Ring-Rod Detection Array for Gas–Solid Flow Pattern Based on Electrostatic Induction Principle

The core of electrostatic tomography (EST) is to solve the inverse problem, but the EST independent measurement data are much smaller than the value that needs to be reconstructed, resulting in a more serious inverse problem. This paper presents an improved ResNet-34 network (P-ResNet), which consists of an input layer, a residual feature extraction layer, and an output layer. The number of residual blocks is 3, 4, 4, and 3. After the second convolution in the main path of each residual block, a ReLU activation function is added to enhance the nonlinear expression ability of the network, and the generalization ability of the model is improved by introducing the L2 regularization loss function. A total of 15 930 sets of samples were simulated for the simulation test. After 200 rounds of iteration, the reconstruction results show that the network achieves high accuracy in EST image reconstruction tasks. In addition, the model is tested under different degrees of Gaussian white noise to verify its anti-noise ability. Compared with the traditional algorithms, the image correlation coefficients of this proposed model network are higher. In addition, this paper designs a small sensor to obtain the induced charge values through the principle of electrostatic induction. The reconstructed results obtained from the experimental data are consistent with the simulation results, which verifies the effectiveness and generalization ability of the proposed model.

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Ring-Rod Detection Array for Gas–Solid Flow Pattern Based on Electrostatic Induction Principle

Cited by 2 publications

References 25 publications

Advanced image reconstruction for electrostatic tomography in gas-solid two-phase flow based on convolutional autoencoder neural network

Advanced image reconstruction for electrostatic tomography in gas-solid two-phase flow based on convolutional autoencoder neural network

Image reconstruction of electrostatic tomography based on the improved residual network

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