A robust baseline elimination method based on community information

Wu, Yanling; Gao, Qingwei; Zhang, Yuanyuan

doi:10.1016/j.dsp.2015.02.015

Cited by 3 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Foist et al first noticed this problem and proposed a method to denoise multidimensional spectral data collaboratively [ 27 ]. For background correction, few approaches have been proposed by utilizing the common characteristics shared in a set of related spectra [ 6 , 28 , 29 ]. For instance, the multiple spectra baseline correction (MSBC) algorithm designed in [ 28 ] assumed that the pairwise differences between the background removed spectra are small and inserted a regularization for this prior to the asymmetric least squares.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

Chen

et al. 2018

Journal of Analytical Methods in Chemistry

View full text Add to dashboard Cite

Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually.

show abstract

Section: Introductionmentioning

confidence: 99%

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

Chen

et al. 2018

Journal of Analytical Methods in Chemistry

View full text Add to dashboard Cite

show abstract

Blind IR spectral deconvolution for image feature extraction via sparse representation regularization

Xiao

Tian

2019

Infrared Physics & Technology

View full text Add to dashboard Cite

Numerical study on cavitation–vortex–noise correlation mechanism and dynamic mode decomposition of a hydrofoil

2022

View full text Add to dashboard Cite

The large eddy simulation model coupled with the modified Schnerr-Sauer cavitation model has been used to numerically simulate the unsteady cavitation and noncavitation flow of the three-dimensional NACA66 hydrofoil under different operating conditions. The resulted show that the magnitude of the cavitation number plays a decisive role in the hydrofoil cavitation quasiperiodic phenomenon. The cavitation number 1.25 is used as a typical working condition for analysis. Using the Ffowcs Williams-Hawkings acoustic analogy approach accompanied with the vorticity transport equation splitting, the growth and shedding of cavitation also lead to the growth and shedding of the vortex structure. The cavitation-vortex interaction is mainly influenced by the vortex stretching term and vortex dilatation term and amplitude of them are larger than 500. The baroclinic torque term may be responsible for generating vorticity during the cloud cavitation collapse and has a lower amplitude about 200. The cavity volume acceleration is the main influencing factor of the low-frequency pressure fluctuation around the cavitating hydrofoil. Moreover, the NACA66 hydrofoil surface-pressure data are collected for dynamic mode decomposition to locate the hydrofoil surface noise sources. The alternate high and low amplitude regions in the mode results overlap highly with the cavitation transformation regions. The cavity transformation and pressure fluctuations are the main reason for the generation of periodic low-frequency noise source regions on the hydrofoil surface. Moreover, the corresponding frequencies of each order mode are linearly correlated with the cavitation shedding frequency of 5.70 Hz. Combined with the results of the multiple mode comparisons.

show abstract

A robust baseline elimination method based on community information

Cited by 3 publications

References 20 publications

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

Blind IR spectral deconvolution for image feature extraction via sparse representation regularization

Numerical study on cavitation–vortex–noise correlation mechanism and dynamic mode decomposition of a hydrofoil

Contact Info

Product

Resources

About