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Two training samples are used.
Training samples are gained by FEM analysis.
For each object we provide 200 training samples.
However, FR often suffers from insufficient training samples.
This proposed method can improve the NN's generalization ability in case of the lacking training samples.
In SPACC, the training samples are nodes of an undirected weighted network.
In the real world this analysis fails because there is a limited number of training samples.
And then you could do it with lower numbers of training samples.
The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples.
In this work we propose to 'learn' the reconstruction from training samples using an autoencoder.
The scattered displacement responses required in training samples are calculated from the strip element method (SEM).
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