고 앙상블 모델을 사용하여 매 시간 정상 동작하는 전력 설비의 온도 평균값을 산출했다. 온도 분석에 대한 결과와 논의는 전력 데이터에 분석 결과에 명확하게 설명되어 있다. 마지막으로, 분석된 데이터를 통해 전력 장치가 정확히 동작하는 지를 판단할 수 있는 온도값의 정상범위를 확인하였다.