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SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data

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Title
SVD-aided non-orthogonal decomposition (SANOD) method to exploit prior knowledge of spectral components in the analysis of time-resolved data
Author(s)
Hosung Ki; Yunbeom Lee; Eun Hyuk Choi; S. Lee; Hyotcherl Ihee
Publication Date
2019-03
Journal
STRUCTURAL DYNAMICS, v.6, no.2, pp.024303 -
Publisher
AMER INST PHYSICS
Abstract
Analysis of time-resolved data typically involves discriminating noise against the signal and extracting time-independent components and their time-dependent contributions. Singular value decomposition (SVD) serves this purpose well, but the extracted time-independent components are not necessarily the physically meaningful spectra directly representing the actual dynamic or kinetic processes but rather a mathematically orthogonal set necessary for constituting the physically meaningful spectra. Converting the orthogonal components into physically meaningful spectra requires subsequent posterior analyses such as linear combination fitting (LCF) and global fitting (GF), which takes advantage of prior knowledge about the data but requires that all components are known or satisfactory components are guessed. Since in general not all components are known, they have to be guessed and tested via trial and error. In this work, we introduce a method, which is termed SVD-aided Non-Orthogonal Decomposition (SANOD), to circumvent trial and error. The key concept of SANOD is to combine the orthogonal components from SVD with the known prior knowledge to fill in the gap of the unknown signal components and to use them for LCF. We demonstrate the usefulness of SANOD via applications to a variety of cases. © 2019 Author(s)
URI
https://pr.ibs.re.kr/handle/8788114/5887
ISSN
2329-7778
Appears in Collections:
Center for Nanomaterials and Chemical Reactions(나노물질 및 화학반응 연구단) > Journal Papers (저널논문)
Files in This Item:
Struct. Dyn. 6, 024303 (2019).pdfDownload

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