• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1351-1353
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• 1999

전력계통의 고장용량 억제를 위한 송전망 구조의 방사상화는 경제적이고 적용이 간단하다는 장점을 갖는다. 그러나 선로개방에 따라 선로조류의 편중과 모선전압의 변동이 발생하기 때문에 계통의 안정성 검토가 전제되어야 한다. 본 연구에서는 고장용량의 감소를 목적으로 하면서 선로조류와 모선전압에 대한 제약조건이 반영되는 최적화 문제를 다룬다. 최적성의 향상을 위해서 호필드 신경망이 사용되며 이를 위해서 관련된 가중행렬 식을 유도한다.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.189-198
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• 2024

Research combining deep learning-based models and physical simulations is making important advances in the medical field. This extracts the necessary information from medical image data and enables fast and accurate prediction of deformation of the skeleton and soft tissue based on physical laws. This study proposes a system that integrates Neural Radiance Fields (NeRF), Position-Based Dynamics (PBD), and Parallel Resampling to generate 3D volume data, and deform and visualize them in real-time. NeRF uses 2D images and camera coordinates to produce high-resolution 3D volume data, while PBD enables real-time deformation and interaction through physics-based simulation. Parallel Resampling improves rendering efficiency by dividing the volume into tetrahedral meshes and utilizing GPU parallel processing. This system renders the deformed volume data using ray casting, leveraging GPU parallel processing for fast real-time visualization. Experimental results show that this system can generate and deform 3D data without expensive equipment, demonstrating potential applications in engineering, education, and medicine.