• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.3
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• pp.228-240
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• 2012

Morphing is a geometric interpolation technique that is often used by the animation industry to transform one form into another seemingly seamlessly. It does this by producing a large number of 'intermediate' forms between the two 'extreme' or 'parent' forms. It has already been shown that morphing technique can be a powerful tool for form design and as such can be a useful addition to the armoury of product designers. Morphing procedure itself is simple and consists of straightforward linear interpolation. However, establishing the correspondence between vertices of the parent models is one of the most difficult and important tasks during a morphing process. This paper discusses the mesh-merging method employed for this process as against the already established mesh-regularising method. It has been found that the merging method minimises the need for manual manipulation, allowing automation to a large extent.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.1
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• pp.74-85
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• 2010

In order to formulate the compounded polypropylene(C-PP) which is suitable to an automotive door trim panel, 9 sorts of properties were measured after manufacturing the C-PP using an extruder and an injection machine with polypropylene(PP), ethylene-octene rubber(EOR) and talc. Mixture design, especially extreme vertices design, in DOE with MINITAB - commercial software was used to analyze the data. The relations between each property and each component, for example, $y=0.00907222x_1+0.00870556x_2+0.0155722x_3$ for specific gravity, were found out by the regression analysis and the variance analysis. The optimized formulation of the C-PP for an automotive door trim panel was acquired at PP(77.6962), EOR(11.0238) and talc(10.2800) by use of the response optimizer(mixture) in MINITAB.

• Journal of the East Asian Society of Dietary Life
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• v.22 no.6
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• pp.782-794
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• 2012

The objective of this study was to determine the optimal conditions for preparation of cooked brown rice by blending brown rice, white rice and glutinous rice to improve the palatability. Formulations composed of brown rice (10~100%), white rice (0~90%) and glutinous rice (0~90%) were generated from an extreme-vertices of mixture experimental design, which showed ten experimental points for brown rice, with white rice and glutinous rice as the independent variables. The sensory evaluation, color, and texture profile analysis (TPA) of cooked brown rice and pasting characteristics of blending cereals flour were measured as response variables. Regression analysis showed that all responsible variables fit linear, quadratic or special cubic models (p<0.1), except for the cohesiveness of TPA. The goals of optimization of the blending ratio of brown rice, white rice and glutinous rice were given as appearance, flavor, texture and overall acceptability (lower: 5.50, target: 6.62). The optimal conditions were determined to be 34.55% brown rice, 42.71% white rice and 22.74% glutinous rice.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.267-280
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• 2007

Various single-valued design optimality criteria such as D-, G-, and V-optimality are used often in constructing optimal experimental designs for mixture experiments in a constrained region R where lower and upper bound constraints are imposed on the ingredients proportions. Even though they are optimal in the strict sense of particular optimality criterion used, it is known that their performance is unsatisfactory with respect to the prediction capability over a constrained region. (Vining et at., 1993; Khuri et at., 1999) We assume the quadratic polynomial model as the mixture response surface model and are interested in finding efficient designs in the constrained design space for a mixture. In this paper, we make an expanded list of candidate design points by adding interior points to the extreme vertices, edge midpoints, constrained face centroids and the overall centroid. Then, we want to propose a robust design with respect to D-optimality, G-optimality, V-optimality and distance-based U-optimality. Comparing scaled prediction variance quantile plots (SPVQP) of robust designs with that of recommended designs in Khuri et al. (1999) and Vining et al. (1993) in the well-known examples of a four-component fertilizer experiment as well as McLean and Anderson's Railroad Flare Experiment, robust designs turned out to be superior to those recommended designs.