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Deep-Learning Based Honeycomb Pattern Removal in Fiber Bundle Imaging
김은찬,서태원,양성욱 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.4
Fiber bundle endomicroscopy has potential in in-vivo or in-situ optical diagnostics because of its compact size and flexible structure. However, acquired images through fiber bundles suffer from artifact such as a honeycomb pattern. The fiber bundle composed of thousands of single optical fibers inevitably includes void imaging space blocked by cladding layers, while cores are subject to delivering light and imaging. Therefore, restoration of images from the fiber bundle imaging is necessary for improving the quality of imaging and the accuracy of diagnosis. Herein, we introduce a honeycomb pattern removal method based on a deep-learning approach. A training dataset is created by overlaying an initially captured white-reference-image with intact images from ImageNet, while simply taking ground truth from the intact images without supplementary optical instruments. A total number of training images is 276,480, and Adam optimizer is used with 1e-4 learning rate. Evaluation metrics, such as variance-based smoothness and Rayleigh-based line separation criteria were used to verify the pattern removal with preservation of detail. As a result, the quality measure of the proposed method is 0.819, and the other methods, such as a median filter, Gaussian filter, and a barycentric interpolation method, are 0.744, 0.758, and 0.702, respectively.
SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm
김은찬,Jinyoung Lee,Hyunjik Jo,Kwangtek Na,Eunsook Moon,Gahgene Gweon,Byungjoon Yoo,경연웅 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.8
Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.
A Case Study of Digital Transformation: Focusing on the Financial Sector in South Korea and Overseas
김은찬,김민재,경연웅 한국경영정보학회 2022 Asia Pacific Journal of Information Systems Vol.32 No.3
This study investigates the adoption and application of digital transformation in the financial sector and analyzes the process and outcomes of digitization and digitalization in the field of the finance industry of South Korea and overseas, in order to seek both managerial and strategic implications for successful implementation of digital transformation in the future. The findings show that, for successful digital transformation, it is necessary to maximize active and systematic use of advanced online and digital technologies that form the basis of business and create an open, horizontal organizational culture and communication system to equally share and distribute advanced technologies and competencies through the entire organization. Furthermore, this study also discovers the legitimacy to concentrate the organizational competencies and know-how in providing technical training for members, expanding customer experience, and improving customer satisfaction services to contribute to improving the quality of life for members of the organization and creating and improving social and public infrastructures, instead of using digital transformation only to improve productivity of organizations or firms. As such, it is necessary to concentrate corporate competencies in establishing and supplying digital transformation that is not just human-centered but also has productivity, innovativeness, and reliability at the same time.