• Korean Journal of Human Ecology
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• v.16 no.6
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• pp.1141-1152
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• 2007

The purpose of this study was to investigate developmental changes of metamemory. The subjects were 120 5, 7, and 9 year-old children. All children performed metamemory tests which were composed of person variable, task variables, and strategy variable. There were significant age differences in metamemory awareness. As for person variable, prediction accuracy was increased with age. As for task variables, older children recognized aims of tasks, whereas younger children perceived salient properties of tasks. Also, as for strategy variable, number and complexity of strategy for memory retrieval were increased with age.

• Journal of Environmental Science International
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• v.24 no.11
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• pp.1443-1450
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• 2015

Wastewater treatment plant(WWTP) has been recognized as a high energy consuming plant. Usually many WWTPs has been operated in the excessive operation conditions in order to maintain stable wastewater treatment. The energy required at WWTPs consists of various subparts such as pumping, aeration, and office maintenance. For management of energy comes from process operation, it can be useful to operators to provide some information about energy variations according to the adjustment of operational variables. In this study, multiple regression analysis was used to establish an energy estimation model. The independent variables for estimation energy were selected among operational variables. The $R^2$ value in the regression analysis appeared 0.68, and performance of the electric power prediction model had less than ${\pm}5%$ error.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.228-239
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• 2022

Many countries around the world using ICT technologies have various technologies to keep pace with the 4th industrial revolution, and various algorithms and systems have been developed accordingly. Among them, many industries and researchers are investing in unmanned automation systems based on AI. At the time when new technology development and algorithms are developed, decision-making by big data analysis applied to AI systems must be equipped with more sophistication. We apply, Pearson's correlation analysis is applied to six independent variables to find out the job satisfaction that office workers feel according to their job characteristics. First, a correlation coefficient is obtained to find out the degree of correlation for each variable. Second, the presence or absence of correlation for each data is verified through hypothesis testing. Third, after visualization processing using the size of the correlation coefficient, the degree of correlation between data is investigated. Fourth, the degree of correlation between variables will be verified based on the correlation coefficient obtained through the experiment and the results of the hypothesis test

• Transactions of Materials Processing
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• v.25 no.2
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• pp.116-123
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• 2016

Press-brake bending is used to shape flat and thick plates into a targeted circular configuration without excessive localized thinning or thickening. A brake bending press called 'a knife press bending apparatus' has been widely adopted to manufacture thick, large and long pipe from initially thick plate. Submerged Arc Welded (SAW) pipes are also produced by employing press-brake bending. These pipes are mainly used for oil, natural gas and water pipelines. The principal process variables for press-brake bending can be summarized as stroke of the press-brake knife, the distance between both roll in the lower die, and the feeding length of the plate. Many combinations of these process variables are available, thus various pipe diameters can be realized. In the current study, a series of repetitive numerical simulations by feeding a thick plate with initial thickness of 25.4mm were conducted with the consideration of elastic recovery. Furthermore, an index for SAW pipe production is proposed which can be widely used in industry.

• Transactions of Materials Processing
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• v.25 no.4
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• pp.235-241
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• 2016

Dimensional accuracy of hot coil is improved by precise control of thickness profiles, flatness, width and winding profile. Especially, precise width control is important because yield could be increased significantly. Precise width control can be improved by predicting the amount of width spread. The purpose of this study is to develop the advanced prediction model for width spread in hot finish rolling for controlling width precisely. FE-simulations were performed to investigate the effect of process variables on width spread such as reduction ratio, forward and backward tension and initial width at each stand. From the statistical analysis of simulated data, advanced model was developed based on the existing models for strip width spread. The experimental hot rolling trials showed that newly developed model provided fairly accurate predictions on the strip width spread during the whole hot finishing rolling process.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.52-61
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• 2021

This study using experimental design and linear regression technique was implemented in order to predict the pitting potential of stainless steel in marine environments, with the target materials being AL-6XN and STS 316L. The various variables (inputs) which affect stainless steel's pitting potential included the pitting resistance equivalent number (PRNE), temperature, pH, Cl^- concentration, sulfate levels, and nitrate levels. Among them, significant factors affecting pitting potential were chosen through an experimental design method (screening design, full factor design, analysis of variance). The potentiodynamic polarization test was performed based on the experimental design, including significant factor levels. From these testing methods, a total 32 polarization curves were obtained, which were used as training data for the linear regression model. As a result of the model's validation, it showed an acceptable prediction performance, which was statistically significant within the 95% confidence level. The linear regression model based on the full factorial design and ANOVA also showed a high confidence level in the prediction of pitting potential. This study confirmed the possibility to predict the pitting potential of stainless steel according to various variables used with experimental linear regression design.

• Journal of Korea Artificial Intelligence Association
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• v.2 no.2
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• pp.1-7
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• 2024

The main purpose of the study is to predict mental health status, that is, Alzheimer's diagnosis, by analyzing factors tailored to each data set using machine learning models. This study aims to find more relevant factors by analyzing unique factors existing in each data set. To this end, this study used decision tree models, random forest models, KNN models, SVM models, artificial neural network models, naive Bayesian models, logistic regression analysis, and XG boost models among the developed machine learning models. In the process of training the model using medical data, we went through trial and error, such as increasing variable values to increase the model performance index value that determines the degree of learning. In addition, we did not end this study by comparing the performance of the models but ended the study by finding out which variables are closely related to dementia prediction and their weights. These results can provide a foundation for what approach is needed when processing medical data. In addition, it will be helpful for research that predicts results through medical data and finds out which variables are closely related.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.6
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• pp.1021-1030
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• 1997

Physical variables related to the subjective evaluation of wear comfort were explored. Experimental fabrics was those used in the previous paper where subjective sensations of women's thin shirts were reported. Fabrics include 100% cotton (unfinished), 100% cotton (water repellent finished), cotton/polyester 35/65 (unfinished), cotton/polyester 35/65 (peach skin finished), 100% polyester fabric(plain), 100% polyester crepe. Among various physical properties of the experimental fabrics, heat 8E moisture transport properties and surface properties were chosen as important variables based on the regression coefficient. Especially, humidity at the microclimate in dynamic mode was highly correlated to the subjective evaluation and appeared to be a sensitive physical predictor, compared to dry thermal transmission rate or water vapor transmission rate. Surface characteristic parameters, however, did not show consistant trend in the prediction of the human's subjective sensation. Interaction between surface properties and humidity measurement was also observed.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.198-201
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• 2002

It has been well-blown that sandwich structures are efficient to resist bending loads by increasing the moment of inertia of the panel. However, the accurate theoretical prediction of failure load and its optimization of sandwich beams for strength under concentrated loads were so complicated. Moreover, the appropriate selection of the variables, such as face thickness, core density and core thickness of the sandwich beam with many theories has continuously researched to satisfy for the given strength to weight structural requirement. There will be interesting to investigate the effect of those variables with its optimization for the load resistance.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.224-233
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• 1999

The quality of injection molded parts is affected by the variables such as materials, design variables of part and mold, molding machine, and processing conditions. It is difficult to consider all the variables at the same time to predict the quality. In this paper neural network was applied to analyze the relationship between processing conditions and volumetric shrinkage of part. Engineering plastic gear was used for the study, and the learning data was extracted by the simulation software like Moldflow. Results of neural network was good agreement with simulation results. Nonlinear regression model was formulated using the test data of 3,125 obtained from neural network, Optimal processing conditions were calculated to minimize the volumetric shrinkage of molded part by the application of RQP(Recursive Quadratic Programming) algorithm.