Publication

Abstract : Electrical resistance tomography (ERT) uses a network of boundary electrodes to visualize and analyze processes involving multiphase flow effectively. Nonlinearity and ill-posed problems are challenging for ERT reconstruction. However, reconstruction image accuracy is in demand. Due to a lack of suitable training, the existing deep learning network for ERT image reconstruction depends on sparse information flow and gradient flow. It is challenges to represent the structural topological link among internal conductivity characteristics using the usual reconstruction approach. By propagating feature representation across topological connections, the non-Euclidean structure of the ERT measurement encodes the problems. Capturing spatial and topological dependencies requires adaptive learning to handle irregular prominent feature positions and conductivity domains. The adaptive learning is modulated by edge priority for different graph states in a dynamic graph where edge features embed spatial constraints and graph geometry. Therefore, a multi-head attention mechanism is incorporated into the graph convolutional network to maintain the local smoothness while preserving sharp transitions. The dynamic graph is learned through edge weights jointly with node features using the RMSprop optimization method. Adaptive edge learning can identify object geometries and real-time flow patterns. Using extensive numerical simulation and experiment results, the proposed algorithm improves the imaging accuracy compared to the traditional methods without dense connections. It is compared with existing deep learning methods.