• Journal of Educational Research in Mathematics
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• v.8 no.1
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• pp.327-350
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• 1998

The purpose of this study is to examine the applicability of educational games to open mathematic learning and consideration to the position of educational games in mathmatical education as powerful technology. For these purposes, previous literatures about games are reviewed. The concept of simulation games are defined and explore the characteristics of games includings game structure and process. And some typical educational games - Israel games, ORDA - are introduced. The main focus of the deliberation and survey of previous literatures is educational games as meaningful learning medium of mathematics and other subjuct matters. Especially educational games take a meaningful role for an implication of applicability of games especially for higher order thinking skills like problem solving, decision making, and creativity. To realize this alternative learning method of mathematics, first of all the attituce of teachers are have to change and accumulate field studies.

• Journal of The Korean Association of Information Education
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• v.6 no.2
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• pp.120-129
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• 2002

In this paper, I survey the field which students use ICT and propose a teaching and learning model to improve students' ability in using ICT. Also, I apply it and prove its' effect. Because Problem Based Learning treats ill-structured problem which reflects actuality, Students can pick up the actual knowledge and become verse in general principle or concept which can transmit resemble problem or situation. Therefore, I hope a teaching and learning model which I propose in this paper has an effect to improve students' ability in using ICT.

• Journal of KSNVE
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• v.10 no.2
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• Journal of the Korean Society for information Management
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• v.17 no.2
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• pp.189-205
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• 2000

We investigate the appropriate design and implementation of an Inductive Learning Alogrithm with a Neural Network in order to solve both inconsistent indexing and incomplete query problems on the web. Specifically, the proposed system based queries and documents in the field of Mathematics shows how inductive learning method and neural networks can apply to information retreival. Also, this study examines all of parameters of the neural networks -- the number of node in input and output, hidden layer size and learning parameters etc. -- which are significant in determining how well the neural network will converge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.31 no.3
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• pp.97-104
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• 2020

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

• Journal of Power Electronics
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• v.2 no.4
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• pp.240-249
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• 2002

Power electronics Is an emerging technology New applications are added every year as well as the power handling capabilities are steadily increasing. The demands to the education of engineers in this field are also increasing. Basically the content of the curriculum should be more expanded without extra study time. This paper present a teaching approach which makes it possible very fast for the student to get in-deplh skills in this important area which is the problem-oriented and project-based learning. The trend and application of power electronics are illustrated. The necessary skills for power electronic engineers are outlined followed up by a discussion on how problem-oriented and project-based learning are implemented. A complete curriculum at Aalborg University is presented where different power electronics related projects at different study levels are carried out.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.36-39
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• 2004

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.71-86
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• 2019

The unprecedented demands of mobile communication networks by the rapid rising popularity of mobile applications and services require future networks to support the exploding mobile traffic volumes, the real time extraction of fine-rained analytics, and the agile management of network resources, so as to maximize user experience. To fulfill these needs, research on the use of emerging deep learning techniques in future mobile systems has recently emerged; as such, this study deals with deep learning based mobile communication research activities. A thorough survey of the literature, conference, and workshops on deep learning for mobile communication networks is conducted. Finally, concluding remarks describe the major future research directions in this field.

• Hwankyungkyoyuk
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• v.13 no.1
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• pp.14-22
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• 2000

This attempts to find out training system of environment education teacher in Korea. The results are summarized as follows. The primary and secondary school have focused on environment education and the environment course was designated as a subject, but only 12% of the 2741 middle school chose the environment subject in 1998. The environment education course is not popular among students. The environment education is an interdisciplinary subject, which is composed of natural science, social studies, earth science, and medical science, that is why the subject is so unsystematic and complicated that appropriate teaching methods and contents for school classes are not able to be developed. Moreover, material and manuals in environment education for students and teachers are limited. While the contents of environment education is composed of field experience learning and experiment learning, but lecture-centered instruction is emphasized in school because of materials, time and experts. Over 300 environmental education teachers are annually produced, but the ratio of employment low. is, Therefore, a retraining program for environment education teacher needs to be developed.