• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• 한국유체기계학회 논문집
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• 제19권2호
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• pp.27-35
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• 2016

The purpose of this study is to develop a method to predict the performance and economics of a micro gas turbine cogeneration system using performance correction curves. The variables of correction curves are ambient temperature, ambient pressure, relative humidity and load fraction. All of the values of correction factors were expressed as relative values with respect to design values at the ISO conditions. Once the correction curves are obtained, system performance can be predicted relatively easily compared to a detailed performance analysis method through a simple multiplication of the correction factors of various variables at any operating conditions. The predicted results using the correction curve method were compared with those by the detailed and more complex performance analysis in a wide operating range, and its feasibility was confirmed. To illustrate the usability of the correction curve method, the results of an economic analysis of a cogeneration system considering varying operating ambient condition and load was presented.

• 아동학회지
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• 제17권2호
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• pp.181-193
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• 1996

The purpose of this study was: (1) to examine relationships between social development and day care quality, child-care experience and family characteristics, and (2) to investigate the explainability of those related variables for social development. Subjects for this study were 252 4-year-old children and their mothers from 32 day care centers in Seoul. Harms & Clifford's Early Childhood Environment Rating Scale was used to measure the quality of day care. The main results were as follows: (1) Day care quality, child-care experience and family characteristics were significantly related to social development. (2) Child's gender, months of age, mother's child rearing attitude, the length of child-care experience, overall quality of day care, and group size significantly predicted social development. 33％ of the variance of social development was explained by these variables. The relative influence of these variables to the prediction of social development was about the same.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.26-32
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• 2020

There are many airline customer evaluation data, but they are insufficient in terms of predicting customer satisfaction in practice. In particular, they are generally insufficient in case of verification of data value and development of a customer satisfaction prediction model based on customer evaluation data. In this paper, airline customer satisfaction analysis is conducted through an experiment of correlation analysis between customer evaluation data provided by Google's Kaggle. The difference in accuracy varied according to the three types, which are the overall variables, the top 4 and top 8 variables with the highest correlation. To build an airline customer satisfaction prediction model, they are applied to three classification algorithms of Random Forest, SVM, DNN and conduct a classification experiment. They are divided into training data and verification data by 7:3. As a result, the DNN model showed the lowest accuracy at 86.4%, while the SVM model at 89% and the Random Forest model at 95.7% showed the highest accuracy and performance.

• 대한간호학회지
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• 제29권2호
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• pp.281-292
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• 1999

This study was designed to construct a model that predicts the health promoting behavior of the Korean elderly. Data were collected by self-reported questionaires from 254 Korean elderly in seoul, from June 1 to July 15, 1998. Data were analyzed by descriptive statistics and correlational analysis using pc-SAS program. The Linear Structural Modeling(LISREL) 8.0 program was used to find the best fit model which predicts causal relationships of variables. The overall fit of the hypothetical model to the data was moderate［X$^2$＝249.83(df＝83, p＝.00), RMR＝.07, GFI＝.90, NNFI＝.92, NFI ＝.91］. The predictable variables of health promoting behavior of the Korean elderly were social activity. social support. self-integrity and helplessness except the perceived health status. These variables explained 17.1% of health promoting behavior of the Korean elderly.

• 가족자원경영과 정책
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• 제22권1호
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• pp.63-78
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• 2018

This study examined the insolvency of debtors using multiple-indicator approaches and compared the outcomes across income levels with the 2016 'Household Financial and Welfare Survey'. This study used (1) the total debt to total assets ratio (DTA), (2) the total debt service ratio (DSR), and (3) the Household Default Risk Index (HDRI) recently developed by the Bank of Korea. Households in the lowest income quintile were more likely to be insolvent than any other income group. Demographics, such as age and gender of the household head, and most of the financial variables significantly increased the likelihood of insolvency based on the DTA. The number of household members and job status increased the likelihood of insolvency based on the DSR. Also, age, gender of the household head, and most of the financial variables increased the likelihood of household insolvency based on the HDRI after controlling for other demographics and financial variables.

• 콘크리트학회논문집
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• 제11권4호
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• pp.129-136
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• 1999

For the prediction of concrete-steel bond ability in reinforced concrete, many countries establish specifications for the pullout test. But these methods hardly to consider many parameters such as strength, shape, diameter and location of steel, concrete restrict condition by loading plate, strength of concrete and cover depth etc, and it is difficult to solve concentration and disturbance of stress. The purpose of this study is to propose a New Ring Test method which can be rational quantity evaluations of bond splitting mechanism. For this purpose, pullout test was carried out to assess the effect of several variables on bond splitting properties between reinforcing bar and concrete. Key variables are concrete compressive strength, concrete cover, bar diameter and rib spacing. Failure mode was examined and maximum bond stress-slip relationships were presented to show the effect of above variables. As the result, it appropriately expressed general characteristics of bond splitting mechanism, and it proved capability for standard test method.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.159-159
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• 2019

In this study, the simulation of sediment deposition at Sangju weir reservoir, South Korea, was carried out using artificial neural networks. The ANNs have typically been used in water resources engineering problems for their robustness and high degree of accuracy. Three basic variables namely turbid water inflow, outflow, and water stage have been used as input variables. It was found that ANNs were able to establish valid relationship between input variables and target variable of sedimentation. The R value was 0.9806, 0.9091, and 0.8758 for training, validation, and testing phase respectively. Comparative analysis was also performed to find optimum structure of ANN for sediment deposition prediction. 3-14-1 network architecture using BR algorithm outperformed all other combinations. It was concluded that ANN possess mapping capabilities for complex, non-linear phenomenon of reservoir sedimentation.

• 한국위성정보통신학회논문지
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• 제10권4호
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• pp.77-82
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• 2015

본 논문은 빅 데이터 분석을 이용하여 산악 안전사고를 예방하기 위하여 사고 예측 모델을 제시하였다. 산악 안전사고의 축적된 데이터를 파악하기 쉽게 그래프로 나타내었다. 사고가 발생하는 패턴을 알기 위하여 산악 안전사고 발생 건수의 연도별 분석, 연간 월별 사고 발생 건수, 요일별, 시간대별 분석을 수행하였다. 나타낸 그래프를 이용하여 산악 안전사고의 영향을 미치는 변수들을 가중치 모델링을 통하여 사고 예측 모델을 구성하였다. 산악 지역의 사고 다발 구역에 제시한 모델을 적용하여 예측 모델의 성능을 검정하였다.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2003년도 한국우주과학회보 제12권2호
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• pp.26-26
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• 2003

During fabrication process for the large space optical surfaces, the traditional bound abrasive grinding with bronze bond cupped diamond wheel tools leaves the machine marks and the subsurface damage to be removed by subsequent loose abrasive lapping. We explored a new grinding technique for efficient quantitative control of precision CNC grinding for space optics materials such as Zerodur. The facility used is a NANOFORM-600 diamond turning machine with a custom grinding module and a range of resin bond diamond tools. The machining parameters such as grit number, tool rotation speed, work-piece rotation speed, depth of cut and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis methods. The effectiveness of the grinding prediction model was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment details, the results and implications are presented.