• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2A
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• pp.97-108
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• 2012

Dynamic responses were measured using long-term monitoring system for Uldolmok tidal current pilot power plant which is one of jacket-type offshore structures. Among the dynamic quantities, the tilt angle was chosen because the low frequency response components can be precisely measured by dynamic tiltmeter, and the natural frequencies and modal damping ratio were successfully identified using proposed LS-FDD (least squared frequency domain decomposition) method. And the effects of tidal height and tidal current velocity on the variation of natural frequencies and modal damping ratios were investigated in time and frequency domain. Also the non-parametric models were tested to model the relationship between tidal conditions and modal properties such as natural frequencies and damping ratios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.641-648
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• 2022

This paper is a study to find out the factors affecting the defects that occur during the use of Electron Beam Melting (EBM), one of the 3D printer output methods, through data analysis. By referring to factors identified as major causes of defects in previous studies, log files occurring between processes were analyzed and related variables were extracted. In addition, focusing on the fact that the data is time series data, the concept of a window was introduced to compose variables including data from all three layers. The dependent variable is a binary classification problem with the presence or absence of defects, and due to the problem that the proportion of defect layers is low (about 4%), balanced training data were created through the SMOTE technique. For the analysis, I use XGBoost using Gridsearch CV, and evaluate the classification performance based on the confusion matrix. I conclude results of the stuy by analyzing the importance of variables through SHAP values.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.918-923
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• 2021

The Korean government is developing a 10-year master plan pertaining to the Public Waters Management and Reclamation Act. However, it was observed that implementation of the reclamation project through frequent changes would occupy a significant proportion. Thus, questions are being raised about the effectiveness of the master plan. In view of this, the need for a trend analysis on long-term reclamation demand is growing. Accordingly, in this study, a trend analysis of reclamation demand was carried out using the annual reclamation performance data. The results of the analysis indicate that the demand for reclamation of public waters continued to decline, and the trend has been particularly evident since the 1990s, when it was converted into a reclamation master plan. In addition, the total demand for reclamation during 2021-2030 was calculated to be at a maximum of 13.8 km² and minimum of 1.7 km².

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.82-89
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• 2021

The objective of this study is to propose a new Glide Path that dynamically adjusts the risky asset inclusion ratio of the Target Date Fund by simultaneously considering the market's forecast volatility as well as the time of investor retirement, and to compare the investment performance with the traditional Target Date Fund. Forecasts of market volatility utilize historical volatility, time series model GARCH volatility, and the volatility index VKOSPI. The investment performance of the new dynamic Glide Path, which considers stock market volatility has been shown to be excellent during the analysis period from 2003 to 2020. In all three volatility prediction models, Sharpe Ratio, an investment performance indicator, is improved with higher returns and lower risks than traditional static Glide Path, which considers only retirement date. The empirical results of this study present the potential for the utilization of the suggested Glide Path in the Target Date Fund management industry as well as retirees.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1036-1056
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• 2017

The rapid increase of ageing population and chronic disease patients cause high medical expenses, and it led an increased attention to digital healthcare. Smart car technologies for healthcare have been developing to recognize drivers' status and predict diverse driving environments. The present study aimed to understand the research trends of autonomous vehicle technologies of Korea and the United States through time series analysis, network analysis, visualization, and comparison between the two countries. The results suggest that cooperative study needs to be done in common research areas such as driver's safety and algorithms. It is also needed to conduct studies and benchmark about liking technique related to part-to-part and vehicle-to-vehicle as America's competitive advantaged area. In the US, diverse approaches of autonomous vehicle technologies have used to consider the characteristics of various age groups and passengers' health status through sensor, while in Korea, only one aspect, older drivers, is mentioned. Implications for the development direction of autonomous vehicle technologies with competitiveness in considering public health, ethics, and driver's safety and convenience are discussed in detail.

• Water for future
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• v.28 no.6
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• pp.147-158
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• 1995

The purpose of this study is to establish a method of estimating the daily urban water demand using statistical model. This method will be used for the development of the efficient management and operation of the water supply facilities. The data used were the daily urban water use, the population, the year lapse and the weather conditions such as temperature, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. The raw data used in this study were rearranged either by month or by season for the purpose of analysis, and the statistical analysis was applied to the data to obtain the regression model. As a result, the multiple linear regression model was developed to estimate the daily urban water use based on the seather condition. The regression constant and the model coefficients were determined for each month of a year. The accuracy of the model was within 3% of average error and within 10% of maximum error. The developed model was found to be useful to the practical operation and management of the water supply facilities.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.51-59
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• 2000

A dynamic model, which combined time series model with distributed-lag model, is applied to understand the relationship between rainfall and groundwater level. In the model, rainfall with distribution lags and past groundwater level as a dependent variables were used to estimate present groundwater level. The distribution of the lagged rainfall effects for groundwater levels was modeled by Almon polynomials. The model was applied to Banglim and Tanbu groundwater stations in Pyungchang river and Bocheong stream watershed which are representative basins for International Hydrological Program (IHP). The dynamic model represents observed groundwater levels very well and can be used to predict the levels. The model parameters reflect hydraulic characteristics of aquifer. In addition, from the parameters it appears that the increase in groundwater level due to rainfall takes place significantly within first two days of the rainfall event. The rainfall of the order of 18mm/day and 30mm/day at Banglim and Tanbu, respectively, had no significant effect on the groundwater levels.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.75-84
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• 2019

In this study, the random forest approach was used to predict the national mean rice yield of South Korea by using mean climatic factors at a national scale. A random forest model that used monthly climate variable and year as an important predictor in predicting crop yield. Annual yield change would be affected by technical improvement for crop management as well as climate. Year as prediction factor represent technical improvement. Thus, it is likely that the variables of importance identified for the random forest model could result in a large error in prediction of rice yield in practice. It was also found that elimination of the trend of yield data resulted in reasonable accuracy in prediction of yield using the random forest model. For example, yield prediction using the training set (data obtained from 1991 to 2005) had a relatively high degree of agreement statistics. Although the degree of agreement statistics for yield prediction for the test set (2006-2015) was not as good as those for the training set, the value of relative root mean square error (RRMSE) was less than 5%. In the variable importance plot, significant difference was noted in the importance of climate factors between the training and test sets. This difference could be attributed to the shifting of the transplanting date, which might have affected the growing season. This suggested that acceptable yield prediction could be achieved using random forest, when the data set included consistent planting or transplanting dates in the predicted area.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.88-88
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• 2021

최근 수자원과 수질관리 분야에 자료기반 머신러닝 모델과 딥러닝 모델의 활용이 급증하고 있다. 그러나 딥러닝 모델은 Blackbox 모델의 특성상 고전적인 질량, 운동량, 에너지 보존법칙을 고려하지 않고, 데이터에 내재된 패턴과 관계를 해석하기 때문에 물리적 법칙을 만족하지 않는 예측결과를 가져올 수 있다. 또한, 딥러닝 모델의 예측 성능은 학습데이터의 양과 변수 선정에 크게 영향을 받는 모델이기 때문에 양질의 데이터가 제공되지 않으면 모델의 bias와 variation이 클 수 있으며 정확도 높은 예측이 어렵다. 최근 이러한 자료기반 모델링 방법의 단점을 보완하기 위해 프로세스 기반 수치모델과 딥러닝 모델을 결합하여 두 모델링 방법의 장점을 활용하는 연구가 활발히 진행되고 있다(Read et al., 2019). Process-Guided Deep Learning (PGDL) 방법은 물리적 법칙을 반영하여 딥러닝 모델을 훈련시킴으로써 순수한 딥러닝 모델의 물리적 법칙 결여성 문제를 해결할 수 있는 대안으로 활용되고 있다. PGDL 모델은 딥러닝 모델에 물리적인 법칙을 해석할 수 있는 추가변수를 도입하며, 딥러닝 모델의 매개변수 최적화 과정에서 Cost 함수에 물리적 법칙을 위반하는 경우 Penalty를 추가하는 알고리즘을 도입하여 물리적 보존법칙을 만족하도록 모델을 훈련시킨다. 본 연구의 목적은 대청호의 수심별 수온을 예측하기 위해 역학적 모델과 딥러닝 모델을 융합한 PGDL 모델을 개발하고 적용성을 평가하는데 있다. 역학적 모델은 2차원 횡방향 평균 수리·수질 모델인 CE-QUAL-W2을 사용하였으며, 대청호를 대상으로 2017년부터 2018년까지 총 2년간 수온과 에너지 수지를 모의하였다. 기상(기온, 이슬점온도, 풍향, 풍속, 운량), 수문(저수위, 유입·유출 유량), 수온자료를 수집하여 CE-QUAL-W2 모델을 구축하고 보정하였으며, 모델은 저수위 변화, 수온의 수심별 시계열 변동 특성을 적절하게 재현하였다. 또한, 동일기간 대청호 수심별 수온 예측을 위한 순환 신경망 모델인 LSTM(Long Short-Term Memory)을 개발하였으며, 종속변수는 수온계 체인을 통해 수집한 수심별 고빈도 수온 자료를 사용하고 독립 변수는 기온, 풍속, 상대습도, 강수량, 단파복사에너지, 장파복사에너지를 사용하였다. LSTM 모델의 매개변수 최적화는 지도학습을 통해 예측값과 실측값의 RMSE가 최소화 되로록 훈련하였다. PGDL 모델은 동일 기간 LSTM 모델과 동일 입력 자료를 사용하여 구축하였으며, 역학적 모델에서 얻은 에너지 수지를 만족하지 않는 경우 Cost Function에 Penalty를 추가하여 물리적 보존법칙을 만족하도록 훈련하고 수심별 수온 예측결과를 비교·분석하였다.