Shuang-Cheng Sun

Image Reconstruction of Two-Dimensional Highly Scattering Inhomogeneous Medium Using MAP-Based Estimation

A maximum a posteriori (MAP) estimation based on Bayesian framework is applied to image reconstruction of two-dimensional highly scattering inhomogeneous medium. The finite difference method (FDM) and conjugate gradient (CG) algorithm serve as the forward and inverse solving models, respectively. The generalized Gaussian Markov random field model (GGMRF) is treated as the regularization, and finally the influence of the measurement errors and initial distributions is investigated. Through the test cases, the MAP estimate algorithm is demonstrated to greatly improve the reconstruction results of the optical coefficients.

Inverse Geometry Design of Radiative Enclosures Using Particle Swarm Optimization Algorithms

Three different Particle Swarm Optimization (PSO) algorithms—standard PSO, stochastic PSO (SPSO) and differential evolution PSO (DEPSO)—are applied to solve the inverse geometry design problems of radiative enclosures. The design purpose is to satisfy a uniform distribution of radiative heat flux on the designed surface. The design surface is discretized into a series of control points, the PSO algorithms are used to optimize the locations of these points and the Akima cubic interpolation is utilized to approximate the changing boundary shape. The retrieval results show that PSO algorithms can be successfully applied to solve inverse geometry design problems and SPSO achieves the best performance on computational time. The influences of the number of control points and the radiative properties of the media on the retrieval geometry design results are also investigated.