• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1492-1499
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• 2021

Skin prick test is widely used in diagnosing allergic sensitization to common inhalant or food allergens, in which positivities are manually determined by calculating the areas or mean diameters of wheals and erythemas provoked by allergens pricked into patients' skin. In this work, we propose a segmentation algorithm over U-Net, one of the FCN models of deep learning, to help us more objectively grasp the erythema boundaries. The performance of the model is analyzed by comparing the results of automatic segmentation of the test data to U-Net with the results of manual segmentation. As a result, the average Dice coefficient value was 94.93%, the average precision and sensitivity value was 95.19% and 95.24% respectively. We find that the proposed algorithm effectively discriminates the skin's erythema boundaries. We expect this algorithm to play an auxiliary role in skin prick test in real clinical trials in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.685-688
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• 2020

안면 인식은 Face ID를 비롯하여 미아 찾기, 범죄자 추적 등의 분야에 도입되고 있다. 안면 인식은 최근 딥러닝을 통해 인식률이 향상되었으나, 측면에서의 인식률은 정면에 비해 특징 추출이 어려우므로 비교적 낮다. 이런 문제는 해당 인물의 정면이 없고 측면만 존재할 경우 안면 인식을 통한 신원확인이 어려워 단점으로 작용될 수 있다. 본 논문에서는 측면 이미지를 바탕으로 정면을 생성함으로써 안면 인식을 적용할 수 있는 상황을 확장하는 인공지능 기반의 안면 정면화 모델을 구현한다. 모델의 안면 특징 추출을 위해 VGG-Face를 사용하며 특징 추출에서 생길 수 있는 정보 손실을 막기 위해 U-Net 구조를 사용한다.

• Smart Media Journal
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• v.13 no.4
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• pp.86-93
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• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• The Mathematical Education
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• v.26 no.2
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• pp.55-61
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• 1988

이 논문에서는 CS-Semistratifiable 공간보다 더 일반화된 공간 Cn-Semistratifiable을 정의 하며 그에 따른 여러가지 성질들을 조사하였다. 위상 공간(X, $\tau$)에 대하여 $\alpha$$\times$$\tau$에서 X의 폐집합족으로의 함수 S가 존재하여 다음 조건들을 만족할 때 공간X는 Cn-Semistratifiable over $\alpha$라 정의한다. a) 임의의 개집합 U에 대하여 U=U{S($\beta$, U) : $\beta$<$\alpha$} b) U, V가 X의 개집합이고 U⊂CV이면 모든 $\beta$<$\alpha$에 대하여 S($\beta$, V)⊂S($\beta$, V)이다. c) 만약 ${\gamma}$<$\beta$<$\alpha$ 이라면 임의의 개집합 U에 대하여 S(${\gamma}$, U)⊂S($\beta$, U)이다. d) X의 수렴하는 net $X_{\beta}$$\longrightarrow$X와 X를 품는 임의의 개집합 U에 대하여 적당한 $\beta$<$\alpha$가 존재하여 X$\in$S($\beta$. U)이고 { $X_{\beta}$}는 S($\beta$, U)안에 eventual하게 들어간다. 위의 정의에 의하여 다음과 같은 성질들이 증명되었다. 1 . Strstifiable over $\alpha$$\longrightarrow$cn-semistratifiable over$\longrightarrow$semistratifiable over $\alpha$ 2, 어떤 공간이 cn-Semistratifiable over $\alpha$이기 위한 필요충분 조건은 그것이 linearly cushioned cn-pairnet를 갖는 것이다. 3. cn-semistratifiable over $\alpha$의 부분공간 역시 cn-semistratifiabie over $\alpha$ 하다. 4. on-semistratifiable over $\alpha$의 유한개의 적공간 역시 cn-semistratifiabie over $\alpha$한다. 5. 폐 cn-semistratifiable over $\alpha$ 부분공간들의 합공간 역시 on-semistrbtifiable over $\alpha$ 하다. 6. 폐연속 net-cevering 함수에 의하여 cn-semistratifiable over $\alpha$ 성질이 보존된다.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.649-652
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• 2000

Beyond free choice net system this paper presents some liveness knowledge in asymmetric net system including necessary and sufficient condition for an asymmetric net system being live and having liveness monotonicity, and an algorithm, polynomial time complexity, for such deciding. Also two conjectures about system livenss are in the contribution.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.300-302
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• 2022

Palm vein identification has attracted attention due to its distinct characteristics and excellent recognition accuracy. However, many contactless palm vein identification systems suffer from the issue of having low-quality palm images, resulting in degradation of recognition accuracy. This paper proposes the use of U-Net architecture to correctly segment the vascular blood vessel from palm images. Attention gate mechanism and residual block are also utilized to effectively learn the crucial features of a specific segmentation task. The experiments were conducted on CASIA dataset. Hessian-based Jerman filtering method is applied to label the palm vein patterns from the original images, then the network is trained to segment the palm vein features from the background noise. The proposed method has obtained 96.24 IoU coefficient and 98.09 dice coefficient.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.5
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• pp.535-546
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• 2020

Land cover maps are derived from satellite and aerial images by the Ministry of Environment for the entire Korea since 1998. Even with their wide application in many sectors, their usage in research community is limited. The main reason for this is the map compilation cycle varies too much over the different regions. The situation requires us a new and quicker methodology for generating land cover maps. This study was conducted to automatically generate land cover map using aerial ortho-images and Landsat 8 satellite images. The input aerial and Landsat 8 image data were trained by Residual U-Net, one of the deep learning-based segmentation techniques. Study was carried out by dividing three groups. First and second group include part of level-II (medium) categories and third uses group level-III (large) classification category defined in land cover map. In the first group, the results using all 7 classes showed 86.6 % of classification accuracy The other two groups, which include level-II class, showed 71 % of classification accuracy. Based on the results of the study, the deep learning-based research for generating automatic level-III classification was presented.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.33-42
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• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

• Nuclear Engineering and Technology
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• v.39 no.5
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• pp.617-626
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• 2007

A centrifugal atomization process for uranium fuel was developed in order to fabricate high uranium density dispersion fuel for advanced research reactors. Spherical powders of $U_3Si$ and U-Mo were successfully fabricated and dispersed in aluminum matrices. Thermal and mechanical properties of dispersion fuel meat were characterized. Irradiation tests at the research reactor HANARO confirm the excellent performance of high uranium density dispersion fuel.