Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion–emission matrices-Reference-Cited by-同舟云学术

Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion–emission matrices

Published:2023 Issue:13 Volume:15 Page:1649-1660
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Gong Bowen¹²^ORCID,Zhang Hongji¹,Wang Xiaodong¹,Lian Ke³,Li Xinkai¹,Chen Bo¹,Wang Hanlin¹²,Niu Xiaoqian¹²

Affiliation:

1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin Province, 130033, China

2. University of Chinese Academy of Sciences, Beijing, 100049, China

3. Shanghai Institute of Spacecraft Equipment, Shanghai, 200240, China

Abstract

The fluorescence of oil films with different thicknesses and mixing proportions was measured and classified using an improved co-training algorithm, which improves the classification accuracy and provides prospects for recognition of similar oil species.

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/AY/D2AY01776H

Reference38 articles.

1. Oil spills: impacts and perspectives of treatment technologies with focus on the use of green surfactants

2. Excitation emission matrix fluorescence spectroscopy and parallel factor framework-clustering analysis for oil pollutants identification

3. History of a disaster: A baseline assessment of the Wakashio oil spill on the coast of Mauritius, Indian Ocean

4. Research on the layout and value space of China’s 5G industry——Based on the Sino-US “trade war” perspective

5. Two Stream Convolution Fusion Network based on Attention Mechanism

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