Publications

Okafor, E., Adeyemi, K., Mohammed, T.

IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

We propose a lightweight digital twin architecture optimized for environments with limited compute, intermittent connectivity, and legacy industrial equipment. Our approach reduces deployment overhead by 60% compared to conventional DT frameworks.

Adeyemi, K., Okafor, E., Chen, L.

ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)

A machine learning approach to automatically verify Simulink and System Composer models against requirements, reducing manual review time by 45% while maintaining verification completeness.

Okafor, E.

arXiv preprint

We define Engineering Intelligence as the integration of AI, simulation, and domain expertise for engineering tasks. The paper presents a reference architecture and case studies from deployments across Africa.

Mohammed, T., Okafor, E., Adeyemi, K.

IEEE Conference on Control Technology and Applications (CCTA)

An adaptive control framework that combines model predictive control with learned terrain models for autonomous vehicle navigation on unpaved and dynamically changing road surfaces.

Adeyemi, K., Okafor, E.

ASEE Annual Conference & Exposition

A survey of 47 engineering programs across 12 African countries assessing MBD tool adoption, curriculum integration, and infrastructure challenges.

Okafor, E., Chen, L.

Journal of Industrial Information Integration

A five-stage maturity model that helps organizations assess and plan their digital twin adoption journey, validated through case studies in manufacturing, energy, and logistics.

Okafor, E.

Engineering for Africa — Technical Whitepaper Series

An analysis of how Engineering Intelligence — the convergence of AI, simulation, and domain expertise — can accelerate industrial development across African economies.