Outputs (86)

Visual Attention Assisted Games (2023)
Presentation / Conference Contribution
Mandal, B., Puhan, N. B., & Homi Anil, V. (2023, August). Visual Attention Assisted Games. Presented at IEEE Symposium on Computational Intelligence and Games, CIG, Boston, MA, USA

In this work, we propose a committee of attention models developed for improving the deep reinforcement learning frequently used for games. The game environment is manifested with spatial and temporal attention mechanisms so as to focus on important... Read More about Visual Attention Assisted Games.

Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners (2023)
Journal Article
Miah, S., Sooriyakanthan, Y., Ledger, P. D., Gil, A. J., & Mallett, M. (2023). Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners. Engineering with Computers, 39(6), 4103-4127. https://doi.org/10.1007/s00366-023-01870-3

The design of magnets for magnetic resonance imaging (MRI) scanners requires the numerical simulation of a coupled magneto-mechanical system where the effects that different material parameters and in-service loading conditions have on both imaging a... Read More about Reduced order modelling using neural networks for predictive modelling of 3d-magneto-mechanical problems with application to magnetic resonance imaging scanners.

Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction (2023)
Presentation / Conference Contribution
Goparaju, S. U., Biju, R., M, P., MC, B., Gangadharan, D., Mandal, B., & C, P. (2023, June). Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction. Presented at 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), Florence, Italy

Traffic flow prediction has been regarded as a critical problem in intelligent transportation systems. An accurate prediction can help mitigate congestion and other societal problems while facilitating safer, cost and time-efficient travel. However,... Read More about Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction.

A Restricted Parametrized Model for Interval-Valued Regression (2023)
Presentation / Conference Contribution
Ying, J., Kabir, S., & Wagner, C. (2023, August). A Restricted Parametrized Model for Interval-Valued Regression. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Incheon, Republic of Korea

This paper explores the parameter generation of the existing ‘Parametrized Model’ (PM) as the state-of-the-art linear interval-valued regression model, highlighting that its strong performance may arise from unexpected behavior. Focusing on the appro... Read More about A Restricted Parametrized Model for Interval-Valued Regression.

Characterising small objects in the regime between the eddy current model and wave propagation (2023)
Journal Article
Ledger, P. D., & Lionheart, W. R. B. (2023). Characterising small objects in the regime between the eddy current model and wave propagation. European Journal of Applied Mathematics, 1-24. https://doi.org/10.1017/S0956792523000207

Being able to characterise objects at low frequencies, but in situations where the modelling error in the eddy current approximation of the Maxwell system becomes large, is important for improving current metal detection technologies. Importantly, th... Read More about Characterising small objects in the regime between the eddy current model and wave propagation.

Exploring the Security Culture of Operational Technology (OT) Organisations: The Role of External Consultancy in Overcoming Organisational Barriers (2023)
Presentation / Conference
Evripidou, S., Ani, U. D., Hailes, S., & McK. Watson, J. D. (2023, August). Exploring the Security Culture of Operational Technology (OT) Organisations: The Role of External Consultancy in Overcoming Organisational Barriers. Presented at Nineteenth Symposium on Usable Privacy and Security (SOUPS 2023), Anaheim, California, USA

Operational Technology (OT) refers to systems that control and monitor industrial processes. Organisations that use OT can be found in many sectors, including water and energy, and often operate a nation's critical infrastructure. These organisations... Read More about Exploring the Security Culture of Operational Technology (OT) Organisations: The Role of External Consultancy in Overcoming Organisational Barriers.

Measuring the Magnetic Polarizability Tensor of Non-Symmetrical Metallic Objects (2023)
Journal Article
Özdeğer, T., Ozdeger, T., Davidson, J. L., Davidson, J., Ledger, P. D., Conniffe, D., Lionheart, W. R. B., Lionheart, W., Peyton, A. J., & Peyton, A. (2023). Measuring the Magnetic Polarizability Tensor of Non-Symmetrical Metallic Objects. IEEE Sensors Journal, 23(17), 20027-20036. https://doi.org/10.1109/jsen.2023.3296439

The Magnetic Polarizability Tensor (MPT) is a representative electromagnetic property of a metallic object, which depends on the size, material, shape, and excitation frequency of the object. The MPT can be used to describe the response of metal dete... Read More about Measuring the Magnetic Polarizability Tensor of Non-Symmetrical Metallic Objects.

libSBOLj3: a graph-based library for design and data exchange in synthetic biology. (2023)
Journal Article
Mısırlı, G. (2023). libSBOLj3: a graph-based library for design and data exchange in synthetic biology. Bioinformatics, 39(8), 3. https://doi.org/10.1093/bioinformatics/btad525

The Synthetic Biology Open Language version 3 data standard provides a graph-based approach to exchange information about biological designs. The new data model has major updates and offers several features for software tools. Here, we present libSBO... Read More about libSBOLj3: a graph-based library for design and data exchange in synthetic biology..

Deep Reinforcement Learning for Smart Energy Networks (2023)
Thesis
Harrold, D. J. B. Deep Reinforcement Learning for Smart Energy Networks. (Thesis). Keele University. https://keele-repository.worktribe.com/output/530054

To reduce global greenhouse gas emissions, the world must find intelligent solutions to maximise the utilisation of carbon-free renewable energy sources (RES). Energy storage systems (ESS) can be used to store energy when RES generation exceeds deman... Read More about Deep Reinforcement Learning for Smart Energy Networks.

Asymptotic formulations of anti-plane problems in pre-stressed compressible elastic laminates (2023)
Journal Article
Helmi, M. M., Althobaiti, S., Mubaraki, A. M., & Rogerson, G. A. (2023). Asymptotic formulations of anti-plane problems in pre-stressed compressible elastic laminates. Open Physics, 21(1), https://doi.org/10.1515/phys-2022-0265

This article investigates the long-wave anti-plane shear motion in a symmetric three-layered laminate composed of pre-stressed compressible elastic layers. The layers of the laminate are perfectly bonded, while traction-free and fixed boundary condit... Read More about Asymptotic formulations of anti-plane problems in pre-stressed compressible elastic laminates.