• Journal of Science and Technology Studies
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• v.9 no.1
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• pp.89-127
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• 2009

The Korean Astronaut Project(KAP) aims to make the first Korean astronaut fly and take part in space activities in the International Space Station(ISS) in April 2008. KAP was on the purpose of studying the requirement to master manned space technologies as part of the long-term basic plan for national space development. However, people criticized that Ms. Yi was a 'space tourist' not an 'astronaut' because KAP was a program for pride, prestige in 21c's new space race, not specifically science and technology. The government emphasized that Yi carried out her 15 experiments in ISS very competently. In contrast people devaluated Yi's space experiments as below the level, though some of them are enough meaningful to be published on SCI journals. Why did the government fail to make people take Ms. Yi as an astronaut? I answer to this question using the notion of "Network Analysis" based on Actor-Network Theory(ANT).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.12 s.243
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• pp.1615-1620
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• 2005

We present a novel flow-rate independent cell counter using a fixed control volume between double electrical sensing zones. The previous device based on the single electrical cell sensing in a given flow-rate requires an accurate fluid volume measurement or precision flow rate control. The present cell counter, however, offers the flow-rate independent method for the cell concentration measurement with counting cells in a fixed control volume of $22.9{\pm}0.98{\mu}{\ell}$. In the experimental study, using the RBC (Red Blood Cell), we have compared the measured RBC concentrations from the fabricated devices with those from Hemacytometer. The previous and present devices show the maximum errors of $20.3\%\;and\;16.1\%$, which are in the measurement error range of Hemacytometer (about $20\%$). The present device also shows the flow-rate independent performance at the constant flow-rates ($5{\mu}{\ell}/min$ and $10{\mu}{\ell}/min$) and the varying flow-rate (4, 2, and $4{\mu}{\ell}/min$). Therefore, we demonstrate that the present cell counter is a simple and automated method for the cell concentration measurement without requiring an accurate fluid measurement and precision flow-rate control.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1135-1139
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• 2004

This paper presents a microextractor for the separation of DNA molecules by their sizes. The DNA microextractor immobilizes the DNA molecules of specific size in the micropillar array by adjusting the period of the crossed electric field, thus providing a starting-point independent target DNA extraction method without separation process monitoring. The DNA microextractor has been fabricated by a three-mask micromachining process. The velocity of three different DNA molecules has been measured at the electric field of E=5V/0.8cm in the fabricated DNA microextractor, resulting in the reorientation times of $4.80{\pm}0.44sec,\;7.12{\pm}0.75sec$, and $9.88{\pm}0.30sec$ for ${\lambda}$ DNA, micrococcus DNA, and T4 DNA, respectively. T4 DNA is trapped in the micropillar array when the crossed electric field of 5V/0.8cm is applied alternately at a 10 second time interval. The present DNA microextractor filters the DNA in a specific size range by adjusting the magnitude and/or the period of the crossed electric field applied in the micropillar array.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.