• Journal of the Korean Society for Aviation and Aeronautics
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• v.13 no.3
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• pp.22-33
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• 2005

Response Surface Method (RSM) constructs approximate response surfaces using sample data from experiments or simulations and finds optimum levels of process variables within the fitted response surfaces of the interest region. It will be necessary to get the most suitable response surface for the accuracy of the optimization. The application of RSM plan experimental designs. The RSM is used in the sequential optimization process. The first goal of this study is to improve the plan of central composite designs of experiments with various locations of axial points. The second is to increase the optimal efficiency applying a modified method to update interest regions.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.59-79
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• 1999

The fitting of a response surface model and the subsequent exploration of the response surface are usually based on the assumption that the experimental runs are carried out under homogeneous conditions. This, however, may be quite often difficult to achieve in many experiments. To control such an extraneous source of variation, the response surface design should be arranged in several blocks within which homogeneity of conditions can be maintained. In this case, when fitting a response surface model, the least squares estimates of the model's parameters and the prediction variance will generally depend on how the response surface design is blocked. That is, the choice of a blocking arrangement for a response surface design can have a considerable effect on estimating the mean response and on the size of the prediction variance. In this paper, we propose a measure for evaluating the effect of blocking of response surface designs using cuboidal regions.

• Women's Health Nursing
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• v.4 no.1
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• pp.139-160
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• 1998

The purpose of this study was to identify the research contents and designs of nursing papers on women's health. 1010 articles published in 9 Korean nursing journals and 2070 matser's theses and doctoral dissertations in nursing science for 10 years from 1988 through 1997 were reviewed. The percent of nursing papers on women's health was 12.7%(390) out of a total of 3080 papers. Major findings of 390 nursing papers analyzed were as following : 1. The contents of nursing papers on women's health were classified by 8 categories such as childbirth(19.5%), women's health state and behavior(18.5%), motherhood(14.9%), cultural and social issues(12.8%), menstruation(8.7%), mental health(8.2%), caring activities(8.2%), and surgery(7.7%). 2. The principal designs used in nursing papers on women's health were descriptive and correlational survey(70.8%), experimental design(14.4%), and qualitative research design(14.1%).

• Advances in Computational Design
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• v.5 no.3
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• pp.323-347
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• 2020

The enhancements of bioenergy production effectiveness require the comprehensively experimental study of several parameters affecting these bioprocesses. The interpretation of the obtained experimental results and the estimation of optimum yield are extremely complicated such as misinterpreting the results of an experiment. The use of mathematical modeling and statistical experimental designs can consistently supply the predictions of the potential yield and the identification of defining parameters and also the understanding of key relationships between factors and responses. This paper summarizes several mathematical models used to achieve an adequate overall and maximal production yield and rate, to screen, to optimize, to identify, to describe and to provide useful information for the effect of several factors on bioenergy production processes. The usefulness, the validity and, the feasibility of each strategy for studying and optimizing the bioenergy-producing processes were discussed and confirmed by the good correlation between predicted and measured values.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.1-10
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• 2006

Many of statistical experimental designs have multiple goals. It is often impractical to use the single-objective criterion for this purpose. It is necessary to modify the existing optimum experimental design criteria. There exist three criteria handling this problem in general: compound, constrained, maxi-min approach. This paper extends Kahng and Kim's idea to develop another approach to incorporate several experimental design criteria in accordance of their importance in practical way. Furthermore this paper investigate its relationship with the maxi-min approach. It shows logically that the often realized infeasibility can be still avoided with the rank of importance of the objectives intact.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.15-20
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• 2022

The study investigated the effects of differences in some electronic course designs on university students' Computational Thinking Skills (CTS). Towards this end, the researcher adopted the experimental research design of a quasi-experimental of two experimental groups. The first group was taught an e-course designed in a sequential pattern, and the other group's course was designed according to the holistic model. A CTSs test was prepared to collect the relevant data, and the data were analyzed statistically using these tests- Pearson correlation Mann Whitney and Alpha Cronbach. Results revealed statistically-significant differences at the level α=0.05 between the mean scores of the first and second experimental groups in favor of the latter in the CTS test. The findings gave ground to put forward some salient recommendations, including the need to expand computational thinking in universities' educational process. It also recommends urging faculty members to enhance e-courses in the educational process and provide technical support to students and faculty members.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.215-223
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• 2012

Response surface methodology(RSM) is a very useful statistical techniques for improving and optimizing the product process. By this reason, RSM has been utilized extensively in the industrial world, particularly in the circumstances where several product variables potentially influence some quality characteristic of the product. In order to estimate the optimal condition of product variables, an experiment is being conducted defining appropriate experimental region. However, this experimental region can vary with the experimental circumstances and choice of a researcher. Response surface designs can be classified, according to the shape of the experimental region, into spherical and cuboidal. In the spherical case, the design is either rotatable or very near-rotatable. The central composite design(CCD)s widely used in RSM is an example of 5-level and spherical design. The cuboidal CCDs(CCDs with ${\alpha}=1$) is appropriate when an experimental region is cuboidal but this design dose not satisfy the rotatability as it is not spherical. Practically, a 3-level spherical design is often required in the industrial world where various level of experiments are not available. Box-Behnken design(BBD)s are a most popular 3-level spherical designs for fitting second-order response surfaces and satisfy the rotatability but the experimental region does not vary with the number of variables. The new experimental design with expanded experimental region can be considered if the predicting response at the extremes are interested. This paper proposes a new 3-level spherical RSM which are constructed to expand the experimental region together with number of product variables.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1491-1498
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• 2014

The major criticism of optimal experimental design is that it depends heavily on the model and its accompanying assumption that often leads the number of support points equal to the number of parameters in the model. Often in the past, a polynomial model of higher degree is assumed to handle the experimental design for the polynomial regression of lower degree. In this paper we searched the possible set of designs which are robust to the departure of the assumed model. The designs are categorized with respect to D-efficiency. The approach by O'Brien (1995) was discussed in univariate polynomial regression model setting.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.950-954
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• 2001

Gasoline engine manufacturers are currently considering designs that will result in low combustion air temperature for improvement of fuel consumption and emission levels. There are a variety of cooling systems that can be used to accomplish this goal. Coolong is therefore normally achieved through a balance of ram and fan action. This paper studies the various systems and compare the cooling performance for several conditions, based on a automotive engine. An experimental analysis was developed to predict the interaction of the fan system and the heat exchangers of the engine cooling system. The local temperature induced by the fan on the cooling system is measured. These experimental result were accomplished using air flow management techniques.

• Journal of Korean Institute of Industrial Engineers
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• v.7 no.2
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• pp.55-59
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• 1981

An Application of Experimental Designs to Improve the quality of Industrial product : optimization Methodology of statistical model. The primary object of this paper is to aid scientists and Engineers, in applying response surface procedures to obtain operating conditions for many technical fields, particularly for industrial manufacturing processes. The problem considered in this paper is to select technically and scientifically some important factors affecting the quality of products through the experimental design and analysis of response surface. Even though the mathematical model is unknown these statistical analysis can be applicable to control the quality of industrial products and to determine optimum operating conditions for many technical fields, particularly, for industrial manufacturing processes. This paper proposes a method to obtain the optimum operating condition, and how to find the condition by using table of orthogonal array experiments, and optimization methodology of statistical model.