• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.165-171
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• 2022

Human heart rate can be used to measure exercise intensity as an important indicator. If heart rate can be predicted, exercise can be performed more efficiently by regulating the intensity of exercise in advance. In this paper, a FitRec-based prediction model is proposed for estimating running heart rate for users. Endomondo data is utilized for training the proposed prediction model. The processing algorithms for time-series data, such as LSTM(long short term memory) and GRU(gated recurrent unit), are employed to compare their performance. On the basis of simulation results, it was demonstrated that the proposed model trained with running exercise performed better than the model trained with several cardiac exercises.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.

• Geomechanics and Engineering
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• v.39 no.3
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• pp.241-256
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• 2024

The shear strength (SS) of soil is a critical parameter utilized in the design of civil engineering projects. The SS parameters, including cohesion (c) and friction angle (𝜑), can be determined through methods conducted either in the field or within a laboratory environment. However, the traditional method for determining SS parameters are not only costly but also time-consuming. Recently, the application of machine learning (ML) in geotechnical problems has received increasing attention. In order to select an appropriate ML model and assess the effect of physical properties on the SS of soil. This research endeavors to predict critical SS parameters of soil through the application of five machine learning (ML) models, integrating easily-available physical soil index, including specific gravity (G), saturation degree (Sr), liquid limit (LL), silt content (SC), and clay content (CC). The used ML techniques include Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). A range of metrics, encompassing the root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R²) were used to measure the predictive efficacy of the employed models as well as compare the performance of the used ML models. The values of R² range from 0.769 to 0.987 indicate that all ML models exhibit excellent predictive capabilities for estimating SS parameters, in which the XGBoost, and CNN techniques show outperforming results compared to the other models. The study uses decision tree feature importance (DTFI) and coefficient feature importance (CFI) techniques to investigate how various physical properties impact the predictive capabilities of the model and indicates that both G and LL have a substantial impact on the predictive accuracy of cohesion and friction angle.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.429-457
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• 2024

Purpose: This study aims to provide useful information to real estate investors by developing a profit determination model using artificial intelligence. The model analyzes the real estate markets of six selected cities from multiple perspectives, incorporating characteristics of the real estate market, economic indicators, and policies to determine potential profits. Methods: Data on real estate markets, economic indicators, and policies for five cities were collected and cleaned. The data was then normalized and split into training and testing sets. An AI model was developed using machine learning algorithms and trained with this data. The model was applied to the six cities, and its accuracy was evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared by comparing predicted profits to actual outcomes. Results: The profit determination model was successfully applied to the real estate markets of six cities, showing high accuracy and predictability in profit forecasts. The study provided valuable insights for real estate investors, demonstrating the model's utility for informed investment decisions. Conclusion: The study identified areas for future improvement, suggesting the integration of diverse data sources and advanced machine learning techniques to enhance predictive capabilities.

• Journal of the Korean Solar Energy Society
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• v.33 no.1
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• pp.1-6
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• 2013

It is difficult to avoid measurement errors caused by the shading effect of the meteorological tower, which is used for wind resource assessment according to the IEC Standard. This paper presents a validation of the computational flow analysis results by comparing the results with the wind tunnel experiment conducted for Reynolds numbers in the $10^4$ to $10^5$ range, for the preparation of a database for use in an automatic method of correcting met-tower shading errors. A three-dimensional simulation employing the MP (Modified Production) $k-{\varepsilon}$ turbulence model predicted a wind speed deficit in the wake region according to minimum wind speed ratio, within an MAE (Mean Absolute Error) of 2.4%.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.70-72
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• 2019

For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger as the operation time accumulates due to aging. In this paper a novel Deep Neural Network (DNN) based SOC estimation method for multi cell application is proposed. In the proposed method DNN is implemented to learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.124.1-124.1
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• 2012

We have made a comparison of the WSA-ENLIL CME propagation model with three cone types and an empirical model using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters from Michalek et al. (2007) as well as their associated interplanetary (IP) shocks. For this study we consider three different cone models (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine CME cone parameters (radial velocity, angular width and source location), which are used for input parameters of the WSA-ENLIL CME propagation model. The mean absolute error (MAE) of the arrival times at the Earth for the elliptical cone model is 10 hours, which is about 2 hours smaller than those of the other models. However, this value is still larger than that (8.7 hours) of an empirical model by Kim et al. (2007). We are investigating several possibilities on relatively large errors of the WSA-ENLIL cone model, which may be caused by CME-CME interaction, background solar wind speed, and/or CME density enhancement.