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All Journal JOIV : International Journal on Informatics Visualization
Yaacob, Siti Salwani
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Computer Science & IT

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A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System Khaleefah, Shihab Hamad; A. Mostafa, Salama; Gunasekaran, Saraswathy Shamini; Khattak, Umar Farooq; Yaacob, Siti Salwani; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2701

Abstract

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.
An Efficient Unknown Detection Approach for RFID Data Stream Management System Yaacob, Siti Salwani; Mahdin, Hairulnizam; Wijayanto, Inung; Aamir, Muhammad; Jaya, M. Izham; Mohd Radzuan, Nabilah Filzah; Mubarak-Ali, Al-Fahim
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.2824

Abstract

The presence of unknown RFID tags can occur when new, unread tagged objects are introduced into the system, either accidentally or intentionally. Additionally, unknown tags can result from tag duplication errors, where multiple tags have the same identifier, or tag malfunctions, where a tag fails to transmit its identifier correctly. This research addresses the critical issue of detecting unknown tags, focusing on optimizing processing time and energy efficiency in terms of memory usage when detecting these tags. A novel algorithm called SWOR (Sliding Window XOR-based Detection) is introduced, specifically designed to identify unknown tags within RFID data streams. SWOR utilizes a sliding window mechanism combined with an XOR filter, enabling efficient detection of unknown tags while reducing unnecessary processing, which can lead to prolonged processing times, high memory consumption, and scalability issues. Experimental results demonstrate that SWOR decreases execution time by an average of 27% across various tests, outperforming existing approaches in terms of processing time for RFID event streams. The materials and methods employed include comprehensive simulations and real-world RFID data streams to validate the algorithm's effectiveness. This study highlights the potential for significant improvements in RFID system efficiency and paves the way for future research in optimizing RFID tag detection methodologies. The implications for further research include exploring the integration of SWOR with other RFID system components and examining its performance in diverse operational environments. This research contributes to the development of more robust and efficient RFID systems, thereby enhancing their reliability and scalability for various future applications.
Co-Authors A. Mostafa, Salama Alde Alanda, Alde Gunasekaran, Saraswathy Shamini Hairulnizam Mahdin, Hairulnizam Inung Wijayanto Jaya, M. Izham Khaleefah, Shihab Hamad Khattak, Umar Farooq Mohd Radzuan, Nabilah Filzah Mubarak-Ali, Al-Fahim Muhammad Aamir