Morphological classification of fine particles in transmission electron microscopy images by using pre-trained convolution neural networks

IF 2.8 4区 环境科学与生态学 Q2 ENGINEERING, CHEMICAL Aerosol Science and Technology Pub Date : 2024-03-08 DOI:10.1080/02786826.2024.2322010
Jasmita Khadgi, Haebum Lee, Juchan Seo, Jin-hyuk Hong, Kihong Park
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Abstract

Morphological information on fine particles is essential for understanding their transport behavior in the ambient atmosphere and in the human respiratory system. More than 3000 transmission electr...
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利用预训练卷积神经网络对透射电子显微镜图像中的细颗粒进行形态分类
细颗粒物的形态信息对于了解其在环境大气和人体呼吸系统中的传输行为至关重要。超过 3000 个透射电镜可以测量细颗粒物的形态。
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来源期刊
Aerosol Science and Technology
Aerosol Science and Technology 环境科学-工程:化工
CiteScore
8.40
自引率
7.70%
发文量
73
审稿时长
3 months
期刊介绍: Aerosol Science and Technology publishes theoretical, numerical and experimental investigations papers that advance knowledge of aerosols and facilitate its application. Articles on either basic or applied work are suitable. Examples of topics include instrumentation for the measurement of aerosol physical, optical, chemical and biological properties; aerosol dynamics and transport phenomena; numerical modeling; charging; nucleation; nanoparticles and nanotechnology; lung deposition and health effects; filtration; and aerosol generation. Consistent with the criteria given above, papers that deal with the atmosphere, climate change, indoor and workplace environments, homeland security, pharmaceutical aerosols, combustion sources, aerosol synthesis reactors, and contamination control in semiconductor manufacturing will be considered. AST normally does not consider papers that describe routine measurements or models for aerosol air quality assessment.
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