• 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.

• Horticultural Science & Technology
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• v.29 no.3
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• pp.181-186
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• 2011

To predict the bud bursting date of 'Campbell Early' grapevines, the bud developmental rate (DVR) models were constructed. The DVRs for bud bursting were calculated from the demanded times at controlled air temperatures. The DVRs were examined on the 'Campbell Early' grapevines incubated in three different temperatures at 4.6, 11.8, and $16.6^{\circ}C$. The DVR increased exponentially or linearly on the air temperature with a slope of about 0.0019. The DVR equations were computed as $DVR=0.0249+0.0020e^{0.1654x}$ or DVR = 0.0019x + 0.0187. These DVR equations offered developmental indices and predicted dates for bud bursting with air temperature data. The DVR equations were validated to the bud bursting data observed in the field. When bud bursting dates were calculated with daily temperature data, the root mean squared error (RMSE) between the observed and the predicted dates was less than 4 days. When those were calculated with hourly temperature data, on the other hand, the RMSE was less than 3 days. These results suggest that the DVR models are useful to predict bud bursting date of 'Campbell Early' grapevines.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1129-1140
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• 2013

Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

• Environmental Engineering Research
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• v.25 no.4
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• pp.479-487
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• 2020

Current assumptions are used in the formulation of pseudo-first (PFO) and second-order (PSO) models to describe the kinetic data of filtration based on ideal operating conditions. This paper presents a new model developed with pseudo n^th order and based on real assumption. A comparison was performed between PFO, PSO and the new model to highlight their performance and the optimisation of the pseudo-order equation, using MATLAB software. Adsorption characteristic of bovine serum albumin adsorption on the track-etched membrane are used as a medium based on protein filtration data were extracted from the literature for different concentrations to demonstrate the comparison between PFO/PSO and the new model. The pseudo first and second-order kinetic models were applied to test the experimental data and they did not provide reasonable values. The results show that the predicted values are consistent with experimental values giving a good correlation coefficient R² = 0.997 and a minimum root mean squared error RMSE = 0.0171. Indeed, the experimental results follow the new model and the optimal pseudo equation order n = 1.115, the most suitable curves for the new model. As a result, we used different experimental adsorption data from the literature to examine and check the applicability and validity of the model.

• Journal of the Korea Organic Resources Recycling Association
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• v.21 no.2
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• pp.88-96
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• 2013

The objective of this research was to evaluate the suitability of sigmoidal and firstorder kinetic equations for simulating the methane production from solid wastes. The sigmoidal kinetic equations used were modified Gompertz and Logistic equations. Statistical criteria used to evaluate equation performance were analysis of goodness-of-fit (Residual sum of squares, Root mean squared error and Akaike's Information Criterion). Akaike's Information Criterion (AIC) was employed to compare goodness-of-fit of equations with same and different numbers of parameters. RSS and RMSE were decreased for first-order kinetic equation with lag-phase time, compared to the first-order kinetic equation without lag-phase time. However, first-order kinetic equations had relatively higher AIC than the sigmoidal kinetic equations. It seemed that the sigmoidal kinetic equations had better goodness-of-fit than the first-order kinetic equations in order to simulate the methane production.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.224-224
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• 2017

지구온난화로 인한 기후변화 등으로 안전한 하천구조물을 설계하기 위해서는 신뢰할 수 있는 홍수량 산정이 필요하다. 신뢰할 수 있는 홍수량 산정을 위해서는 정도 높은 과거 수문자료가 필요하나 국내의 많은 중소 규모유역이 미계측 유역 또는 과거 수문자료 부족으로 신뢰 할 수 있는 홍수량 산정이 어려운 실정이다. 본 연구에서는 미계측 유역의 홍수량 산정을 위하여 확률분포모형(PDM)의 매개변수 지역화를 수행하였다. 매개변수 지역화 연구를 수행하기 위하여, 금강 25개 유역을 대상으로 유역별 9~18개의 단기홍수수문사상을 선정하였다. 선정된 단기홍수수문사상을 확률분포모형에 적용하기위하여, MCAT (Monte Carlo Analysis Toolbox)을 활용하여 검정 및 검증을 수행하였으며, 목적함수는 수문곡선 모든 구간을 반영하는 NSE (Nash Sutcliffe Efficiency)와 고유량 부분을 반영하는 RMSE (Root Mean Squared Error) - FH를 적용하였다. 각각의 목적함수에 대하여 검정 모형 매개변수와 유역 특성인자의 다중 선형회귀식을 강우유출모형 매개변수 지역화 모형으로 제시하였다. 매개변수 지역화 결과의 평가를 위하여 청주 유역을 미계측 유역으로 가정하였다. 청주 유역에 대하여 지역화 매개변수를 적용한 결과, 17개의 사상 중 11개의 사상에서 NSE 목적함수 값이 0.5이상으로 전체적인 수문곡선의 경향성을 보였으며, 첨두 홍수량은 17개 사상 중 11개 사상에서 관측 첨두 홍수량 값의 20%이내를 제시하여 적합한 결과를 제시하였다. 또한 금강 25개 유역에 Jackknife 방법으로 검정 결과 관측 첨두 홍수량 값 20%이내의 성능을 보이는 사상이 56%를 포함하고 있어 의미있는 지역화 모형을 제시하였다고 판단된다. 본 연구에서 제시한 매개변수 지역화 방법은 미계측 유역의 유출모의에 활용될 수 있음을 확인하였다.