Contact Dr Boyu Kuang
- Email: Neil.Kuang@cranfield.ac.uk
- Twitter: @BoyuKuang
- ORCID
- Google Scholar
- ResearchGate
Areas of expertise
- Aerospace Manufacturing
- Carbon, Climate and Risk
- Computing, Simulation & Modelling
- Engines, Powertrains & Alternative Fuels
- Industrial Automation
- Instrumentation, Sensors and Measurement Science
- Process Systems Engineering
Background
Dr. Boyu Kuang is a research fellow in Computer Vision and Artificial Intelligence at Cranfield University, affiliated with the Centre for Computational Engineering Sciences. He is currently involved in the UKRI, ATI, and Airbus-funded ONEHeart project, which addresses challenges in autonomous systems and advanced technologies for aviation applications. Since publishing his first paper in the autumn of 2021, Dr. Kuang has built an impressive research record, with over 450 citations, an h-index of 10, and 10 i10-index publications within just three years. His 23 high-impact publications, including 14 in JCR Q1 journals, have appeared in leading venues such as IEEE Transactions on Cybernetics, Expert Systems with Applications, and the Chemical Engineering Journal. These achievements highlight his growing influence in artificial intelligence, robotics, and engineering research.
Dr. Kuang’s expertise spans deep learning, weak and self-supervised learning, semantic segmentation, target detection, 3D reconstruction, multi-modal data fusion, and domain adaptation. His research focuses on tackling real-world challenges in areas such as aviation maintenance, renewable energy systems, and intelligent robotics. His work aims to advance the safety, efficiency, and sustainability of industrial systems through innovative AI-driven solutions and interdisciplinary approaches.
Committed to collaboration, Dr. Kuang has worked with renowned institutions, including Stanford University, King’s College London (KCL), and the Civil Aviation University of China (CAUC), as well as global industry leaders such as Airbus, Leidos, and the Austrian Institute of Technology (AIT). These collaborations ensure that his research remains closely aligned with the needs of academia and industry, addressing critical technological and sustainability challenges.
In addition to his research contributions, Dr. Kuang plays an active role in academic leadership. As the module lead for Artificial Intelligence and Machine Learning at Cranfield University, he combines theoretical depth with practical relevance to provide students with the skills required for industry and research. He has also demonstrated his commitment to advancing the field through peer review, having reviewed 59 manuscripts for 15 prestigious journals, including IEEE Transactions on Industrial Electronics and Neural Networks. His contributions as a session chair at international conferences further reflect his leadership in fostering scholarly exchange and innovation.
Dr. Kuang is a member of IEEE and other professional organisations, demonstrating his active engagement with the academic and industrial communities. Supported by Cranfield University's state-of-the-art facilities, including the Aerospace Integration Research Centre (AIRC) and the Digital Aviation Research and Technology Centre (DARTeC), he is well-positioned to drive advancements in AI, robotics, and intelligent systems.
Through his interdisciplinary work, Dr. Kuang is committed to developing transformative solutions that address key challenges in automation, intelligent systems, and sustainability. Leveraging his expertise and Cranfield University's strengths in aerospace and engineering, he continues to advance technologies that enhance efficiency, reduce carbon emissions, and improve safety across industries. Dr. Kuang welcomes collaboration opportunities with academic and industrial partners and invites inquiries from prospective PhD students interested in contributing to impactful research in artificial intelligence, robotics, and engineering applications.
Research opportunities
1. Advanced Perception and AI Systems
This area focuses on enhancing autonomous systems through cutting-edge perception and artificial intelligence technologies. Opportunities include developing innovative algorithms for data fusion, machine learning, and domain-specific AI models, addressing real-world challenges in complex environments. These advancements have applications across aerospace, robotics, and intelligent automation.
2. Intelligent Automation and Maintenance
Explore research in intelligent automation aimed at improving the safety, efficiency, and sustainability of industrial processes. This area involves applying artificial intelligence, robotics, and data-driven approaches to tackle complex maintenance tasks, with a focus on reducing human involvement in hazardous operations and optimising workflows.
3. Data-Driven Insights and Modelling
Investigate advanced data generation and modelling techniques to improve the robustness and scalability of AI systems. Research in this area involves leveraging state-of-the-art learning frameworks, such as self-supervised and generative models, to address challenges in resource-limited environments or specialised industrial applications.
Why Join?
Cranfield University offers a unique ecosystem for impactful research, supported by world-class facilities such as the Aerospace Integration Research Centre (AIRC) and the Digital Aviation Research and Technology Centre (DARTeC). Collaborating with global industry leaders and academic institutions, researchers will have access to cutting-edge infrastructure and multidisciplinary expertise to drive innovations in AI and engineering.
Current activities
Dr. Boyu Kuang is actively contributing to several interdisciplinary projects that integrate artificial intelligence, computer vision, and robotics to address complex real-world challenges. As part of the UKRI, ATI, and Airbus-funded ONEHeart project, he is exploring cutting-edge technologies to enhance automation and decision-making processes in aviation. His work focuses on advancing AI-driven systems that improve efficiency, safety, and sustainability across industrial applications.
At Cranfield University, Dr. Kuang leads teaching modules on Artificial Intelligence and Machine Learning, where he combines theoretical insights with hands-on applications to prepare students for real-world challenges. He is also engaged in academic collaborations with institutions such as Stanford University, King’s College London, and the Civil Aviation University of China, as well as industry partners like Airbus and Leidos. These partnerships ensure that his research aligns with both academic advancements and practical industry needs.
Dr. Kuang’s ongoing research emphasises the development of advanced perception systems, data-driven modelling, and sustainable automation solutions. By leveraging Cranfield University’s state-of-the-art facilities, including the Aerospace Integration Research Centre (AIRC) and the Digital Aviation Research and Technology Centre (DARTeC), his work continues to contribute to innovative solutions that support the global transition towards more efficient and environmentally sustainable technologies.
Clients
- Airbus SE
- Renault SA
- BAE Systems PLC
- Turkish Aerospace Industries
Publications
Articles In Journals
- Falope TO, Lao L, Huo D & Kuang B. (2024). Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control. Sustainable Energy Grids and Networks, 39
- Nnabuife SG, Hamzat AK, Whidborne J, Kuang B & Jenkins KW. (2024). Integration of renewable energy sources in tandem with electrolysis: A technology review for green hydrogen production. International Journal of Hydrogen Energy
- Sun S, Huang Z, Kang J, Sun X, Kuang B, .... (2024). Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method. Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering, 238(1)
- Kuang B, Nnabuife SG, Whidborne JF, Sun S, Zhao J, .... (2024). Self-supervised learning-based two-phase flow regime identification using ultrasonic sensors in an S-shape riser. Expert Systems with Applications, 236(February)
- Thomas J, Kuang B, Wang Y, Barnes S & Jenkins K. (2024). Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach. The Visual Computer
- Nnabuife SG, Darko CK, Obiako PC, Kuang B, Sun X, .... (2023). A Comparative Analysis of Different Hydrogen Production Methods and Their Environmental Impact. Clean Technologies, 5(4)
- Nnabuife SG, Oko E, Kuang B, Bello A, Onwualu AP, .... (2023). The prospects of hydrogen in achieving net zero emissions by 2050: A critical review. Sustainable Chemistry for Climate Action, 2
- Kuang B, Gu C, Rana ZA, Zhao Y, Sun S, .... (2022). Semantic terrain segmentation in the navigation vision of planetary rovers – a systematic literature review. Sensors, 22(21)
- Anika OC, Nnabuife SG, Bello A, Okoroafor ER, Kuang B, .... (2022). Prospects of low and zero-carbon renewable fuels in 1.5-degree net zero emission actualisation by 2050: a critical review - WITHDRAWN DUPLICATE: see https://dspace.lib.cranfield.ac.uk/handle/1826/19347 . Carbon Capture Science & Technology, 5(December)
- Kuang B, Nnabuife SG, Sun S, Whidborne JF & Rana ZA. (2022). Gas-liquid flow regimes identification using non-intrusive Doppler ultrasonic sensor and convolutional recurrent neural networks in an S-shaped riser. Digital Chemical Engineering, 2(March)
- Kuang B, Chen Y & Rana ZA. (2022). OG-SLAM: a real-time and high-accurate monocular visual SLAM framework. Trends in Computer Science and Information Technology, 7(2)
- Were S, Nnabuife SG & Kuang B. (2022). A Computational Fluid Dynamics Study of Flared Gas for Enhanced Oil Recovery Using a Micromodel. AppliedMath, 2(4)
- Nnabuife SG, Kuang B, Rana ZA & Whidborne JF. (2021). Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system. Chemical Engineering Journal Advances, 9(March)
- Nnabuife SG, Kuang B, Whidborne JF & Rana ZA. (2021). Development of gas-liquid flow regimes identification using a noninvasive ultrasonic sensor, belt-shape features, and convolutional neural network in an S-shaped riser. IEEE Transactions on Cybernetics, 53(1)
- Kuang B, Rana ZA & Zhao Y. (2021). Sky and ground segmentation in the navigation visions of the planetary rovers. Sensors, 21(21)
- Kuang B, Wisniewski M, Rana ZA & Zhao Y. (2021). Rock Segmentation in the Navigation Vision of the Planetary Rovers. Mathematics, 9(23)
- Nnabuife SG, Kuang B, Whidborne JF & Rana Z. (2020). Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline-riser using doppler ultrasonic sensor and deep neural networks. Chemical Engineering Journal, 403
- Kuang B, Nnabuife SG & Rana Z. (2020). Pseudo-image-feature-based identification benchmark for multi-phase flow regimes. Chemical Engineering Journal Advances, 5
Conference Papers
- Kuang B, Barnes S, Tang G & Jenkins K. (2024). Faster RCNN-based Refueling Port Detection for the Autonomous Aircraft Ground Refueling System
- Wang Y, Kuang B, Durazo I & Zhao Y. (2024). 3D reconstruction of rail tracks based on fusion of RGB and infrared sensors
- Kuang B, Barnes S, Tang G & Jenkins K. (2023). A Dataset for Autonomous Aircraft Refueling on the Ground (AGR)
- Kuang B, Rana Z & Zhao Y. (2020). A novel aircraft wing inspection framework based on multiple view geometry and convolutional neural network