• Knowledge Management Research
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• v.19 no.2
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• Fire Science and Engineering
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• v.20 no.2 s.62
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• pp.14-20
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• 2006

The aim of this study is to predict the ignition times and the HRR(heat release rate) for building interior materials. By using the literature data and RSM(response surface methodology), the new equations for predicting the ignition time and the HRR of building interior materials are proposed. The A.A.P.E.(average absolute percent error) and the A.A.D.(average absolute deviation) of the reported and the calculated ignition times by means of the thickness and the density were 4.35 sec and 1.57 sec, and the correlation coefficient was 0.987. The correlation coefficient of the reported and the calculated the net HRR by means of burner width and power was 0.983. Also the correlation coefficient of the reported and the calculated the total HHR by means of burner width and power was 0.999. The correlation coefficient of the reported and the calculated the maximum flame height by means of burner width and power was 0.999. The values calculated by the proposed equations were in good agreement with the literature data.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.4
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• pp.311-320
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• 2019

This study aimed to optimize mixing conditions (adding amount of squid skin and sea tangle Saccharina japonica) for concentrates of octopus Octopus vulgaris cooking effluent (COCE) and roasting conditions (temperature and time) of seasoned Laver Pyropia yezoensis with concentrates of octopus cooking effluent (SL-COCE) using response surface methodology (RSM). The results of RSM program for COCE showed that the optimum independent variables ($X_1$, squid skin amount; $X_2$, sea tangle amount) based on the dependent variables ($Y_1$, odor intensity; $Y_2$, amino-N content; $Y_3$, sensory overall acceptance) for high-quality COCE were 0.53% (w/w) for $X_1$ and 0.48% (w/w) for $X_2$ for uncoded values. The results of the RSM program for SL-COCE showed that the optimum independent variables ($X_1$, roasted temp.; $X_2$, roasted time) based on the dependent variables ($Y_1$, burnt odor intensity; $Y_2$, water activity; $Y_3$, sensory overall acceptance) for high-quality SL-COCE were $344^{\circ}C$ for $X_1$ and 8 sec for $X_2$ for uncoded values. The SL-COCE prepared under optimum procedure was superior in sensory overall acceptance to commercial seasoned laver.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.247-255
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• 2018

In this research, the effect of normal load, sliding velocity, and texture density on thefriction coefficient of surfaces micro-textured on AISI 4140 under paraffin oil lubrication were investigated. The predicted tribological behavior by numerical calculation can be serves as guidance for the designer during the machine development stage. Therefore, in this research friction coefficient prediction model based on response surface methodology (RSM), support vector machine (SVM), and artificial neural network (ANN) were developed. The experimental result shows that the variation of load, speed and texture density were influence the friction coefficient. The RSM, ANN and SVM model was successfully developed based on the experimental data. The ANN model can effectively predict the tribological characteristics of micro-textured AISI 4140 in paraffin oil lubrication condition compare to RSM and SVM.

• CELLMED
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• v.11 no.3
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• pp.15.1-15.9
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• 2021

The chemical constitution of a drug has been accepted as an important basis for pharmacological action in Unani medicine. Various dosage forms have been developed on this concept, such as decoctions (Joshanda), infusions (Khesanda), extract (Rub / Usara), and syrup. Zarawand Mudaharaj (ZM.) / Aristolochia rotunda L. root was subjected to extraction process using Soxhlet's apparatus by using Response Surface Methodology (RSM) to design the number of random runs of the extracts with variation in the factors of temperature, the concentration of ethanol in water, time for extraction, for optimizing and maximizing the yield concentration. The data obtained, was analyzed with regression equation and ANOVA two-way summary to interpret the interaction of the factors for yield maximization. Minitab version 18 was used to design and analyze data. Validation of the optimum conditions for maximum yield of the whole extract of ZM. Root was carried out by re-run of the extract using the optimized conditions. The maximum yield percentage thus obtained using RSM was 20.87% whereas using these optimum conditions 21.35 % yield was obtained thereby validating the method. The association between the response functions and the process variables was identified by a three-factor recorded Box-Behnken design. In the present study RSM is used because itis a cheap and affordable method to optimize maximum yield percentage which may be reliably used by researchers. The study set in the surface conditions for ZM. root extraction by the Soxhlet apparatus for maximizing the yield percentage.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.82-90
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• 2019

The Box-Benhken Design (BBD) model of response surface methodology (RSM) was used to optimize fluoride adsorption conditions in water using a 350℃ thermally treated cow bone. Water temperature, pH, contact time, and initial fluoride concentration were selected as variables to be optimized. A second order reaction equation was obtained from a Box-Behnken Design DoE experimental matrix of 29 runs. R² and p-value of the model were 0.9242 and <0.0001, respectively, indicating that the selected variables had a very substantial effect on the adsorption results. The optimized adsorption capacity of the thermally synthesized bone char was estimated to be 6.46 mgF/g at the water temperature of 39.68℃, pH 6.25, contact time of 88.81 minutes and an initial fluorine concentration of 14.64 mgF/L.

• Food Science and Biotechnology
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• v.17 no.2
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• pp.379-383
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• 2008

Extraction characteristics of garlic and functional properties of corresponding extract were monitored by response surface methodology (RSM). Maximum extraction yield of 26.41% was obtained at microwave power of 146.29 W, ethanol concentration of 63.31 %, and extraction time of 5.88 min. At microwave power, ethanol concentration, and extraction time of 114.84 W, 58.83%, and 1.42 min, respectively, maximum electron-donating ability (EDA) was 72.86%. Maximum nitrite-scavenging ability was 94.62% at microwave power, ethanol concentration, and extraction time of 81.83 W, 2.65%, and 3.83 min, respectively. Superoxide dismutase (SOD) showed maximum pseudo-activity of 49.12% at microwave power of 34.23 W, ethanol concentration of 33.11 %, and extraction time of 4.40 min. Based on superimposition of 4-dimensional RSM with respect to extraction yield, electron-donating ability, nitrite-scavenging ability, and pseudo-activity of SOD, optimum ranges of extraction conditions were microwave power of 0-100 W, ethanol concentration of 40-70%, and extraction time of 2-8 min.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.3
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• pp.58-63
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• 2005

In order to minimize weight of vehicle seat, an optimum design of aluminum seat is presented while satisfying stress and fatigue life constraints. In this study, the analysis model is validated by comparing it's stress with that of test. Then, two-level orthogonal array is used to estimate the design sensitivity for 7 design variables. Finally, the sequential approximate optimization (SAO) is performed using the constructed RSM models. The approximate RSM models are sequentially updated using the analysis results corresponding to the approximate optimum obtained during the SAO. After 14 analyses, the SAO gives an optimal design that can reduce 16.7$\%$ of weight while increasing 369$\%$ of fatigue life and satisfying stress constraint.

• Journal of the Korean Society of Safety
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• v.32 no.1
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• pp.27-32
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• 2017

The experimental design methodology was applied in the real scale coal-fired boiler 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 provided with the numerical simulation of the coal-fired boiler. The three independent variables, high heating value of coal (HHV), overall stoichiometry excess air ratio (OST), and burner-side stoichiometry excess air ratio (BST), were set to characterize the cross section averaged NOx concentration and temperature distribution. The maximum NOx concentration was predicted accurately and mainly controlled by BST in the boiler. The parabola function was assumed for the zone averaged peak temperature distribution, and the prediction was in a fairly good agreement with the experiments except downstream. Also, the location of the peak temperature was compared with that of maximum NOx, which implies that thermal NOx formation is the main mechanism in the coal-fired boiler. These results promise the wide use of statistical models for the fast prediction and safety assessment.