Matches in SemOpenAlex for { <https://semopenalex.org/work/W2340486515> ?p ?o ?g. }
- W2340486515 endingPage "1221" @default.
- W2340486515 startingPage "1214" @default.
- W2340486515 abstract "This paper proposes a transform-based compression algorithm for waveforms associated with power quality and transient phenomena in power systems. This method uses the wavelet transform, a dynamic bit allocation in the transform domain through estimation of the spectral shape, as well as entropy coding in order to minimize residual redundancy. Five distinct approaches for estimating the spectral shape are proposed. Four of them are based on analytical models that seek to describe the decreasing behavior of the transformed coefficients: (1) decreasing linear bit allocation shape; (2) decreasing quadratic bit allocation shape; (3) decreasing exponential bit allocation shape; (4) rotated sigmoid bit allocation shape; and (5) the fifth approach-the neural shape estimator (NSE)-is an adaptive model that uses an artificial neural network to map the changes in the spectrum shape. Results with databases of real signals and a performance evaluation using objective measures are reported. The results indicate that the NSE approach outperforms the other proposed solutions that use spectral shape estimation for coding, as well as other compression contributions reported in the literature." @default.
- W2340486515 created "2016-06-24" @default.
- W2340486515 creator A5030042313 @default.
- W2340486515 date "2016-05-01" @default.
- W2340486515 modified "2023-10-06" @default.
- W2340486515 title "Spectral Shape Estimation in Data Compression for Smart Grid Monitoring" @default.
- W2340486515 cites W1890834058 @default.
- W2340486515 cites W1988344262 @default.
- W2340486515 cites W1999148066 @default.
- W2340486515 cites W2001577852 @default.
- W2340486515 cites W2003160087 @default.
- W2340486515 cites W2013762526 @default.
- W2340486515 cites W2020604746 @default.
- W2340486515 cites W2047667901 @default.
- W2340486515 cites W2061288717 @default.
- W2340486515 cites W2061462530 @default.
- W2340486515 cites W2061536341 @default.
- W2340486515 cites W2063850918 @default.
- W2340486515 cites W2067063452 @default.
- W2340486515 cites W2074989321 @default.
- W2340486515 cites W2076522182 @default.
- W2340486515 cites W2088939727 @default.
- W2340486515 cites W2092395488 @default.
- W2340486515 cites W2093855461 @default.
- W2340486515 cites W2099605119 @default.
- W2340486515 cites W2102594219 @default.
- W2340486515 cites W2106342127 @default.
- W2340486515 cites W2108545600 @default.
- W2340486515 cites W2109753595 @default.
- W2340486515 cites W2110828252 @default.
- W2340486515 cites W2114264037 @default.
- W2340486515 cites W2131853629 @default.
- W2340486515 cites W2137203768 @default.
- W2340486515 cites W2142123810 @default.
- W2340486515 cites W2143995107 @default.
- W2340486515 cites W2145125280 @default.
- W2340486515 cites W2148666284 @default.
- W2340486515 cites W2149669452 @default.
- W2340486515 cites W2151693816 @default.
- W2340486515 cites W2152742487 @default.
- W2340486515 cites W2155647922 @default.
- W2340486515 cites W2156125143 @default.
- W2340486515 cites W2166949990 @default.
- W2340486515 cites W2167065262 @default.
- W2340486515 cites W2169318603 @default.
- W2340486515 cites W2170948846 @default.
- W2340486515 cites W2545667300 @default.
- W2340486515 cites W3036512766 @default.
- W2340486515 cites W4249354340 @default.
- W2340486515 cites W2033633281 @default.
- W2340486515 doi "https://doi.org/10.1109/tsg.2015.2500359" @default.
- W2340486515 hasPublicationYear "2016" @default.
- W2340486515 type Work @default.
- W2340486515 sameAs 2340486515 @default.
- W2340486515 citedByCount "19" @default.
- W2340486515 countsByYear W23404865152016 @default.
- W2340486515 countsByYear W23404865152017 @default.
- W2340486515 countsByYear W23404865152018 @default.
- W2340486515 countsByYear W23404865152019 @default.
- W2340486515 countsByYear W23404865152020 @default.
- W2340486515 countsByYear W23404865152021 @default.
- W2340486515 countsByYear W23404865152022 @default.
- W2340486515 countsByYear W23404865152023 @default.
- W2340486515 crossrefType "journal-article" @default.
- W2340486515 hasAuthorship W2340486515A5030042313 @default.
- W2340486515 hasConcept C105795698 @default.
- W2340486515 hasConcept C106301342 @default.
- W2340486515 hasConcept C11413529 @default.
- W2340486515 hasConcept C115961682 @default.
- W2340486515 hasConcept C121332964 @default.
- W2340486515 hasConcept C126255220 @default.
- W2340486515 hasConcept C1276947 @default.
- W2340486515 hasConcept C152822103 @default.
- W2340486515 hasConcept C154945302 @default.
- W2340486515 hasConcept C155512373 @default.
- W2340486515 hasConcept C169805256 @default.
- W2340486515 hasConcept C185429906 @default.
- W2340486515 hasConcept C196216189 @default.
- W2340486515 hasConcept C2221639 @default.
- W2340486515 hasConcept C33923547 @default.
- W2340486515 hasConcept C41008148 @default.
- W2340486515 hasConcept C47432892 @default.
- W2340486515 hasConcept C4839761 @default.
- W2340486515 hasConcept C50644808 @default.
- W2340486515 hasConcept C62520636 @default.
- W2340486515 hasConcept C78548338 @default.
- W2340486515 hasConcept C81388566 @default.
- W2340486515 hasConceptScore W2340486515C105795698 @default.
- W2340486515 hasConceptScore W2340486515C106301342 @default.
- W2340486515 hasConceptScore W2340486515C11413529 @default.
- W2340486515 hasConceptScore W2340486515C115961682 @default.
- W2340486515 hasConceptScore W2340486515C121332964 @default.
- W2340486515 hasConceptScore W2340486515C126255220 @default.
- W2340486515 hasConceptScore W2340486515C1276947 @default.
- W2340486515 hasConceptScore W2340486515C152822103 @default.
- W2340486515 hasConceptScore W2340486515C154945302 @default.
- W2340486515 hasConceptScore W2340486515C155512373 @default.
- W2340486515 hasConceptScore W2340486515C169805256 @default.