• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2008년도 공동추계학술대회
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• pp.251-253
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• 2008

Erosion and landslide cause serious damage to forest areas. As a consequence, partial or complete destruction of vegetation occurs, which leads to many cascading problems. In this study, an attempt has been made to identify the forest areas, which are under different risk categories of erosion and landslide, in part of Eastern Ghats of Tamil Nadu. Relevantthematic maps were generated from satellite data, topographical maps, primary and secondary data and weights to each map were assigned appropriately. Weighted overlay analysis was carried out to identify the erosionprone forest areas. The result of erosion and landslide prone model reveals that 4712 ha(17%) of forest area is under high risk category and 15879 ha(58.65%) isunder medium risk category. The results of spatial modeling would be very much useful to the forest officials and conservationist to plan for effective conservation.

• Structural Monitoring and Maintenance
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• 제1권1호
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• pp.1-45
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• 2014

The developments and implementations of the structural health monitoring (SHM) system for large infrastructures have been gradually recognized by researchers, engineers and administrative authorities in the last decades. This paper summarizes an updated review on innovations and applications in SHM for infrastructures carried out by researchers at Dalian University of Technology. Invented sensors and data acquisition system are firstly briefly described. And then, some proposed theories and methods including the sensing technology, sensor placement method, signal processing and data fusion, system identification and damage detection are discussed in details. Following those, the activities on the standardization of SHM and several case applications on specific types of structure are reviewed. Finally, existing problems and promising research efforts in the field of SHM are given.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.274-274
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• 2002

Damage to forest trees, caused by wildfire, changes their spectral reflectance signature. This factor led to the initiation of a research project at the Remote Sensing & GIS Laboratory, Kookmin University, to determine if multispectral data acquired by IKONOS could provide fire scar and bum severity mapping. This paper will present detail mapping of burned areas in the eastern coast of Korea with IKONOS imagery. In addition, a single post-burn Landsat-7 ETM＋ data was used to compare with IKONOS, the study area. Burn severity map based on IKONOS image was found to be affected by strong topographic illumination effects in the mountain forest. But it has better the delineation of the bum-scarred area. In this study the NDVI was analyzed for geometric illumination conditions influenced by topography(slop, aspect and elevation) and shadow(solar elevation and azimuth angle).

• 대한원격탐사학회지
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• 제20권1호
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• pp.13-21
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• 2004

The usefulness of the multi-temporal satellite image to monitoring the vegetation recovery process after forest fire was tested. Using multi-temporal Landsat TM and ETM＋data, NDVI and NBR changes over times were analyzed. Both NDVI and NBR values were rapidly decreased after the fire and gradually increased for all forest type and damage class. However, NBR curve showed much clearer tendency of vegetation recovery than NDVI. Both indices yielded the lowest values in severely damaged red pine forest. The results show the vegetation recovery process after forest fire can detect and monitor using multi-temporal Landsat image. NBR was proved to be useful to examine the recovering and development process of the vegetation after fire. In the not damaged forest, however the NDVI shows more potential capability to discriminate the forest types than NBR..

• 디지털산업정보학회논문지
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• 제17권4호
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• pp.95-102
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• 2021

As social interest in the metaverse increases, various metaverse platforms and services are appearing, and various security issues are emerging accordingly. In particular, since all activities are performed in a variety of virtual spaces, and the metaverse utilizes sensing data using various hardware devices, more information is accumulated than other Internet services, and more damage can occur if information security is not guaranteed. Therefore, in this paper, we propose a metaverse security model that considers the major issues mentioned in previous papers and the necessary evaluation factors for the security functions required in the metaverse platform. As a result of performing the performance evaluation of the proposed model and the existing attribute information collection model, the proposed model can provide security functions such as anonymity and source authentication, which were not provided by the existing models.

• Journal of Mechanical Science and Technology
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• 제17권10호
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• pp.1423-1430
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• 2003

In this paper, we develop a real time monitoring system to detect third-party damage on natural gas pipeline. When the damage due to third-party incidents causes an immediate rupture, the developed on-line monitoring system can help reducing the sequences of event at once. Moreover, since many third-party incidents cause damage that does not lead to immediate rupture but can grow with time, the developed on-line monitoring system can execute a significant role in reducing many third-party damage incidents. Also, when the damage is given at a point on natural gas pipeline, the acoustic wave is propagated very fast about 421.3 m/s. Therefore, the data processing time should be very short in order to detect precisely the impact position. Generally, the pipeline is laid under ground or sea and the length is very long. So a wireless data communication method is recommendable and the sensing positions are limited by laid circumstance and setting cost of sensors. The calculation and monitoring software is developed by an algorithm using the propagation speed of acoustic wave and data base system based on wireless communication and DSP systems. The developed monitoring system is examined by field testing at Balan pilot plant, KOGAS being done in order to demonstrate its validity through reactive detection of third-party contact with pipelines. Furthermore, the development system was set at the practical pipelines such as an offshore pipeline between two islands Yul-Do and Youngjong-Do, and a land branch of Pyoungtaek, Korea and it has been operating in real time.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.443-449
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• 1999

The investigation of the amount of the ecosystem damage on the shoreline due to the NAHODKA oil-spill accident, which occurred in the Sea of Japan, was attempted by using geoinformatics. At first, it was assumed that symbolical vegetation's distribution could be specified in the coast in Ishikawa Pref. where the heavy oil was washed, and surveyed the regional distribution. Then, the presumption result of those environmental capacities was arranged by GIS. In addition, the amount of the ecosystem damage was presumed as cost necessary though a symbolical living thing for the retreat because of the base line by the heavy oil drifting ashore was recovered. By comparing the vegetation line and the surveying data which shows environmental capacity, the retreat areas of the vegetation were 1100-1200 $m^2$. When the amount of damage on the ecosystem of the NAHODKA oil-spill accident was presumed based on the retreat area of this vegetation and the restoration cost, the amount of damage within Shioya beach which 150m in the surveying range became 2 to 2.5 million Yen. Because the extension distance from the Shioya beach to the Katano beach was about 3,500m, the amount of damage became about 46 to 65 million Yen. As a result of calculation for the amount of damage on the ecosystem of the NAHODKA oil-spill accident, it was estimated approximately 1,400 to 2,000 million Yen in the shoreline of Ishikawa Pref., because the total extension of beaches in Ishikawa Pref. is about 110km.

• 대한기계학회논문집A
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• 제41권5호
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• pp.401-407
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• 2017

본 연구에서는 레이저 스캐닝 및 단일 주파수 정상파 가진과 파수 분석을 통해 구조물의 손상을 탐지하는 기법을 개발하였다. 구조물에 부착된 압전소자를 통해 단일 주파수로 가진하고, 이때 발생한 구조물의 정상상태 응답을 레이저 도플러 속도계와 거울 방향조절 장치를 통해 측정하였다. 구조물의 결함을 탐지하기 위해 정상상태 응답에서 파수 필터링을 이용한 손상 탐지 기법을 개발 및 적용하였다. 부식결함이 발생한 알루미늄 평판과 층간 분리가 발생한 복합재료 구조물에 대한 손상 탐지를 수행하여 손상의 위치와 크기를 정확히 파악할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.3-5
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• 2008

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmental description of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapour Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapour Explosion), Fireball and so on, among them, we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.

• Smart Structures and Systems
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• 제31권4호
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.