• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.111-118
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• 2018

Korea is facing a surge in the aging population, showing that population aged 65 and above will be accounted for 42.5% of the total population in 2065 with the emphasis on the over-80 population consisting of 19.2%. In response to this abrupt change in population structure, the number of traffic fatality accident referring to older driver as aged 65+ years had been increasing from 605 fatalities in 2011 to 815 fatalities in 2015 resulting in increases in 34.7% in oppose to happening to decreases in 17.2% about non-older driver. With Logit analysis based on Newton-Raphson algorithm utilizing older driver's traffic fatality data for the 2011-2015 years, it was found that the likelihood of an accident resulting in a fatality for super older driver aged 80 years and above considerably increased compared to other older driver with aging classification: 2.24 times for violation of traffic lane, 2.04 times for violation of U-turn, 1.48 times for violation of safety distance, 1.35 times for violation of obstacle of passing; also average annual increase of traffic accident cost related to super older driver was fairly increased rather than other older driver groups. Hence, this study proposes that improving and amending transport safety system and Road Traffic Act for super older driver needs to be urgently in action about license management, safe driving education, etc. when considering the increase of over-80 population in the near future. Also, implementing a social agreement with all ages and social groups to apply with advanced driver assistance system for older driver groups will be able to become a critical factor to enhance safe driving over the face of the country.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Tunnel and Underground Space
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• v.29 no.5
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• pp.305-317
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• 2019

The RMR and Q-system for characterizing rock mass and drilling core, and for estimating the support and reinforcement measures in mine galleries, tunnels and caverns have been widely used by engineers. SMR has been widely used in the rock mass classification for rock slope, but Q-Slope has been introduced into slopes since 2015. In the last ten years, a modified Q-system called Q-slope has been tested by the many authors for application to the benches in open pit mines and excavated road rock slopes. The results have shown that a simple correlation exists between Q-slope values and the long-term stable and unsupported slope angles. Just as RMR and Q have been used together in a tunnel or underground space and complemented by comparison, Q-Slope can be used in parallel with SMR. This paper introduces how to use Q-Slope which has not been announced in Korea and application examples of Pasir open pit coal mine in Indonesia.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.363-376
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• 2022

An area under construction for a living facility collapsed around 12:48 KST on 13 January 2021 in Sa-dong, Ansan-si, Gyeonggi-do. There were no casualties due to the rapid evacuation measure, but part of the temporary retaining facility collapsed, and several cracks occurred in the adjacent road on the south side. This study used the potential of synthetic aperture radar (SAR) satellite for surface property changes that lies in backscattering characteristic to map the collapsed structure. The interferometric SAR technique can make a direct measurement of the decorrelation among different acquisition dates by integrating both amplitude and phase information. The damage proxy map (DPM) technique has been employed using four high-resolution Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) data spanning from 2020 to 2021 during ascending observation to analyze the collapse of the construction. DPM relies on the difference of pre- and co-event interferometric coherences to depict anomalous changes that indicate collapsed structure in the study area. The DPMs were displayed in a color scale that indicates an increasingly more significant ground surface change in the area covered by the pixels, depicting the collapsed structure. Therefore, the DPM technique with SAR data can be used for damage assessment with accurate and comprehensive detection after an event. In addition, we classify the amplitude information using support vector machine (SVM) and maximum likelihood classification algorithms. An investigation committee was formed to determine the cause of the collapse of the retaining wall and to suggest technical and institutional measures and alternatives to prevent similar incidents from reoccurring. The report from the committee revealed that the incident was caused by a combination of factors that were not carried out properly.

• Convergence Security Journal
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• v.22 no.2
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• pp.105-114
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• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.59-64
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• 2024

Recently, research on the convergence of drones and artificial intelligence technologies have been conducted in various industrial fields. In this paper, we propose an illegal parking vehicle recognition model using deep learning-based object recognition and classification algorithms. The model of object recognition and classification consist of YOLOv8 and ResNet18, respectively. The proposed model was trained using image data collected in general road environment, and the trained model showed high accuracy in determining illegal parking. From simulation results, it was confirmed that the proposed model has generalization performance to identify illegal parking vehicles from various images.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.69-78
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• 2009

Korea has achieved significant economic growth with building the Gyeongbu Expressway. As the number of new road construction projects has decreased, it becomes more important to maintain optimal status of the current road networks. One of the best ways to accomplish it is weight enforcement as active control measure of traffic load. This study is to develop High-speed Weigh-in-motion System in order to enhance efficiency of weight enforcement, and to analyze patterns of overloaded trucks on highways through the system. Furthermore, it is to review possibilities of developing overweight control system with application of the HS-WIM system. The HS-WIM system developed by this study consists of two sets of an axle load sensor, a loop sensor and a wandering sensor on each lane. A wandering sensor detects whether a travelling vehicle is off the lane or not with the function of checking the location of tire imprint. The sensor of the WIM system has better function of classifying types of vehicles than other existing systems by detecting wheel distance and tire type such as single or dual tire. As a result, its measurement errors regarding 12 types of vehicle classification are very low, which is an advantage of the sensor. The verification tests of the system under all conditions showed that the mean measurement errors of axle weight and gross axle weight were within 15 percent and 7 percent respectively. According to the WIM rate standard of the COST-323, the WIM system of this study is ranked at B(10). It means the system is appropriate for the purpose of design, maintenance and valuation of road infrastructure. The WIM system in testing a 5-axle cargo truck, the most frequently overloaded vehicle among 12 types of vehicles, is ranked at A(5) which means the system is available to control overloaded vehicles. In this case, the measurement errors of axle load and gross axle load were within 8 percent and 5 percent respectively. Weight analysis of all types of vehicles on highways showed that the most frequently overloaded vehicles were type 5, 6, 7 and 12 among 12 vehicle types. As a result, it is necessary to use more effective overweight enforcement system for vehicles which are seriously overloaded due to their lift axles. Traffic volume data depending upon vehicle types is basic information for road design and construction, maintenance, analysis of traffic flow, road policies as well as research.

• Journal of Environmental Impact Assessment
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• v.29 no.4
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• pp.252-271
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• 2020

In this study, we investigated physical and chemical properties such as grain size, heavy metal pollution, magnetic properties, and their environmental impacts of road dusts (RD) collected from 14 sampling points in Daebul industrial Complex. Heavy metal concentrations in RD were in the order of Fe>Zn>Cu>Pb>Cr>Ni>As>Cd>Hg, and this pollution pattern was related to major industries and traffic activities in this area. The results of the correlation analysis between heavy metal elements and particle size in RD showed that Fe and all of analyzed heavy metals had a significant correlation with each other and metal concentrations had a significantly negative correlation (p<0.05). However, due to the input of large metal particles some heavy metal concentrations in the particle fraction of >1000 ㎛ were highest. Pollution load per unit area of this fraction was the highest among the grain size fractions. Cr, Ni, Cu, Zn, Cd, Pb levels in RD decreased and the levels of Cr, Ni, Cu, Zn, Cd, and Pb were reduced to 85 (As) -22 (Ni)% of the whole after removal of MFs fraction from RD. The mean heavy metal levels in the study area did not exceed the soil contamination guide value of Korea, indicating that heavy metal levels in RD were not a concern. However, at some sampling points, Zn concentrations were exceeded the soil contamination guide value for the 3rd areas of Korea and this result indicated that further studies of the impact of RD on the surrounding environment through re-suspension or non-point pollution, and of effective management methods are required.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.15-21
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• 2007

This paper proposes an improved methodology for estimating traffic accident cost savings in the transport appraisal. Four major problems from the existing framework are identified and their alternatives are suggested. First, casualties in the established approach are classified by just two types of 'killed' and 'injured'. This study supplies the indices of fatality further details. Namely, road victims are regrouped by 'killed', 'seriously injured', 'slightly injured', and 'accident reports'. Those of railways are similarly sorted by 'killed', 'seriously injured', and 'slightly injured'. Second, damage only accidents are not satisfactorily considered in the current arrangement. The accidents should be considered as one of the accident types and the social cost of them should also be evaluated. Third, the unit cost of accidents is given by the total value. The unit cost is consisted of several elements and each loss would be useful for a policy frame. This study breaks down the total figure into four pieces of costs, namely production loss, medical treatment, property loss, and administrative costs. Finally, there is inconsistency in the audit between roads and railways. Road accidents are analyzed by road types. On the other hand, patronage or others is the classification rule of rail accident costs. This paper suggests a way that the accident costs of two modes can be coherently estimated based on the level of services by each mode. The result of this study is expected to help frame more cautious social overhead capital investment policies.