• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.941-953
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• 2022

In this study, monthly water use data were collected for 5 years from the 65 local governments included in the Han-river basin and a typical water usage ratios and patterns were calculated. The difference in water shortage was compared by considering the water usage patterns using the water balance and budget analysis model (MODSIM) and data base. As a result, it was confirmed that the change occurred in the range of -3.120% to +4.322% compared to the monthly constant ratio by period. In addition, when applying the patterns in the water balance model, 17 of the 28 middle watershed showed changes in the quantity of water shortage and the domestic and industrial water shortage would decrease about 8.0% during the maximum drought period. If it is applied in conjunction with predictive research on water usage patterns reflecting climate change, social and regional characteristics in the future, it will be possible to establish a more realistic water supply forecasts and a reliable national water resources plan.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.140-161
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• 2022

Seismic data with missing traces are often obtained regularly or irregularly due to environmental and economic constraints in their acquisition. Accordingly, seismic data interpolation is an essential step in seismic data processing. Recently, research activity on machine learning-based seismic data interpolation has been flourishing. In particular, convolutional neural network (CNN) and generative adversarial network (GAN), which are widely used algorithms for super-resolution problem solving in the image processing field, are also used for seismic data interpolation. In this study, CNN-based algorithm, U-Net and GAN-based algorithm, and conditional Wasserstein GAN (cWGAN) were used as seismic data interpolation methods. The results and performances of the methods were evaluated thoroughly to find an optimal interpolation method, which reconstructs with high accuracy missing seismic data. The work process for model training and performance evaluation was divided into two cases (i.e., Cases I and II). In Case I, we trained the model using only the regularly sampled data with 50% missing traces. We evaluated the model performance by applying the trained model to a total of six different test datasets, which consisted of a combination of regular, irregular, and sampling ratios. In Case II, six different models were generated using the training datasets sampled in the same way as the six test datasets. The models were applied to the same test datasets used in Case I to compare the results. We found that cWGAN showed better prediction performance than U-Net with higher PSNR and SSIM. However, cWGAN generated additional noise to the prediction results; thus, an ensemble technique was performed to remove the noise and improve the accuracy. The cWGAN ensemble model removed successfully the noise and showed improved PSNR and SSIM compared with existing individual models.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.169-181
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• 2022

Purpose: This paper addresses a case study of developing a flame retardant wood-plastic composites (WPC) by adding tannic acid to the existing synthetic wood. The optimal mixing ratios of six components are explored to minimize the burning time using two mixture designs. Methods: In the preliminary experiment, six components are considered to find important components and their ranges. Seven D-optimal mixture design points are generated. Two points are removed for the balance of plastic components to be maintained, and the remaining five points are augmented with two basic compositions. Four components are selected to be considered in the main experiment. In the main experiment, pellets are extruded at the eight mixture design points. In-house testing of burning time is executed three times. Specimens made of pellets from two promising flame retardant compositions are sent to the accredited laboratories and tested. Results: The test results are as follows: 1) The best composition (Wood flour, Tannic acid, PE, Lubricant) = (25, 41, 10, 2) (wt%) shows the burning time of 1 second, which is 9-fold improvement compared to the the burning time of 9 seconds from the existing composition (58, 0, 10, 2) (wt%). 2) The second best composition (41, 25, 10, 2) (wt%) results in the burning time of 2 seconds. This composition is inferior to the best composition in terms of the flame retardancy, but more economical since it needs less tannic acid which is 100-fold expensive than the wood flour. Conclusion: Flame retardant compositions are found by adding tannic acid to the existing WPC employing optimal mixture designs. This case study will be helpful to practitioners who try to develop new products with additional physical properties with as small number of experimental trials as possible. Future research direction includes exploring conditions which satisfy both performance level and cost limitation simultaneously.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6A
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• pp.625-639
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• 2009

Experimental and analytical studies are performed on the mechanical behavior of concrete-filled tubular(CFT) truss girders for different f/L ratios. Bending tests are conducted on two CFT truss girder specimens to determine fundamental structural characteristics such as the strength and deformation properties. Nonlinear material models for CFT members subjected to an axial compressive force are compared in this paper by using the nonlinear finite element program, ABAQUS. Previous researchers have proposed several nonlinear stress-strain models of confined concrete. In this study, the nonlinear analyses are performed applying several stress-strain models for confined concrete proposed by Mander, Sakino, Han, Susantha and Ellobody, and the results are compared with the experimental results in terms of load-deflection and load-strain relationships. Based on the comparisons of the load-deflection relationships, the models proposed by Mander and Susantha provide a maximum load about 12.0~13.8% higher and that by Sakino gives a maximum load about 7.6% higher than the experimental results. The models proposed by Han and Ellobody give a maximum load only about 0.2~1.2% higher than the test results, showing the best agreement among the proposed stress-strain models. However, the load-strain relations predicted by the existing models generally provide conservative results exhibiting larger strains than the experimental data.

• Journal of the Korean School Mathematics Society
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• v.26 no.2
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• pp.111-135
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• 2023

This study conducted a student-centered inquiry lesson on the similarity of figures using AlgeoMath, with student learning aspects analyzed from a communication perspective. This approach aimed to inform pedagogical implications related to teaching geometric similarity. Through utilizing AlgeoMath, students were able to visually confirm that their chosen figures were similar, experiencing key mathematical concepts such as the ratio of similarity to the area of similar figures, and congruency and similarity conditions of triangles. In the lessons applying this concept, we categorized the features of similarity learning displayed by students, as seen in the communication aspects of their exploratory activities, into 'Understanding similarity ratios', 'Grasping conditions of similarity in triangles', and 'Comparing concepts of congruency and similarity'. Through exploratory activities based on AlgeoMath, students discussed the meaning and mathematical relationships of key concepts related to similarity, such as the ratio of similarity to the area of figures, and the meaning and conditions of congruence and similarity in triangles. By improving misconceptions about the similarity of figures, they were able to develop deeper mathematical understanding. This study revealed that in teaching and learning the geometric similarity using AlgeoMath, obtaining meaningful pedagogical outcome was not solely due to the features of the AlgeoMath environment, but also largely depended on the teacher's guidance and intervention that stimulated students' thinking.

• Analyses & Alternatives
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• v.7 no.3
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• pp.181-230
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• 2023

Evaluating the empirical validity of Modern Monetary Theory, this study implements panel cointegration analysis on annual panel data (2000-2022) of OECD countries. Specifically, the sample countries are divided into groups based on the presence of their own sovereign currencies, and for each group, the long-run equilibrium relation (cointegration) between the ratio of public debt or fiscal deficit and government bond rates is tested and estimated. Main findings are as follows: applying the pooled mean-group estimation for panel cointegration, it is found that both the ratios of public debt and fiscal deficit have significantly positive long-run correlation with government bond rates in countries without sovereign currency such as the Euro-zone or fixed exchange rate regime countries. However, in countries with sovereign currency such as non-Euro-zone or floating exchange rate regime countries, the long-run correlation is either negative or not statistically significant. Particularly, in countries without sovereign currency, the ratio of public debt has significantly positive correlation with the real government bond rates in the short run as well as the long run. These results are consistent with the prediction of Modern Monetary Theory, thus providing a supporting evidence for the empirical validity of the theory.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1341-1351
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• 2021

Objective: To compare the quality of various polychromatic and monochromatic images with or without using an iterative metal artifact reduction algorithm (iMAR) obtained from a dual-energy computed tomography (CT) to evaluate total knee arthroplasty. Materials and Methods: We included 58 patients (28 male and 30 female; mean age [range], 71.4 [61-83] years) who underwent 74 knee examinations after total knee arthroplasty using dual-energy CT. CT image sets consisted of polychromatic image sets that linearly blended 80 kVp and tin-filtered 140 kVp using weighting factors of 0.4, 0, and -0.3, and monochromatic images at 130, 150, 170, and 190 keV. These image sets were obtained with and without applying iMAR, creating a total of 14 image sets. Two readers qualitatively ranked the image quality (1 [lowest quality] through 14 [highest quality]). Volumes of high- and low-density artifacts and contrast-to-noise ratios (CNRs) between the bone and fat tissue were quantitatively measured in a subset of 25 knees unaffected by metal artifacts. Results: iMAR-applied, polychromatic images using weighting factors of -0.3 and 0.0 (P_-0.3i and P_0.0i, respectively) showed the highest image-quality rank scores (median of 14 for both by one reader and 13 and 14, respectively, by the other reader; p < 0.001). All iMAR-applied image series showed higher rank scores than the iMAR-unapplied ones. The smallest volumes of low-density artifacts were found in P_-0.3i, P_0.0i, and iMAR-applied monochromatic images at 130 keV. The smallest volumes of high-density artifacts were noted in P_-0.3i. The CNRs were best in polychromatic images using a weighting factor of 0.4 with or without iMAR application, followed by polychromatic images using a weighting factor of 0.0 with or without iMAR application. Conclusion: Polychromatic images combined with iMAR application, P_-0.3i and P_0.0i, provided better image qualities and substantial metal artifact reduction compared with other image sets.

• Journal of Korea Water Resources Association
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• v.57 no.8
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• pp.549-559
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• 2024

This study aims to investigate the feasibility of using image analysis methods to examine the flow characteristics of air bubbles discharged underwater. A bubble screen was created using multiple nozzles in a laboratory flume filled with stagnant water. The flow characteristics of the bubbles were analyzed, and the suitability of the analysis method was evaluated. Several parameters, such as projection area ratio and depth ratio, were defined to conduct laboratory experiments and analyze the flow characteristics of the bubbles. Correlation and regression analyses were performed to assess the relationships between various variables. Specifically, the correlation between the bubble's projection area and its rising speed across eight water depth ratios was examined. The results indicated that as the depth ratio increased, the bubble size exhibited a linear increase with a strong correlation as it rose to the water surface due to pressure effects. Regarding the sensitivity of different grid sizes in the ten analysis grids when applying image analysis methods, it was observed that the sensitivity to grid size based on the projection area ratio (0.09~0.96) was not significant. These findings suggest that image analysis techniques can be effectively utilized to observe the flow characteristics of bubbles.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.3
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• pp.317-324
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• 2007

Fluorescence measurements of dissolved organic matter(DOM) have the superior advantages over other analysis tools for applying to water quality management. A preliminary study was conducted to test the feasibility of applying synchronous fluorescence measurements for tracing and monitoring pollution sources in a small stream located in an upstream area of the Sooyoung watershed in Busan. The water quality of the small stream is affected by leachate from sawdust pile and discharge of untreated sewage. The sampling sites included an upstream site, two pipes discharging untreated sewage, leachate from sawdust, and a downstream site. Of the five field samples, the leachate was distinguished from the other samples by a high peak at a lower wavelength range and a blunt peak at 350nm, suggesting that synchronous fluorescence can be used as a discrimination tool for monitoring the pollution. The efficacy of various indices derived from the spectral features to discriminate the pollution source was tested for well-defined mixture of the sawdust leachate and the upstream stream by comparing (1)the difference between measured values and those predicted based on mass balance and the characteristics of the two samples and (2)the linear correlations between index values and mass ratios of the sample mixtures. Of various discrimination indices selected, fluorescence intensities at 276 nm$({\Delta}\lambda=30nm)$and 347 nm$({\Delta}\lambda=60nm)$ were suggested as promising potential discrimination indices for the sawdust pollution source. Despite the limited number of samples and the study area, this study illustrates the evaluation process that should be followed to develop rapid, low-cost discrimination indices to monitor pollution sources based on end member mixing analyses.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.