Journal of Engineering Research and Education (JERE)http://dspace.unimap.edu.my:80/xmlui/handle/123456789/19472024-03-29T09:20:34Z2024-03-29T09:20:34ZSelf-assessing psychomotor skills using thinking-aloud technique via smartphoneZol Bahri, RazaliN. S. S., MizamM. M. A., Kaderhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/768492022-11-10T01:13:25Z2022-01-01T00:00:00ZSelf-assessing psychomotor skills using thinking-aloud technique via smartphone
Zol Bahri, Razali; N. S. S., Mizam; M. M. A., Kader
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.
Link to publisher's homepage at http://jere.unimap.edu.my
2022-01-01T00:00:00ZPower transformer health prediction using machine learningChia, Kwang TanJun, Ying WongYuh, Ru WongYogendra A/L BalasubramaniamChong, Tak YawSiaw, Paw Kohhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/768482022-11-10T01:03:18Z2022-01-01T00:00:00ZPower transformer health prediction using machine learning
Chia, Kwang Tan; Jun, Ying Wong; Yuh, Ru Wong; Yogendra A/L Balasubramaniam; Chong, Tak Yaw; Siaw, Paw Koh
Ensuring good conditions and functionalities of these power transformers, these units are
constantly monitored and maintained through the implementation of various conditionbased
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 be used 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.
Link to publisher's homepage at http://jere.unimap.edu.my
2022-01-01T00:00:00ZFeature extraction for underground object reconstruction from Ground Penetrating Radar (GPR) dataS. A., Abdul ShukorHavenderpal, SinghNurush Syamimie, MahmudH., AliA. F., Ahmad ZaidiM. S., Zanar AzalanT. S., Tengku AmranM. R., Ahmadhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/768472022-11-10T00:57:03Z2022-01-01T00:00:00ZFeature extraction for underground object reconstruction from Ground Penetrating Radar (GPR) data
S. A., Abdul Shukor; Havenderpal, Singh; Nurush Syamimie, Mahmud; H., Ali; A. F., Ahmad Zaidi; M. S., Zanar Azalan; T. S., Tengku Amran; M. R., Ahmad
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.
Link to publisher's homepage at http://jere.unimap.edu.my
2022-01-01T00:00:00ZDesign of remote warning system for Miniature Circuit Breaker (MCB) power shortage via Internet of Things (IoT)Lim, Hong QuanNurul Syahirah, Khalidhttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/767262022-11-08T02:37:48Z2022-01-01T00:00:00ZDesign of remote warning system for Miniature Circuit Breaker (MCB) power shortage via Internet of Things (IoT)
Lim, Hong Quan; Nurul Syahirah, Khalid
In this modern era, the Internet of Things (IoT) technology is a new profound technology
which leads to a better and easy lifestyle world. With this technology, office workers will not
require to present in a company and industrial workers will no longer needed in a factory to
monitor manufacture process. With this new and promising technology, most of the work
can be done at home by using mobile phone or a computer and even a machine can run itself
with a single button. As the title of the project presented, the project proposed an application
of the Miniature Circuit Breaker (MCB) using the technology of the IoT. This project is a
warning system when a black out occurs, it will send a message to the user to inform about
the trips through a mobile phone. Sometimes, workers overlook the short circuit of the
machine which results in a loss to the company and delay of the product. In this project, a
prototype of the MCB system with the IoT technology will be developed and created to
achieve the objective of project. Furthermore, the prototype will be tested to show that the
prototype is functional and efficient as it is a remote warning system via Wi-Fi.
Link to publisher's homepage at http://jere.unimap.edu.my
2022-01-01T00:00:00Z