• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.315-322
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• 2019

Recently, the Marine Corps of South Korea started to introduce and possess its own weapon systems. On the other hand, the level of maintenance is lower than that of other military forces, and depending on the other military forces, maintenance occurs intermittently when transporting weapons systems. In this case, unmanned vehicles can be used to reduce the cost, manpower, time, and risk of carrying weapons systems. In addition, the transport of weapon systems between islands or between an island and inland of the Marine Corps using unmanned vehicles is easier in terms of the maintenance level and surrounding environment than other military forces. This paper compares the improvement of operational availability and cost of spare parts in terms of logistics support when using unmanned vehicles in the West Sea area, and quantitatively show the efficiency and usability of the weapon system. To compare operational availability and costs for spare parts, a simulation was performed based on the OO weapons system between islands or between an island and inland, and the results were compared and analyzed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.233-242
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• 2019

ZHD (Zenith Hydrostatic Delay) model is important parameter in estimating of GNSS (Global Navigation Satellite System) PWV (Precipitable Water Vapor) along with weighted mean temperature. The ZWD (Zenith Wet Delay) is tend to accumulate the ZHD error, so that biases from ZHD will be affected on the precision of GNSS PWV. In this paper, we compared the accuracy of GNSS PWV with radiosonde PWV using three ZHD models, such as Saastamoinen, Hopfield, and Black. Also, we adopted the KWMT (Korean Weighted Mean Temperature) model and the mean temperature which was observed by radiosonde on the retrieval processing of GNSS PWV. To this end, GNSS observation data during one year were processed to produce PWVs from a total of 5 GNSS permanent stations in Korea, and the GNSS PWVs were compared with radiosonde PWVs for the evaluating of biases. The PWV biases using mean temperature estimated by the KWMT model are smaller than radiosonde mean temperature. Also, we could confirm the result that the Saastamoinen ZHD which is most used in the GNSS meteorology is not valid in South Korea, because it cannot be exclude the possibility of biases by latitude or height of GNSS station.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.247-254
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• 2019

Recently, in designing military equipment, considerable attention has been paid to maintaining operations, including reliability, maintenance, and maintenance time of equipment, from the early stages of development. Therefore, both users and developers stress the importance of design considering the maintenance time. This study evaluated the specific methodologies for predicting the realistic maintenance time, such as the access complexity of equipment, other than the standard maintenance time provided by the conventional method mil-hdbk-470a at the beginning of the design, and applied the time conversion factor using a measure of the maintenance complexity. In addition, the actual maintenance time reflecting the actual maintenance time of the developed equipment and the time-conversion factor applied was compared/verified to confirm the reliability of the data. In a study to set a target for repair and the repair of equipment design in the future, it is expected that the maintenance time of equipment that fails to measure the maintenance time for the initial actual equipment will be reflected as a more realistic time. Moreover, activities, such as research and design reflection activities, will be performed to reduce the maintenance time, operational maintenance cost, etc.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.381-381
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• 2019

동남아시아의 급격한 도시인구 증가는 도시화로 파생되는 제반문제를 유발하고 있으며 특히 집중호우와 홍수배제 시설의 부족 및 유관시설의 정보관리체계 부재는 홍수 피해규모를 가중시키고 있다. 인도네시아의 경우 물 재해 관리기관 간의 정보공유체계 부재로, 홍수로 인한 문제해결에 대하여 효과적인 대응이 어려운 실정이다. 주요 물 관리 기관인 유역관리청(BBWS)의 경우 조기홍수경보시스템을 보유하고 있으나 단순 수문현황 모니터링에 국한되어 운영되고 있다. 이에 본 연구에서는 홍수피해를 최소화 할 수 있는 동남아시아 맞춤형 물 재해 관리 클라우드 플랫폼을 개발하여 비구조적 홍수 문제해결의 매개체로 활용하고자 한다. 기본적인 유역 수문현황 모니터링과 함께 댐, 보, 배수문 및 펌프장 등 홍수방어시설물의 운영현황 정보, 홍수상황분석, 홍수위험지도 등 종합적인 물 재해 정보를 제공하고 사전에 홍수위험 지역을 분석하여 유관기관과 공유할 수 있는 물 재해 관리 의사결정지원시스템을 개발하고자 한다. 기본적인 정보관리 체계화를 위하여 인도네시아의 다양한 물 재해 관련기관에서 보유하고 있는 자료들의 통합 클라우드 DB관리 시스템을 구축하였다. 연구대상지역은 인도네시아 수도인 자카르타의 Pesanggrahan유역과 인근 Batam섬 Baloi유역을 선정하였으며 대상 유역의 수문, 기상자료 및 GIS 정보수집은 공동연구기관인 인도네시아 공공사업부 수자원청(MPWH)과 주요 물 관리기관인 유역관리청(BBWS)의 협조를 통하여 진행하였다. 수집된 자료들은 관계형 데이터베이스 관리시스템인 MySQL을 사용하여 통합 물 재해 정보 데이터베이스를 구축하였으며 완성된 데이터베이스의 정보제공 및 공유시스템은 웹기반 인터페이스를 통해 관리되도록 설계하였다. 홍수유출 해석을 위한 분석 엔진은 K-water의 홍수분석 시스템인 FAS를 이용하였다. FAS의 홍수분석모형인 COSFIM과 수리모형인 Fldwav를 연계하는 데이터 분석 플랫폼을 완성하였으며 인도네시아 현지 조건에 부합하는 홍수분석 시스템으로 Customizing과정을 수행하였다. 또한 FAS의 PC기반 시뮬레이션 형식을 DB 연계형 웹서비스 방식으로 연동되도록 개량하였으며 추후 SaaS형 물 재해 분석시스템으로 전환할 수 있는 개발환경을 확보하였다. 개발된 물 재해 분석 플랫폼(WRMP)을 활용하여 인도네시아 공동연구기관과의 협의를 통해 물 재해 관리 시나리오를 수립하고 그 대안을 제시하였으며, 적용 시나리오별 홍수피해 저감 효과를 분석하였다. 또한 향후 방재시설물까지 연계하여 운영효과를 분석할 수 있도록 구조화하였다. 개발된 물 재해 관리 시스템은 개선된 정보처리 및 분석시스템을 활용하여 종합적인 물 재해정보를 제공하고, 사전에 홍수위험 지역을 분석하여 유관기관과 공유할 수 있는 물 재해 관리 의사결정 지원시스템으로써 유용하게 활용될 수 있을 것이다.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.447-452
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• 2020

Extratropical cyclone in winter season is very important in safe operation of ships because it is a major cause of marine accidents due to its strong power. In this study, we used meteorological data, to analyze extratropical cyclones occurring near the 1st Pacific polar front from December 2019 to February 2020. The analysis results are as follows. During those three months, we recorded 41 extratropical cyclones, 8 of which were remarkably developed. The central pressure of the strongest cyclone was 947hPa. The highest number of cyclones were generated in the East P acific Ocean around J apan (16), followed by the areas around Korea, the East China Sea, and the southern Sea of J apan. The cyclones followed five major tracks with a common northeast pattern. We thus concluded that the optimal route for a ship encountering an extratropical cyclone in the North P acific in winter would be south of the cyclone's center traveling eastbound and north of the center traveling westbound.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.463-473
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• 2021

Recently, United States Geological Survey (USGS) distributed Landsat 8 Collection 2 Level 2 Science Product (L2SP). This paper aims to derive land surface temperature from L2SP and to validate it. Validation is made by comparing the land surface temperature with the one calculated from Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) and the one from Automated Synoptic Observing System (ASOS). L2SP is calculated from Landsat 8 Collection 2 Level 1 data and it provides land surface temperature to users without processing surface reflectance data. Landsat 8 data from 2018 to 2020 is collected and ground sensor data from eight sites of ASOS are used to evaluate L2SP land surface temperature data. To compare ground sensor data with remotely sensed data, 3×3 grid area data near ASOS station is used. As a result of analysis with ASOS data, L2SP and L1TP land surface temperature shows Pearson correlation coefficient of 0.971 and 0.964, respectively. RMSE (Root Mean Square Error) of two results with ASOS data is 4.029℃, 5.247℃ respectively. This result suggests that L2SP data is more adequate to acquire land surface temperature than L1TP. If seasonal difference and geometric features such as slope are considered, the result would improve.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.35-44
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• 2022

The V_S30 map is used as a key variable for site amplification in the ShakeMap, which predicts ground motion at any site. However, no V_S30 map considering Korean geology and geomorphology has been developed yet. To develop a proxy-based V_S30 map, we used 1,101 V_S profiles obtained from a geophysical survey and collected proxy layers of geological and topographical information for the Korean Peninsula. Then, V_S30 prediction models were developed using linear regression analysis for each geological age considering the distribution of V_S30. As a result, models depending on geomorphology were suggested per each geologic group, including Quaternary, Fill, Ocean, Mesozoic group and Precambrian. Resolution of map is doubled from that of V_S30 map by U.S. Geological Survey (USGS). Standard deviation of residual in natural log of proxy-based V_S30 map is 0.233, whereas standard deviation of slope-based USGS V_S30 map is 0.387. Therefore, the proxy-based V_S30 map developed in this study is expected to have less uncertainty and to contribute to predicting more accurately the ground motion amplitude.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.311-326
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• 2022

The Compact Advanced Satellite 500-4 (CAS500-4) is under development to efficiently manage and monitor forests in Korea and is scheduled to launch in 2025. The National Institute of Forest Science is developing 36 types of forestry applications to utilize the CAS500-4 efficiently. The products derived using the remote sensing method require validation with ground reference data, and the quality monitoring results for the products must be continuously reported. Due to it being the first time developing the national forestry satellite, there is no official calibration and validation site for forestry products in Korea. Accordingly, the author designed a calibration and validation site for the forestry products following international standards. In addition, to install calibration and validation sites nationwide, the authors selected appropriate sensors and evaluated the applicability of the sensors. As a result, the difference between the ground observation data and the Sentinel-2 image was observed to be within ±5%, confirming that the sensor could be used for nationwide expansion.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.