• Journal of Environmental Science International
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• v.24 no.5
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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.

• Journal of Bio-Environment Control
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• v.11 no.2
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• pp.93-100
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• 2002

This study was carried out not only to develop CFD model for numerically simulating fog cooling system but also to verify the validity of the developed model by data measured in fag cooling greenhouse. In addition the developed model was applied to investigate the effects of spraying water temperature, spraying water amount, spraying interval and evaporation percentage on the performance of the fog cooling system. According to the simulation results, the temperature differences between the measured and predicted temperatures at each measurement point were $0.1~1.4^{\circ}C$ in case of no shading and $0.2~2.3^{\circ}C$ in close of shading. The humidity differences were 0.3~6.0% and 0.7~10.6%, respectively in the cases of no shading and shading. Because the predicted data showed a good agreement with the measured ones, the developed model is supposed to be able to predict the cooling effect of the fog cooling system. The performance of fog cooling system was greatly influenced by spraying water amount, spraying interval and evaporation percentage, but it was not influenced by spraying water temperature.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.E2
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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.

• Journal of ILASS-Korea
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• v.27 no.4
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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.

• Atmosphere
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• v.23 no.4
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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.

• Proceedings of the KSRS Conference
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• 2007.10a
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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.

• Journal of the Korean Society of Safety
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• v.26 no.2
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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.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.60-71
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• 2019

As IoT systems such as smart farms and smart cities become popular, a large amount of data collected from many sensor nodes is sent to a server in the Internet, which causes network traffic explosion, delay in delivery, and increase of server's workload. To solve these problems, the concept of fog computing has been proposed to store data between IoT systems and servers. In this study, we implemented a software platform of the fog node and applied it to the prototype smart farm system in order to check whether the problems listed above can be solved when using the fog node. When the fog node is used, the time taken to control an IoT device is lower than the response time of the existing IoT device-server case. We confirmed that it can also solve the problem of the Internet traffic explosion and the workload increase in the server. We also showed that the intelligent control of IoT system is feasible by having the data visualization in the server and real time remote control, emergency notification in the fog node as well as data storage which is the basic capability of the fog node.

• Journal of Bio-Environment Control
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• v.20 no.1
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• pp.1-7
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• 2011

The objective of the present study is to identify the applicability of a low pressure fogging system for cooling commercial tomato greenhouse. In particular, the cooling system in this experiment utilizes low pressure spray nozzles which were developed in Korea recently. The experimental result that the temperature in fog-cooled greenhouse was lower than the non-cooled greenhouse showed the cooling effect by the low pressure fogging system. But because the relative humidity in fog-cooled greenhouse was comparatively low, the satisfactory cooling effect could be acquired by narrowing the space of fog nozzles and extending fogging time to supply more fog spray quantity. The variation of temperature distribution in fog-cooled greenhouse along timelag was insignificant during short time, but that was great during long period of day. This result showed the variation of temperature along timelag was slight by fog cooling but great by other factors like radiation, ventilation, air flow, etc. The advanced operation technology of fog system was required to reduce the variation of temperature along time lag. We plan to suggest the advanced installation and operation technology of low pressure fogging system for cooling commercial tomato greenhouse by further experiments in near future.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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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.