• Smart Media Journal
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• v.9 no.1
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• pp.16-22
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• 2020

In this study, a neural network method performing both Detection and Classification of diseases and insects in paprika is proposed with Multi-Tasking U-net. Paprika on farms does not have a wide variety of diseases in this study, only two classes such as powdery mildew and mite, which occur relatively frequently are made as the targets. Aiming to this, a U-net is used as a backbone network, and the last layers of the encoder and the decoder of the U-net are utilized for classification and segmentation, respectively. As the result, the encoder of the U-net is shared for both of detection and classification. The training data are composed of 680 normal leaves, 450 mite-damaged leaves, and 370 powdery mildews. The test data are 130 normal leaves, 100 mite-damaged leaves, and 90 powdery mildews. Its test results shows 89% of recognition accuracy.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.100-106
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• 2021

Measuring Intima-media thickness (IMT) with ultrasound images can help early detection of coronary artery disease. As a result, numerous machine learning studies have been conducted to measure IMT. However, most of these studies require several steps of pre-treatment to extract the boundary, and some require manual intervention, so they are not suitable for on-site treatment in urgent situations. in this paper, we propose to use deep learning networks U-Net, Attention U-Net, and Pretrained U-Net to automatically segment the intima-media complex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diagnostic device images. As a result, The average dice coefficient of HE applied Models is 71% and CLAHE applied Models is 70%, while the HS applied Models have improved as 72% dice coefficient. Among them, Pretrained U-Net showed the highest performance with an average of 74%. When comparing this with the mean value of IMT measured by Conventional wired ultrasound equipment, the highest correlation coefficient value was shown in the HS applied pretrained U-Net.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.34 no.9
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• pp.10-20
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• 2016

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.16-19
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• 2011

최근에 Smart Phone 보급의 급격한 확산에 따라 2012년경에는 국내에서 약 2천만명 정도가 Smart Phone을 사용할 것이며 전 세계적으로도 약 3억5천만대 정도의 사용자가 Smart Phone을 사용할 것으로 예상되고 있다. 이러한 Smart Phone에서 시작된 u-Device 변혁은 Smart Phone, Tablet-PC, Smart TV, Desk Top Computer를 연계한 Seamless 학습 환경 및 최근의 N-Screen 환경의 구현을 가능하게 하고 있다.

• Digital Contents
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• no.7 s.158
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• pp.39-45
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• 2006

상반기 국내 디지털 콘텐츠 시장은 전반적으로 상승세를 이어갔지만, 분야별로는 명암이 엇갈렸다. 특히 국내DC 산업을 견인하고 있는 온라인게임의 거침없는 행보가 약간 주춤거리는 모습을 보였다. 이는 메이저 온라인게임업체들이 야심차게 선보인일부 MMORPG 대작들의 예상 밖 부진이 큰 영향을 준 것으로 보인다. 이밖에 올해 상반기 DC시장은 이통사들의 폐쇄적DRM 논란, 온라인상의 UCC 열풍, u러닝시장개화등급 변하는DC산업의 특성을 다시금 확인할 수 있었다.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• 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.

• Hwankyungkyoyuk
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• v.22 no.4
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• pp.95-110
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• 2009

The purpose of the study was to develop The Stream Environmental education u-learning contents for elementary school students. For the development of content, the researchers commissioned detailed examination to experts to confirm validity, did a literature review and hosted expert forums. In addition, to enhance accessibility, they asked fairytale writers to develop easier and more valid scenarios and narrations of u-learning contents for elementary school students. The development content is for 18 hours of education and has three sections: i) Preparation, ii) Exploration, and iii) Arrangement. Since the content has been developed based on SCROM, it is expected to have re-usability, accessibility, compatibility and durability. Based on evaluation criteria of u-learning contents suggested in the research methods, the research group commissioned evaluation to ten experts in environmental education of each school level. Recommendations for applying the content developed in this study and further research are as follows: First, the developed content should be actively promoted and provided both online and offline so that elementary school students can fully utilize them. To this end, the website of the Ministry of Environment and u-learning training centers of universities of education should be used. Since content requires interaction not only between learners of the content but also between learners and operators, additional administrative and financial support should be provided. Second, this study focuses on the development of u-learning contents for elementary school students. Further studies are needed to develop content for secondary school students.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.552-555
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• 2008

This paper represent a method of constructing the improved virtual reality systems. The proposed method be able to decrease the difference between reality and virtual reality. For the future, the proposed improved virtual reality systems are applied to advanced education, for example U-Learning is mixed with virtual reality based on Ubiquitous computing that is important highly information technology(IT) at $21^{th}$ knowledge based on informational society. Also, we respect to The Control system which is embedded in mixed reality.