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and extract discriminative features from encrypted IoT traffic that reliably distinguish between benign and malicious behaviour. These features must be extracted in a lightweight manner suitable for
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reliability and operational efficiency. Determining the optimal size and location of PSTs within a network is inherently complex due to the nonlinear and dynamic nature of power systems, necessitating the use
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design that is applicable to different types of energy harvesters. This project will explore how PMC can be optimised to maximise efficiency, reliability and scalability using novel circuit topologies
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augmentation and model optimisation to deliver a reliable prediction of patient needs into recovery services after surgery, improving the deployment of available resources, ensuring patient quality-of-care and
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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metrics during both standard operation (primarily governed by system reliability) and extreme events (primarily governed by robustness and restoration). This will be achieved by building on previous
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noise from other mechanical components such as gears, screws, etc., fault diagnosis using such signals is not an easy task. Having a robust and reliable CM system for low-speed bearings will have
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subsurface layers of components and even transform their microstructure, potentially introducing additional defects. Thus, assessment of these effects on structural reliability and durability of systems
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Project description: Reliable, high-resolution electricity demand data are scarce across the global south, limiting our understanding in these regions of the relationship between weather and energy demand
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable