• Journal of Elementary Mathematics Education in Korea
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• v.18 no.3
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• pp.459-474
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• 2014

The purpose of this study was to investigate the effects of mathematical instruction using GSP program on the symmetrical figures learning and self-directed learning attitude. According to the pretest result, the experiment group and the comparison group showed to be homogeneous groups. The experiment group has learned symmetrical figures for 9 hours using the GSP program and the comparison group has learned for 9 hours using the traditional method(paper and pen lesson). As the posttests, self-directed learning attitude test and symmetry figure understanding test were performed. The results obtained in this research are as follows; First, there was a significant difference in symmetry figure understanding test between the experiment group which learned through GSP program and the comparison group which learned through traditional method. Since there showed a very high achievement in the experiment group which learned using GSP, it can be inferred that GSP was very effective in the lessons of symmetrical movements. Second, there was a significant difference in self-directed learning attitude test between the experiment group and the comparison group. This seems to be because the length of the sides of the figures, size of the angles of the figures etc can be verified instantly and the students can correct by themselves and give feedbacks when they use GSP program. Students preferred drawing using the GSP over drawing using rulers and pencils, and they showed interest in the GSP program and they did not have burden in being wrong in their study and studied in various methods. And as they become familiar with the GSP program, they even studied other contents beyond the scope presented in the textbook.

• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.31-38
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• 2019

The systems engineering standard process is intended to be customized for a given project environment and system characteristics. This study describes the experience gained by applying systems engineering to government-funded projects. The characteristics of government funded project are lack of common development process among the participating organizations and mechanism to determine system requirements. This study describes the contents of the systems engineering activities, including development of operational concept, system requirements, verification requirements (test cases), test verification plan, and implementation of system test and lessons learned from these activities.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.125-134
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• 2015

This paper designs a peer tutoring in Flipped Learning environments for effective game programming(TGF), suggests a result of survey and a lesson learned from game programming in terms of students' and professors' perspectives in hands-on program training using Snake game programming as an applied example. The TGF is more effective than the traditional classroom to achieve the learning goals of game programming course.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.04a
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• pp.83-90
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• 1998

The morphologies of the wear debris are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify friction condition from the lubricated moving system. The four parameter(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction coefficient. It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We dicuss between the characteristic of wear debris and the friction coefficient and how the network determines difference in wear debris feature.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1997.04a
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• pp.25-32
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• 1997

The neural network was applied to identify wear debris generated from the lubricated machine moving surface. The wear test was carried out under different experimental conditions. In order to describe characteristics of debris of various shapes and sizes. The four parameter(50% volumetric diameter, aspect, roundness and reflec- tivity) of wear debris are used as inputs to the network and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.45.1-45.1
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• 2017

A Korean VLBI Network Key Science Program, the Interferometric Monitoring of Gamma-ray Bright AGNs (iMOGABA) program continues to aim at revealing the origins of the gamma-ray flares that are often detected in active galactic nuclei (AGNs). Here in this presentation, we would like to present what we have learned about the Gamma-ray bright AGNs based on the recent results of the Korean VLBI Network Key Science Program: the iMGOABA. The results will include a) the source properties of the whole samples obtained from a single-epoch observation, and b) some of scientific highlights for the iMOGAGBA on specific sources. From those highlighted works, we find that the Gamma-ray bright AGNs become fainter at higher frequencies, yielding optically thin spectra at mm wavelengths. Based on the studies on specific sources, taking into account the synchrotron self-absorption model of the relativistic jet, we estimated the magnetic field strength in the mas emission region during the observing period.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.4
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• pp.42-49
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• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1997.10a
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• pp.135-144
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• 1997

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surfaces from which the particles originated. The morphologies of the wear particles are therefore directly indica- rive of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the lubricated moving system. The four parameter(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We dicuss how the network determines difference in wear debris feature, and this approach can be applied to condition diagnosis of the lubricated moving system.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.359-367
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• 2015

In this paper, we propose intelligent fire learning and detection system using hybrid visual attention mechanism of human. Proposed fire learning system generates leaned data by learning process of fire and smoke images. The features used as learning feature are selected among many features which are extracted based on bottom-up visual attention mechanism of human, and these features are modified as learned data by calculating average and standard variation of them. Proposed fire detection system uses learned data which is generated in fire learning system and features of input image to detect fire.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.5
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• pp.92-97
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• 1998

The morpholgies of the wear particles are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the machine driving system. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different patter characteristic and recognized the friction condition and materials very well by artificial neural network. We discussed how the network determines differencee in wear debris feature, and this approach can be applied to foreseeability and decisio for moving condition of the Machine driving system.