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Outputs (4350)

Geotechnical characterisation of recycled biopolymer-stabilised earthen materials (2019)
Presentation / Conference Contribution
Muguda, S., Hughes, P., Augarde, C., Perlot, C., Bruno, A., & Gallipoli, D. (2019, December). Geotechnical characterisation of recycled biopolymer-stabilised earthen materials. Presented at The XVII European Conference on Soil Mechanics and Geotechnical Engineering, Reykjavik Iceland

Earthen structures (i.e. structural units manufactured from soil) are often regarded as sustainable forms of construction due to their characteristically low carbon footprint. Unstabilised earthen materials can easily be recycled or disposed, however... Read More about Geotechnical characterisation of recycled biopolymer-stabilised earthen materials.

AMPLE: A Material Point Learning Environment (2019)
Presentation / Conference Contribution
Coombs, W., Augarde, C., Bing, Y., Charlton, T., Cortis, M., Ghaffari Motlagh, Y., & Wang, L. (2019, December). AMPLE: A Material Point Learning Environment. Paper presented at Second International Conference on the Material Point Method for Modelling Soil-Water-Structure Interaction, Cambridge, UK

Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field (2019)
Presentation / Conference Contribution
Hua, W., You, M., & Sun, H. (2019, December). Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field. Presented at 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)., Changchun, China

Energy hub scheduling plays a vital role in optimally integrating multiple energy vectors, e.g., electricity and gas, to meet both heat and electricity demand. A scalable scheduling model is needed to adapt to various energy sources and operating con... Read More about Real-Time Price Elasticity Reinforcement Learning for Low Carbon Energy Hub Scheduling Based on Conditional Random Field.

Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items (2019)
Presentation / Conference Contribution
Bhowmik, N., Gaus, Y., & Breckon, T. (2019, November). Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items. Presented at 2019 IEEE International Symposium on Technologies for Homeland Security, Boston, USA

X-ray baggage security screening is widely used to maintain aviation and transport safety and security. To address the future challenges of increasing volumes and complexities, the recent focus on the use of automated screening approaches are of part... Read More about Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items.

On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding (2019)
Presentation / Conference Contribution
Ismail, K., & Breckon, T. (2019, December). On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

Whilst real-time object detection has become an increasingly important task within urban scene understanding for autonomous driving, the majority of prior work concentrates on the detection of obstacles, dynamic scene objects (pedestrians, vehicles)... Read More about On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding.

Region Based Anomaly Detection With Real-Time Training and Analysis (2019)
Presentation / Conference Contribution
Adey, P., Bordewich, M., Breckon, T., & Hamilton, O. (2019, December). Region Based Anomaly Detection With Real-Time Training and Analysis. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

We present a method of anomaly detection that is capable of real-time operation on a live stream of images. The real-time performance applies to the training of the algorithm as well as subsequent analysis, and is achieved by substituting the region... Read More about Region Based Anomaly Detection With Real-Time Training and Analysis.