This paper proposes an FPGA-based compressed 1-D convolutional neural network for long-horizon FCS-MPC, significantly reducing online computation and hardware resource usage while maintaining control performance.
This study proposes a novel deep learning method, SA-HFL, that fuses multi-branch features to improve the accuracy and robustness of EIT conductivity reconstruction against noise and modeling errors.
