Kamarulzaman Kamarudin
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Kamarulzaman Kamarudin
Official Name
Kamarudin, Kamarulzaman
Alternative Name
Kamarudin, K.
Kamaruddin, Kamarulzaman
Main Affiliation
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
    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
    Theoretical and numerical study on the effect of ambient temperature towards gas dispersion in indoor environment using CFD approach
    (Semarak Ilmu Publishing, 2023)
    Zaffry Hadi Mohd Juffry
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Hamid Adom
    ;
    Muhammad Fahmi Miskon
    ;
    Abdulnasser Nabil Abdullah
    The usage of harmful chemical gas and natural gas has been increasing rapidly which increase the risk of gas leakage incident to occur especially in the indoor environment. It is important to learn the gas dispersal behavior in order to mitigate the casualty caused by gas leakage. In addition, one of the factors that contribute to the dispersion of gas is temperature. This paper focuses to study the role of ambient temperature toward gas dispersion in an indoor environment by looking at the theoretical and numerical knowledge of gas diffusion's relationship with temperature. Computational Fluid Dynamics (CFD) has been utilized to simulate gas dispersion at different ambient temperature levels in an indoor environment. This study released ethanol vapor to simulate gas dispersion at 5°C, 25°C, and 40°C of ambient temperature to observe the way gas distribute in the indoor environment. Both results from the theoretical calculation and simulation were compared. The result indicates that the gas diffusivity has an increment of 3.5% for every 5°C increment of the temperature. This causes the gas to diffuse rapidly in the warm air compared to the cool air. This paper also finds out that when the initial ambient temperature which is 5°C, was increased to 25°C and 40°C it causes the spread distance of the gas increased by 13.75% and 32.50% respectively.
      4  20
  • 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
    ;
    Abdulnasser Nabil Abdullah
    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  32
  • Publication
    Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment
    ( 2020-05-18)
    Visvanathan R.
    ;
    Kamarulzaman Kamarudin
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Toyoura M.
    ;
    Ali Yeon A.S.
    ;
    Ammar Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Mao X.
    ;
    Shazmin Aniza Abdul Shukor
    Mobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered.
      1  30
  • Publication
    2D LiDAR based reinforcement learning for Multi-Target path planning in unknown environment
    ( 2023)
    Nasr Abdalmanan
    ;
    Kamarulzaman Kamarudin
    ;
    Muhammad Aizat Abu Bakar
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Latifah Munirah Kamarudin
    Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.
      4  41
  • 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