• Journal of the Korean Solar Energy Society
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• v.37 no.6
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• pp.25-37
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• 2017

A study on factors influencing measurement error of Ground-based LiDAR(Light Detection And Ranging) system was conducted in Kimnyeong wind turbine test site on Jeju Island. Three properties of wind including inclined angle, turbulence intensity and power law exponent were taken into account as factors influencing the measurement error of Ground-based LiDAR. In order to calculate LiDAR measurements error, 2.5-month wind speed data collected from LiDAR (WindCube v2) were compared with concurrent data from the anemometer on a nearby 120m-high meteorological mast. In addition, data filtering was performed and its filtering criteria was based on the findings at previous researches. As a result, at 100m above ground level, absolute LiDAR error rate with absolute inclined angle showed 4.58~13.40% and 0.77 of the coefficients of determination, $R^2$. That with turbulence intensity showed 3.58~23.94% and 0.93 of $R^2$ while that with power law exponent showed 4.71~9.53% and 0.41 of $R^2$. Therefore, it was confirmed that the LiDAR measurement error was highly affected by inclined angle and turbulence intensity, while that did not much depend on power law exponent.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.253-264
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• 2018

The GPS radiosonde is designed to conduct a full synoptic sounding to balloon burst using data generated from precision meteorological sensors and the GPS satellite network. The GPS radiosonde include proven, accurate temperature, humidity and capacitance aneroid pressure sensors. The atmospheric boundary layer was intensively observed in three islands of the west sea from 18 LST on March 9, 2016 to 06 LST on March 12, 2016. We investigated the restriction of nocturnal stable layer and rather the development of the mixed layer at night. On March 9, nocturnal mixed layer was developed by buoyancy heat flux. On the other hand, on March 10, the shear production was higher especially at 21 LST when the mixed layer height was the highest during the intensive observation period. The wind shear and the surface heat flux which produce the turbulent kinetic energy played an important role to grow the mixed layer even at night.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.1-9
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• 2021

Many factors such as wind direction, wind strength, temperature, and obstacles affect a munition's trajectory. Since these factors eventually determines the probability of hit and the hitting point of a target, these factors should be considered to obtain reliable weapon effectiveness data. In this study, we propose the extension of the MSDL(Military Scenario Definition Language) to reflect these factors to improve the reliability of weapon effectiveness data. Based on the existing MSDL, which has been used to set the initial condition of a military simulation scenarios, the newly identified subelements are added in ScenarioID, Environment, Organizations, and Installations as a scenario schema. Also, DamageAssessment and DesignOfExperiments element are added to make weapon effectiveness data easily. The extended MSDL enables to automatically generate the simulation scenarios that reflect various factors which affect the probability of hit or kill. This extended MSDL is applied to an integrated simulation software of weapon systems, named AddSIM version 4.0 for generation of weapon effectiveness data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1905-1912
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• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.6
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• pp.465-472
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• 2013

In this paper, we suggest how to determine the parachute deploy position for accurate landing of a UAV at a desired position. The 9-DOF dynamic modeling of UAV-parachute system is required to construct the proposed algorithm based on neural network nonlinear function approximation technique. The input and output data sets to train the neural network are obtained from simulation results using UAV-parachute 9-DOF model. The input data consist of the deploy position, UAV's velocity, and wind velocity. The output data consist of the cross range and down range of landing positions. So we predict the relative landing position from the current UAV position. The deploy position is then determined through distance compensations for the relative landing positions from the desired landing position. The deploy position is consistently calculated and updated.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.49-56
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• 2017

Among the techniques for analyzing big data, the association rule mining is a technique for searching for relationship between some items using various relevance evaluation criteria. This associative rule scheme is based on the direction of rule creation, and there are positive, negative, and inverse association rules. The purpose of this paper is to investigate the applicability of various types of relatively causal strength measures to the types of association rules from the point of view of interestingness measure. We also clarify the relationship between various types of confidence measures. As a result, if the rate of occurrence of the posterior item is more than 0.5, the first measure ($RCS_{IJ1}$) proposed by Good (1961) is more preferable to the first measure ($RCS_{LR1}$) proposed by Lewis (1986) because the variation of the value is larger than that of $RCS_{LR1}$, and if the ratio is less than 0.5, $RCS_{LR1}$ is more preferable to $RCS_{IJ1}$.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.84-84
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• 2011

저수지에서 수체의 거동정보는 저수지관리를 위해 매우 중요하며, 이를 수리 및 수질모형에 적용하여 다양한 예측에 활용할 수 있는 기본적인 정보이다. 그러나 저수지 수체의 흐름은 매우 느리고 조사시기에 따라 혹은 수심에 따라 영향을 받으며, 바람 등 외부요인에도 영향을 받게 됨으로 정확한 측정에 많은 어려운 문제가 있다. 따라서 보다 정확한 측정을 위해서는 최신의 기술이 적용되어야 한다. 현장조사에 적용한 저수지 수체거동조사장비(drifter)는 수체의 유동을 관측하는 장치로 GPS 및 Drogue를 장착한 장치가 저수지 수체의 흐름에 따라 이동하면서 위치정보를 무선통신으로 실시간 전송할 수 있도록 만들어진 장치이다. 수체흐름 측정장치는 크게 수체거동정보 생산부이 시스템과 수집서버 및 데이터베이스 서버로 구성되어 있다. 현장 조사시에는 부이에 장착되어 있는 GPS를 통해 현재의 위치가 매번 정해진 시간 간격마다 본체 내부의 메모리에 저장되며, 측정자가 설정한 시간간격으로 위치정보를 수신국으로 전송하도록 구성되어 있다. 수체이동부이의 데이터 수신은 CDMA를 통해 송신된 메일 데이터를 자체적으로 수집하여 데이터형태로 변환한 뒤 실시간으로 표출하고 저장되며, 최대 10개의 부이를 동시에 추적 및 모니터링할 수 있게 구성되어 있다. 본 연구는 용담댐저수지에서 평갈수기 및 풍수기로 구분하여 수체거동을 조사하였다. 평갈수기 조사는 2010년 4월부터 6월까지 총 3차례에 걸쳐 실시하였으며, 풍수기 조사는 8월에 3차례에 걸쳐 실시하였다. 수집된 자료에 의하면 평갈수기 drifter의 평균 속도는 2.25 cm/sec, 최고 속도는 3.46 cm/sec, 최저 속도는 1.15 cm/sec로 나타났으며, 풍수기 조사에서는 평균 속도 2.0 cm/sec, 최고 속도는 2.5 cm/sec, 최저 속도는 1.29 cm/sec로 조사되어 평갈수기와 풍수기간 저수지 수체의 이동속도는 유사한 것으로 평가되었다. 그러나 수기별 조사는 보다 더 많은 정보가 필요하며, 수심에 따른 차이도 있을 것으로 판단되어 좀더 다양한 조사가 수행되어야 보다 정확한 결과를 도출할수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.77-77
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• 2015

기상이변으로 인한 사회 경제적 피해의 증가로 기상정보에 대한 중요성이 커지면서 해외에서는 민간 기업이 기상 관측망을 구축하는 사례가 나타났다. 미국의 Earth Network은 전 세계에 1만개의 기상 관측센서를 설치하였고, 일본의 통신회사인 NTT DoCoMo는 일본에 4000여 개의 기상 및 환경관측 센서를 구축하였다. 국내에서는 SK플래닛이 자사의 플랫폼 기술과 SK텔레콤의 기지국 인프라를 활용하여 수도권 지역에 국지기상 관측망을 구축하였다. SK플래닛은 2013년 서울지역에 1km 간격으로 264개의 기상센서를 설치하고, 2014년 인천 경기지역에 3km 간격으로 825개의 기상센서를 추가 설치하여, 현재 1089개의 국지기상 관측망을 운용하고 있다. 관측에 사용한 센서는 우량계와 복합 기상센서로 강수량, 기온, 습도, 바람, 기압을 측정한다. 관측된 자료는 데이터로거에서 기상청의 자료처리 표준규격에 따라 처리한 후 M2M 모뎀을 통해 1분마다 서버로 전송한다. 전송된 자료는 기상정보 플랫폼의 수집 서버에서 프로토콜 변환 후 원본자료 DB에 저장하고, 실시간 품질관리를 마친 후 품질관리 자료 DB에 저장한다. 관측 지점의 기본정보 및 작업이력은 메타데이터 DB에 저장되고 관리자 페이지를 통해 조회 및 수정 된다. 관측 자료의 품질 보증은 제조사의 센서 Calibration부터 서비스 모니터링 까지 각 단계별로 체계적인 품질관리를 통해 이루어진다. 품질관리를 마친 국지기상 관측 데이터는 응용프로그램 개발자가 편리하게 사용할 수 있는 API(Application Programming Interface)형태로 제공된다. 2013년 여름부터 수집된 1~3km 해상도의 SK플래닛 국지기상 관측 자료를 통해 그 동안 정량적으로 확인하지 못한 국지성 호우 시의 강수량 편차에 대해 알 수 있었다. 2014년 7월 31일 양평지역에 내린 국지성 호우는 시간당 최대 90mm 이상의 비가 내린 사례로, 귀여리 관측소(SK 플래닛)에 시간당 93.1mm가 내리는 동안 퇴촌 관측소(기상청)에는 17.5mm의 비가 내려, 두 관측지점 간 거리가 3.4km 임에도 불구하고 시간당 75mm 이상의 강수량 차이를 보였다. 앞으로 SK플래닛의 국지기상 관측 자료가 국지성 호우의 조기 경보 및 예측 정확도 향상에 활용되어 재난으로부터 국민의 생명과 재산을 지키는데 많은 도움이 되기를 기대한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1049-1059
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• 2019

We propose a learning-based image segmentation algorithm. Starting from super-pixels, our method learns the probability of merging two regions based on the ground truth made by humans. The learned information is used in determining whether the two regions should be merged or not in a segmentation stage. Unlike exiting learning-based algorithms, we use both local and object information. The local information represents features computed from super-pixels and the object information represent high level information available only in the learning process. The object information is considered as privileged information, and we can use a framework that utilize the privileged information such as SVM+. In experiments on the Berkeley Segmentation Dataset and Benchmark (BSDS 500) and PASCAL Visual Object Classes Challenge (VOC 2012) data set, out model exhibited the best performance with a relatively small training data set and also showed competitive results with a sufficiently large training data set.