• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.585-588
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• 2008

KICT has been carried out inventory research on the cut slopes of national roads. Inventory research results are basic data using cut slope management system. Inventory data are classified by general status, cut slope characteristics and inspector opinion. Inventory data are utilized to figure out dangerous slopes and decide survey ranking of detailed safety diagnostication. This paper drew the distribution area of dangerous cut slopes using inventory data in Chungcheongdo, and verified efficiency on distribution area of dangerous cut slopes by comparing occurrence frequency of real collapsed cut slopes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.438-444
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• 2019

With the increasing supply of vehicles, construction of new roads and expansion of existing roads are growing and this is leading to a proportional rise in diverse hazards on a road. These hazards are classified into fixed hazards and variable hazards. Currently, drivers receive information of fixed hazards, such as overspeed, frequent accidents, and rock fall through navigations. However, variable hazards are more hazardous than fixed hazards. Map companies frequently enter information of variable hazards manually, but it is less real-time and hard to deal with unforseen hazards. This paper is intended to implement a road hazard warning system for making a contribution to pubic interests by improving this problem and delivering real-time information of hazards to drivers, and suggest a direction for using information of hazards on a road.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.63-73
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• 2024

Autonomous vehicles at Levels 3 to 5, currently under global research and development, seek to replace the driver's perception, judgment, and control processes with various sensors integrated into the vehicle. This integration enables artificial intelligence to autonomously perform the majority of driving tasks. However, autonomous vehicles currently obtain temporary driving permits, allowing them to operate on roads if they meet minimum criteria for autonomous judgment abilities set by individual countries. When autonomous vehicles become more widespread in the future, it is anticipated that buyers may not have high confidence in the ability of these vehicles to avoid hazardous situations due to the limitations of temporary driving permits. In this study, we propose a method for grading the judgment abilities of autonomous vehicles based on a driving simulator experiment comparing and evaluating drivers' abilities to avoid hazardous situations. The goal is to derive evaluation criteria that allow for grading based on specific scenarios and to propose a framework for grading autonomous vehicles. Thirty adults (25 males and 5 females) participated in the driving simulator experiment. The analysis of the experimental results involved K-means cluster analysis and independent sample t-tests, confirming the possibility of classifying the judgment abilities of autonomous vehicles and the statistical significance of such classifications. Enhancing confidence in the risk-avoidance capabilities of autonomous vehicles in future hazardous situations could be a significant contribution of this research.

• Korean Journal of Environmental Agriculture
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• v.15 no.4
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• pp.494-500
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• 1996

To investigate the relationship between the atmospheric pollution level and the pollution level of soil adjacent to the roadside in Suwon suspended particles and soil samples were collected in August to October 1993, and analyzed by AAS and ICP. The Ca, K, Mg concentration in atmosphere were observed as high level and assumed that it was effected by the acid-rain originated from the erosion of concreate vicinity to the roadside, and the Pb, Zn concentration were higher at outer area than that of central area. Hazardous heavy metal (Cu, Pb, Zn) concentrations in soil were observed as high level at all sampling sites. The relationship between the heavy metal concentrations in the atmosphere and those in the soil were analyzed, by using the correlation coefficient value(r) and the result was appeared similarly. And this study indicated that the atmospheric pollution affect the level of the soil pollution adjacent to the road side.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.593-596
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• 2018

The speed of vehicles has remained a significant factor that influences the severity of accidents and traffic accident rate in many parts of the world including South Korea. This behavior where drivers drive at speeds which exceed a posted safe threshold is known as 'speeding'. Over the past twenty years, the Korean National Police Agency (NPA) has become aware of an increased frequency of drivers who are speeding. Therefore, fixed-type ASE systems [1] have been installed on hazardous road sections of many highways. These system monitor vehicle speeds using a camera. However, the use of ASE systems has changed the behavior of the drivers. Specifically, drivers reduce speed or avoid the route where the cameras are mounted. It is not practical to install cameras at every possible location. Therefore, it is challenging to thoroughly explore the location where speeding occurs. In view of these problems, the author of this paper designed and implemented a prototype visualization system in which point and color are used to show vehicle location and associated over-speed information. All of this information was used to create a comprehensive visualization application to show information about vehicle driving. In this paper, we present an approach detecting vehicles moving at speeds which exceed a threshold and visualizing the points those violations occur on a map. This was done using vehicle trajectory data collected in Daegu city. We propose steps for exploring the data collected from those sensors. The resulting mapping has two layers. The first layer contains the dynamic vehicle trajectory data. The second underlying layer contains the static road networks. This allows comparing the speed of vehicles on roads with the known maximum safe speed of those roads, and presents the results with a visualization tool. We also compared data about people who drive over threshold safe speeds on each road on days and weekends based on vehicle trajectories. Finally, our study suggests improved times and locations where law enforcement should use monitoring with speed cameras, and where they should be stricter with traffic law enforcement. We learned that people will drive over the speed limit at midnight more than 1.9 times as often when compared with rush hour traffic at 8 o'clock in the morning, and 4.5 times as often when compared with traffic at 7 o'clock in the evening. Our study can benefit the government by helping them select better locations for installation of speed cameras. This would ultimately reduce police labor in traffic speed enforcement, and also has the potential to improve traffic safety in Daegu city.

• Journal of Korean Society of Disaster and Security
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• v.16 no.1
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• pp.23-35
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• 2023

Recently, the damage caused by natural disasters such as typhoons and localized torrential rains has been increasing rapidly. The Ministry of the Interior and Safety enacted a ｢law on safety management of small sized infrastructures｣ and local governments have to register small sized infrastructures with the National Disaster and Safety Management System (NDMS) until March 31st every year. Recently, each local government has ordered Safety inspections of small sized infrastructures and maintenance plans and six types of facilities, including small streams, small bridges, farm roads, access roads to village, inlet weirs, and drop structures are being surveyed and digitized into a database. Each facility is being evaluated for risk, and for those deemed hazardous, maintenance plans are being developed. However, since the risk assessment method of small sized infrastructures is not clear so that is conducted through visual investigation by field investigators, risk assessment is conducted in a subjective and ambiguous form. Therefore, this study presented a reasonable and quantitative risk assessment method by providing a quantitative evaluation indicator for small stream, which has the highest disaster risk among other small sized infrastructures, so that small sized hazard infrastructures can be selected to secure transparent evidence for improvement plans and action plans.

• Journal of KIISE
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• v.43 no.3
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• pp.389-397
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• 2016

In this paper, we propose a highway safety system that comprises a small number of central servers and smart mobile devices. To implement this system, we constructed a central server that collects GPS location information on cars, whose update messages are decreased via the car location estimation algorithm. The in-car mobile devices use the accelerometer sensors to detect hazardous situations; this information is updated to the central server that relays the information to the corresponding endangered cars via location-based unicast using LTE communication. To evaluate the proposed algorithm, we equipped a mobile device app on a real car and conducted real experiments in various environments such as city streets, rural areas, and highway roads. Furthermore, we conducted simulations to evaluate the propagation of danger information. Finally, we conducted simulated experiments to detect car collisions as well as exceptions, such as falling of the mobile device from the cradle.

• 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.