• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1269-1278
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• 2014

Despite the D-optimality criterion becomes very popular in designing an experiment for nonlinear models because of theoretical foundations it provides, it is very critical that the criterion depends on the unknown parameters of the nonlinear model. But some nonlinear models turned out to be partially nonlinear in sense that the optimal design depends on the subset of parameters only. It was a strong belief that the maximin approach to find a robust design to protect against the uncertainty of parameters is not guaranteed to be successful in nonlinear models. But the maximin approach could be a success for the partial nonlinear model, because often the optimal design depends on only one unknown value of parameter, easier to handle than the full parameters. We deal with maximin approach for Michaelis-Menten model with respect to D- and $D_s$-optimality.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.171-178
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• 1991

For the polynomial spline regression with fixed knots, some properties of the D-optimal design are discussed. Also the D-optimal design for some cases are found analytically by using a normalized B-spline basis for $S(P_m : k : \Delta)$. Based on the Kiefer-Wolfowitz equivalence theorem, the D-optimal design for some cases are found by numerical methods.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.393-405
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• 2000

Box and Draper( 19(5) listed some properties of a design that should be considered in design selection. But it is impossible that one design criterion from optimal experimental design theory reflects many potential objectives of an experiment, because the theory was originally based on the underlying model and its strict assumption about the error structure. Therefore, when it is neces::;ary to implement multi-objective experimental design. it is common practice to balance out the several optimal design criteria so that each design criterion involved benefits in terms of its relative "high" efficiency. In this study, we proposed several composite design criteria taking the case of heteroscedastic model. WVhen the heteroscedasticity is present in the model. the well known equivalence theorem between 1)- and C-optimality no longer exists and furthermore their design characteristics are sometimes drastically different. We introduced three different design criteria for this purpose: constrained design, combined design, and minimax design criteria. While the first two methods do reflect the prior belief of experimenter, the last one does not take it into account. which is sometimes desirable. Also we extended this method to the case when there are uncertainties concerning the error structure in the model. A simple algorithm and concluslOn follow.On follow.

• 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.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.93-102
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• 2015

The D-optimal design for the nonlinear model typically depends on the unknown parameters to be estimated. Therefore, it is strongly recommended in literature to use a sequential experimental design for estimating the parameters. In this paper two stage experimental design is discussed under many different circumstances including estimating parameters. The method is so universal to be applied to any mixture of objectives for any model including linear model. A hybrid approach is suggested to handle more than 2 objectives in two-stage experimental design. The design is discussed in approximate design framework.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.5
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• pp.345-352
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• 2014

In this paper, the CAD data for the optimal shape design obtained by isogeometric shape optimization is directly used to fabricate the specimen by using 3D printer for the experimental validation. In a conventional finite element method, the geometric approximation inherent in the mesh leads to the accuracy issue in response analysis and design sensitivity analysis. Furthermore, in the finite element based shape optimization, subsequent communication with CAD description is required in the design optimization process, which results in the loss of optimal design information during the communication. Isogeometric analysis method employs the same NURBS basis functions and control points used in CAD systems, which enables to use exact geometrical properties like normal vector and curvature information in the response analysis and design sensitivity analysis procedure. Also, it vastly simplify the design modification of complex geometries without communicating with the CAD description of geometry during design optimization process. Therefore, the information of optimal design and material volume is exactly reflected to fabricate the specimen for experimental validation. Through the design optimization examples of elasticity problem, it is experimentally shown that the optimal design has higher stiffness than the initial design. Also, the experimental results match very well with the numerical results. Using a non-contact optical 3D deformation measuring system for strain distribution, it is shown that the stress concentration is significantly alleviated in the optimal design compared with the initial design.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1549-1554
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• 2015

This study examined experimentally the characteristics of the flow pattern and particle suspension in an agitated vessel with a bottom baffle. A flow pattern of the particles was shown to increase the upward flow from the center of the agitated vessel bottom. The suspended particles from the experiment found that the particle suspension was promoted by the development of an Ekman boundary layer. The optimal conditions of the impeller, and the agitated vessel bottom baffle within the experimental range were as follows: Impeller, $n_p=6$, d/D=0.5, and b/d=0.3; and bottom baffle, $n_b=6$, $d_b/D=0.5$ and $b_w/D=0.05$.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.3
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• pp.191-196
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• 2022

In this study, the anisotropic mechanical property of fused deposition modeling three-dimensional (3D) printing based on lamination direction was verified by a tensile test. Moreover, the property was applied to solid isotropic materials with penalization-based topology optimization. The case of the lower control arm, one of the automotive suspension components, was considered as a benchmark problem. The optimal topological results varied depending on the external load and anisotropic property. Based on these results, two test specimens were fabricated by varying the lamination direction of 3D printing; a tensile test utilizing 3D non-contact strain gauge was also conducted. The measured strain was compared with that obtained by computer-aided engineering response analysis. Quantitatively, the measurement and analysis results are found to have good agreement. The effectiveness of topology optimization considering the lamination direction of 3D printing was confirmed by the experimental result.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.617-626
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• 2023

This study aims to investigate the preferred sound level of water sound for various levels of open-plan-office noise regarding soundscape quality and speech privacy. And assessment of the work efficiency of the water sound. For the laboratory experiment, office noise was recorded using a binaural microphone in a real open-plan office. For the assessment of the soundscape quality and speech privacy, Overall Soundscape Quality (OSQ) and Listening Difficulty (LD) were evaluated under three different sound levels (55 dBA, 60 dBA, and 65 dBA) and five different signal-to-noise ratios (SNR -10 dB, -5 dB, 0 dB, +5 dB, and +10 dB). After the evaluation, the preferred SNR was proposed according to OSQ and LD. For the assessment of to work efficiency of water sound, this study evaluated the cognitive performance of both of the condition noise only and combine the water sound with office noise. The results showed that LD increased as the water sound level increased, but OSQ decreased. When the water sound level was more than the office noise level, the OSQ decreased from noise only. Therefore, considering OSQ and LD, the preferred SNR of water sound was -5 dB for all noise levels. At the preferred level of water sound, the cognitive performance results were shown to decrease at 55 dBA compared to noise only, but at 60 dBA and 65 dBA combine the water sound results were increased than the noise only.