• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.794-796
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• 2016

Recently terrestrial mobile multimedia broadcasting(T-DMB) service is being provided throughout the country are expanding and demand is increasing day by day. T-DMB has the advantage of being cheaper in cost than installing another mobile multimedia broadcasting. However, there are a variety data of additional and provide it difficult for localized emergency alert broadcasting services. In this paper, a method to solve this problem feature was designed to restrict incoming unidirectional / bidirectional authentication via T-DMB system. In the mobile device is received by the T-DMB broadcasting service authentication mechanism for re-transmission to the mobile device, and T-DMB receiving other registered users can view it impossible to receive the broadcast. Through the proposed system it is considered to be able to solve the problems of the existing T-DMB technology.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.626-632
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• 2017

Since natural disaster occurs increasingly and becomes complicated, it causes deaths, disappearances, and damage to property. As a result, there is a growing interest in the development of ICT-based natural disaster response technology which can minimize economic and social losses. In this letter, we introduce the main functions of the forest fire management platform by using images from an UAV. In addition, we propose a disaster image analysis technology based on the deep learning which is a key element technology for disaster detection. The proposed deep learning based disaster image analysis learns repeatedly generated images from the past, then it is possible to detect the disaster situation of forest-fire similar to a person. The validity of the proposed method is verified through the experimental performance of the proposed disaster image analysis technique.

• Journal of Korean Society of Transportation
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• v.23 no.1
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• pp.33-44
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• 2005

Railroad crossing crashes are fewer than road crashes, but with regard to crash severity, they can be serious injury crashes. There should be, therefore, enormous efforts to increase the safety of railroad crossings. The objective of this paper is to identify and understand factors associated with railroad crossing crashes. Statistical models are used to examine the relationships between crossing accidents and geometric elements of crossings. The results show the Poisson model is the most appropriate method for the crossing accidents, because overdispersion was not observed. This study identifies seven significant factors associated with railroad crossing crashes through the main and variant models. With regard to explanatory factors on crossing safety, the total traffic volume, daily train volume, presence of commercial area around crossings, distance of train detector from crossings, time duration between the activation of warning signals and gates, crossing types, and speed hump were found to affect the safety of railroad crossings.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.476-476
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• 2017

최근 갑작스런 폭우로 인한 제방 붕괴, 침수 및 지진 등과 같은 재해 발생 시 추가 피해를 방지하고 주민들의 긴급대피를 도운 건 SNS를 통한 현장 정보와 경보 메시지의 지속적인 전파이다. 최근의 SNS는 재난정보에서도 활용할 수 있을 정도로 진화하였다. 국가재난정보 중 수재해 관련 정보를 추출하여 다양한 주제도위에 중첩으로 공간정보를 제공할 수 있는 재난정보 제공을 위한 웹서비스를 개발하고자 하였다. 수재해 정보를 필터링하기 위하여 우선 관련된 키워드 선정이 필요하며, 기본적인 키워드는 하천일람표를 참고하여 6개 권역 및 하천이름을 선정하였다. 또한, 한강 홍수 통제소의 수자원 용어사전과 (사)한국물학술단체연합회에서 발간한 물용어집을 참고하여 수재해 관련 용어들 약 300여개를 추가하였다. 선정된 용어들은 1차적으로 적재된 데이터베이스에서 수재해 정보 관련 필터링을 하는데 사용되며, 비정형 데이터들을 필터링하고 주소 정보 검색 및 추출을 통하여 정형화 하게 된다. 추출된 주소정보에 대하여 개발한 지오코딩 모듈을 적용하여 수재해 항목에 대해 좌표정보를 업데이트 하게 된다. 가뭄, 집중호우, 홍수 등의 수재해 정보별, 또한 일자별 그룹화 및 구조화를 진행하고 해당되는 정보를 공간정보 오픈플랫폼 API를 활용하여 지도상에 가시화할 수 있다. 개발한 지오코딩 모듈을 이용하여 실제 테이블 정보를 구성하여 데이터베이스에 수재해 정보 지오코딩 테이블을 구성하여 테스트 모의하였다. 재난정보 중 홍수, 가뭄에 대한 선택정보와 시간정보를 매개변수로 받는 XML 웹서비스 테스트로 검증을 하였다. 본 연구를 통하여 재난정보 가시화에 있어서 사용자가 조회하고자 하는 유형별, 날짜별 선택이 가능한 공간적 정보를 검색 및 확인할 수 있게 되었다. 개발한 수재해 정보 지오코딩 모듈 프로토 타입은 수재해 정보 플랫폼 융합기술 연구단에서 개발하는 핵심 목표시스템 내 재난정보 제공시스템에 적용 가능하며, 수재해 정보에 대하여 대국민 서비스가 가능할 것으로 사료된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.464-464
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• 2018

최근 풍수해 현황 분석(국민안전처, 2016)에서는 2003년 이후 태풍 루사와 매미와 같은 대형태풍이 최근에 발생하지 않아 대부분 하천급류로 인한 인명피해가 대부분이라고 언급하였다. 최근 풍수해로 인한 피해가 발생하지는 않았지만 호우/태풍이 발생할 경우 인명보호와 불편해소를 최우선에 두고 각종 정책들을 선제적으로 추진하고 있어 홍수범람발생 예상지역에 대한 인명피해 분석은 반드시 필요하다고 판단된다. 최근들어 인명피해를 평가하는 기술은 피해자료로부터 비교적 간단히 분석되는 경험적 방법에서 2차원 동적 수리모형과 연계, 그리고 정밀한 인구, 건물 등의 자료를 활용하여 대피율, 사전경보 등 인명피해에 영향을 미치는 다양한 요소를 복합적으로 고려하고 개념적이고 기계적 방법으로 발전하는 추세이다. 우리나라의 경우 인명피해 평가와 관련한 연구사례가 거의 전무한 상태이고, 치수경제성분석에서 제시하는 침수면적에 기반한 간략한 방법만이 실무에서 활용되고 있다. 최근 국외에서 제시한 접근방법은 본 연구에서의 개발하고자 하는 목적과 방향에 부합하지 않다고 판단되며, 국내 실정을 고려할 때 주요 영향인자를 추가하고, 특히 노출인구, 인명 인벤토리의 해상도를 높이는 데 주안점을 두고자 한다. 홍수로 인한 인명피해 발생확률은 사후분석의 일환으로 침수흔적도를 통해 총 2개의 침수구간을 설정한 후 Census data를 활용한 위험인구(Population at Risk, PAR)를 산정한후, NDMS 인명피해 자료를 활용하여 침수구간별 인명피해 발생확률을 제시하였다. 여기서 제시한 침수구간의 경우 데이터의 축적정도에 따라 구간을 세밀화 할 수 있는데, 본 연구에서는 총 2개구간(0-1m, 1m 이상)으로 계략화 하여 제시하였다. 본 연구에서는 4개의 지자체의 인명피해 자료를 통해 인명피해 발생확률을 산정하였으며, 해당내용을 시범유역의 빈도별 침수구역도에 적용하여 인명피해 발생을 분석하였다. 해당 연구결과의 경우 인명피해에 대한 명확한 결과를 유추하는데에는 한계가 있지만, 인명피해에 기반한 해당지역의 장래피해규모를 예측하는 데에는 기초가 될 수 있을 것으로 판단된다.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.7-14
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• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1524-1530
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• 2018

In this paper, we designed a product that can prevent environmental pollution, waste of resources, and leakage of foreign currency by commercializing a green IT solution by merging a used smart phone with the IoT object communication technology for the first time in the world. For the experiment of the designed system, various performance and communication condition was experimented by installing it in the actual crop cultivation facility. As a result, when a problem occurs, the alarm sound and video notification are generated by the user's smart phone, and remote control of various installed devices and data analysis in real time are possible. In this study, it is thought that the terminal management board developed for the utilization of the used smart phone can be applied to various fields such as agriculture and environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1139-1145
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• 2019

In order to efficiently use defined satellite network resources, it is a priority to understand the performance and usage of the network. In this paper, in order to analyze the operational efficiency and stability of the system in the satellite communication system operated by K-water flood forecast and alarm network, FTP and ping testing and network traffic analysis methods of measuring download and upload speed between central and observational countries were introduced. As a result of measuring the transmission speed by the introduced test method, the effects of TCP accelerators have been improved by 120% upon download from the observational station. Through the performance test and traffic analysis of the satellite hydrologic observation system introduced, environmental improvement and improvement points of the satellite communication system were derived so that the operational efficiency and stability of the communication network could be expected.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.53-59
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• 2018

Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In this paper, we proposed a deep learning-based violence recognition method for surveillance cameras. A convolutional neural network (CNN) model is trained and fine-tuned on available benchmark datasets of fights and knives for violence recognition. When an abnormal event is detected, an alarm can be sent to the nearest police station to take immediate action. Moreover, when the probabilities of fight and knife classes are predicted very low, this situation is considered as normal situation. The experimental results of the proposed method outperformed other state-of-the-art CNN models with high margin by achieving maximum 99.21% accuracy.