• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.483-495
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• 2007

In a general fractional factorial design, the n-levels of a factor are coded by the $n^{th}$ roots of the unity. Pistone and Rogantin (2007) gave a full generalization to mixed-level designs of the theory of the polynomial indicator function using this device. This article discusses the optimal blocking scheme for nonregular designs. According to hierarchical principle, the minimum aberration (MA) has been used as an important criterion for selecting blocked regular fractional factorial designs. MA criterion is mainly based on the defining contrast groups, which only exist for regular designs but not for nonregular designs. Recently, Cheng et al. (2004) adapted the generalized (G)-MA criterion discussed by Tang and Deng (1999) in studying $2^p$ optimal blocking scheme for nonregular factorial designs. The approach is based on the method of replacement by assigning $2^p$ blocks the distinct level combinations in the column with different blocks. However, when blocking level is not a power of two, we have no clue yet in any sense. As an example, suppose we experiment during 3 days for 12-run Plackett-Burman design. How can we arrange the 12-runs into the three blocks? To solve the problem, we apply G-MA criterion to nonregular mixed-level blocked scheme via the mixed-level indicator function and give an answer for the question.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.235-244
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• 2008

Balanced arrays which are generalized orthogonal arrays, introduced by Chakravarti (1956) can be used to construct the fractional factorial designs. Especially for 2-level factorials, balanced arrays with strength 4 are identical to the resolution-V fractional designs. In this paper criteria for evaluation the degree of the orthogonality of balanced arrays of 2-levels with strength 4 are developed and some application methods of the suggested criteria are discussed. As a result, in this paper, we introduce the constructing methods of near orthogonal saturated balanced resolution-V fractional 2-level factorial designs.

• Journal of the Korean Society of Tobacco Science
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• v.5 no.1
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• pp.49-56
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• 1983

• Journal of the Korean Society of Food Science and Nutrition
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• v.32 no.4
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• pp.539-543
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• 2003

To produce alcohol from Mume (Prunus mume Sieb. et Zucc) fruits, fermentation conditions were optimized by a response surface methodology (RSM) using the fractional factorial design with 3 variables and 5 levels. The coefficient of determination ($R^2$) of response surface regression equation for alcohol production were 0.9276. Optimum conditions for alcohol production were involved with 8.39% of sugar content, 28.86$^{\circ}C$ of fermentation temperature and 3.84 days of fermentation time. Finally, predicted level of alcohol production at these conditions was 5.24%.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.862-865
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• 2000

The object of this paper is to evaluate the surface roughness using the experimental equation of surface roughness, which is developed in turning by an orthogonal array method. $L_9{3^4}$ orthogonal array method, one of fractional factorial design has been used to study effects of main cutting parameters such as cutting speed, feed rate and depth of cut, on the surface roughness. And the analysis of variance (ANOVA)-test has been used to check the significance of cutting parameters. Using the result of ANOVA-test, the experimental equation of surface roughness, which consists of only significant cutting parameter - feed rate, has been developed. The coefficient of determination of this equation is 0.962.

• The Korean Journal of Applied Statistics
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• v.5 no.1
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• pp.19-28
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• 1992

In this paper, minimal balanced $2^t$ fractional factorial designs which permit the estimation of main effects and 2-factor interactions are developed by using a partially balanced array. Such designs are characterized by a minimum number of runs and some balancedness property of the variance-covariance matrix of the estimates. In addition to describing the designs, optimality criteria are discussed and the trace-optimal designs are presented. The proposed designs are especially useful in Taguchi method, where we need to investigate up to 2-factor interactions of the control factors.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.430-435
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.234-238
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• 1996

In this paper, Plasma-Enhanced Chemical Vapor Deposition (PECVD) modeling using Polynomial Neural Networks (PNN) has been introduced. The deposition of SiO2 was characterized via a 25-1 fractional factorial experiment, was used to train PNNs using predicted squared error (PSE). The optimal neural network structure and learning parameters were determined by means of a second fractional factorial experiment. The optimized networks minimized both learning and prediction error. From these PNN process models, the effect of deposition conditions on film properties has been studied. The deposition experiments were carried out in a Plasma Therm 700 series PECVD system. The models obtained will ultimately be used for several other manufacturing applications, including recipe synthesis and process control.

• Nuclear Engineering and Technology
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• v.33 no.6
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• pp.605-618
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• 2001

For the mechanical stability assessment of a deep underground high-level waste repository. computer simulations using FLAC3D were carried out and important parameters including stress ratio, depth, tunnel size, joint spacing, and joint properties were chosen from sensitivity analysis. The main effect as well as the interaction effect between the important parameters could be investigated effectively using fractional factorial design . In order to analyze the stability of the disposal tunnel and deposition hole in a discontinuous rock mass, different modelings were performed under different conditions using 3DEC and the influence of joint distribution and properties, rock properties and stress ratio could be determined. From the three dimensional modelings, it was concluded that the conceptual repository design was mechanically stable even in a discontinuous rock mass.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.357-360
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. In this research, we tried the back-propagation algorithm of artificial neural network to find an optimum solution in the cooling system design of injection mold. The cooling system optimization problem that was once solved by a response surface method with 4 design variables was solved by applying the back-propagation algorithm, resulting in a solution with a sufficient accuracy. Furthermore the number of training points was much reduced by applying the fractional factorial design without losing solution accuracy.