• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.1
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• pp.1-9
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• 2009

The objective of this paper was to evaluate the auto-calibration with multi-objective optimization method to calibrate the parameters of the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated by using nine years (1996-2004) of measured data for the 384-ha Baran reservoir subwatershed located in central Korea. Multi-objective optimization was performed for sixteen parameters related to runoff. The parameters were modified by the replacement or addition of an absolute change. The root mean square error (RMSE), relative mean absolute error (RMAE), Nash-Sutcliffe efficiency index (EI), determination coefficient ($R^2$) were used to evaluate the results of calibration and validation. The statistics of RMSE, RMAE, EI, and $R^2$ were 4.66 mm/day, 0.53 mm/day 0.86, and 0.89 for the calibration period and 3.98 mm/day, 0.51 mm/day, 0.83, and 0.84 for the validation period respectively. The statistical parameters indicated that the model provided a reasonable estimation of the runoff at the study watershed. This result was illustrated with a multi-objective optimization for the flow at an observation site within the Baran reservoir watershed.

• Journal of Biosystems Engineering
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• v.37 no.2
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• pp.82-89
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• 2012

Purpose: The simulation model of a pneumatic conveying drying (PCD) for sawdust was developed and verified with the experiments. Method: The thermal behavior and mass transfer of a PCD were modeled and investigated by comparing the experimental results given by a reference (Kamei et al. 1952) to validate the model. Momentum, energy and mass balance, one dimensional first order ordinary differential equations, were coded and solved into Matlab V. 7.1.0 (2009). Results: The simulation results showed that the moisture content reduced from 194% to 40% (dry basis), air temperature decreased from $512^{\circ}C$ to $128^{\circ}C$ with the particle residence time of 0.7 seconds. The statistical indicators, root mean square error and R-squared, were calculated to be 0.079, and 0.998, respectively, between the measured and predicted values of moisture content. The relative error between the measured and predicted values of the final pressured drop, air temperature, and air velocity were only 8.96%, 0.39% and 1.05% respectively. Conclusions: The predicted moisture content, final temperature, and pressure drop values were in good agreement with the experimental results. The developed model can be used for design and estimation of PCD system for drying of wood dust particles.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.459-473
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• 2007

In this paper, we suggest a spatial regression model to predict AADT. Although Euclidian distances between one monitoring site and its neighboring sites were usually used in the many analysis, we consider the shortest travel path between monitoring sites to predict AADT for unmonitoring site using spatial regression model. We used universal Kriging method for prediction and found that the overall predictive capability of the spatial regression model based on shortest travel path is better than that of the model based on multiple regression by cross validation.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.191-201
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• 1991

Given a set of data pxN$_{i}$, matrices X$_{i}$ observed from p-variate normal populations $\prod$$_{i}$~N($\mu$$_{I}$, $\Sigma$$_{i}$) for i=1, …, K, the test for equality form of the covariance matrices is to choose a hypothetical model which best explains the homogeneity/heterogeneity structure across the covariance matrices among the hypothesized class of models. This paper describes a test procedure for selecting the best model. The procedure is based on a synthesis of Bayesian and a cross-validation or sample reuse methodology that makes use of a one-at-a-time schema of observational omissions. Advantages of the test are argued on two grounds, and illustrative examples and simulation results are given.are given.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.713-727
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• 2005

Among different regression approaches, nonparametric procedures perform well under different conditions. In practice it is very hard to identify which is the best procedure for the data at hand, thus model combination is of practical importance. In this paper, we focus on one dimensional regression with fixed design. Polynomial regression, local regression, and smoothing spline are considered. The data are split into two parts, one part is used for estimation and the other part is used for prediction. Prediction performances are used to assign weights to different regression procedures. Simulation results show that the combined estimator performs better or similarly compared with the estimator chosen by cross validation. The combined estimator generates a similar risk to the best candidate procedure for the data.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.802-809
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• 2010

An automatic calibration tool of Hydrological Simulation Program-Fortran (HSPF), Parameter Estimation (PEST) program, was applied at the Imha lake watershed to get optimal hydrological parameters of HSPF. Calibration of HSPF parameters was performed during 2004 ~ 2008 by PEST and validation was carried out to examine the model's ability by using another data set of 1999 ~ 2003. The calibrated HSPF parameters had tendencies to minimize water loss to soil layer by infiltration and deep percolation and to atmosphere by evapotranspiration and maximize runoff rate. The results of calibration indicated that the PEST program could calibrate the hydrological parameters of HSPF with showing 0.83 and 0.97 Nash-Sutcliffe coefficient (NS) for daily and monthly stream flow and -3% of relative error for yearly stream flow. The validation results also represented high model efficiency with showing 0.88 and 0.95, -10% relative error for daily, monthly, and yearly stream flow. These statistical values of daily, monthly, and yearly stream flow for calibration and validation show a 'very good' agreement between observed and simulated values. Overall, the PEST program was useful for automatic calibration of HSPF, and reduced numerous time and effort for model calibration, and improved model setup.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.615-631
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• 2008

Current credit evaluation models based on only financial data except non-financial data are used continuous data and produce credit scores for the ranking. In this work, some problems of the credit evaluation models based on transformed continuous financial data are discussed and we propose improved credit evaluation models based on categorized financial data. After analyzing and comparing goodness-of-fit tests of two models, the availability of the credit evaluation models for categorized financial data is explained.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.