• 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 Electrical Engineering and Technology
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• v.6 no.2
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• pp.202-207
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• 2011

Numerous methodologies have been developed in an effort to reduce cogging torque. However, most of these methodologies have side effects that limit their applications. One approach is the optimization methodology that determines an optimized design variable within confined conditions. The response surface methodology (RSM) and the genetic algorithm (GA) are powerful instruments for such optimizations and are matters of common interest. However, they have some weaknesses. Generally, the RSM cannot accurately describe an object function, whereas the GA is time consuming. The current paper describes a novel GA and RSM hybrid algorithm that overcomes these limitations. The validity of the proposed algorithm was verified by three test functions. Its application was performed on a surface-mounted permanent magnet.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1097-1104
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• 2008

Working condition is one of the most important factors in precision working. In this study, we optimized the vibration acceleration of working progress direction using RSM(response surface methodology) by table of orthogonal array. RSM was well adapted to make analytic model for minimizing vibration acceleration, created the objective function and saved a great deal of computational time. Therefore, it is expected that the proposed optimization procedure using RSM can be easily utilized to solve the optimization problem of working condition. The experimental results of the surface roughness and vibration acceleration showed the validity of the proposed working condition of side wall end-milling as it can be observed.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.964-966
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• 2002

This paper presents a magnet circuit design procedure to reduce mover mass of the moving coil type linear direct motor (LDM). The procedure of optimization is based on the response surface methodology (RSM) and Sequential Quadratic Problem (SQP). This procedure of optimization is verified by the comparison of the result of the initial design between the result of the optimum design.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1313-1317
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• 2007

Manufacturing condition is one of the most important factors in precision manufacturing. In this study, we optimized minimizing the Z vibration acceleration using RSM(response surface methodology) by table of orthogonal array. RSM was well adapted to make the analytical model of the minimum vibration acceleration and enable the objective function to be easily created and a great deal of the time in computation to be saved. Therefore, it is expected that the proposed optimization procedure using RSM can be easily utilized to solve the optimization problem of manufacture condition.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.69-75
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• 2006

This paper deals with the optimum design solution on reduction of torque ripple for a Synchronous Reluctance Motor with concentrated winding using response surface methodology. The coupled Finite Elements Analysis (FEA) & Preisach model have been used to evaluate the nonlinear solution. Comparisons are given with characteristics of a SynRM according to the stator winding, slot number, open width of slot, slot depth, teeth width variation in concentrated winding SynRM, respectively. This paper presents an optimization procedure using Response Surface Methodology (RSM) to determine design parameters for reducing torque ripple. RSM has been achieved to use the experimental design method in combination with finite Element Method (FEM) and well adapted to make analytical model for a complex problem considering a lot of interaction of design variables. Moreover, Sequential Quadratic Problem (SQP) method is used to solve the resulting of constrained nonlinear optimization problem.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2183-2188
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• 2016

This paper deals with the design process for an outer-rotor-type surface-mounted permanent magnet synchronous motor (SPMSM) used in continuous variable valve timing (CVVT) systems in automobiles with internal combustion engines. When the same size, outer-rotor-type SPMSMs generate larger torque and more stable than inner-rotor-type SPMSMs. For the initial design, space harmonic analysis (SHA) is used. In order to minimize the cogging torque, an optimization was conducted using Response Surface Methodology (RSM). At the end of the paper, Finite Element Analysis (FEA) is performed to verify the performance of the optimum model.

• Integrative Medicine Research
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• v.6 no.4
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• pp.388-394
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• 2017

Background: The present study optimized ultrasound-assisted extraction conditions to maximize extraction yields of glycyrrhizic acid from licorice. Methods: The optimal extraction temperature ($X_1$), extraction time ($X_2$), and methanol concentration ($X_3$) were identified using response surface methodology (RSM). A central composite design (CCD) was used for experimental design and analysis of the results to obtain the optimal processing parameters. Results: Statistical analyses revealed that three variables and the quadratic of $X_1$, $X_2$, and $X_3$ had significant effects on the yields and were followed by significant interaction effects between the variables of $X_2$ and $X_3$ (p<0.01). A 3D response surface plot and contour plots derived from the mathematical models were applied to determine the optimal conditions. The optimum ultrasound-assisted extraction conditions were as follows: extraction temperature, $69^{\circ}C$; extraction time, 34?min; and methanol concentration, 57%. Under these conditions, the experimental yield of glycyrrhizic acid was 3.414%, which agreed closely with the predicted value (3.406%). Conclusion: The experimental values agreed with those predicted by RSM models, thus indicating the suitability of the model employed and the success of RSM in optimizing the extraction conditions.

• Journal of the Korean Society of Safety
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• v.30 no.1
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• pp.8-13
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• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.1
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• pp.51-57
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• 2007

The aim of this study was to improve rice muffin quality with sorbitol using response surface methodology(RSM). Response surface experimental design was made by central composite design using several independent factors. In preliminary experiment of chlorella rice muffin, rice flour(RF), chlorella(CH) and sorbitol(SO) were chosen as independent factors. Response factor was the overall acceptability obtained from sensory evaluation. The regression model equation could be predicted as $OV=6.70-0.45CH-0.44RF^2-0.81CH^2-0.60SO^2$. The optimal conditions for chlorella rice muffin substituted with sorbitol were determined to be 60.8% of RF, 4.7% of CH and 35.45% of SO. Rice muffin was superior to flour muffin in sensory evaluation using the prediction value derived from RSM. Therefore, the optimum condition of muffin could be developed by RSM.