• Natural Product Sciences
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• v.22 no.4
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• pp.270-274
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• 2016

Green tea, the leaves of Camellia sinsneis (Theaceae), is generally acknowledged as the most consumed beverage with multiple pharmacological functions including antioxidant activity. This study was performed to analyze the effect of extraction conditions of green tea on its antioxidant effects using DPPH assay. Three extraction factors such as extraction solvent (EtOH, 0 - 100%), extraction time (3 - 15 min) and extraction temperature ($10-70^{\circ}C$) were analyzed and optimized extraction condition for antioxidant activity of green tea extract (GTE) was determined using response surface methodology with three-level-three-factor Box-Behnken design (BBD). Regression analysis showed a good fit of data and the optimal conditions of extraction were found to be 57.7% EtOH, 15 min and $70^{\circ}C$. Under this condition, antioxidant activity of experimental data was 88.4% which was almost fit to the ideal value of 88.6%. As epigallocatechin gallate (EGCG) is known for the major ingredient for antioxidant activity of green tea, we investigated the effect of EGCG on antioxidant activity of GTE. EGCG showed antioxidant activity with the $IC_{50}$ value of $4.2{\mu}g/ml$ and a positive correlation was observed between EGCG content and the antioxidant activity of GTE with $R^2=0.7134$. Interestingly, however, GTE with 50 - 70% antioxidant activity contain less than $1.0{\mu}g/ml$ of EGCG, which is much lower than $IC_{50}$ value of EGCG. Therefore, we suppose that EGCG together with other constituents contribute to antioxidant activity of GTE. Taken together, these results suggest that green tea is more beneficial than EGCG alone for antioxidant ability and optimal extraction condition of green tea will be useful for the development of food and pharmaceutical applications

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.3
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• pp.165-172
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• 2017

In this study, centrifugal fan blades used to circulate cold air inside a household refrigerator were optimized to achieve high performance and low noise by using the response surface method, which is frequently employed as an optimization algorithm when multiple independent variables affect one dependent variable. The inlet and outlet blade angles, and the inner radius, were selected as the independent variables. First, the fan blades were optimized to achieve the maximum volume flow rate. Based on this result, a prototype fan blade was manufactured using a 3-D printer. The measured P-Q curves confirmed the increased volume flow rate of the proposed fan. Then, the rotation speed of the new fan was decreased to match the P-Q curve of the existing fan. It was found that a noise reduction of 1.7 dBA could be achieved using the new fan at the same volume flow rate.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.195-208
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• 2009

A conventional single design optimality criterion has been used to select an efficient experimental design. But, since an experimental design is constructed with respect to an optimality criterion pre specified by investigators, an experimental design obtained from one optimality criterion which is superior to other designs may perform poorly when the design is evaluated by another optimality criterion. In other words, none of these is entirely satisfactory and even there is no guarantee that a design which is constructed from using a certain design optimality criterion is also optimal to the other design optimality criteria. Thus, it is necessary to develop certain special types of experimental designs that satisfy multiple design optimality criteria simultaneously because these multi-optimal designs (MODs) reflect the needs of the experimenters more adequately. In this article, we present a heuristic approach to construct second-order response surface designs which are more flexible and potentially very useful than the designs generated from a single design optimality criterion in many real experimental situations when several competing design optimality criteria are of interest. In this paper, over cuboidal design region for $3\;{\leq}\;k\;{\leq}\;5$ variables, we construct multi-optimal designs (MODs) that might moderately satisfy two famous alphabetic design optimality criteria, G- and IV-optimality criteria using a GA which considers a certain amount of randomness. The minimum, average and maximum scaled prediction variances for the generated response surface designs are provided. Based on the average and maximum scaled prediction variances for k = 3, 4 and 5 design variables, the MODs from a genetic algorithm (GA) have better statistical property than does the theoretically optimal designs and the MODs are more flexible and useful than single-criterion optimal designs.

• Steel and Composite Structures
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• v.42 no.1
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• pp.59-74
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• 2022

This study introduces a general reliability-based, performance-based design framework to design frames regarding their uncertainties and user-defined design goals. The Iterative-R method extracted from the main framework can designate a proper R (i.e., response modification factor) satisfying the design goal regarding target reliability index and pre-defined probability of collapse. The proposed methodology is based on FEMA P-695 and can be used for all systems that FEMA P-695 applies. To exemplify the method, multiple three-dimensional, four-story steel special moment-resisting frames are considered. Closed-form relationships are fitted between frames' responses and the modeling parameters. Those fits are used to construct limit state functions to apply reliability analysis methods for design safety assessment and the selection of proper R. The frameworks' unique feature is to consider arbitrarily defined probability density functions of frames' modeling parameters with an insignificant analysis burden. This characteristic enables the alteration in those parameters' distributions to meet the design goal. Furthermore, with sensitivity analysis, the most impactful parameters are identifiable for possible improvements to meet the design goal. In the studied examples, it is revealed that a proper R for frames with different levels of uncertainties could be significantly different from suggested values in design codes, alarming the importance of considering the stochastic behavior of elements' nonlinear behavior.

• Journal of Environmental Science International
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• v.23 no.3
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• pp.359-368
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• 2014

The aim of our research was to apply experimental design methodology in the optimization of N, N-Dimethyl-4-nitrosoaniline (RNO, which is indictor of OH radical formation) degradation using gas mixing-circulation plasma process. The reaction was mathematically described as a function of four independent variables [voltage ($X_1$), gas flow rate ($X_2$), liquid flow rate ($X_3$) and time ($X_4$)] being modeled by the use of the central composite design (CCD). RNO removal efficiency was evaluated using a second-order polynomial multiple regression model. Analysis of variance (ANOVA) showed a high coefficient of determination ($R^2$) value of 0.9111, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the experimental data. The application of response surface methodology (RSM) yielded the following regression equation, which is an empirical relationship between the RNO removal efficiency and independent variables in a coded unit: RNO removal efficiency (%) = $77.71+10.04X_1+10.72X_2+1.78X_3+17.66X_4+5.91X_1X_2+3.64X_2X_3-8.72X_2X_4-7.80X{_1}^2-6.49X{_2}^2-5.67X{_4}^2$. Maximum RNO removal efficiency was predicted and experimentally validated. The optimum voltage, air flow rate, liquid flow rate and time were obtained for the highest desirability at 117.99 V, 4.88 L/min, 6.27 L/min and 24.65 min, respectively. Under optimal value of process parameters, high removal(> 97 %) was obtained for RNO.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1960-1965
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• 2001

In the practical design, it is important to extract the design space information of a complex system in order to optimize the design because the design contains huge amount of design conflicts in general. In this research FEA (finite element analysis) has been successfully implemented and integrated with a statistical approach such as DOE (design of experiments) based RSM (response surface model) to optimize the thermal deformation of an automotive disk brake. The DOE is used for exploring the engineer's design space and for building the RSM in order to facilitate the effective solution of multi-objective optimization problems. The RSM is utilized as an efficient means to rapidly model the trade-off among many conflicting goals existed in the FEA applications. To reduce the computational burden associated with the FEA, the second-order regression models are generated to derive the objective functions and constraints. In this approach, the multiple objective functions and constraints represented by RSM are solved using the sequential quadratic programming to archive the optimal design of disk brake.

• Journal of the Korean Wood Science and Technology
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• v.46 no.4
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• pp.380-392
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• 2018

The importance of polysaccharides is increasing globally due to their role as a significant source of dietary prebiotics in the human diet. In the present study, in order to maximize the yield of crude polysaccharides from Pinus densiflora, response surface methodology (RSM) was used to optimize a two-stage extraction process consisting of steam explosion and water extraction. Three independent main variables, namely, the severity factor (Ro) for the steam explosion process, the water extraction temperature ($^{\circ}C$), and the ratio of water to raw material (v/w), were studied with respect to prebiotic sugar content. A Box-Behnken design was created on the basis of the results of these single-factor tests. The experimental data were fitted to a second-order polynomial equation for multiple regression analysis and examined using the appropriate statistical methods. The data showed that both the severity factor (Ro) and the ratio of water to material (v/w) had significant effects on the prebiotic sugar content. The optimal conditions for the two-stage process were as follows: a severity factor (Ro) of 3.86, a water extraction temperature of $89.66^{\circ}C$, and a ratio of water to material (v/w) of 39.20. Under these conditions, the prebiotic sugar content in the extract was 332.45 mg/g.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.6
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• pp.954-960
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• 2012

Nowadays, rapidly changing and unstable global economic environments request a lot of roles to engineers. In this situation, product should be designed to make more profit by cost down and to satisfy distinguished performance comparing to other competitive ones. In this research, the optimization design of the industrial robot casting will be done. The weight and deflection have to be reduced as objective functions and stress has to be constrained under some constant value. To reduce time cost, CCD (Central Composite Design) will be used to make experimental design. And RSM (Response Surface Methodology) will be taken to make regression model for objective functions and constraint function. Finally, optimization will be done with Genetic Algorithm. In this problem, the objective functions are multiple, so NSGA-II which is brilliant and efficient for such a problem will be used. For the solution quality check, the diversity between Pareto solutions will be also checked.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• Journal of Ocean Engineering and Technology
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• v.27 no.2
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• pp.27-32
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• 2013

A valve is a marine structure that is subjected to multiple seawater loads. Therefore, it is necessary to define the kind of loads applied to it to confirm whether the structure has sufficient strength. In this research, we aimed to find the optimal solution for the stress and deformation of valves under various loads. We first selected design variables and implement a finite element analysis according to changes in the thickness of each component of a valve based on a central composite design. Next we developed a regression model of the response surface. Using this model, we calculated the optimal objective value based on NSGA-II. Finally, to confirm the correspondence between the optimal objective value and the real FEM value, we compared the optimal result and structural analysis result to verify the performance of NSGA-II.