Kamarulzaman Kamarudin
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Kamarulzaman Kamarudin
Official Name
Kamarudin, Kamarulzaman
Alternative Name
Kamarudin, K.
Kamaruddin, Kamarulzaman
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Scopus Author ID
55193266400
Researcher ID
DVV-8479-2022

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  • Publication
    An improved mobile robot based gas source localization with temperature and humidity compensation via slam and gas distribution mapping
    ( 2016)
    Kamarulzaman Kamarudin
    This research is concerned with the problem of localizing gas source in indoor environment using a mobile robot. The problem could be seen as similar to the event of hazardous gas leak in a building. Since the environment is often unknown to the robot, the Simultaneous Localization and Mapping (SLAM) operation is required. Two open source SLAM techniques (i.e. Gmapping and Hector SLAM) were implemented to provide this crucial information. Extensive experiments and analysis on both SLAM techniques yielded that the Hector SLAM is more suitable for gas distribution mapping (GDM) application due to the improved robot pose estimation, less computational requirement and only performs map correction locally. Therefore, the Hector SLAM is combined with Kernel DM+V algorithm to achieve real-time SLAM-GDM for predicting gas source location. Rigorous real-time experiments were conducted to verify the performance of the proposed SLAM-GDM method in an uncontrolled office building with the presence of ethanol emission. The experimental results showed that the prediction of gas source location is often accurate to 0.5 to 2.0m. Furthermore, an Epanechnikov based Kernel DM+V algorithm was also introduced to limit extrapolation range in GDM computations. The observed advantages were lower computational requirement and slightly more accurate prediction on gas source location. More importantly, it was found that the maps produced were able to indicate the areas of unexplored gas distribution and therefore could be used for the robot‘s path planning. The final and the main part of the thesis deals with the effect of ambient temperature and humidity on metal oxide gas sensor (i.e. TGS 2600) response; which could affect the GDM results. Linear regression processes were conducted to create a model to correct the temperature and humidity drift of the gas sensor response. The model (i.e. function) was tested in various configurations and was found to minimize the effects of the two environmental factors on the gas sensor response in different gas concentrations. Finally, two versions of Kernel DM+V/T/H algorithms were proposed and coupled with the drift model to compensate for temperature and humidity variation during the GDM task. The experimental results showed that the Kernel DM+V/T/H algorithms were able to produce more stable gas distribution maps and improve the accuracy of gas source localization prediction by 34%.
  • Publication
    Numerical investigation of immersion cooling performance for Lithium-ion polymer (LiPo) battery: effects of dielectric fluids and flow velocity
    (IOP Publishing, 2023)
    A Z A Akmal
    ;
    Muhammad Faiz Hilmi Rani
    ;
    Wong Keng Yinn
    ;
    Mohd Ibthisham Ardani
    ;
    Zuradzman Mohamad Razlan
    ;
    Shahriman Abu Bakar
    ;
    Kamarulzaman Kamarudin
    ;
    M S A Kadir
    ;
    Rishan Murali
    ;
    Sukport Sunan
    This study investigates the enhancement of immersion cooling performance for a single 14.6 Ah lithium-ion polymer (LiPo) battery cell by using air, palm oil, and engineered fluid (3M Novec 7000) as dielectric fluids. The research aims to observe the temperature distribution and rate of heat transfer on the battery cell at a 3C discharge rate, while varying the fluid velocity flow (0 mm/s, 1 mm/s, and 50 mm/s) and fluid types. Computational fluid dynamics (CFD) simulations were performed using ANSYS Fluent software, with heat generation from the LiPo battery simulated using the Newman, Tiedmann, Gu, and Kim (NTGK) semi-empirical electrochemical model. Results revealed that palm oil demonstrated the optimum cooling effect, reducing peak temperature to safe operating temperature region by 62.4% within 1020 seconds. Fluid flow velocity strongly influenced temperature distribution and heat transfer rates, with 50 mm/s resulting in a more uniform temperature distribution compared to 1 mm/s and 0 mm/s. The rate of heat transfer was highest at 1 mm/s and intermediate at 50 mm/s. Considering the abundance of palm oil in Malaysia, utilizing it as the dielectric fluid with a 50 mm/s flow velocity yields the best cooling effect for the 14.6 Ah LiPo battery at a 3C discharge rate.
  • Publication
    Customer survey analysis for design and development of golf ball retriever prototype
    (Springer, 2023)
    Iszmir Nazmi Ismail
    ;
    Nur Najwa Umirah Nor Azman
    ;
    Nursyadzatul Tasnim Roslin
    ;
    Mohd Rashdan Isa
    ;
    Nazirul Mubin Zahari
    ;
    Syazwani Zainal Abidin
    ;
    Ahmad Wafi Mahmood Zuhdi
    ;
    Eqwan Roslan
    ;
    Hassan M.
    ;
    Mohd Zakwan Ramli
    ;
    Mohd Helmi Mansor
    ;
    Fevi Syaifoelida
    ;
    Azrul Abidin Zakaria
    ;
    Mohd Hafiz Zawawi
    ;
    Daud Mohamad
    ;
    Mohd Firdaus Jaafar
    ;
    Kamarulzaman Kamarudin
    ;
    Mohamed Saiful Firdaus Hussin
    This paper focuses on using customer analysis in the design and development of golf ball retriever that can be operated in the driving range. The identified problems with conventional method of retrieving the balls are the golf attendant is not able to collect the balls effectively, the number of balls collected is relatively low and the operator will experience strain in their wrists while collecting the balls with the method of scooping. Hence, the methodology and results are presented according to the customer and engineering requirements. Quality Function Deployment (QFD) method by using House of Quality (HOQ) is used to align both the customers’ and engineering requirements.
  • Publication
    Parameters tuning for enhanced automated guided vehicle navigation in ROS/gazebo simulation environment
    (Semarak Ilmu Publishing, 2025)
    Muhammad Aizat Abu Bakar
    ;
    Nurakasyah Qistina
    ;
    Kamarulzaman Kamarudin
    ;
    Heng Han
    ;
    Hafizul Imran
    ;
    Wan Rahiman
    Automated Guided Vehicle (AGV) robot, a type of ground transportation vehicle that follows a predetermined path, is now in high demand in industrial operations and among researchers. AGV robot may improve it carrying capacity in the delivery operation through consistent and safe behaviour. The main challenge is in its navigation system when obstacles appear unexpectedly on its desired path, its limited abilities make it unable to avoid obstacles that would interfere with the smooth operation and decrease the quality of time. The aim of this research work is to present a tuning parameter of algorithms, namely the Pure Pursuit based on coordinates look-ahead distance for navigation and the Vector Field Histogram based on safety distance for avoiding obstacles. Robot Operating System (ROS) platform and Gazebo simulator environment are used to simulate the simulation testing for algorithms. According to the test results, the combination of these algorithms produced promising outcomes by demonstrating the AGV's capability to manoeuvre along a predetermined path, avoid obstacles, and return to its original path in order to reach its goal position.
  • Publication
    Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map
    ( 2022-01-01)
    Zaffry Hadi Mohd Juffry
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Hamid Adom
    ;
    Mao X.
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    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.
      1  29
  • Publication
    Two-stream deep convolutional neural network approach for RGB-D face recognition
    ( 2021-07-21)
    Shunmugam P.
    ;
    Kamarulzaman Kamarudin
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Nishizaki H.
    Two-dimensional face recognition has been researched for the past few decades. With the recent development of Deep Convolutional Neural Network (DCNN) deep learning approaches, two-dimensional face recognition had achieved impressive recognition accuracy rate. However, there are still some challenges such as pose variation, scene illumination, facial emotions, facial occlusions exist in the two-dimensional face recognition. This problem can be solved by adding the depth images as input as it provides valuable information to help model facial boundaries and understand the global facial layout and provide low-frequency patterns. RGB-D images are more robust compared to RGB images. Unfortunately, the lack of sufficient RGB-D face databases to train the DCNN are the main reason for this research to remain undiscovered. So, in this research, new RGB-D face database is constructed using the Intel RealSense D435 Depth Camera which has 1280 x 720-pixel depth. Twin DCNN streams are developed and trained on RGB images at one stream and Depth images at another stream, and finally combined the output through fusion soft-max layers. The proposed DCNN model shows an accuracy of 95% on a newly constructed RGB-D database.
      1
  • Publication
    Gas Source Localization via Mobile Robot with Gas Distribution Mapping and Deep Neural Network
    ( 2022-01-01)
    Ahmad Shakaff Ali Yeon
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Visvanathan R.
    ;
    Kamarulzaman Kamarudin
    ;
    Latifah Munirah Kamarudin
    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.
      2  27
  • Publication
    Effect of environmental temperature and humidity on different metal oxide gas sensors at various gas concentration levels
    (IOP Publishing Ltd., 2020)
    Abdulnasser Nabil Abdullah
    ;
    Kamarulzaman Kamarudin
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Zaffry Hadi Mohd Juffry
    ;
    Abdul Hamid Adom
    Metal Oxide (MOX) semiconductor gas sensors have been widely used in monitoring targeted gases that are present in the environment. This type of gas sensor can also be utilized as a safety device to detect the source of gas leakage. Their uses in many applications are due to being user-friendly, lower in cost, high sensitivity and relatively quick response time. However, there are several factors that could affect their performance. This work investigates the effects of the changes in ambient temperature and humidity on the readings of these sensors at various gas concentration levels. A PCB board was developed, which consists of temperature and humidity sensors, as well as eight different MOX gas sensors (TGS2600, TCS2602, CCS803, MiCS552, GM-402B, GM-502B, GM-702B and MiCS6814). The board was subjected to various temperatures (16˚C to 30˚C) and humidity levels (45% to 75%). At each of these parameter settings, the gas sensor responses were recorded at different ethanol gas concentrations. The results of the study showed that the temperature and humidity affected all the gas sensor response. The magnitude of the sensors responses was observed to decrease with rising temperature and humidity levels, except for MICS6814 (NH3 sensor) which responses in the opposite manner. Hence, there is the need to take into consideration of the drift of gas sensors’ responses when there are changes in temperature and humidity.
      4  74
  • 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
    Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques
    ( 2023-01-15)
    Mohd Hilmi Tan M.I.S.
    ;
    Jamlos M.F.
    ;
    Omar A.F.
    ;
    Kamarulzaman Kamarudin
    ;
    Mohd Aminudin Jamlos
    Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a severe threat to the palm oil industry. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease unless ergosterol, a biomarker of G. boninense can be detected. There is yet a non-destructive and in-situ technique explored to detect ergosterol. Capability of NIR to detect few biomarkers such as mycotoxin and zearalenone (ZEN) has been proven to pave the way an effort to explore NIR's sensitivity towards detecting ergosterol, as discussed in this paper. A compact hand-held NIR with a measurement range of 900–1700 nm is utilized by scanning the leaves of three oil palm seedlings inoculated with G. boninense while the other three were non-inoculated from 16-weeks-old to 32-weeks-old. Significant changes of spectral reflectance have been notified occur at the wavelength of ∼1450 nm which reflectance of infected sample is higher 0.2–0.4 than healthy sample which 0.1–0.19. The diminishing of the spectral curve at approximately 1450 nm is strongly suspected to happened due to the loss of water content from the leaves since G. boninense attacks the roots and causes the disruption of water supply to the other part of plant. However, a few overlapped NIRs' spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. It is found the spectra of healthy samples are scattered on the negative sides of PC-1 while infected samples tend to be on a positive side with large loading coefficients marked significant discriminatory effect on healthy and infected samples at the wavelength of 1310 and 1452 nm. A PLS regression is used on NIR spectra to implement the prediction of ergosterol concentration which shows good corelation of R = 0.861 between the ergosterol concentration and oil palm NIR spectra. Four different ML algorithms are tested for prediction of G. boninense infection: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested which depicted DT algorithm achieves a satisfactory overall performance with high accuracy up to 93.1% and F1-score of 92.6% compared to other algorithms. High accuracy shows the capability of the classification model to correctly predict the G. boninense detection while high F1-score indicates that the classification is able to validate the detection of G. boninense correctly with low misclassification rate. The result represents a significant step in the development of a non-destructive and in-situ detection system which validated by both chemometric and machine learning (ML) classification techniques.
      3  26