Ahmad Firdaus Ahmad Zaidi
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Ahmad Firdaus Ahmad Zaidi
Official Name
Ahmad Firdaus, Ahmad Zaidi
Alternative Name
Ahmad Zaidi, Ahmad Firdaus
Ahmad Firdaus, A. Z.
Zaidi, Ahmad Firdaus Bin Ahmad
Firdaus, A. Z.Ahmad
Main Affiliation
Scopus Author ID
55992689600
Researcher ID
S-8233-2019

Research Output

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  • Publication
    Investigating the Effect of EEG Channel Location on Emotion Classification using EEG Signal
    ( 2023-01-01)
    Mohd Shuhanaz Zanar Azalan
    ;
    Hisham N.A.W.N.N.
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Jusman Y.
    EEG channel location on emotion classification and the choice of classifier are the factor that significantly impact the emotion recognition accuracy. Currently, there is a gap in the literature concerning the identification of optimal channel locations and classifier choice for effective emotion recognition. This study aims to investigate the influence of EEG channel electrode locations and classifiers on the accuracy of emotion recognition. Emotion classification was conducted using three classifiers namely Neural Network (NN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) - on two EEG channels configuration with 62 channels and 32 channels, as well as five separate brain regions: Frontal, Temporal, Central, Parietal, and Occipital. The analysis was performed on the SEED-IV dataset. Features were extracted using Differential Entropy. Result indicates that SVM outperformed NN and KNN across all configurations. When assessing EEG channel location, the 32-channel setup yield higher average accuracies for both SVM (91.07%) and NN (86.63%), whereas the 62-channel setup was optimal for KNN (88.28%). The most significant results were identified in the Parietal and Occipital regions. SVM achieved the highest accuracies in these regions (96.55% in the Parietal and 97.18% in the Occipital), with NN (90.17% in Parietal and 92.65% in Occipital) and KNN (82.76% in Parietal and 92.81% in Occipital). These findings emphasize the crucial roles of the Parietal and Occipital regions, associated with sensory integration and visual processing, in emotion recognition. The study highlights the importance of EEG channel location and classifier selection in enhancing the reliability of EEG-based emotion recognition systems.
  • Publication
    A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images
    ( 2021-08-27)
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Tengku Amran T.S.
    ;
    Ahmad M.R.
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator's knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD and DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.
      1
  • Publication
    Fuzzy Logic Cascaded Current Control of DC Motor Variable Speed Drive using dSPACE
    ( 2023-01-01)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Ni L.P.
    ;
    Leong Jenn Hwai , Jenn
    ;
    Syahrul Ashikin Azmi
    ;
    Kamarulzaman Kamarudin
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Jusman Y.
    Two-wheel e-scooter falls under low power segment for Battery Electric Vehicle (BEV) and has gain more popularity in urban commuting. Most entry level e-scooter is still powered by DC motor due to low cost and ease of control. However basic open-loop DC Motor control employed through throttling is plugged with limited efficiency, precision, and range of speed control. Closed-loop control enables real time adjustment according to preset speed which becomes handy during auto cruising. To ensure good dynamic response, improved robustness and stable wide speed control range, a good control scheme for the motor is essential. In this project, a variable speed control scheme, namely fuzzy logic cascaded current control system was designed using MATLAB Simulink, comprising speed control loop and a current control loop 185 W Separately Excited Brushed DC Motor. The proposed control system was tested on hardware using dSPACE DS1104 platform. The system's output speed is obtained using an incremental encoder, while the output current is measured with a current sensor. Subsequently, the control system's stability, robustness, and dynamic performance were evaluated by driving the system on 120 W electrical load at varying speed. The system performance has proved superior to closed-loop by 70% on low speed ripple reduction and is on par with PI cascaded current control scheme.
      1
  • Publication
    3D Reconstruction of embedded object using ground penetrating radar
    ( 2023-01-01)
    Fadil N.D.
    ;
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Kamal W.H.B.W.
    ;
    Basri N.A.M.
    Ground Penetrating Radar (GPR) is a non-destructive device widely used to locate and map underground utilities such as pipes, cables, etc. Its principle is based on the reflection signal of a transmitter-receiver antenna that strikes underground objects by means of the propagation of a short pulse of electromagnetic waves into the ground. The GPR will produce a hyperbolic curve as a result of the object's presence. Accurate interpretation of hyperbola curves is greatly important and highly depends on user expertise; thus, it is considered a challenge. To address this issue, this study aims to develop 3D reconstructions of embedded objects. In this study, C-scan images were acquired, and 3D interpolation and the Synthetic Aperture Focusing Technique (SAFT) were introduced. In this framework, the acquired data is subjected to pre-processing techniques via time-zero correction, background removal using average background subtraction, and Kirchoff's migration method. The software Reflex 3D Scan has been used to analyse and preprocess the 3D reconstruction of embedded objects. The obtained results show that 3D interpolation and SAFT methods are not only able to reconstruct 3D models but are also able to reveal information on the dimension and location of the buried object represented by voxel points in the 3D space cube.
      8  29
  • Publication
    Assessment of Control Drive Technologies for Induction Motor: Industrial Application to Electric Vehicle
    ( 2021-06-11)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Syahrul Ashikin Azmi
    ;
    Kamarulzaman Kamarudin
    ;
    Leong Jenn Hwai , Jenn
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Zamri Che Mat Kasa
    Nowadays electric vehicle has increasingly gained much popularity indicated by growing global share market targeted at 30% by 2030 after recording 7.2million global stock in 2019. Compared to Internal Combustion Engine (ICE) counterpart, Battery Electric Vehicles (BEV) produce zero tailpipe emission which greatly reducing carbon footprints. Induction motor has been widely used and its control technology has evolved from scalar type volt/hertz to recent predictive control technology. This allows induction motor's application to expand from being the workhorse of industry to become prime mover in electric vehicle, where high performance is expected. Among vector control scheme, Direct Torque Control (DTC) has gained interest over Field Oriented Control (FOC) with simpler structure, better robustness and dynamics performance yet suffer from high torque and flux ripple. In electric vehicle applications, high ripple at low speed is highly undesirable, potentially causing torsional vibration. High performance control requires speed sensor integration, which often increase complexity in the design. The work aims to review the best control technology for induction motor in electric vehicle application through performance parameter evaluation such as improvement on dynamic response, torque and flux ripple reduction, and component optimization. Several arise issues in motor control and possible methods to circumvent are highlighted in this work. In conclusion, model predictive torque control (MPTC) is the most promising scheme for electric vehicle with excellent dynamic response, good low speed performance, and 50% torque ripple reduction compared to conventional DTC and potential integration with sliding mode observer for sensorless solution.
      1  38
  • Publication
    dSPACE Implementation of Motor Drives using Asymmetric Converter
    ( 2024-02-01)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Azmi S.A.
    ;
    Hwai L.J.
    ;
    Kamarudin K.
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Noor A.M.
    ;
    Ramzan N.I.
    This paper deals with the dSPACE DS1104 based implementation of closed-loop motor drive system using asymmetric converter. The mathematical model of the drive has been simulated in the MATLAB/Simulink environment to analyze the performance of the drive system. The simulated results are then validated with the experimental investigation. For experimental work, the pulse width modulation (PWM) has been implemented in MATLAB environment with Simulink real-time interface. Meanwhile, the hardware implementation consisting of dSPACE digital signal processor, voltage source inverter and generator-coupled motor. Variable speed test was performed on the loaded motor in open loop and closed-loop design to obtain speed tracking response parameter as well as speed ripple. Overall performance of developed system is satisfactory where in low-speed operation, experimental results show good speed tracking performance with ripple within 20%.
      32  2
  • Publication
    Design and Development of Cascaded Current Control in DC Motor Variable Speed Drive using dSPACE
    ( 2023-01-01)
    Ahmad Firdaus Ahmad Zaidi
    ;
    Davendren T.
    ;
    Syahrul Ashikin Azmi
    ;
    Leong Jenn Hwai , Jenn
    ;
    Kamarulzaman Kamarudin
    ;
    Hassan A.
    ;
    Hasimah Ali
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    Normahira Mamat @ Mohamad Nor
    Even today, DC motors are still used in variety of applications, including home appliances, transportation, as well as industrial crane and rolling machine. However, achieving precise speed and torque control in DC drives at industry level could be challenging, as instability and reduced efficiency remains at large. This project focuses on developing a cascaded control system for a Separately Excited Brushed DC motor using dSPACE platform. The cascaded control system, designed using MATLAB Simulink, incorporates a proportional-integral (PI) controller at the speed loop and a Hysteresis controller at the current loop to improve robustness and dynamic performance. The experimental setup utilizes the dSPACE 1104 platform, an asymmetric bridge converter board, gate driver, and electrical load. Speed measurement is done using an incremental encoder, while current is measured using the ACS712 current sensor. The drive system was tested in alternate low and high speed cycle on various load level to test for stability, robustness and dynamic performance. The proposed control system was compared with PI-closed-loop control and open-loop control determine the best drive performance. Experimental results showed significant improvement in term of transient response and ripple reduction of speed and current for proposed cascaded current control over the closed-loop design.
      6  33
  • Publication
    Classification Size of Underground Object from Ground Penetrating Radar Image using Machine Learning Technique
    ( 2023-01-01)
    Mohd Shuhanaz Zanar Azalan
    ;
    Esian T.
    ;
    Hasimah Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Amran T.S.T.
    Ground Penetrating Radar (GPR) is a useful tool in detecting subsurface object or hidden structure defects However, the time-consuming problems and high requirement of professional manpower is required to analyse the GPR data. Machine learning is a tool that endowed with the ability to learn, and it can reduce time taken for the GPR data analysing. To simplify the identification process, a framework is proposed to classify the size of underground metallic pipe by using Histogram of Oriented Gradient (HOG) as a feature extraction algorithm. Two machine learning algorithms namely Support Vector Machines (SVM) and Backpropagation Neural Network were proposed to classify the size of the underground metallic pipe. As a result, the accuracy from the identification is more than 98% for both classifier algorithm.
      2  38
  • Publication
    Feature extraction for underground object reconstruction from Ground Penetrating Radar (GPR) data
    ( 2022)
    Shazmin Aniza Abdul Shukor
    ;
    Havenderpal Singh
    ;
    Nurush Syamimie Mahmud
    ;
    H. Ali
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Mohd Shuhanaz Zanar Azalan
    ;
    T.S. Tengku Amran
    ;
    M.R. Ahmad
    Ground Penetrating Radar (GPR) is very beneficial for underground object scanning and detection. It utilises radar pulses as the signal, hence it able to penetrate surfaces in obtaining the underneath information without disturbing and destructing the ground. However, its radargram output in hyperbolic signal are very challenging to be analysed. Thus, suitable algorithm has to be designed and developed to interpret the data. This work highlights on the usage of drop-flow algorithm in detecting important features of the hyperbolic signal. Previous study has shown that these features is promising in understanding and further, reconstructing the GPR data. Results show that the features extracted from the hyperbolic signal able to be identified for further processing, which is necessary for visualization purpose.
      2  20
  • Publication
    Effect of elevated temperature on the tensile strength of Napier/glass-epoxy hybrid reinforced composites
    ( 2017-11-07)
    Mohd Ridzuan Mohd Jamir
    ;
    Mohd Shukry Abdul Majid
    ;
    Mohd Afendi Rojan
    ;
    Ahmad Firdaus Ahmad Zaidi
    ;
    Azduwin Khasri
    The effects of elevated temperature on the tensile strength of Napier/glass-epoxy hybrid reinforced composites and its morphology of fractured surfaces are discussed. Napier/glass-epoxy hybrid reinforced composites were fabricated by using vacuum infusion method by arranging Napier fibres in between sheets of woven glass fibres. Napier and glass fibres were laminated with estimated volume ratios were 24 and 6 vol. %, respectively. The epoxy resin was used as matrix estimated to 70 vol. %. Specimens were tested to failure under tension at a cross-head speed of 1 mm/min using Universal Testing Machine (Instron) with a load cell 100 kN at four different temperatures of RT, 40°C, 60°C and 80°C. The morphology of fractured surface of hybrid composites was investigated by field emission scanning electron microscopy. The result shows reduction in tensile strength at elevated temperatures. The increase in the temperature activates the process of diffusion, and generates critical stresses which cause the damage at first-ply or at the centre of the hybrid plate, as a result lower the tensile strength. The observation of FESEM images indicates that the fracture mode is of evolution of localized damage, from fibre/matrix debonding, matric cracking, delamination and fibre breakage.
      12  38