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  • 1.
    Dahlberg, Emil
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Mineur, Mattias
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Shoravi, Linus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Swartling, Holger
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Replacing Setpoint Control with Machine Learning: Model Predictive Control Using Artificial Neural Networks2020Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Indoor climate control is responsible for a substantial amount of the world's total energy expenditure. In a time of climate crisis where a reduction of energy consumption is crucial to avoid climate disaster, indoor climate control is a ripe target for eliminating energy waste. The conventional method of adjusting the indoor climate with the use of setpoint curves, based solely on outdoor temperature, may lead to notable inefficiencies. This project evaluates the possibility to replace this method of regulation with a system based on model predictive control (MPC) in one of Uppsala University Hospitals office buildings. A prototype of an MPC controller using Artificial Neural Networks (ANN) as its system model was developed. The system takes several data sources into account, including indoor and outdoor temperatures, radiator flowline and return temperatures, current solar radiation as well as forecast for both solar radiation and outdoor temperature. The system was not set in production but the controller's predicted values correspond well to the buildings current thermal behaviour and weather data. These theoretical results attest to the viability of using the method to regulate the indoor climate in buildings in place of setpoint curves.

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  • 2.
    Shoravi, Linus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Compressing Pointers for the Z Garbage Collector: Runtime compression of pointers in a concurrent setting2023Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Pointers in 64-bit architectures are unlikely to exhaust their vast address range, and are as such needlessly big. Reducing the amount of memory a pointer occupies leads to reduced memory demands, better usage of memory, and better locality. Pointer compression is a term that encompasses techniques that aim to make pointers occupy less memory, often to 32-bit for the sake of word alignment. Pointers that are 32-bit embody the opposite problem of having too restricted of an address range, being able to address only 4 GB. Z is a garbage collector in the HotSpot JVM which does not support pointer compression. Partly because the aforementioned address range restriction, and partly because the implementation of compressed pointers which exist in HotSpot would clash with the goals of the garbage collector. This project explores ways of implementing pointer compression for Z that isn't detrimental to the goals of the garbage collector, and aims to find where problems may occur. The outset was to explore compressing speculatively during runtime. The result is a design that relies on a custom bit layout for compressed pointers, inspecting bit layouts of the pointers on each read and write to detect the compression status. This seems to be the most promising in terms of code maintainability and ease of implementation. 

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