Mohd Shuhanaz Zanar Azalan
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Mohd Shuhanaz Zanar Azalan
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Mohd Shuhanaz , Zanar Azalan
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Zanar Azalan, M. S.
Azalan, Mohd Shuhanaz Zanar
Azalan, M. S. Z.
Azalan, M. S. Zanar
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36469819700

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Assessment of Control Drive Technologies for Induction Motor: Industrial Application to Electric Vehicle

2021-06-11 , Ahmad Firdaus A.Z. , Azmi S.A. , Kamarulzaman Kamarudin , Leong Jenn Hwai , Jenn , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Kasa Z.C.M.

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.

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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%.

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Hyperbola detection of ground penetrating radar using deep learning

2024-02-08 , Zahir N.H.M. , Hasimah Ali , Nasri M.I.S. , Masuan N.A. , Zaidi A.F.A. , Mohd Shuhanaz Zanar Azalan , Amin M.S.M. , Ahmad M.R. , Elshaikh M.

Ground Penetrating Radar (GPR) is a geophysical method using high resolution electromagnetic used to acquire the information of underground. The electromagnetic (EM) waves produces from the antenna consisting of transmitter and receiver. The waves from the transmitter penetrates into the ground and reflect backs to the surface that receive by the antenna receiver. The antenna can lie within the range of 10MHz to 1000MHz to determine the shallow or deep penetration. Higher value of antenna will result in shallow penetration and otherwise for lower antenna. The process of recognition of buried objects is challenging task especially in the construction area to ensure safety and the quality of civil building. The GPR will display the mapping image on its control unit screen. If there are objects underground have detected, the image will display the hyperbola shape to indicate the target of the object. A vast number of data makes it difficult to classify each and every one of it either the image data is in which classes or categories. If there are many hyperbola present in image also makes it difficult to locate the accurate position. Due to this, deep learning technique by means of ResNet50 has been used in this research for hyperbola recognition in GPR image. A series of experiments has been conducted on the GPR dataset collected at Agency Nuclear Malaysia. Based on the results obtained, the deep learning model successfully learn the image feature. The accuracy of the model classified for this GPR data using ResNet50 gives 90% accuracy. Therefore, the proposed method for image recognition shows the promising results with all the GPR images are correctly recognize. Further, region of interest of hyperbola signature has been represented by a rectangular box indicates the hyperbola location

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Motor imagery task classification enhancement using raw signal energy data dimension reduction approaches

2019 , Mohd Shuhanaz Zanar Azalan

Brain Computer Interface (BCI) is defined as machines using the brain signals for its control. The motor neural activity of the brain can be recorded from the human scalp using EEG recording equipment and converted into control commands representing the needs of the person. These can be used for the control of devices such as a prosthetic arm, a joystick or a wheelchair and may be very useful for persons with physical disabilities. The performance of such system, however, depends heavily on the quality of the recorded signals and the subsequent features extracted from it. This research proposes a new protocol for experimental setup, frequency band as well as channel selection approach based on raw signal energy to improve data dimension reduction process. A new protocol for acquiring brain EEG signal using 19 non-invasive scalp electrodes were developed. The frequency bands related to the motor actions, namely alpha1 (8-10 Hz), alpha 2 (11-12 Hz), beta 1 (13-15 Hz), beta 2 (16-18 Hz) and beta 3 (19-25 Hz) were extracted using a customized filter. A novel four class brain computer interface (BCI) based on four tasks motor imagery signals was also designed. Classifications of the tasks using three spectral and fractal-based features with three different types of neural networks were performed. A novel method to minimize the number of frequency bands and EEG channels for the classification of tasks is proposed without sacrificing the classification accuracy. The performance of the neural network models with features from the selected channels were compared to that with all the 19 channels. The proposed approach with limited number of chosen channels were able to achieve a minimum average accuracy of 90% similar to that of the present methods but with reduced computational times. The proposed method also has the advantage of being able to identify the most discriminating regions for the EEG electrode channels for motor imagery tasks classification. The results show that the proposed frequency band selection and channel reduction method is a viable data dimension approach with reduced computational time to classify a motor imagery task for a motor imagery BCI system. This research work will reduce the computational load, and hence, time in BCI systems. This opens up the possibilities for the migration of pc-based to microcontroller-based implementations for better mobility.

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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.

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Ground penetrating radar for buried utilities detection and mapping: a review

2021-12-01 , Hasimah Ali , Ideris N.S.M. , Ahmad Firdaus Ahmad Zaidi , Mohd Shuhanaz Zanar Azalan , Amran T.S.T. , Ahmad M.R. , Rahim N.A. , Shazmin Aniza Abdul Shukor

This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.

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Feature Extraction based on Empirical Mode Decomposition for Shapes Recognition of Buried Objects by Ground Penetrating Radar

2021-06-11 , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Ahmad Firdaus Ahmad Zaidi , Tengku Sarah Tengku Amran , Mohamad Ridzuan Ahmad , Mohamed Elshaikh Elobaid Said Ahmed

Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.

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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.

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Feature Extraction based on Empirical Mode Decomposition for Shapes Recognition of Buried Objects by Ground Penetrating Radar

2021-06-11 , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Zaidi A.F.A. , Amran T.S.T. , Ahmad M.R. , Elshaikh M.

Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.

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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.

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