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A Teaching Framework Based on Big Data of Students’ Learning Behaviors: A Case Study of Academic Performance Prediction Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Source Number Enumeration Approach Based on CEEMD

Authors : Shengguo Ge, Siti Nurulain Mohd Rum, Hamidah Ibrahim, Erzam Marsilah, Thinagaran Perumal

Published in: Frontier Computing

Publisher: Springer Nature Singapore

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Abstract

Empirical mode decomposition (EMD) can be used to decompose complex signals into a limited number of intrinsic mode functions (IMFs), and each decomposed IMF component contains local characteristic signals of different time scales of the original signal. Thus, EMD can be used to transform the problem of source number enumeration into a pattern recognition problem, thereby improving the accuracy of source number enumeration. However, EMD is prone to mode mixing, which also affects the accuracy of source number enumeration. To overcome this issue, this research work proposes the replacement of EMD decomposition with the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to suppress the mode mixing phenomenon. The result of the experimental simulation in this proved that CEEMD achieved a better accuracy of source number enumerations compared to three other approaches namely based on EMD, SORTE and GED.

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Metadata
Title
Source Number Enumeration Approach Based on CEEMD
Authors
Shengguo Ge
Siti Nurulain Mohd Rum
Hamidah Ibrahim
Erzam Marsilah
Thinagaran Perumal
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Frontier Computing

Print ISBN: 978-981-9914-27-2

Electronic ISBN: 978-981-9914-28-9

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-1428-9_73