• Transactions of The Korea Fluid Power Systems Society
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• v.6 no.4
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• pp.24-32
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• 2009

For a design of on/off solenoid actuator for valve actuating, designer must have the experimental knowledge as well as general electromagnetic formulas to design object. It is possible for theoretical knowledge to do the out-line design, but it is impossible to optimal design without experimental knowledge which only can be achieved through many repeated experiments. In addition, in present on/off type solenoid actuator field, the smaller, lightening, lower consumption power, high response time are effected as the most important design factor. So, experimental knowledge is more needed for optimal design of solenoid actuator. In this study, we derived the governing equations for optimal design of on/off solenoid actuator for valve actuating and developed a design program composed electromagnetic theories and experimental parameter values for inexperienced designers. And we proved the propriety of this program by experiments.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.929-931
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• 2002

For design of On/Off type solenoid actuator, designer must have the experimental knowledge as well as general electromagnetic formulas to design object. It is possible for theoretical knowledge to do the out-line design, but it is impossible to optimal design without experimental knowledge which only can achieve through many repeated experiments. In addition, in present On/Off type solenoid actuator field, smaller, lightening, lower consumption power, high response time are effected as the most important design factor. So, experimental knowledge is more needed for optimal design of solenoid actuator. In this study, we developed a design program composed electromagnetic theories and experimental parameter values for inexperienced designers. And we proved the propriety of this program by experiments.

• Journal of Engineering Education Research
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• v.11 no.1
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• pp.34-47
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• 2008

The purpose of this paper is to design the curriculum by considering the experimental design and analysis for enhancing an engineer's creative problem-solving ability. This ability is one of the important objectives in modern engineering education. To achieve this purpose, first, it is suggested that the experimental design and analysis, a specific area of engineering education, is highly relevant to the creative problem-solving ability, one of the basic engineering competencies and of the final goals in engineering education. And also, the curriculum already introduced the experimental design and analysis in departments of mechanical engineering of universities are surveyed and reviewed. 59 papers are also analyzed to know how engineers applicate the knowledge of the experimental design and analysis to their activities. Finally, the module of engineering education curriculum introduced the experimental design and analysis to enhance effectively the engineer's creative problem-solving ability is suggested.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.289-289
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• 2001

Experimental design has become one of the primary tools for achieving quality of manufactured products. Because of a devision in the diciplines of quality and reliability, this important tool has not been used to extensively to achieve reliability. In this talk, we describe how experimental design can be used to achieve reliability.

• Journal of Applied Reliability
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• v.2 no.1
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• pp.47-61
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• 2002

Experimental design has become one of the primary tools for achieving quality of manufactured products. However, this important tool has not been extensively used in achieving reliability. In this paper, we describe how experimental design can be used to achieve reliability.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.72-76
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• 2001

Experimental design in simulation provides an efficient way of economizing simulation runs since a considerable number of simulation runs that originally were planned can be reduced by this approach. This experimental design method is an active area of research together with the output analysis and so no single panacea seems to exist so far. Thus, selection of techniques of experimental design and output analysis more lithely depends upon the objective of simulation analysis, budget constraint and sometimes the analysts subjective judgment. This paper attempts to describe an experimental design methodology for port investment analysis using a case study in a bulk terminal in Korea. Detailed display will be focused on simulation period, warm-up period, the number of replications needed in production runs after brief explanation on the system configuration.

• 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.

• 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.

• 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.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.557-563
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• 2009

The research develops measurement processes for estimating and evaluating the gauge R&R(Reproducibility & Repeatability) using ANOVA(Analysis of Variance) of experimental design tools. The ten-step processes developed include experimental goal setting, the selection of characteristics(factors, levels), data model, ANOVA, EMS(Expected Mean Square), estimation of gauge precisions, and evaluation indexes. The three-factor combined measurement models are presented to show the processes developed in this paper.