• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.347-348
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• 2022

본 연구에서는 무인이동체를 활용한 철도교량의 외관조사 점검을 보다 효율적이고 객관성 있게 수행하기 위하여 무인이동체를 통해 촬영된 이미지를 딥러닝 기반 분석기술을 활용하여 손상 자동으로 분석 하기위한 기술을 연구하였다. 철도교량의 외관 손상 중 균열, 콘크리트 박리·박락, 누수, 철근노출에 대한 손상 이미지를 추출하여 딥러닝 분석 모델을 생성하고 학습한 분석 모델을 적용한 시스템을 실제 현장에 적용 테스트를 수행하였으며 학습 구현된 분석모델의 검측 재현율을 검토한 결과 평균 95%이상의 감지성능을 검토할 수 있었다. 개발 제안된 자동손상분석 기술은 기존 육안점검 결과 대비 보다 객관적이고 정밀한 손상 검측이 가능하며 철도 유지관리 분야에서 무인이동체를 활용한 외관조사 업무를 수행함에 있어 기존 대비 객관적인 결과도출과 소요시간, 비용저감이 가능할 것으로 기대된다.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.185-189
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• 2009

무선 센서 네트워크는 화산 감시, 전장 감시, 동물 서식지 감시, 건축물의 감시, 농장 관리, 의료분야등 다양한 분야에서 연구되고 있다. 국내에서도 국가 정책 사업으로 교량 및 건축물의 균열 감시, 표적의 침입 탐지 및 식별을 위한 무선 센서 네트워크 연구가 활발히 진행 중이다. 특히, 무선 센서 네트워크의 다양한 분야의 연구 중에서 철조망을 이용한 표적의 침입 탐지 및 식별에 관한 연구는 산업 시설, 보안지역, 교도소, 군사지역, 공항 등 다양한 분야에서 사용된다. 현재 철조망 감시는 대부분 유선 센서 노드를 통한 유선 센서 네트워크 환경에서 이루어지고 있다. 기존의 유선 센서 네트워크는 높은 데이터 전송률을 통해 수신되는 높은 정보의 신호를 이용하여 고속 푸리에 변환에 의한 신호의 주파수 분석 기법을 사용해 왔다. 하지만, 유선 센서 네트워크의 높은 데이터 전송률과 비교하여 무선 센서 네트워크의 센서 노드는 유선 센서 네트워크에 비해 매우 낮은 데이터 전송률을 가진다. 따라서 무선 센서 네트워크에서 수신되는 신호의 정보가 매우 낮고, 유선 센서 네트워크에서 사용된 고속 푸리에 변환에 의한 신호의 주파수 분석에 따른 주파수별 특징 추출을 할 수 없다. 따라서 본 논문에서는 철조망 감시를 위한 높은 데이터 전송률을 보장하는 유선 센서 네트워크에 비해 제한된 통신자원과 센서 노드의 낮은 데이터 전송률로 인해 수신되는 한정적인 신호의 정보를 이용한 무선 센서 네트 워크에서 철조망의 표적 침입 탐지 및 식별을 위한 특징 추출 알고리즘을 제안한다.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.400-414
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• 2021

Under high-level radioactive waste repository conditions, bentonite as an engineered barrier material undergoes thermal, hydrological, mechanical, and chemical processes. We report the applications of X-ray Computed Tomography (CT) imaging technique on the characterization and analysis of bentonite over the past decade to provide a reference of the utilization of this technique and the recent research trends. This overview of the X-ray CT technique applications includes the characterization of the bentonite either in pellets or powder form. X-ray imaging has provided a means to extract grain information at the microscale and identify crack networks responsible for the pellets' heterogeneity. Regarding samples of pellets-powder mixtures under hydration, X-ray CT allowed the identification and monitoring of heterogeneous zones throughout the test. Some results showed how zones with pellets only swell faster compared to others composed of pellets and powder. Moreover, the behavior of fissures between grains and bentonite matrix was observed to change under drying and hydrating conditions, tending to close during the former and open during the latter. The development of specializing software has allowed obtaining strain fields from a sequence of images. In more recent works, X-ray CT technique has served to estimate the dry density, water content, and particle displacement at different testing times. Also, when temperature was added to the hydration process of a sample, CT technology offered a way to observe localized and global density changes over time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.481-487
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• 2018

This paper proposes a method to detect and monitor the land creeping area using a UAV to analyze the damaged area efficiently. Using a UAV, it was possible to secure the safety of the investigators before the field survey and effectively utilize it to establish an investigation plan because an orthophoto can be used to detect and scale the cracks in a land creeping area. In addition, it was possible to analyze the scale of the crack quantitatively by extracting the topographic information from the orthophoto. The study sites were found to have a total crack area of 1.01 ha, a length of 1.07 km, an average width of 10 m, and a step distance of 1 to 10 m. Periodic UAV measurements can be used to detect displacements on the land creeping area and monitor the direction and scale of crack spread. Therefore, it is expected to be used effectively during recovery planning. Applying the UAV to the land creeping area resulted in the qualitative and quantitative results quickly and easily in dangerous mountainous watersheds. Therefore, it is expected that it will contribute to the development of related industries because of the high availability of a UAV in forest soil sediment disasters, such as landslides, debris flow, and land creeping area.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.213-215
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• 2022

Recently, deep learning technology that processes images has been widely used in fire detection, autonomous driving, and defective product detection. In particular, in order to determine whether a product is contaminated or not, it can be identified through the contaminants passed from the existing sensor data, but technologies for recognizing cracks in products or contaminants themselves as images are being actively studied in various fields. In this paper, a system for classifying uncontaminated normal cups and contaminated cups through images was designed and implemented. The image was analyzed using an open image and a photographed image, and the image was analyzed by extracting the upper part of the cup image using Google Objectron for 3D object recognition. Through this study, it is thought that it will be used in various ways for research that can extract the contamination level of products required in the hygiene field based on images.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.447-456
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• 2020

The study area is a slope in Dogye-eup, Samcheok-si, Gangwon-do. The cutting method was applied to this slope for stabilization in 2009 due to the influence of the waste-rock dump located at the top of slope. Recently, soil cracks and creep have occurred on this slope, and the drainage channel was damaged. Therefore, it was analyzed the topography change through photogrammetry using a UAV. Orthophotos were taken in April and October 2019 respectively. From the Orthophots, Digital Surface Model (DSM) was extracted. Time series analysis was performed by comparing each DSM. The topography of October was pushed forward while maintaining the topography of April. Through these features, it is judged that the soil creep is occurring in this study area.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.803-811
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• 2021

In this study, the exposure amount of IASCC test worker was evaluated by applying the process simulation technology. Using DELMIA Version 5, a commercial process simulation code, IASCC test facility, hot cells, and workers were prepared, and IASCC test activities were implemented, and the cumulative exposure of workers passing through the dose-distributed space could be evaluated through user coding. In order to simulate behavior of workers, human manikins with a degree of freedom of 200 or more imitating the human musculoskeletal system were applied. In order to calculate the worker's exposure, the coordinates, start time, and retention period for each posture were extracted by accessing the sub-information of the human manikin task, and the cumulative exposure was calculated by multiplying the spatial dose value by the posture retention time. The spatial dose for the exposure evaluation was calculated using MCNP6 Version 1.0, and the calculated spatial dose was embedded into the process simulation domain. As a result of comparing and analyzing the results of exposure evaluation by process simulation and typical exposure evaluation, the annual exposure to daily test work in the regular entrance was predicted at similar levels, 0.388 mSv/year and 1.334 mSv/year, respectively. Exposure assessment was also performed on special tasks performed in areas with high spatial doses, and tasks with high exposure could be easily identified, and work improvement plans could be derived intuitively through human manikin posture and spatial dose visualization of the tasks.

• Tunnel and Underground Space
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• v.16 no.1 s.60
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• pp.1-10
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• 2006

Rock masses in nature include various rock discontinuities such as faults, joints, bedding planes, fractures, cracks, schistosities, and cleavages. The behavior of rock structures, therefore, is mainly controlled by various rock discontinuities. In many tunnels, enormous cost and time are consumed to cope with the failing or sliding of rock blocks, which cannot be predicted because of the complexity of rock discontinuities. It is difficult to estimate the properties of rock masses before the rock excavation. The observational design and construction method of tunnels in rock masses is becoming important recently. In this paper, a new observational design and construction method for rock block evaluation of tunnels in discontinuous rock masses is proposed, and then applied to the tunnel based on actual rock discontinuity information observed in the field. It is possible to detect key blocks all along the tunnel exactly by using the numerical analysis program developed far the new observational design and construction method. This computer simulation method with user-friendly interfaces can calculate not only the stability of rock blocks but also the design of supplementary supports. The effectiveness of the proposed observational design and construction method has been verified by the confirmation of key block during the enlargement excavation.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.