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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Published in 5th International Conference on Materials and Reliability Jeju, Korea, 2019
This short paper proposes a kernel principal component analysis (KPCA) feature fusion technique for degradation assessment, and a deep learning model for prognostics of rolling element bearings. Read more
Recommended citation: U. E. Akpudo and J. H. Hur, “A Feature Fusion-based Prognostics Approach for Rolling Element Bearings,” 5th International Conference on Materials and Reliability Jeju, Korea, Nov. 27-29, 2019.
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Published in IEEE Access, 2020
This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequency-domain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Read more
Recommended citation: U. E. Akpudo and H. Jang-Wook, "A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features," in IEEE Access, vol. 8, pp. 175020-175034, 2020, doi: 10.1109/ACCESS.2020.3025909..
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Published in Journal of Mechanical Science and Technology (JMST), 2020
This study presents a methodology for constructing a reliable HI for bearing prognostics, choosing a reliable TSP, and provides a comparison between ML and DL methods for bearing prognostics. Read more
Recommended citation: Akpudo, U.E., Hur, JW. Towards bearing failure prognostics: a practical comparison between data-driven methods for industrial applications. Journal of Mechanical Science and Technology (JMST) 34, 4161–4172 (2020). https://doi.org/10.1007/s12206-020-0908-7
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Published in IEEE Access, 2021
This study introduces a multi-sensor prognostics approach which merges highly prognosible statistical features from vibrational and pressure sensor measurements after a multi-level wavelet decomposition of the signals. Read more
Recommended citation: U. E. Akpudo and H. Jang-Wook, "An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps," in IEEE Access, vol. 9, pp. 38920-38933, 2021, doi: 10.1109/ACCESS.2021.3063676.
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Published in Electronics, 2021
This study proposes the use of common rail (CR) pressure differentials and a deep one-dimensional convolutional neural network (1D-CNN) with the local interpretable model-agnostic explanations (LIME) for empirical diagnostic evaluations (and validations) using a KIA Sorento 2004 four-cylinder line engine as a case study. Read more
Recommended citation: Akpudo, U.E.; Hur, J.-W. An Explainable DL-Based Condition Monitoring Framework for Water-Emulsified Diesel CR Systems. Electronics 2021, 10, 2522. https://doi.org/10.3390/electronics10202522.
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Published in Energies, 2021
This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for fault detection and isolation (FDI). Read more
Recommended citation: Akpudo, U.E.; Hur, J.-W. D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics. Energies 2021, 14, 5286. https://doi.org/10.3390/en14175286
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Published in Algorithms, 2022
This study explores artificial intelligence-based models for learning the discriminant spectral information stored in the vibration signals and considers the accuracy and cost implications of spectral isolation of the critical spectral segments of the signals for accurate equipment monitoring. Read more
Recommended citation: Akpudo, U.E.; Hur, J.-W. Exploring the Efficiencies of Spectral Isolation for Intelligent Wear Monitoring of Micro Drill Bit Automatic Regrinding In-Line Systems. Algorithms 2022, 15, 194. https://doi.org/10.3390/a15060194.
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Published in Data for Policy 2022, 2022
This brief unveils some vulnerabilities surrounding the use of AI in SSA and promotes equitable access to new technologies in SSA amidst the anxiety around AI and concerns about data governance. Read more
Recommended citation: J. O. Effoduh, U. E. Akpudo, and J. D. Kong, “Toward a trustworthy and inclusive data governance policy for the use of artificial intelligence in Africa,” Data & Policy, vol. 6, p. e34, 2024. doi:10.1017/dap.2024.26.
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Published in 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2023
This study explores and draws for the first time, qualitative insights into Ultra-FGVC images through saliency-based explanation methods that provide intuitive hints on where the models are looking at the images. Read more
Recommended citation: U. E. Akpudo, X. Yu, J. Zhou and Y. Gao, "What EXACTLY are We Looking at?: Investigating for Discriminance in Ultra-Fine-Grained Visual Categorization Tasks," 2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Port Macquarie, Australia, 2023, pp. 129-136, doi: 10.1109/DICTA60407.2023.00026.
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Published in 2024 IEEE International Conference on Multimedia and Expo (ICME), 2024
This paper extends the Invertible Concept-based Explainer (ICE) to introduce a new ingredient measuring concept consistency. Read more
Recommended citation: U. E. Akpudo, Y. Gao, J. Zhou and A. Lewis, "Coherentice: Invertible Concept-Based Explainability Framework for CNNs beyond Fidelity," 2024 IEEE International Conference on Multimedia and Expo (ICME), Niagara Falls, ON, Canada, 2024, pp. 1-6, doi: 10.1109/ICME57554.2024.10687699.
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I had the opportunity to present my insights on how Big Data-driven Prognostics and Health Management (PHM) is shaping the future of industrial reliability. Read more
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Addressed AI’s role in early disaster warning systems, climate adaptation strategies, and risk mitigation. Showcased case studies on AI-driven sustainable urban water systems. Read more
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Discussed how AI can enhance predictive flood monitoring, real-time water quality assessment, and smart irrigation systems. Explored machine learning applications in optimizing hydropower efficiency and water distribution networks. Read more
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Anchored a one-week workshop organised by Defense Reliability Laboratory, Kumoh National Institute of Technology for graduate and undergraduate students for practical approaches to data-driven prognostics and health management of mechanical, electrical and electronic components. Read more
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Anchored a one-week workshop organised by Defense Reliability Laboratory, Kumoh National Institute of Technology for graduate and undergraduate students for practical approaches to data-driven prognostics and health management of mechanical, electrical and electronic components. Read more
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Honoured to pitch my research proposal to Hanwa Systems on Emulsified Diesel Injection Systems Monitoring using intelligent PHM frameworks Read more
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Pitched my proposal Smart Factory Automation: A correlation-based GA-DNN Model for Smart AC Monitoring and Control at the Daewoong Foundation AI & Big Data Hackathon organised by Daewoong Foundation. Read more
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This initiative provides a platform for fostering cultural awareness and inclusivity through structured dialogues, exhibitions, presentations, and interactive sessions amongst high and middle school children. Read more
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Honoured to be on a panel discussion at the Data for Policy Conference 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 (she/her). Read more
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Pitched my proposal to 3rd Smart Agriculture Symposium organised by the ARC Research Hub for Driving Farming Productivity and Disease Prevention, Griffith University Australia. Read more
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Pitched my proposal to different talent groups at the Emerging Industry Leaders Program organised by Griffith University Australia. Read more
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Pitched my proposal on Unveiling AI Concerns for Sub-Saharan Africa and Other Vulnerable Groups’ at the ICONIC 2024 Read more
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post. Read more
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post. Read more