A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification
• It is the first method for power quality analysis on combining 1D and 2D CNN features. • A robust and effective framework is created by combining signal and image features. • It provides better performance than state-of-the-art power quality classification methods.
Autor*in: |
Sindi, Hatem [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:174 ; year:2021 ; day:15 ; month:07 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.eswa.2021.114785 |
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Katalog-ID: |
ELV053982169 |
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A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification |
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• It is the first method for power quality analysis on combining 1D and 2D CNN features. • A robust and effective framework is created by combining signal and image features. • It provides better performance than state-of-the-art power quality classification methods. |
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• It is the first method for power quality analysis on combining 1D and 2D CNN features. • A robust and effective framework is created by combining signal and image features. • It provides better performance than state-of-the-art power quality classification methods. |
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A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification |
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