• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.109-118
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• 2022

The data of the missing section among the vertex surface sea temperature observation data was imputed using the Bidirectional Recurrent Neural Network(BiRNN). Among artificial intelligence techniques, Recurrent Neural Networks (RNNs), which are commonly used for time series data, only estimate in the direction of time flow or in the reverse direction to the missing estimation position, so the estimation performance is poor in the long-term missing section. On the other hand, in this study, estimation performance can be improved even for long-term missing data by estimating in both directions before and after the missing section. Also, by using all available data around the observation point (sea surface temperature, temperature, wind field, atmospheric pressure, humidity), the imputation performance was further improved by estimating the imputation data from these correlations together. For performance verification, a statistical model, Multivariate Imputation by Chained Equations (MICE), a machine learning-based Random Forest model, and an RNN model using Long Short-Term Memory (LSTM) were compared. For imputation of long-term missing for 7 days, the average accuracy of the BiRNN/statistical models is 70.8%/61.2%, respectively, and the average error is 0.28 degrees/0.44 degrees, respectively, so the BiRNN model performs better than other models. By applying a temporal decay factor representing the missing pattern, it is judged that the BiRNN technique has better imputation performance than the existing method as the missing section becomes longer.

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.2 s.2
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• pp.115-121
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• 2001

The purpose of the synthetic generation of monthly river flows based on the short term observed data by means of stochastic models is to provide abundant input data to the water resources systems of which the system performance and operation policy are to be determined beforehand. In this study, a multivariate autoregressive model has been applied to generate monthly flows of the multi sites considering the correlations between each site. The model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, variance, skewness and correlation coefficients. The results of this study showed that the modeled generated flows were statistically similar to the historical flows.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.197-211
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• 2024

This research studies the effect of geotechnical factors on EPB-TBM performance parameters. The modeling was performed using simple and multivariate linear regression methods, artificial neural networks (ANNs), and Sugeno fuzzy logic (SFL) algorithm. In ANN, 80% of the data were randomly allocated to training and 20% to network testing. Meanwhile, in the SFL algorithm, 75% of the data were used for training and 25% for testing. The coefficient of determination (R²) obtained between the observed and estimated values in this model for the thrust force and cutterhead torque was 0.19 and 0.52, respectively. The results showed that the SFL outperformed the other models in predicting the target parameters. In this method, the R² obtained between observed and predicted values for thrust force and cutterhead torque is 0.73 and 0.63, respectively. The sensitivity analysis results show that the internal friction angle (φ) and standard penetration number (SPT) have the greatest impact on thrust force. Also, earth pressure and overburden thickness have the highest effect on cutterhead torque.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.383-401
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• 2016

In this paper, we described the statistical modeling of crop yields using satellite images, climatic datasets, soil property maps, and fertilizer data for the Midwestern United States during 2001-2012. Satellite images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic datasets were provided by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group. Soil property maps were derived from the Harmonized World Soil Database (HWSD). Our multivariate regression models produced quite good prediction accuracies, with differences of approximately 8-15% from the governmental statistics of corn and soybean yields. The unfavorable conditions of climate and vegetation in 2012 could have resulted in a decrease in yields according to the regression models, but the actual yields were greater than predicted. It can be interpreted that factors other than climate, vegetation, soil, and fertilizer may be involved in the negative biases. Also, we found that soybean yield was more affected by minimum temperature conditions while corn yield was more associated with photosynthetic activities. These two crops can have different potential impacts regarding climate change, and it is important to quantify the degree of the crop sensitivities to climatic variations to help adaptation by humans. Considering the yield decreases during the drought event, we can assume that climatic effect may be stronger than human adaptive capacity. Thus, further studies are demanded particularly by enhancing the data regarding human activities such as tillage, fertilization, irrigation, and comprehensive agricultural technologies.

• Journal of Environmental Health Sciences
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• v.42 no.4
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.217-234
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• 2018

Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

• Journal of the Korean Magnetic Resonance Society
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• v.25 no.3
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• pp.33-38
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• 2021

Type 1 diabetes mellitus (T1DM) is caused by insufficient production of insulin, which is involved in carbohydrate metabolism. Type 2 diabetes mellitus (T2DM) has insulin resistance in which cells do not respond adequately to insulin. The purpose of this study was to estimate the characteristics of type 1 diabetes using streptozotocin-treated mice (STZ-mouse). The sera samples were collected from the models of hyperglycemic mouse and healthy mouse. Based on the pair-wise comparison, five metabolites were found to be noticeable: glucose, malonic acid, 3-hyroxybutyrate, methanol, and tryptophan. It was very natural glucose was upregulated in STZ-mouse. 3-hyroxybutyrate was also increased in the model. However, malonic acid, tryptophan, and methanol was downregulated in STZ-mouse. Several metabolites acetoacetate, acetone, alanine, arginine, asparagine, histidine, lysine, malate, methionine, ornithine, proline, propylene glycol, threonine, tyrosine, and urea tended to be varied in STZ-mouse while the statistical significance was not stratified for the variation. The multivariate model of PCA clearly showed the group separation between healthy control and STZ-mouse. The most significant metabolites that contributed the group separation included glucose, citrate, ascorbate, and lactate. Lactate did not show the statistical significance of change in t-test while it tends to down-regulated both in DNP and Diabetes.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.393-401
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• 2007

Affective aspects of design attributes such as color, Pattern, and texture are important to the overall impression and the success of interior products. Among all the interior materials, wallpapers and flooring materials take up largest construction area and they are main components in creating affective impression for customers. This study aims to investigate the relationship between luxuriousness and related affective variables and design elements of wallpapers and flooring materials. The approach consists of 3 steps: (1) selecting related affective features and product design attributes through a literature survey, opinion of expert panel, and focus group interview, (2) conducting evaluation experiments, and (3) developing Kansei models using multivariate statistical analysis and analyzing critical attributes. Evaluation experiment was conducted using a questionnaire made up of 7-point scale and 100-point scale and 30 housewives and 20 interior designers participated in the evaluation experiment. The result of evaluation was analyzed through principal component regression and quantification I analysis. As a result of analyzing the survey data, the relationship between luxuriousness and related affective features and product design attributes was identified, moreover a optimal combination of the design component was identified. Consequently, it is expected that the results of the study would be a basis of the concept of emotion-based design by giving insights about how customers perceive the luxuriousness and suggesting the optimal combination, and providing specific quantitative design guidelines.