• Journal of Families and Better Life
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• v.24 no.5 s.83
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• pp.161-174
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• 2006

This study examined different individual and parental factors that affect children's achievement motivation. For an analysis, perceived competence, intrinsic locus of control were included in individual variables. For parental variables, parental support and achievement pressure and marital conflict were examined. The sample consisted of 561 fifth and sixth grade children. Statistics and methods used for the data analysis were Cronbach's alpha, Factor analysis, frequency, percentage, Pearson's correlation, and Hierarchical Regression. Several major results were found from the analysis. First, girl's achievement motivation was higher than boys. No age difference was found between fifth and sixth grade. Second, boy's and girl's achievement motivation had a positive correlation with perceived competence, intrinsic locus of control, parental support and achievement pressure but a negative correlation with parental marital conflict. Third, important variables predicting boy's and girl's achievement motivation were perceived academic competence, parental achievement pressure and perceived social competence. Important variables predicting boy's individual and social oriented achievement motivation were perceived academic competence and parental achievement pressure. On the other hand, important variables predicting girl's individual oriented achievement motivation were perceived social competence, perceived academic competence, intrinsic locus of control and parental achievement pressure. Important variables predicting girl's social oriented achievement motivation were parental achievement pressure, perceived academic competence and mother's support.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.92-99
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• 2010

This study aimed to provide empirical evidence about expert performance approach in aviation field and the results suggested that the amount of experience(e.g. total flight hour) is necessary but not sufficient index of a pilot's expertise or superior performance. 43 pilots participated and completed a spatial span task and SA (situation awareness) tasks. To explore the factors predicting the performance in routine and non-routine situations, discriminant analysis was conducted. The results of discriminant analysis indicated that different variables are related with the performance in routine and non-routine situation. The factors predicting performance in routine situation were the spatial span scores and total flight hours. On the other hand, the factors predicting performance in non-routine situation were age and the qualification for instrument flying. In real world, total flight time which represents the quantity of experience has been frequently used to predict flight abilities and as an important index of expertise. The results of this study suggest that these kinds of factors have to be used cautiously to predict the performance in abnormal situation.

• Journal of the Korean Home Economics Association
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• v.48 no.3
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• pp.15-30
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• 2010

The objective of this study is to identify the relationship quality and relationship benefit, which has greater explanatory power in predicting fashion consumers' future loyalty. This study is particularly interested in the different explanatory power of each relationship quality with various relationship partners of fashion consumers. The participants were 507 female consumers over 20 years old and they responed questionnaire. The result showed that relationship quality types and relationship benefits having greater explanatory power in predicting consumers' loyal relationship intention varied with multi-loyal relations. Consumers' intention to be loyal to an apparel brand and apparel company was more explained by self attachment than by any other relationship quality types, whereas the intention to be loyal to specific department store was predicted by low involved relationship quality types such as habitual alternative and compensational bind. Trusted intimacy was the only relationship quality type that was significant in predicting consumers' intention to be loyal to salesperson in the future. Among relationship benefits, the influence of convenience benefit was significant in predicting consumers' future loyalty in most relations.

• Journal of Welding and Joining
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• v.13 no.3
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• pp.77-88
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• 1995

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO$_{2}$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a backpropagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the ailure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.34-40
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• 2020

This study aims to create a model for predicting the number of extinguishment manpower to put out forest fires by taking into account the climate, the situation, and the extent of the damage at the time of the forest fires. Past research has been approached to determine the cause of the forest fire or to predict the occurrence of a forest fire. How to deal with forest fires is also a very important part of how to deal with them, so predicting the number of extinguishment manpower is important. Therefore predicting the number of extinguishment manpower that have been put into the forest fire is something that can be presented as a new perspective. This study presents a model for predicting the number of extinguishment manpower inputs considering the scale of the damage with forest fire on a scale bigger than 0.1 ha as data based on the forest fire annual report(Korea Forest Service; KFS) from 2015 to 2018 using the moderated multiple regression analysis. As a result, weather factors and extinguished time considering the damage show that affect forest fire extinguishment manpower.

• Journal of the Korea Safety Management & Science
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• v.8 no.3
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• pp.1-9
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• 2006

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare algorithms for data analysis of industrial accidents and this paper provides an optimal predicting model of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. Also, this paper provides an approximation model for an optimal predicting model based on NN. The approximation model provided in this study can be utilized for easy interpretation of data analysis using NN. This study uses selected ten independent variables to group injured people according to a dependent variable in a way that reduces variation. In order to find an optimal predicting model among 5 algorithms, a retrospective analysis was performed in 67,278 subjects. The sample for this work chosen from data related to industrial accidents during three years ($2002\;{\sim}\;2004$) in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.947-955
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• 2020

Predicting the sloshing motion of a coolant during a seismic assessment of a rectangular spent fuel pool is of critical concern. Linear theory, which provides a simple analytical method, has been used to predict the sloshing motion in rectangular pools and tanks. However, this theory is not suitable for the high-frequency excitation problem. In this study, the authors developed a simple analytical method for predicting the sloshing motion in a rectangular pool for a wide range of excitation frequencies. The correlation among the linear theory parameters, influencing on excitation and convective waves, and the excitation frequency is investigated. Sloshing waves in a rectangular pool with several liquid heights are predicted using the original linear theory, a modified linear theory and computational fluid dynamics analysis. The results demonstrate that the developed method can predict sloshing motion over a wide range of excitation frequencies. However, the developed method has the limitations of linear solutions since it neglects the nonlinear features of sloshing motion. Despite these limitations, the authors believe that the developed method can be useful as a simple analytical method for predicting the sloshing motion in a rectangular pool under various external excitations.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.51-60
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• 2001

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermo-mechanical analysis has been performed for the $CO_2$ arc welding using the finite element method. The first part of numerical analysis performs a three-dimensional transient heat transfer analysis, and the second part then uses the results of the first part and performs a three-dimensional transient thermo-elastic-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method are used to train a back propagation neural network to predict the residual stress. Architecturally, the fully interconnected network consists of an input layer for the voltage and current, a hidden layer to accommodate the failure mechanism mapping, and an output layer for the residual stress. The trained network is then applied to the prediction of residual stress in the four specimens. It is concluded that the accuracy of the neural network predicting method is fully comparable with the accuracy achieved by the traditional predicting method.

• Journal of the Korean Society of Food Culture
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• v.21 no.5
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• pp.439-445
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• 2006

The purpose of this study was to measure respondent's demographic characteristics, respondent's attitudes toward chicken, and factor influencing on the level of perceived helpfulness of country of origin in predicting the quality of chicken. The data was collected through a consumer survey during the March 2006. A total number of 250 meat consumers living in Suncheon, the eastern part of Chonnam, were randomly selected as respondents. Eleven respondents did not complete the survey instrument, resulting in a final sample size of 239. All estimations were carried out using chi-square, correlation, and logistic procedure of SAS package. The results are as follows. The level of perceived helpfulness of country of origin in predicting the quality of chicken was significantly different by age and occupation of demographic variables, and was significantly correlated with respondent informed of attitude variables. The proportional odds assumption of model was not violated at p<0.05. The effects of income, occupation and respondent informed on the level of perceived helpfulness of country of origin in predicting the quality of chicken. The results from this study could be useful in developing marketing and health promotion strategies, as well as government trade policy.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.304-307
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• 2013

Various programs have been developed to predict the energy consumption of a building as a result of recent increased social interest in the environmental friendliness of construction as measured by energy efficiency. The goal of environmental-friendliness, which is achieved by predicting the energy consumption of a building, can be realized in the design stage by applying a variety of technologies, planning factors and planning systems. However, most energy analyzing engines are only suitable for use in the advanced stages of design because of the large amount of design information that must be entered. Thus, because the simulation programs currently used are not suitable for use in the early stages of design, this study suggests a prediction logic that provides an overview of the energy consumption of a building according to its size, scope, and purpose by analyzing statistics collected by government agencies.