• Smart Structures and Systems
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• 제4권6호
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.371-374
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• 2007

For development of landslide risk assessment techniques using GIS(Geographic Information System), this study classifies the category of socioeconomic factors. The landslide quantitative risk assessment performs first prediction of flow trajectory and runout distance of debris flow over natural terrain. Based on those results, it can be analyzed the factors of socioeconomic which are directly related to the magnitude of risk due to landslide hazards. Those risk assessment results can deliver factual damage situation prediction to policy making for the landslide damage mitigation. Therefore, this study is based on feature classification of the digital map ver. 2.0 provided by the National Geographic Information Institute. The category of factors can be used as useful data in preventing landslide.

• 대한원격탐사학회지
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• 제7권2호
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• pp.149-163
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• 1991

This paper discusses the capabilities of airborne remotely sensed data to detect and classify forest damades. In this work the AMS (Aircraft Multiband Scanner) was used to obtain digital imagery at 300m altitude for forest damage inventory in the Black Forest of Germany. MSS(Multispectral Scanner) digital numbers were converted to spectral emittance and radiance values in 8 spectral bands from the visible to the thermal infrared and submitted to a maximum-likelihood classification for : (1) tree species ; and. (2) damage classes. As expected, the resulted, the results of MSS data with high spatial resolution 0.75m$\times$0.75m enabled the detection and identification of single trees with different damages and were nearly equivalent to the truth information of ground checked data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.426-429
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• 2006

Flood caused by Typhoon and severe rain during summer is the most destructive natural disasters in Korea. Almost every year flood has resulted in a big lost of national infrastructure and loss of civilian lives. It usually takes time and great efforts to estimate the flood-related damages. Government also has pursued proper standard and tool for using state-of-art technologies. High resolution satellite imagery is one of the most promising sources of ground truth information since it provides detailed and current ground information such as building, road, and bare ground. Once high resolution imagery is utilized, it can greatly reduce the amount of field work and cost for flood related damage assessment. The classification of high resolution image is pre-required step to be utilized for the damage assessment. The classified image combined with additional data such as DEM and DSM can help to estimate the flooded areas per each classified land use. This paper applied object-oriented classification scheme to interpret an image not based in a single pixel but in meaningful image objects and their mutual relations. When comparing it with other classification algorithms, object-oriented classification was very effective and accurate. In this paper, IKONOS image is used, but similar level of high resolution Korean KOMPSAT series can be investigated once they are available.

• 대한원격탐사학회지
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• 제36권1호
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• pp.29-39
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• 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.

• 한국재난정보학회 논문집
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• 제9권2호
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• pp.153-163
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• 2013

최근 국내외적으로 자연 및 인적재난에 의한 피해는 매년 증가하고 있다. 이들 재난에 대한 피해는 완전히 방지할 수는 없으나 국가적으로 잘 준비된 예방 및 관리대책 수립에 의하여 막대한 인명 및 재산피해가 발생되는 경우에 이를 충분히 저감할 수 있다. 방재자원은 재난 발생 시 투입되는 인력, 물자, 장비, 시설자원을 의미한다. 실제 재난발생 시 이들 자원의 신속하고 적절한 투입은 실제 그 재난을 최소화하는데 매우 중요한 요소이다. 그러나 현재 국내 방재자원의 경우, 적절한 방재자원 동원을 위한 기준이나 분류가 체계적으로 구성되어 있지 못한 상황으로 이로 인한 효과적인 자원관리 및 투입이 효율적으로 운영되지 못하고 있는 실정이다. 본 연구는 현행 국내에서 불규칙적으로 적재, 활용되고 있은 방재자원의 효율적인 동원체계 구축을 위한 초기단계의 연구로서 보다 효율적인 방재자원의 관리 및 운영을 위한 방재자원의 기능별, 역할별 분류를 구축하고 이를 제시 보다 효과적인 방재자원 관리 및 동원시스템을 구축하는 토대를 마련하고자 한다.

• 대한원격탐사학회지
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• 제38권5_1호
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• pp.545-557
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• 2022

본 연구는 직접적인 접근이 어려운 demilitarized zone (DMZ)의 산불 피해 지역을 파악하기 위하여, 고해상도 위성영상 및 머신러닝 기반의 감독 분류 기법을 이용하였다. 고해상도 위성 영상은 Sentinel-2 A/B를 이용하였으며, SVM 감독분류 기법을 기반으로 토지피복도를 산출하였다. DMZ 산불 피해 지역을 분류하기 위한 최적의 조합을 찾기 위하여 SVM 내에 다양한 커널과 밴드 조합에 따른 감독 분류를 진행하고 오차 행렬을 통해 정확도를 평가하였다. 또한, 2020년, 2021년은 위성영상 자료 기반의 산불 탐지 결과와 산불 연보의 피해 지역 면적 간의 비교를 통한 검증을 수행하였다. 이후, 현재 피해 면적 자료가 없는 2022년의 산불 피해 지역을 탐지함으로써 신뢰할 만한 수준의 결과를 신속적으로 파악하고자 하였다.

• 스마트미디어저널
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• 제7권2호
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• pp.34-39
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• 2018

국내 유해 적조발생에 따른 어패류 양식장에 지속적인 피해가 증가함에 따라서 적조에 대하여 많은 연구가 이루어지고 있다. 그러나 자동으로 적조 이미지를 인식하여서 유해적조생물을 판별하는 적조생물 이미지 검색에 대한 국내의 연구는 미흡한 실정에 있다. 본 논문은 오픈소스 기반의 딥러닝을 이용하여 적조생물 이미지를 분류할 수 있는 방법을 제안한다. 제안방법은 다양하게 표현되는 적조생물 이미지의 인식문제를 해결하기 위하여 텐서프로 프레임워크와 구굴 이미지 분류 모델을 이용하여 구현하였다.

• 한국안전학회지
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• 제20권3호
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• pp.112-119
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• 2005

In road tunnel, in order to prevents an accident and minimize the damage of an accident in the case of fire, safety facilities and equipments are integral parts. The type and amount of safety facilities are based on tunnel type and length, traffic flow rate, etc. Therefore many countries use a tunnel classification system that categories tunnel into groups, and specifies the necessary emergency equipment for each group. In this study, for the purpose of classifying tunnel based on tunnel ist investigated the domestic and foreign standards and regulations for safety of road tunnel. As a results, we suggest the method of classification of tunnel by traffic performance, tunnel grade, the volume of traffic, fraction of HGV, rules or regulations for transports of dangerous good through tunnel.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.211-214
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• 2007

After the forest fire, as access is not easy, forest damage degree are determined with Landsat TM image rather than visual inspection. Therefore in this study, damaged areas are extracted with factor analysis and cluster analysis. Second factor analysis was performed for areas suspicious as forest fire damage areas to evaluate accuracy after separating into strong, medium and light forest fire areas.