• Journal of Korean Society for Atmospheric Environment
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• v.30 no.4
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• pp.378-386
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• 2014

The use of bus stop in the center lane has reduced the emissions of exhaust gas on the road due to the improvement of the traffic speed but has caused a health problem for the citizens who are waiting for the bus in the platform, and thus the air pollution control of bus stop in the center lane is emerging as a more important part. This study was conducted to investigate the air pollution degree for the center lane-bus stops in four regions using mobile air measuring vehicle, and to evaluate the characteristics of air pollution by comparing with the data measured at the urban air monitoring site close to the bus stops. In addition, the correlation analysis was performed to analyze the impact to neighboring region by vehicle exhaust gas. The regional mean concentration of nitrogen dioxide in the center lane-bus stops ranged from 0.025 to 0.043 ppm which shows from 2.5 times to 5.3 times higher than the values of urban air monitoring site selected as a control group. The regional mean concentration of ozone in the center lane-bus stops ranged from 0.023 to 0.034 ppm which shows from 3% to 28% lower than the values of urban air monitoring site selected as a control group. The concentrations of nitrogen dioxide and ozone for the sampling regions did not exceed one hour-air quality environmental standard (0.1 ppm). The mean concentration of particulate matter for four center lane-bus stops was $28{\mu}g/m^3$ which shows about 27% higher than the values of urban air monitoring site selected as a control group, and that of particulate matter did not exceed one day-air quality environmental standard ($100{\mu}g/m^3$). In the results of correlation analysis between data from center lane-bus stops and data from urban air monitoring sites, the correlation coefficient (r) of nitrogen dioxide was relatively low as 0.316 to 0.416, and the correlation coefficient was high as the distance was close and vice versa. However, the correlation coefficient of ozone ranged from 0.167 to 0.658 and the correlation coefficient was high as the distance was far and vice versa.

• Smart Structures and Systems
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• v.9 no.3
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• pp.287-301
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• 2012

It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.239-245
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• 2016

In the midst of continuing international recession, the rate of maritime traffic and marine leisure markets are consistently growing. The Republic of Korea controls the marine traffic volume through vessel traffic centers and various other management facilities. Nevertheless, the continuous growth and complexity of marine traffic is resulting in repeated occurrences of marine accidents. Recovery is very difficult in cases of human injuries or deaths caused by marine accidents due to its nature, and the scale of marine accidents is also becoming greater with advanced ship building technologies. Passenger ships, oil tankers, and other such vessels used for specific purposes requires a more detailed navigational status surveillance and analysis, and numerous research has been conducted with an objective for monitoring such special purpose vessels. However, the data elements transmitted from the ocean to the shore station are limited to AIS and ARPA. We are implementing IoT ship sensor collection and a syncing system capable of transmitting various ship sensing data to the shore station, and also proposing a Safe Navigation Status Analysis System utilizing the collected data.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.459-473
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• 2007

In this paper, we suggest a spatial regression model to predict AADT. Although Euclidian distances between one monitoring site and its neighboring sites were usually used in the many analysis, we consider the shortest travel path between monitoring sites to predict AADT for unmonitoring site using spatial regression model. We used universal Kriging method for prediction and found that the overall predictive capability of the spatial regression model based on shortest travel path is better than that of the model based on multiple regression by cross validation.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.2
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• pp.113-126
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• 2002

This study was carried out to evaluate the temporal (daily, weekly, and seasonal) variations of volatile organic compounds (VOCs) concentrations at a road-side site in a heavy-traffic central area of Metropolitan Taegu. Ambient air sampling was undertaken continuously for 14 consecutive days in each of four seasons from the spring of 1999 to the winter of 2000. The VOC samples were collected using adsorbent tubes, and were determined by thermal desorption coupled with GC/MS analysis. A total of 10 aromatic VOCs of environmental concern were determined, including benzene, toluene, ethylbenzene, m＋p-xylenes, styrene, o-xylene, 1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, and naphthalene. Among 10 target VOCs, the most abundant compounds appeared to be toluene (1.5 ∼ 102 ppb) and xylenes (0.1 ∼ 114 ppb), while benzene levels were in the range of 0.3 ∼6 ppb. It was found that the general trends of VOC levels were significantly dependent on traffic conditions at the sampling site since VOC concentrations were at their maximum during rush hours (AM 7∼9 and PM 7 ∼9). However, some VOCs such as toluene, xylenes, and ethylbenzene were likely to be affected by a number of unknown sources other than vehicle exhaust, being attributed to the use of paints, and/or the evaporation of solvents used nearby the sampling site. In some instances, extremely high concentrations were found for these compounds, which can not be explained solely by the impact of vehicle exhaust. The results of this study may be useful for estimating the relative importance of different emission sources in large urban areas. Finally, it was suggested that the median value might be more desirable than the arithmetic mean as a representative value for the VOC data group, since the cumulative probability distribution (n=658) does not follow the normal distribution pattern.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.429-437
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• 2015

DDoS attacks have been used for paralyzing popular Internet services. Especially, amplification attacks have grown dramatically in recent years. Defending against amplification attacks is challenging since the attacks usually generate extremely hugh amount of traffic and attack traffic is coming from legitimate servers, which is hard to differentiate from normal traffic. Moreover, some of protocols used by amplification attacks are widely adopted in IoT devices so that the number of servers susceptible to amplification attacks will continue to increase. This paper studies on the analysis of amplification attack mechanisms in detail and proposes defense methodologies for scenarios where attackers, abused servers or victims are in a monitoring network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.222-234
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• 2020

Autonomous vehicles are equipped with a wide rage of sensors such as GPS, RADAR, LIDAR, camera, IMU, etc. and are driven by recognizing and judging various transportation systems at intersections in the city. The accident ratio of the intersection of the autonomous vehicles is 88% of all accidents due to the limitation of prediction and judgment of an area outside the sensing distance. Not only research on non-signalized intersection collision avoidance strategies through V2V and V2I is underway, but also research on safe intersection driving in failure situations is underway, but verification and fragments through simple intersection scenarios Only typical V2V failures are presented. In this paper, we analyzed the architecture of the V2V module, analyzed the causal factors for each V2V module, and defined the failure mode. We presented intersection scenarios for various road conditions and traffic volumes. we used the ISO-26262 Part3 Process and performed HARA (Hazard Analysis and Risk Assessment) to analyze the risk of autonomous vehicle based on the simulation. We presented ASIL, which is the result of risk analysis, proposed a monitoring concept for each component of the V2V module, and presented monitoring coverage.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.10
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• pp.987-994
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• 2011

This paper presents An efficient method of antenna placement considering EMI(Electromagnetic Interference) between equipments which are mounted on the UAV(Unmanned Air Vehicle). The analysis is accomplished for voice communication radio, control datalink, TCAS(Traffic Alert Collision & Avoidance System) and GPS(Global Positioning System) which are vulnerable to EMI because the frequencies are close to each other. There are two steps for analysis procedure : The first one is selecting antenna position on the UAV by monitoring return loss and pattern variation of each antenna. The second one is analyzing EMI via antennas between equipments. In the EMI analysis, spurious level of each transmitter, coupling level between antennas and system noise property are considered. This procedure can be used to predict EMI between equipments in development stage.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.226-233
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• 2022

Video surveillance is widely used in security surveillance, military navigation, intelligent transportation, etc. Its main research fields are pattern recognition, computer vision and artificial intelligence. This article uses OpenCV to detect and track vehicles, and monitors by establishing an adaptive model on a stationary background. Compared with traditional vehicle detection, it not only has the advantages of low price, convenient installation and maintenance, and wide monitoring range, but also can be used on the road. The intelligent analysis and processing of the scene image using CAMSHIFT tracking algorithm can collect all kinds of traffic flow parameters (including the number of vehicles in a period of time) and the specific position of vehicles at the same time, so as to solve the vehicle offset. It is reliable in operation and has high practical value.

• ETRI Journal
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• v.33 no.5
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• pp.770-779
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• 2011

Security quad and cube (SQC) is a network attack analyzer that is capable of aggregating many different events into a single significant incident and visualizing these events in order to identify suspicious or illegitimate behavior. A network administrator recognizes network anomalies by analyzing the traffic data and alert messages generated in the security devices; however, it takes a lot of time to inspect and analyze them because the security devices generate an overwhelming amount of logs and security events. In this paper, we propose SQC, an efficient method for analyzing network security through visualization. The proposed method monitors anomalies occurring in an entire network and displays detailed information of the attacks. In addition, by providing a detailed analysis of network attacks, this method can more precisely detect and distinguish them from normal events.