摘要
目前,向量相似度测量(VSM)算法被应用于分析前向散射颗粒测试技术的颗粒粒径分布(PSD)。但其对多峰分布颗粒情况的反演结果并不理想,尤其是对3个峰值以上的颗粒分布。为了满足预测多峰分布颗粒的需要及提高普遍适用性,提出了一种基于向量相似度的优化迭代算法,并将其用于颗粒粒径分布函数的反演计算。模拟计算和实验研究均表明:该算法在较高的测量误差情况下仍可得到合理的颗粒粒径分布。
Recently, the vector similarity measurement （VSM） algorithm has been introduced to analyze particle size distribution （PSD） of the forward scattering. However, the retrieval result is not ideal for multi-modal distribution particle systems, especially for the particle distributions of more than three peaks. To satisfy the need of predicting multi-peak distribution particle and enhancing common applicability, a modified iterative algorithm is proposed based on vector similarity measurement, and it is applied in retrieval calculation of PSD function. Simulated results and experimental researches show that the algorithm can also get the reasonable PSI） with high measurement error.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2016年第6期294-302,共9页
Acta Optica Sinica
基金
国家自然科学基金(NSFC 51476104)
关键词
散射
向量相似度测量
欧氏距离
颗粒粒径
反演算法
scattering
vector similarity measurement
Euclidean distance
particles size
inverse algorithm
