• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.82-91
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• 1997

In this paper, we analyze the dynamics of langevine competitive learning neural network based on its fokker-plank equation. From the viewpont of the stochastic differential equation (SDE), langevine competitive learning equation is one of langevine stochastic differential equation and has the diffusin equation on the topological space (.ohm., F, P) with probability measure. We derive the fokker-plank equation from the proposed algorithm and prove by introducing a infinitestimal operator for markov semigroups, that the weight vector in the particular simplex can converge to the globally optimal point under the condition of some convex or pseudo-convex performance measure function. Experimental resutls for pattern recognition of the remote sensing data indicate the superiority of langevine competitive learning neural network in comparison to the conventional competitive learning neural network.

• Transactions of Materials Processing
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• v.30 no.4
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• pp.186-194
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• 2021

Finite element simulation is a widely applied method for practical purpose in various metal forming process. However, in the simulation of elasto-plastic behavior of porous material or in crystal plasticity coupled multi-scale simulation, it requires much calculation time, which is a limitation in its application in practical situations. A machine learning model that directly outputs the constitutive equation without iterative calculations would greatly reduce the calculation time of the simulation. In this study, we examined the possibility of artificial intelligence based constitutive equation with the input of existing state variables and current velocity filed. To introduce the methodology, we described the process of obtaining the training data, machine learning process and the coupling of machine learning model with commercial software DEFROM^TM, as a preliminary study, via rigid plastic finite element simulation.

• Journal of Digital Convergence
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• v.6 no.1
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• pp.83-91
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• 2008

The objective of this research is to assess various factors affecting E-learning in Korean language education. In this research, we hypothesize that several factors such as absorption, motivation and tutors increase the educational effects of E-learning and ultimately affect learning attitude and satisfaction of students in E-learning. To discuss the hypothesis, we analyzed survey data of 300 students who were taking E-learning class of Korean language for three weeks in October 2007 based on Structural Equation Model. The result of our analysis shows that the factors such as absorption, motivation, tutors have positive effects on E-learning in Korean language education and positive influence on learning attitude and satisfaction on students as well.

• Journal of Digital Convergence
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• v.6 no.2
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• pp.145-155
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• 2008

The purpose of this research is to investigate the effects of the Cyber Home Learning Motivation Factors on its satisfaction and activation through surveying the actual conditions among the students present at a cyber home learning class. For this study, samples were collected around the end of a term from the students(300 in pilot test and 248 in main test) who were taking Cyber Home Lecture at high school level. Structural equation model by AMOS 5.0 was used to analyze the data. The result of our analysis is summarized as follows. First, the cyber home learning satisfaction has a positive effect on the cyber home learning activation. Second, the 4 factors of the cyber home learning motivation: relevancy, self-confidence and satisfaction has a positive effect on the cyber home learning satisfaction. But the factor 'attention' has no positive effect on the cyber home learning satisfaction. Therefore, the Good Cyber Home Learning Contents should provide the information quality which meets 3 conditions: relevancy, self-confidence and satisfaction.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.135-143
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• 2009

The purpose of this study was to explore factors that affected the satisfaction of students' experiences in an education course, with the educational method and educational technology designed with a blended learning strategy. Blended learning is currently recognized as a good solution for the problems posed by both online and face-to-face learning, because it has features like flexibility and accessibility by using tools supporting both individualization and socialization. This study is one case that illustrates how blended learning can be applied at the university level. Subjects were 56 students who had participated in the class and responded to the survey questions. The gathered data were analyzed by using Factor Analysis and the Structural Equation Model. Based on the results of Factor Analysis, data revealed 5 factors: learning motivation, previous experience, ability to use information & technology, capability of self-regulated learning, and learning satisfaction. The results of the Structural Equation Model revealed causal relationships among the aforementioned factors as follows: (a) there was a statistically meaningful causal relationship between "learning motivation" and "capability of self-regulated learning", (b) there was a statistically meaningful casual relationship between "previous experience" and "capability of self-regulated learning", and (c) "capability of self-regulated learning" directly affected "learning satisfaction".

• Journal of Agricultural Extension & Community Development
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• v.22 no.4
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• pp.455-464
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• 2015

The purposes of this study are to explain and identify the frame of structural relations of learning orientation, self-efficacy, learning transfer and job performance of farmers who participated in the strong and small farms education. This is an experimental research with the data collected from 495 farmers who have taken the farm education. Based on the collected data, the study conducted a structural equation modeling(SEM) to confirm the validity and analyze the structural relations of the suggested model. Using measured and latent variables drew from the analyses, the study set a structural equation model and tested the model by analysis of the structural equation modeling with AMOS 18.0. The results found from the empirical analysis can be summarized as follows. 1) Learning orientation and self-efficacy positively influenced job performance through learning transfer. 2) The hypothesis that learning orientation would have direct impact on job performance was not supported. 3) The strong and small farms education is useful to expand learning transfer and to enhance job performance. So, government policy support has to reinforce learning support on farmers in order to achieve high performance of learning and job management through farm educations.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.6
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• pp.935-949
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• 2012

The purpose of this study was to investigate the structural relationship among the self-efficacy, self-directed learning ability, school adjustment and learning flow in middle school students by the structural equation modeling analysis. The subjects of this study consisted of 553 middle school students. The data were analyzed with descriptive statistics, Pearson correlations and structural equation modeling analysis by using the SPSS 12.0 and AMOS 5.0 statistical program. The results of this study were as followed: First, there were significant correlations among the self-efficacy, self-directed learning ability, school adjustment and learning flow. Second, the self-directed learning ability and school adjustment directly affected the learning flow. Third, self-efficacy and school adjustment variables indirectly affected learning flow. The indices of the best fit model on these variable were adequate. This study shows that the self-efficacy, self-directed learning ability, school adjustment are the significant predictor for the learning flow during adolescent.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.91-94
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• 1996

This paper presents an approach to estimation of learning gain in iterative learning control for discrete-time affine nonlinear systems. In iterative learning control, to determine learning gain satisfying the convergence condition, we have to know the system model. In the proposed method, the input-output equation of a system is identified by neural network refered to as Piecewise Linearly Trained Network (PLTN). Then from the input-output equation, the learning gain in iterative learning law is estimated. The validity of our method is demonstrated by simulations.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.77-84
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• 2014

The purpose of this study is to explore factors affecting the effect of e-learning, and to find out the casual relationship among these factors. Subjects are 2,091 students who have participated in e-learning based class during the period of second semester in 2013. Those of them, 1,732 students response to the survey questions. After gathering data, they are analyzed by using Confirmative Factor Analysis and Structural Equation Modeling. From the result of Confirmative Factor analysis, data have reduced four factors, and are named as four latent variables likes e-learning effect, contents satisfaction, managing assistant factor, and system functional factor. From the result of Structural Equation Modeling, it is known as the relation and impact among factors: (a) "managing assistant factor" affects to "contents satisfaction" directly. (b) "contents satisfaction" affects to "e-learning effect" directly. (c) "system function factor" affects directly to "contents satisfaction", but does not affect directly to "e-learning effect". (d) both "managing assistant factor" and "system function factor" have an indirect effect on "e-learning effect" via "contents satisfaction".

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.158-165
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• 2011

Statistics by structural equation modeling techniques were used to assess a model of chemistry learning strategy based on performance goal orientation. In the optimal Model III of this research, Performance-approach goal was positively related to the use of learning strategy(p<.05) and achievement need(p<.05). Performance-avoidance goal was negatively related to learning strategy(p<.05) and was positively related to self-handicapping tendency(p<.15). Performance-approach goal affected learning strategy indirectly through achievement need(p<.05). Use of achievement need was positively related to learning strategy(p<.05) and self-handicapping tendency(p<.35). Self-handicapping tendency affected learning strategy negatively(p<.05). Implications of these findings for learning strategy in chemistry education are discussed.