Machine-learning-based computationally efficient particle size distribution retrieval from bulk optical properties-Reference-Cited by-同舟云学术

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Machine-learning-based computationally efficient particle size distribution retrieval from bulk optical properties

Published:2020-08-18 Issue:24 Volume:59 Page:7284
ISSN:1559-128X
Container-title:Applied Optics
language:en
Short-container-title:Appl. Opt.

Author:

Jia Ruhui,Zhang Xiaohao,Cui Fenping,Chen Gongye,Li Haomiao,Peng Haochen,Cao Zhaolou,Pei Shixin

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Startup Foundation for Introducing Talent of NUIST

Undergraduate innovation and entrepreneurship training program of NUIST

Publisher

The Optical Society

Subject

Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering

Reference28 articles.

1. Stratospheric aerosol particle size distribution based on multi-color polarization measurements of the twilight sky

2. Milk homogenization monitoring: Fat globule size estimation from scattering spectra of milk

3. A model-based approach for controlling particle size distribution in combined cooling-antisolvent crystallization processes

4. Improved Size Determination by Nanoparticle Tracking Analysis: Influence of Recognition Radius

5. An ultra-compact particle size analyser using a CMOS image sensor and machine learning

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identification of Model Particle Mixtures Using Machine-Learning-Assisted Laser Diffraction;Photonics;2022-01-28

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