• Journal of the Korean Statistical Society
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• v.34 no.3
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• pp.209-218
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• 2005

In response surfaces analysis, we often proceed by supposing that, over a limited region of factor space, a polynomial of only first or second degree might adequately approximate the true function. To find the best subset model, all possible quadratic regressions for response surfaces can be very valuable to get optimum solutions under some reasonable experimentations. However, there is a very hard computational burden to get all possible quadratic regressions. In practice, it is sufficient to consider only hierarchical models. In this paper, we propose an algorithm to get all possible hierarchical quadratic regressions for fitting response surfaces.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.109-116
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• 2002

The solution of the aircraft wing spar cross-section area optimization problem is obtained by the response surface method. The object function of the problem is wing total weight, design variables are spar cross-section areas, constraints are the conditions that the stresses at the each spar is less than the allowable stress. D-Optimal condition is utilized to obtain the experimental points to construct the response surfaces. D-Optimal experimental points are obtained by the commercial software "Deign-Expert". Response values for the object function and constraints for each experimental point are calculated by the NASTRAN. Response surfaces for object function and constraints are approximated from the response values by the least square method. The optimization solution is obtained by the DOT for the response surfaces of object function and constraints. The optimization results obtained from the response surface are compared with the results obtained by the NASTRAN SOL200.

• Korean Management Science Review
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• v.24 no.2
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• pp.73-80
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• 2007

Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

• 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.

• The Journal of Korean Academy of Prosthodontics
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• v.25 no.1
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• pp.341-353
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• 1987

The purpose of this study was to investigate the gingival response to the location of the crown margin and the gingival response to the period of crown placement. Twenty one patients were selected for this study. The patients fitted into the research condition. The crown margins of anterior teeth were located at crest and supragingivally. The crown margins of posterior teeth were located at crest, subgingivally and supragingivally. Plaque index and gingival index were measured before tooth preparation and 1 week,4 weeks, 6 weeks after crown placement. The results were as follows: 1. There was no significance in the gingival response to the location of crown margin in short term period. 2. As the period of crown placement was extended, plaque index and gingival index were gradually increased. 3. Plaque index and gingival index of tooth surfaces were increased in the order of the interproximal surfaces, lingual surfaces and buccal or labial surfaces.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.199.1-199.1
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• 2014

In nonlinear optics, the properties of nonlinear optical responses such as polarization and nonlinear analysis of the nonlinear surfaces were investigated using the jellium model by optical second harmonic generation. The nonlinear response of the Al metal surfaces were calculated using TDLDA. Band structure, lattice constant and bulk modulus of the Al metal were investigated. Effective potential and electron density were compared by changing different.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.334-340
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• 2013

This paper deals with the optimization of blade stiffened composite panels. The main objective of the research is to make response surfaces for the constraints. The response surface for warping thermal deformation was previously made for a fixed dimension composite structure. In this study, the dimensions of the blade stiffener were treated as design variables. This meant that a new response surface technique was required for the constraints. For the response surfaces, the lamination parameters, linear thermal expansions and dimensions of the structures were used as variables. A genetic algorithm was adopted as an optimizer, and an optimal result, which satisfied two constraints, was obtained. As a result, a new response surface was obtained, for predicting warping thermal deformation.

• Journal of the Korean Statistical Society
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• v.8 no.1
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• pp.37-64
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• 1979

Criteria and designs are developed for estimating derivatives of P-variable second order polynomial response surfaces. The basic criterion used is mean square error of the estimated derivative, averaged over all directions and then averaged over a region of interest. A new design concept called slope-rotatability is introduced. A simplex optimization program is used to find minimum mena square error designs for the two variable case for $6 \leq N \leq 12$.

• Journal of Industrial Technology
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• v.34
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• pp.39-43
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• 2014

This paper represents a highly porous MWCNT film electrode with interconnected open pores and demonstrated the possibility of using an MWCNT network film as the top electrode for polyimide capacitive humidity sensors. Polyimide humidity sensors with MWCNT electrodes exhibited about 6 times faster response than equivalent Cr electrode sensors. This result may be due to their percolated pore structures, which make water molecules accessible to all polyimide surfaces. The much faster response times of MWCNT electrode sensors is attributed to the percolated pore network, which allows more water molecules to be accessible to polyimide surfaces.

• Journal of Information Technology Application
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• v.3 no.3
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• pp.1-24
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• 2001

This paper introduces the genetic programming algorithm(GP), which can approximate highly nonlinear functions, as a tool for the modeling of response surfaces. When the response surfaces is approximated, the very small or minimal teaming set should be used, and thus it is almost certain that GP trees will show overfilling that must be avoided at all costs. We present a novel method, calledDDBS(DirectionalDerivative-Based Smoothering), which very effectively eliminates the unwanted behaviors of GP trees such as large peaks, oscillations, and also overfitting. Four illustrative numerical examples are given to demonstrate the performance of the genetic programming algorithm that adopts DDBS.