• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.715-726
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• 2023

This study suggested that protective panels should be installed as sacrificial members as a safety design method for structures with potential explosions such as hydrogen charging stations to minimize direct damage to the structure and have resilience. To this end, the focus of the experiment is on quantitatively evaluating the impact of the structure when the protection panel is installed closely or spaced apart from the structure in a high-speed collision situation of the projectile. The experimental design used steel plates instead of concrete structural members mainly used in the past for excellent reproducibility, and the impact of structural members was compared and analyzed through deformation differences on the back of the steel plate. In addition, the impact of changes in the physical properties of the elastic body used as a separation material for the protective member and the difference in shock wave transmission time according to the protective member and the elastic body on the structural member was investigated.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.8
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• pp.1129-1136
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• 2009

The paper presents the performance characteristics for a cooling system using EEV. The water cooler was used to reduce thermal deformation and contraction due to high speed of machine tools and the EEV was used for capacity control for water cooler. The apparatus was designed for hot-gas bypass system which a hot-gas can flows from outlet of compressor to the inlet of evaporator. This experiment is the intermediary study for precise temperature control through PID control. The results show that the evaporator pressure increased and refrigeration capacity decreased as the EEV opening step of hot-gas bypass increased. These results can be used as basic data for the design of effective water cooler.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.