On the Efficient Graph Representation of Collinear Relation in the 3D Shape Grammars

3D shape matching is widely used in engineering design for model retrieval, shape similarity assessment or collaborative development. In this paper, an enhanced graph representation and heuristic tabu search based approach is presented to enable flexible and efficient 3D shape matching. The core idea includes (1) generic shape features are recognized from boundary representation (B-rep) of 3D shape as many as possible to incorporate domain design knowledge, (2) an enhanced graph representation of 3D shape is constructed by mixing faces of B-rep and recognized features, to achieve meaningful matching results at low-level of faces or high-level of features satisfying various design intents, and (3) a tabu list of possible improper matches is built to reduce the search space so that the optimal result can be efficiently obtained. Finally, Two examples are demonstrated to show that both two levels of 3D shape matching results can be efficiently obtained for various design intents in the engineering applications, only not more than 18% computation time is required when compared with a typical shape matching method, and it takes only 20 s when the number of matching nodes is more than 460.

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Representing 3D Shape Grammars in a Generative Product Design System

Design Computing and Cognition '12 ◽

10.1007/978-94-017-9112-0_21 ◽

2014 ◽

pp. 377-392 ◽

Cited By ~ 2

Author(s):

Jia Cui ◽

Ming-Xi Tang

Keyword(s):

Product Design ◽

Design System ◽

Shape Grammars ◽

3D Shape

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Supporting product innovation using 3D shape grammars in a generative design framework

International Journal of Design Engineering ◽

10.1504/ijde.2014.062376 ◽

2014 ◽

Vol 5 (3) ◽

pp. 193 ◽

Cited By ~ 1

Author(s):

Ming Xi Tang ◽

Jia Cui

Keyword(s):

Product Innovation ◽

Design Framework ◽

Shape Grammars ◽

Generative Design ◽

3D Shape

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Practical electron tomography

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100140087 ◽

1995 ◽

Vol 53 ◽

pp. 742-743

Author(s):

C.L. Woodcock

Keyword(s):

Electron Tomography ◽

Tomographic Reconstruction ◽

Three Dimensional ◽

3D Shape ◽

Computational Resources ◽

Shape Of Particles

Despite the potential of the technique, electron tomography has yet to be widely used by biologists. This is in part related to the rather daunting list of equipment and expertise that are required. Thanks to continuing advances in theory and instrumentation, tomography is now more feasible for the non-specialist. One barrier that has essentially disappeared is the expense of computational resources. In view of this progress, it is time to give more attention to practical issues that need to be considered when embarking on a tomographic project. The following recommendations and comments are derived from experience gained during two long-term collaborative projects.Tomographic reconstruction results in a three dimensional description of an individual EM specimen, most commonly a section, and is therefore applicable to problems in which ultrastructural details within the thickness of the specimen are obscured in single micrographs. Information that can be recovered using tomography includes the 3D shape of particles, and the arrangement and dispostion of overlapping fibrous and membranous structures.

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HLW Disposal in Japan. Hydrological Research for Understanding of 3D Shape of the Seawater/Freshwater Interface.

Shigen-to-Sozai ◽

10.2473/shigentosozai.117.816 ◽

2001 ◽

Vol 117 (10) ◽

pp. 816-821

Author(s):

Atsunao MARUI ◽

Takeshi HAYASHI

Keyword(s):

3D Shape

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Graph Neural Networks for Prediction of Fuel Ignition Quality

10.26434/chemrxiv.12280325.v1 ◽

2020 ◽

Author(s):

Artur Schweidtmann ◽

Jan Rittig ◽

Andrea König ◽

Martin Grohe ◽

Alexander Mitsos ◽

...

Keyword(s):

Neural Networks ◽

Octane Number ◽

Molecular Graph ◽

Chemical Properties ◽

Graph Representation ◽

Structure Property ◽

Oxygenated Hydrocarbons ◽

Physico Chemical ◽

Ignition Quality ◽

Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

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