Woven-reinforcement CMCs are promising materials in aeronautic industry. their matrix is often prepared by Chemical Vapor Infiltration. To increase their competitiveness with respect to more classical materials, their...
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ISBN:
(纸本)9781118932988
Woven-reinforcement CMCs are promising materials in aeronautic industry. their matrix is often prepared by Chemical Vapor Infiltration. To increase their competitiveness with respect to more classical materials, their fiber architecture and matrix processing conditions need more control and optimization. this paper summarizes recent developments of computer methods aimed at providing design tools in this context, paying special attention to imageprocessing techniques and image-based modeling. A first code extracts morphological features from X-ray CMT images of raw woven fabrics. An algorithm for segmentation and labeling produces the yams envelopes;then, fiber labeling is done at a higher resolution. Based on the analysis results, a synthesis technique allows numerical reconstitutions of yams as filament bundles. We present an "object dynamics" method placing fiber sections in a yam section as repelling disks. Another synthesis is the "macro-wire dynamics" approach in which a yam is divided in a small number of large filaments whose mechanics are simulated in detail. Once realistic 3D images of woven architectures are obtained, numerical simulations of matrix infiltration take place. they are either based on region dilation tools or on realistic simulations of gas diffusion and deposition;the latter have been implemented as random-walk algorithms at two scales.
the scene simulation is an important content for simulator development. the fidelity of simulation directly affects the effect of the training. In this paper, first we analyse the difficulty of the scene simulation, t...
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One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In our paper, we investigate a new method for auto...
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One of the major problems in cluster analysis is the determination of the number of clusters in unlabeled data, which is a basic input for most clustering algorithms. In our paper, we investigate a new method for automatically estimating the number of clusters in unlabeled data sets, which is based on an existing algorithm for Spectral Visual Assessment of Cluster Tendency (SpecVAT) of a data set, using several common image and signalprocessing techniques. Its basic steps include 1) generating a VAT image of an input dissimilarity matrix, 2) Constructing Laplacian matrix 3) Normalize the rows and 4) Apply SpecVAT. Our new method is nearly “automatic,” depending on just one easy-to-set parameter. In this paper we propose direct visual validation method and divergence matrix for finding the automatic clustering. the experimental result shows that the proposed algorithm is much better than the other algorithms.
the selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn l...
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