An enhance approach of filtering to select adaptive IMFs of EEMD in fiber optic sensor for oxidized carbon steel
Number of existing signal processing methods can be used for extracting useful information. However, receiving desired and eliminating undesired information is yet a significant problem of these methods. Empirical Mode Decomposition (EMD) algorithm shows promising results in comparison to other sign...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Springer Verlag
2019
|
| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047859630&doi=10.1007%2f978-3-319-91189-2_24&partnerID=40&md5=a16ae21db8a5efb5e6a131d50c009adf http://eprints.utp.edu.my/23587/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Number of existing signal processing methods can be used for extracting useful information. However, receiving desired and eliminating undesired information is yet a significant problem of these methods. Empirical Mode Decomposition (EMD) algorithm shows promising results in comparison to other signal processing methods especially in terms of accuracy. For example, it shows an efficient relationship between signal energy and time frequency distribution. Though, EMD algorithm still has a noise contamination which may compromise the accuracy of the signal processing. It is due to the mode mixing phenomenon in the Intrinsic Mode Function�s (IMF) which causes the undesirable signal with the mix of additional noise. Therefore, it has still a room for the improvements in the selective accuracy of the sensitive IMF after decomposition that can influence the correctness of feature extraction of the oxidized carbon steel. This study has used two datasets to compare the parameters analysis of the Ensemble Empirical Mode Decomposition (EEMD) algorithm for constructing the signal signature. © 2019, Springer International Publishing AG, part of Springer Nature. |
|---|