Mekatronika: Journal of Intelligent Manufacturing and Mechatronics

Thermal Monitoring and Modelling of Eletrical Machine – A Review

2024-02-06T01:30:26+00:00

The temperature of an electrical machine can affect its performance and lifespan, as high temperatures can lead to thermal stress, material degradation, and reduced efficiency. Therefore, thermal monitoring and modelling of electrical machines are crucial for ensuring their optimal operation and maintenance. This paper provides overview of the recent studies and developments in these two areas, highlighting their advantages, challenges, and applications. Focuses on two aspects of thermal management in electrical machines: monitoring temperature response and modelling temperature response. The paper also identifies some future research directions and opportunities for improving thermal management in electrical machines.

Enhancing Decision-Making Based on Social Responses for Human-Robot Interactions (HRI) Applications

2024-01-25T14:34:21+00:00

Making decisions, especially in uncertain situations, can be challenging. This study explores how a social robot, acting as an advisor, affects human decision-making in a specially designed game. The social robot facilitated the decision-making process using verbal cues in a study with a 2x2 between-subject (controlling language and social praise) design experiment. Drawing from the Technology Acceptance Model (TAM) and the Persuasive Robots Acceptance Model (PRAM), the study assess how human responses influence the acceptance of this technology. Sixty participants took part in the experiment, and as results, their anxiety levels decreased after interacting with the robot and playing the game. Also, the outcomes highlight positive social responses, suggesting that social robots have potential in supporting decision-making even though the specific impact of social cues on participant responses is somewhat limited. In conclusion, incorporating social responses such as liking and beliefs enhances the ability to predict acceptance, emphasizing the importance of considering social aspects in the acceptance of robots. This research contributes to the understanding of Human-Robot Interactions (HRI) and provides valuable insights for future developments in social robots for decision-making support.

Effective Maintenance of Aircraft Antiskid Brake System

2024-01-28T08:07:20+00:00

The most effective way to reduce Mean Time to Repair (MTR) is to get to the root cause of the failure before it even occurs. Closely monitoring operational data of each component of an aircraft enables the early detection of possible causes of failure. The purpose of this research work is to determine the cause of the frequent failure of the antiskid brake UA-51, using the data collected on the performance of the component, with the aim of improving on the maintenance process. A close examination of the antiskid brake system showed that the failures occurred due to wear and tear of the drive gear. The external condition of the brake was examined and a calculation was done to ascertain the hardness of the gear teeth. The results of the calculation are presented in the work. Based on the results, it was discovered that the wear and tear of the drive gear was caused by negligence of the technical procedure for carrying out maintenance on the antiskid brake system. A recommended technical procedure of maintaining the antiskid brake was given.

Tele-operated Rehabilitation Robot for Forearm Pronation and Supination in Home-Based Therapy

2024-04-18T07:02:15+00:00

Patients suffering from neurological injuries undergo clinical rehabilitation to regain functionality of the affected limb. However, frequent visits to the therapist tend to be tedious and time consuming. Tele-operated rehabilitation has the advantage of being accessible to patients from the comfort of their homes. This paper presents a tele-operated master-slave rehabilitation robot for forearm pronation and supination for home-based rehabilitation. The prototype of the robot has been developed to provide the rotational motion at the forearm. Arduino is used as the microcontroller board and DC (direct current) motor is utilized to actuate the system. Potentiometers are incorporated at both sides of the robot to provide angular displacement readings. The position of the master sensor is fed to the slave side and this value is then compared to the current displacement of the slave robot and their difference is adjusted. The communication between the master and slave is carried out using Arduino Ethernet shield over the internet. The mathematical model of the robot has been approximated by the dynamic equation of a flywheel. A Proportional Integral Derivative (PID) controller has been implemented on the system to improve its performance. Hardware experimental tests has been conducted and the results verify that the slave robot has successfully followed the master robot’s trajectory as required in the design objective with a time delay of 0.1 s. The resulting percentage overshoot is obtained as 17% and the steady state error is 4%.

Analysis of GPS and GSM with GPRS-Based Vehicle Tracking and Monitoring System

2024-05-21T11:43:04+00:00

Vehicle security has become a major concern in today's world. Due to the increasing number of vehicle thefts, GSM and GPS-based vehicle tracking systems have gained attention to help users keep track of their vehicles. The main objective of this study is to develop an accurate and reliable device to support vehicle owners in tracking the whereabouts of their vehicles. Such devices will allow vehicles to be tracked remotely from a distance by utilizing the already available mobile networks. Specifically, the system will use the GPS (Global Positioning System) Module to collect the vehicle coordinates (latitude and longitude) and send them to the user's mobile upon request, which also allows for the expansion of knowledge on GPS (Global Positioning System), GSM (Global Systems for Mobile) and GPRS (General Packet Radio Service) technology, as well as the SIM interface. It will take some analysis to make this endeavour succeed. A Google Spreadsheet will be used as a database to store the location data from GPS sensors, and information will be sent to it via a connection to the General Packet Radio Service (GPRS) network system. The information will be synced and shown on a visual map so that the user can simply access and track the whereabouts of the vehicle. There is a discussion concerning the accuracy of the GPS sensor being utilised over several places, including urban and rural areas, to determine the device's accuracy. The system's dependability and ability to perform well based on the data collected also have both been studied and analysed. As a result, this paper offers a well-presented study to develop a tracking system to determine the time and position of the monitoring vehicle.

Performance Characteristics of Stroke Patients using the Motor Activity Log and ANOVA Analysis

2024-01-28T08:16:05+00:00

Scoring system is crucial in evaluating a patient’s stroke severity and monitoring their recovery progress. The current manual and subjective approach heavily relies on the individual expertise of therapists, resulting in inconsistent scores and an increased burden on the therapist’s expertise resulting in inconsistent scores and increased burden on the therapist’s workload. This pilot study automate and refine the scoring methodology utiised for matient’s assessment. This study focuses on Motor Activity Log (MAL), a widely acknowledged standard clinical assessment that incorporates the evaluation of Activities of Daily Living (ADL) in stroke patients. Data are collected from 30 healthy individuals and 30 stroke patients. Two statistical analyses using one-way ANOVA are performed to check the data characteristics and assess the effectiveness of the MAL in this context. The analysis results indicated two scores that did not show significant differences, specifically 0.328 for the Rotation X parameter in the DoorKnob activity and 0.587 for the Time parameter in the Water Faucet activity. This demonstrates that this test method can effectively differentiate between each stroke patient. This initiative represents a significant step towards establishing a more standardised and objective scoring system, contributing to a more consistent and efficient evaluation of stroke patients' performance characteristics and recovery trajectories.

Integrated Hand and Eye Communication Device for Intensive Care Unit (ICU) Patients

2024-04-19T03:44:45+00:00

Numerous Intensive Care Unit (ICU) patients may be unable to speak or move their body due to being intubated or weak muscles. This makes the communication harder, as they are voiceless in expressing their thoughts and needs. This study focuses on the development of an integrated hand and eye communication device based on hand gesture recognition and eye movement tracking with an output display. The device comes with a dual mode, where the hand gesture and eye movement are detected using flex sensors and reflectance sensors respectively. Arduino Uno is used as the microcontroller and the output window is programmed using Java programming language. Four messages communicated in ICU patients are installed on the system. The device has been tested at the laboratory stage with healthy subjects. The results with healthy individuals in the laboratory validate that the device is successful in conveying the intended messages correctly for all trials. The resulting sensors measurement curves are consistent across all subjects and messages. The developed device will contribute towards a better communication between the patients and healthare providers, leading to a more convinient and efficient patient care.

Advancing Security Measures: A Brainwave-Based Biometric System for User Identification and Authentication

2024-01-28T08:32:54+00:00

In contemporary organizational contexts, the imperative for robust user identification and authentication systems to safeguard assets is paramount. Conventional methods like passwords, secret codes, and personal identification numbers are prone to compromise and human error. This study explores the feasibility of utilizing human brainwaves, specifically Electroencephalogram (EEG) signals, as a biometric authentication system. Employing the Unicorn Hybrid Black EEG device for measurement and LabVIEW software for analysis, the research focuses on discerning EEG features pertinent to authentication. Through controlled activities encompassing imaginative (imagining singing a favorite song, imagining opening a locked door) and physical tasks (engaging in a mobile game, solving a Rubik's cube), the study elucidates the dominance of the EEG Theta band across varied cognitive and motor processes. Further analysis underscores the heightened power of the EEG Alpha band during relaxation phases and the prevalence of the EEG Beta band during heightened cognitive engagement. The classification of selected EEG features highlights the efficacy of utilizing Standard Deviation as a discriminative factor, achieving a commendable accuracy of 93.35% with a training-testing ratio of 80:20. This research underscores the potential of EEG-based authentication systems in fortifying organizational security protocols.

Full Hand Pose Recognition in Performing Daily Activities for Tele-Rehabilitation based on Decision Tree Algorithm

2024-04-18T07:24:24+00:00

The older population has the highest risk of getting a stroke, leading to a high healthcare cost and a heavy economic burden to the nation. Tele-rehabilitation aids to enhance the life of stroke survivors by allowing them to conduct the therapy from home which helps the patient with low mobility and living far from the medical centers. This work focuses on the development of full hand pose recognition in performing daily activities for tele-rehabilitation treatment using Decision Tree algorithm under the Machine Learning. Force sensor, flexible sensors and MPU6050 Micro Electro-Mechanical system (MEMS) are used for the data collection. The sensors’ resistance and acceleration are the input to the Machine Learning algorithm and the type of hand pose acts as the output. Three hand gesture procedure are chosen in this study, which are grasping a glass, turning the pipe and switching on the plug. The procedure for data collection has been devised. The Decision Tree has been trained and tested using Python programming language on Jupyter Notebook web-based interactive computing platform. At this stage of study, tests are conducted with healthy subjects to validate the effectiveness of the proposed recognition system. An accuracy of 94%. has been achieved. The sensor readings show different patterns of the curves for each activity. This project will assist the medical staffs in delivering a better treatment for the patients and will lead to a faster recovery process.

Mechanomyography in Assessing Muscle Spasticity: A Systematic Literature Review

2024-04-19T04:42:59+00:00

Mechanomyography (MMG) has gained significant prominence in the domain of scientific inquiry, exhibiting widespread applications in diverse areas including sensor advancement, signal processing methodologies, characterization of muscle spasticity, diagnosis of neurological disorders, and as a valuable tool in medical rehabilitation. However, despite the considerable body of existing MMG research, there remains a paucity of comprehensive investigations in these domains in the past. The primary objective of this systematic review is to conduct a comprehensive analysis of the available literature pertaining to the evaluation of muscle spasticity assessment using mechanomyography (MMG) in a systematic and categorical manner. By applying the pre-established search criteria to five prominent databases, a total of 63 pertinent studies that met the inclusion criteria for our review. Through a thorough scrutiny of the 10 meticulously selected records, we unveiled the extensive diversity in protocols and parameters employed in the assessment of muscle spasticity using mechanomyography (MMG). Accelerometers and piezoelectric sensor used for mechanomyography (MMG) are currently in the nascent phase of their development, as evidenced by the findings of this systematic review. Notably, this review also highlights the influence of sensor placement on muscles as a potential factor affecting the acquired signal. In consideration of these findings, it can be concluded that further research is warranted to advance MMG, particularly in the domains of sensor refinement, with specific attention to accelerometers, and the refinement of signal processing techniques. Additionally, future investigations should aim to expand the scope of MMG applications in clinical settings and rehabilitation practices.

Numerical Simulation of Thermal Comfort in Passenger Car Compartment Using CFD-Heat Transfer Coupling

2024-01-28T08:47:53+00:00

While heating, ventilation, and air conditioning (HVAC) systems provide thermal comfort for the car occupants, the passenger compartment’s thermal environment is not uniform and needs to be further assessed. Different cars have different car compartment designs too, thus, the distribution of the airflow and temperature field inside the passenger compartment has to be examined so that improvements in the different car designs can be proposed. Hence, this study aims to investigate the thermal comfort in a Malaysian local brand sedan car which is Proton Saga FLX 2012 using the coupling of computational fluid dynamics (CFD) and heat transfer. A simplified human model was included in the car to allow the understanding of the effect of airflow and temperature field distribution on the passengers sitting under ventilation system conditions. A few conditions also were simulated; without outlets where all windows close (Case 1) and with different window openings (Case 2). The thermal comfort of the passengers was analysed based on the temperature or thermal field displayed on the human models. The head, hand, torso, feet and overall temperature were evaluated. The results showed that in the no outlet condition when all windows were closed, air conditions were on and the initial compartment temperature was at 50°C, it can cause hyperthermia stage to humans. Besides, the hands will have the lowest body temperature in both situations with and without window openings because it is directly facing the air-conditioning. In addition, window opens have pleasant air velocity compared to those without windows open.

Formulation of A Deep Learning Model for Automated Detection Via Segmentation of Lung Cancer

2024-02-28T11:07:06+00:00

In 2020, the International Agency for Research on Cancer recorded nearly 20 million new cases of cancer around the world. It is estimated that cancer will be the second biggest cause of mortality worldwide in 2020, with over 10 million deaths. In Malaysia, the recorded number of new cases and deaths due to cancer in 2020 are 48639 and 29530, respectively. Lung cancer is the third most fre-quent cancer in Malaysia, and it also has the highest mortality rate, at 15.3 per-cent. Lung cancer has become a major public health issue in Malaysia, with only a 11% 5-year survival rate. Computed tomography (CT) scanning is the most common tool for early-stage lung cancer screening. One of the clinical signs of early lung cancer on CT imaging is pulmonary nodules, which are characterized as a small, opaque, roundish growth on the lung with a size of 7-30mm. There are two types of pulmonary nodules: benign and malignant (cancerous). The characteristic difference between malignant and benign nodules had make the pulmonary nodules segmentation significant as the radiologist can classify the malignancy of the nodules with the size of the nodules. Furthermore, radiologist can adjust the dosage of medication for malignant nodules patient, according to the size of the pulmonary nodules. The manual detection of pulmonary nodules in CT images is a tiring job as the radiologist may need to watch over 200 CT imag-es per CT scans. Luckily the advancement in machine learning technologies have paved way to new possibilities of pulmonary nodules detection and segmentation. and can integrate automation in solving repetitive manual intensive tasks. There-fore, this research investigates the diagnosis of lung cancer through CT images by using transfer learning and fine-tuning of the encoder. Hyperparameters such as type of number of epochs, optimizer and loss function are investigated on which combinations of these hyperparameters will yield the highest segmentation dice coefficient and Intersect over Union (IoU). Neural network architectures ResNet101 are evaluated as transfer learning encoder in extracting features from the patient’s CT images. The extracted fea-tures are then fed into the DeepLabV3 segmentation head to form a complete segmentation model. Subsequently, evaluating the combination of various pipe-lines, the loss and dice coefficient graphs are used to find the pipeline which performs the best in pulmo-nary nodules segmentation. This study indicated that the DeepLabV3-ResNet101-Adagrad Optimizer-Dice Loss pipeline yield the best performance. The pulmonary nodule segmentation models achieved a Dice Coefficient of 0.7983. The findings in this research will open new possibilities in screening method of lung cancer screening methods, offering more efficient and accurate detection of pulmonary nodules, ultimately improving patient outcomes.