Taihu Lake is one of the five biggest lakes in China. Surface water samples from 26 sampling sites of Taihu Lake were collected. Furthermore wet chemical analysis （CODCr and BOD5） and measurement of three dimensiona...Taihu Lake is one of the five biggest lakes in China. Surface water samples from 26 sampling sites of Taihu Lake were collected. Furthermore wet chemical analysis （CODCr and BOD5） and measurement of three dimensional excitation-emission matrix （3DEEM） spectra in the laboratory have been conducted. Using parallel factor analysis （PARAFAC） model, three components of colored dissolved organic matter （CDOM） have been identified successfully, based on the analysis of 3DEEM data. The characteristics of the three components also have been described by comparing them to some components of CDOM, identified in earlier researches. Meanwhile, spatial variations of concentration for the three components in Taihu Lake have been analyzed, and the result indicates that the concentration of component 1 depends more on the situation of wastewater pollution and can be used as the indicator of wastewater pollution. The relationship between the concentrations of the three components and results of the wet chemical analysis show that none of the three components can be used as indicators of gross organic matter in water. However, the concentrations of all the three components have obvious linear relationships with the BOD5 value, especially for component 1 （r = 0.72878）. Finally, the potential applications of the composition analysis based on 3DEEM and PARAFAC model in water quality monitoring have been illuminated.展开更多
In tensor theory, the parallel factorization （PARAFAC）decomposition expresses a tensor as the sum of a set of rank-1tensors. By carrying out this numerical decomposition, mixedsources can be separated or unknown sys...In tensor theory, the parallel factorization （PARAFAC）decomposition expresses a tensor as the sum of a set of rank-1tensors. By carrying out this numerical decomposition, mixedsources can be separated or unknown system parameters can beidentified, which is the so-called blind source separation or blindidentification. In this paper we propose a numerical PARAFACdecomposition algorithm. Compared to traditional algorithms, wespeed up the decomposition in several aspects, i.e., search di-rection by extrapolation, suboptimal step size by Gauss-Newtonapproximation, and linear search by n steps. The algorithm is ap-plied to polarization sensitive array parameter estimation to showits usefulness. Simulations verify the correctness and performanceof the proposed numerical techniques.展开更多
基金Project supported by the Knowledge Innovation Project of ChineseAcademy of Sciences (No. KGCX2-SW-111).
文摘Taihu Lake is one of the five biggest lakes in China. Surface water samples from 26 sampling sites of Taihu Lake were collected. Furthermore wet chemical analysis （CODCr and BOD5） and measurement of three dimensional excitation-emission matrix （3DEEM） spectra in the laboratory have been conducted. Using parallel factor analysis （PARAFAC） model, three components of colored dissolved organic matter （CDOM） have been identified successfully, based on the analysis of 3DEEM data. The characteristics of the three components also have been described by comparing them to some components of CDOM, identified in earlier researches. Meanwhile, spatial variations of concentration for the three components in Taihu Lake have been analyzed, and the result indicates that the concentration of component 1 depends more on the situation of wastewater pollution and can be used as the indicator of wastewater pollution. The relationship between the concentrations of the three components and results of the wet chemical analysis show that none of the three components can be used as indicators of gross organic matter in water. However, the concentrations of all the three components have obvious linear relationships with the BOD5 value, especially for component 1 （r = 0.72878）. Finally, the potential applications of the composition analysis based on 3DEEM and PARAFAC model in water quality monitoring have been illuminated.
基金supported by the National Natural Science Foundation of China(61571131)the Technology Innovation Fund of the 10th Research Institute of China Electronics Technology Group Corporation(H17038.1)
文摘In tensor theory, the parallel factorization （PARAFAC）decomposition expresses a tensor as the sum of a set of rank-1tensors. By carrying out this numerical decomposition, mixedsources can be separated or unknown system parameters can beidentified, which is the so-called blind source separation or blindidentification. In this paper we propose a numerical PARAFACdecomposition algorithm. Compared to traditional algorithms, wespeed up the decomposition in several aspects, i.e., search di-rection by extrapolation, suboptimal step size by Gauss-Newtonapproximation, and linear search by n steps. The algorithm is ap-plied to polarization sensitive array parameter estimation to showits usefulness. Simulations verify the correctness and performanceof the proposed numerical techniques.
