Journal of Engineering Research and Education (JERE)
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Journal of Engineering Research and Education (JERE) is an annually engineering journal, scholarly open access and published by the publication of Universiti Malaysia Perlis (UniMAP). JERE is focusing on theories, methods, and applications in Engineering Research and Education. JERE covers all areas of Engineering Research and Education, publishing refereed original research articles. Articles preferably should focus on new methods, report or research, review or research, latest research findings and innovative practices in the engineering field.
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PublicationSelf-assessing psychomotor skills using thinking-aloud technique via smartphone( 2022)This research aims to design and develop an automated device for self-assessing psychomotor skills without an instructor’s observation. The lab assessment usually needs an instructor to observe, measure, and analyze the student's skills. It consumed much time to monitor each student. The problem of assessing psychomotor skills in the laboratory can be solved using the latest technology. Thus, the design of an Automated Psychomotor Testing Kit will be used to measure student psychomotor skills via a smartphone. The result can be transmitted to the instructor's smartphone via the Blynk application using the Arduino Mega and Bluetooth module. For this research, 17 students of Robotic and Automation Technology (Treatment Group) and 19 volunteered students from other engineering technology programs (Control Group) participated. The detailed methodology is described in this paper. The results show that there is a significant difference in mean scores between the treatment and control groups. Thus, the researcher can conclude that changes in students' Psychomotor Skills (P.S.) resulting from laboratory classes are statistically significant and be measured.
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PublicationFeature extraction for underground object reconstruction from Ground Penetrating Radar (GPR) data( 2022)Ground Penetrating Radar (GPR) is very beneficial for underground object scanning and detection. It utilises radar pulses as the signal, hence it able to penetrate surfaces in obtaining the underneath information without disturbing and destructing the ground. However, its radargram output in hyperbolic signal are very challenging to be analysed. Thus, suitable algorithm has to be designed and developed to interpret the data. This work highlights on the usage of drop-flow algorithm in detecting important features of the hyperbolic signal. Previous study has shown that these features is promising in understanding and further, reconstructing the GPR data. Results show that the features extracted from the hyperbolic signal able to be identified for further processing, which is necessary for visualization purpose.
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PublicationPower transformer health prediction using machine learning( 2022)Ensuring good conditions and functionalities of these power transformers, these units are constantly monitored and maintained through the implementation of various condition-based maintenance activities. However, despite all of these preventive maintenance practices in place, some transformer defects are still left undetected, especially at an early stage. There is a lack of a holistic risk evaluation system in the power utility company to support and guide the scheduling and prioritization of condition-based maintenance activities. It is reported that there was a total of 20 power transformer failure cases during the years 2005-2019. These failures led to higher operating expenses, arising from the cost of repair and loss of revenues due to outages and downtime. As such, the outcome of this research aims to fill in this gap in the preventive maintenance system currently in practice in the power utility company by developing a transformer failure prediction system to complement the existing maintenance testing activities that are performed routinely as a part of condition-based maintenance in Malaysia. A Tier 1 to Tier 2 prediction algorithm is developed in this project with the help of artificial intelligence to accelerate the availability of Tier 2 electrical test results. This allows early assessment of the transformer's electrical parameters. Thereafter, the predicted Tier 2 test results can beused in conjunction with transformer age, loading, visual inspection as well as Tier 1 oil test results to predict failure probability and fault type through the development of a lookup table. Overall, this algorithm aims to speed up and improve the transformer health assessment to act as an early warning system for future tripping and failure events. This allows condition-based maintenance activities that are currently in practice to prioritize transformers that are undergoing more severe deterioration before permanent irreversible damage occurs.
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PublicationLow-cost IOT based Energy Monitoring System (EMOSY)( 2022)Energy monitoring system becomes an important subject to provide information of electricity usage for the users. Due to advances in electronics and computing, many technologic solutions are now available. These solutions are very important tool to a sustainable future. Hence, this paper presents the digitization of a low-cost small-scale energy monitoring systems based on IoT. The proposed energy monitoring system known as EMOSY is designed to eliminates the high-cost energy meter. EMOSY is a portable and practical system which can be used without modification of internal or external connection of appliances. EMOSY is developed by using a voltage detector circuit concept by amplifying the existence of electrostatic. This electrostatic reading sends to the database through Wi-Fi module ESP8266 integrated with Arduino NodeMCU. The web page is designed using Adobe Dreamweaver with HTML and PHP coding. In the proposed system, the user able to monitor the energy usage of each appliance and estimated billing time to time. Based on the result, the energy monitoring system successfully can detect the existence of electrostatic, and the webpage database can display the energy usage extended to the estimated electricity bill. The monitoring system is found to be useful to the residential, commercial, and industrial to monitor energy patterns, which is essential to facilitate energy conservation measures for minimizing energy usage.
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PublicationExamining the effects of eWOM and consumer based brand equity on intention to purchase electronic products: a study on Malaysian consumers( 2022)This study aims to examine the impact of eWOM and consumer based brand equity on Malaysian consumers’ intention to purchase electronic products. The primary data for this research was gathered through the distribution of online surveys. The final sample consisted of 245 Malaysian online shoppers selected by the purposive sampling method. The results confirm that eWOM and consumer-based brand equity are the key factors that influence Malaysian consumers’ intention to purchase electronic products. Theoretical and practical implications are discussed as well as recommendations for future research.