• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.145-153
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• 2015

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

• Journal of the Korean Solar Energy Society
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• v.30 no.2
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• pp.16-21
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• 2010

Computer simulation of buildings and solar energy systems are being used increasingly in energy assessments and design. This paper discusses the possibility of using sunshine duration data instead of global hourly solar irradiation (GHSI) data for localities with abundant data on sunshine duration. For six locations in South Korea where global radiation is currently measured, the global radiation was calculated using Sunshine Duration Radiation Model (SDRM), compared and analyzed. Results of SDRM has been compared with the measured data on the coefficients of determination (R2), root-mean-square error (RMSE) and mean bias error (MBE). This study recommends the use of sunshine duration based irradiation models if measured solar radiation data is not available.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.68-68
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• 2022

코로나바이러스는(COVID-19)는 2019년 12일 중국 후베이성 우한시에서 시작된 코로나바이러스감염증으로 2020년 1월부터 전 세계로 퍼져, 일부 국가 및 지역을 제외한 대부분의 나라와 모든 대륙으로 확산되었다. 이에 WHO는 범 유행전염병(Pandemic)을 선언하였다. 2022년 3월 18일 현재 국내 누적 확진환자 8,657,609명과 11,782명의 사망자를 일으켰고 전 세계적으로도 많은 사상자를 내고 있는 실정이고 사회 및 경제적인 피해로도 계속 확대되고 있다. 많은 감염자와 사망자의수에 대한 예측은 코로나바이러스의 전염병을 예방하고 즉각적 조치를 취할 수 있는데 도움이 될 수 있다. 본 연구에서는 문화적 인자를 제외한 국내에서 연구 사례가 많지 않은 기상 요인을 인자로 포함하여 머신러닝 모델을 통해 확진자를 예측하였다. 그리고 여러 가지 모델을 성능 평가 기법인 Root Mean Square Error(RMSE) 및 Mean Absolute Percentage Error(MAPE)를 통해 성능을 평가하고 비교하여 정확도 높은 모델을 제시하였다.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.1
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• pp.89-97
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• 2007

Simple regression models for pollutants load estimation of paddy developed by the Ministry of Environment in 1995 were tested with the data (T-N, T-P, $COD_{Mn}$, and SS) collected from Yeongsan and Seomjin river watersheds, and improvement measures were suggested. Overall, the simulated values showed a great difference from the measured values except for T-P according to the statistical analyses (RMSE, root mean square error; RMAE, root mean absolute error; RB, relative bias; EI, efficiency index). Such difference was assumed due to the fact that the models use only hydrologic factors (quantity factor) associated with precipitation and run-off as input parameters, but do not consider other factors which are likely to affect pollutant concentration (quality factor) including days after fertilization. In addition, in terms of accessibility of the models, some parameters in the models such as run-off depth and run-off amount which can not be obtained from the weather database but should be collected by on-site measurements need to be replaced with other variables.

• Journal of Environmental Science International
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• v.26 no.4
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• pp.421-432
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• 2017

In this study, we examined the suitability of ten disinfection models for predicting the inactivation of Artemia sp. via single or combined physical and chemical treatments. The effect of Hydraulic Retention Time (HRT) on the inactivation of Artemia sp. was examined experimentally. Disinfection models were fitted to the experimental data by using the GInaFiT plug-in for Microsoft Excel. The inactivation model were evaluated on the basis of RMSE (Root Mean Square Error), SSE (mean Sum Square Error) and $r^2$. An inactivation model with the lowest RMSE, SSE and $r^2$ close to 1 was considered the best. The Weibull+Tail model was found to be the most appropriate for predicting the inactivation of Artemia sp. via electrolytic treatment and electrolytic-ultrasonic combined treatment. The Log-linear+Tail model was the most appropriate for modeling inactivation via homogenization and combined electrolytic-homogenization treatment. The double Weibull disinfection model was the most suitable for the predicting inactivation via ultrasonic treatment.

• Computers and Concrete
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• v.25 no.1
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• pp.83-94
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• 2020

The use of fly ash in modern-day concrete technology aiming sustainable constructions is on rapid rise. Fly ash, a spinoff from coal calcined thermal power plants with pozzolanic properties is used for cement replacement in concrete. Fly ash concrete is cost effective, which modifies and improves the fresh and hardened properties of concrete and additionally addresses the disposal and storage issues of fly ash. Soft computing techniques have gained attention in the civil engineering field which addresses the drawbacks of classical experimental and computational methods of determining the concrete compressive strength with varying percentages of fly ash. In this study, models based on soft computing techniques employed for the prediction of the compressive strengths of fly ash concrete are collected from literature. They are classified in a categorical way of concrete strengths such as control concrete, high strength concrete, high performance concrete, self-compacting concrete, and other concretes pertaining to the soft computing techniques usage. The performance of models in terms of statistical measures such as mean square error, root mean square error, coefficient of correlation, etc. has shown that soft computing techniques have potential applications for predicting the fly ash concrete compressive strengths.

• Journal of Biosystems Engineering
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• v.37 no.5
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• pp.327-334
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• 2012

Purpose: The color changes in red pepper during far infrared drying were studied in order to establish a color change model. Methods: The far infrared drying experiments of red pepper were conducted at two temperature levels of 60, $70^{\circ}C$ and two air velocity levels of 0.6 and 0.8 m/s. The results were compared with the hot-air drying method. The surface color changes parameters of red pepper were measured qualitatively based on L (lightness), a (redness), b (yellowness) and total color changes (${\Delta}E$). The goodness of fit of model was estimated using the coefficient of determination ($R^2$), the root mean square error (RMSE), the mean relative percent error (P) and the reduced chi-square (${\chi}^2$). Results: The results show that an increase in drying temperature and air velocity resulted in a decrease in drying time, the values of L (lightness) and a (redness) decreased with drying time during far infrared drying. The developed model showed higher $R^2$ values and lower RMSE, P and ${\chi}^2$ values. Conclusions: The model in this study could be beneficial to describe the color changes of red pepper by far infrared drying.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.2
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.121-145
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• 1992

This paper examines characteristics of time series data related to the construction investment(stationarity and time series components such as secular trend, cyclical fluctuation, seasonal variation, and random change) and surveys predictibility, fitness, and explicability of independent variables of various models to build a short-term construction investment forecasting model suitable for current economic circumstances. Unit root test, autocorrelation coefficient and spectral density function analysis show that related time series data do not have unit roots, fluctuate cyclically, and are largely explicated by lagged variables. Moreover it is very important for the short-term construction investment forecasting to grasp time lag relation between construction investment series and leading indicators such as building construction permits and value of construction orders received. In chapter 3, we explicate 7 forecasting models; Univariate time series model (ARIMA and multiplicative linear trend model), multivariate time series model using leading indicators (1st order autoregressive model, vector autoregressive model and error correction model) and multivariate time series model using National Accounts data (simple reduced form model disconnected from simultaneous macroeconomic model and VAR model). These models are examined by 4 statistical tools that are average absolute error, root mean square error, adjusted coefficient of determination, and Durbin-Watson statistic. This analysis proves two facts. First, multivariate models are more suitable than univariate models in the point that forecasting error of multivariate models tend to decrease in contrast to the case of latter. Second, VAR model is superior than any other multivariate models; average absolute prediction error and root mean square error of VAR model are quitely low and adjusted coefficient of determination is higher. This conclusion is reasonable when we consider current construction investment has sustained overheating growth more than secular trend.