• Journal of the Korea Computer Graphics Society
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• v.18 no.1
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• pp.29-34
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• 2012

High-quality tetrahedral meshes are crucial for FEM-based simulation of large elasto-plastic deformation and tetrahedral-mesh-based simulation of fluid flow. This paper proposes a volume meshing method that exploits an octree-based adaptive signed distance field to fill the inside of a polygonal object with tetrahedra, of which dihedral angles are good. The suggested method utilizes an octree structure to reduce the total number of tetrahedra by space-efficiently filling an object with graded tetrahedra. To obtain a high-quality mesh with good dihedral angles, we restrict the octree in such a way that any pair of neighboring cells only differs by one level. In octree-based tetrahedral meshing, the signed distance computation of a point to the surface of a given object is a very important and frequently-called operation. To accelerate this operation, we develop a method that computes a signed distance field directly on the vertices of the octree cells while constructing the octree using a top-down approach. This is the main focus of the paper. The suggested tetrahedral meshing method is fast, stable and easy to implement.

• Journal of the Korea Computer Graphics Society
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• v.17 no.2
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• pp.45-53
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• 2011

This paper proposes a volume meshing method that fills the inside of an object with tetrahedra, of which dihedral angles are good. The suggested method is fast, stable and easy to implement It can also utilize an octree structure to space-efficiently fill an object with graded tetrahedra by reducing the total number of tetrahedra. To obtain a high-quality mesh with good dihedral angles, we restrict the octree such that any pair of neighboring cells only differs by one level. To efficiently construct a restricted-octree and generate a volume mesh from the octree, we utilize a signed distance field of an object on its bounded workspace. The suggested method can be employed in FEM-based simulation of large elasto-plastic deformation and tetrahedral-mesh-based simulation of fluid flow.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.377-380
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• 2012

본 연구에서는 학습데이터의 빈도요소를 반영하도록 수정된 구조의 FMM 신경망을 소개하고, 이로부터 패턴 분류를 위한 지식 표현을 생성하는 방법론을 제안한다. 하이퍼박스 멤버쉽함수는 5종류의 퍼지 분할을 기반으로 설정한 구간에 대하여 소속정도를 반영하여 결정하며, 각 차원별로 특징범위의 폭과 빈도 요소로부터 가중치 값이 학습된다. 본 연구에서는 제안된 이론을 수화인식 문제를 대상으로 고찰하였다. 인식 시스템의 구성은 특징추출을 위하여 3차원으로 확장된 구조의 CNN 모델을 사용하였으며, 수화패턴 데이터의 표현은 모션 히스토리 볼륨(Motion History Volume) 구조를 기반으로 하였다. 6종류의 수화패턴 동영상으로부터 27개 특징요소를 추출하고 이를 사용한 FMM 신경망의 학습과정과 지식의 추출 과정을 실험으로 보이고 그 유용성을 고찰한다.