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

Research Output

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Performance analysis of the microsoft kinect sensor for 2D Simultaneous Localization and Mapping (SLAM) techniques

2014 , Kamarulzaman Kamarudin , Syed Muhammad Mamduh Syed Zakaria , Ali Yeon Md Shakaff , Ammar Zakaria

This paper presents a performance analysis of two open-source, laser scanner-based Simultaneous Localization and Mapping (SLAM) techniques (i.e., Gmapping and Hector SLAM) using a Microsoft Kinect to replace the laser sensor. Furthermore, the paper proposes a new system integration approach whereby a Linux virtual machine is used to run the open source SLAM algorithms. The experiments were conducted in two different environments; a small room with no features and a typical office corridor with desks and chairs. Using the data logged from real-time experiments, each SLAM technique was simulated and tested with different parameter settings. The results show that the system is able to achieve real time SLAM operation. The system implementation offers a simple and reliable way to compare the performance of Windows-based SLAM algorithm with the algorithms typically implemented in a Robot Operating System (ROS). The results also indicate that certain modifications to the default laser scanner-based parameters are able to improve the map accuracy. However, the limited field of view and range of Kinect's depth sensor often causes the map to be inaccurate, especially in featureless areas, therefore the Kinect sensor is not a direct replacement for a laser scanner, but rather offers a feasible alternative for 2D SLAM tasks.

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Reinforcement Learning for Mobile Robot's Environment Exploration

2023-01-01 , Teoh S.W.H. , Kamarulzaman Kamarudin , Ali N.A.N. , Zainal M.M.M. , Mohd Rizal Manan , Syed Muhammad Mamduh Syed Zakaria

Mobile robots are being are being applied in various industries to perform repetitive or dangerous tasks for humans to carry out. Autonomous mobile robots are more capable than automated guided vehicles (AGV) due to their ability to be adaptable to their environment which is important for exploration of unknown environments. It is difficult to program autonomous mobile robots to adapt to various situations it may face, thus machine learning can be applied to allow a mobile robot to learn how to navigate through environments by itself. Reinforcement learning is applied in this project so that a differential drive mobile robot can learn how to navigate through its environment while avoiding collision with surrounding walls and obstacles. The reinforcement learning process is simulated by using the Robot Operating System (ROS) and its simulator Gazebo. Controlled simulation environments are created using Gazebo for the purposes of training and performance testing. Simultaneous Localization and Mapping (SLAM) will be applied to generate a map of the environment. At the end of this project, the Turtlebot3 is able to map smaller controlled environments ranging between 18m2 to 27m2 without colliding with the surrounding walls.

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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.

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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.

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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%.

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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.

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Design and Development of Cascaded Current Control in DC Motor Variable Speed Drive using dSPACE

2023-01-01 , Ahmad Firdaus Ahmad Zaidi , Davendren T. , Syahrul Ashikin Azmi , Leong Jenn Hwai , Jenn , Kamarulzaman Kamarudin , Hassan A. , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Normahira Mamat @ Mohamad Nor

Even today, DC motors are still used in variety of applications, including home appliances, transportation, as well as industrial crane and rolling machine. However, achieving precise speed and torque control in DC drives at industry level could be challenging, as instability and reduced efficiency remains at large. This project focuses on developing a cascaded control system for a Separately Excited Brushed DC motor using dSPACE platform. The cascaded control system, designed using MATLAB Simulink, incorporates a proportional-integral (PI) controller at the speed loop and a Hysteresis controller at the current loop to improve robustness and dynamic performance. The experimental setup utilizes the dSPACE 1104 platform, an asymmetric bridge converter board, gate driver, and electrical load. Speed measurement is done using an incremental encoder, while current is measured using the ACS712 current sensor. The drive system was tested in alternate low and high speed cycle on various load level to test for stability, robustness and dynamic performance. The proposed control system was compared with PI-closed-loop control and open-loop control determine the best drive performance. Experimental results showed significant improvement in term of transient response and ripple reduction of speed and current for proposed cascaded current control over the closed-loop design.

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Potential of industrial internet of things (IIoT) to improve inefficiencies in food manufacturing

2023 , Noor Zafira Noor Hasnan , Yuzainee Md. Yusoff , Sarina Abdul Halim Lim , Kamarulzaman Kamarudin

The aims of this review were twofold, namely 1) to analyze the main operational inefficiencies in food manufacturing and 2) to identify the main IIoT-related technologies with their potential operational improvement for the food manufacturing sector. An analytical literature review was performed using the main scientific literature databases as the secondary data source. The review has found nine major operational issues that are most frequently reported in the food manufacturing sector namely 1) too long manufacturing lead time, 2) low productivity, 3) absence of systematic quality management, 4) low compliance to food safety requirements, 5) lack of innovations in product development, 6) lack of training, 7) unsustainable marketing strategies, 8) poor traceability and 9) lack of documentation along the supply chain. While IIoT is relatively new, it is important to embrace that food manufacturing can have many of these operational issues solved when incorporating digital technologies. The key starting point is the identification of the correct and effective application that suits the industry's requirements in their pursuit of an improved level of operational efficiencies, productivity and a higher level of quality. In this regard, this review intended to clarify the identified seven groups of IIoT technologies that could improve the above-identified operational issues, whereby these are 1) smart manufacturing technologies, 2) Big Data, Analytics and Artificial Intelligence, 3) robotics, 4) additive manufacturing, 5) augmented reality, 6) manufacturing simulation, and lastly 7) the cloud. The study concluded that food manufacturers could only benefit from the IIoT advantages when the purpose of the technology fulfils their operational objectives and requirement as well as fits within their constraints

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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.

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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.

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