• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.3A
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• pp.139-148
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• 2012

Circular concrete-filled tubes (CFTs) are composite members, which consists of a steel tube and concrete infill. CFTs have been used as building columns and bridge piers due to several advantages such as their strength-to-size efficiency and facilitation of rapid construction. Extensive experimental studies about CFT have been conducted for past decades. However experimental results alone are not sufficient to support the engineering of these components. Complementary advanced numerical models are needed to simulate the behavior of CFT to extend the experimental research and develop predictive tools required for design and evaluation of structural systems. In this study, a finite element modeling technique for CFT was developed. The confinement effects, and behavior of CFT subjected various types of loading predicted by the proposed finite element model for CFT were verified by comparing with test results.

• Genomics & Informatics
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• v.11 no.4
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• pp.263-271
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• 2013

We investigated the contribution of genetic variations of KLF5 to basal metabolic rate (BMR) and resting metabolic rate (RMR) and the inhibition of obesity in Korean children. A variation of KLF5 (rs3782933) was genotyped in 62 Korean children. Using multiple linear regression analysis, we developed a model to predict BMR in children. We divided them into several groups; normal versus overweight by body mass index (BMI) and low BMR versus high BMR by BMR. There were no differences in the distributions of alleles and genotypes between each group. The genetic variation of KLF5 gene showed a significant correlation with several clinical factors, such as BMR, muscle, low-density lipoprotein cholesterol, and insulin. Children with the TT had significantly higher BMR than those with CC (p=0.030). The highest muscle was observed in the children with TT compared with CC (p=0.032). The insulin and C-peptide values were higher in children with TT than those with CC (p=0.029 vs. p=0.004, respectively). In linear regression analysis, BMI and muscle mass were correlated with BMR, whereas insulin and C-peptide were not associated with BMR. In the high-BMR group, we observed that higher muscle, fat mass, and C-peptide affect the increase of BMR in children with TT (p < 0.001, p < 0.001, and p=0.018, respectively), while Rohrer's index could explain the usual decrease in BMR (adjust $r^2$=1.000, p < 0.001, respectively). We identified a novel association between TT of KLF5 rs3782933 and BMR in Korean children. We could make better use of the variation within KLF5 in a future clinical intervention study of obesity.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.3
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• pp.503-515
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• 2018

In this research, the three-dimensional structural and colorimetrical modeling of yarn-dyed woven fabrics was conducted based on the Kubelka-Munk theory (K-M theory) for their accurate color predictions. In the K-M theory for textile color formulation, the absorption and scattering coefficients, denoted K and S, respectively, of a colored fabric are represented using those of the individual colorants or color components used. One-hundred forty woven fabric samples were produced in a wide range of structures and colors using red, yellow, green, and blue yarns. Through the optimization of previous two-dimensional color prediction models by considering the key three-dimensional structural parameters of woven fabrics, three three-dimensional K/S-based color prediction models, that is, linear K/S, linear log K/S, and exponential K/S models, were developed. To evaluate the performance of the three-dimensional color prediction models, the color differences, ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and ${\Delta}E_{CMC(2:1)}$, between the predicted and the measured colors of the samples were calculated as error values and then compared with those of previous two-dimensional models. As a result, three-dimensional models have proved to be of substantially higher predictive accuracy than two-dimensional models in all lightness, chroma, and hue predictions with much lower ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and the resultant ${\Delta}E_{CMC(2:1)}$ values.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Child Health Nursing Research
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• v.12 no.4
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• pp.514-522
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• 2006

Purpose: The purpose of this study was to identify the factors of child behavioral problems and construct a descriptive model that explains child behavioral problems for school age children. Method: The participants in the study were 586 4th, 5th, 6th graders and their mothers. The children attended 8 elementary schools located in Taejon city and their mothers. The tools used in this study was the Mother's Child Raising Behavior Scale by Park, Seong-Yeon and Yi, Sook(1990). To measure child's self esteem, the Self Esteem Scale by Kim(1987) was used; child perceived social support was measured with the Social Support Evaluation Scale by Dubow and Ullman(1989), and childhood behavioral problems were measured with the Korean standardized of version of the Korean-Child Behavior Checklist(K-CBCL)(1997). Descriptive statistics and linear structural relationship(LISREL) modeling were used to analyze the data. SAS and LISREL 8.12a programs were used. Results: The overall fit of the hypothetical model to the data was good $>X^2=103.07(p=0.00)$, GFI=0.96, AGFI=0.94, RMSR=0.04, RMSEA=0.07, NFI=0.94, NNFI=0.95< Maternal child raising behaviors(T=2.21) and child perceived social support(T=10.29) had a significant, direct effect on a child's self esteem. Maternal child raising behaviors(T=-3.87), and child self esteem(T=-2.04) and had a significant total effect on child behavioral problems. These variables accounted for 63% of the variance of the child behavioral problems in the school age children. Conclusion: These finding have provided support for maternal child raising behaviors, child perceived social support, and child self esteem as predictive variables of behavioral problems in school age children.

• Computers and Concrete
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• v.14 no.5
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• pp.577-597
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• 2014

This paper aims to develop a prediction model for the hardened properties of waste LCD glass that is used in concrete by analyzing a series of laboratory test results, which were obtained in our previous study. We also summarized the testing results of the hardened properties of a variety of waste LCD glass concretes and discussed the effect of factors such as the water-binder ratio (w/b), waste glass content (G) and age (t) on the concrete compressive strength, flexural strength and ultrasonic pulse velocity. This study also applied a hyperbolic function, an exponential function and a power function in a non-linear regression analysis of multiple variables and established the prediction model that could consider the effect of the water-binder ratio (w/b), waste glass content (G) and age (t) on the concrete compressive strength, flexural strength and ultrasonic pulse velocity. Compared with the testing results, the statistical analysis shows that the coefficient of determination $R^2$ and the mean absolute percentage error (MAPE) were 0.93-0.96 and 5.4-8.4% for the compressive strength, 0.83-0.89 and 8.9-12.2% for the flexural strength and 0.87-0.89 and 1.8-2.2% for the ultrasonic pulse velocity, respectively. The proposed models are highly accurate in predicting the compressive strength, flexural strength and ultrasonic pulse velocity of waste LCD glass concrete. However, with other ranges of mixture parameters, the predicted models must be further studied.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.261-266
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• 2014

When the number of parameters in the time series model are diverse, it is hard to forecast because of the increasing error by a parameter estimation. If the homogeneity hypothesis which was obtained from the same model about severeal data for the time series is selected, it is easy to get the predictive value better. Nonlinear time-series panel data for each parameter for each time series, since there are so many parameters that are present, and the large number of parameters according to the parameter estimation error increases the accuracy of the forecast deteriorated. Panel present in the time series of multiple independent homogeneity is satisfied by a comprehensive time series to estimate and to test of the parameters. For studying about the homogeneity test for the m independent non-linear of the time series panel data, it needs to set the model and to make the normal conditions for the model, and to derive the homogeneity test statistic. Finally, it shows to obtain the limit distribution according to ${\chi}^2$ distribution. In actual analysis,, we can examine the result for the homogeneity test about nonlinear time series panel data which are 2 groups of stock price data.

• Journal of Conservation Science
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• v.34 no.5
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• pp.387-396
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• 2018

Exposure to the natural environment can cause damage to domestic wooden cultural assets, such as temples. Deterioration is accelerated by biological damage and various environmental factors. In this study, meteorological factors were monitored by equipment installed at Beopjusa temple of Boeun province and Seonamsa temple of Suncheon province. A statistical model was applied to these data to predict the meteorological factors and to compare the predictive performance of each meteorological factor. The resulting correlation coefficient between air and dew point temperatures was highest, at 0.95, while the correlation coefficient for relative humidity had a moderate value(0.65) at both the Beopjusa and Seonamsa temples. Thus, a general linear model was found to be suitable for predicting air and dew point temperatures. An analysis of correlation between meteorological factors showed that there was strong positive correlation between air temperature and dew point temperature, and between solar radiation and evaporation at both sites. There was a weak positive correlation between air temperature and evaporation at Beopjusa temple. Wind speed was negatively correlated with both air temperature and relative humidity at Seonamsa temple. The wind speed at this location is higher than average in winter and lower than average in summer, and it was hypothesized that the low wind speed plays a role in reducing water evaporation in summer, when both air temperature and relative humidity are high. As a result, damage to the wooden buildings of Seonamsa temple is accelerated.