• 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.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1025-1033
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• 2003

In common with workers in hydrologic fields, scientists and engineers relate one variable to two or more other variables for purposes of predication, optimization, and control. Statistics methods have improved to establish such relationships. Regression, as it is called, is indeed the most commonly used statistics technique in hydrologic fields; relationship between the monitored variable stage and the corresponding discharges(rating curve). Regression methods expressed in the form of mathematical equations which has parameters, so called parametric methods. some times, the establishment of parameters is complicated and uncertain. Many non-parametric regression methods which have not parameters, have been proposed and studied. The most popular of these are kernel regression method. Kernel regression offer a way of estimation the regression function without the specification of a parametric model. This paper conducted comparisons of some bandwidth selection methods which are using the least squares and cross-validation.

• 대한공업교육학회지
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• v.34 no.2
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• pp.321-330
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• 2009

This study presents the blow molding of injection stretch-blow molding process for PET bottle. The numerical analysis of the blow molding of PET bottle is considered in this paper using CAE with a view to minimize the thickness difference. In order to determine the design parameters and processing conditions in blow molding, it is very important to establish the numerical model with physical phenomenon. In this study, a shell model with thickness has been introduced for the purpose and blow simulations with 3-type blow process condition are carried out. The simulations resulted in the thickness distribution in good agreement with the physical phenomenon. Also, from the result of numerical analysis, we appropriately predicted the thickness distribution along the PET bottle wall and Using the result of numerical analysis we apply the preform design and blow molding process condition for optimization.

• Journal of the Korean Society for Railway
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• v.10 no.6
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• pp.742-750
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• 2007

This paper presents a mathematical model for a double-fleet operation in Korean high speed rail (HSR). KORAIL has a plan to launch new HSR units in 2010, which are composed of 10 railcars. The double-fleet operation assigns a single-unit or two-unit fleet to a segment, accommodating demand fluctuation. The proposed model assumes stochastic demand and uses chance-constrained constraints to assure a preset service level. It can be used in the tactical planning stage of the rail management as it includes several real-world conditions, such as the capacities of the infra-structures and operational procedures. In the solution approach, the expected revenue in the objective function is linearized by using expected marginal revenue, and the chance-constrained constraints are linearized by assuming that demands are normally distributed. Subsequently, the model can be solved by a mixed-integer linear programming solver fur small size problems. The test results of the model applied to Friday morning train schedules for one month sample data from KTX operation in 2004 shows that the proposed model could be utilized to determine the effectiveness of double-fleet operation, which could significantly increase the expected profit and seat utilization rates when properly maneuvered.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.83-102
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• 1994

Some optimal designs and data analysis methods based on a Gaussian spatial linear model for computer simulation experiments are considered. For designs of computer experiments, Latin-hypercube designs and some optimal designs are combined. A two-stage computational (2-points exchange and Newton-type) algorithm for finding the optimal Latin-hypercube design is presented. The spatial prediction model which was discussed by Sacks, Welch, Mitchell and Wynn(1989) for computer experiments, is used for analysis of the simulated data. Moreover, a method of contructing sequential (optimal) Latin-hypercube designs is considered. An application of this approach to the quality improvement and optimization of the integrated circuit design via the main-effects plot and the sequential experimental strategy is presented.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.2
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• pp.120-128
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• 2000

At the initial design stage of a new vehicle, the chassis layout has the most important influence on the overall vehicle performance. Most chassis designers have achieved the target performances by trial and error method as well as individual knowhow. Accordingly, a general procedure for determining the optimum location of suspension hard points with respect to the kinematic characteristics needs to be developed. In this paper, a method to optimize the toe angle in the double wishbone type front suspension of the four-wheel-drive vehicle is presented using the design of experiment, multibody dynamic simulation, and optimum design program. The handling performances of two full vehicle models having the initial and optimized toe angle are compared through the single lane change simulation. The sensitive design variables with respect to the kinematic characteristics are selected through the experimental design sensitivity analysis using the perturbation method. An object function is defined in terms of the toe angle among those kinematic characteristics. By the design of experiment and regression analysis, the regression model function of toe angle is obtained. The design variables which make the toe angle optimized ae extracted using the optimum design program DOT. The single lane change simulation and test of the full vehicle model are carried out to survey the handling performances of vehicle with toe angle optimized. The results of the single lane change simulation show that the optimized vehicle has the more improved understeer tendency than the initial vehicle.

• Membrane Journal
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• v.7 no.4
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• pp.167-174
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• 1997

The concentration of silica is required to meet a certain level because silica affects fuel and materials integrity by forming a zeolite layer on fuel cladding surfaces. When the established Feed and Bleed method is employed, nuclear waste increase and the corresponding amount of boric acid is constantly consumed. This study concentrates on minimizing the amount of nuclear waste and consumption of boric acid. Using five different membranes, operating conditions such as temperatur, feed water flow rate, boric acid recovery and silica removal rate were examined. A silica-selective removal system was designed based on the above optimization procedures. Three-stage system was designed with two characteristically different membranes so that it could correspond with the different situation easily. Compared to the pevious results of the Feed and Bleed method, the current method showed that the amount of nuclear waste was reduced to 7%, and the consumption of boric acid to 15.7%.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.22-28
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• 2017

The Convolutional Neural Network(CNN) can be divided into two stages: feature extraction and classification. The hyperparameters such as kernel size, number of channels, and stride in the feature extraction step affect the overall performance of CNN as well as determining the structure of CNN. In this paper, we propose a method to optimize the hyperparameter in CNN feature extraction stage using Parameter-Setting-Free Harmony Search (PSF-HS) algorithm. After setting the overall structure of CNN, hyperparameter was set as a variable and the hyperparameter was optimized by applying PSF-HS algorithm. The simulation was conducted using MATLAB, and CNN learned and tested using mnist data. We update the parameters for a total of 500 times, and it is confirmed that the structure with the highest accuracy among the CNN structures obtained by the proposed method classifies the mnist data with an accuracy of 99.28%.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Design Convergence Study
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• v.15 no.6
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• pp.255-270
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• 2016

The purpose of this research is to establish the UX methodology knowledge optimized for small sized companies on the basis of theoretical and practical UX development process models. The 7 UX design process models were analyzed by the outcomes and attributes on each design stage from academic field. Then the interview and observation on 18 domestic companies were conducted to clarify the actual methods in use and the gab from the academic theories. The two different design model were unified as an product lifecycle coupled UX process (PLUS). The 100 theory-industry knowedge combined UX design methodologies were selected and aligned along with 6 design stages of PLUS process. Each method was decomposed as a template format that contains standardized attributes applicable for small companies under consideration of their resources, process and produced items. The result of this research is expected to be applied onto real industry and reduce the risk of small manufacturing companies to escalate the quality of UX in their productions.