• Nuclear Engineering and Technology
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• 제54권2호
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• pp.601-607
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• 2022

In recent years, monitoring of spent nuclear fuel inside dry cask storage has become an important area of national security. Muon tomography is a useful method for monitoring spent nuclear fuel because it uses high energy muons that penetrate deep into the target material and provides a 3-D structure of the inner materials. We designed a muon tomography system consisting of four 2-D position sensitive detector and characterized and optimized the system parameters. Each detector, measuring 200 × 200 cm², consists of a plastic scintillator, wavelength shifting (WLS) fibers and, SiPMs. The reconstructed image is obtained by extracting the intersection of the incoming and outgoing muon tracks using a Point-of-Closest-Approach (PoCA) algorithm. The Geant4 simulation was used to evaluate the performance of the muon tomography system and to optimize the design parameters including the pixel size of the muon detector, the field of view (FOV), and the distance between detectors. Based on the optimized design parameters, the spent fuel assemblies were modeled and the line profile was analyzed to conduct a feasibility study. Line profile analysis confirmed that muon tomography system can monitor nuclear spent fuel in dry storage container.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1818-1832
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• 2022

Violence in the Internet era poses a new challenge to the current counter-riot work, and according to research and analysis, most of the violent incidents occurring are related to the dissemination of violence images. The use of the popular deep learning neural network to automatically analyze the massive amount of images on the Internet has become one of the important tools in the current counter-violence work. This paper focuses on the use of transfer learning techniques and the introduction of an attention mechanism to the residual network (ResNet) model for the classification and identification of violence images. Firstly, the feature elements of the violence images are identified and a targeted dataset is constructed; secondly, due to the small number of positive samples of violence images, pre-training and attention mechanisms are introduced to suggest improvements to the traditional residual network; finally, the improved model is trained and tested on the constructed dedicated dataset. The research results show that the improved network model can quickly and accurately identify violence images with an average accuracy rate of 92.20%, thus effectively reducing the cost of manual identification and providing decision support for combating rebel organization activities.

• Applied Microscopy
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• 제50권
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• pp.25.1-25.11
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• 2020

Scanning acoustic microscopy (SAM) or Acoustic Micro Imaging (AMI) is a powerful, non-destructive technique that can detect hidden defects in elastic and biological samples as well as non-transparent hard materials. By monitoring the internal features of a sample in three-dimensional integration, this technique can efficiently find physical defects such as cracks, voids, and delamination with high sensitivity. In recent years, advanced techniques such as ultrasound impedance microscopy, ultrasound speed microscopy, and scanning acoustic gigahertz microscopy have been developed for applications in industries and in the medical field to provide additional information on the internal stress, viscoelastic, and anisotropic, or nonlinear properties. X-ray, magnetic resonance, and infrared techniques are the other competitive and widely used methods. However, they have their own advantages and limitations owing to their inherent properties such as different light sources and sensors. This paper provides an overview of the principle of SAM and presents a few results to demonstrate the applications of modern acoustic imaging technology. A variety of inspection modes, such as vertical, horizontal, and diagonal cross-sections have been presented by employing the focus pathway and image reconstruction algorithm. Images have been reconstructed from the reflected echoes resulting from the change in the acoustic impedance at the interface of the material layers or defects. The results described in this paper indicate that the novel acoustic technology can expand the scope of SAM as a versatile diagnostic tool requiring less time and having a high efficiency.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.97-99
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• 2022

산업과 기술력이 발전하면서 이에 대한 데이터의 양도 증폭하고 있으며 해당 기술력과 정보 전달에 대한 연구가 활발히 진행되고 있다. 따라서 본 논문에서는 데이터의 양을 줄이기 위해서 압축센싱을 활용하였고 해당 데이터가 객체 검출 알고리즘인 Mask R-CNN 모델에 미치는 영향을 분석하였다. 압축률이 높아질수록 이미지의 데이터 양이 줄어들면서 해상도가 낮아지는 것을 확인할 수 있었지만 객체 검출에서는 원본과 큰 차이를 보이지 않고 대부분의 객체가 검출되는 것을 확인하였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.47-48
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• 2022

The positioning technology that measures the position of a person or object is a key technology to deal with the location of the real coordinate system or converge the real and virtual worlds, such as digital twins, augmented reality, virtual reality, and autonomous driving. In estimating the location of a person or object at an indoor construction site, there are restrictions that it is impossible to receive location information from the outside, the communication infrastructure is insufficient, and it is difficult to install additional devices. Therefore, this study tested the direct sparse odometry algorithm, one of the visual Simultaneous Localization and Mapping (vSLAM) that estimate the current location and surrounding map using only image information, at an indoor construction site and analyzed its applicability as an indoor positioning technology. As a result, it was found that it is possible to properly estimate the surrounding map and the current location even in the indoor construction site, which has relatively few feature points. The results of this study can be used as reference data for researchers related to indoor positioning technology for construction sites in the future.

• Smart Structures and Systems
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• 제30권5호
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Nonlinear Functional Analysis and Applications
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• 제29권1호
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• pp.131-164
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• 2024

In this research, we study a modified relaxed Tseng method with a single projection approach for solving common solution to a fixed point problem involving finite family of τ-demimetric operators and a quasi-monotone variational inequalities in real Hilbert spaces with alternating inertial extrapolation steps and adaptive non-monotonic step sizes. Under some appropriate conditions that are imposed on the parameters, the weak and linear convergence results of the proposed iterative scheme are established. Furthermore, we present some numerical examples and application of our proposed methods in comparison with other existing iterative methods. In order to show the practical applicability of our method to real word problems, we show that our algorithm has better restoration efficiency than many well known methods in image restoration problem. Our proposed iterative method generalizes and extends many existing methods in the literature.

• 한국항행학회논문지
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• 제22권1호
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• pp.20-26
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• 2018

본 논문에서는 오인식률 감소를 위한 다중 프레임 특징점 추적 정보 기반 이동 물체 검출 및 추적 알고리즘을 제안한다. 기존의 연구에서는 이동 물체 탐지의 오인식과 추적의 속도 문제가 존재 하였다. 본 연구에서는 이를 보완하기 위해 먼저, 카메라 이동 보상과 물체의 추적을 위해 다중 프레임의 코너 특징점과 옵티컬 플로우를 계산한다. 다음으로 다중 프레임 전-후방향 추적으로 옵티컬 플로우의 추적 오류를 감소시키고, 카메라 이동 보상을 위해 호모그래피와 RANSAC 알고리즘 기반으로 추적된 코너 특징점을 배경영역과 이동 물체 후보 영역으로 구분한다. 변환된 코너 특징점들 중 RANSAC에 의해 제거되는 이상점들을 군집화하고 일정 크기 이상의 이상점 군집 영역을 이동 물체 후보군으로 구분한다. 이동 물체 후보군으로 구분된 물체는 라벨 추적 기반 데이터 상관 분석에 따라 라벨 번호를 할당하고 추적한다. 이동 물체 후보군으로 구분된 물체는 라벨 추적 기반 데이터 상관 분석에 따라 라벨 번호를 할당하고 추적한다. 본 논문에서는 제안한 알고리즘이 기존 알고리즘에 비해 Precision과 Recall 모두 향상됨을 쿼드로터 영상기반 탐지 및 추적 성능 실험으로 확인하였다.

• 한국재난정보학회 논문집
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• 제20권1호
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• pp.199-205
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• 2024

연구목적: 본 연구는 요양병원 등 재난취약시설에 재난이 발생할 경우 잔류한 요구조자를 정확하게 파악하여 소방 등 대응기관에 제공하는 APC(Auto People Counting)의 인식률 개선에 목적이 있다. 연구방법: 본 연구에서는 실제 재난취약시설에 설치되어 운영 중인 APC를 대상으로 카메라를 통해 출입 인원의 이미지를 인식하는 알고리즘을 개선하기 위해 CNN모델을 활용하여 베이스라인 모델링을 하였다. 다양한 알고리즘의 성능을 분석하여 상위 7개의 후보군을 선정하고 전이학습 모델을 활용하여 성능이 가장 우수한 최적의 알고리즘을 선정하는 방법으로 연구를 수행하였다. 연구결과: 실험결과 시간과 성능이 가장 좋은 Densenet201, Resnet152v2 모델의 정밀도와 재현율을 확인한 결과 모든 라벨에 대해서 정확도 100%를 나타내는 것을 확인할 수 있었다. 이 중 Densenet201 모델이 더 높은 성능을 보여주었다. 결론: 다양한 인공지능 알고리즘 중 APC에 적용할 수 있는 최적의 알고리즘을 선정하였다. 향후 연무 등 다양한 재난상황에서 재난취약시설 내 출입인원을 정확하게 파악할 수 있도록 알고리즘 분석 및 학습에 대한 추가 연구가 요구된다.

• 융합신호처리학회논문지
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• 제3권3호
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• pp.47-53
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• 2002

본 논문은 동영상의 대표프레임에서 eigen value와 휘도 성분을 이용한 내용기반 검색 기법에 관한 연구이다. video를 shot단위로 분할하여 shot을 대표하는 대표프레임을 얻어내고, 그 대표프레임을 Eigen Component Analysis(ECA)를 이용하여 데이터베이스를 생성하여 shot들의 휘도값 분포를 구한다. 생성된 코드북과 각 대표 프레임에 대한 코드북 인덱스 값, 휘도값을 database화하여 질의 영상과 video database간의 저장된 비디오들의 코드북과 코드워드간의 유클리디안 거리를 이용하여 유사도 높은 비디오를 찾고, 검색되어진 video에 포함된 대표프레임들의 코드북 인덱스 값과 입력 영상의 코드북 인덱스 값을 비교하여 입력 영상의 특징과 가장 유사한 대표프레임을 얻어낸다. 실험결과 제안된 방법이 검색에 있어서 영상의 형태에 대한 전체적인 특징을 제공하는 대표프레임의 eigen value와 휘도 성분을 이용함으로서 보다 검색 결과가 우수하며, 영상의 통계적인 특성을 이용함으로서 시간과 메모리 공간을 줄일 수 있음을 확인하였다.