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- W4313443724 abstract "A cavity viscoelastic structure is used as an underwater anechoic coating because of its excellent acoustic performance. To overcome the shortcomings of the traditional numerical analysis method, which is time-consuming, and physical experiment for measuring the absorption coefficient is costly, a convolutional neural network-based (CNN-based) logistical prediction method was investigated. First, the finite element method (FEM) was used to calculate the absorption coefficient. Then, a dataset was constructed based on the FEM; in this way, it avoided repeated experiments. The critical material parameters were processed by the min–max normalization method, and the ground truth of the dataset is the absorption coefficient. Finally, the root-mean-square error (RMSE) predicted by the CNN-based model was 0.1177, and the Pearson correlation coefficient (PCC) was 0.9354. It shows that the CNN-based logistic regression model accurately and rapidly predicts the effect of material properties on the absorption coefficient of the underwater viscoelastic anechoic coating." @default.
- W4313443724 created "2023-01-06" @default.
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- W4313443724 date "2023-01-01" @default.
- W4313443724 modified "2023-10-01" @default.
- W4313443724 title "Convolutional Neural Network Prediction of Underwater Anechoic Coating: Effect of Material Properties on Absorption Coefficient" @default.
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- W4313443724 doi "https://doi.org/10.1007/978-981-19-7184-6_22" @default.
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