• Food Science of Animal Resources
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• v.43 no.2
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• pp.374-381
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• 2023

In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.431-440
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• 2012

Purpose: In this paper we propose modified Notz designs which are useful to experimenters who want to adopt the sequential experimentation strategy and to estimate second-order regression model with as few experimental points as possible. Methods: We first present the design matrices and design points in two or three dimensional spaces for such small sized second-order designs as small composite, hybrid, and Notz designs. Modified Notz designs are proposed and compared with some response surface designs in terms of the total number of experimental points, the estimation capability criteria such as D- and A-optimality. Results: When sequential experimentation is necessary, the modified Notz designs are recommendable. Conclusion: The result of this paper will be beneficial to experimenters who need to do experiments more efficiently, especially for those who want to reduce the time of experimentation as much as possible to develop cutting-edge products.

• Journal of the Korean Applied Science and Technology
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• v.25 no.3
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• pp.282-290
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• 2008

The malting process of rice (OM4080 variety from Mekong Delta Rice Research Institute) was studied under pilot condition plan by means of the second-order experimental design. Processing parameters, such as the steeping time (0-60 hrs), steeping temperature ($5-45^{\circ}C$), germination time (0-8 days), germination temperature ($5-45^{\circ}C$) and gibberellin concentration (0-2 mg/kg) were investigated. As a result, all germination conditions, especially germination time, germination temperature, and gibberellin concentration had a significant effect on the malting loss, amylase activity and starch content. The protein content was not clearly affected by any conditions. The optimum conditions for malting process (with highest amylase activity) were as follows: 30 hrs of steeping time, $30-35^{\circ}C$ of steeping temperature, 5-5.5 days of germination time, $25^{\circ}C$ of germination temperature, and 1.5 mg/kg of giberrellin concentration.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1988-1994
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• 2014

In a single-phase grid-connected power system consisting of a DC/DC converter and a DC/AC converter, the current drawn from renewable energy sources has a tendency to be pulsated and contains second-order frequency ripple components, which results in several drawback such as a power harvesting loss and a shortening of the energy source's life. This paper presents a new second-order harmonic current reduction scheme with a fast dc-link voltage loop for two-stage dc-dc-ac grid connected systems. In the frequency domain, an adequate control design is performed based on the small signal transfer function of a two-stage dc-dc-ac converter. To verify the effectiveness of proposed control algorithm, a 1 kW hardware prototype has been built and experimental results are presented.

• 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.

• Wind and Structures
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• v.1 no.3
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• pp.255-269
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• 1998

Second-order moments of considerable magnitude arise in tall and slender RC chimneys and towers subject to along-wind loading, on account of eccentricities in the distributed self-weight of the tower in the deflected profile. An accurate solution to this problem of geometric nonlinearity is rendered difficult by the uncertainties in estimating the flexural rigidity of the tower, due to variable cracking of concrete and the 'tension stiffening' effect. This paper presents a rigorous procedure for estimating deflections and second-order moments in wind-loaded RC tubular towers. The procedure is essentially based on a generalised formulation of moment-curvature relationships for RC tubular towers, derived from the experimental and theoretical studies reported by Schlaich et al. 1979 and Menon 1994 respectively. The paper also demonstrates the application of the proposed procedure, and highlights those conditions wherein second-order moments become too significant to be overlooked in design.

• Structural Engineering and Mechanics
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• v.89 no.2
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• pp.199-211
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• 2024

The loading capacity of engineering structures/components reduces after the initiation and propagation of crack eventually leads to the final failure. Hence, it becomes essential to deal with the crack and its effects at the design and simulation stages itself, by detecting the prone area of the fracture. The phase-field (PF) method has been accepted widely in simulating fracture problems in complex geometries. However, most of the PF methods are formulated with second order continuity theoryinvolving C⁰ continuity. In the present study, PF method based on fourth-order (i.e., higher order) theory, maintaining C¹ continuity has been proposed for ductile fracture simulation. The formulation includes fourth-order derivative terms of phase field variable, varying between 0 and 1. Applications of fourth-order PF theory to ductile fracture simulation resulted in novelty in this area. The proposed formulation is numerically solved using a two-dimensional finite element (FE) framework in 3-layered manner system. The solutions thus obtained from the proposed fourth order theory for different benchmark problems portray the improvement in the accuracy of the numerical results and are well matched with experimental results available in the literature. These results are also compared with second-order PF theory and a comparison study demonstrated the robustness of the proposed model in capturing ductile behaviour close to experimental observations.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.451-459
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• 1998

The analysis of data from a multiresponse experiment requires careful consideration of the multivariate nature of the data. In a multiresponse sitation, the optimization problem is more complex than in the single response case. The biplot is a graphical tool which make the analyst to understand the correlation of the response variables, the relation of the response variables arid the explanatory variables and the relative importance of the explanatory variables. In case of good fitting of the first order model, we can draw the biplot with the first order experimental design. Otherwise, we can make the biplot with the second order experimental design by adding other experimental points.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.59-74
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• 2007

Computer simulation models are in general quite complex and time-consuming to run, and therefore, a simpler meta-model is usually constructed for further analysis. In this paper, JANUS, a war-game simulator, is used to describe a certain tank combat situation. Then, second-order response surface and artificial neural network meta-models are developed using the data from eight different experimental designs. Relative performances of the developed meta-models are compared in terms of the mean squared error of prediction. Computational results indicate that, for the given problem, the second-order response surface meta-model generally performs better than the neural network, and the orthogonal array-based Latin hypercube design(LHD) or LHD using maximin distance criterion may be recommended.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.133-152
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• 1998

The central composite design is widely used in the response surface analysis, because it can fit the second order model with small experimental points. In practice, the experimental data are not always obtained on all the points. When there are missing observations, many problems due to the missing cells can occur. In this paper, the influence of a missing cell on the central composite design is discussed. First, the influences of a missing cell on the variances of estimated regression coefficents are compared as $\alpha$ varies. Second, how the average predition variance is affected by a missing sell is discussed. And the influence on rotatability is investigated. Third, the influence of a missing cell on optimality, especially on D-optimality and A-optimality, is examined.