• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1423-1444
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• 2022

In this study, the sensitivity of the mid-infrared radiance to atmospheric and surface factors was analyzed using the radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN6)'s simulation data. The possibility of retrieving the land surface temperature (LST) using only the mid-infrared bands at night was evaluated. Based on the sensitivity results, the LST retrieval algorithm that reflects various factors for night was developed, and the level of the LST retrieval algorithm was evaluated using reference LST and observed LST. Sensitivity experiments were conducted on the atmospheric profiles, carbon dioxide, ozone, diurnal variation of LST, land surface emissivity (LSE), and satellite viewing zenith angle (VZA), which mainly affect satellite remote sensing. To evaluate the possibility of using split-window method, the mid-infrared wavelength was divided into two bands based on the transmissivity. Regardless of the band, the top of atmosphere (TOA) temperature is most affected by atmospheric profile, and is affected in order of LSE, diurnal variation of LST, and satellite VZA. In all experiments, band 1, which corresponds to the atmospheric window, has lower sensitivity, whereas band 2, which includes ozone and water vapor absorption, has higher sensitivity. The evaluation results for the LST retrieval algorithm using prescribed LST showed that the correlation coefficient (CC), the bias and the root mean squared error (RMSE) is 0.999, 0.023K and 0.437K, respectively. Also, the validation with 26 in-situ observation data in 2021 showed that the CC, bias and RMSE is 0.993, 1.875K and 2.079K, respectively. The results of this study suggest that the LST can be retrieved using different characteristics of the two bands of mid-infrared to the atmospheric and surface conditions at night. Therefore, it is necessary to retrieve the LST using satellite data equipped with sensors in the mid-infrared bands.

• New & Renewable Energy
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• v.8 no.2
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• pp.14-23
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• 2012

The TMY (Typical Meteorological Year) for the solar energy study is generated using observation data with 22 solar sites from KMA (Korea Meteorological Administration) during 11 years (2000-2010). The meteorological data for calculation the TMY are used solar radiation, temperature, dew point temperature, wind speed and humidity data. And the TMY is calculated to apply the FS (Finkelstein and Schafer) statistics and RMSE (Root Mean Squared Error) methods. FS statistics performed with each point and each variable and then selected top five candidate TMM months with statistical analysis and normalization. Finally TMY is generated to select the highest TMM score with evaluation the average errors for the 22 whole points. The TMY data is represented average state and long time variations with 22 sites and meteorological data. When TMY validated with the 11-year daily solar radiation data, the correlation coefficient was about 0.40 and the highest value is 0.57 in April and the lowest value is 0.23 in May. Mean monthly solar radiation of TMY is 411.72 MJ which is 4 MJ higher than original data. Average correlation coefficient is 0.71, the lowest correlation is 0.43 in May and the highest correlation is 0.90 in January. Accumulated annual solar radiation by TMY have higher value in south coast and southwestern region and have relatively low in middle regions. And also, differences between TMY and 11-year mean of is distributed lower 100 MJ in Kyeongbuk, higher 200 MJ in Jeju and higher 125 MJ in Jeonbuk and Jeonnam, respectively.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.836-845
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• 2017

Gravimetric method is usually used to evaluate air-dry density, which is governing physical or mechanical properties of wood. Although it had high evaluation accuracy, the method is time consuming process. Thus, this study was conducted to estimate air-dry density of wood with high accuracy by using polychromatic X-ray and digital detector as alternative of gravimetric method. To quantify polychromatic X-ray projection for evaluating air-dry density, Lambert-Beer's law with the integral value of probability function was used. The integral value was used as weighting factor in the law, and it was determined by conducting simple test at various penetration depths and tube voltage. Mass attenuation coefficient (MAC) of wood also calculated by investigating polychromatic X-ray projection according to species, penetration depth and tube voltage. The species had not an effect on change of MAC. Finally, an air-dry density of wood was estimated by applying the integral value, MAC and Lambert-Beer's law to polychromatic X-ray projection. As an example, the relation of the integral value (${\alpha}$) according to penetration depth (t, cm) at tube voltage of 35 kV was ${\alpha}=-0.00091t{\times}0.0184$ while the regression of the MAC (${\mu}$, $cm^2/g$) was ${\mu}=0.5414{\exp}(-0.0734t)$. When calculation of root mean squared error (RMSE) was performed to check the estimation accuracy, RMSE at 35, 45 and 55 kV was 0.010, 0.013 and $0.009g/cm^3$, respectively. However, partial RMSE in relation to air-dry density was varied according to tube voltage. The partial RMSE below air-dry density of $0.41g/cm^3$ was $0.008g/cm^3$ when tube voltage of 35 kV was used. Meanwhile, the partial RMSE above air-dry density of $0.41g/cm^3$ decreased as tube voltage increased. It was conclude that the accuracy of estimation with polychromatic X-ray and digital detector was quite high if the integral value and MAC of wood were determined precisely or a condition of examination was chosen properly. It was seemed that the estimation of air-dry density by using polychromatic X-ray system can supplant the gravimetric method.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.173-183
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• 2015

The aim of this study was to develop a soil moisture simulation model equipped with meteorological data enhanced by WRF (Weather Research and Forecast) model, and this soil moisture model was applied for quantifying soil moisture content and irrigation requirement. The WRF model can provide grid based meteorological data at various resolutions. For applicability assessment, comparative analyses were conducted using WRF data and weather data obtained from weather station located close to test bed. Water balance of each upland grid was assessed for soils represented with four layers. The soil moisture contents simulated using the soil moisture model were compared with observed data to evaluate the capacity of the model qualitatively and quantitatively with performance statistics such as correlation coefficient (R), coefficient of determination (R2) and root mean squared error (RMSE). As a result, R is 0.76, $R^2$ is 0.58 and RMSE 5.45 mm in soil layer 1 and R 0.61, $R^2$ 0.37 and RMSE 6.73 mm in soil layer 2 and R 0.52, $R^2$ 0.27 and RMSE 8.64 mm in soil layer 3 and R 0.68, $R^2$ 0.45 and RMSE 5.29 mm in soil layer 4. The estimated soil moisture contents and irrigation requirements of each soil layer showed spatiotemporally varied distributions depending on weather and soil texture data incorporated. The estimated soil moisture contents using weather station data showed uniform distribution about all grids. However the estimated soil moisture contents from WRF data showed spatially varied distribution. Also, the estimated irrigation requirements applied WRF data showed spatial variabilities reflecting regional differences of weather conditions.

• The Journal of Advanced Prosthodontics
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• v.15 no.6
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• pp.281-289
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• 2023

PURPOSE. This study aimed to predict the positional coordinates of incisor points from the scan data of conventional complete dentures and verify their accuracy. MATERIALS AND METHODS. The standard triangulated language (STL) data of the scanned 100 pairs of complete upper and lower dentures were imported into the computer-aided design software from which the position coordinates of the points corresponding to each landmark of the jaw were obtained. The x, y, and z coordinates of the incisor point (X_P, Y_P, and Z_P) were obtained from the maxillary and mandibular landmark coordinates using regression or calculation formulas, and the accuracy was verified to determine the deviation between the measured and predicted coordinate values. Y_P was obtained in two ways using the hamularincisive-papilla plane (HIP) and facial measurements. Multiple regression analysis was used to predict Z_P. The root mean squared error (RMSE) values were used to verify the accuracy of the X_P and Y_P. The RMSE value was obtained after crossvalidation using the remaining 30 cases of denture STL data to verify the accuracy of Z_P. RESULTS. The RMSE was 2.22 for predicting X_P. When predicting Y_P, the RMSE of the method using the HIP plane and facial measurements was 3.18 and 0.73, respectively. Cross-validation revealed the RMSE to be 1.53. CONCLUSION. Y_P and Z_P could be predicted from anatomical landmarks of the maxillary and mandibular edentulous jaw, suggesting that Y_P could be predicted with better accuracy with the addition of the position of the lower border of the upper lip.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.8-16
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• 2007

Finite element analysis (FEA) is an effective means for the analysis of bioelectromagnetism. It has been successfully applied to various problems over conventional methods such as boundary element analysis and finite difference analysis. However, its utilization has been limited due to the overwhelming computational load despite of its analytical power. We have previously developed a novel mesh generation scheme that produces FE meshes that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with far less number of nodes and elements, thus lessen the computational load. In general, the cMesh generation is affected by the quality of feature maps derived from MRI. In this study, we have tested various feature maps created based on the improved differential geometry measures for more effective cMesh head models. As performance indices, correlation coefficient (CC), root mean squared error (RMSE), relative error (RE), and the quality of cMesh triangle elements are used. The results show that there is a significant variation according to the characteristics of specific feature maps on cMesh generation, and offer additional choices of feature maps to yield more effective and efficient generation of cMeshes. We believe that cMeshes with specific and improved feature map generation schemes should be useful in the FEA of bioelectromagnetic problems.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• The Korean Journal of Physiology and Pharmacology
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• v.20 no.3
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• pp.245-251
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• 2016

The objective of this study was to externally validate a new dosing scheme for busulfan. Thirty-seven adult patients who received busulfan as conditioning therapy for hematopoietic stem cell transplantation (HCT) participated in this prospective study. Patients were randomized to receive intravenous busulfan, either as the conventional dosage (3.2 mg/kg daily) or according to the new dosing scheme based on their actual body weight (ABW) ($23{\times}ABW^{0.5}mg\;daily$) targeting an area under the concentration-time curve (AUC) of $5924{\mu}M{\cdot}min$. Pharmacokinetic profiles were collected using a limited sampling strategy by randomly selecting 2 time points at 3.5, 5, 6, 7 or 22 hours after starting busulfan administration. Using an established population pharmacokinetic model with NONMEM software, busulfan concentrations at the available blood sampling times were predicted from dosage history and demographic data. The predicted and measured concentrations were compared by a visual predictive check (VPC). Maximum a posteriori Bayesian estimators were estimated to calculate the predicted AUC ($AUC_{PRED}$). The accuracy and precision of the $AUC_{PRED}$ values were assessed by calculating the mean prediction error (MPE) and root mean squared prediction error (RMSE), and compared with the target AUC of $5924{\mu}M{\cdot}min$. VPC showed that most data fell within the 95% prediction interval. MPE and RMSE of $AUC_{PRED}$ were -5.8% and 20.6%, respectively, in the conventional dosing group and -2.1% and 14.0%, respectively, in the new dosing scheme group. These findings demonstrated the validity of a new dosing scheme for daily intravenous busulfan used as conditioning therapy for HCT.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.811-821
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• 2007

The intensity-duration-frequency (IDF) curves by Talbot, Sherman and Japanese type formulas are widely used in South Korea since the parameters are easily estimated. However, these IDF curves' accuracies are relatively worse than those of the IDF curves developed by Lee et al. (1993) and Heo et al. (1999), and different parameters for the given return periods should be computed. In this study, parameter estimation method for the IDF curve by Heo et al. (1999) is suggested using genetic algorithm (GA). Quantiles computed by at-site frequency analysis using the rainfall data of 22 rainfall gauges operated by Korea Meteorological Administration are employed to estimate the parameters of IDF curves and minimizing root mean squared error (RMSE) and relative RMSE (RRMSE) of observed and computed quantiles are used as objective functions of GA. The comparison of parameter estimation methods between the empirical regression analysis and the suggested method show that the IDF curve in which the parameters are estimated by GA using RRMSE as an objective function is superior to the IDF curves using RMSE.