Characterizing dissolved organic matter in Taihu Lake with PARAFAC and SOM method

The three-dimensional fluorescence spectrum has a significant amount of information than the single-stage scanning fluorescence spectrum. At the same time, the parallel factor (PARAFAC) analysis and neural network method can help explore the fluorescence characteristics further, thus could be used to analyse multiple sets of three-dimensional matrix data. In this study, the PARAFAC analysis and the self-organizing mapping (SOM) neural network method are firstly introduced comprehensively. They are then adopted to extract information of the three-dimensional fluorescence spectrum data set for fluorescence characteristics analysis of dissolved organic matter (DOM) in Taihu Lake water. Forty water samples with DOM species were taken from different seasons with the fluorescence information obtained through the three-dimensional fluorescence spectrum analysis, PARAFAC analysis and SOM analysis. The PARAFAC analysis results indicated that the main fluorescence components of dissolved organic matter in Taihu Lake water were aromatic proteins, fulvic acids, and dissolved microorganisms. While the SOM analysis results exhibited that the fluorescence characteristics of the dissolved organics in Taihu Lake varied seasonally. Therefore, the combined method of the three-dimensional fluorescence spectrum analysis, PARAFAC and SOM analysis can provide important information for the characterization of the fluorescence properties of dissolved organic matter in surface water bodies.

Recognition Method of Petroleum Fluorescence Spectra Based on Convolutional Neural Network

As an important raw material and petrochemical tool, petroleum not only brings convenience to mankind, but also brings huge socio-economic and cultural value to our social development, but at the same time it also causes a lot of serious damage to our ecological environment. The identification and measurement of petroleum pollutants has become the main tool to identify pollution sources, control their pollutants and protect their ecological environment. This paper explores the petroleum fluorescence spectrum identification method based on convolutional neural network. Based on extensive research on this method, a simple analysis and understanding of petroleum fluorescence spectrum identification technology and petroleum-related principles are carried out, and then summarized according to relevant data find out the main factors that affect fluorescence spectrum recognition, and prepare for the experiment. The feasibility of the method is verified through the petroleum fluorescence spectrum recognition experiment of the convolutional neural network. The experimental results show that the relative error of the fluorescence spectrum recognition under different concentrations of petroleum both are within the range of 9%. Through the analysis of the relative error, it can be seen that the relative error of resolution shows a downward trend with the increase of the concentration. According to the above data, it can be seen that when the convolutional neural network algorithm is used to identify the components of the petroleum mixed solution, the qualitative analysis can be completed well. When the components in the mixed solution are quantitatively analyzed, there is a certain relative error.

Low-frequency fluorescence spectrum of a laser driven polar quantum emitter damped by squeezed vacuum with finite bandwidth

A two-level quantum emitter with broken inversion symmetry driven by external semiclassical monochromatic high-frequency electromagnetic (e.g., laser) field and damped by squeezed vacuum reservoir with finite bandwidth is presented. The squeezed vacuum source is assumed to be either degenerate parametric oscillator (DPO) or a non-degenerate parametric oscillator (NDPO). It is shown that the shape of low-frequency fluorescence spectrum of the emitter can be effectively alternated by controlling the degree of the squeezed vacuum source degeneration and phase of the squeezing.