• 한국방재안전학회논문집
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• 제7권2호
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• pp.35-41
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• 2014

지하철과 같은 지하공간에서 재난이 발생할 경우, 신속하게 안전한 대피 경로로 승객을 유도하여 인명피해를 최소화 하여야 한다. 이를 위해서 빠르게 재난을 감지하고 중앙관리센터에서 신속한 대응이 이루어지지 않는 경우에 자율적으로 의사결정을 할 수 있는 분산형 방재 시스템이 필요하다. 본 연구에서는 분산 자율형 방재 시스템과 재난전조 감지에 관한 개념적인 연구를 수행하였다.

• 한국정보전자통신기술학회논문지
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• 제9권6호
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• pp.624-629
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• 2016

Recently various types of disaster monitoring system using smart-phones are under active studying. In this paper, we propose a system that automatically performs the disaster and fire detection. Additionally we implement the Arduino-based smart image sensor system in the web platform. When a fire is detected, an SMS is sent to the Fire and Disaster Management Agency. In order to improve fire detection probability, we proposed a smart Arduino fire detection sensor simulation which searches the smart sensor inference algorithm using fuzzy rules.

• 한국재난정보학회 논문집
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• 제10권3호
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• pp.452-457
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• 2014

화학재난 발생시 현장대응 요원들은 사건 원인물질의 성상과 잔류오염 농도를 신속 정확하게 파악해야 한다. 또한 화학재난 현장에서의 적절한 대응절차 진행을 위해서는 화학물질의 성상과 오염농도 확인은 필수적이다. 이를 위해 현장에서 사용하는 각 장비의 특징을 알아보고자 한다. 현장대응장비는 모든 화학물질을 확인할 수 없으며, 각 장비별로 물질탐지에 제한적이다. 장비별 물질탐지 범위와 상호보완성을 고려해야 한다. 본 연구에서는 현장 활용장비인 간이탐지 킷과 검지관식 탐지장비, 전자식 탐지장비의 신속한 현장 활용을 위한 대응절차를 마련하여 현장대응에 도움을 주고자 한다.

• Journal of information and communication convergence engineering
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• 제9권1호
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• pp.69-73
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• 2011

Twitter is a microblogging service that allows its user to share short messages called tweets with each other. All the tweets are visible on a public timeline. These tweets have the valuable geospatial component and particularly time critical events. In this paper, our interest is in the rapid detection of disaster events such as tsunami, tornadoes, forest fires, and earthquakes. We describe the detection system of disaster events and show the way to detect a target event from Twitter data. This research examines the three disasters during the same time period and compares Twitter activity and Internet news on Google. A significant result from this research is that emergency detection could begin using microblogging service.

• 한국안전학회지
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• 제31권6호
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.

• 전자공학회논문지CI
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• 제49권2호
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• pp.90-95
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• 2012

위성영상은 광범위한 지역에 걸쳐 실시간으로 정확한 지표 상태에 대한 정보를 수집할 수 있어 재난재해관리에도 효율적 수단으로 사용되고 있다. 특히 고해상도 영상은 1m급 이하 지표 물체를 탐지할 수 있어 도심지역 정보 획득에 매우 유용하다. 본 논문에는 재난 발생 시 고해상도 위성영상으로부터 변화탐지 기법을 사용하여 피해를 탐지하고 피해정보를 추출하는 방법론이 제안되었다. 사용된 영상분석기법은 텍스쳐 정보를 이용하여 시간적 변화를 탐지하는 기법으로 특징 추출과 변화탐지 단계로 구성되어있다. 특징 추출 단계에서는 wavelet과 GLCM을 이용하여 텍스쳐가 추출되었고 변화탐지 단계에서는 영역간 텍스쳐의 상관관계를 이용한 분류기법이 사용되었다. 제안된 방법은 고해상도 위성영상을 사용하여 지진피해지역을 탐지하는 예에 적용되어 테스트 되었다.

• Computers and Concrete
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• 제32권6호
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• pp.615-623
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• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.25-33
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• 2019

In this paper, we propose an intelligent CCTV technology which is applied to a recent attracted attention real-time object detection technology in a disaster alarm system. Natural disasters are rapidly increasing due to climate change (global warming). Various disaster alarm systems have been developed and operated to solve this problem. In this paper, we detect object through Neuron Network algorithm and test the difference from existing SVM classifier. Experimental results show that the proposed algorithm overcomes the limitations of existing object detection techniques and achieves higher detection performance by about 15%.

• 한국측량학회지
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• 제34권2호
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• pp.195-206
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• 2016

The remote sensing technique using SAR data have many advantages when applied to the disaster site due to its wide coverage and all-weather acquisition availability. Although a single-pol (polarimetric) SAR image cannot represent the land surface better than a quad-pol SAR image can, single-pol SAR data are worth using for disaster-induced change detection. In this paper, an automatic change detection method based on a mixture of GGDs (generalized Gaussian distribution) is proposed, and usability of the textural features and intensity is evaluated by using the proposed method. Three ALOS/PALSAR images were used in the experiments, and the study site was Norita City, which was affected by the 2011 Tohoku earthquake. The experiment results showed that the proposed automatic change detection method is practical for disaster sites where the large areas change. The intensity information is useful for detecting disaster-induced changes with a 68.3% g-mean, but the texture information is not. The autocorrelation and correlation show the interesting implication that they tend not to extract agricultural areas in the change detection map. Therefore, the final tsunami-induced change map is produced by the combination of three maps: one is derived from the intensity information and used as an initial map, and the others are derived from the textural information and used as auxiliary data.

• 드라이브 ㆍ 컨트롤
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• 제17권3호
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• pp.1-8
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• 2020

Recently, a special purpose machine with two manipulators and quadruped crawler system has been developed for rapid life-saving and initial restoration work at disaster sites. This special purpose machine provides the driver with various environmental recognition functions for accurate and rapid task determination. In particular, the human detection technology assists the driver in poor working conditions such as low-light, dust, water vapor, fog, rain, etc. to prevent secondary human accidents when moving and working. In this study, a human detection module is developed to be mounted on a special purpose machine. A thermal sensor and CCD camera were used to detect victims and nearby workers in response to the difficult environmental conditions present at disaster sites. The performance of various AI-based life detection algorithm were verified and then applied to the task of detecting various objects with different postures and exposure conditions. In addition, image visibility improvement technology was applied to further improve the accuracy of human detection.