• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.117-137
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• 2012

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.7
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• pp.706-717
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• 1991

In order to remove the assumption of full state availability which is one of the major difficulties with the practical realization of variable structure control systems,a new switching function with a dynamic structure is proposed. And the control performances of the output feedback variable structure control systems with the dynamic switching function are evaluated through simulation studies. The proposed dynamic switching function is driven by small number of measured output and input variables while conventional static switching function requires full state information. Therefore, the proposition of the dynamic swiching function makes practical implementation of output feedback variable structure control scheme possible for the systems with unmeasurable state variables, high order systems and large scale systems that the conventional variable structure control schemes with static switching function cannot be applied. In the variable structure control systems with the dynamic switching function, desired control performance can be guaranteed by proper choice of design parameters such as poles of switching function dynamic equation and switching control gains even though small number of measured output and input variables are provided as shown in simulation resuls.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.93-100
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• 2019

The interest in the Fourth Industrial Revolution along with the development of ICT makes the software get the attention of the world. This phenomenon naturally leads to the concern for software education. Learning software coding is not easy for students whose major is in humanities or social sciences. This paper is a study of how cognition on software education affects to education of computational thinking. For research method, moderator variables were adopted on the proposed research model to prove that positive cognition can derive good influence on improvement of computational thinking. To find out moderator variables of the research model, we have conducted the questionnaire over three years for total of 928 students who took the software coding courses. As the result of the study, we proved that the positive cognition on software education can make the better improvement of computational thinking within proper moderator variables.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.97-97
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• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.

• Journal of the Korean Home Economics Association
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• v.37 no.11
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• pp.33-48
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• 1999

The purpose of this study was to suggest some ways to utilize effectively the labor forces of employed women. For this purpose, this stud\ulcorner examined the family’s attitudes toward the work of married women and the effects of determinants including personal characteristics, household related variables, work related variables and family’s attitudes toward the work of married women on the employment status of married women. KLFI(1995)’s National data were used and one Logistic model and one Calmed model were employed to analyze the efficients of the independent variables. The resets of this study were as follows. The family’s attitudes toward the work of married women among the employed women was highly positive and the one toward the unemployment among the unemployed women was positive too. The variables having significant effects on the husbands’attitudes and the parents(parents in law)’attitudes toward the work of married women were different. The family’s altitudes had a very significant effect on the employment status of married women. Also, the financial situation of the household and some difficulties to find proper house keepers or child care types were important variables to predict the employment status of married women.

• Korean Journal of Health Education and Promotion
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• v.26 no.2
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• pp.1-13
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• 2009

Objectives: This study identified how personal characteristics, family environment, governmental policy for the prevention and cessation of smoking might influence on adolescent smoking. Methods: This study used data from the 2006 Korea Youth Risk Behavior Web-based Survey of 71,404 middle school and high school students, giving a response rate of 90.9%. We selected 61,508 adolescents subjects of the final analysis without missing data on independent variables and dependent variables which are used in this study. This study used $\chi^2$ tests and logistic regression models. Variables were added to the regression model in three groups using a hierarchical approach.Results: Adolescents were significantly more likely to become current smokers if they were boys, were in a higher grade, and had lower academic achievement. Adolescents experiencing stress and depression were associated with increased risk of current smoking. Adolescents with single parents or students of non-living with parents comparing with students of living with parents showed the high possibility of smoking. Lower father's education was associated with increased likelihood of current smoking. Adolescents who were exposed to smoking at home were more likely to smoke. Adolescents without contacting with the antismoking media campaign was associated with increased likelihood of current smoking. Conclusion: Promoting antismoking media campaigns targeted at adolescent is required, and the smoking prevention education which are proper for subjects are required. Proper plans which could decrease the exposure of secondhand smoking should be established.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.699-709
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• 2011

The uses of multi-dimensional hydrodynamic and water quality models are increasing to support a sustainable management of large dam reservoirs in Korea. Any modeling study requires selection of a proper spatial dimension of the model based on the characteristics of spatial variability of concerned simulation variables. For example, a laterally averaged two-dimensional (2D) model, which has been widely used in many large dam reservoirs in Korea, assumes that the lateral variations of hydrodynamic and water quality variables are negligible. However, there has been limited studies to give a justification of the assumption. The objectives of this study were to present the characteristics of spatial variations of water quality variables through intensive field monitoring in Daechung Reservoir, and provide information on a proper spatial dimension for different water quality parameters. The monitoring results showed that the lateral variations of water temperature are marginal, but those of DO, pH, and conductivity could be significant depending on the hydrological conditions and local algal biomass. In particular, the phytoplankton (Chl-a) and nutrient concentrations showed a significant lateral variation at R2 (Daejeongri) during low flow periods in 2008 possibly because of slow lateral mixing of tributary inflow from So-oak Stream and wind driven patchiness.

• (The) Korean Journal of Educational Psychology
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• v.23 no.3
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• pp.543-560
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• 2009

The purpose of this study was to identify the proper identification method of the gifted students in math and science. The subjects were 6,237 students from 3rd to 7th graders. The subjects took nation-wide tests which were made for identifying the gifted students. The tests were composed of creativity, thinking skill, and academic achievement in math and science. The results of this study were as follows; First, creativity and thinking skill were positively correlated with the academic achievements. Specially, the academic achievement of science was positively correlated with the all of the sub-factors of creativity and thinking skill variables. Second, the influential power of each variable differed depending on the identification methods. Also, group 1, which was considered all variables such as, creativity, thinking skill, and academic achievement, was the most proper way to identifying the top 1% students from the subjects. These results implies the variables of creativity, thinking skill, and academic achievement have to consider identifying the gifted students in math and science.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.537-552
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• 2019

Purpose: The development of information technology makes it easy to utilize high-dimensional categorical data. In this regard, the purpose of this study is to propose a novel method to select the proper categorical variables in high-dimensional categorical data. Methods: The proposed feature selection method consists of three steps: (1) The first step defines the goodness-to-pick measure. In this paper, a categorical variable is relevant if it has relationships among other variables. According to the above definition of relevant variables, the goodness-to-pick measure calculates the normalized conditional entropy with other variables. (2) The second step finds the relevant feature subset from the original variables set. This step decides whether a variable is relevant or not. (3) The third step eliminates redundancy variables from the relevant feature subset. Results: Our experimental results showed that the proposed feature selection method generally yielded better classification performance than without feature selection in high-dimensional categorical data, especially as the number of irrelevant categorical variables increase. Besides, as the number of irrelevant categorical variables that have imbalanced categorical values is increasing, the difference in accuracy between the proposed method and the existing methods being compared increases. Conclusion: According to experimental results, we confirmed that the proposed method makes it possible to consistently produce high classification accuracy rates in high-dimensional categorical data. Therefore, the proposed method is promising to be used effectively in high-dimensional situation.