Search for dissertations about: "Reinforcement Learning"
Showing result 21 - 25 of 172 swedish dissertations containing the words Reinforcement Learning.
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21. On Deep Machine Learning Based Techniques for Electric Power Systems
Abstract : This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. READ MORE
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22. Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
Abstract : Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. READ MORE
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23. Data-Efficient Learning of Semantic Segmentation
Abstract : Semantic segmentation is a fundamental problem in visual perception with a wide range of applications ranging from robotics to autonomous vehicles, and recent approaches based on deep learning have achieved excellent performance. However, to train such systems there is in general a need for very large datasets of annotated images. READ MORE
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24. Enabling Enterprise Live Video Streaming with Reinforcement Learning and Graph Neural Networks
Abstract : Over the last decade, video has vastly become the most popular way the world consumes content. Due to the increased popularity, video has been a strategic tool for enterprises. READ MORE
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25. Learning to Control the Cloud
Abstract : With the growth of the cloud industry in recent years, the energy consumption of the underlying infrastructure is a major concern.The need for energy efficient resource management and control in the cloud becomes increasingly important as one part of the solution, where the other is to reduce the energy consumption of the hardware itself. READ MORE