• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.49-56
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• 2023

Impact based forecast refers to providing information on potential socioeconomic risks according to weather conditions, away from the existing weather factor-oriented forecast. Developed weather countries are investing manpower and finances in technology development to provide and spread impact information, but awareness of impact based forecasts has not spread in Korea. In addition, the focus is on disasters such as floods and typhoons, which cause a lot of damage to impact based forecasts, and research on evaluating the impact of vehicle risks due to strong winds in the transportation sector with relatively low damage is insufficient. In Korea, there are not many cases of damage to vehicle conduction caused by strong winds, but there are cases of damage and the need for research is increasing. Road vulnerability is required to evaluate the risk of vehicles caused by strong winds, and the purpose of this study was to calculate the criteria for road vulnerability. The road vulnerability evaluation was evaluated by the altitude of the road, the number of lanes, the type of road. As a result of the analysis, it was found that the vulnerable area was well reproduced. It is judged that the results of this study can be used as a criterion for preparing an objective evaluation of potential risks for vehicle drivers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.162-177
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• 2020

The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

• Journal of architectural history
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• v.20 no.6
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• pp.169-184
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• 2011

The following research of the road network of Jeju-Eupseong during Daehan Empire period has a twofold purpose: to study some characteristics of the road network at that time; and, to restore it to the original form of that period before a newly constructed road, called Shinjakro, has been established. As an attempt to trace the old shape of Jeju-Eupseong, this study analyzed some historical factors based on the first land cadastral map which was made in 1914, including outskirts of Jeju-Eupseong; such as castle itself, castle gate, road, bridge, lots of land, etc. Then this study also tried to restore the old road network of Jeju-Eupseong, through finding the original land-lot shape in the land cadastral map. There was five Shinjakro made between 1914 and 1917. The road network before then was composed of the double east-west axes and the single north-south axis. These axes was connected to some important place of the inside of Jeju-Eupseong; such as castle gates, fountains, Gaek-sa, etc. There were many branch lines between these main axes at about 80-120m intervals. Also there was an outer road along the outer wall of castle, connected with each castle gates. Especially, the north-west axis was the baseline which divided into two large parts, a government office area and non-government area (housing and commercial street for the people). Finally, this paper examines that the road network of Jeju-Eupseong was the true result for the efficient function of the city, especially considering natural geographical conditions and environment of living of that time.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.47-55
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• 2011

The purpose of this study was to determine the legibility distance for traffic road sign and traffic sign, fixation duration and number of fixation during the time of recognition of traffic road signs under different vision conditions. This experiment was conducted on a closed-road circuit which has realistic driving road and environment Each participant drove the real vehicle for the experiment and specially built traffic road sign for the experiment and traffic road signs on the side of closed-road circuit were used. Different vision conditions were simulated using spectacle lenses to reach visual acuity 1.0 and 0.8 and it was 1.2 without spectacles and each participant tested under 3 vision conditions.. The result of this study demonstrated that there was a significant difference on legibility distance between visual acuity of 1.2 and 0.8 and there were also significant difference on fixation duration and number of fixations with smaller traffic signs. This study demonstrated the importance of vision correction for driving at night-time, also showed there would be difference on legibility distance and efficiency of eye movement such as fixation duration and number of fixation despite of satisfied visual acuity for driver's license requirement.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Interaction and multiscale mechanics
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• v.5 no.1
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• pp.41-56
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• 2012

This study is focused on evaluation of the tensile properties of concrete exposed to acid rain environment. Acid rain environment was simulated by the mixture of sulfate and nitric acid in the laboratory. The dumbell-shaped concrete specimens were submerged in pure water and acid solution for accelerated conditioning. Weighing, tensile test, CT, SEM/EDS test and microanalysis were performed on the specimens. Tensile characteristics of the damaged concrete are obtained quantitatively. Evolution characteristics of the voids, micro cracks, chemical compounds, elemental distribution and contents in the concrete are examined. The deterioration mechanisms of concrete exposed to acid rain are well elucidated.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.3
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• pp.143-150
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• 2008

In this paper, we investigate a real-time environment recognition system based on stereo vision for moving object. This system consists of stereo matching, obstacle detection and distance estimation. In stereo matching part, depth maps can be obtained real road images captured adjustable baseline stereo vision system using belief propagation(BP) algorithm. In detection part, various obstacles are detected using only depth map in case of both v-disparity and column detection method under the real road environment. Finally in estimation part, asymmetric parabola fitting with NCC method improves estimation of obstacle detection. This stereo vision system can be applied to many applications such as unmanned vehicle and robot.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.2
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• pp.191-206
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• 2018

In this study, the HAPs(Harzard Air Pollutants) emission factor level of Starex and Grand Carnival was tested using PEMS (Portable Emissions Measurement System) on real road driving. As a result of RDE (Real Driving Emission test), the overall vehicle speed pattern showed similar characteristics. The real-road driving test at constant speed revealed emission factor is inversely proportional relationship to constant speed. Results of accelerating with speed limit on the real-road were shown as followings; Uran (less than 45 km/h)>Rural (<45 km/h, less than 80 km/h)>Motorway (>80 km/h). Moreover, the sudden acceleration and deceleration in driving at high speed was the increasing factor to the HAPs emission factor. This tendency is considered to be influenced by the operating environment on real roads.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.103-110
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• 2012

PURPOSES : In Korea, over 70 percent of the land consists of mountainous and rolling area. Thus, tunnels continue its upward trend as road network are extended. In these circumstances, the importance of tunnel has been increased nowadays and then its safety investigation and research should be performed. This study is focus on confirming and improving the safety of tunnel. On tunnel hood, sunglare effect can irritate driver's behavior instantly and this can result in incident. METHODS : The study of this phenomenon is rarely conducted in domestic and foreign papers, so there is no proper measure for this. This study analyzes the driving environment of the effect of sunglare effect on tunnel hood. RESULTS : Traffic accidents stem from complex set of factors. This study build the Traffic Accident Prediction Models to find out the effect of sunglare effect on tunnel's hood. The independent variables are traffic volume, geometric design of road, length of tunnel and road side environment. Using these variables, this model estimates accident frequency on tunnel hood by Poisson regression model and Negative binomial regression model. Although Poisson regression model have more proper goodness of fit than Negative binomial regression model, Poisson regression model has overdipersion problem. So the Negative binomial regression model is used in this analysis. CONCLUSIONS : Consequently, the model shows that sunglare effect can play a role in driving safety on tunnel hood. As a result, the information of sunglare effect should be noticed ahead of tunnel hood so this can prevent drivers from being in hazard situation.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.4
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• pp.440-450
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• 2006

PM10, NOx, and $O_3$ were measured at six locations, of which each three is horizontally and vertically distributed respectively, in an apartment complex around the heavily traffic road. Those were measured seven times a day with two hours interval starting from 8 o'clock in the morning for 15 days during May 2005 $\sim$ September 2005. PM10 and NOx showed high concentrations in rush hours while low concentrations in midday due to the direct emissions from automobiles in operation. Temporal variations of 01 showed very much similar trend appeared in normal urban atmospheres. The spatial distributions of PM10, NOx and $O_3$ showed that almost all of concentrations were higher in a row of Roadside > Surface at 130 m apart from the road > Surface at 230 m apart from the road > 3rd floor of apartment building > 15th floor of apartment building > 27th floor of apartment building. Model equations, which can project spatial concentration distributions, were constructed by combining the horizontal and the vertical linear regression equations derived from six mean values corresponding to six measuring locations. According to inter-comparison of PM10, NOx, and $O_3$ with the constructed model equations, concentration gradients were higher in a row of Vertical direction of NOx > Vertical direction of PM10 > Horizontal direction of NOx > Horizontal direction of PMIO > Vertical direction of $O_3$ > Horizontal direction of $O_3$. Why concentration gradient of particulate PM10 is lower than that of gaseous NOx is in question, and should be studied.