Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Applied Power Engineering (IJAPE) International Journal of Robotics and Control Systems
Nur Zawani Saharuddin
Universiti Teknikal Malaysia Melaka
Control & Systems Engineering Electrical & Electronics Engineering

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Comparative Analysis of 1D – CNN, GRU, and LSTM for Classifying Step Duration in Elderly and Adolescents Using Computer Vision Lee, Teng Hong; Shair, Ezreen Farina; Abdullah, Abdul Rahim; Rahman, Kazi Ashikur; Ali, Nursabillilah Mohd; Saharuddin, Nur Zawani; Nazmi, Nurhazimah
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1588

Abstract

Developing a classification system that can predict the onset of neurodegenerative diseases or gait-related disorders in elders is vital for preventing incidents like falls. Early detection allows reduction in symptoms and treatment cost for the elderly. In this study, step duration data from five healthy adolescents with age range of 23 – 29 years old and five healthy elderly individuals with age range of 71 – 77 years old were sourced from PhysioNet. To ensure proper training of the deep learning models, synthetic data was generated from the original dataset using a noise jittering technique with random noise of a range between -0.01 and 0.01 added to the original data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and 1D Convolutional Neural Network (1D-CNN) are used for training the data since the data is available in the form time series data. LSTM and GRU are advanced forms of Recurrent Neural Network (RNN) while 1D – CNN can capture temporal dependencies in sequential data. 1D – CNN has the advantages over GRU and LSTM of being more robust to noise and can capture complex patterns behind the data. These methods will be compared in terms of processing time and accuracy. Results show that 1D – CNN outperforms both LSTM and GRU with accuracy of 1.000 in less than 60 seconds. The novelty and contribution of this research shows that healthy old people and healthy young people can be classified with deep learning. Further direction of the research can explore the deep learning in classification of Parkinson’s disease.
Enhanced intentional controlled islanding with BESS integration Nasir, Amar Saufi; Saharuddin, Nur Zawani; Abidin, Izham Zainal; Shair, Ezreen Farina; Ghani, Sharin Ab
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp80-89

Abstract

Severe power system outages can lead to uncontrolled failures and system instability. Intentional controlled islanding is a strategy that deliberately splits the power system into balanced, stand-alone islands to ensure continuous electricity supply until full restoration. However, the execution of islanding may result in certain islands being unbalanced in terms of generation and load. In such cases, load shedding is implemented to achieve balanced stand-alone islands. Nevertheless, load shedding is not the best option as it will result in more users experiencing power disruptions. Therefore, this study explores the integration of battery energy storage systems (BESS) to enhance intentional controlled islanding, with the aim to form balance islands without the need to execute load shedding. This study evaluates the effectiveness of BESS in forming balanced islands and optimizing islanding strategies. The IEEE 30-bus and IEEE 118-bus test systems were used to validate the effectiveness of BESS in enhancing the intentional controlled islanding implementation. The results demonstrated the role of BESS in facilitating intentional controlled islanding, forming stable and balanced island operations without the need for a load shedding scheme. These findings highlight the potential of BESS to enhance the reliability and effectiveness of intentional controlled islanding.
Co-Authors Abdullah, Abdul Rahim Abidin, Izham Zainal Ali, Nursabillilah Mohd Ghani, Sharin Ab Lee, Teng Hong Nasir, Amar Saufi Nazmi, Nurhazimah Rahman, Kazi Ashikur Shair, Ezreen Farina