• Computational Structural Engineering
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• v.11 no.4
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• pp.5-12
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• 1998

• Journal of Korean Association for Spatial Structures
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• v.4 no.2 s.12
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• pp.99-105
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• 2004

The structure systems with non-rigid member were classified by the composition type of line and surface members. As a result of the classification, there are 1-way cable structure, cable net and radial cable net structure in the line member system. And there are pneumatic structure and suspension membrane structure in surface member system. In addition, when the line and surface members are composed together, there is the hybrid membrane system which are divided into hanging type and supported type. In this paper, the Korean terms of structure systems with non-rigid member are recommended through this classification.

• Journal of Korean Association for Spatial Structures
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• v.4 no.3 s.13
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• pp.93-101
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• 2004

Structural systems have a lot of architectural meaning concerning historical context of structural technology. Therefore, surveying constructed examples and their constructed year, the character and development of various formations of structure systems with non-rigid member were investigated. At the result, the early modem structure systems with non-rigid member were made up from the cable structures, then membrane structures have mainly used after 1970's. The early structural systems had intended to make the large scale space, after 1970's, they have been adopted into the smaller scale space structure, and cable net structure, pneumatic structure and dome typed hybrid membrane system tend to compose the larger scale spare structure.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.213-225
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• 2018

For constructing three-dimensional (3D) spatial information occlusion region problem arises in the process of taking the texture of the building. In order to solve this problem, it is necessary to investigate the automation method to automatically recognize the occlusion region, issue it, and automatically complement the texture. In fact there are occasions when it is possible to generate a very large number of structures and occlusion, so alternatives to overcome are being considered. In this study, we attempt to apply an approach to automatically create an occlusion region based on learning by patterning the blocked region using the recently emerging deep learning algorithm. Experiment to see the performance automatic detection of people, banners, vehicles, and traffic lights that cause occlusion in building walls using two advanced algorithms of Convolutional Neural Network (CNN) technique, Faster Region-based Convolutional Neural Network (R-CNN) and Mask R-CNN. And the results of the automatic detection by learning the banners in the pre-learned model of the Mask R-CNN method were found to be excellent.