• 대한원격탐사학회지
• /
• 제36권5_4호
• /
• pp.1139-1151
• /
• 2020

국립재난안전연구원에서는 인공위성, 드론, 조사차량 등 다양한 지구관측 플랫폼 기반의 재난 피해 분석·모니터링과 재난 활용 기술을 개발하고 있다. 본 특별호에서는 연구원에서 수행하고 있는 재난현장 조사와 위성기반 재난관리 기술, GIS를 활용한 재난정보 분석과 관련된 최근 연구성과를 자세히 기술하였다. 이러한 지구관측 기술개발 연구성과를 토대로 기존 공간정보 서비스 기술과 재난현장에서 수집되는 각종 정보들을 서로 연계하여 통합적인 국가 재난 조사·분석·모니터링 기술 고도화를 위해 지속적으로 노력을 해나갈 것이다.

• 한국복합재료학회:학술대회논문집
• /
• 한국복합재료학회 2002년도 추계학술발표대회 논문집
• /
• pp.212-215
• /
• 2002

Interfacial evaluation and damage sensing of the carbon fiber/epoxy-amine terminated (AT)-polyetherimide (PEI) composites were performed using micromechanical test and electrical resistance measurement. As AT-PEI content increased, the fracture toughness of epoxy-AT-PEI matrix increased, and thus their interfacial shear strength (IFSS) was improved due to the improved toughness. After curing process, the changes in electrical resistance (ΔR) with increasing AT-PEI contents increased gradually because of the changes in thermal expansion coefficient (TEC) and thermal shrinkage of matrix. Matrix fracture toughness was correlated to the IFSS, residual stress and electrical resistance. The results obtained from the electrical resistance measurement during curing process, reversible stress/strain, and durability test were consistent with modified matrix toughness properties.

• 대한원격탐사학회지
• /
• 제36권5_3호
• /
• pp.1125-1137
• /
• 2020

본 연구에서는 산불 발생 위치와 면적만 기록하는 산불피해대장 정보의 한계점을 보완하기 위해 위성 영상 기반의 산불발생데이터의 연계 방법을 제시하고자 하였다. 이를 위해 2012년 1월 ~ 2019년 12월 사이 산림청에서 제공하는 산불피해대장 자료와 MODIS 및 VIIRS 기반 산불발생데이터를 연계하는 방안을 도출하였다. 연구 결과 191건의 산불피해대장 산불 중 MODIS와 VIIRS 기반 산불발생데이터는 각각 약 11%와 44%의 산불에 대해 발생지역 확인이 가능하였다. VIIRS 기반 산불발생데이토로 추출한 피해지역에 대해 고해상도의 Sentinel-2A 위성으로 확인한 산불 영역과 비교한 결과 평균 56% 정도의 산림지역 피해가 추출되었다. 따라서 대규모 산불에 대해서는 VIIRS 산불발생데이터를 이용하면 면적과 발화점 위치만 기록하는 산불피해대장 정보의 한계점을 보완할 수 있을 것이다.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.962-964
• /
• 2003

In 2002 Thailand was faced with severe flooding in the North, Northeast and Central parts of the country caused by heavy rainfall of the monsoonal depression which brought about significant damages. According to the report by the Ministry of Interior and the Ministry of Agricultural and Co-operatives, the total damages were estimated to be about 6 billion bath. More than 850,000 farmers and 10 million livestock were effected. An area of 1,450,000 ha of farmland in 59 Provinces were put under water for a prolonged period. Satellite imageries were employed for mapping and monitoring the flood-inundated areas, flood damage assessment, flood hazard zoning and post-flood survey of river configuration and protection works. By integrating satellite data with other updated spatial and non-spatial data, likely flood zones can be predicted beforehand. Some examples of satellite data application to flood dis aster mitigation in Thailand during 2002 using mostly Radarsat-1 data and Landsat-7 data were illustrated and discussed in the paper. The results showed that satellite data can clearly identify and give information on the status, flooding period, boundary and damage of flooding. For comprehensive flood mitigation planning, other geo-informatic data, such as the elevation of topography, hydrological data need to be integrated. Ground truth data of the watershed area, including the water level, velocity, drainage pattern and direction were also useful for flood forecasting in the future.

• Smart Structures and Systems
• /
• 제6권5_6호
• /
• pp.731-748
• /
• 2010

Impact detection and health monitoring are very important tasks for civil infrastructures, such as bridges. Piezoceramic based transducers are widely researched for these tasks due to the piezoceramic material's inherent advantages of dual sensing and actuation ability, which enables the active sensing method for structural health monitoring with a network of piezoceramic transducers. Wireless sensor networks, which are easy for deployment, have great potential in health monitoring systems for large civil infrastructures to identify early-age damages. However, most commercial wireless sensor networks are general purpose and may not be optimized for a network of piezoceramic based transducers. Wireless networks of piezoceramic transducers for active sensing have special requirements, such as relatively high sampling rate (at a few-thousand Hz), incorporation of an amplifier for the piezoceramic element for actuation, and low energy consumption for actuation. In this paper, a wireless network is specially designed for piezoceramic transducers to implement impact detection and active sensing for structural health monitoring. A power efficient embedded system is designed to form the wireless sensor network that is capable of high sampling rate. A 32 bit RISC wireless microcontroller is chosen as the main processor. Detailed design of the hardware system and software system of the wireless sensor network is presented in this paper. To verify the functionality of the wireless sensor network, it is deployed on a two-story concrete frame with embedded piezoceramic transducers, and the active sensing property of piezoceramic material is used to detect the damage in the structure. Experimental results show that the wireless sensor network can effectively implement active sensing and impact detection with high sampling rate while maintaining low power consumption by performing offline data processing and minimizing wireless communication.

• Earthquakes and Structures
• /
• 제22권2호
• /
• pp.185-201
• /
• 2022

Structural Health Monitoring (SHM) is used to provide reliable information about the structure's integrity in near realtime following extreme incidents such as earthquakes, considering the inevitable aging and degradation that occurs in operating environments. This paper experimentally investigates an integrated wireless sensor network (Wi-SN) based monitoring technique for damage detection in concrete structures. An effective SHM technique can be used to detect potential structural damage based on post-earthquake data. Two novel methods are proposed for damage detection in reinforced concrete (RC) building structures including: (i) Jerk Energy Method (JEM), which is based on time-domain analysis, and (ii) Modal Contributing Parameter (MCP), which is based on frequency-domain analysis. Wireless accelerometer sensors are installed at each story level to monitor the dynamic responses from the building structure. Prior knowledge of the initial state (immediately after construction) of the structure is not required in these methods. Proposed methods only use responses recorded during ambient vibration state (i.e., operational state) to estimate the damage index. Herein, the experimental studies serve as an illustration of the procedures. In particular, (i) a 3-story shear-type steel frame model is analyzed for several damage scenarios and (ii) 2-story RC scaled down (at 1/6th) building models, simulated and verified under experimental tests on a shaking table. As a result, in addition to the usual benefits like system adaptability, and cost-effectiveness, the proposed sensing system does not require a cluster of sensors. The spatial information in the real-time recorded data is used in global damage identification stage of SHM. Whereas in next stage of SHM, the damage is detected at the story level. Experimental results also show the efficiency and superior performance of the proposed measuring techniques.

• Smart Structures and Systems
• /
• 제31권5호
• /
• pp.485-500
• /
• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• 대한원격탐사학회지
• /
• 제36권5_4호
• /
• pp.1255-1266
• /
• 2020

본 연구에서는 화학사고에 따른 인명피해 저감을 위해서 신속하고 정확한 화학물질 확산 범위 예측을 위한 복합시뮬레이션 프로토타입을 개발하였다. 복합시뮬레이션은 화학물질의 누출 특성을 고려하고자 근거리 확산과정에서 누출 운동량을 고려하였다. 원거리 확산과정에서는 사고지점 주변의 기상 및 지형정보를 이용하여 획일적으로 제시되었던 기존 모델의 바람 분포를 개선하여 실제와 유사한 바람장을 구현하였다. 개선된 근·원거리 확산과정에 따라 최종적으로 피해확산 범위는 기존의 모델에 비해서 정밀한 분포를 나타냈다. 본 연구에서 개발된 복합시뮬레이션의 시간대별 피해 범위 예측 결과 통해서 화학사고 발생 후 주민 대피 및 복귀 등 정책적 의사결정의 지원시스템으로서 활용도가 높을 것으로 기대된다.

• 대한원격탐사학회지
• /
• 제36권1호
• /
• pp.29-39
• /
• 2020

Forest fire is a critical disaster that causes massive destruction of forest ecosystem and economic loss. Hence, accurate estimation of the burned area is important for evaluation of the degree of damage and for preparing baseline data for recovery. Since most of the area size damaged by wildfires in Korea is less than 1 ha, it is necessary to use satellite or drone images with a resolution of less than 10m for detecting the damage area. This paper aims to detect wildfire-damaged area from a Kompsat-3 image using the indices such as NDVI (normalized difference vegetation index) and FBI (fire burn index) and to examine the classification characteristics according to the methods such as Otsu thresholding and ISODATA(iterative self-organizing data analysis technique). To mitigate the salt-and-pepper phenomenon of the pixel-based classification, a gaussian filter was applied to the images of NDVI and FBI. Otsu thresholding and ISODATA could distinguish the burned forest from normal forest appropriately, and the salt-and-pepper phenomenon at the boundaries of burned forest was reduced by the gaussian filter. The result from ISODATA with gaussian filter using NDVI was closest to the official record of damage area (56.9 ha) published by the Korea Forest Service. Unlike Otsu thresholding for binary classification,since the ISODATA categorizes the images into multiple classes such as(1)severely burned area, (2) moderately burned area, (3) mixture of burned and unburned areas, and (4) unburned area, the characteristics of the boundaries consisting of burned and normal forests can be better expressed. It is expected that our approach can be utilized for the high-resolution images obtained from other satellites and drones.

• 대한원격탐사학회지
• /
• 제35권5_2호
• /
• pp.873-881
• /
• 2019

재난관리에서 담당 공무원(관리자)이 모든 피해 현장을 조사하고 복구 계획을 수립하기에는 인력과 비용 측면에서 한계점이 있다. 이를 극복하기 위해 다수의 지역민이 참여하는 커뮤니티매핑(community mapping) 과정에서 등록되는 현장 정보들을 통하여 효율적인 재난관리 의사결정에 활용할 수 있다. 하지만 여러 피해지역에서 검증되지 않은 다수의 정보가 등록되면 오히려 복구 우선순위 결정 등에 걸림돌이 되기도 한다. 또한 커뮤니티매핑 정보는 사용자가 업로드한 사진이나 사전 분류된 내용 또는 콘텐츠만 업로드할 수 있어 관리자가 현장 상황을 명확히 파악하기 어려운 문제점이 있으므로 이를 개선할 여지가 있다. 따라서 본 연구에서는 커뮤니티매핑에 등록되는 정보와 드론으로 촬영한 3D매핑 정보를 연계하여 재난관리 의사결정을 지원하기 위한 방안을 제시하였다. 이를 통하여 관리자가 피해 상황을 종합적으로 판단할 수 있어 피해복구에 대한 계획수립 시 최종 결정권자의 의사결정을 지원할 수 있을 뿐만 아니라 피해복구 단계에서 시간, 인력, 비용의 절감이 가능할 것으로 기대한다.