• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.1999-2010
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• 2023

Undesirable flapping motion of discs can cause the failure of swing check valves in nuclear passive safety systems. Time-resolved particle image velocimetry (PIV) was employed to investigate the flow characteristics around a free-to-rotate plate and the motion response, with the Reynolds numbers, based on the hydraulic diameter of the channel, from 1.32 × 10⁴ to 3.95 × 10⁴. Appreciable flapping motion (±3.52°) appeared at the Reynolds number of 2.6 × 10⁴ with the frequency of 5.08 Hz. In the low-Reynolds-number case, the plate showed negligible flapping. In the high-Reynolds-number case, the deflection angle increased with reduced flapping amplitude. The torque from the fluid determined the flapping amplitude. In the low-Reynolds-number case, Karman vortices were absent. With increasing Reynolds numbers, Karman vortices developed behind the plate with larger deflection angles. Strong interaction between the wake flow from the leading and trailing edge of the plate was observed. Based on power spectrum density (PSD) analysis, the vortex shedding frequency coincided with the flapping frequency, and the amplitude was positively correlated to the strength of the vortices. Proper orthogonal decomposition (POD) modes evince that, in the case of appreciable motion, coherent structures exhibited a larger spatial scale, enhancing the magnitude of the external torque on the plate.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.315-316
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• 2022

최근 전 세계적인 탄소 중립 정책으로 인해 전기차 보급 속도는 예상보다 훨씬 빠르게 증가하고 있다. 하지만 늘어나는 수요에 비해 전기차 충전기 수는 턱없이 부족하다. 그뿐만 아니라 일반 차들의 전기차 충전소 불법주차로 인해 전기차가 충전하지 못하는 불편함이 발생하고 있다. 본 논문에서는 에지 컴퓨터(edge computer)와 딥러닝 기반 객체 감지 시스템 YOLO(You only look once)를 이용한 전기차 충전소 불법주차 방지 시스템을 개발한다. 먼저, 이 시스템은 카메라를 통해 실시간으로 영상을 받아 YOLO를 이용하여 차량 번호판 인식이 되면 전기차 번호판의 특정 마크를 인식하여 전기차인지 일반 차인지를 판별하여 판별된 값에 따라 주차 차단기가 작동되는 시스템이다. 전기차이면 차단기가 내려가서 충전소를 이용할 수 있게 하고 일반차일 경우 주차 차단기가 내려가지 않고 막아 불법주차를 차단한다. 이와 같은 기술을 활용하여 전기차 충전소 불법주차 방지에 기여하고자 한다.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• Smart Structures and Systems
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• v.33 no.2
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• pp.105-118
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• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• Journal of Biomedical Engineering Research
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• v.23 no.3
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• pp.253-261
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• 2002

A spatially adaptive falter was formulated to imrove PET image qualify and the Performance of the filter was evaluated using simulation and phantom and human PET studies. In the proposed filter. if a pixel was identified as the edge Pixel, the Pixel value was Preserved. Otherwise a Pixel was replaced by the mean of the pixel values weighted by 2:7: 2. A Pixel was identified as the edge Pixel. if it satisfies the following conditions : the number of ADs (absolute difference between center and neighborhood pixels) which is smaller than THl (($pix_max{\times}0.1/log_2(NPM)$, NPM : mean of 6 neighborhood pixels excluding minimum and maximum) is 8-k and the number of ADs which is lager than TH2 ($NPM{\times}0.1$) is k. where k : 2, 3, …, 6. The results of this study demonstrate the superior performance of the Proposed titter compared to Gaussian fitter, weight median filter and subset averaged median filter. The proposed tittering method is simple but effective in increasing uniformity and contrast with minimal degradation of spatial resolution of PET images and thus. is expected to Provide improved diagnositc quality PET images .

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1159-1174
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• 2004

In this paper, we present an efficient algorithm for region analysis of business card images acquired in a PDA by using DCT and information pixel density. The proposed method consists of three parts: region segmentation, information region classification, and text region classification. In the region segmentation, an input business card image is partitioned into 8 f8 blocks and the blocks are classified into information and background blocks using the normalized DCT energy in their low frequency bands. The input image is then segmented into information and background regions by region labeling on the classified blocks. In the information region classification, each information region is classified into picture region or text region by using a ratio of the DCT energy of horizontal and vertical edge components to that in low frequency band and a density of information pixels, that are black pixels in its binarized region. In the text region classification, each text region is classified into large character region or small character region by using the density of information pixels and an averaged horizontal and vertical run-lengths of information pixels. Experimental results show that the proposed method yields good performance of region segmentation, information region classification, and text region classification for test images of several types of business cards acquired by a PDA under various surrounding conditions. In addition, the error rates of the proposed region segmentation are about 2.2-10.1% lower than those of the conventional region segmentation methods. It is also shown that the error rates of the proposed information region classification is about 1.7% lower than that of the conventional information region classification method.

• Journal of the Korea Computer Graphics Society
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• v.16 no.1
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• pp.1-7
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• 2010

With the widespread use of vision-based surveillance systems, the capability for person identification is now an essential component. However, the CCTV cameras used in surveillance systems tend to produce relatively low-resolution images, making it difficult to use face recognition techniques for person identification. Therefore, an algorithm is proposed for person identification in CCTV camera images based on the clothing. Whenever a person is authenticated at the main entrance of a building, the clothing feature of that person is extracted and added to the database. Using a given image, the clothing area is detected using background subtraction and skin color detection techniques. The clothing feature vector is then composed of textural and color features of the clothing region, where the textural feature is extracted based on a local edge histogram, while the color feature is extracted using octree-based quantization of a color map. When given a query image, the person can then be identified by finding the most similar clothing feature from the database, where the Euclidean distance is used as the similarity measure. Experimental results show an 80% success rate for person identification with the proposed algorithm, and only a 43% success rate when using face recognition.

• Korean Journal of Optics and Photonics
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• v.26 no.3
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• pp.162-167
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• 2015

In this paper, we developed a MTF (Modulation Transfer Function) measurement system using a knife-edge scanning method for infrared optics. It consists of an objective part to generate the target image, a collimator to make the beam parallel, and a detector to analyze the image. We used a tungsten filament as the light source and MCT (Mercury Cadmium Telluride) to detect the mid-infrared(wavelength $3-5{\mu}m$) image. We measured the MTF of a standard lens (f=5, material ZnSe) to test this instrument and compared the result to the theoretical value calculated using the ZEMAX commercial software. It was found that the difference was within ${\pm}0.035$ at the cut-off frequency (50 1/mm). Also, we calculated the A-type measurement uncertainty to check the reliability of the measurement. The result showed only 0.002 at 20 1/mm in spatial frequency, which means very little variation in the MTF measurement under the same conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.126-133
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• 2003

This paper is related to a morphological segmentation method for extracting the moving object in video sequence using global motion compensation and two-dimensional spatio-temporal entropic thresholding. First, global motion compensation is performed with camera panning vector estimated in the hierarchical pyramid structure constructed by wavelet transform. Secondly, the regions with high possibility to include the moving object between two consecutive frames are extracted block by block from the global motion compensated image using two-dimensional spatio-temporal entropic thresholding. Afterwards, the LUT classifying each block into one among changed block, uncertain block, stationary block according to the results classified by two-dimensional spatio-temporal entropic thresholding is made out. Next, by adaptively selecting the initial search layer and the search range referring to the LUT, the proposed HBMA can effectively carry out fast motion estimation and extract object-included region in the hierarchical pyramid structure. Finally, after we define the thresholded gradient image in the object-included region, and apply the morphological segmentation method to the object-included region pixel by pixel and extract the moving object included in video sequence. As shown in the results of computer simulation, the proposed method provides relatively good segmentation results for moving object and specially comes up with reasonable segmentation results in the edge areas with lower contrast.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.551-562
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• 1998

Amultiresolution filtering algorithm based on the physical SPECT lesion detachability provides and optimal solution for SPECT reconstruction problem. Related to the previous study, we estimated the SPECT lesion detection capability by m minimum detectable lesion sizes (MDLSs), and generated m reconstruction filters which are designed to maximize the smoothing effect at a fixed MDLS-dependent resolution level $\frac{MDLS}{4\sqrt{2In2}}$. The proposed multiresolution filtering algorithm used a coarse-to-fine approach for the m-level resolution filter images obtained from these m filters for a given projection image. First, the local homogeneity is determined for every pixel of the filter images, by comparing the local variance value computed in a window centered at the pixel and the mode determined from the distribution of the local variances. Based on the local homogeneity, the pixels declared as homogeneous are chosen from the filter image of the lowest resolution, and for the other pixels the same process is repeated for the higher resolution filter images. For the non-homogeneous pixels after this pixels after this repetition process ends, the pixel values of the highest resolution filter image are substituted. From the results of the simulated experiments, the proposed multiresolution filtering algorithm showed a strong smoothing effect in the homogeneous regions and a significant resolution improvement near the edge regions of the projection images, and so produced good adaptability effects in the reconstructed images.