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In this project, different optimal control problems will be considered under a contagious financial and insurance market with regime switching and risk uncertainty. In the first chapter, an optimal
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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its environment and respond optimally in dynamic operating conditions. Meanwhile, you will also develop intelligent control strategies that minimise energy use while ensuring punctuality and safety
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electronic converters are required to connect renewable energy sources and energy storage systems to the power network. These converters employ sophisticated control algorithms that must simultaneously achieve
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and embrittlement by precisely optimizing additive manufacturing parameters. By combining experimental investigations, advanced microstructural analyses, and numerical simulations, a novel manufacturing
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and blackouts. In addition to their volatile nature, RESs cannot provide the ancillary services (such as voltage and frequency control) that conventional synchronous generators naturally deliver
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while discarding those that contribute minimally, thus reducing the model's order without compromising accuracy. The framework will employ advanced techniques such as machine learning, optimization
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. This can impair performance and result in injury or illness. Therefore, identification of optimal recovery strategies to alleviate post-exercise muscle damage and soreness is crucial. Post-exercise nutrition
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electronic converters are required to connect renewable energy sources and energy storage systems to the power network. These converters employ sophisticated control algorithms that must simultaneously achieve
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. Therefore, identification of optimal recovery strategies to alleviate post-exercise muscle damage and soreness is crucial. Post-exercise nutrition and sleep practices are key components in an optimal recovery