Han Wang | Hai Lin

Electromagnetic Characteristics Optimization Based on Shape Deformation

Thu, 15 Sep 2022 15:42:18 +0800

A feasible approach to optimize the characteristics of electromagnetic (EM) objects through shape deformation is proposed. We provide a complete and detailed procedure of the optimization and investigate the shape deformation formulations for some typical EM objects. Numerical results demonstrate the validity and performance of the optimization approach.

Deep Learning with Attention Mechanism for Electromagnetic Inverse Scattering

Fri, 15 Jul 2022 17:04:51 +0800

The inverse scattering problems (ISPs) aim for reconstructing the unknown targets from the measured scattered fields. Due to its highly nonlinear and ill-posed properties, solving ISPs is a challenging task. In this paper, a new deep learning technique is investigated to improve the solution of electromagnetic inverse scattering problems (ISPs). The basic idea of this technique is to introduce the attention mechanism to U-Net. By doing this, the deep learning model can automatically learn to focus on target areas of the measured scattered field matrix. In this way, the proposed technique can offer higher accuracy and faster convergence compared with the traditional deep learning networks. Numerical results validate the efficacy of the proposed technique.

Enhanced solution to the surface-volume-surface EFIE for arbitrary metal-dielectric composite objects

Sat, 01 Jan 2022 00:00:00 +0000

A GPU-based radio wave propagation prediction with progressive processing on point cloud

Fri, 01 Jan 2021 00:00:00 +0000

Accuracy improvement of the algebraic fast methods for the volume-surface integral equation

Fri, 01 Jan 2021 00:00:00 +0000

Iterative MLFMA-MADBT technique for analysis of antenna mounted on large platforms

Wed, 01 Jan 2020 00:00:00 +0000

Solving the scattering of combined conductor-dielectric bodies based on EPA

Wed, 01 Jan 2020 00:00:00 +0000

Electromagnetic characteristics optimization based on shape deformation

Tue, 01 Jan 2019 00:00:00 +0000