Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204278368> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W3204278368 endingPage "378" @default.
- W3204278368 startingPage "369" @default.
- W3204278368 abstract "Storage, processing, and transfer of huge matrices are becoming challenging tasks in the process analytical technology and scientific research. Matrix compression can solve these problems successfully. We developed a novel compression method of spectral data matrix based on its low-rank approximation and the fast Fourier transform of the singular vectors. This method differs from the known ones in that it does not require restoring the low-rank approximated matrix for further Fourier processing. Therefore, the compression ratio increases. A compromise between the losses of the accuracy of the data matrix restoring and the compression ratio was achieved by selecting the processing parameters. The method was applied to multivariate chemometrics analysis of the cow milk for determining fat and protein content using two data matrices (the file sizes were 5.7 and 12.0 MB) restored from their compressed form. The corresponding compression ratios were about 52 and 114, while the loss of accuracy of the analysis was less than 1% compared with processing of the non-compressed matrix. A huge, simulated matrix, compressed from 400 MB to 1.9 MB, was successfully used for multivariate calibration and segment cross-validation. The data set simulated a large matrix of 10 000 low-noise infrared spectra, measured in the range 4000-400 cm-1 with a resolution of 0.5 cm-1. The corresponding file was compressed from 262.8 MB to 19.8 MB. The discrepancies between original and restored spectra were less than the standard deviation of the noise. The method developed in the article clearly demonstrated its potential for future applications to chemometrics-enhanced spectrometric analysis with limited options of memory size and data transfer rate. The algorithm used the standard routines of Matlab software." @default.
- W3204278368 created "2021-10-11" @default.
- W3204278368 creator A5029033313 @default.
- W3204278368 date "2021-10-01" @default.
- W3204278368 modified "2023-09-23" @default.
- W3204278368 title "A Novel Compression Method of Spectral Data Matrix Based on the Low-Rank Approximation and the Fast Fourier Transform of the Singular Vectors" @default.
- W3204278368 cites W1495773224 @default.
- W3204278368 cites W1985275422 @default.
- W3204278368 cites W2076611334 @default.
- W3204278368 cites W2080747833 @default.
- W3204278368 cites W2139522708 @default.
- W3204278368 cites W2287570459 @default.
- W3204278368 cites W2491676022 @default.
- W3204278368 cites W2775346211 @default.
- W3204278368 cites W3027593411 @default.
- W3204278368 cites W3088121114 @default.
- W3204278368 doi "https://doi.org/10.1177/00037028211044759" @default.
- W3204278368 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34596451" @default.
- W3204278368 hasPublicationYear "2021" @default.
- W3204278368 type Work @default.
- W3204278368 sameAs 3204278368 @default.
- W3204278368 citedByCount "1" @default.
- W3204278368 countsByYear W32042783682022 @default.
- W3204278368 crossrefType "journal-article" @default.
- W3204278368 hasAuthorship W3204278368A5029033313 @default.
- W3204278368 hasConcept C102519508 @default.
- W3204278368 hasConcept C104317684 @default.
- W3204278368 hasConcept C106487976 @default.
- W3204278368 hasConcept C11413529 @default.
- W3204278368 hasConcept C114614502 @default.
- W3204278368 hasConcept C115961682 @default.
- W3204278368 hasConcept C119857082 @default.
- W3204278368 hasConcept C134306372 @default.
- W3204278368 hasConcept C151304367 @default.
- W3204278368 hasConcept C154945302 @default.
- W3204278368 hasConcept C159985019 @default.
- W3204278368 hasConcept C164226766 @default.
- W3204278368 hasConcept C180016635 @default.
- W3204278368 hasConcept C185592680 @default.
- W3204278368 hasConcept C192562407 @default.
- W3204278368 hasConcept C193252679 @default.
- W3204278368 hasConcept C22789450 @default.
- W3204278368 hasConcept C2780985081 @default.
- W3204278368 hasConcept C33923547 @default.
- W3204278368 hasConcept C41008148 @default.
- W3204278368 hasConcept C44465124 @default.
- W3204278368 hasConcept C55493867 @default.
- W3204278368 hasConcept C78548338 @default.
- W3204278368 hasConcept C99498987 @default.
- W3204278368 hasConceptScore W3204278368C102519508 @default.
- W3204278368 hasConceptScore W3204278368C104317684 @default.
- W3204278368 hasConceptScore W3204278368C106487976 @default.
- W3204278368 hasConceptScore W3204278368C11413529 @default.
- W3204278368 hasConceptScore W3204278368C114614502 @default.
- W3204278368 hasConceptScore W3204278368C115961682 @default.
- W3204278368 hasConceptScore W3204278368C119857082 @default.
- W3204278368 hasConceptScore W3204278368C134306372 @default.
- W3204278368 hasConceptScore W3204278368C151304367 @default.
- W3204278368 hasConceptScore W3204278368C154945302 @default.
- W3204278368 hasConceptScore W3204278368C159985019 @default.
- W3204278368 hasConceptScore W3204278368C164226766 @default.
- W3204278368 hasConceptScore W3204278368C180016635 @default.
- W3204278368 hasConceptScore W3204278368C185592680 @default.
- W3204278368 hasConceptScore W3204278368C192562407 @default.
- W3204278368 hasConceptScore W3204278368C193252679 @default.
- W3204278368 hasConceptScore W3204278368C22789450 @default.
- W3204278368 hasConceptScore W3204278368C2780985081 @default.
- W3204278368 hasConceptScore W3204278368C33923547 @default.
- W3204278368 hasConceptScore W3204278368C41008148 @default.
- W3204278368 hasConceptScore W3204278368C44465124 @default.
- W3204278368 hasConceptScore W3204278368C55493867 @default.
- W3204278368 hasConceptScore W3204278368C78548338 @default.
- W3204278368 hasConceptScore W3204278368C99498987 @default.
- W3204278368 hasIssue "3" @default.
- W3204278368 hasLocation W32042783681 @default.
- W3204278368 hasLocation W32042783682 @default.
- W3204278368 hasOpenAccess W3204278368 @default.
- W3204278368 hasPrimaryLocation W32042783681 @default.
- W3204278368 hasRelatedWork W1515733880 @default.
- W3204278368 hasRelatedWork W1516708947 @default.
- W3204278368 hasRelatedWork W2038922352 @default.
- W3204278368 hasRelatedWork W2105730801 @default.
- W3204278368 hasRelatedWork W2154249783 @default.
- W3204278368 hasRelatedWork W2495424399 @default.
- W3204278368 hasRelatedWork W2770973630 @default.
- W3204278368 hasRelatedWork W2964991158 @default.
- W3204278368 hasRelatedWork W3204278368 @default.
- W3204278368 hasRelatedWork W349217628 @default.
- W3204278368 hasVolume "76" @default.
- W3204278368 isParatext "false" @default.
- W3204278368 isRetracted "false" @default.
- W3204278368 magId "3204278368" @default.
- W3204278368 workType "article" @default.