Mohd Azri Abd Aziz
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Preferred name
Mohd Azri Abd Aziz
Official Name
Mohd Azri , Abd Aziz
Alternative Name
Aziz, Azri A.
Aziz, Azri Abdul
Aziz, Azri
Main Affiliation
Scopus Author ID
57192086971
Researcher ID
CCT-1519-2022

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Development of Automatic Mini Fan with Human Detector by Using PIR Sensor

2020-12-18 , Noor Anida Abu Talib , Kamarudin, Farhah , Shema S.S. , Mohd Azri Abd Aziz

This project will present the design, construction, development, control and evaluation of an automatic function of electric fan. Fan is the important thing for circulation of air. The development of Automatic Mini Fan with Human Detector System by Using PIR Sensor presented in this project is required to fulfill the requirement of technologies today and it had been fabricate with new design. The automatic mini fan with two types of power supply which is Alternating Current (AC) and Direct Current (DC) can continuously function if one of the power supplies cannot be used. The human detection systems by using PIR sensor are implemented in this project and the detection range is up to 13 feet. The temperature sensors which maximum 70 C that are used in this project can automatically control the speed of fan up to 225 rpm. This automatic mini fan with human detection system contains combination of sensor, controller, motor and two types of power supply that controlled by Arduino UNO as the main controller. This project also presents the expected performance of the automatic mini fan with human detector system which the fan can rotate 0 to 180 and the construction of hardware and software development to gather the performance data. Finally, this project can give many benefits to people because it is portable and can save electricity. The result of this project becomes useful in the future.

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Characteristic Study of Supercapacitor's Discharging Process Base on BLDC Motor

2023-01-01 , Mohd Azri Abd Aziz , Mohd Saifizi Saidon , Muhammad Izuan Fahmi Romli , Siti Marhainis Othman , Wan Azani Wan Mustafa , Al-Dolaimy F. , Alkhayyat A.

Energy storage has become a key issue for achieving goals connected with increasing the efficiency of both producers and users. In particular, supercapacitors currently seem to be interesting devices for many applications because they can supply high power for a significant amount of time and can be recharged more quickly than electrochemical batteries. Supercapacitors, as similar to conventional capacitors build on two plates separated by a dielectric and an electrolyte, can store more energy than conventional capacitors because they can produce two distinct layers of separated charges between plates, which are typically made of porous, carbon-based materials. The supercapacitors module serves as the power supply for the discharging process as called Energy Storages (ES). The main purpose of this research is to investigate the behaviour and properties of discharged supercapacitors. The design is created using Simulink and includes a circuit schematic and scope label. Using Brushless Direct Current Motor (BLDCM) as a load, study proposed steady state condition and dynamic state condition of BLDCM operation are investigated. With comparison to battery, preliminary finding state that value of SoC and voltage of battery higher but value of current less than supercapacitor in a certain amount of time.

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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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Graduate Data Job Classification Using Support Vector Machine with Radial Basis Function Kernel

2023-01-01 , Hisham M.H.H. , Mohd Azri Abd Aziz , Sulaiman A.A.

The escalating rates of unemployment among recent graduates constitute a pressing concern, with farreaching implications for a nation's future. Graduates often encounter challenges in aligning their skills and interests with suitable positions, while employers grapple with identifying the ideal candidates for their job openings. To address this issue, this study focuses on graduate-job classification using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, based on graduates' data. The SVM - RBF model's performance was evaluated with a consistent C value of 10, while the Gamma value underwent variations (0.125, 0.25, and 0.75). In addition, a linear SVM was included for comparative analysis. Various metrics including classification accuracy, Root Mean Square Error (RMSE), and the receiver operating characteristic (ROC) curve were employed to ascertain the optimal classifier performance. The results indicate that the SVM - RBF model with a Gamma value of 0.125 demonstrated the most robust performance, surpassing SVM - RBF models with Gamma values of 0.25 and 0.75, as well as the linear SVM.

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WiFi Approximated Strength Measurement Method with Brute Force Algorithm for a Minimum Number of AP and Maximum WiFi Coverage

2020-04-01 , Fatihah Wan Mustapha W.N. , Mohd Azri Abd Aziz , Masrie M. , Sam R. , Tan M.N.M.

The implementation of a wireless network in indoor premises has increased due to its easy and flexible access. This however requires a good strategy in placing the access point (AP) in order to cover as much area as possible with a small number of AP as possible. This paper proposed a WiFi approximated signal quality measurement method to be used with a Brute Force algorithm in looking for the best placement of AP in indoor locations. Only one time measurement of WiFi signal quality for each AP was done and our proposed algorithm will predict the strength of this AP as it was installed in a different location. The result shows the approximated signal quality generated by our algorithm almost equals the actual strength measured with an acceptable error. The new placement of AP proposed by our algorithm also manages to ensure a minimum of 84% WiFi strength in each room if all 4 APs were used. Experimental results have also shown a minimum of 2 APs is adequate to ensure at least 72% of WiFi signal quality can be received in each room.

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Fusion wind and solar generation prototype design with 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 with neural network was proposed to support a small microgrids system. All the system performance measurement such as sensitivity, specificity and accuracy give higher than 90%.

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Development of gesture database for an adaptive gesture recognition system

2017 , Mohd Azri Abd Aziz

Human gestural motion is one of the areas in studying human behaviour regardless the physical capability and intellectuality of each individual. In this research, the focus is to investigate human physical characteristics which contribute to the performance of gestural motions. Every person has different body structure and physical distinctive that can be determined by calculating the person’s body mass index (BMI) and measuring the size represented by the weight an geometrical gestures. The geometrical gesture databases are developed based on human body characteristic features. These gesture databases are utilized to recognize and identify an unknown gesture by gathering some information of human features for further analysis. A motion capture system was used to capture gestural motions. Three dimensional data obtained from motion capture system are analysed, classified and stored in the gesture database. The resampling algorithm is developed to diminish the excessive movement information which to be used in the represented form. Principal Component Analysis (PCA) is used to reduce dimension of data and classify the gesture data. PCA classifies three groups of people based on gestural motions of subjects. For further clarification, data inside the group database were tested for similarity and dissimilarity measured using Jaccard Similarity Measure; the result of total average is 90.8% dissimilarity of all five geometrical gestures between group #1, group #2 and group #3 for all the three axes: X-axis, Y-axis and Z-axis. Consequently, adaptive gesture recognition is introduced to select the suitable database especially for identifying unknown gestures inserted into the system. The result of recognition shows that recognition of individual database is 86.5%, group database 83.7% and the lowest is recognition of universal database which is 82.8%. The experimental result shows that the group database is preferable for an adaptive gesture recognition system.

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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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