• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.1058-1068
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• 2013

The purpose of this study was to examine how participation in R-Learning university club management program affects to R-Learning professionalism of pre-teachers in field of early childhood education related to knowledge, function, and attitude. Upon investigation for knowledge part, those answers: 'I know the role of teachers when education based on robot, 'I know how much education based on robot affects to development of early childhood', and 'I know the necessary of education based on robot' appear highly. 'I can give lessons by connecting robot and computer' for function part, and 'I think using robot for class positively' for attitude part show highly. Also, professionalism of the pre-teachers improved after participating in R-running club, especially, function and attitude part. Thus, R-Learning university club management program is effective by the research.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.133-143
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• 2019

The purpose of this paper was to analyze the differences in executive function according to type of early English learning experience. The subjects were 75 5-years-old who had immersive early English learning in language school, and daycare center. The measurement tools were stroop, DCCST, memorize numbers, pattern fluency, and maze. We conducted covariance analysis with total intelligence as the covariates. In the results, there were significant differences in attention control and cognitive flexibility, but weren't significant differences in information processing and goal setting according to type of early English learning experience. This study suggests that experience of immersive early English learning positively affected attention control and cognitive flexibility, and didn't affect information processing and goal setting.

• Journal of Digital Convergence
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• v.5 no.2
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• pp.25-36
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• 2007

In this paper, we studied on the effective structure of an e-Learning Korean Support System for foreigner based on computer systems which is to obey the rules of IMS/AICC International Standard regulations based on LCMS and SCORM. The most important task on this study is to support the function of self-study module through the review of the analysis and results of Korean learning and learning customs. We studied the effective PMS detail modules as well as the Standard Competency Module Management System, which related to LMS/LCMS, Learning an Individual Competency Management System, Competency Registry/Repository System, Knowledge Management System based on Community Competency Module, Education e-survey System and Module learning Support Service System. We suggested one of standard Effective Model of learning Korean Support System which is adopted in a various techniques for foreigner.

• Journal of The Korean Association For Science Education
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• v.24 no.1
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• pp.171-182
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• 2004

The purpose of this study is to analyze lesson plans from third to sixth grades of science and to find out teaching strategies in respects of learning functions provided by preservice elementary teachers in education university. On the whole, to control students' learning process preservice teachers used more shared-regulation strategy than strong teacher-regulation one. Teaching activities for regulative learning function were most used in strategy of strong teacher-regulation, and in strategy of shared-regulation those for cognitive learning functions were most used. But teaching activities for affective learning functions were used a little considered in both teaching strategies. In introduction step of instruction, affective and regulative learning functions were more instructed by strong teacher-regulation strategy and cognitive learning functions were more instructed by shared-regulation strategy. The affective, cognitive, and regulative learning functions were largely planned by shared-regulation teaching strategy in development. The regulative learning functions were planned by strong teacher-regulation strategy than by shared-regulation strategy and affective learning functions were considered a little bit in consolidation. There was a tendency that strong teacherregulation strategy was increased in lessons for fifth and sixth grade.

• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.211-216
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• 1996

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture o fuzzy neural networks with fuzzy weights and fuzzy biases is shown. Next, a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so 솜 t the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.6-157
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• 2001

We estimated various neuron functions to construct of engineering neurons, which are the combination of sigmoid, linear, sine, quadric, double/single bended, soft max/minimum functions. These combinations are estimated by the property on the potential surface between the learning points, calculation speed, and learning convergence; because the surface depends on the inference ability of a neuron system; and speed and convergence are depend on the efficiency on the points of engineering applications. After the evaluating discussions, we can select more appropriate combination than original sigmoid function´s, which is single bended function and linear one. The combination ...

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1996.10a
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• pp.211-216
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• 1996

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture of fuzzy nerual networks with fuzzy weights and fuzzy biases is shown. Next a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value.A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.225-230
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• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.389-396
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• 2022

Deep Learning is based on a perceptron, and is currently being used in various fields such as image recognition, voice recognition, object detection, and drug development. Accordingly, a variety of learning algorithms have been proposed, and the number of neurons constituting a neural network varies greatly among researchers. This study analyzed the learning characteristics according to the number of neurons of the currently used SGD, momentum methods, AdaGrad, RMSProp, and Adam methods. To this end, a neural network was constructed with one input layer, three hidden layers, and one output layer. ReLU was applied to the activation function, cross entropy error (CEE) was applied to the loss function, and MNIST was used for the experimental dataset. As a result, it was concluded that the number of neurons 100-300, the algorithm Adam, and the number of learning (iteraction) 200 would be the most efficient in deep learning learning. This study will provide implications for the algorithm to be developed and the reference value of the number of neurons given new learning data in the future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.