• Convergence Security Journal
• /
• v.22 no.4
• /
• pp.161-168
• /
• 2022

After the corona virus, as non-face-to-face services are activated, domain services that guarantee integrity by embedding sensing information of the Internet of Things (IoT) with block chain technology are expanding. For example, in areas such as safety and security using CCTV, a process is required to safely update firmware in real time and to confirm that there is no malicious intrusion. In the existing safe security processing procedures, in many cases, the person in charge performing official duties carried a USB device and directly updated the firmware. However, when private blockchain technology such as Hyperledger is used, the convenience and work efficiency of the Internet of Things environment can be expected to increase. This article describes scenarios in how to prevent vulnerabilities in the operating environment of various customers such as firmware updates and device changes in a non-face-to-face environment. In particular, we introduced the optimal blockchain technique for the Internet of Things (IoT), which is easily exposed to malicious security risks such as hacking and information leakage. In this article, we tried to present the necessity and implications of security management that guarantees integrity through operation applying block chain technology in the increasingly expanding Internet of Things environment. If this is used, it is expected to gain insight into how to apply the blockchain technique to guidelines for strengthening the security of the IoT environment in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.6
• /
• pp.114-123
• /
• 2023

With the development of computer vision technology, video sensors such as CCTV are detecting incident. However, most of the current incident have been detected based on existing fixed imaging equipment. Accordingly, there has been a limit to the detection of incident in shaded areas where the image range of fixed equipment is not reached. With the recent development of edge-computing technology, real-time analysis of mobile image information has become possible. The purpose of this study is to evaluate the possibility of detecting expressway emergencies by introducing computer vision technology to dash cam. To this end, annotation data was constructed based on 4,388 dash cam still frame data collected by the Korea Expressway Corporation and analyzed using the YOLO algorithm. As a result of the analysis, the prediction accuracy of all objects was over 70%, and the precision of traffic accidents was about 85%. In addition, in the case of mAP(mean Average Precision), it was 0.769, and when looking at AP(Average Precision) for each object, traffic accidents were the highest at 0.904, and debris were the lowest at 0.629.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.1
• /
• pp.76-81
• /
• 2018

The Internet of Things (IoT), which is emerging as a future economic growth engine, has been actively introduced in areas close to our daily lives. However, there are still IoT security threats that need to be resolved. In particular, with the spread of smart homes and smart cities, an explosive amount of closed-circuit televisions (CCTVs) have been installed. The Internet protocol (IP) information and even port numbers assigned to CCTVs are open to the public via search engines of web portals or on social media platforms, such as Facebook and Twitter; even with simple tools these pieces of information can be easily hacked. For this reason, a big-data analytics system is needed, capable of supporting quick responses against data, that can potentially contain risk factors to security or illegal websites that may cause social problems, by assisting in analyzing data collected by search engines and social media platforms, frequently utilized by Internet users, as well as data on illegal websites.

• Journal of Broadcast Engineering
• /
• v.20 no.3
• /
• pp.430-439
• /
• 2015

The demand of violence detection techniques using a video analysis to help prevent crimes is increasing recently. Many researchers have studied vision based behavior recognition but, violent behavior analysis techniques usually focus on violent scenes in television and movie content. Many methods previously published usually used both a color(e.g., skin and blood) and motion information for detecting violent scenes because violences usually involve blood scenes in movies. However, color information (e.g., blood scenes) may not be useful cues for violence detection in surveillance videos, because they are rarely taken in real world situations. In this paper, we propose a method of violent behavior detection in surveillance videos using motion vectors such as flow vector magnitudes and changes in direction except the color information. In order to evaluate the proposed algorithm, we test both USI dataset and various real world surveillance videos from YouTube.

• Journal of the Korea Convergence Society
• /
• v.11 no.3
• /
• pp.77-83
• /
• 2020

Recently, intelligent predictive surveillance system has emerged. It is a system that can probabilistically predict the future situation and event based on the existing data beyond the scope of the current object or object motion and situation recognition. Since such intelligent predictive monitoring system has a high possibility of handling personal information, security consideration is essential for protecting personal information. The existing video surveillance framework has limitations in terms of privacy. In this paper, we proposed a security framework for intelligent predictive surveillance system. In the proposed method, detailed components for each unit are specified by dividing them into terminals, transmission, monitoring, and monitoring layers. In particular, it supports active personal information protection in the video surveillance process by supporting detailed access control and de-identification.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.6-6
• /
• 2022

최근 수재해에 대응하기 위한 물관리 환경은 기후변화에 따른 홍수 피해 심화와 댐과 하천 시설의 노후화 점증, 하천관리일원화 등 정책적 변화, 그리고 포스트코로나 디지털 혁신 등 복합적 대전환 시대 진입에 따라 복잡다단한 양상을 보이고 있다. 디지털 트윈은 디지털 대전환(digital transformation) 시대 다양한 산업 영역에서 지능화와 생산성 향상을 목적으로 도입되고 있다. 본 국가 시범사업에서는 170 km에 달하는 섬진강 유역 전체를 대상으로 홍수에 대응하기 위한 디지털 트윈 플랫폼(K-Twin SJ)을 구축하고 있다. 본 플랫폼은 국가 인프라 지능정보화 사업의 일환으로 시작되었으며, 공간정보와 시설물 모델링, 홍수 분석 등 수재해에 대응하기 위한 수자원 분야의 다학제적인 강소기업들과 K-water에서 컨소시엄을 구성하여 추진하고 있다. 본 사업의 내용은 섬진강 댐-하천 유역에 대하여 고정밀도 3D 공간정보화, 실시간 물관리 데이터 연계, 홍수 분석 시뮬레이션, AI 댐 운영 최적화, AI 사면 정보 생성, 하천 제방 안전성 평가, AI 지능형 CCTV 영상분석, 간이 침수피해 예측, 드론 제약사항 조사 체계 개발을 포함하고 있다. 물관리 데이터와 하천 시설정보를 트윈 플랫폼 상에서 위치기반으로 시각화 표출하기 위해서는 유역의 공간정보를 3차원으로 구축하는 과정이 필수적이다. 따라서 GIS 기반의 섬진강 하천 중심 공간정보 구축을 위해 유역의 국가 정사영상과 5m 수치표고모형(DEM)은 최신성과를 협조 받아 적용하였으며, 홍수 분석을 위한 하천 중심 공간정보는 신규 헬기에 LiDAR 매핑을 수행하여 0.5m 급 DEM을 신규 구축하였다. 또한 하천 시설물 중 섬진강댐과 79개 주요 하천 횡단 교량과 3개 보 시설을 지상기준점 측량과 드론 매핑, 패턴 방식의 경량화 작업을 통해 트윈에 탑재할 수 있는 시설물 3D 객체 모델을 제작하였다. 홍수 분석을 위해서는 섬진강 유역에 대해 K-Drum, K-River, K-Flood 모델을 구축하였으며, AI 하천 수위 예측 학습 모델을 개발하였다. 섬진강 디지털 트윈 유역 물관리 플랫폼을 통해 데이터 기반의 똑똑한 물관리를 구현하고자 한다.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.10 no.1 s.20
• /
• pp.9-14
• /
• 2004

Many lighthouses have been built for safety navigation of vessel, but the management of lighthouses had to paid for maintenance costs. For that reason, the remote control system for the lighthouse is to be used, but the communication expense is very expensive because of the use of satellite communication network or the RF communication network Also, the state of lighthouse is difficult to analyze as transmit only measured data. Therefore, this paper embodied the remote control system for the lighthouse using CDMA method, that was extended to island area and we verified the effectiveness of the proposed system.

• Journal of Korean Society of Transportation
• /
• v.19 no.6
• /
• pp.171-182
• /
• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

• Journal of IKEEE
• /
• v.25 no.1
• /
• pp.83-87
• /
• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.258-258
• /
• 2023

도시침수는 하천홍수와는 달리 짧은 시간에 발생하며 저지대 우수 유입, 배수관로 용량 부족등으로 인해 발생한다. 추가적 원인으로 국지성 집중호우가 있으며 짧은 시간에 많은 비가 집중적으로 내리는 현상을 의미한다. 한두 시간 혹은 몇 분 동안의 짧은 시간에 좁은 지역에서 발생하는특성 때문에 발생 시간, 지점, 강우량에 대한 정확한 예측이 어려워 도심지의 저지대가 침수되는등 예상치 못하는 침수피해가 자주 발생한다. 강우량별 피해 범위를 보면 시간당 30~40mm 정도에서 하수관이 역류하고, 시간당 50mm 강우량에서 지하실이나 지하상가와 같은 지하공간에서 침수피해가 발생할 수 있으며, 시간당 100mm 이상의 강우에서는 대규모 재해가 발생할 우려가 높아진다. 도시침수 피해를 줄이기 위해 지자체에서는 CCTV를 운영하여 위험을 감시하고 있지만다수의 인력과 환경에 따라 영상 확인의 한계가 있다. 그러나 침수센서는 침수 정도를 수치로 표현하여 데이터를 확보함과 동시에 다수의 지역을 모니터링하는데 유용하다. 또한 주변 환경에 상관없이 계측된 자료를 모니터링 할 수 있다. 기존 센서를 설계할 때는 도시 미관을 해치는 경우가있었으나 본 연구에서는 도심지의 여건에 맞추어 인도용, 차도용, 공원용의 용도에 맞는 센서를구축하여 미관을 해치지 않으면서 기존의 지형을 활용하고자 하였다. 이 후 구축된 센서를 이용하여 리빙랩 개념의 테스트베드를 통해 다양한 도시침수의 원인이 되는 조건을 검토하여 실증 실험을 진행할 예정이다.