• Journal of the Korean GEO-environmental Society
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• v.12 no.9
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• pp.55-61
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• 2011

This study investigate the use of ultraviolet(UV) light with hydrogen peroxide($H_2O_2$) for Methyl Tert Butyl Ether(MTBE) degradation in photolysis reactor. The process in general demands the generation of OH radicals in solution at the presence of UV light. These radicals can then attack the MTBE molecule and it is finally destroyed or converted into a simple harmless compound. The MTBE removal by photolysis were mathematically described as the independent variables such as irradiation intensity, initial concentration of MTBE and $H_2O_2$/MTBE ratio, and these were modeled by the use of response surface methodology(RSM). These experiments were carried out as a Box-Behnken Design(BBD) consisting of 15 experiments. Regression analysis term of Analysis of Variance(ANOVA) shows significantly p-value(p<0.05) and high coefficients for determination values($R^2$=94.60%) that allow satisfactory prediction of second-order regression model. And Canonical analysis yields the stationery point for response, with the estimate ridge of maximum responses and optimal conditions for Y(MTBE removal efficiency, %) are $x_1$=25.75 W of irradiation intensity, $x_2$=7.69 mg/L of MTBE concentration and $x_3$=11.04 of $H_2O_2$/MTBE molecular ratio, respectively. This study clearly shows that RSM is available tool for optimizing the operating conditions to maximize MTBE removal.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.365-370
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• 2007

Many engineering problems often require the large amount of computing resources for iterative simulations of problems treating many parameters and input files. In order to overcome the situation, this paper proposes an e-Science based computational system. The system exploits the Grid computing technology to establish an integrated web service environment which supports distributed high throughput computational simulations and remote executions. The proposed system provides an easy-to-use parametric study service where a computational service includes real time monitoring. To verify usability of the proposed system, two kinds of applications were introduced. The first application is an Aerospace Integrated Research System (e-AIRS). The e-AIRS adapts the proposed computational system to solve CFD problems. The second one is design and optimization of protein 3-dimensional structures in structural biology.

• Journal of Fashion Business
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• v.18 no.6
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• pp.57-66
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• 2014

The purpose of this study is to design appropriate digital tools for the production of makeup designs. In this study, we used a three-dimensional facial avatar simulation program developed by the Electronics and Telecommunications Research. This study is based on the creation of three-dimensional CG digital art of facial avatar makeup, produced by using simulation technology. First, the actual application and the tools for digital-optimization and media features were created, leading to the research and cleanup. Second, the theoretical background was applied to the formative elements of oriental colors in the designing process. Makeup design elements include point, line, surface, color, and texture. In this study, effective makeup design was interpreted to be based on the representation of particular elements, notably the design principles of balance, proportion, rhythm, repetition, emphasis, contrast, harmony, and unity. In Asia, design is based on the visibility of red, blue, black, yellow, and white-the colors of the five elements-and the use of points, lines, and shapes. This study was recently under scrutiny in relations to digital simulation and various three-dimensional designs, in terms of how to take advantage of a wide range of applications, and how to apply the findings through media and the dissemination of basic research. This study applies the characteristics of the limited existing stereoscopic three-dimensional and digital simulation programs in order to take advantage of the empirical research, providing a basis to implement this research in a meaningful way. A follow-up study is needed to extend these findings and theoretical foundation through continuous observation and in-depth technical development and research.

• The Korean Journal of Food And Nutrition
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• v.30 no.2
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• pp.319-325
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• 2017

In this study, we optimized the coffee extraction conditions for instant coffee production in two stage percolators, which is the most common coffee extractor for instant coffee production. A central composite design was used to build mathematical model equations for response surface methodology (RSM). In these equations, the yield and overall acceptability of the coffee extracts were expressed as second-order functions of three factors, the feed water temperature, draw-off factor (DOF), and extraction time (cycle time). Based on the result of RSM, the optimum conditions were obtained with the use of desirability function approach (DFA) which find the best compromise area among multiple options. The optimum extraction conditions to maximize the yield and overall acceptability over 40% of yield were found with $163^{\circ}C$ of feed water temperature, 4.3 of DOF and 27 minutes of extraction time (cycle time). These results provide a basic data for the coffee extraction conditions for the competitive instant coffee in the industry.

• KIISE Transactions on Computing Practices
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• v.23 no.10
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• pp.579-587
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• 2017

Cyber Mission Impact Assessment is one of the essential tasks which many militaries and industrial major companies should perform to effectively achieve their mission. The unexpected damage to an organization's assets results in damage to the whole system's performance of the organizations. In order to minimize the damage, it is necessary to quantify the available capacity of the mission, which can be achieved only with the remaining assets, and to immediately prepare a new second best plan in a moment. We therefore need to estimate the exact cyber attack's impact to the mission when the unwanted damage occurs by modeling the relationship between the assets and the missions. In this paper, we propose a new model which deals with the dependencies between assets and missions for obtaining the exact impact of a cyber attack. The proposed model distinguishes task management from asset management for an efficient process, and it is implemented to be optimized using a vectorized operation for parallel processing and using a buffer to reduce the computation time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• Journal of Korean Society of Steel Construction
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• v.16 no.1 s.68
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• pp.21-32
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• 2004

In this study, a continuous optimum design model with its application program for plane steel frame structures developed. In the model, the sequential unconstrained minimization technique (SUMT) transforming the nonlinear optimization problem with multidesign variables and constraints into an unconstrained minimization problem and the refined plastic hinge analysis method as one of the most effective second-order inelastic analysis methods for steel frame structures were implemented. The total weight of a steel frame structure was taken as the objective function, and the AISC-LRFD code requirements for the local and member buckling, flexural strength, shear strength, axial strength and size of the cross-sectional shapes of members were used for the derivation of constraint equations. To verify the appropriateness of the present model, the optimum designs of serveral plane steel frame structures subject to vertical and horizontal loads were conducted.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.81-87
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• 2010

In order to calculate the accurate runoff from a basin, nonlinearity in the relationship between rainfall and runoff has to be considered. Many runoff calculation models assume the linearity in the relationship or are too complicated to be analyzed. Therefore, the storage function method has been used in the prediction of flood because of the simplicity of the model. The storage function method has five parameters with related to the basin and rainfall characteristics which can be estimated by the empirical trial and error method. To optimize these parameters, regression method or optimization techniques such as genetic algorithm have been used, however, it is not easy to optimize them because of the complexity of the method. In this study, the metamodel is proposed to estimate those model parameters. The metamodel is the combination of artificial neural network and genetic algorithm. The model is consisted of two stages. In the first stage, an artificial neural network is constructed using the given rainfall-runoff relationship. In the second stage, the parameters of the storage function method are estimated using genetic algorithm and the trained artificial neural network. The proposed metamodel is applied in the Peong Chang River basin and the results are presented.

• Environmental Engineering Research
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• v.15 no.2
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• pp.63-70
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• 2010

In this study, the response surface method and experimental design were applied as an alternative to conventional methods for the optimization of coagulation tests. A central composite design, with 4 axial points, 4 factorial points and 5 replicates at the center point were used to build a model for predicting and optimizing the coagulation process. Mathematical model equations were derived by computer simulation programming with a least squares method using the Minitab 15 software. In these equations, the removal efficiencies of turbidity and total organic carbon (TOC) were expressed as second-order functions of two factors, such as alum dose and coagulation pH. Statistical checks (ANOVA table, $R^2$ and $R^2_{adj}$ value, model lack of fit test, and p value) indicated that the model was adequate for representing the experimental data. The p values showed that the quadratic effects of alum dose and coagulation pH were highly significant. In other words, these two factors had an important impact on the turbidity and TOC of treated water. To gain a better understanding of the two variables for optimal coagulation performance, the model was presented as both 3-D response surface and 2-D contour graphs. As a compromise for the simultaneously removal of maximum amounts of 92.5% turbidity and 39.5% TOC, the optimum conditions were found with 44 mg/L alum at pH 7.6. The predicted response from the model showed close agreement with the experimental data ($R^2$ values of 90.63% and 91.43% for turbidity removal and TOC removal, respectively), which demonstrates the effectiveness of this approach in achieving good predictions, while minimizing the number of experiments required.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.390-395
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• 2020

For some processes with saturations, economical operating points are on the saturation edges. Traditional feedback controllers cannot be used to regulate such processes on the saturation edges because there are abrupt dynamics changes and no feedback information at saturations. Optimization-based methods such as the model predictive control can treat this control problem without difficulty when the saturation levels and dynamics are known and not varying. Otherwise, an adaptation scheme to track the saturation levels and dynamics should be included. Here, for very simple methods to treat this control problem, two control methods based on the recent slope seeking method and the relay feedback method are proposed. Their performances are evaluated with simulations applying them to a second order liquid level system with saturation. Simulations show that these proposed control methods can find and maintain operating point of the saturation edge under 5% relative error.