• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• 한국위성정보통신학회논문지
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• 제11권1호
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• pp.26-31
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• 2016

본 논문에서는 이기종 통신 시스템에서 Empirical Mode Decomposition(EMD) 기법을 활용하여 통신 신호의 잡음을 완화시키는 방안을 제시하였다. EMD는 노이즈가 인가된 신호를 여러 개의 Intrinsic Mode Function(IMF)로 분할하여 노이즈가 포함된 IMF를 제거하는 방법으로 노이즈를 줄이는 방법이다. 본 논문에서는 EMD의 연산량을 줄이기 위해 새로운 반복 중지 규칙을 제시하였다. EMD의 적용 방법을 수식 및 알고리즘으로 구현하였다. 3종류의 잡음이 인가된 신호를 시뮬레이션을 통해 효과적으로 잡음이 완화되는 것을 확인하였다.

• 한국위성정보통신학회논문지
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• 제12권1호
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• pp.43-48
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• 2017

Recently, demands on civilian UAV (Unmanned Aerial Vehicle) has been increasing and appropriate communication system is required for the UAV. In this paper, the performance of the UAV communication system using commercial wireless mobile network is discussed. The main service area of the wireless mobile network is ground level however the flying range of the UAV is normally in high altitude. Because of this mismatch of service area the performance of the UAV communication system is degraded in high altitude. To compensate performance degradation of the UAV communications system in high altitude, adaptive array antenna is introduced which is able to overcome altitude limitation of the UAV communication system.

• Journal of Communications and Networks
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• 제12권6호
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• pp.533-543
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• 2010

This paper proposes an orthogonal frequency division multiplexing (OFDM) waveform for the forward link of a fixed broadband satellite system. We focus on the synchronization tasks in the receiver. Our objective is to minimize the required overhead, in order to improve the spectral efficiency with regard to a single carrier waveform system. A non pilot aided algorithm is used for fine synchronization. It is preceded by a coarse synchronization stage, which relies on a limited overhead (short cyclic prefix associated to some pilots). The performance of the proposed receiver is assessed through simulation results.

• Journal of Communications and Networks
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• 제13권6호
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• pp.625-632
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• 2011

This paper presents an overview of recent advances in radio frequency and microwave filter topologies for satellite communication systems. Many types of filters have been developed during the last years in order to satisfy the demands of modern applications in both terrestrial systems and onboard spacecrafts, leading to a great variety of aspects such as transfer functions, resonator implementations or coupling structures. This paper revisits some of the last advances in this area, including the modeling and full-wave simulation. Some recent designs using dual-mode cavities along with other novel implementations in ridge waveguide will be shown.

• 정보보호학회논문지
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• 제6권3호
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• pp.23-30
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• 1996

본 논문에서는 Diffie-Hellman 방법을 위성을 이용한 다중 방송통신망에 적용하여 키 분배하는 방법을 제안하고 있다.

• 천문학회보
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• 제46권1호
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• pp.41.1-41.1
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• 2021

We generate solar magnetograms and EUV images from Galileo sunspot drawings using a deep learning model based on conditional generative adversarial networks. We train the model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding magnetogram (or UV/EUV images) from 2011 to 2015 except for every June and December by the SDO (Solar Dynamic Observatory) satellite. We evaluate the model by comparing pairs of actual magnetogram (or UV/EUV images) and the corresponding AI-generated one in June and December. Our results show that bipolar structures of the AI-generated magnetograms are consistent with those of the original ones and their unsigned magnetic fluxes (or intensities) are well consistent with those of the original ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. We hope that the EUV intensities can be used for estimating solar EUV irradiance at long-term historical times.

• 한국농공학회논문집
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• 제62권1호
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• pp.83-93
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• 2020

East Asia, which includes China, Japan, Korea, and Mongolia, is highly impacted by hydroclimate extremes such drought, flood, and typhoon recent year. In 2017, more than 18.5 million hectares of crops have been damaged in China, and Korea has suffered economic losses as a result of severe drought. Satellite-derived rainfall products are becoming more accurate as space and time resolution become increasingly higher, and provide an alternative means of estimating ground-based rainfall. In this study, we verified the availability of rainfall products by comparing widely used satellite images such as Climate Hazards Groups InfraRed Precipitation with Station (CHIRPS), Global Precipitation Climatology Centre (GPCC), and Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with ground stations in East Asia. Also, the satellite-based rainfall products were used to calculate the Standardized Precipitation Index (SPI). The temporal resolution is based on monthly images and compared with the past 30 years data from 1989 to 2018. The comparison between rainfall data based on each satellite image products and the data from weather station-based weather data was shown by the coefficient of determination and showed more than 0.9. Each satellite-based rainfall data was used for each grid and applied to East Asia and South Korea. As a result of SPI analysis, the RMSE values of CHIRPS were 0.57, 0.53 and 0.47, and the MAE values of 0.46, 0.43 and 0.37 were better than other satellite products. This satellite-derived rainfall estimates offers important advantages in terms of spatial coverage, timeliness and cost efficiency compared to analysis for drought assessment with ground stations.

• Water Engineering Research
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• 제3권2호
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• pp.123-134
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• 2002

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

• Water Engineering Research
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• 제3권2호
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.