Conference Publications

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Latifah Mohamed

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Anita Ahmad

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Ruzairi Abdul Rahim

This manuscript presents a new method to monitor and localize the moisture distribution in a rice silo based on tomography images. Because the rice grain is naturally hygroscopic, the stored grains’ quality depends on their level of moisture content. Higher moisture content leads to fibre degradation, making the grains too frail and possibly milled. If the moisture is too low, the grains become brittle and are susceptible to higher breakage. At present, the single-point measurement method is unreliable because the moisture build-up inside the silo might be distributed unevenly. In addition, this method mostly applies gravimetric analysis, which is destructive. Thus, we proposed a radio tomographic imaging (RTI) system to address these problems. Four simulated phantom profiles at different percentages of moisture content were reconstructed using Newton’s One-Step Error Reconstruction and Tikhonov Regularization algorithms. This simulation study utilized the relationship between the maximum voxel weighting of the reconstructed RTI image and the percentage of moisture content. The outcomes demonstrated promising results, in which the weighting voxel linearly increased with the percentage of moisture content, with a correlation coefficient higher than 0.95 was obtained. Therefore, the results support the possibility of using the RTI approach for monitoring and localizing the moisture distribution inside the rice silo.

Publication

Flashover voltage prediction on polluted cup-pin the insulators under polluted conditions

( 2021-01-01)

Salem A.A.

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Abd-Rahman R.

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Kamarudin M.S.

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Othman N.A.

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Jamail N.A.M.

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Nuriziani Hussin

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Haziah Abdul Hamid

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Rawi I.M.

The natural pollution which is mainly affected by the weather conditions are the main cause of flashovers on high voltage insulators leading to outages in power systems. In this work, characteristics of flashover for contaminated cup-pin insulators have been studied based on experiential test and a mathematical model. Information from laboratory test combined with new mathematical model results are used to define Artificial Neural Network (ANN) algorithm and Adaptive Neuro-fuzzy Inference System (ANFIS) for calculated the flashover characteristics (current IF and voltage UF). several of experiments and measurement are carried out for 1:1, 5:1, 10:1 and 15:1 ratios of bottom to top surface salt deposit density on contaminated samples (z). Dimensional Analysis Method (DAM) was used to derive new model for the variables which often effective in the flashover phenomenon of polluted insulators. The model was derived by establishment the relationship between flashover voltage UF and current IF, length of pollution layer LP, exposure time t, arc constant A and layer pollution conductivity of insulator σ. The both arc constants A and n is computed using genetic algorithm. Comparative investigates have clearly shown that the approach AI-based method gives the agreeable results compared to the mathematical model.

Publication

Effect of distributed generation to the faults in medium voltage network using ATP-EMTP simulation

( 2021-01-01)

Wan Syaza Ainaa Wan Salman

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Muzamir Isa

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Nur Syazlin Bakhtiar Jamili

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Muhd Hafizi Idris

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Melaty binti Amirruddin

During past few years, distributed generation (DG) technology has been widely known in the industry as it can helps in providing backup power during high power demand. However, adding a new system may changes the traditional power system that are usually work in one direction which is from the generation to the consumer. When DG is added, the power flows from two direction. Therefore, a short circuit study was done to study the effects of DG to the local system during fault. An IEC Standard was also calculated as guidance to determine the thermally permissible of a cable.

Publication

Analysis on renewable energy sources for electricity generation in remote area of Iraq by using HoMER: A case study

( 2021-05-03)

Zaidoon Waleed Jawad Al-Shammari

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Muhammad Mokhzaini Azizan

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Ahmad Shukri Fazil Rahman

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Khairunnisa Hasikin

Zerbattiya is one of the remote areas in Iraq that have retained their old generation stations and networks despite their expansion. As the rehabilitation of these stations and networks is extremely costly, supplying renewable energy sources to these regions is reasonable. The problem in Zerbattiya is that electricity supply comprises 75% of the total energy load. In this regard, we propose the use of a hybrid system using renewable energy with the national grid as a means of compensating for power supply shortage. The proposed system composed of grid, wind turbines, solar panels, converters, batteries, and auxiliary tools. The most optimal system configuration was determined with HOMER. Meanwhile, the average life expectancy of the proposed system is 20 years. Three on-grid scenarios were considered for power generation. The most suitable system was determined in WT-grid hybrid systems. The results showed that the NWT, NBT, Nconv, COE, NPC, Operating cost, and IC are 61, 4, 12, 0.0349 US$/KWh, US$1.32 million, 23,982 US$/year, and US$1.05 million respectively.

Publication

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

( 2021-06-11)

Hasimah Ali

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Mohd Shuhanaz Zanar Azalan

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Ahmad Firdaus Ahmad Zaidi

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Tengku Sarah Tengku Amran

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Mohamad Ridzuan Ahmad

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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.

Publication

Arcing fault diagnosis using first peak arrival of EM radiation signal

( 2021-06-11)

Melaty binti Amirruddin

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Nur Adyani Mohd Affendi

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Azralmukmin Azmi

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Muhd Hafizi Idris

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Hana Abdull Halim

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Muzamir Isa

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Halim S.A.

The objective of this study was to diagnose the arcing fault signals based on the first peak of arrival method using antenna to assess its use as potential arcing fault detection in power system network. Square patch antenna and circle patch antenna were employed for detection on artificial arcing in real environment. First peak of arcing signal arrival was measured through an analysis over a range of time and amplitude signals detected. For accurate results, Discrete Wavelet Transform (DWT) denoising technique was applied to the arcing signals detected as denoising tools. Analysis of first peak of signal arrival time and amplitude were carried out using MATLAB software to measure the changes in signals detected caused by di different placements of antenna. The results revealed that the first peak of signal arrival time, amplitude, type of antenna used and placement of the antenna around arcing source point all reflect the signals measurement.

Publication

Investigation of Spherical Electrode Grounding System under High Impulse Conditions with Different Polarities

( 2021-06-11)

Haziah Abdul Hamid

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Noor Hasnizam Hanafi

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Nor N.M.

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Abdullah S.

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Reffin M.S.

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Etobi N.A.

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Abd-Rahman R.

There have been many studies in the pasts, which showed that under high impulse conditions, soil ionization process around the electrode would occur, which results in lower resistance and lower transient voltage on the electrode. Several factors have also been found to affect the ionization process in soil such as; soil types and its grain sizes, moisture contents in the soil, earth electrode configurations, impulse polarity, the location of the injection point, the shape of response time, etc. In this paper, spherical electrodes grounding is used to study the ionization process in the grounding system under different impulse polarity of the current. The experiment was done by field measurement using a commercially available impulse current generator. It was found that lower earth resistance was seen to decrease with increasing current magnitudes for both impulse polarities. In addition, it was found that at low impulse current with negative polarity has a higher resistance than that of positive polarity. This paper is, therefore, to investigate towards improvement on grounding electrode's design, which would consider soil ionization process

Publication

PV array and inverter optimum sizing for grid-connected photovoltaic power plants using optimization design

( 2021-06-11)

Zidane Tekai Eddine Khalil

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Samila Mat Zali

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Mohammad Faridun Naim Tajuddin

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Durusu A.

This paper aims to select the optimum inverter size for large-scale PV power plants grid-connected based on the optimum combination between PV array and inverter, among several possible combinations. Inverters used in this proposed methodology have high-efficiency conversion in the range of 98.5% which is largely used in real large-scale PV power plants to increase the financial benefits by injecting maximum energy into the grid. To investigate the PV array-inverter sizing ratio, many PV power plants rated power are considered. The proposed method is based on the modelling of several parts of the PV power plant taking into account many design variables and constraints. The objective function is the levelized cost of energy (LCOE) and the optimization is performed by a multi-verse algorithm. The optimization method results in an optimum inverter size that depends on the PV plant rated capacity by providing an optimum number of inverters required in the installation site. The optimum sizing ratio (Rs) between PV array and inverter were found equal to 0.928, 0.904, and 0.871 for 1 MW, 1.5 MW, and more than 2 MW, respectively, whereas the total power losses reached 8% of the total energy generation during the PV power plant operational lifetime.

Publication

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

( 2021-06-11)

Ahmad Firdaus Ahmad Zaidi

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Syahrul Ashikin Azmi

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Leong Jenn Hwai , Jenn

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Hasimah Ali

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Mohd Shuhanaz Zanar Azalan

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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.

Publication

Classification of Body Mass Index Based Facial Images using Empirical Mode Decomposition

( 2021-06-11)

Hasimah Ali

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Yee, O.S.

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Wan Khairunizam Wan Ahmad

Human faces contain rich information. Recent studies found that facial features have relation with human weight or body mass index (BMI). Decoding "facial information"from the face in predicting the BMI could be linked to the various health marker. This paper proposed the classification of body mass index (BMI) based on appearance based features of facial images using empirical mode decomposition (EMD) as feature extraction technique. The facial images that describe the body mass index was extracted using EMD to obtain a set of significant features. In this framework, the facial image was decomposed using EMD to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF features which exhibit the unique pattern were used to classify the BMI. The obtained features were then fed into machine learning classifier such as k-nearest neighbour and support vector machines (SVM) to classify the three BMI classes namely normal, overweight and obese. The obtained results show that the IMF2 feature using SVM classifier achieved recognition rate of 99.12% which show promising result.

Publication

Non-Contact breathing monitoring using Sleep Breathing Detection Algorithm (SBDA) based on UWB radar sensors

( 2022)

Muhammad Husaini

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Intan Kartika Kamarudin

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Muhammad Amin Ibrahim

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Hiromitsu Nishizaki

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Masahiro Toyoura

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Xiaoyang Mao

Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.

Publication

Gas Source Localization via Mobile Robot with Gas Distribution Mapping and Deep Neural Network

( 2022-01-01)

Ahmad Shakaff Ali Yeon

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Syed Muhammad Mamduh Syed Zakaria

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Visvanathan R.

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With the growth of artificial intelligence compute technology, the gas source localization problem would be solved by mobile robots equipped with gas sensing system and artificial intelligence compute units. This work presented a feasibility study of deep learning approach towards gas source localization by mobile robots. A deep neural network strategy was developed and incorporated with the Kernel DM+V gas distribution mapping method. The gas source localization work in this paper was performed on a controlled indoor testbed. From this work, it is shown that by incorporating the developed deep neural network model, it may help improved the gas source location prediction accuracy. A comparison of accuracy between Kernel DM+V and the neural network model is also presented to better visualize the improvement.

Publication

Gas Source Localization via Mobile Robot with Gas Distribution Mapping and Deep Neural Network

( 2022-01-01)

Ahmad Shakaff Ali Yeon

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Syed Muhammad Mamduh Syed Zakaria

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Visvanathan R.

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With the growth of artificial intelligence compute technology, the gas source localization problem would be solved by mobile robots equipped with gas sensing system and artificial intelligence compute units. This work presented a feasibility study of deep learning approach towards gas source localization by mobile robots. A deep neural network strategy was developed and incorporated with the Kernel DM+V gas distribution mapping method. The gas source localization work in this paper was performed on a controlled indoor testbed. From this work, it is shown that by incorporating the developed deep neural network model, it may help improved the gas source location prediction accuracy. A comparison of accuracy between Kernel DM+V and the neural network model is also presented to better visualize the improvement.

Publication

Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map

( 2022-01-01)

Zaffry Hadi Mohd Juffry

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Abdul Hamid Adom

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Mao X.

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Syed Muhammad Mamduh Syed Zakaria

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Abdullah A.N.

The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.

Publication

Different Devices Behavior in Fingerprinting Indoor Positioning for Mobile Robot in Healthcare Industries

( 2022-01-01)

Ahmad Hakimi Ahmad Sa'ahiry

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Abdul Halim Ismail

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Toyoura M.

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