• 지역사회간호학회지
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• 제19권1호
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• pp.13-26
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• 2008

Purpose: The purpose of this paper was to investigate community health status and related factors using community health and social indicators. Method: Data sources were reviewed and data for 10 categories, 75 indicators were collected. Community health status and health-related factors were categorized, and the means and standard deviation of individual indicators were obtained and standardized scores were calculated. In addition, through factor analysis of individual indicators by category using the scores and using the resultant factor coefficients as weights, indexes were calculated by area. Correlation and regression were analyzed. Result: Each indicator was highly correlated with each index, and the indexes were highly correlated with one another. Correlation coefficients were above 0.8 between community health index and population, education, housing, and economy, between population and education, housing and economy, between education and housing and economy, and between housing and economy, environment and industry. But multicollinearity was not found in the result. Significant factors on community health index were population, health personnel and facilities, education, housing and economy, and R-square were 92.4%. Conclusion: Health determinants such as population, health personnel and facilities, education, housing and economy could be influencing factors on community health in community level. These results showed the importance of intersectoral collaboration within a local government. Overall community health can be enhanced by intersectoral collaboration.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.707-717
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• 2020

Global warming affects climate change and has an overall impact on all aspects of life. On the other hand, community behavior and disaster aspects also have an important role in people's lives. This will also have an impact on regional development. This study aims to find the effect of climate, disaster, and social community on rural development. This study uses data on the potential of rural development from PODES 2014, and 2018 data collection on climate conditions and regional status is sourced from relevant ministries. This research uses Ordinary Least Square (OLS) Regression Analysis method, then continued with CHAID analysis to find the segmentation of the role of climate, disaster, and social factors on rural development. The results of this study found that all research regressor variables significantly influence the Rural Development Index (IPD2018), with an R-squared value of 32.9 percent. Efforts need to be taken in order to implement policies that are targeted, effective, and efficient. The results of this study can be a reference for the government in determining policies by focusing on rural development that have high duration of sunshine, cultivating natural disaster warnings, especially in areas prone to natural disasters, and need to focus on underdeveloped areas.

• 대한기계학회논문집
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• 제12권5호
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• pp.992-1002
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• 1988

본 연구에서는 Monte Carlo 새뮬레이션 기법을 이용하여 지반가속도의 스펙트 럼 밀도함수(power spectral densities)로부터 여섯 성분의 지반가속도 시간이력곡선 을 얻고, 이들을 입력 데이터로 하여 운동방정식에 Newmark의 직접적분법을 이용하여 회전축-베어링 시스템의 응답상태벡터(response state vector)를 얻기로 한다. 충분 히 많은 수의 지반가속도 시간이력곡선을 시뮬레이션하고, 각 경우에 대응하는 응답상 태벡터들을 얻은 다음 일반적인 통계학 방법을 적용하여 평균함수, 표준편차 및 r.m.s (root mean square)등을 얻는다.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• 농촌지도와개발
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• 제29권4호
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• pp.225-250
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• 2022

This study aims to examine the effects of infant's social development forest experience activities through meta-analysis. The final nine studies(total of 165 in the experimental group and 159 in the control group) were selected as a method of systematic review. Meta-analysis on overall effect size estimation, chi-square test, significance analysis, publication bias analysis, and subgroup analysis was performed using the R program. The overall effect size of 9 studies was 1.59, indicating a large effect size. As a result of subgroup analysis of the sub-factors of sociality, autonomy showed the largest effect size at 1.47, the adjusted effect size of cooperation was 1.34, the effect size adjusted for peer interaction was 1.29, and the adjusted effect size for perspective-taking ability was 0.97. All were found to have a statistically significant effect. To analyze the moderating effect, a meta-regression analysis was conducted on the participation period(4, 5~6, 7~8weeks), the number of sessions(6~10, 11~15, 16~20), the frequency per week(1, 2, 5), and the participation time(40, 60, 90, 120, 150min), but there was no statistical difference. Although not statistically significant, the effect size was larger when the participation period was 4 weeks, the number of sessions was 16 to 20, the frequency was 2 times per week, and the participation time was 40 minutes. This results can be usefully utilized by policy makers and forest commentators related to the vitalization of forest education through forest experience activities.

• Journal of Ecology and Environment
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• 제38권4호
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• pp.485-491
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• 2015

This study was conducted to develop stem volume models for the volume estimation of Quercus glauca Thunb. in Jeju Island, Republic of Korea. Furthermore, this study validated the developed stem volume models using an independent dataset. A total of 167 trees were measured for their diameter at breast height (DBH), total height and stem volume using non-destructive sampling methods. Eighty percent of the dataset was used for the initial model development while the remaining 20% was used for model validation. The performance of the different models was evaluated using the following fit statistics: standard error of estimate (SEE), mean bias absolute mean deviation (AMD), coefficient of determination (R²), and root mean square error (RMSE). The AMD of the five models from the different DBH classes were determined using the validation dataset. Model 5 (V = aD^bH^c), which estimates volume using DBH and total height as predicting variables, had the best SEE (0.02745), AMD (0.01538), R² (0.97603) and RMSE (0.02746). Overall, volume models with two independent variables (DBH and total height) performed better than those with only one (DBH) based on the model evaluation and validation. The models developed in this study can provide forest managers with accurate estimations for the stem volumes of Quercus glauca in the subtropical forests of Jeju Island, Korea.

• Journal of Biosystems Engineering
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• 제36권5호
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• pp.369-376
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• 2011

The overall goal of this study was to examine the feasibility of predicting firmness of pear fruit by analyzing laser-induced light backscattering images. Thirty-five image analysis characteristics extracted from the laser-induced light backscattering images were used to build partial least squares regression (PLSR) models for predicting firmness of pear fruit. Experiments were conducted with three sets of pear samples which were in same "Shingo" cultivar, harvested in a same season, but produced in different counties. In every experiments with fruit samples produced in a same county, the correlation coefficients of prediction ($r_p$) and root mean square errors of prediction (RMSEP) of the models were 0.550~0.761 and 4.039~6.154 N, respectively. In an experiment with mixed fruit samples produced in different counties, the $r_p$ and RMSEP of the model were 0.669 and 5.02 N, respectively. The experiment results indicate that the analysis of laser-induced light backscattering images could be a useful tool for predicting firmness of pear fruit nondestructively.

• 한국해안·해양공학회논문집
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• 제22권1호
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• pp.10-17
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• 2010

투과성 이중 반원통 구조물과 경사 입사파간의 상호작용문제를 선형포텐셜 이론을 사용하여 살펴보았다. 투과성 이중 반원통 구조물은 바닥에 고정된 동심원상으로 배열된 2개의 원통 구조물로 이루어지며 각 원통 구조물의 전면은 일정한 공극율을 갖는 투과벽으로 후면은 투명한 벽으로 구성된다. 전면 투과벽의 공극율과 간격 그리고 파랑특성(주파수, 입사각)을 변화시키면서 파랑하중과 처올림 파형 그리고 파 차단성능을 살펴보았다. 파 차단 성능의 척도로서 차단영역내의 평균제곱 변위의 제곱근(R.M.S.)을 사용하였다. 투과성 반원통 구조물은 불투과성 구조물과 비교하여 차단영역내의 파도응답을 감소시키며 구조물에 작용하는 파랑하중을 크게 줄여준다. 특히 이중 구조물은 고주파주 영역에서 단일 구조물보다 파를 차단하는데 효과적이다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.153-153
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• 2019

Evapotranspiration (ET) is an important component of hydrological processes. Accurate estimates of ET variation are of vital importance for natural hazard adaptation and water resource management. This study first developed a soil water index (SWI)-based Priestley-Taylor algorithm (SWI-PT) based on the enhanced vegetation index (EVI), SWI, net radiation, and temperature. The algorithm was then compared with a modified satellite-based Priestley-Taylor ET model (MS-PT). After examining the performance of the two models at 10 flux tower sites in different land cover types over East Asia and Australia, the daily estimates from the SWI-PT model were closer to observations than those of the MS-PT model in each land cover type. The average correlation coefficient of the SWI-PT model was 0.81, compared with 0.66 in the original MS-PT model. The average value of the root mean square error decreased from $36.46W/m^2$ to $23.37W/m^2$ in the SWI-PT model, which used different variables of soil moisture and vegetation indices to capture soil evaporation and vegetative transpiration, respectively. By using the EVI and SWI, uncertainties involved in optimizing vegetation and water constraints were reduced. The estimated ET from the MS-PT model was most sensitive (to the normalized difference vegetation index (NDVI) in forests) to net radiation ($R_n$) in grassland and cropland. The estimated ET from the SWI-PT model was most sensitive to $R_n$, followed by SWI, air temperature ($T_a$), and the EVI in each land cover type. Overall, the results showed that the MS-PT model estimates of ET in forest and cropland were weak. By replacing the fraction of soil moisture ($f_{sm}$) with the SWI and the NDVI with the EVI, the newly developed SWI-PT model captured soil evaporation and vegetation transpiration more accurately than the MS-PT model.

• 한국지진공학회논문집
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• 제28권4호
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• pp.193-203
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• 2024

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R²), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.