• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.11-18
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• 2016

The local wind systems in the Pohang region were categorized into wind sectors. Still, thorough knowledge of wind resource assessment, wind environment analysis, and atmospheric environmental impact assessment was required since the region has outstanding wind resources, it is located on the path of typhoon, and it has large-scale atmospheric pollution sources. To overcome the resolution limitation of meteorological dataset and problems of categorization criteria of the preceding studies, the high-resolution wind resource map of the Korea Institute of Energy Research was used as time-series meteorological data; the 2-step method of determining the clustering coefficient through hierarchical clustering analysis and subsequently categorizing the wind sectors through non-hierarchical K-means clustering analysis was adopted. The similarity of normalized time-series wind vector was proposed as the Euclidean distance. The meteor-statistical characteristics of the mean vector wind distribution and meteorological variables of each wind sector were compared. The comparison confirmed significant differences among wind sectors according to the terrain elevation, mean wind speed, Weibull shape parameter, etc.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.190.2-190.2
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• 2010

LIDAR는 레이저를 대기에 송출하여 미세먼지의 이동에 의한 도플러 위상차를 검출함으로써 3차원 풍속벡터를 측정하는 원격탐사 장비로, 한국에너지기술연구원은 국내최초로 WindCube LIDAR를 도입하여 운영 중에 있다. LIDAR의 장점은 이동성, 설치의 편리성 외에도 현재까지 풍황탑이 모든 범위를 측정하지 못한 풍력발전기 블레이드 회전면 최고 높이인 지면 150m 까지의 풍속분포를 상세하게 측정할 수 있다는 특장점이 있다. WindCube LIDAR는 총 10개의 측정 고도를 설정할 수 있으며 1Hz로 원시자료를 획득하여 10분 평균자료로 저장한다. 이러한 측정자료를 통하여 기존 기상탑에서 불가능하였던 풍속분포의 정확한 이해와 난류특성의 파악이 가능하게 되었으나 반대급부로 급증한 측정자료의 정리와 분석에 많은 시간과 노력이 필요하게 되었다. 이에 한국에너지기술연구원에서는 LIDAR 측정자료의 가공 및 분석에 편리성을 제공하기 위해 KIER-$LidarWind^{TM}$ 프로그램을 개발하였으며, 2차원 등치선도 및 3차원 풍속분포 그래프를 시각함으로써 입체적인 가공 및 분석이 가능하도록 하였다.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.755-767
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• 2014

In this study, detailed air flow characteristics in an urban areas were analyzed using GIS data and a Computational Fluid Dynamics (CFD) model. For this, a building construction algorithm optimized for Geographic Information System (GIS) data with a vector format (Los Angeles region imagery acquisition consortium 2 geographic information system, LARIAC2 GIS) was used. In the LARIAC2 GIS data, building vertices were expressed as latitude and longitude. Using the model buildings constructed by the algorithm as the surface boundary data in the CFD model, we performed numerical simulations for two building-congested areas in Los Angeles using inflow information provided by California Air Resources Board. Comparing with the inflow, there was a marked difference in wind speed and direction within the target areas, which was mainly caused by the secondarily induced local circulations such as street-canyon vortices, horse-shoe vortices, and recirculation zones. In street canyons parallel to the inflow direction, wind speed increased due to a channeling effect and, in street canyons perpendicular to the inflow direction, vertically well developed vortices were induced. In front of a building, a horse-shoe vortex was developed near the surface and, behind a building, a recirculation zone was developed. Near the surface in the areas where the secondarily induced local circulations, wind speed remarkably increased. Overall, wind direction little (largely) changed at the areas where wind speed largely increased (decreased).

• Journal of Power System Engineering
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• v.4 no.4
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• pp.25-31
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• 2000

This paper represents the vector fields and three dimensional mean velocities in the X-Y plane of cone type swirl gas burner measured by using X-probe from the hot-wire anemometer system. This experiment is carried out at flowrate 350 and $450{\ell}/min$ respectively in the test section of subsonic wind tunnel. The vector plot shows that the maximum axial mean velocity component is focused in the narrow slits distributed radially on the edge of a cone type swirl burner, for that reason, there is some entrainment of ambient air in the outer region of the burner and the rotational flow can be shown in the inner region of the burner because mean velocity W is distributed about twice as large as mean velocity V due to inclined flow velocity ejecting from the swirl vanes of a cone type baffle plate of burner. Moreover, the mean velocities are largely distributed near the outer region of burner within $X/R{\fallingdotseq}1.5$, hence, the turbulent characteristics are anticipated to be distributed largely in the center of this region due to the large inclination of mean velocity and swirl effect.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.86-92
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• 2017

In this study, a model to classify frost occurrence and frost free day was developed using the digital weather forecast data provided by Korea Meteorological Administration (KMA). The minimum temperature, average wind speed, relative humidity, and dew point temperature were identified as the meteorological variables useful for classification frost occurrence and frost-free days. It was found that frost-occurrence date tended to have relatively low values of the minimum temperature, dew point temperature, and average wind speed. On the other hand, relatively humidity on frost-free days was higher than on frost-occurrence dates. Models based on machine learning methods including Artificial Neural Network (ANN), Random Forest(RF), Support Vector Machine(SVM) with those meteorological factors had >70% of accuracy. This results suggested that these models would be useful to predict the occurrence of frost using a digital weather forecast data.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Journal of Power System Engineering
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• v.6 no.4
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• pp.23-29
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• 2002

This paper is studied to investigate the effect of slits and swirl vanes on the main flow fields of a gun-type gas burner through X-Y plane and Y-Z plane respectively by using X-probe from hot-wire anemometer system. This experiment was carried out with flow rate $450{\ell}/min$ in respective burner models installed in the test section of a subsonic wind tunnel. The burner models with only slits and only swirl vanes respectively were made by modifying original gun-type gas burner. The fast jet flow spurted from slits played a role such as an air-curtain because it encircled rotational flow by swirl vanes and drives mixed main flow to axial direction. As a result, the gun-type gas burner had a wider flow range up to about Y/R=1.5 deviated from slits and maintains a comparatively large velocity in the central part of burner within the range of about X/R=2.5. Therefore, it was very desirable that swirl vanes were installed within slits in gun-type gas burner in order to control the main flow fields effectively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Journal of Conservation Science
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• v.37 no.3
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• pp.209-219
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• 2021

In this study, the wind environment in an urban area near Sungneymun gate was numerically investigated in the cases of inflow directions. The wind fields for the target area were simulated using Geographic Information System data and Computational Fluid Dynamics model. Results, including vector fields, three-dimensional wind velocity components, and wind speeds, were analyzed to examine flow characteristics. Wind direction variability affected by buildings was shown in the target area. The complex flows around Sungneymun did not depend on the inflow direction as a boundary condition. The wind speed around Sungneymun was generally 3 times stronger at 14 m above ground level (AGL) compared to the surface wind at 2 m AGL and relatively high in the case of easterly inflow. The effect of wind was also analyzed to be relatively significant at the southeast side of Sungneymun. Thus, it was suggested that the assessment of wind environment affected by high-rise and high-density buildings should be necessary for the architectural heritage in urban areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.