• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1430-1435
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• 2003

Satellite upper platform is optimized by response surface method which has non-gradient, semi-glogal, discrete and fast convergency characteristics. Sampling points are extracted by design of experiments using Central Composite Method and Factorial Design. Also response surface is generated by the various regression functions. Structure analysis is execuated with regard for static and dynamic environment in launching stage. As a result response surface method is superior to other optimization method with respect to optimum value and cost of computation time. Also a confidence is varified in the various regression models.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.95-100
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• 2005

In this paper a class of multifactor designs for estimating the slope of second order response surface regression models with correlated errors is considered. General conditions for second order slope rotatability over all directions and also with respect to the maximum directional variance in case of k=2 have been derived assuming errors have a general correlated error structure. And we consider the measures for evaluating slope rotatability with correlated errors similar to in case of uncorrelated error structures.

• Proceedings of the Korean Nuclear Society Conference
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• 2002.05a
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• pp.54-54
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• 2002

• Journal of the East Asian Society of Dietary Life
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• v.1 no.3
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• pp.305-311
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• 1991

The study was designed to investigate overall acceptability of rice extrudate with added ginseng flour extruded by single screw extruder. Graphic three dimension analysis on response surface regression was conducted for overall acceptability evaluated by balanced incomplete block design. Overall acceptability, which formed a saddle point, increased as moisture content increased at lower die temperature, and as moisture content decreased at higher die temperature. Critical values of each variable which indicated optimum response are 5.0% ginseng content, 17.8% moisture content and 104.6$^{\circ}C$ die temperature, and optimum inferred score of overall acceptability is 59.6 and 90. Key words: extrdate, overall acceptability, response surface regression analysis, balanced incomplete block method.

• The Korean Journal of Food And Nutrition
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• v.6 no.3
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• pp.178-188
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• 1993

This research was attempted to investigate changes in physicochemical properties of rice extrudate with added ginger powder extruded by single screw extruder. Graphic three dimensional analysis on response surface regression was used to evaluate effects of extrusion variables on quality factors of the extrudate according to two independent variables, ginger consent 0∼12%, moisture content 14∼26%. The summarized results are as follows : 1) Regarding proximate composition of rice extrudate with added ginger powder, as ginger powder content of raw material Increased, crude tat, crude protein, crude ash and crude fiber increased, while soluble nitrogen free extract decreased. 2) Graphic three dimensional analysis on response surface regression was conducted for each dependent variable which revealed statistically significant relationship with independent variables, 0∼120A ginger and 14∼26% moisture content. Expansion ratio had a critical point as moisture content changed. As ginger and moisture content Increased, bulk density, break strength and water absorption Index Increased, while water solubility Index decreased. The predicted maximum degree of gelatinization in 6.15% ginger and 15.56% moisture content is 88.27%, and lightness decreased as ginger content Increased. According to the microstructure for the cross section of extrudate obsorbed with image analyzer, air cell number and perimeter revealed saddle point, meanwhile total area and fractarea of air cell had critical points as moisture content changed. In view of the results, quality of rice extrudate with added ginger powder was optimum when rice flour was fed to the extruder with 2∼7% singer powder and 15∼20% moisture content.

• Journal of Dairy Science and Biotechnology
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• v.26 no.2
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• pp.51-56
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• 2008

Using central composite design, we have designed optimization of the manufacturing of processed butter. And response surface analysis by least-square regression was used Statistical Analysis System(SAS). Central composite design can be achieved by response surface techniques that allow flexibility in modeling and analysis. Response surface methodology(RSM) was used to optimize hardness(%) using as independent variables; the content of butter($X_1$), ranging from 50 to 90(%), the content of soybean oil($X_2$), from 0 to 20(%), and the hydrogenated soybean oil($X_3$) from 0 to 4(%). The results on the regression coefficients calculated for overrun by response surface by least-square regression(RSREG) were followed. It was considered that the linear regression was significant(p<0.01). As for the processed butter, the regression model equation for the hardness(Y, %) to the change of an independent variable could be predicted as follow: $Y=60.88-8.92X_2-{29.3X_2}^2$. The optimal for the manufacturing of processed butter were determined at the content of butter of 88.22%, soybean oil of 6.71% and hydrogenated soybean oil of 2.36%, respectively. Optimum compositions were resulted in hardness of 65.78 N. Finally the reference sample(Butter in the morning, Seoul Dairy Co-op.) and processed butter manufacturing under the optimal conditions were compared with spreadability test. The spreadability scores result from reference sample and butter under optimal conditions was not found a significant difference.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.557-578
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• 2007

In Response Surface Methodology (RSM), rotatability is a natural and highly desirable property. For second order general correlated regression model, the concept of robust rotatability was introduced by Das (1997). In this paper a new measure of robust rotatability for second order response surface designs with correlated errors is developed and illustrated with an example. A comparison is made between the newly developed measure with the previously suggested measure by Das (1999).

• Journal of the Korean Statistical Society
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• v.9 no.1
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• pp.61-77
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• 1980

In response surface experiments, a polynomial model is often used to fit the response surface by the method of least squares. However, if the vectors of predictor variables are multicollinear, least squares estimates of the regression parameters have a high probability of being unsatisfactory. Hoerland Kennard have demonstrated that these undesirable effects of multicollinearity can be reduced by using "ridge" estimates in place of the least squares estimates. Ridge regrssion theory in literature has been mainly concerned with selection of k for the first order polynomial regression model and the precision of $\hat{\beta}(k)$, the ridge estimator of regression parameters. The problem considered in this paper is that of selecting k of ridge regression for a given polynomial regression model with an arbitrary order. A criterion is proposed for selection of k in the context of integrated mean square error of fitted responses, and illustrated with an example. Also, a type of admissibility condition is established and proved for the propose criterion.criterion.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.1
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• pp.26-32
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• 2005

In this paper, the effect of various regression models is investigated on structural optimization using the central composite method. Three bar truss and the upper platform of a satellite are optimized using various regression models that are polynomial, exponential and log functions. Response surface method is non-gradient, semi-global, discrete and fast converging in optimization problem. Sampling points are extracted by the design of experiments using the central composite method. Response surface is generated using the various regression functions. Structural analysis for calculating constraints is executed to find static and dynamic responses. From this study, it is verified that the response surface method has advantage in optimum value and computation time in comparison to other optimization methods.

• Journal of the Korean Statistical Society
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• v.7 no.2
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• pp.109-119
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• 1978

When a response surface by a seconde order polynomial regression model, the stationary point is obtained by solving simultaneous linear equations. But the point is a function of random variables. We can find a confidence region for this point as Box and Hunter provided. However, the confidence region is often too large to be useful for the experiments, and it is necessary to augment additional design points in order to obtain a satisfactory confidence region for the stationary point. In this note, the author suggests a method how to augment design points "eficiently", and shows the change of the confidence region of the estimated stationary point in a response surface.e surface.