Journal of Advances in Engineering and Technology [JAET]

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The Journal of Advances in Engineering and Technology (JAET) is an international, open access, double blind peer-reviewed journal. It is published by the Faculty of Engineering of Sri Lanka Institute of Information Technology (SLIIT). The JAET aims at fostering research and development work in Engineering and Technology and bringing researchers on to a common platform. Furthermore, JAET will also accept review articles on appropriate subject areas including concept papers of academic opinions, book reviews, etc. for publication therein.

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Author: search.filters.author.Jones, K.OStart date: 2022

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    PublicationOpen Access
    Enhancing Load Frequency Control in Interconnected Power Systems with Zone-Specific Fuzzy Controllers: Principles and Methods
    (SLIIT Faculty of Engineering, 2025-02) Jahangiri, S; Jones, K.O
    This work focuses on load frequency control in interconnected power systems, a critical aspect of modern power grid operations. However, sudden load disturbances and generator outages can lead to transient oscillations between control areas, posing challenges to frequency control. The aim of the work was to investigate and enhance load frequency control behaviour, considering dynamic load changes and uncertainties. Fuzzy Logic Controllers optimized with Particle Swarm Optimization were applied to improve control robustness. The Particle Swarm Optimisation algorithm was used to tune the scaling factors and parameters of the fuzzy controllers to optimize their performance. The methods were tested on a standard four-area interconnected power system model equipped with load frequency control blocks, reheaters, governors, rate constraints, and thermal components. Different disturbance scenarios including parameter fluctuations and load changes were evaluated. The Fuzzy Logic Controllers demonstrate resilient response across scenarios without needing extensive tuning. Particle Swarm Optimization improves robustness through systematic exploration for constraint-based nonlinear optimization. Tuning fuzzy controllers with bio-inspired algorithms enhances efficiency in addressing complex grid conditions. The results provide insights into designing more secure and resilient grid controls, contributing to power system stability research.
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    PublicationOpen Access
    How Frequency and Harmonic Profiling of a ‘Voice’ Can Inform Authentication of Deepfake Audio: An Efficiency Investigation
    (SLIIT, Faculty of Engineering, 2025-01) Williams, E.L; Jones, K.O; Robinson, J.C; handler-Crnigoj, S; Burrell, H; McColl, S
    As life in the digital era becomes more complex, the capacity for criminal activity within the digital realm becomes even more widespread. More recently, the development of deepfake media generation powered by Artificial Intelligence pushes audio and video content into a realm of doubt, misinformation, or misrepresentation. The instances of deepfake videos are numerous, with some infamous cases ranging from manufactured graphic images of the musician Taylor Swift, through to the loss of $25 million dollars transferred after a faked video call. The problems of deepfake are becoming increasingly concerning for the general public when such material is submitted into evidence in a court case, especially a criminal trial. The current methods of authentication against such deepfake evidence threats are insufficient. When considering speech within audio forensics, there is sufficient ‘individuality’ in one’s own voice to enable comparison for identification. In the case of authenticating audio for deepfake speech, it is possible to use this same comparative approach to identify rogue or incomparable harmonic and formant patterns within the speech. The presence of deepfake media within the realms of illegal activity demands appropriate legal enforcement, resulting in a requirement for robust detection methods. The work presented in this paper proposes a robust technique for identifying such AI-synthesized speech using a quantifiable method that proves to be justified within court proceedings. Furthermore, it presents the correlation between the harmonic content of human speech patterns and the AI-generated clones they produce. This paper details which spectrographic audio characteristics were found that may prove helpful towards authenticating speech for forensic purposes in the future. The results demonstrate that using specific frequency ranges to compare against a known audio sample of a person’s speech, indicates the presence of deepfake media due to different harmonic structures.