CNNs lack human-centred explainability in their natural forms, despite their efficiencies. An ongoing project and a requirement for the completion of a PhD at The School of Engineering and Built Environment, Griffith University, I aim to reveal what a CNN sees and not just where it looked. 
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This project addresses the challenge of extracting discriminant information from multiple sensors exhibiting unique spectral and transient behaviors—a critical need in modern diagnostic systems. Focusing on high-precision automatic regrinding equipment used for micro drill bits, the study emphasizes real-time monitoring during the grinding process to safeguard drill bit longevity and overall equipment performance. 
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Honoured to be on a panel discussion at the Data for Policy Conference hosted at the Evans School of Public Policy & Governance, University of Washington on Towards an Inclusive Data Governance Policy for the use of AI in Africa alongside Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) colleagues (Jude Kong and Jake Okechukwu Effoduh), moderated by the International Development Research Centre (IDRC) project officer Chaitali Sinha.
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Under Prof. Hur Jang-Wook’s supervision, I spearheaded this project between March 2019 and May 2021 as a requirement for fulfilling the requirements of masters (by research) at the Department of Mechanical Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology (KIT), Republic of Korea. 
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Under Prof. Hur Jang-Wook’s supervision, I collaborated with a team member as part of his Masters (by research) requirements at the Department of Mechanical Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology (KIT), Republic of Korea. 
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About 30% of greenhouse gas emissions come from diesel engines, which contribute to a variety of health and environmental problems. This project was motivated by the need to discover the optimal diesel emulsion to reduce fuel emmision, maintain engine efficiency and lower costs. 
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The project proposed an informed decision-making framework for remaining compliant with domestic and international standards while also improving performance and extending the life cycles of proton-exchange membrane fuel cells (PEMFC) using Finite Element Analysis (FEA). 
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The project aimed at developiing fault disgnostic frameworks for electrical motors. Supervised by Prof. Hur Jang Wook and spearheaded by myself, the team designed test beds, conducted experiments and authored a journal articles. 
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The project aimed at developiing fault disgnostic frameworks for electronic (capacitors, etc) components. Supervised by Prof. Hur Jang Wook and spearheaded by myself, the team designed test beds, conducted experiments and authored a journal articles. 
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About 30% of greenhouse gas emissions come from diesel engines, which contribute to a variety of health and environmental problems. This project was motivated by the need to discover the optimal diesel emulsion to reduce fuel emmision, maintain engine efficiency and lower costs. 
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The project Explores different statistical and machine learning methods for estimating the remaining useful life (RUL) of Rolling Elemnet Bearings. 
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