FEMHUB

The Hub addresses key manufacturing challenges in the production of high integrity and high value electrical machines for the aerospace, energy, high value automotive and premium consumer sectors. 

DECHI

The programme researches new digital approaches to expedite the adoption of circular innovation within the MedTech sector.  

Data Augmentation Framework for Improved Classification in Object Detectors. 

IEEE Access

This study proposes a framework for choosing an appropriate augmentation method in various scenarios by investigating pixel-level transformations in the context of data augmentation to enhance the training performance of Quality Inspection (QI) models in scenarios with extremely limited data. 

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Time Series Anomaly Detection in Vehicle Sensors Using Self-Attention Mechanisms

IEEE Transactions on Intelligent Transportation Systems

This study proposes a novel anomaly detection method, namely Dual-channel Self-attention-based Convolutional Neural Network (DSA-CNN) for multivariate time series data. 

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