Mohd Rizal Manan
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Preferred name
Mohd Rizal Manan
Official Name
Mohd Rizal , Manan
Alternative Name
Manan, M. R.
Main Affiliation
Scopus Author ID
36514937100
Researcher ID
FKS-3641-2022

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Identification of ARX Model for Thermoelectric Cooling on Glass Windows

2022-01-01 , Mohd Saifizi Saidon , Nasrul Amri Mohd Amin , ‘Aqilah Che Sulaiman , Mohd Rizal Manan , Siti Marhainis Othman , Wan Azani Wan Mustafa , Arith F.

Thermoelectric cooling (TEC) is a solid-state heat pump that uses the Peltier effect to dissipate the heat generated by the electronic packaging system. TECs are widely used in aerospace, military, scientific work and industry due to small size, lack of moving parts, and ease of integration. In this study, a cooling system integrated with TEC is developed in a testing area (lecturer’s office) with the aim to reduce the temperature of the hot glass window area due to solar radiation that passes through it. This cooling system used direct TEC, for keeping the cooling temperature on the window to about 26 °C which is equivalent to an air conditioning setting temperature of 26 °C set during the experiment. This work includes experimental and modelling studies conducted on cooling systems integrated with TEC. The main target of this study is to develop a dynamic model of a cooling system integrated with TEC. The black box modelling approach in producing a mathematical model was selected based on the ARX model that corresponds to the actual dynamic state of the cooling system. The best model was finalized based on the best match on curve patterns when comparing the real and estimated models using the system identification tools in MATLAB, and also had the least error. The accuracy of the models was compared and analysed. The results showed that the 4th order of the ARX model produced a higher best fitting and standard deviation values of 78.14% and 0.030769. This system accuracy is almost within the acceptable range for most error calculations in the validation method. In addition, the ARX model is found incapable of achieving the highest fitting due to the losses from the dynamic environment and losses from the TEC itself. Still, the use of this black box model used in this study is a significant variation where system parameters can be identified even offline.

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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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Real-time vision-based hand gesture to text interpreter by using artificial intelligence with augmented reality element

2024-03-07 , Rosnazri M.H. , Mohd Rizal Manan , Rosnazri Ali , Baseemah Mat Jalaluddin , Aimi Salihah Abdul Nasir , Muhammad Aizat Abu Bakar , Zamri N.F. , Rahmat M.A. , Zamzuri M.A. , Azmi M.A.A.

Real-time Vision-based Hand Gesture to Text Interpreter by Using Artificial Intelligence with Augmented Reality Element is a device that can interpret sign language to text in real-time. This communicator used a machine learning approach with a slight touch of deep learning elements, which are OpenCV, MediaPipe, and TensorFlow algorithms. Those algorithms have been used to differentiate the hand from other objects, detect the movement and coordinate of hands and perform imagery data analysis to produce output instantly in real-time. The camera will detect the user's hand movement, and the output will be produced on an LCD monitor. This project has been developed by using Python programming language. 13,000 of ASL's alphabets and 5,000 of ASL's number imagery datasets have been collected and trained by using cloud platforms which are Google Teachable Machine and Google Colab. The training process produced 99.85% of accuracy for the alphabets and 100% accuracy for the number. Finally, the constructed machine learning algorithm able to display alphabets and numbers on an LCD monitor by performing ASL's alphabet and number hand gesture in real-time. The performance of the prototype has been analyzed and experimented by two users at plain and noise background with different determined distances.

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The ARX and ARMAX Models for thermoelectric cooling on glass windows: A Comparative study

2022-01-01 , Mohd Saifizi Saidon , Nasrul Amri Mohd Amin , Aqilah Che Sulaiman , Mohd Rizal Manan , Siti Marhainis Othman , Wan Azani Wan Mustafa , Wahab N.A.

Thermoelectric cooling (TEC), in particular, can be combined with a heat sink for local cooling, but they can also be integrated into electronic chips for point-to-point cooling. The study aims to develop a dynamic model of a cooling system integrated with TEC for glass window. The main target of this study is to develop a dynamic model of a cooling system integrated with TEC. The black box modelling approach in producing a mathematical model was selected based on the ARMAX and ARX model that corresponds to the actual dynamic state of the cooling system. The best model was finalized based on the best match on curve patterns when comparing the real and estimated models using the system identification tools in MATLAB, and also having the least error. The accuracy of the models was compared and analysed. The results showed that the 4th order of the ARMAX model produced a higher best fitting and standard deviation values of 80.23% and 0.027592 compared to the 4th order of the ARX model of 78.14% and 0.030769 respectively. This system accuracy is almost within the acceptable range for most error calculations in the validation method. Yet, this cooling system integrated with TEC is found more suitable for the 4th order of the ARMAX model when compared to the ARX model due to the noise parameter in the ARMAX model. Nevertheless, the noise order in this system is not dominant, therefore, whenever the noise order of the system in the ARMAX model is high than the second structure (nb), the number of errors is also high. In addition, the ARMAX model is found incapable of achieving the highest fitting due to the losses from the dynamic environment and losses from the TEC itself. Still, the use of this black box model used in this study is a significant variation where system parameters can be identified even offline.

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Fusion wind and solar generation forecasting via neural network

2021-08-27 , Mahmoud Mustafa Yaseen Mohammed Al Asbahi , Muhammad Naufal Mansor , Mohd Rizal Manan , Mohd Azri Abd Aziz , Roejhan Md Kawi , Farah Hanan Mohd Faudzi

Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore. power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction proposed in this paperwork to support a small microgrids system. The system is a combination of hardware of solar panel, wind turbine, hybrid charge controller, current sensor, voltage sensor circuit, battery, Arduino Mega and personal computer that is install with MATLAB along with artificial neural network model for load forecast. The prediction model is known as Feedforward back propagation (FFBP) artificial neural network (ANN), this method utilizes a learning relationship between wind-solar power output and predicted weather. The FFBP model trained ANN to recognize similar pattern and to predict the output power based on train and tested data and the results achieved 99.5 accuracy, 6.25% MAPE and 1.2 % MAD.

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Two phase medium identification using ultrasonic tomography technique

2013 , Mohd Rizal Manan

The use of tomographic techniques has been widely used in pipeline and oil industry. These techniques have potential applications for flow visualization and measurement in producing wells. One of the important processes is in multiphase characterization; that serve in monitoring, measuring or controlling industrial processes. Multiphase represents the condition of more than one medium phase. The identification for two phase medium is carried out in this research. Research on industrial tomography process revolved in obtaining estimated images in cross section of a pipe or vessel containing or carrying the substances in the process. Ultrasonic tomography technique is one of the categories in process tomography. A simple tomography system can be built by mounting a number of sensors around the circumference of a horizontal pipe. In this research, sixteen pairs of 40 kHz ultrasonic sensor have been non-invasively mounted around the pipe. The characteristic of the sensor is an important factor that needs to be considered. Grease was used as the coupling material to mount these ultrasonic sensors. The output data from the sensors were processed to obtain the information of the spatial distributions of liquid and gas in an experimental column. Time of Flag (TOF) method has been chosen to extract the data from the ultrasonic signals. Analysis on the transducers’ signals has been carried out to distinguish the observation time between the longitunal (straight) propagation waves and the Lamb waves. The information obtained from the observation time is useful for further development of the images. The Linear Back Projection (LBP) algorithm has been applied to obtain concentration profiles or also called tomograms. The results obtained through LBP were filtered using Gaussian Filter and Enhancement Filter Technique. From the filtered images, further development was made by extracting features information such as mean, standard deviation, skewness, kurtosis, energy and entropy. Two approaches were applied for classification purposes using single and combination of features. Comparison between K-Nearest Neighbor (k-NN) and Linear Discriminant Analysis (LDA) classifiers have been made. From the observation, non-linear classifier (k-NN) produced a better result over linear classifier (LDA). Furthermore, it has been found that combination of features gives better performance over single feature classification.

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Adaptive PD Controller Performance for Direct Cooling of Thermoelectric Refrigerator

2020-12-18 , Mohd Saifizi Saidon , Mohd Rizal Manan , Baseemah Mat Jalaluddin , Lee T.W. , Junaidah Ali Mohd Jobran , Roejhan Md Kawi , Mat Piah M.S. , Diana N.S. , Wan Azani Wan Mustafa

Refrigerator is the key component to keep the medicine and biological sample in the hospital. The domestic refrigerator has the problem of larger size and heavier weight since to the compact system like condenser, compressor, evaporator and expansion valve are assemble and using in the refrigerator. This project focused on design of temperature control of the portable thermoelectric refrigerator for medical purpose. Thermoelectric refrigerator is using the direct cooling method through thermoelectric module. Thermoelectric refrigerator has several advantages such as smaller size, lighter and silent when operated. Since maintain a constant temperature for the storage of medical product is important, a specific refrigerator is needed to ensure the medicine is stored in desired temperature. This project is to design and develop an adaptive control system which can perform a good temperature control for the thermoelectric refrigerator. The second order model is applied to design adaptive Proportional-Derivative (PD) controller. The selected controller is the adaptive PD controller because the performance of response shows 0.42 C of less steady state error and 0.21 C of lower undershoot. The adaptive PD control system designed able to let the refrigerator operate in different operating condition without influence the performance of the refrigerator.

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Implementation of Two-Stage Multilevel Inverter System using PIC Controller

2022-10-01 , Mohd Saifizi Saidon , Muhammad Izuan Fahmi Romli , Muzaidi Othman @ Marzuki , Wan Azani Wan Mustafa , Muhammad Zaid Aihsan , Mohd Rizal Manan , Mohd Azri Abd Aziz

In this study, the Cascaded H-Bridge Multilevel Inverter (CMLI) is described for use with infrared dryer loads. Because of the low harmonic distortion content and reduced voltage stress in the switching devices, CMLI is one of particular interest. The CMLI topology and the Selective Harmonic Elimination Pulsed Width Modulation (SHEPWM) technology were studied and evaluated. To evaluate the inverter, SHEPWM modulation was studied and applied. The system also includes a DC-DC converter. The converter was designed to be used in infrared drying system powered by direct current (DC) power where an increased output voltage is required. This study also presented an evaluation of performance using infrared load of the CMLI based on power used at 100 W. As a result, a comparison of input power was made, and an assessment into the converter's power quality in terms of harmonic content and overall efficiency was conducted. The implementation of the system by hardware had been able to reduce the harmonic to 15.5%.

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A Review on BLDC Motor Application in Electric Vehicle (EV) using Battery, Supercapacitor and Hybrid Energy Storage System: Efficiency and Future Prospects

2023-04-01 , Aziz M.A.A. , Mohd Saifizi Saidon , Fahmi M.I. , Othman S.M. , Wan Azani Wan Mustafa , Mohd Rizal Manan , Muhammad Zaid Aihsan

The automotive industry has rapidly introduced pollution-free vehicles such as Electric Vehicle (EV). The development and improvement of the EV to replace the conventional vehicle become crucial to obtain the customer satisfaction and high technology achievements. The main systems in EV that are improvise to be switch from the conventional engine with a fuel source to an electric type drive system, include the electric motor and the energy/power storage called battery. There are several types of electric motors that suitable for EV and the best solution was Brushless Direct Current (BLDC) motor in terms of power, speed, torque and low maintenance. Meanwhile, the fuel source replacement is the electrical energy/power storage such as batteries. The aims were to study the best Energy Storage System (ESS) in EV which leads to introducing Battery Energy Storage System (BESS), but the drawbacks of the system give the opportunity improvement, in replacement using Supercapacitor Energy storage System (SESS) and Hybrid Energy Storage System (HESS). SESS is a reliable source, but the stand-alone Supercapacitor also has a minimum operation time. With several adjustments in the energy management control strategy, the discharge rate of energy from a supercapacitor can be minimized to prolong its operation.

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System Design for Early Detection of Explosive and Flammable Gas Leaks Using Mobile Robot in Confined Space

2021-12-01 , Muhammad Aizat Abu Bakar , Mohd Rizal Manan , Roejhan Md Kawi , Yunn, L. J.

The presence of explosive or flammable gases in confined space may contribute towards accidents that threaten the workers safety and industrial progress. Conventionally, the existing instrument for gas detection in confined space is manually carried by humans whereby the workers or competence person itself were exposed directly to the gases. This project is aim to develop a prototype system to detect the presence of gases leak where the robotic system replaces humans to carry gas sensors. Users only need to maneuver the robot using a mobile phone to monitor the specific area that may have an explosive or flammable gas leak which includes Liquefied Petroleum Gas (LPG) and methane gases. The sensors will detect if a change in the gas concentration has exceeded a safety limit and will activate the alarm as an alert signal. The readings of gases as input signals were sent wirelessly to the Personal Computer (PC) as a user device for monitoring purposes. This prototype is successfully developed, tested and calibrated using the samples of LPG gas, methane, smoke and environment temperature. The result proved that the developed system is able to detect an air sample using selected gas sensors and display the data in graph form with live monitoring. This will contribute significantly to acquiring a new and alternative method using the system for detecting the presence of gases in confined space application.

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