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M. F. Ghazali

Researcher at Universiti Malaysia Pahang

Publications -  47
Citations -  531

M. F. Ghazali is an academic researcher from Universiti Malaysia Pahang. The author has contributed to research in topics: Hilbert–Huang transform & Signal. The author has an hindex of 7, co-authored 37 publications receiving 360 citations. Previous affiliations of M. F. Ghazali include University of Sheffield.

Papers
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A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel

TL;DR: It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods, and a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.
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Comparative study of instantaneous frequency based methods for leak detection in pipeline networks

TL;DR: In this paper, a comparative study of instantaneous frequency analysis techniques based on pressure transients recorded within a live distribution network is presented, where the instantaneous frequency of the signals are analysed using the Hilbert transform (HT), the Normalised Hilbert transform(NHT), Direct Quadrature (DQ), Teager Energy Operator (TEO), and Cepstrum.
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Leak detection in gas pipeline by acoustic and signal processing - A review

TL;DR: In this article, a discussion about gas leak detection in pipeline system using acoustic method is presented in this paper, the wave propagation in the pipeline is a key parameter in acoustic method when the leak occurs and the pressure balance of the pipe will generated by the friction between wall in the pipe.
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Instantaneous phase and frequency for the detection of leaks and features in a pipeline system

TL;DR: In this paper, the authors used the Hilbert transform (HT) and the Hilbert Huang transform (HHT) to detect pipeline leaks and features based on analysis of a pressure transient.
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Porosity detection by analyzing arc sound signal acquired during the welding process of gas pipeline steel

TL;DR: In this paper, the acquired signal was analyzed using Hilbert Huang transform (HHT), which uses empirical mode decomposition for the purpose of filtering unrelated-to-damage signal components, and Hilbert spectral analysis to obtain the energy-frequency distance plot.