Examensarbeten för masterexamen
Länka till denna samling:
Browse
Senast publicerade
Visar 1 - 5 av 419
- PostDO SWEDISH MUTUAL FUNDS DESERVE THEIR FEES?- A statistical resampling approach(2024) Brantberg, Jesper; Chalmers tekniska högskola / Institutionen för matematiska vetenskaper; Mostad, Petter
- PostPatient outcome prediction using knowledge graph representation learning(2021) Fazlinovic, Adnan; Modi, Trilokinath; Chalmers tekniska högskola / Institutionen för matematiska vetenskaper; Lundh, Torbjörn; Kjellberg, Magnus; Lundh, TorbjörnThe project focuses on using knowledge graphs in a healthcare setting, classifying patient re-admissions. Knowledge graphs are a type of heterogeneous network consisting of entities and relations. Knowledge graph embedding method aims to generate lower-dimensional latent vector representation of these entities and relations while preserving their relational properties. The data consists of patient admission details along with their underlying diagnoses, prescriptions consumed and procedures performed. To exploit the true nature of knowledge graphs, more information to the patient graph is added by combining various biomedical databases to obtain a richer set of relationships. Cleaning patient records and converting the information in more standardized form, as well as gathering information and create a knowledge graph structure in the form of triplets are conducted. The generation of latent vector representations of the entities and relations are done with various embedding methods, where the final phase is to classify patient re-admissions. The methods investigated achieves to represent entities and relations in latent vector form when evaluating the embeddings based on the proposed loss functions. However, the embeddings generated doesn’t supply enough information that can accurately predict the patient readmission status in an extended down-stream fashion. The potential problems could be either of not enough features that explains the variability, not enough rich information regarding the different data sources used, or the effect of class imbalance. A stratified test subset was created from the same excerpt of training data to quantify the results.
- PostEnhancing Rigid Body Path Planning via Anchor Frame Optimization: Anchor Frame Impact Assessment and Strategization(2024) Alvinge, Samuel; Blom, Axel; Chalmers tekniska högskola / Institutionen för matematiska vetenskaper; Strömberg, Ann-Brith; Bohlin, Robert; Åblad, EdvinIn the field of computational geometry, a classical problem is the piano movers’ problem, which involves finding a path for a rigid body, such as a lumbering piano, through a confined and challenging environment. Solving this requires path planning algorithms that precisely control and manipulate the object’s position and orientation. Most path planning algorithms depend on a so-called anchor frame, which is fixed to the rigid body. Many path planning algorithms find a path through a six-dimensional configuration space rather than the physical space, often from the perspective of the anchor frame. This study assesses the impact of the anchor frame’s pose on the performance of Industrial Path Solutions’ path planning algorithm. Strategies are proposed that optimize the anchor frame’s orientation and position for enhanced performance. Sampled measurements from the path planner are utilized by the strategies to predict the subsequent movement of the rigid body. Results indicate that appropriate alignment of the anchor frame significantly improves the navigability of the configuration space, reducing computational efforts and enhancing overall performance. However, the placement strategy remains challenging due to the local nature of the available data, and no successful strategy has been found. The findings underscore the potential of refined orientation strategies and highlight the need for further research into robust placement techniques.
- PostMultidimensional Data-Driven Modelling of Engine Test Cell Data(2021) Andersson, Helena; Chalmers tekniska högskola / Institutionen för matematiska vetenskaper; Sagitov, Serik; Andersson-Hedberg, Per; Johansson, AntonIn the journey towards a more sustainable vehicle fleet, requirements for lower emissions and improved energy efficiency in gasoline engines lead to more components being added to the internal combustion engines. This adds to the degrees of freedom when trying to model air flow in the engine using volumetric efficiency. This paper presents a way of modelling volumetric efficiency from engine test cell data provided by T-Engineering – a company that designs and develops control systems for vehicles. The model uses Gaussian process regression (GPR) for inter- and extrapolation, including noise reduction of the measurement data. Furthermore, a local interpretable model-agnostic explainer (LIME) is used to find regions of uncertainty by explaining what features contribute to increasing the variance of the GPR predictions. In addition, a neural network model is implemented in order to improve the prediction runtime, with the purpose of enabling real-time predictions in the control systems. The model(s) were found to give a more physically accurate description of volumetric efficiency than the one currently used at T-Engineering. The runtime for making predictions for 50 data points with the neural network was ~ 0.14 ms on an AMD Ryzen 7 PRO 4750U with Radeon Graphics 1.70 GHz and 32.0GB RAM. It remains to investigate what the runtime on a limited CPU in the control systems will be.
- PostThe abundance and selection of antibiotic resistance genes: A metagenomic study based on wastewater and human gut data.(2024) Holmström, Michaela; Chalmers tekniska högskola / Institutionen för matematiska vetenskaper; Kristiansson, Erik; Johnning, Anna; Lund, DavidAntibiotic resistance is a global issue, with many consequences for the individual person and society as a whole. Studying potential new genes conferring resistance (latent ARGs) and the flow of resistance genes through environments could be crucial for the prevention and control of ARGs. This project aimed firstly to investigate how antibiotic usage affects new antibiotic resistance genes in the human gut. A metagenomic pipeline including mapping of reads and statistical models using a study of 12 healthy individuals treated with three broad-spectrum antibiotics enabled analysis of ARGs and taxonomy abundances. It was shown that after treatment with the broad-spectrum antibiotics, several latent ARGs were more abundant in the short term, implying the potential selection of these ARGs. The change in ARGs can not be ruled out as being due to the recolonization of fast-growing bacterial species, normally carrying such genes, as also the taxonomy composition was largely affected. This effect was not seen on the last sampling day, after 180 days, in which only one single ARG was found at higher abundance. The short-term effect could, nevertheless, impact the spread of ARGs due to the potential for these ARGs to spread in the disrupted microbiota. Secondly, this project aimed to investigate the flow of ARGs between the human gut and wastewater. For this, a large number of metagenomic samples were used. Similarly to the first part, a metagenomic pipeline was utilized for ARG and taxonomy analysis. Contrary to previous findings, the results implied that ARGs flow from the human gut to the wastewater, while not the other way around. Furthermore, pathogenic presence was highest for ARGs found at high prevalence in both the human gut and wastewater. Interestingly, ARGs present only in wastewater were more prevalent in pathogens compared to ARGs present only in the human gut. This implies that the spread of ARGs to pathogens could be linked to presence in wastewater environments. Moreover, the taxonomy of the human gut and wastewater differed. The results of this project can inform several future research directions, from broadening the data of longitudinal studies to a deeper dive into the flow of ARGs.