• Journal of the Korean Statistical Society
• /
• v.31 no.3
• /
• pp.277-299
• /
• 2002

Most statistical designs, such as orthogonal designs and optimal designs, are based on a specific statistical model. It is very often that the experimenter does not completely know the underlying model between the response and the factors. In computer experiments, the underlying model is known, but too complicated. In this case we can treat the model as a black box, or model to be unknown. Both cases need a space filling design. The uniform design is one of space filling designs and seeks experimental points to be uniformly scattered on the domain. The uniform design can be used for computer experiments and also for industrial experiments when the underlying model is unknown. In this paper we shall introduce the theory and method of the uniform design and related data analysis and modelling methods. Applications of the uniform design to industry and other areas are discussed.

• Communications for Statistical Applications and Methods
• /
• v.7 no.3
• /
• pp.759-766
• /
• 2000

In order to design fractional factorial experiments which include some debarred combinations, we should select defining contrasts so that those combinations are to be excluded. Choi(1999) presented a method of selectign defining contrasts to construct orthogonal 3-level fractional factorial experiments which exclude some debarred combinations. In this paper, we extend Choi's method to general p-level fractional factorial experiments to select defining contrasts which cold exclude some debarred combinations.

• Applied Chemistry for Engineering
• /
• v.31 no.2
• /
• pp.138-146
• /
• 2020

Design of experiments (DOE) is a method that has been applied in the industry to improve value for many decades. This study provides an overview of 115 cases of statistical DOE applications in the field of chemical engineering. All cases were published in important scientific journals for the last ten years. The applied design type, the experiment size, the number of factors and levels affecting the response variable, and the area of application for the design were all analyzed. Obviously, the number of publications related with statistical DOE increased over time.

• The Korean Journal of Applied Statistics
• /
• v.33 no.1
• /
• pp.87-105
• /
• 2020

Statistical engineering contains the design of experiments, quality control/management, and reliability engineering. R is a free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. R package has many functions and libraries for statistical engineering. We can use R package as a useful tool for statistical engineering. This paper shows the applications of R package for statistical engineering and suggests a R Task View for statistical engineering.

• The Korean Journal of Applied Statistics
• /
• v.10 no.1
• /
• pp.15-27
• /
• 1997

Randomized block design (RBD) with three replication is very frequently adopted in agricultural experiments of the Rural Development Administration of Korea. Even though it works well in field trials of traditional crops, it may not accomodate trial site conditions and/or experimental environment. In this research report, we deal with two such cases. The first case is for a crop experiment in green houses. In house conditions, RBD may not be appropriate since it cannot reflect two directions of the yield gradient. So, a Latin square design is suggested as an alternative. The second case is for local field experiments of the newly-inbred rice. RBD with three replications is used without doubt for decades, even though the site layout is not appropriately shaped for the design. In this case, we suggest the RBD in two blocks with multiple replicates for control varieties as an alternative. To improve the quality of statistical experimental designs in over one-thousand agricultural trials performed annually in the Rural Development Administration, we need to re-train agricultural researchers on the design and analysis of experiments and call for concerns of Korean statisticians.

• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.633-658
• /
• 2021

Statistical engineering contains design of experiments, quality control/ management, and reliability engineering. Python is a free software environment for machine learning, data science, and graphics. Python package has many functions and libraries for statistical engineering. We can use Python package as a useful tool for statistical engineering. This paper shows applications of Python package for statistical engineering and suggests a total Python projects for statistical engineering.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.9
• /
• pp.15-24
• /
• 1998

In this research. we have proposed a new technique to determine .the combination of design parameters for the process design of a hot forged product using artificial neural network(ANN) and statistical design of experiments(DOE). The investigated problem involves the adequate selection of the aspect ratio of billet, the ram velocity and the friction factor as design parameters. An optimal billet satisfying the forming limitation, die filling, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of artificial neural network and considering the analysis of mean and variation on the functional requirement. This methodology will be helpful in designing and controlling parameters on the shop floor which would yield the best design solution.

• Journal of the Korean Statistical Society
• /
• v.14 no.1
• /
• pp.48-55
• /
• 1985

In clinical trials subjects are avalible sequentially and must be assigned to treatments immediately. Completely randomized procedure for the allocation of treatments to each subject may result in severe imbalance among the number of subjects in treatment groups, especially for small experiments or interim analyses of large experiments. In this study, restricted randomization methods such as biased coin designs (Efron, 1971), permuted block design, and truncated binomial design are compared to teh completely randomized design in the presence of selection and/or accidential bias by Monte Carlo simulations.

• Transactions on Electrical and Electronic Materials
• /
• v.10 no.5
• /
• pp.177-181
• /
• 2009

Employing statistical design of experiments, we have performed studies on the characterization of electrodes using $TiO_2$ and process variables in the fabrication process of nanocrystalline dye sensitized solar cell. Systematic experiment to identify the effects of process variables on cell's efficiency has based on broad-band absorption of light by tailor made organometallic dye molecules dispersed on a high surface of $TiO_2$. Employing statistical design of experiment on $TiO_2$ photoelectrode forming process, structural characterization of electrodes and process variable have been investigated. Through the statistical analysis we have found that the particle size of $TiO_2$ and the amount of PEG/PEO are significantly affecting on the cell efficiency. In addition, a significant amount of interaction exists between the particle size and the amount of PEG/PEO.

• Journal of the Korean Statistical Society
• /
• v.11 no.1
• /
• pp.36-44
• /
• 1982

A new design concept, called axis-slope-rotatability, is presented for the design of experiments with mixtures. This is an analogue of the Box-Hunter (1957) rotatability for second order response surface designs. By choice of design, it is possible to make the variance of the estimated slopes along the component axes constant for all axial points equidistant from the center point of the factor space. This property is called axis-slope-rotatability for mixture experiments. When the Scheffe's second degree polynomial is used, it is shown that some symmetry conditions are sufficient for axis-slope-rotatability. Several designs having this property are illustrated.