Examensarbeten för masterexamen
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- PostFermi Surfaces of Holographic Metals(2024) ISMAILOV, ELI; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Gran, Ulf; Nilsson, EricOne of the most challenging endeavours in theoretical condensed matter physics is solving models of strongly correlated metals. In these systems, the standard techniques from Fermi liquid theory have limited applicability, necessitating new descriptions. One particularly promising approach is known as holographic duality, which conjectures a relation between the seemingly unapproachable physics of strongly coupled quantum field theories and classical gravitational theories in one higher dimension. While successful in many regards, the usual holographic approach for metals fails to incorporate a satisfactory description of a Fermi surface, an indisputably important ingredient for any theory describing a metal. Specifically, any theory of a metal ought to have a Fermi surface that satisfies Luttinger’s theorem. In this thesis, we introduce a holographic model that exhibits the necessary behaviour of metal. Diverging from the typical holographic treatment where all scales are described, we instead assume the dual theory to be an infrared effective field theory. We explore the behaviour of the theory across various temperatures by numerically solving the differential equations of motion for the gravity theory. Motivated by the numerical predictions, we suggest a UV cutoff scale for the theory. We discuss some potential limitations and plausible modifications of the model.
- PostSparsely Annotated Semantic Segmen tation of Weather-Related Road Surface Conditions(2024) Rumar Karlquist, Johan; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Mirkhalaf, Mohsen; Andersson, PontusThis project aims to build upon a machine learning pipeline for semantic segmentation of road conditions, focusing on classifying weather-affected surfaces such as dry, wet, slush, snow, and ice. Accurate detection of road surface conditions is crucial for autonomous driving systems and advanced driver-assistance systems as it directly influences vehicle control strategies, safety measures, and overall driving experience. Unlike most research in semantic segmentation, which relies heavily on densely annotated datasets that require significant manual labor to generate, this project utilises sparsely annotated data. These sparse labels, though containing less information, substantially reduce the need for manual annotation. Additionally, the provided data uses soft labels, representing a probability distribution over class conditions, which differs from the commonly used hard labels representing a single class. Data collection involves vehicles equipped with a front-facing camera recording the road and two laser detectors that gathers information about the road surface conditions. Two pre-processing approaches were explored: one crops the original input image, and the other performs an image transformation to simulate a bird’s-eye view of the road. Multiple new machine learning models were implemented, but it was observed that the choice of model did not significantly affect performance, indicating possible limitations in the provided data. Consequently, various approaches for augmenting data and methods to extract further information from unlabeled pixels were explored, some of which marginally enhanced performance. The pipeline’s performance was evaluated using conventional metrics such as accuracy and mean intersection over union, as well as through visualisation of the resulting semantic segmentation.
- PostMaterial exploration through active learning(2024) Hildingsson, Joel; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Hellman, Anders; Moberg, Henrik
- PostInteger Linear Programming Applied to Production Planning(2024) BUSKE, JESPER; JENDLE, THEODOR; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Gustafsson, Kristian; Johannesson, GustavThe Job-Shop Scheduling Problem (JSSP) is a classic optimization problem that has been a focal point in the field of operational research for decades. As industries advance into Industry 4.0, optimizing production planning becomes increasingly cru cial to enhance efficiency and competitiveness. This thesis explores the application of Integer Linear Programming (ILP) to an extended version of the JSSP, intro ducing new constraints and utilizing a heuristic when solving the problem. In this work, we present our mathematical formulation for the extended job-shop schedul ing problem. Our approach embeds additional constraints and variables that reflect real-world production scenarios more accurately than traditional JSSP models. The performance of our formulation is evaluated by comparing our results against two benchmarks, where the first benchmark compares the results to a scheduler solely based on heuristics, and the other compares the result to a lower bound of the optimal solution. These comparisons provide insight into the performance of our proposed model. Furthermore, we discuss difficulties associated with solving this NP problem. Expressing the complications of computational complexity and its ef fects on our extension. This research not only advances the theoretical understand ing and exploration of different useful techniques regarding job-shop scheduling but also provides practical tools and insights for optimizing production planning in the era of Industry 4.0.
- PostStudying Interactions of Complex Lipid Vesicles with Cell Membrane Mimics(2024) Kosta, Eleftheria; Chalmers tekniska högskola / Institutionen för fysik; Chalmers University of Technology / Department of Physics; Höök, Fredrik; Holme, Margaret