Conference Publications

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https://hdl.handle.net/20.500.14170/204

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Browsing Conference Publications by Title

  • Publication
    A new method of rice moisture content determination using voxel weighting-based from radio tomography images
    ( 2021)
    Nurul Amira Mohd Ramli
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Latifah Munirah Kamarudin
    ;
    Latifah Mohamed
    ;
    Ammar Zakaria
    ;
    Anita Ahmad
    ;
    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.
      4  6
  • Publication
    An analysis of kinect-based human fall detection system
    (IEEE, 2020)
    Nor Asilah Saidin
    ;
    Shazmin Aniza Abdul Shukor
    Human fall detection system has become one of the most important things especially for indoor environment application. This system has been used in respective areas of elderly care and at child care houses. It helps to detect any human fall and will alert the caretaker about the accident. Kinect sensor can be used to perform the detection due to its capability in scanning and tracking human as well as its affordability. One of the widely used algorithm in human fall detection using Kinect is the skeleton-based method where it works by calculating the distances of every joint with the floor-plane. The joints are detected using the skeleton space coordinate system. When the floor-plane is not visible and the Y-coordinate is less than the given value, a fall is detected. Due to its widely usage, there is a need to study its performance to know the best condition that this algorithm could offer. Performance of selected parameters were observed through a few experiments conducted using Visual Studio as the interface. In this work, a mobile-based Kinect is used due to its mobility and better future implementation for indoor navigation. The best parameter can be identified quantitatively in order to choose the ideal scene that can be used to detect human fall detection using this skeleton-based method. Among the parameters are the distance of the human to the Kinect, the light intensity, the time to track human and the speed of fall. It can be concluded that the most ideal conditions would be at a distance of 3 meters to 3.5 meters with lightings of 1007 lux and of 2 persons at the scene. These conditions can be helpful for others when considering to use the algorithm for human fall detection using Kinect.
      3  3
  • Publication
    An overview on overvoltage phenomena in power systems
    ( 2019-06-28)
    N. S. Othman
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Mustafa W. A.
    ;
    Wooi Chin Leong
    ;
    Afifah Shuhada Rosmi
    ;
    Shakur N.F.M.
    ;
    Juliangga R.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Zunaidi I.
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    Overvoltage happens in a condition where the voltage is increased and exceed its design limit. This situation may lead to harmful damage to machines or related equipment that connected to the system. Overvoltage can exist in a form of transient, voltage spike or permanent, depending on its duration. Types of overvoltage consist of lightning overvoltage and switching overvoltage. Overvoltage that caused by lightning is considerate as natural phenomena, while switching overvoltage exists from the system itself, either by the interruption of faults or inappropriate connection of circuit breaker contacts. This paper is discussed about overvoltage phenomenon including causes and effects of overvoltage and overvoltage protection towards power system.
      1  3
  • Publication
    Analysis of flexible antenna performance on partial discharges detection
    (IOP Publishing, 2020)
    Nur Adyani Mohd Affendi
    ;
    Melaty Amirruddin
    ;
    Salsabila Ahmad
    This paper reports a short finding on the use of a flexible substrate in the design of Partial discharge (PD) detection antenna. This flexible antenna was designed and simulated using CST Microwave Studio to detect corona discharge which is one types of PD that occur in power transformers. The flexible substrate that been used in designing the antenna is paper that has permittivity of 3.2 and the chosen frequency is between 300MHz until 3GHz. The overall dimension is 208×168×1.6 mm3. The simulations of the antenna’s return loss were conducted under two conditions; 1) flat and 2) bending along H- planes. The result were been analysed to understand the performance of paper properties, so that practical system requirements are met. This implies that the performance of paper antennas can be used in the PD field with altering some of the characteristic to upgrade its performance.
  • Publication
    Analysis of WiFi Spatio-Temporal Data for Organic Fingerprinting-based Indoor Positioning System
    ( 2022-01-01)
    Abdul Halim Ismail
    ;
    Amirah Husna Mohd Hajazi
    ;
    Muhamad Safwan Muhamad Azmi
    ;
    Mohd Sani Mohamad Hashim
    ;
    Mohd Nasir Ayob
    ;
    Hassrizal Hassan Basri
    The mobile robot navigation is the next huge topic after positioning utilizing fingerprinting-based Wireless Positioning System (WPS). Many of recent works does not discuss this topic yet since many open problems in positioning topic are not yet solved, for instance the issues on multi-devices heterogeneity, instability of WiFi signals, granularity problems in grid-based indoor environment and many others. However, we anticipate that both positioning and navigation works must run in parallel so that the succession are guaranteed. This paper describes the analysis of spatio-temporal data of the signal obtained from the WiFi Access Point. Initial results suggest that the difference between transmitter heights have an effect on the spatio-temporal data while the handover of maximum signal strengths is inherent when three WiFi APs are used.
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  • 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
    ;
    Muhammad Mokhzaini Azizan
    ;
    Ahmad Shukri Fazil Rahman
    ;
    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.
      1  10
  • Publication
    Arcing fault diagnosis using first peak arrival of EM radiation signal
    ( 2021-06-11)
    Melaty binti Amirruddin
    ;
    Mohd Rafi Adzman
    ;
    Nur Adyani Mohd Affendi
    ;
    Azralmukmin Azmi
    ;
    Muhd Hafizi Idris
    ;
    Hana Abdull Halim
    ;
    Muzamir Isa
    ;
    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.
      1  25
  • 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.
      3  2
  • Publication
    Assessment of functional and dysfunctional on implant stability measurement for quality of life
    ( 2017)
    Norshahrizan Nordin
    ;
    Razli Che Razak
    ;
    Mohd. Mustafa Al Bakri Abdullah
    ;
    Muhammad Safizal Abdullah
    ;
    Fauziah Che Mat
    ;
    Abdul Hamid Adom
    This study was conducted to investigate the effect of an implant wearer comprising among orthopedic patients as well as the use of implant dentistry in Northern Malaysia. A total of 100 questionnaires were distributed and 70 questionnaires can be used to record, analyze and test hypotheses. Data for all variables were collected through a questionnaire administered alone and analyzed by using SmartPLS V3. A total of four (4) hypotheses have been formulated and the results show that the hypothesis is supported. The results show that: (1) limit the functionality and quality of life was significantly (0.904) in connection with the implant wearer, (2) physical pain was significantly (0.845) relating to the quality of life, (3) physical discomfort was significantly (0.792) in connection with quality of life, and also (4) social discomfort is significant as well (0.809). This finding suggests that there are positive effects on the implant wearer who through life routine. The results of the study may also serve as a basis for reliable decisions related to quality of life and for the implementation of awareness campaign that increase how the need for humanity in the field of quality involvement.
      14  1
  • Publication
    Beyond trends: Tiktok’s educational symphony by unmasking the digital revolution
    (IEEE, 2023)
    Miharaini Md Ghani
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Ekram Alhafis Bin Hashim
    ;
    Hafizul Fahri Hanafi
    ;
    Laith H. Alzubaidi
    In the fast-paced digital age, TikTok has emerged as an unlikely protagonist in the realm of education, ushering in a bite-sized learning revolution. This comprehensive study delves into the captivating phenomenon of TikTok's educational content, unveiling its transformative impact on learners across generations and disciplines. Drawing from extensive empirical research and expert insights, the article will explore the intricate interplay between TikTok's snackable video format and its ability to foster knowledge acquisition and skill development. It illuminates how this platform's unique amalgamation of entertainment and education has redefined traditional learning paradigms, empowering users to consume and share knowledge in a highly engaging and democratized manner. Beyond trends unravels the cognitive mechanisms underpinning the effectiveness of bite-sized learning, shedding light on its potential to cater to diverse learning styles and attention spans. It examines the platform's role in democratizing access to education, enabling content creators and subject matter experts to reach unprecedented global audiences. Moreover, It provides a critical analysis of the challenges and opportunities that arise as bite-sized learning gains traction, offering invaluable insights for educators, policymakers, and stakeholders invested in shaping the future of education. With its multidisciplinary approach and forward-thinking perspectives, Beyond Trends serves as a comprehensive guide to navigating the digital learning landscape, empowering readers to harness the transformative potential of TikTok and its bite-sized educational content.
      1  3
  • Publication
    Clasification of Malaria images in thropozoid stages using deep learning models
    (IEEE, 2024-03)
    Wikan Tyassari
    ;
    Yessi Jusman
    ;
    Novian Dwi Payana
    ;
    Siti Nurul Aqmariah Mohd Kanafiah
    ;
    Zeehaida Mohamed
    The risk of malaria infection is very high, especially for people living in eastern Indonesia, such as Papua, Maluku, and Nusa Tenggara. In Indonesia there are several types of malaria parasite infected, Plasmodium Falciparum, Plasmodium Vivax, and Plasmodium Malaria. Identifying malaria at an early stage is an important to reduce the risk of death and find suitable treatment. However, identifying and diagnosing malaria is time consuming. Therefore, it is necessary to apply technology in detecting the class of malaria parasites. This study classified images of malaria parasites Plasmodium Falciparum, Plasmodium Vivax, and Plasmodium Malarie at the trophozoite stage using the deep learning pre-trained models AlexNet and Inception-V3. According to accuracy of training, Inception-V3 is the best deep learning model. The performance analysis result of inception is accuracy 98.98% ± 0.71%, precision 98.83% ± 1.44%, recall 98.83% ± 1.38%, specificity 99.11% ± 1.09%, and F-score 98.82% ± 0.83%. However, despite having lower accuracy and performance AlexNet have faster in computational training time.
  • Publication
    Classification of Body Mass Index Based Facial Images using Empirical Mode Decomposition
    ( 2021-06-11)
    Hasimah Ali
    ;
    Yee, O.S.
    ;
    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.
      3  3
  • Publication
    Classification of human emotions using EEG Signals in a simulated environment
    ( 2022-01-01)
    Hafiz Halin
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Wan Azani Wan Mustafa
    ;
    Muhajir Ab. Rahim
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    The Brain-Computer Interface (BCI) is a computer-based system that acquires and analyses brain signals. The analysis of brain signals shows the physiological change that happens to the drivers. The physiological changes detected by the BCI system may not be visible to the naked eye. By using the BCI, it increases the diagnostic capability to detect the drivers' emotions. The negative drivers' emotions may cause bad decision making during driving the vehicle. The proposed method was developed to study the related emotions that occur during driving in the simulation environment. The experiments were designed in two situations, which are manual and autonomous drive. In the manual mode, the subjects will control the steering wheel and acceleration of the simulated vehicle. While in autonomous mode, all controls are disable and the subjects will experience the automatic simulation drive. The EEG data was recorded during the simulated drive (manual and autonomous). The EEG data from the subjects were then categorised into five emotions classifications.
      4  3
  • Publication
    Combination effects of fault resistance and remote in feed current on simple impedance based fault location
    (IOP Publishing, 2020)
    Muhd Hafizi Idris
    There are many types of fault location algorithms and one of it is simple impedance based fault location algorithm which is still widely used by power system utilities around the world mainly because of no communication channel exists between local and remote substations. The aim of this research is to study the combination effects of remote in feed current and fault resistance on the accuracy of simple impedance based fault location algorithm which is used to estimate fault location when the transmission line is connected to equivalent Thevenin sources from both ends. Simulation has been carried out using Matlab Simulink software for single line-to-ground fault and line-to-line fault. For each type of fault, fault location and fault resistance were varied for two conditions which were when remote breaker in open and remote breaker in close conditions. From the results, it can be concluded that remote in feed current will add additional error to the error due to the existence of fault resistance when fault resistance not equal to zero.
  • Publication
    Comparative analysis between chaos theory method and power system for WPT energy security application
    (AIP Publishing, 2020)
    N. H. Hussin
    ;
    M. M. Azizan
    ;
    Rafikha Aliana A Raof
    This paper reviews the techniques used in Wireless power transfer (WPT). WPT is one of the most useful ways to transfer power. Based on power transfer distances, the WPT system can be divided into three categories, namely, near medium, and far fields. Inductive coupling and capacitive coupling contactless techniques are used in the near-field WPT magnetic resonant coupling technique is used in the medium-field WPT. Electromagnetic radiation is used in the far-field WPT. In addition, energy encryption plays a major role in ensuring that power is transferred to the true receiver. Therefore, this paper reviews the energy encryption techniques in WPT system. A comparison between different technique shows that the distance, efficiency, and number of receivers are the main factors in selecting the suitable energy encryption technique.
      1  9
  • Publication
    Comparative study of optimization algorithms for SHEPWM five-phase multilevel inverter
    (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    Muhammad Aniq Shahmi Bin Bimazlim
    ;
    Baharuddin Ismail
    ;
    Muhammad Zaid Aihsan
    ;
    Siti Khodijah Mazalan
    ;
    Muhammad Sirajuddin Muhammad Azhar Walter
    ;
    Mohamad Nur Khairul Hafizi Rohani
    Conversion of DC to AC is widely used as an essential need for the power system to harness renewable power source in this era. Multilevel inverter is a mechanism which capable of harnessing these renewable energies. Unfortunately, harmonics occur in the system due to distortions and causing much unwanted problems at the power output. Therefore, the elimination of harmonics and reduce Total Harmonics Distortion (THD) are needed to maintain good power output. Application of Selective Harmonics Elimination Pulse Width Modulation (SHEPWM) can be used in the multilevel inverter to solve the problem. In this paper, optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Sine Cosine Algorithm (SCA) were applied for finding suitable switching angle for SHEPWM. The calculated switching angle was applied into seven-level five-phase multilevel inverter for various modulation indexes. In addition, the simulation of the multilevel inverter also done using PSIM software. The results show that the selective lower order harmonics, 3rd and 7th are completely eliminated for all three algorithms. In addition, the time taken for newly discovered algorithms to find switching angles for seven-level five-phase inverter is faster compared to PSO.
  • Publication
    Decoding digital dynamics: attitude algorithms in online gaming redefining communication experiences
    (IEEE, 2024-07)
    Miharaini Md Ghani
    ;
    Wan Azani Wan Mustafa
    ;
    M. Al Haroni
    ;
    Mohd Ekram Alhafis Bin Hashim
    ;
    Hafizul Fahri Hanafi
    The proliferation of online gaming has given rise to vast virtual worlds where communication and social interaction take on new dimensions. At the forefront of this digital frontier are "attitude algorithms"- advanced computational models designed to detect, interpret, and respond to the emotional states and behavioral patterns of players. These algorithms have the potential to fundamentally redefine the nature of communication experiences within online gaming environments. This study examines the impact of attitude algorithms on player interactions and social dynamics in popular multiplayer online games. Through a combination of data analysis, user surveys, and in-game observations, we explore how these algorithms shape how players express themselves, form connections, and navigate complex social landscapes within virtual gaming worlds. Our findings suggest that attitude algorithms introduce a new layer of emotional intelligence and responsiveness to online gaming communities. By tailoring in-game experiences based on players' attitudes and behaviors, these algorithms can foster more engaging and personalized forms of communication. However, we also uncover potential concerns regarding privacy, manipulation, and the erosion of authentic human-to-human interactions. Ultimately, this research sheds light on the profound impact of attitude algorithms, highlighting their capacity to redefine the boundaries of communication and social dynamics in the digital realm. As these technologies continue to evolve, it becomes increasingly important to understand their implications and guide their development in a responsible and ethical manner, ensuring that they enhance rather than diminish the richness of human connections in online gaming and beyond.
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