Habibah Mokhtaruddin

Habibah Mokhtaruddin

Preferred name

Habibah Mokhtaruddin

Official Name

Habibah, Mokhtaruddin

Alternative Name

Habibah, H. M.

Main Affiliation

Universiti Malaysia Perlis

Scopus Author ID

57221196456

Research Output

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Performance Analysis of Unipolar SPWM Inverter: Resistive load and Inductive load

( 2020-12-18)
Muhammad Zaid Aihsan
;
Yusof A.M.
;
Ahmad N.I.
;
Mohd Saifizi Saidon
;
Habibah Mokhtaruddin
;
Rahman D.H.A.
;
Wan Azani Wan Mustafa

This paper presents the performance analysis of the Unipolar SPWM Inverter for the resistive and inductive load testing. The common criteria reported in various technical papers where the resistive load will produce a unity power factor correction and lagging power factor behaviour for inductive load. This paper is to demonstrate the performance of both loads that are tested to the single phase Unipolar SPWM inverter under the modulation ratio of 0.8. The performances will be covered in term of the waveform behavior and THDv performance. The project are carried out through the simulation using PSIM software and real implementation to the real hardware. The selection of filter for this paper is the low pass passive LC filter.
Liquid Composition Identification and Characteristic Measurement Using Ultrasonic Transmission Technique via Neural Network

( 2023-08-01)
Muhammad Naufal Mansor
;
Yahya S.
;
Wan Azani Wan Mustafa
;
Habibah Mokhtaruddin
;
Syahrul Affandi Saidi
;
Ilham Shafini Ahmad Mahyudin
;
Mohd Aminudin Jamlos
;
Noor Anida Abu Talib
;
Mohd Zamri Hasan

This project is to determine the composition of liquids solvent by using the ultrasonic frequency signal from echoscope scan machine. The transmission technique of ultrasonic signal is focused. On the research experiment, studies on mixing of distilled water with control sodium chloride (Kitchen Salt), kitchen sugar and monosodium glutamate (MSG). The Parameters such as Fast Fourier Transform (FFT) which is the parameters are using to identify the ratio of composition of liquid solvent. The feature extraction of median, average and root mean square (RMS) from FFT is represented with different result analysis such as sensitivity, specificity, accuracy, Area under curve, kappa, F-measure and precision. The results performed more than 90% with Neural Network.

Habibah Mokhtaruddin

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Habibah Mokhtaruddin

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