Changchun Institute of Optics,Fine Mechanics and Physics,CAS
| Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices | |
| B. Gong; H. Zhang; X. Wang; K. Lian; X. Li; B. Chen; H. Wang and X. Niu | |
| 2023 | |
| 发表期刊 | Analytical Methods
 |
| ISSN | 17599660 |
| 卷号 | 15期号:13页码:1649-1660 |
| 摘要 | In recent years, marine oil spill accidents have been occurring frequently during extraction and transportation, and seriously damage the ecological balance. Accurate monitoring of oil spills plays a vital role in estimating oil spill volume, determination of liability, and clean-up. The oil that leaks into natural environments is not a single type of oil, but a mixture of various oil products, and the oil film thickness on the sea surface is uneven under the influence of wind and waves. Increasing the mixed oil film thickness dimension and the mix proportion dimension has been proposed to weaken the effect of the detection environment on the fluorescence measurement results. To preserve the relationships between the data of oil films with different thicknesses and the relationships between the data of oil films with different mixing proportions, the three-dimensional fluorescence spectral data of mixed oil films on a seawater surface were measured in the laboratory, producing a thickness-fluorescence matrix and a proportion-fluorescence matrix. The nonlinear variation of the fluorescence spectra was investigated according to the fluorescence lidar equation. This work pre-processes the data by sum normalization and two-dimensional principal component analysis (2DPCA) and uses the dimensionality reduction results as two feature-point views. Then, semi-supervised classification of collaborative training (co-training) with K-nearest neighbors (KNN) and a decision tree (DT) is used to identify the samples. The results show that the average overall accuracy of this coupling model can reach 100%, which is 20.49% higher than that of the thickness-only view. Using unlabeled data can reduce the cost of data acquisition, improve the classification accuracy and generalization ability, and provide theoretical significance and application prospects for discrimination of spectrally similar oil species in natural marine environments. © 2023 The Royal Society of Chemistry. |
| DOI | 10.1039/d2ay01776h |
| URL | 查看原文 |
| 收录类别 | sci ; ei |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ciomp.ac.cn/handle/181722/67484 |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | B. Gong,H. Zhang,X. Wang,et al. Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices[J]. Analytical Methods,2023,15(13):1649-1660. |
| APA | B. Gong.,H. Zhang.,X. Wang.,K. Lian.,X. Li.,...&H. Wang and X. Niu.(2023).Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices.Analytical Methods,15(13),1649-1660. |
| MLA | B. Gong,et al."Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion-emission matrices".Analytical Methods 15.13(2023):1649-1660. |
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