• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.219-223
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• 2006

Variable Message Sign(VMS) is one of the most popular travel information system. In this study, the evaluation categories, each category's datum value and evaluation procedure for enhancing the visibility of VMS are studied and the physical evaluation system is developed. The evaluation categories have been developed based on the analysis of previous guidelines used in Korea, Europe and the United States. The evaluation categories include luminance, luminance ratio, beam width, color, and uniformity. The physical evaluation system consists of goniometer, solar simulator, luminance meter, sensor and rack system. As a result of this study, the quantitative and effective evaluation of VMS visibility would become possible.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.117-136
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• 2019

In this study, a hybrid-type of day fog detection algorithm (DFDA) was developed based on the optical and textural characteristics of fog top, using the Himawari-8 /Advanced Himawari Imager data. Supplementary data, such as temperatures of numerical weather prediction model and sea surface temperatures of operational sea surface temperature and sea ice analysis, were used for fog detection. And 10 minutes data from visibility meter from the Korea Meteorological Administration were used for a quantitative verification of the fog detection results. Normalized albedo of fog top was utilized to distinguish between fog and other objects such as clouds, land, and oceans. The normalized local standard deviation of the fog surface and temperature difference between fog top and air temperature were also assessed to separate the fog from low cloud. Initial threshold values (ITVs) for the fog detection elements were selected using hat-shaped threshold values through frequency distribution analysis of fog cases.And the ITVs were optimized through the iteration method in terms of maximization of POD and minimization of FAR. The visual inspection and a quantitative verification using a visibility meter showed that the DFDA successfully detected a wide range of fog. The quantitative verification in both training and verification cases, the average POD (FAR) was 0.75 (0.41) and 0.74 (0.46), respectively. However, sophistication of the threshold values of the detection elements, as well as utilization of other channel data are necessary as the fog detection levels vary for different fog cases(POD: 0.65-0.87, FAR: 0.30-0.53).

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.548-551
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• 2007

Among components of digital topographic maps used officially in Korea, only contours have 3D values except buildings and trees that are demanded in landscape planning. This study presented a series of processes for 3Dreconstructing landscape features such as terrain, buildings and standing trees using LiDAR (Light Detection And Ranging) data and aerial digital photo graphs. The 3D reconstructing processes contain 1) building terrain model, 2) delineating outline of landscape features, 3) extracting height values, and 4) shaping and coloring landscape features using aerial photograph and 3-D virtual data base. LiDAR data and aerial photograph was taken in November 2006 for $50km^{2}$ area in Sorak National Park located in eastern part of Korea. The average scanning density of LiDAR pulse was 1.32 points per square meter, and the aerial photograph with RGB bands has $0.35m{\times}0.35m$ spatial resolution. Using reconstructed 3D landscape features, visibility with the growing trees with time and at different viewpoints was analyzed. Visible area from viewpoint could be effectively estimated considering 3D information of landscape features. This process could be applied for landscape planning like building scale with the consideration of surrounding landscape features.

• Journal of Environmental Science International
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• v.22 no.6
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• pp.745-759
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• 2013

The characteristics of meteorological conditions and air pollution were investigated in a valley city (Yangsan) on bad visibility days (from 05:00 to 09:00 LST) of the cold half year (November 2008 to April 2009). This analysis was performed using the hourly observed data of meteorological variables (temperature, wind speed and direction, relative humidity, and 2 m and 10 m temperature) and air pollutants ($NO_2$, $SO_2$, $PM_{10}$, and $O_3$). In addition, visibility data based on visual measurements and a visibility meter were used. The bad visibility days were classified into four types: fog, mist, haze, and the mixture (mist+haze). The results showed that the bad visibility days of the four types in the valley city were observed to be more frequently (about 50% of the total study period (99 days except for missing data)) than (27%) those near coastal metropolitan city (Busan). The misty days (39%) in the valley city were the most dominant followed by the hazy (37%), mixture (14%), and foggy days (10%). The visibility degradation on the misty days in Yangsan was closely related to the combined effect of high-level relative humidity due to the accumulation of water vapor from various sources (e.g. river, stream, and vegetation) and strong inversion due to the development of surface radiative cooling within the valley. On the hazy days, the visibility was mainly reduced by the increase in air pollutant (except for $O_3$) concentrations from the dense emission sources under local conditions of weaker winds from the day before and stronger inversion than the misty days. The concentrations of $NO_2$, $PM_{10}$, and $SO_2$ (up to +36 ppb, $+25{\mu}g/m^3$, and +7 ppb) on the hazy days were a factor of 1.4-2.3 higher than those (+25 ppb, $+14{\mu}g/m^3$, and +3 ppb) on the misty days.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.105-118
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• 2023

In this study, we predicted the presence of fog with a one-hour delay using the XGBoost DART machine learning algorithm for Andong, which had the highest occurrence of fog among inland stations from 2016 to 2020. We used six datasets: meteorological data, agricultural observation data, additional derived data, and their expanded data. The weather phenomenon numbers obtained through naked-eye observations and the visibility distances measured by visibility meters were classified as fog [1] or no-fog [0]. We set up twelve machine learning modeling experiments and used data from 2021 for model validation. We mainly evaluated model performance using recall and AUC-ROC, considering the harmful effects of fog on society and local communities. The combination of oversampled meteorological data features and the target induced by weather phenomenon numbers showed the best performance. This result highlights the importance of naked-eye observations in predicting fog using machine learning algorithms.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1996.09a
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• pp.117-124
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• 1996

When navigating in or near an area of restricted visibility it is necessary to be heard the whistle bell and/or the siren of lighthouses or ships at times. Even though we can get the brief informations about the property of sound the direction and range of a sound radiator it is not easy to get the accurate informations for decision making. generally the audio frequency is known as 16-20,000Hz but the earshot is shorten and discrimination of sound is more difficult when there is some noise. The sound pressure is 60dB at the moment when human speaks 1 meter away. Usually the noise pressure in a silent room is 40dB and 60dB on the quiet street. In this study we suggest the basic algorithm to trace the direction and range of the source radiator using the signal received through not a physical sense but the microphone sensors and a series of signal of signal processing.

• Journal of the Korean Institute of Navigation
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• v.20 no.4
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• pp.71-79
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• 1996

When navigating in or near an area of restricted visibility, it is necessary to be heard the whistle, bell and/or the siren of lighthouses or ships at times. Even though we can get the brief informations about the property of sound, the direction and range of a sound radiator, it is not enough to get the accurate informations for decision making. Generally the audio frequency is known as 16~20, 000Hz, but the earshot is shorten and discrimination of sound is more difficult when there is some noise. The sound pressure is 60dB at the moment when human speaks 1 meter away. Usually the noise pressures are 40dB in a silent room and 60dB on the quiet street, respectively. It this study, the basic algorithm and a method of signal processing are suggested to trace the direction and range of the source radiator using the signals received through not a physical sense but the microphone sensors.

• Journal of radiological science and technology
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• v.28 no.1
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• pp.13-17
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• 2005

In domestic nondestructive testing(NDT) field, there have recently been radiation exposure accidents due to a disregard for confirmation of the position of radioisotope during the test. In order to prevent these kinds of accidents, a scintillating film has been developed. The scintillating film that can convert gamma-ray to visible light has a function of the position detection of radioisotope in a opaque guide tube of an NDT apparatus. The aim of this study is to enhance the visibility performance of the scintillating film and find out the best configuration of the scintillating film. In order to find appropriate materials for the scintillating film, various inorganic scintillating materials were evaluated in this work. An absolute luminance of the scintillating films was measured by luminance meter for evaluation of visibility performance. Ir-192 gamma projector was used for NDT apparatus. The experiment shows that the scintillating film with reflective layer was the more effective performance for visibility. The higher mixing ratio of scintillating material to binding material, the higher luminance was measured. $Gd_2O_2S(Tb)$ inorganic powder as the scintillating materials had the best performance for visibility of the scintillating film. The developed scintillating film helps to ensure safer environment to the operators.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.188-196
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• 2023

Among marine accidents caused by bad weather, visibility restrictions caused by sea fog occurrence cause accidents such as ship strand and ship bottom damage, and at the same time involve casualties caused by accidents, which continue to occur every year. In addition, low visibility at sea is emerging as a social problem such as causing considerable inconvenience to islanders in using transportation as passenger ships are collectively delayed and controlled even if there are local differences between regions. Moreover, such measures are becoming more problematic as they cannot objectively quantify them due to regional deviations or different criteria for judging observations from person to person. Currently, the VTS of each port controls the operation of the ship if the visibility distance is less than 1km, and in this case, there is a limit to the evaluation of objective data collection to the extent that the visibility of sea fog depends on the visibility meter or visual observation. The government is building a marine weather signal sign and sea fog observation networks for sea fog detection and prediction as part of solving these obstacles to marine traffic safety, but the system for observing locally occurring sea fog is in a very insufficient practical situation. Accordingly, this paper examines domestic and foreign policy trends to solve social problems caused by low visibility at sea and provides basic data on the need for government support to ensure maritime traffic safety due to sea fog by factually investigating and analyzing social problems. Also, this aims to establish a more stable maritime traffic operation system by blocking marine safety risks that may ultimately arise from sea fog in advance.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.359-375
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• 2017

We developed fog detection algorithm (KNU_FDA) based on the optical and textural properties of fog using satellite (COMS) and ground observation data. The optical properties are dual channel difference (DCD: BT3.7 - BT11) and albedo, and the textural properties are normalized local standard deviation of IR1 and visible channels. Temperature difference between air temperature and BT11 is applied to discriminate the fog from other clouds. Fog detection is performed according to the solar zenith angle of pixel because of the different availability of satellite data: day, night and dawn/dusk. Post-processing is also performed to increase the probability of detection (POD), in particular, at the edge of main fog area. The fog probability is calculated by the weighted sum of threshold tests. The initial threshold and weighting values are optimized using sensitivity tests for the varying threshold values using receiver operating characteristic analysis. The validation results with ground visibility data for the validation cases showed that the performance of KNU_FDA show relatively consistent detection skills but it clearly depends on the fog types and time of day. The average POD and FAR (False Alarm Ratio) for the training and validation cases are ranged from 0.76 to 0.90 and from 0.41 to 0.63, respectively. In general, the performance is relatively good for the fog without high cloud and strong fog but that is significantly decreased for the weak fog. In order to improve the detection skills and stability, optimization of threshold and weighting values are needed through the various training cases.