• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1777_1778
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• 2009

한국수자원공사에서는 1998년부터 단계적으로 12~14 GHz의 Ku-band 대역을 사용하고 있는 무궁화위성을 이용하여 우량 및 수위, 경보 등의 데이터를 송수신하고 있으며, 특히 강우가 집중되는 기간 동안에는 그 데이터의 필요성이 더욱 큰 실정이다. 사용하고 있는 위성통신망 주파수의 파장은 2~2.5 cm 이므로 물방울 입자에 의해 산란되는 특징을 가지고 있으며, 이로 인해 안테나 및 휘드혼의 표면에 물방울이 묻어 있으면 전파가 산란되어 신호가 감쇠되는 특성을 가지고 있다. 따라서 본 연구에서는 강우시 안테나 및 휘드혼 표면에 발생하는 물방울 맺힘 현상에 대해 물방울과 전파신호간의 상관관계를 분석하고, 위성안테나 성능을 개선방안을 시험하여, 강우로 인한 위성신호의 감쇠를 최소화하여, 최적화된 안테나 성능 구현으로 강우 영향을 최소화하여 안정적인 위성단말국을 운영하고자 한다.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.143-152
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• 2019

This paper presents two new techniques for solving the two problems of the water curtain: 'shape distortion' caused by gravity and 'resolution degradation' caused by fine satellite droplets around the shape. In the first method, when the user converts a three-dimensional model to a vertical sequence of slices, the slices are evenly spaced. The method is to adjust the time points at which the equi-distance slices are created by the nozzle array. In this method, even if the velocity of a water drop increases with time by gravity, the water drop slices maintain the equal interval at the moment of forming the whole shape, thereby preventing distortion. The second method is called the minimum time interval technique. The minimum time interval is the time between the open command of a nozzle and the next open command of the nozzle, so that consecutive water drops are clearly created without satellite drops. When the user converts a three-dimensional model to a sequence of slices, the slices are defined as close as possible, not evenly spaced, considering the minimum time interval of consecutive drops. The slices are arranged in short intervals in the top area of the shape, and the slices are arranged in long intervals in the bottom area of the shape. The minimum time interval is pre-determined by an experiment, and consists of the time from the open command of the nozzle to the time at which the nozzle is fully open, and the time in which the fully open state is maintained, and the time from the close command to the time at which the nozzle is fully closed. The second method produces water drop sculptures with higher resolution than does the first method.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.71-87
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• 2007

In this study, 14 heavy snow events in Yeongdong area which are local phenomena are analyzed using MODIS cloud products provided from NASA/GSFC. The clouds of Yeongdong area at observed at specific time by MODIS are classified into A, B, C Types, based on the characteristic of cloud properties: cloud top temperature, cloud optical thickness, Effective Particle Radius, and Cloud Particle Phase. The analysis of relations between cloud properties and precipitation amount for each cloud type show that there are statistically significant correlations between Cloud Optical Thickness and precipitation amount for both A and B type and also significant correlation is found between Cloud Top Temperature and precipitation amount for A type. However, for C type there is not any significant correlations between cloud properties and precipitation amount. A-type clouds are mainly lower stratus clouds with small-size droplet, which may be formed under the low level cold advection derived synoptically in the East sea. B-type clouds are developed cumuliform clouds, which are closely related to the low pressure center developing over the East sea. On the other hand, C-type clouds are likely multi-layer clouds, which make satellite observation difficult due to covering of high clouds over low level clouds directly related with Yeongdong heavy snow. It is, therefore, concluded that MODIS cloud products may be useful except the multi-layer clouds for understanding the mechanism of heavy snow and estimating the precipitation amount from satellite data in the case of Yeongdong heavy snow.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.