• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.151-156
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• 2011

Previously most of flood prevention efforts have been made for relatively large watersheds near to channel flow. However, as economical development and the expansion of leisure areas to mountainous region, human casualty by flash flood occurs frequently, requiring additional prevention activity. Therefore, to reduce the damage of human lives and property by flash flood, we develop an assessment method for flash flood occurrence for mountainous areas considering various factors involving it. PROMETHEE(Preference Ranking Organization METHod for Enrichment Evaluations) which is one of the MCDM(Multi-Criteria Decision Making) was adopted to assess the contribution of each factor to the risk of the flash flood in the mountainous area. The main evaluation criteria are classified into three categories, namely, the regional and rainfall characteristics, and geographical features. Also, the Entropy method is used to determine the weight of each evaluation criteria without survey. The suggested method based on PROMETHEE with Entropy method is applied to BongHwa region to verify its applicability. After applied, the method successfully assesses the relative risk of flash flood occurrence of each sub region in the BongHwa region. Out of the seventeen sub-regions, five, seven and five of them are evaluated as high-risk, medium-risk, and low-risk, respectively. To verify the results, we searched the historical data of flash flood and the flash flood had occurred in one of high-risk sub-regions at 2008.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.105-116
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• 2023

The fatality rate of secondary accidents is seven times that of general traffic accidents. If limited to highways, one in four deaths are said to occur from secondary accidents. Unexpected situations which do not give drivers time to prepare are the cause of secondary accidents. This risk results in more fatalities on highways with high driving speeds. Existing studies have conducted research on traffic accidents and on secondary traffic accidents that occur after a primary traffic accident, without considering unexpected situations that may occur on the road. Therefore, to reduce damage and casualties caused by secondary accidents, there is a need to create a safe road environment by removing the possibility of causing accidents. This study analyzes whether the day of occurrence, time of occurrence, and radius of the curve of an unexpected situation are related to the occurrence of an unexpected situation. This study was based on data of accidents that occurred in 2022 on the Cheonan-Nonsan Expressway and the Seoul-Yangyang Expressway. The radius of the curve was calculated by dividing the section of the highway into straight, clothoid, and curved sections through cluster analysis. Results of the analysis indicate that the day and time of occurrence and the curve radius are associated with unexpected situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.38-50
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• 2023

Recent advances in artificial intelligence (AI) technology have enabled the integration of AI technology into image sensors, such as Closed-Circuit Television (CCTV), to detect specific traffic incidents. However, most incident detection methods have been carried out using fixed equipment. Therefore, there have been limitations to incident detection for all roadways. Nevertheless, the development of mobile image collection and analysis technology, such as image sensors and edge-computing, is spreading. The purpose of this study is to estimate the reducing effect of the incident detection time according to the introduction level of mobile image collection and analysis equipment (or connected vehicles). To carry out this purpose, we utilized data on the number of incidents collected by the Suwon branch of the Gyeongbu expressway in 2021. The analysis results showed that if the market penetration rate (MPR) of connected vehicles is 4% or higher for two-lane expressway and 3% or higher for three-lane expressways, the incident detection time was less than one minute. Furthermore, if the MPR is 0.4% or higher for two-lane expressways and 0.2% or higher for three-lane expressways, the incident detection time decreased compared to the average incident detection time announced by the Korea Expressway Corporation for both two-lane and three-lane expressways.

• Clinical and Experimental Pediatrics
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• v.52 no.5
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• pp.619-621
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• 2009

Paroxysmal hemicrania (PH) is rare in children and not widely recognized. It is characterized by pain attacks and associated symptoms and signs similar to those experiencing cluster headaches, but the features have a shorter effect, are more frequent, and respond completely to indomethacin. Some patients with PH may experience slight pain across the midline. There are only four cases of bilateral PH in the literature and it is very rare in children. Here, I report the case of a 10-year-old female with bilateral PH diagnosed by the typical symptoms along with the favorable response to indomethacin therapy.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.811-815
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• 2005

산악지역의 유출은 지형적 특성 때문에 매우 빠른 반응시간을 가지고 첨두유량 또한 매우 크게 마련인데 이러한 특성 때문에 산악지역의 돌발홍수 발생 메카니즘과 이것의 정확한 규명은 지금까지 수많은 연구과제의 주제가 되어왔다. 본 연구는 산악지역의 유출 특성을 잘 반영한다고 알려진 수문지형학을 기초한 지형기후학적단위도(geomorphoclimatic unit hydrograph, GCUH) 이론을 토대로 단일유역 산악지역과 분할유역 규모의 유출 특성을 규명하고 각각의 유역특성에 맞는 돌발홍수예경보시스템을 제안 및 비교 검토하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1341-1345
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• 2010

본 연구에서는 부산시 44개 지방하천을 대상으로 하여 홍수량 산정지점을 기준으로 175개의 소단위지구의 한계유출량을 산정하기 위하여 지형상관인자를 소단위지구별로 조사하였다. 한편, 한계유효강우는 한계홍수량에 해당하는 돌발홍수능(FFG, Flash Flood Guidance)을 산정하기 위한 전단계로 김홍태(2009) 등이 지형수문단위도 개발에서 제시한 KGCUH 공식을 이용하여 유역규모 $50km^2$을 기준으로 구분하여 적용하였다. 빈도별 지속시간별 한계유효강우를 산정하였으며, SCS유효우량 산정법을 이용하여 부산시 44개 지방하천 175개의 소단위지구별 및 빈도별(2, 10, 30, 50, 80, 100년) 돌발홍수능을 산정하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.179-179
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• 2019

전 세계적으로 빈번히 발생하고 있는 홍수, 그중에서도 국지성 집중호우로 인한 돌발홍수에 대응하려면 정확한 강수예측자료를 빠르게 생산하는 것이 필수적이다. 본 연구에서는 최근 딥러닝(머신러닝)을 이용한 강수예측방법에 대하여 고찰하고, 특히 레이더 이미지를 기반으로 한 강수예측방법에 중점을 두고 그 적용성을 살펴보았다. 그 결과 딥러닝(머신러닝)을 이용한 강수예측자료는 예측의 정확성을 높일 수 있을 뿐 아니라 돌발홍수에 대응할 수 있는 자료로 충분히 활용할 수 있음을 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.178-178
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• 2019

소형레이더(X밴드 이중편파레이더)는 시공간적으로 고해상도의 자료를 제공하고 있어 도시지역에서 돌발홍수 감시에 많은 역할을 할 것으로 기대되어 왔다. 이에 한국건설기술연구원은 2013년에 수도권에서의 돌발홍수 및 악기상 감시를 위해 소형레이더를 도입하였다. 수재해플랫폼 연구단에서도 서울 지역에서의 돌발홍수 및 악기상을 감시를 위해 같은 기종의 소형레이더를 고려대학교와 연세대학교에 도입하여 소형레이더 망인 X-Net을 구축하였다. 본 연구에서는 고려대학교와 연세대학교에 도입된 소형레이더의 수문 적용성 평가를 위해 2018년에 관측된 38개의 강우사례에 대한 레이더 강우량 정확도 평가를 실시하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.181-184
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• 2021

자율주행 기술이 고도화됨에 따라 사용자가 주행 상황을 실시간으로 모니터링하고 주행을 제어할 수 있는 자율주행 서비스가 필요하다고 생각했다. 또한, 돌발 장애물을 고려하며 정해진 경로로 주행하는 자율주행을 구현하고자 해당 시스템을 설계하게 되었다. 해당 시스템은 차량, 서버, 애플리케이션으로 구성되어있으며 구성요소 간의 실시간 통신을 통해 차량 주행 상황 및 사용자 제어 명령을 자유롭게 전달하고자 했다. 차량의 자율주행 알고리즘을 구현하기 위해 이미지 데이터 처리에 효과적인 CNN을 활용하여 장애물 회피 모델과 라인 트레이서 모델을 구현하여 해당 모델들을 하나의 솔루션으로 통합하였다. 해당 솔루션 구현을 통해 차량이 마주할 수 있는 돌발 상황에 대처하는 자율주행의 안전성을 높이고자 했으며 자율주행 환경에서 사용자 조작을 용이하게 하고자 하였다.