Anes Saoudi

This study introduces AIA-Net, standing for Autoencoder Improved Attention Network, a novel neural-network (NN) architecture based on an attention mechanism for solving the non-linear inverse problem of Electrical Impedance Tomography (EIT). It aims at producing reliable reconstructed images of the conductivity distribution within a probed medium. To overcome the generalization problem inherent to deep learning approaches, AIA-Net is further improved using a latent space generative NN based on a Conditional Latent Diffusion Model. We present an end-to-end workflow from measurements to image reconstructions using AIA-Net. The EIT voltage excitations protocol considered relies on the ONe Excitation for Simultaneous High-speed Operation Tomography method. Static experimental tests are conducted, with plastic rods of different shapes and combinations emulating anomalous objects within a reference medium. Using a total of 16 320 data, comprising both voltage measurements and ground-truth images, some reference NNs for reconstructing conductivity images are deployed and compared, qualitatively and quantitatively, with the proposed AIA-Net architecture. The results presented highlight the excellent overall performance of AIA-Net compared to existing architectures, along with a further improvement provided by a generative model to overcome the generalization problem.

This study is concerned with the classification of two-phase flow regimes. The data considered are provided by electrical measurements at the boundary of the probed medium. In this context, our aim is to measure and characterize flow regimes, and their transitions, in real time. To this end, we investigate a neural-network approach based on spectrogram representations of time-dependent measurements in order to improve the accuracy compared to time series analyses. In the present study, dynamical tests in an experimental two-phase flow loop are considered, making use of a sensor developed at the Atomic Energy Commission coupled with a simultaneous voltage excitation method. A total of 80 configurations are studied, with different air/water mass flow rates leading to five different flow regime classes, namely slug, plug, wavy, stratified and annular. For each configuration, the available electrical measurements are processed into a normalized impedance matrix, whose important features are extracted using singular value decomposition. The time evolution of the maximum singular value is then passed to a neural network, which is trained from labeled data while making use of the stratified k -fold cross-validation method. Two different network architectures are considered: the first one processes raw time-series, leading to a regime classification accuracy of 89.4%. The second one uses spectrogram images, resulting in a more accurate classification reaching 97.4%. A comparison with other methods for identifying two-phase flows is also proposed, which supports the main contribution of this study in providing evidence of the advantages of using spectrograms of electrical impedance tomography measurements.