CPFI-EIT: A CNN-PINN Framework for Full-Inverse Electrical Impedance Tomography on Non-Smooth Conductivity Distributions

This paper introduces a hybrid learning framework that combines convolutional neural networks (CNNs) and physics-informed neural networks (PINNs) to address the challenging problem of full-inverse electrical impedance tomography (EIT). EIT is a noninvasive imaging technique that reconstructs the spatial distribution of internal conductivity based on boundary voltage measurements from injected currents. This method has applications across medical imaging, multiphase flow detection, and tactile sensing. However, solving EIT involves a nonlinear partial differential equation (PDE) derived from Maxwell's equations, posing significant computational challenges as an ill-posed inverse problem. Existing PINN approaches primarily address semi-inverse EIT, assuming full access to internal potential data, which limits practical applications in realistic, full-inverse scenarios. Our framework employs a forward CNN-based supervised network to map differential boundary voltage measurements to a discrete potential distribution under fixed Neumann boundary conditions, while an inverse PINN-based unsupervised network enforces PDE constraints for conductivity reconstruction. Instead of traditional automatic differentiation, we introduce discrete numerical differentiation to bridge the forward and inverse networks, effectively decoupling them, enhancing modularity, and reducing computational demands. We validate our framework under realistic conditions, using a 16-electrode setup and rigorous testing on complex conductivity distributions with sharp boundaries, without Gaussian smoothing. This approach demonstrates robust flexibility and improved applicability in full-inverse EIT, establishing a practical solution for real-world imaging challenges.

Multi-Scale RCNN Model for Financial Time-series Classification

Financial time-series classification (FTC) is extremely valuable for investment management. In past decades, it draws a lot of attention from a wide extent of research areas, especially Artificial Intelligence (AI). Existing researches majorly focused on exploring the effects of the Multi-Scale (MS) property or the Temporal Dependency (TD) within financial time-series. Unfortunately, most previous researches fail to combine these two properties effectively and often fall short of accuracy and profitability. To effectively combine and utilize both properties of financial time-series, we propose a Multi-Scale Temporal Dependent Recurrent Convolutional Neural Network (MSTD-RCNN) for FTC. In the proposed method, the MS features are simultaneously extracted by convolutional units to precisely describe the state of the financial market. Moreover, the TD and complementary across different scales are captured through a Recurrent Neural Network. The proposed method is evaluated on three financial time-series datasets which source from the Chinese stock market. Extensive experimental results indicate that our model achieves the state-of-the-art performance in trend classification and simulated trading, compared with classical and advanced baseline models.