• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.7
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• pp.10-16
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• 2012

In this paper, we tried to realize a next generation MILES detection system which is robust to optical noise using a narrow band interference optical filter. Applying a narrow band interference optical filter which has the wavelength range of 895~915nm to the LASER wavelength of 900nm, we could obtain detection characteristics robust to strong optical noise which can be occurred in street battles. However, the MILES detection system has the blind range of detection in the incident angle range of $30^{\circ}{\sim}90^{\circ}$. To solve this problem we proposed a method of incident angle scatter using refractors. Applying a concave meniscus lens refractor which has diopter of 5.4 to the MILES detection system, we could eliminate the blind angle of detection.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1234-1239
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• 2006

신속하고 정확한 교통정보 서비스의 제공은 원활한 교통소통을 위하여 필수적인 요소이다. 특히, 교통사고, 도로보수 그리고 자연재해와 같은 유고가 발생할 경우, 운전자에게 즉시 통보해주어 우회할 수 있도록 조치하는 것이 필요하다. 이를 위하여 다양한 교통정보 수집기에서 수집된 교통정보를 바탕으로 실시간으로 유고상황을 판별하는 연구가 많이 진행되고 있다. 유고상황 분석은 다양한 환경요인으로 인해 판별이 어렵고, 최근에 활용되고 있는 인공지능 기법은 검지에 드는 시간 비용이 많다는 문제를 가지고 있다. 본 연구에서는 과거에 발생한 각종 돌발 상황을 분석하여 실시간으로 유고상황을 검지하는 것이 목적이다. 유고검지를 위해 GPS를 탑재한 probe car에서 수집된 차량속도와 온라인으로 제보된 유고정보를 ANFIS를 이용하여 분석 후 유고상태를 판별한다. 본 연구를 통해 실시간 도로 이용자들이 유고 발생 지역의 정보를 제공받고 그 상황에 신속하게 대처하게 함으로써 교통 혼잡 완화에 기여할 것으로 기대한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.793-799
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• 2021

Recently, there has been a growing interest in threat hunting presented to overcome the limitations of existing security solutions. Threat hunting is generally recognized as a technique for identifying and eliminating threats that exit inside the system. But, the definition is not clear, so there is confusion in terms with penetration testing, intrusion detection, and incident analysis. Therefore, in this paper, compare and analyze the definitions of threat hunting extracted from reports and papers to clarify their implications and compare with defense techniques.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.221-227
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• 2008

• Transportation Technology and Policy
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• v.9 no.4
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• pp.53-58
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• 2012

• 한국ITS학회:학술대회논문집
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• 2004.11a
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• pp.175-180
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• 2004

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2430-2436
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• 2013

This study suggests a method of detecting the still object, which becomes a cause of danger within the crossroad. The Inverse Perspective Transform was performed in order to make the object size consistent by being inputted the real-time image from CCTV that is installed within the crossroad. It established the detection area in the image with the perspective transform and generated the adaptative background image with the use of the moving information on object. The detection of the stop object was detected the candidate region of the stop object by using the background-image differential method. To grasp the appearance of truth on the detected candidate region, a method is proposed that uses the gradient information on image and EHD(Edge Histogram Descriptor). To examine performance of the suggested algorithm, it experimented by storing the images in the commuting time and the daytime through DVR, which is installed on the cross street. As a result of experiment, it could efficiently detect the stop vehicle within the detection region inside the crossroad. The processing speed is shown in 13~18 frame per second according to the area of the detection region, thereby being judged to likely have no problem about the real-time processing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.581-586
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• 2006

Incidents, which is unexpected unusual events such as traffic accidents, have increased on the most roads in Korea. The obstruction of a fluent traffic flow occurred by incidents causes the traffic congestion and decreases the capacity. The Wavelet technique was applied to detect the road section and the happening time of incidents on urban freeways in this study, and this technique has been widely used in many engineering fields such as an electrical engineering, etc. The availability and validity of the Wavelet technique to the detection of incidents was examined by the occupancy rate, the important element of traffic flows, which is extracted from the data of detectors installed on Seoul Urban freeways. Then, this result is compared to the California Algorithm and the Low-Pass Filtering Algorithm among basic present detection algorithms, which are based on the occupancy rate. As a result, the false alarm rate of this method was similar as that of the California algorithm and the Low-Pass Filtering algorithm, but the detection rate is higher.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.39-50
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• 2006

This study aims to introduce a basic principle to improve the incident detection algorithm using traffic flow diagrams that can classify traffic states with a high reliability on the basis of the analysis of traffic flow characteristics under the recurrent or incident congestions. It is tried to newly classify the traffic states with the speed-flow and speed-occupancy diagrams. This is because McMaster algorithm has a tendancy on not identifying the traffic states exactly using the flow-occupancy diagram. In this study it shows that the classification of traffic states is applicable to use speed-occupancy relationship Therefore, it is necessary to determine some parameters to correctly classify the areas representing the traffic states and it may be possible to develop a new algorithm to detect the incident with a high reliability.