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Updated: Jul 20, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

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开发一种循环的时空深度学习方法,加上数据融合,以纠正每小时的臭氧预测

Jie Li1, Ji-Cheng Jang1, Yun Zhu1

  • 1Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China.

Environmental pollution (Barking, Essex : 1987)
|August 1, 2023
PubMed
概括

这项研究引入了一种新的深度学习方法,可以提前72小时准确预测环境臭氧 (O3) 度. 这种先进的模型显著改进了传统方法,提高了空气质量预测.

关键词:
空气质量建模空气质量建模数据融合数据融合深度学习是一种深度学习.臭氧光化学 臭氧的光化学臭氧预测的预测

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