• 한국환경과학회지
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• 제24권5호
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• pp.657-670
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• 2015

Visibility and Automatic Weather System(AWS) data near Nakdong river were analyzed to characterize fog formation during 2012-2013. The temperature was lower than its nearby city - Daegu, whereas the humidity was higher than the city. 157 fog events were observed in total during the 2 year period. About 65% of the events occurred in fall (September, October, and November) followed by winter, summer, and spring. 94 early morning fog events of longer than 30 minutes occurred when south westerly wind speed was lower than 2 m/s. During these events, the water temperature was highest followed by soil surface and air temperatures due to the advection of cold and humid air from nearby hill. The observed fog events were categorized using a fog-type classification algorithm, which used surface cooling, wind speed threshold, rate of change of air temperature and dew point temperature. As a result, frontal fog observed 6 times, radiation 4, advection 13, and evaporation 66. The evaporation fog in the study area lasted longer than other reports. It is due to the interactions of cold air drainage flow and warm surface in addition to the evaporation from the water surface. In particular, more than 60% of the evaporation fog events were accompanied with cold air flows over the wet and warm surface. Therefore, it is needed for the identification of the inland fog mechanism to evaluate the impacts of nearby topography and land cover as well as water body.

• 생물환경조절학회지
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• 제11권2호
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• pp.93-100
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• 2002

본 연구에서는 포그냉방시스템을 수치적으로 시뮬레이션하기 위한 CFD 모델을 개발하였으며, 포그냉방온실에서 측정된 데이터에 의해 개발된 모델의 유효성을 검증하였다. 또한 분무수온, 분무수량, 분무정지시간과 분무입자의 증발률이 포그냉방시스템의 성능에 미치는 영향을 알아보기 위해 개발된 모델을 적용하였다. 시뮬레이션 결과에 의하면, 각 측점에서 실측치와 예측치의 온도차가 무차광조건에서는 $0.1~1.4^{\circ}C$, 차광조건에서는 $0.2~2.3^{\circ}C$였으며, 상대습도차는 무차광조건에서는 0.3~6.0%, 차광조건에서는 0.7~10.6%였다. 예측치가 실측치와 비교적 잘 일치하는 것으로 나타나 개발된 모델이 포그냉방시스템의 냉방효과를 예측할수 있는 것으로 판단된다. 포그냉방시스템 성능은 분무수량, 분무정지시간과 분무입자의 증발률의 영향을 많이 받지만 분무수온의 영향은 받지 않는 것으로 나타났다.

• Journal of Korean Society for Atmospheric Environment
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• 제24권E2호
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• pp.92-101
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• 2008

The objective of this study is to evaluate and improve the capability of the Weather Research and Forecasting (WRF) model in simulating fog and visibility in local airports over Korea. The WRF model system is statistically evaluated for the 48-fog cases over Korea from 2003 to 2006. Based on the 4-yr evaluations, attempts are made to improve the simulation skill of fog and visibility over Korea by revising the statistical coefficients in the visibility algorithms of the WRF model. A comparison of four existing visibility algorithms in the WRF model shows that uncertainties in the visibility algorithms include additional degree of freedom in accuracy of numerical fog forecasts over Korea. A revised statistical algorithm using a linear-regression between the observed visibility and simulated hydrometeors and humidity near the surface exhibits overall improvement in the visibility forecasts.

• 한국분무공학회지
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• 제27권4호
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• pp.173-180
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• 2022

Cooling fog is being used in various parts of society such as fine dust reduction, cleanliness, and temperature drop. Cooling fog has the advantage of low flow rate and ease of use compared to other spray systems. In the case of cooling fog, it was confirmed that the injection angle increased as the pressure increased and the nozzle diameter increased. In this study, the minimum injection angle was 33.61 degrees and the maximum injection angle was 107.38 degrees. It was confirmed that the larger the nozzle diameter and the smaller the pressure, the larger the droplet size. In addition, it was confirmed that the Sauter Mean Diameter (SMD) increased along the X and Y axis directions. It was confirmed that the size of the droplet decreases as it approaches the nozzle tip due to the characteristics of the nozzle design factor.

• 대기
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• 제23권4호
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• pp.471-483
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• 2013

Nighttime sea fog detection from satellite is very hard due to limitation in using visible channels. Currently, most widely used method for the detection is the Dual Channel Difference (DCD) method based on Brightness Temperature Difference between 3.7 and 11 ${\mu}m$ channel (BTD). However, this method have difficulty in distinguishing between fog and low cloud, and sometimes misjudges middle/high cloud as well as clear scene as fog. Using CALIPSO Lidar Profile measurements, we have analyzed the intrinsic problems in detecting nighttime sea fog from various satellite remote sensing algorithms and suggested the direction for the improvement of the algorithm. From the comparison with CALIPSO measurements for May-July in 2011, the DCD method excessively overestimates foggy pixels (2542 pixels). Among them, only 524 pixel are real foggy pixels, but 331 pixels and 1687 pixels are clear and other type of clouds, respectively. The 514 of real foggy pixels accounts for 70% of 749 foggy pixels identified by CALIPSO. Our proposed new algorithm detects foggy pixels by comparing the difference between cloud top temperature and underneath sea surface temperature from assimilated data along with the DCD method. We have used two types of cloud top temperature, which obtained from 11 ${\mu}m$ brightness temperature (B_S1) and operational COMS algorithm (B_S2). The detected foggy 1794 pixels from B_S1 and 1490 pixel from B_S2 are significantly reduced the overestimation detected by the DCD method. However, 477 and 446 pixels have been found to be real foggy pixels, 329 and 264 pixels be clear, and 989 and 780 pixels be other type of clouds, detected by B_S1 and B_S2 respectively. The analysis of the operational COMS fog detection algorithm reveals that the cloud screening process was strictly enforced, which resulted in underestimation of foggy pixel. The 538 of total detected foggy pixels obtain only 187 of real foggy pixels, but 61 of clear pixels and 290 of other type clouds. Our analysis suggests that there is no winner for nighttime sea fog detection algorithms, but loser because real foggy pixels are less than 30% among the foggy pixels declared by all algorithms. This overwhelming evidence reveals that current nighttime sea fog algorithms have provided a lot of misjudged information, which are mostly originated from difficulty in distinguishing between clear and cloudy scene as well as fog and other type clouds. Therefore, in-depth researches are urgently required to reduce the enormous error in nighttime sea fog detection from satellite.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.294-297
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• 2007

Steam and advection fogs are frequently observed in the Yellow Sea located between Korea and China during the periods of March-April and June-July respectively. This study uses the remote sensing (RS) data for monitoring sea fog. Meteorological data obtained from the Ieodo Ocean Research Station provided an informative synopsis for the occurrence of steam and advection fogs through a ground truth. The RS data used in this study was GOES-9, MTSAT-1R images and QuikSCAT wind data. A dual channel difference (DCD) approach using IR and near-IR channel of GOES-9 and MTSAT-1R satellites was applied to estimate the extension of the sea fog. For the days examined, it was found that not only the DCD but also the texture-related measurement and the weak wind condition are required to separate the sea fog from the low cloud. The QuikSCAT wind is used to provide a weak wind area less than threshold under stable condition of the surface wind around a fog event. The Laplacian computation for a measurement of the homogeneity was designed. A new combined method of DCD, QuikSCAT wind speed and Laplacian was applied in the twelve cases with GOES-9 and MTSAT-1R. The threshold values for DCD, QuikSCAT wind speed and Laplacian are -2.0 K, 8 m $s^{-1}$ and 0.1, respectively. The validation methods such as Heidke skill score, probability of detection, probability of false detection, true skill score and odds ratio show that the new combined method improves the detection of sea fog rather than DCD method.

• 한국안전학회지
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• 제26권2호
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• pp.99-106
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• 2011

There is a high potentiality of large traffic accident due to the dense fog when road is developed along the coast or river. The establishment of national level control system against the fog is necessary because the accident due to the creation of fog has a high fatality ratio than other weather conditions. The selection method for the frequent foggy area on highway was suggested to control the fog on the highway effectively because the establishment of the countermeasure against the fog in every range in highway is difficult practically. 44 ranges where the fog control is necessary throughout the year and the 45 ranges where the control is necessary in specific months were selected from the result of application of the weighted value on each visible distance data except the fog with beyond 250 m visible distance which does not affect on the safe driving out of the surveyedjsh fog visible distances. The preferential fog control countermeasure shall be provided to prevent the traffic accident and to reduce the severeness of the accident in case of fog creation for 89 ranges which were selected for frequent foggy area in highway.

• 한국차세대컴퓨팅학회논문지
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• 제15권6호
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• pp.60-71
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• 2019

스마트팜이나 스마트시티와 같은 IoT 시스템이 보편화되면, 많은 센서 노드들로부터 수집된 대량의 데이터가 인터넷 내 서버로 전송되기 때문에 네트워크 트래픽 폭증, 전달 지연, 서버 부하증가 문제가 발생한다. 이러한 문제를 완화하기 위해 IoT 시스템과 서버와 사이에 데이터를 저장하는 포그 컴퓨팅 개념이 제안된 바 있다. 본 연구에서는 포그 노드의 소프트웨어 플랫폼을 구현하여 스마트팜(smart farm) 시험 구현물에 적용해 봄으로써, 포그 노드를 사용하는 경우 위에서 나열된 문제를 해결할 수 있음을 확인하였다. 포그 노드 플랫폼을 이용했을 때 IoT 장치를 제어하는데 걸리는 시간이 기존 IoT-서버 방식보다 더 낮아지는 것을 확인하였으며, 인터넷 내부 트래픽 폭증, 부하 증가 문제를 해결할 수 있음을 확인하였다. 또한 포그 노드의 기본 기능인 IoT 데이터 저장뿐만 아니라, 실시간 원격제어, 긴급 알림, 데이터 시각화의 기능을 본 논문의 포그 노드에 구현해 봄으로써 보다 지능적인 IoT 제어가 가능함을 보였다.

• 생물환경조절학회지
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• 제20권1호
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• pp.1-7
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• 2011

토마토 재배용 상엽용 온실의 여름철 냉방에 최근 국내에서 개발된 저압분무식 포그냉방시스템을 사용하기 위한 시스템 설치 및 관리기술을 규명하기 위하여 저압 포그시스템을 온실에 설치하여 실험을 통하여 그 적용 가능성을 분석하였으며 결과를 요약하면 다음과 같다. 포그를 분사한 온실이 분사하지 않은 온실보다 온도가 더 낮은 값을 보여 포그분사에 의한 냉방효과가 있음을 확인할 수 있었다. 그러나 전체적으로 상대습도가 비교적 낮게 나타나 냉각효과를 충분히 얻지 못한 것으로 판단되며, 포그노즐의 설치간격을 더 줄이거나 포그분사 시간을 더 늘리는 등의 조정을 통해서 충분한 냉방효과를 얻을 수 있을 것으로 판단되었다. 온실 전체의 시간에 따른 온도분포는 짧은 시간 동안에는 온도분포의 큰 변화는 없었으나 하루의 긴 시간 동안에는 온도분포의 변화가 다소 크게 나타났다. 이는 포그분사에 따른 온도편차의 발생은 크지 않으나 일사, 공기유동 등 다른 환경요인들에 의해 발생된 편차가 큰 것으로 판단된다. 특히 본 자료에는 나타내지 않았지만 온실 하부에서의 수평방향의 온도편차는 더 크게 나타나는 경향이 있었기 때문에 온도편차를 줄일 수 있는 포그시스템 관리방안에 대한 연구가 필요할 것으로 판단되었다. 앞으로 추가 실험을 통해 자연환기 토마토재배온실의 냉방을 위한 저압포그시스템 설치 및 관리기술을 제시하고자 한다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.