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박상근(Sangkun Park) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.2
This paper proposes a 3D image augmentation method for improving the generalization performance of deep neural networks. It allows us to enrich the diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. It also enables us to predict medical segmentation surfaces in Euclidean space without additional labeled datasets. This method includes image transformation functions, which are comprised of a spatial deformation and image intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.
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품질기능전개와 공리설계를 이용한 고객지향 굴삭기 프론트초기설계 시스템
전기현(Ki Hyun Jeon),배일주(Ilju Bae),이수홍(Soo-hong Lee) (사)한국CDE학회 2009 한국CDE학회 논문집 Vol.14 No.2
A design needs various experience and design knowledge through a whole design process. Despite of all efforts and time, it is not easy to introduce a product that meets all customer"s needs and expectation in time. To achieve the product goal, designers need a set of sequential process to find appropriate design parameters and ensure customers" needs and requirements. In this research we propose a design methodology for the initial design of an excavator front group with existing QFD(Quality Function Deployment) and Axiomatic Design to satisfy customer"s requirements. It turns out that the proposed methodology can support designers more effectively, objectively, and systematically.
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