Muhajir Ab. Rahim
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Preferred name
Muhajir Ab. Rahim
Official Name
Muhajir, Ab. Rahim
Alternative Name
Rahim, Muhajir
Rahim, M. A.
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Scopus Author ID
56739343200
Researcher ID
FXA-2537-2022

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Characterization of Hardening Duffing Oscillator based on a Tensioned Wire System

2021-06-11 , Muhajir Ab. Rahim , Mohd Shuhanaz Zanar Azalan , Mohd Noor Arib Md Rejab

In this paper, the characterization of mechanical system that behaves as a hardening Duffing oscillator is presented. This mechanical system comprises a mass attached to a tensioned wire which exhibits a hardening stiffness behavior when the displacement of the mass is large. Firstly, the equation of motion of the system is derived to provide the relationship between the applied static force and the resulting displacement. Then, the effect of initial tension, and number of the wires on the force-displacement relationship are analyzed. It has been found that a higher tension will produce higher linear stiffness, whilst having a negligible effect on cubic stiffness. Moreover, the nonlinearity is less sensitive for small inequality between the length of wire on the left and right side of the mass. The results presented herein provide an insight of the system behavior for its application as a vibration isolator.

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Wireless mass air flow device for thermal comfort data acquisition

2024 , Ismail I. Ibrahim , M. N. I. Mohamad Zubir , Shazmin Aniza Abdul Shukor , Muhajir Ab. Rahim , K. Kohlhof

This paper aims to build and implement an IoT-based mass air flow sensor device using the FS7 sensor from IST Innovative Sensor Technology and the ESP32. The scope of the project includes design and implementation of the device, the evaluation of its performance, and the presentation of the results. To achieve its objective, the project will employ literature evaluation, hardware design, programming, testing, and data analysis methodologies. The IoT-based mass air flow sensing device has the potential to improve the performance of air flow-dependent systems by providing real-time data, remote monitoring and control, and enhanced precision and dependability. In addition, it will be calibrated, maintained, and upgraded remotely, decreasing the need for on-site maintenance and extending the device's lifespan.

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Connected car: Engines diagnostic via Internet of Things (IoT)

2017-10-29 , Azrul Fahmi A.Hamid , Md. Tasyrif bin Abdul Rahman , Shah Fenner Khan Mohamad Khan , Abdul Hamid Adom , Muhajir Ab. Rahim , Norasmadi Abdul Rahim , Ismail M.H.N. , Norizan A.

This paper is about an experiment for performing engines diagnostic using wireless sensing Internet of Thing (IoT). The study is to overcome problem of current standard On Board Diagnosis (OBD-II) data acquisition method that only can be perform in offline or wired method. From this paper it show a method to determined how the data from engines can be collected, make the data can be easily understand by human and sending data over the wireless internet connection via platform of IOT. This study is separate into three stages that is CAN-bus data collection, CAN data conversion and send data to cloud storage. Every stage is experimented with a two different method and consist five data parameter that is Revolution per Minute (RPM), Manifold Air Pressure (MAP), load-fuel, barometric pressure and engine temperature. The experiment use Arduino Uno as microcontroller, CAN-bus converter and ESP8266 wifi board as transfer medium for data to internet.

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Classification of Human Emotions Using EEG Signals in a Simulated Environment

2022-01-01 , Hafiz Halin , Wan Khairunizam Wan Ahmad , Wan Azani Wan Mustafa , Muhajir Ab. Rahim , Zuradzman Mohamad Razlan , Shahriman Abu Bakar

The Brain-Computer Interface (BCI) is a computer-based system that acquires and analyses brain signals. The analysis of brain signals shows the physiological change that happens to the drivers. The physiological changes detected by the BCI system may not be visible to the naked eye. By using the BCI, it increases the diagnostic capability to detect the drivers' emotions. The negative drivers' emotions may cause bad decision making during driving the vehicle. The proposed method was developed to study the related emotions that occur during driving in the simulation environment. The experiments were designed in two situations, which are manual and autonomous drive. In the manual mode, the subjects will control the steering wheel and acceleration of the simulated vehicle. While in autonomous mode, all controls are disable and the subjects will experience the automatic simulation drive. The EEG data was recorded during the simulated drive (manual and autonomous). The EEG data from the subjects were then categorised into five emotions classifications.

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Numerical Study on Effect of Phase Angle on Torsional Vibration in Double Cardan Joint Driveline System

2023-11-09 , Omar M.H. , Muhajir Ab. Rahim , Mohd Noor Arib Md Rejab , Rani M.N.A. , Mutra R.R.

A driveline system with a double Cardan joint may be subject to severe vibration due to fluctuations in angular speed determined by the degree of angular misalignment and the phasing of the joint yokes. The inner yokes of the two joints are usually in the same plane or in phase. In this study, the potential of an out-of-phase position displaced by a phase angle to attenuate torsional vibration of the driveline is investigated. The governing equations describing the torsional dynamics of the system are derived. The torsional vibration is indicated by a maximum and minimum value of the steady-state twist calculated over a range of input rotational speeds. When attenuating the vibration, the phase angle is set equal to the maximum twist that occurs during the in-phase position. Relative attenuation is used to study the percentage of attenuation of the twist due to phase angle effects. The effectiveness is investigated for different levels of angular misalignment. For the system considered, the results showed that the phase angle can attenuate the torsional vibration by more than 10 percent for angular misalignment greater than 20 degrees.

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Design of Experiment (DOE) for the Investigation of Human Emotions while Driving in a Virtual Environment through Brain Signal (EEG)

2021-01-01 , Hafiz Halin , Wan Khairunizam Wan Ahmad , Wan Azani Wan Mustafa , Muhajir Ab. Rahim , Zuradzman Mohamad Razlan , Shahriman Abu Bakar

The transition from the conventional vehicle to the autonomous vehicle is going to take place but, the acceptance of users to the autonomous vehicle still lacking. The past research more focusses on the driver attention, drowsiness, fatigue or the alertness of the driver. This research aims to study the drivers' emotions/reactions during the autonomous and manual drive in the simulated environment. The environment for the manual and autonomous drive is developed by using simulator software, Unity. This paper focus only on the experimental setup for the human emotions' detection using EEG signal during the manual and autonomous drive. The Emotiv Epoc+ use for the EEG signal acquisition. The simulated environments are displayed through a Head Mount Display (HMD). The analysis of the EEG signal which includes the pre-processing, feature extraction, and classification will be discussed in future works.

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Classification of human emotions using EEG Signals in a simulated environment

2022-01-01 , Hafiz Halin , Wan Khairunizam Wan Ahmad , Wan Azani Wan Mustafa , Muhajir Ab. Rahim , Zuradzman Mohamad Razlan , Shahriman Abu Bakar

The Brain-Computer Interface (BCI) is a computer-based system that acquires and analyses brain signals. The analysis of brain signals shows the physiological change that happens to the drivers. The physiological changes detected by the BCI system may not be visible to the naked eye. By using the BCI, it increases the diagnostic capability to detect the drivers' emotions. The negative drivers' emotions may cause bad decision making during driving the vehicle. The proposed method was developed to study the related emotions that occur during driving in the simulation environment. The experiments were designed in two situations, which are manual and autonomous drive. In the manual mode, the subjects will control the steering wheel and acceleration of the simulated vehicle. While in autonomous mode, all controls are disable and the subjects will experience the automatic simulation drive. The EEG data was recorded during the simulated drive (manual and autonomous). The EEG data from the subjects were then categorised into five emotions classifications.

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Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal

2024-06-01 , Abdul Hafiz Abd Halin , Wan Khairunizam Wan Ahmad , Wan Azani Wan Mustafa , Muhajir Ab. Rahim , Zuradzman Mohamad Razlan , Shahriman Abu Bakar , Saidatul Ardeenawatie Awang

The Brain-Computer Interface (BCI) is a field of research that studies the EEG signal in order to elevate our understanding of the human brain. The applications of BCI are not limited to the study of the brain wave but also include its applications. The studies of human emotions specific to the vehicle driver are limited and not vastly explored. The EEG signal is used in this study to classify the emotions of drivers. This research aims to study the emotion classifications (surprise, relax/neutral, focus, fear, and nervousness) while driving the simulated vehicle by analyse the EEG signals. The experiments were conducted in 2 conditions, autonomous and manual drive in the simulated environment. In autonomous driving, vehicle control is disabled. While in manual drive, the subjects are able to control the steering angle, acceleration, and brake pedal. During the experiments, the EEG data of the subjects is recorded and then analyzed.

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Suppresion of sommerfeld effect in power transmission system employing cardan shaft through phase angle arragement

2024-07-01 , Omar M.H. , Muhajir Ab. Rahim , Mohd Noor Arib Md Rejab , Rani M.N.A.

In a power transmission system with a cardan shaft, the Sommerfeld effect occurs, which is characterized by speed capture and release at the resonance range. Suppression of the Sommerfeld effect is critical for smooth and reliable operation. This study aims to suppress the Sommerfeld effect in a transmission system by compensating the phase angle between the two universal joints installed in the cardan shaft. The differential equations of motion representing the dynamics of the system are derived using the Lagrange equation. The responses are simulated numerically using the Runge–Kutta algorithm for scenarios with constant and gradually varying input torque. To suppress the Sommerfeld effect, the phase angle is set to 25%, 50%, 75% and 100% of the maximum twist angle observed in the subcritical speed range of the in-phase configuration. With the phase angle of 25%, the Sommerfeld effect is damped, where the output speed only deviates by 5% from the estimated value for both input torque scenarios. It is shown how the change of the phase angle attenuates the Sommerfeld effect and the system vibrations, which should be considered in the development and practical implementation.

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Numerical Study on the Torsional and Lateral Vibrations of Double Universal Joint Driveline System

2023-01-01 , Omar M.H. , Muhajir Ab. Rahim , Mohd Noor Arib Md Rejab

Utilizing a universal joint can lead to significant vibration within a driveline system. This study presents a model for analyzing the torsional and lateral vibrations of a driveline connected by a double universal joint. The governing equations of motion are derived, and the Runge-Kutta method computes steady-state responses across a spectrum of input rotational speeds. The focus is to examine the effect of system parameters, including static angular misalignment, load torque, and lateral stiffness. Relative amplification is used to analyze the effects of parameters on system vibration. Results indicated that the second-order component of input rotational speed induced by the universal joint was the factor that caused the vibrations. For the considered system, static angular misalignment significantly impacts both the torsional and lateral vibrations. Increasing the angular misalignment from 15° to 30° results in a threefold increase in lateral vibration amplification, while torsional vibration amplification is increased by nearly two times. The effect of load torque is almost linearly proportional to torsional vibration but is nonlinear to lateral vibration. Thus, lateral vibration is significantly impacted compared to torsional vibration for higher load torque. Changing the stiffness leads to a modification of the natural frequency. Increasing the lateral stiffness shifts the critical speed to a higher speed range, resulting in reduced lateral vibration amplitude. It is demonstrated that a slight fluctuation in angular misalignment due to lateral vibration will not affect the torsional vibration even if both vibrations are coupled. The findings may enhance understanding of how changing system parameters affects vibration.

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