• 대한핵의학회:학술대회논문집
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• 1973.11a
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• pp.51.3-52
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• 1973

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2009.06a
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• pp.233-234
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• 2009

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2010.10a
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• pp.179-180
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• 2010

• The Journal of the Korean dental association
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• v.36 no.3 s.346
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• pp.204-214
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• 1998

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.305-306
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• 2018

• 대한핵의학회:학술대회논문집
• /
• 1972.11a
• /
• pp.65.2-66
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• 1972

• 대한핵의학회:학술대회논문집
• /
• 1972.11a
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• pp.66.1-66.1
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• 1972

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.33-40
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• 1996

In this paper, we resolved a trade-off problem of the blind equalizer based on the stop-and-go algorithm that is commonly used for QAM demodulation in CATV receiver. The stop-and-go algorithm has used the LMS(least mean square) algorithm in the updating operation of tap weights so that the structure of equalizer is simple, but there is a trade-off between convergence speed and steady state error as in the typical LMS algorithm. We used the variable step size algrithm to improve the convergence speed with the steady state error in the constant level. With respect to the same level of the steady state error, the variable step size stop-and-go algortihm improved convergence speed by about $36%{\sim}56%$ as compared with that of the constant step size algortihm.

• Journal of Korea Game Society
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• v.9 no.1
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• pp.105-114
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• 2009

In the game of Go, the life-and-death problem is a fundamental problem to be definitely overcome when implementing a computer Go program. To solve local Go problems such as life-and-death problems, an important consideration is how to tackle the game tree's huge branching factor and its depth. The basic idea of the experiment conducted in this article is that we modelled the human behavior to get the recognized first moves to kill the surrounded group. In the game of Go, similar life-and-death problems(patterns) often have similar solutions. To categorize similar patterns, we implemented Kohonen Neural Network(KNN) based clustering and found that the experimental result is promising and thus can compete with a pattern matching method, that uses supervised learning with a neural network, for solving life-and-death problems.

• Journal of Environmental Science International
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• v.22 no.10
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• pp.1263-1271
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• 2013

Graphene oxide (GO)-titania composites have emerged as an attractive heterogeneous photocatalyst that can enhance the photocatalytic activity of $TiO_2$ nanoparticles owing to their potential interaction of electronic and adsorption natures. Accordingly, $TiO_2$-GO mixtures were synthesized in this study using a simple chemical mixing process, and their heterogeneous photocatalytic activities were investigated to determine the degradation of airborne organic pollutants (benzene, ethyl benzene, and o-xylene (BEX)) under different operational conditions. The Fourier transform infrared spectroscopy results demonstrated the presence of GO for the $TiO_2$-GO composites. The average efficiencies of the $TiO_2$-GO mixtures for the decomposition of each component of BEX determined during the 3-h photocatalytic processes were 26%, 92%, and 96%, respectively, whereas the average efficiencies of the unmodified $TiO_2$ powder were 3%, 8%, and 10%, respectively. Furthermore, the degradation efficiency of the unmodified $TiO_2$ powder for all target compounds decreased during the 3-h photocatalytic processes, suggesting a potential deactivation even during such a short time period. Two operational conditions (air flow entering into the air-cleaning devices and the indoor pollution levels) were found to be important factors for the photocatalytic decomposition of BEX molecules. Taken together, these results show that a $TiO_2$-GO mixture can be applied effectively for the purification of airborne organic pollutants when the operating conditions are optimized.