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이수현(Lee Su-Hyun),이재홍(Lee, Jae-Hong) 대한건축학회 2011 大韓建築學會論文集 : 構造系 Vol.27 No.11
This paper presents form-finding of Tensegrity structures by using Force method. The Force method is generalized to all types of skeletal structures, such as rigid-jointed frames, pin-jointed planar trusses and ball-jointed space trusses. This method has easier basic concept, which is based on equilibrium equation, than Finite Element Method. In addition, this method is appealing to engineers because the properties of members of structures most often depend on the member force than joint displacement. Therefore, this study applies to analyze about form-finding of Tensegrity structures by using Force method’s merit.
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척추 MRI의 공간 정보 활용을 위한 비지도 학습 방법 고찰
이수현(Soohyun Lee),변윤수(Yunsu Byeon),이정룡(Jeong Ryong Lee),황도식(Dosik Hwang) 대한전자공학회 2024 대한전자공학회 학술대회 Vol.2024 No.6
This study explores effective deep learning-based pre-training methods that leverage spatial information for precise semantic segmentation of vertebral magnetic resonance imaging (MRI). Specifically, it examines and compares the Jigsaw puzzle and RotNet techniques, as well as assesses the efficacy of Masked Autoencoders (MAE) within limited medical datasets. The results confirm that the Jigsaw puzzle approach effectively acquires spatial information, achieving superior performance in semantic segmentation of vertebral MRI. Additionally, it was observed that in scenarios with extremely limited medical data, pre-training methods from the Deep Convolutional Neural Networks (DCNN) are more effective than MAE, which employs Vision Transformer, especially when compared to natural images.
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