1.模型结构。 WGANGP由生成器(Generator)和判别器(Discriminator)两部分组成,其中生成器负责生成逼真的样本,而判别器则负责区分生成样本和真实样本。 1.1生成器(Generator)。 生成器的作用是将随机噪声转换为逼真的样本。它通常由多层神经网络组成,采用反卷积操作将低维的随机向量映射到高维空间,生成样本。 具体参数包括...
In the process of fault diagnosis, the wavelet transform is firstly used to change the one-dimensional time series signal into the time-frequency domain signal; then the SA-DCWGAN-GP model is used to expand the data samples, and the samples generated by it are evaluated for their similarity...
Based on a deep convolutional neural network (DCNN), the model combines a conditional variational autoencoder (DCCVAE) and auxiliary conditional Wasserstein GAN with gradient penalty (ACWGAN-GP) to gradually expand and generate various coating defect images for solving the ...