基于PSO-OSSART算法的非轴对称电弧发射系数重建质量评价

针对有序子集-联合重建算法(ordered subsets-simultaneous algebraic reconstruction technique, OSSART)重建参数选取主观性强的不足，提出以重建区域误差最小为适应度的随机优化粒子群算法(particle swarm optimization, PSO)来获取最佳重建参数，并对非轴对称电弧发射系数稀疏角度重建质量进行评价. 结果表明，与最大似然函数-期望值最大化算法相比，基于粒子群的OSSART算法不仅能够在大投射角度间隔条件下使重建误差明显降低，而且具有更强的边缘保持能力，能够有效提高电弧中心区域的重建质量.采用OSSART算法，应在180°范围内至少等间距采集6次特征谱线投影，才能保证变极性等离子-熔化极气体保护复合焊(variable polarity plasma arc-metal inert gas, VPPA-MIG复合焊)电弧发射系数场的重建质量.试验结果为非轴对称电弧可靠光谱诊断提供理论依据.

Reconstructed quality evaluation of asymmetric arc emission coefficient by PSO-OSSART algorithm

Due to the shortcomings of strong subjectivity for selecting the parameters of ordered subsets-simultaneous algebraic reconstruction technique(OSSART), in this paper, a random particle swarm optimization algorithm with reconstruction area minimal error as fitness function is proposed to obtain the best reconstruction parameters. The quality of reconstruction from sparse angle of the asymmetric arc emission coefficient is evaluated. The results show that, compared with the maximum likelihood expectation maximum (MLEM) algorithm, the OSSART algorithm based on particle swarm optimization can not only reduce the reconstruction error significantly under the condition of large projection angle interval, but also have a stronger edge retention ability, which can effectively improve the reconstruction quality of the central area of the arc. In order to ensure the reconstructed quality of VPPA-MIG hybrid welding arc emission coefficient using OSSART algorithm, six feature line projections with equal spacing should be collected at least in the range of 180 degrees. The results provide a theoretical basis for the reliable study of asymmetric arc spectroscopic diagnostics.