• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.339-351
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• 2016

For the safety of sea, it is important to monitor sea fog, one of the dangerous meteorological phenomena which cause marine accidents. To detect and monitor sea fog, Moderate Resolution Imaging Spectroradiometer (MODIS) data which is capable to provide spatial distribution of sea fog has been used. The previous automatic sea fog detection algorithms were focused on detecting sea fog using Terra/MODIS only. The improved algorithm is based on the sea fog detection algorithm by Wu and Li (2014) and it is applicable to both Terra and Aqua MODIS data. We have focused on detecting spring season sea fog events in the Yellow Sea. The algorithm includes application of cloud mask product, the Normalized Difference Snow Index (NDSI), the STandard Deviation test using infrared channel ($STD_{IR}$) with various window size, Temperature Difference Index(TDI) in the algorithm (BTCT - SST) and Normalized Water Vapor Index (NWVI). Through the calculation of the Hanssen-Kuiper Skill Score (KSS) using sea fog manual detection result, we derived more suitable threshold for each index. The adjusted threshold is expected to bring higher accuracy of sea fog detection for spring season daytime sea fog detection using MODIS in the Yellow Sea.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.87-96
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• 2016

OBJECTIVES : A fog generated field test was conducted to analyze the relationship between different sizes of variable speed limit signs and the legibility distance under various fog density conditions. By using this study, appropriate sizes of signs can be selected depending on the density of fog. METHODS : An actual tunnel was selected as the area for this test, as other places cannot maintain the fog condition because of rapid air current. A total 121 subjects were recruited for this test, which took place over the course of four days. The test on the first day was conducted under normal weather conditions for comparison. Visibility-distance detecting sensor was used to measure the visibility distance due to the fog density time, simultaneously with the evaluation of legibility distance by subjects. RESULTS : The test results show the relationship between the different sizes of signs and the legibility distance corresponding to the visibility distance due to both non-fog and fog generated conditions. According to the technical test results, appreciable amount of reduction in legibility distance due to fog was resulted in all sizes of signs. Moreover, the legibility distance is reduced proportionately with the decrease in the visibility distance due to fog. CONCLUSIONS : The results of this study can be used to select appropriate sizes of valuable speed signs under fog conditions. Hence, drivers can expect to have more room to respond to adverse weather conditions, thereby reducing the risk of accidents.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.77-85
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• 2016

OBJECTIVES : A fog generated field test was conducted to analyze the relationship between the luminance of variable speed-limit signs and the legibility distance under various fog density conditions. By using this study, appropriate luminance values can be selected depending on the density of fog. METHODS : An actual tunnel was selected as the area to conduct the test, as other places cannot maintain the fog condition because of rapid air current. Ninety-two subjects were recruited for this test, which took place during the course of three days. Visibility-distance detecting sensor was used to measure the visibility distance due to the fog density time, simultaneously with the evaluation of legibility distance by subjects. RESULTS : The test results show the relationship between luminance values and the legibility distance corresponding to the visibility distance due to fog. According to the technical test results, lower luminance value such as $7000cd/m^2$ corresponds to less legibility distance compared to higher values such as $20000cd/m^2$ or $40000cd/m^2$. However, the amount of difference between $20000cd/m^2$ and $40000cd/m^2$ is negligible in our test. CONCLUSIONS : The results of this study can be used to select appropriate luminance of valuable speed signs under fog conditions. Hence, drivers can expect to have more room to respond to adverse weather conditions, thereby reducing the risk of accidents.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• 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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.643-648
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• 2013

In this paper, we developed a system which can detect and can alarm about the sailing provocative climate by using a photo. The research on domestic shipbuilding industry and in IT fusion technology is under construction, but a reliable safety device which can alarm a sailor about the circumstances of the fog and rain during ship operation as soon as possible due to the constant state in domestic. In this paper, a compact, for system low-power transceiver and data processing equipment for sensing were developed, also a performance evaluation got accomplished with simulation analysis. In results, it is operating normally at least 32.36[dB] and maximum values f 89.20[dB] in the domestic, and 32.55 to 60.66[dB] in the outdoors.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1081-1088
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• 2020

In this paper, the method of detecting sea fog through CCTV image is proposed based on convolutional neural networks. The study data randomly extracted 1,0004 images, sea-fog and not sea-fog, from a total of 11 ports or beaches (Busan Port, Busan New Port, Pyeongtaek Port, Incheon Port, Gunsan Port, Daesan Port, Mokpo Port, Yeosu Gwangyang Port, Ulsan Port, Pohang Port, and Haeundae Beach) based on 1km of visibility. 80% of the total 1,0004 datasets were extracted and used for learning the convolutional neural network model. The model has 16 convolutional layers and 3 fully connected layers, and a convolutional neural network that performs Softmax classification in the last fully connected layer is used. Model accuracy evaluation was performed using the remaining 20%, and the accuracy evaluation result showed a classification accuracy of about 96%.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.12
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• pp.164-175
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• 2008

Atmospheric effects on laser beam propagation can be broken down into two categories: attenuation of the laser power and fluctuation of laser power due to laser beam deformation. Attenuation consists of scattering of the laser light photons by the fog. Laser beam deformation occurs because of small-scale dynamic changes in the index of refraction of the atmosphere. This causes pointing error. In order to analyse these effect on optical wireless communication system, in this paper uses cassegrain optics as a transmitting and receiving telescope, AID as a detecting device and ill as a light source. The signal modulating and demodulating method is a IM/DD. I show the effects of fog and pointing error and calculate the possible communication distance for BER is $10^{-9}$.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.101-115
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• 2022

This study compared the performance of LiDAR in detecting objects in rough weather with that in clear weather. An experiment that reproduced rough weather divided the fog visibility into four stages from 200 m to 50 m and controlled the rainfall by dividing it into 20 mm/h and 50 mm/h. The number of points cloud and intensity were used as the performance indicators. The difference in performance was statistically investigated by a T-Test. The result of the study indicates that the performance of LiDAR decreased in the order in situations of 20 mm/h rainfall, fog visibility less than 200 m, 50 mm/h rainfall, fog visibility less than 150 m, fog visibility less than 100 m, and fog visibility less than 50 m. The decreased performance was greater when the measurement distance was greater and when the color was black rather than white. However, in the case of white, there was no difference in performance at a measurement distance of 10 m even at 50 m fog visibility, which is considered the worst situation in this experiment. This no difference in performance was also statistically significant. These performance verification results are expected to be utilized in the manufacture of road facilities in the future that improve the visibility of sensors.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.103-112
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• 2022

This study aims to obtain empirical evidence regarding the link between tax avoidance (TA) and the readability of financial statements. This is a quantitative research using Ordinary Least Squares regression analysis which is then processed using STATA 14.0. A total of 278 companies listed on the Indonesia Stock Exchange during the period 2017-2019 is the data of this study. In detecting TA in a company, this study uses the ETR and CashETR and for the measurement of financial statement readability, this study uses gunning fog index and length of the document. The findings of this study suggest that tax avoidance and clear financial statements are mutually exclusive in the sense that when tax avoidance is practiced, companies will tend to conceal the information conveyed by financial statements. In other words, it is concluded that the more a company engages in tax avoidance, the lower the readability of the company's financial statements. This study provides in-depth evidence that tax avoidance is indirectly related to the disclosure of information by the company. Users of financial statements will realize that the company seeks to make disclosures that are in their best interests to avoid their tax avoidance strategy being detected.