• Computers and Concrete
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• v.26 no.4
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• pp.309-316
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• 2020

The application of multi-variable adaptive regression spline (MARS) in predicting he long-term compressive strength of a concrete with various admixtures has been investigated in this study. The compressive strength of concrete specimens, which were made based on 24 different mix designs using various mineral and chemical admixtures in different curing ages have been obtained. First, The values of fly ash (FA), micro-silica (MS), water-reducing admixture (WRA), coarse and fine aggregates, cement, water, age of samples and compressive strength were defined as inputs to the model, and MARS analysis was used to model the compressive strength of concrete and to evaluate the most important parameters affecting the estimation of compressive strength of the concrete. Next, the proposed equation by the MARS method using particle swarm optimization (PSO) algorithm has been optimized to have more efficient equation from the economical point of view. The proposed model in this study predicted the compressive strength of the concrete with various admixtures with a correlation coefficient of R=0.958 rather than the measured compressive strengths within the laboratory. The final model reduced the production cost and provided compressive strength by reducing the WRA and increasing the FA and curing days, simultaneously. It was also found that due to the use of the liquid membrane-forming compounds (LMFC) for its lower cost than water spraying method (SWM) and also for the longer operating time of the LMFC having positive mechanical effects on the final concrete, the final product had lower cost and better mechanical properties.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.1-7
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• 2008

This study quantified 7 trunk muscles' physiological cross-sectional areas (PCSAs) and developed prediction equations for the physiological cross-sectional area as a function of anthropometic variables for Korean people. Nine females and nine males were participated in the magnetic resonance imaging (MRI) scans approximately from S1 through T8. Muscle fiber angle corrected cross-sectional areas (anatomical cross sectional areas: ACSAs) were recorded at each vertebral level and maximum value of ACSAs were determined as physiological cross sectional area (PCSA). There was a significant gender difference in PCSAs of all muscles (p<0.05). Stepwise linear regression techniques using anthropometric measures (e.g., height, weight, trunk depths and widths) as independent variables were conducted to develop prediction equations for the PCSA for each muscle. For males, six muscles' significant prediction equations (p<0.05) were developed except quadratus lumborum. For females, three prediction equations were developed for psoas, quadratus lumborum, and erector spinae muscles (p<0.05).

• Korean Journal of Food Science and Technology
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• v.23 no.5
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• pp.605-609
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• 1991

The physical properties which must be considered as engineering factors affecting on the evaporation process of fruit juices are boiling point rise, density, viscosity, thermal conductivity and specific heat. These factors are varied with food ingredients, soluble solids, pressure and temperature. In the reserch, it has been worked to obtain the data and to develop prediction model for the boiling point rise as a faction of soluble solid and pressure by the regression of SPSS package program. For the prediction model of density, it was developed as a fuction of soluble solid content on apple and pear juices. For the viscosity model, it was establised by the factors of temperature and content of soluble solid through the optimization program.

• Journal of Korean Society on Water Environment
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• v.25 no.1
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• pp.96-101
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• 2009

The aim of this research was to apply experimental design methodology to optimizetion the photolytic degradation of N-nitrosodimethylamine (NDMA). Reactions were mathematically described as a function of parameters such as pH, initial NDMA concentration, and UV intensity using the Box-Behnken method. The results showed that the responses of NDMA removal (%) in photolysis were significantly affected by the synergistic effect of linear term of pH, initial NDMA concentration and UV intensity. The application of Response Surfase Methodology (RSM) using the Box-Behnken method yielded the following regression equation, which is an empirical relationship between the removal (%) of NDMA and test variables in coded unit: Y = 50.929 + 16.073(UV) - 7.909(NDMA) - 27.432(pH) - 11.385(UV)(NDMA) - 7.363(UV)(pH) +13.811(NDMA)(pH). The model predictions agreed well with the experimentally observed result ($R_2(ad.)=89%$).

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.186-191
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• 2015

Presently, rapidly changing and unstable global economic environments demand engineers. Products should be designed to increase profits by lowering costs and provide distinguished performance compared with competitors. This study aims to optimize the design of the power-transmission drive shaft. The mass is reduced as an objective function, and the stress is constrained under a constant value. To reduce the number of experiments, CCD (central composite design) and D-Optimal are used for the experimental design. RSM (response surface methodology) is employed to construct a regression model for the objective functions and constraint function. In this problem, there is only one objective function for the mass. The other objective function gives 1; thus, NSGA-II is used.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.10a
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• pp.344-347
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• 2007

For modeling the non-periodic and randomly scattered powder particles, the quasi-random multi-particle array is introduced. The multi-scale process simulation, which enables to formulate a regression model with a response surface method, is performed by employing a homogenization method. The size of ${Al_2}{O_3}$ particle, amplitude of cyclic compaction pressure, and friction coefficient are considered as optimal process parameters. The optimal conditions of process parameters providing the highest relative density are finally found by using the grid search method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.21-29
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• 1993

We want to know the interrelationship among the four components of Total Quality Cost. So that we will be able to say what changes will occur in one when another is changed Even though the relationship among the component Cost is as varied as there are companies keeping such cost systems, existence of some general pattern is hypothesized at least among similar companies doing similar business or producing similar products. The purpose of this study is to drive Optimum Quality Cost on base of the result of the quality cost analyses in N business, after multiple regression model with failure cost as dependent variable is established. Vector Optimization (VOP) method were used for solving multiobjective decision ploblem.

• Archives of Pharmacal Research
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• v.19 no.3
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• pp.183-190
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• 1996

There are a wide variety of semi-solid ointments used for healing the skin diseases, whose therapeutic and skin penetration abililties may greatly differ from one another depending on the compositions of ointment vehicles. A computer optimization technique was applied to obtain the optimum formula of o/w type ointment giving the in vitro maximum absorption rate through hairless rat skin membrane. Some of the formulations were selected to find out a relationship between skin penetration of ointment and its Theological characteristics. The experimental value of absorption rate obtained from the ointment by optimum formula agreed well with the theoretical value obtained from a polynomial regression analysis, Three kinds of ointments selected among 15 formulations were obtained with a concentric cylinder type rheometer (Model; Rheolab SM-HM Physica, Germany) at 20, 30, 40 and $50^{\circ}C$ for rheograms of rhelolgical properties of o/w type ointments. As the temperature was raised, all products showed a decrease in both shear stress and yield values. The higher skin penetration, the lower shear stress showed.

• Journal of Welding and Joining
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• v.29 no.6
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• pp.82-87
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• 2011

In this study, we conducted laser welding by using remote scanner that is 5J32 aluminum alloy to observe the mechanical properties and optimize welding process parameters. As the control factors, laser incident angle, laser power and welding speed were set and as the result of weldablility, tensile shear tests were performed. ANOVA (Analysis of Variation) was also carried out to identify the influence of process variables on tensile shear strength. Strength estimation models were suggested using regression alnalysis and 2nd order polynomial model had the best estimation performance. In addition optimal welding condition was determined in terms with wedalility and productivity using objective function and fitness function. Final optimized welding condition was laser power was 4 kW, and welding speed was 4.6 m/min.