%X Number of existing signal processing methods can be used for extracting useful information. However, the problem of signal processing method, essential to highlight the wanted information and attenuate the undesired signal is trivial. Several signal processing methods have been implemented to solve this issue. Research using Empirical Mode Decomposition (EMD) algorithm shows promising results in comparison to other signal processing methods, especially in the accuracy showing the relationship between signal energy and time � frequency distribution by represents series of the stationary signals with different amplitudes and frequency bands. However, this EMD algorithm will still have noise contamination that may compromise the accuracy of the signal processing to highlight the wanted information. It is because the mode mixing phenomenon in the Intrinsic Mode Function�s (IMF) due to the undesirable signal with the mix of additional noise. There is still room for the improvement in the selective accuracy of the sensitive IMF after decomposition that can influence the correctness of feature extraction of the oxidized carbon steel. Using four datasets, analysis parameters of the Ensemble Empirical Mode Decomposition (EEMD) algorithm has been conducted. © Springer Nature Switzerland AG. 2019.
%K Carbon steel; Functions; Intelligent systems; Mixing; Processing, Empirical Mode Decomposition; Ensemble empirical mode decomposition; Intrinsic Mode functions; Mode mixing; Noise contamination; Selective accuracy of IMF, Signal processing
%R 10.1007/978-3-030-00211-4₂₃
%D 2019
%L scholars12313
%J Advances in Intelligent Systems and Computing
%O cited By 0; Conference of 2nd Computational Methods in Systems and Software, CoMeSySo 2018 ; Conference Date: 12 September 2018 Through 14 September 2018; Conference Code:217959
%V 859
%I Springer Verlag
%A N.S.M. Jaafar
%A I.A. Aziz
%A J. Jaafar
%A A.K. Mahmood
%T An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel
%P 255-273