• Korean Journal of Remote Sensing
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• 제24권2호
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• pp.179-188
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• 2008

This study aims for generating a city spatial model required for the creation of a 3D noise map. In this study, we propose an efficient method to generate 3D models of the terrain and buildings using only a digital map and draft maps previously established without using any sensory data. The terrain model is generated by interpolating into a grid the elevation values derived from both the contour lines and the elevation point of the digital map. Building model is generated by combining the 2D building boundaries and the building elevations extracted from the digital map and the draft map, respectively. This method has been then applied to a digital map and three sets of draft maps created in the different times. covering the entire area of Yeongdeungpo-gu. The generated city spatial model has been successfully utilized for the noise analysis and the 3D visualization of the analysis results.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2015년도 학술발표회
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• pp.213-213
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• 2015

밭에서의 점적 관개를 이용한 노지 재배의 경우 적정 관개 계획 수립을 위해서는 작물 및 토양의 수분 정보에 대한 정확한 파악이 필요하다. 본 연구에서는 밭 토양을 GIS(Geographic Information System)를 통해 격자 형태로 분할하여 작물의 증발산량 및 토양의 수분함량을 모의할 수 있는 격자 기반 토양 물수지 모형을 개발하였다. 본 모형을 통해 작물의 소비수량 및 필요 수량을 파악함으로써 작부기간 중 필요한 관개수량을 제시하는 것이 가능하다. 고도화 기상자료로는 국가농림기상센터에서 운영 중인 고해상도 WRF(Weather Research and Forecasting) 모형에서 생산된 격자 형태의 복사, 온도, 바람, 강수 자료를 사용하였고 고도화 기상자료의 격자 해상도 별로 모의되는 작물 및 토양의 수분 정보 간 비교 및 분석을 실시하였다. 토양 물수지 모형에 입력되는 격자형태의 자료로는 기상, 토성 및 토지이용 자료가 있으며 기상자료의 경우 가로 및 세로의 크기가 각 270, 810, 2430m로 동일한 3가지 경우로 나누어 적용했으며 토성 및 토지이용 자료의 경우 기상 격자의 최소 크기에 맞춰 가로 및 세로의 크기가 각 270m인 격자로 분할하였다. 이와 같은 과정에 의한 모의 결과 각 격자별 작물 증발산량, 토양수분함량 및 관개수량의 일 연별 시계열 자료를 얻을 수 있으며 동시간대 격자별 수문인자 값을 산정하고 위치에 따른 공간적 상호 상관성을 분석하였다. 결과적으로, 고도화 기상자료의 격자 크기에 따른 밭 토양 물수지 분석 결과를 통해 고도화 기상 격자의 규모별 밭 토양 물수지 분석 효용성을 파악하고자 하였다. 더불어, 시험 지역(Test Bed) 선정을 통해 토양수분 및 증발산량을 실측하고 본 모형의 모의 결과와 비교함으로써 검정하는 것을 향후 연구 계획으로 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제19권5호
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• pp.93-99
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• 2019

In this paper, we propose a smart meter that uses GRU model, which is one of artificial neural networks, for the efficient energy management. We collected power consumption data that train GRU model through the proposed smart meter. The implemented smart meter has automatic power measurement and real-time observation function and load control function through power consumption prediction. We determined a reference value to control the load by using Root Mean Squared Error (RMS), which is one of performance evaluation indexes, with 20% margin. We confirmed that the smart meter with automatic load control increases the efficiency of energy management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.613-616
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• 2009

Information present condition is using situation board by manual processing that is consisted of ship arrangement plan and letterpress and magnet etc. in Pyeongtaekhang's deep-sea fishing vessel company. Study that mark open sea far from land ship information of underway 37 ships that is accepted in every time in internet web application environment that is based on Ubiquitous Network in PC that is linked to internet. 3 through practical use of RIA of Flash technology base compose Digital Dash-Board in width grid structure only and do ship sailing addition that is operating in 6 oceans and latitude, hardness indication as well as various informations to do visual display do. Emphasized in dynamic Web Application construction because can heighten the convenience to operator and user, and take advantage of real time data.

• Clinical and Experimental Reproductive Medicine
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• 제32권2호
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• pp.91-99
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• 2005

Objective: To define an appropriate vitrification condition of preantral follicle that yields high survival and to evaluate growth and ovulation rate of mouse follicles during in vitro culture after vitrification. Methods: Preantral follicles were isolated mechanically from mouse ovaries that were surgically recovered from mice aged 14 days. Retrieved preantral follicles were placed in EG (Ethylene Glycol) for 2, 5, 10 minutes and transferred to EFS-40 (40% EG, 18% Ficoll-70, 0.5 M sucrose) for 0.5, 1, 2 minutes. And then, preantral follicles were placed onto an EM grid and submerged immediately in liquid nitrogen. Thawing was carried out at room temperature. After defining the most appropriate vitrification condition that yields high survival, in vitro growth and ovulation rate of follicles were evaluated. Results: Appropriate vitrification condition that yield high survival rate ($83.2{\pm}2.1%$) of preantral follicle was EG for 5 minutes and EFS-40 for 0.5 minutes. In vitro survival rate of the vitrified preantral follicles were $85.5{\pm}0.5%$, $67.9{\pm}0.8%$ and $40.2{\pm}0.5%$ on day 2, 6 and 10. And in vitro growth of the vitrified preantral follicles were $107.1{\pm}16.1{\mu}m$, $117.1{\pm}18.4{\mu}m$, $178.4{\pm}45.6{\mu}m$ and $325.4{\pm}54.4{\mu}m$ on day 0, 2, 6 and 10. Although in vitro survival rate and growth of vitrified preantral follicles were lower than that of non-vitrified preantral follicles, the patterns of survival and growth were similar in vitrified and non-vitrified preantral follicles. The ovulation rate of antral follicles that was grown from vitrified preantral follicles was $32.6{\pm}1.2%$. Conclusion: Vitrified preantral follicles could be grown to antral sizes, and mature oocytes that can be used for IVF-ET programs were produced successfully. These data suggest that cryopreservation of preantral follicle by vitrification can be used for the preservation of the fertility.

• KIPS Transactions on Software and Data Engineering
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• 제10권7호
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• pp.263-270
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• 2021

Reinforcement learning(RL) is the method to find an optimal policy through training. and it is one of popular methods for solving lifesaving and disaster response problems effectively. However, the conventional reinforcement learning method for disaster response utilizes either simple environment such as. grid and graph or a self-developed environment that are hard to verify the practical effectiveness. In this paper, we propose the design of a disaster response RL environment which utilizes the detailed property information of the disaster simulation in order to utilize the reinforcement learning method in the real world. For the RL environment, we design and build the reinforcement learning communication as well as the interface between the RL agent and the disaster simulation. Also, we apply the dimension reduction method for converting non-image feature vectors into image format which is effectively utilized with convolution layer to utilize the high-dimensional and detailed property of the disaster simulation. To verify the effectiveness of our proposed method, we conducted empirical evaluations and it shows that our proposed method outperformed conventional methods in the building fire damage.

• Atmosphere
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• 제29권2호
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• pp.149-164
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• 2019

The purpose of this study is to introduce the improvement of current operational climate prediction system of KMA and to compare previous and improved that. Whereas the previous system is based on GloSea5GA3, the improved one is built on GloSea5GC2. GloSea5GC2 is a fully coupled global climate model with an atmosphere, ocean, sea-ice and land components through the coupler OASIS. This is comprised of component configurations Global Atmosphere 6.0 (GA6.0), Global Land 6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0 (GSI6.0). The compositions have improved sea-ice parameters over the previous model. The model resolution is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, the predictability of each system is evaluated using by RMSE, Correlation and MSSS, and the variables are 500 hPa geopotential height (h500), 850 hPa temperature (t850) and Sea surface temperature (SST). A predictive performance shows that GloSea5GC2 is better than GloSea5GA3. For example, the RMSE of h500 of 1-month forecast is decreased from 23.89 gpm to 22.21 gpm in East Asia. For Nino3.4 area of SST, the improvements to GloSeaGC2 result in a decrease in RMSE, which become apparent over time. It can be concluded that GloSea5GC2 has a great performance for seasonal prediction.

• Journal of Climate Change Research
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• 제6권2호
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• pp.61-72
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• 2015

In this study, we have investigated monthly changes in temperature extremes in South Korea for the past (1921~2010) and the future (2011~2100). We used seven stations' (Gangneung, Seoul, Incheon, Daegu, Jeonju, Busan, Mokpo) data from KMA (Korea Meteorological Administration) for the past. For the future we used the closest grid point values to observations from the RCP8.5 scenario of 1 km resolution. The Expert Team on Climate Change Detection and Indices (ETCCDI)'s climate extreme indices were employed to quantify the characteristics of temperature extremes change. Temperature extreme indices in summer have increased while those in winter have decreased in the past. The extreme indices are expected to change more rapidly in the future than in the past. The number of frost days (FD) is projected to decrease in the future, and the occurrence period will be shortened by two months at the end of the $21^{st}$ century (2071~2100) compared to the present (1981~2010). The number of hot days (HD) is projected to increase in the future, and the occurrence period is projected to lengthen by two months at the end of the $21^{st}$ century compared to the present. The annual highest temperature and its fluctuation is expected to increase. Accordingly, the heat damage is also expected to increase. The result of this study can be used as an information on damage prevention measures due to temperature extreme events.

• Journal of Climate Change Research
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• 제4권1호
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• pp.63-75
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• 2013

The purpose of this study is to analyze the green-house gas emission reduction of the pump scheduling system applied to the water-supply facilities in all objectivity with AMS-II.C/Version 13 in CDM methodology. To calculate the baseline and project emission in Paldang Pumping Station (III) the data about water flow, water level, electricity consumption, etc. before and after the implementation of project was used. This study considers internal facility (mostly for lighting) electricity consumption and grid loss in order to get more accurate emission reductions. The methodology used in this study will be able to apply to different energy improvement techniques to calculate emission reductions in water supply facilities.

• Atmosphere
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• 제29권3호
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• pp.295-309
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• 2019

Due to frequent occurrence of abnormal weather, the need to improve the accuracy of subseasonal prediction has increased. Here we analyze the performance of weekly predictions out to 6 weeks by GloSea5 climate model. The performance in circulation field from January 1991 to December 2010 is first analyzed at each grid point using the 500-hPa geopotential height. The anomaly correlation coefficient and mean-square skill score, calculated each week against the ECWMF ERA-Interim reanalysis data, illustrate better prediction skills regionally in the tropics and over the ocean and seasonally during winter. Secondly, we evaluate the predictability of 7 major teleconnection patterns in the Northern Hemisphere: North Atlantic Oscillation (NAO), East Atlantic (EA), East Atlantic/Western Russia (EAWR), Scandinavia (SCAND), Polar/Eurasia (PE), West Pacific (WP), Pacific-North American (PNA). Skillful predictability of the patterns turns out to be approximately 1~2 weeks. During summer, the EAWR and SCAND, which exhibit a wave pattern propagating over Eurasia, show a considerably lower skill than the other 5 patterns, while in winter, the WP and PNA, occurring in the Pacific region, maintain the skill up to 2 weeks. To account for the model's bias in reproducing the teleconnection patterns, we measure the similarity between the teleconnection patterns obtained in each lead time. In January, the model's teleconnection pattern remains similar until lead time 3, while a sharp decrease of similarity can be seen from lead time 2 in July.