Examensarbeten och uppsatser / Final Theses

Framläggningar på IDA / Presentations at IDA

Se även framläggningar annonserade hos ISY och ITN i Norrköping / See also presentations announced at ISY and ITN in Norrköping (in Swedish)

If nothing is stated about the presentation language then the presentation is in Swedish.

• 2021-12-01 kl 13:30 i https://liu-se.zoom.us/j/62958256201?pwd=SUZvbDhzOEd0ejNFd1AvaklsV2w2QT09
  
  Körvägsplannering i storskaliga och flervåniga stadsmiljöer med tillämpningar mot autonom robotsopning
  
  Författare: Daniel Engelsons
  Opponent: Jacob Ljungberg
  Handledare: Mattias Tiger
  Examinator: Fredrik Heintz
  Nivå: Avancerad (30hp)
  
  Autonomous lawn mowers and floor cleaning robots are today easily accessible and are driving using well-studied Coverage Path Planning algorithms. They operate in one-floor environments that are small with simple geometry compared to general urban environments such as city parking garages, highway bridges or city crossings. The next step in the automation of cleaning is autonomous road sweeping of these complex urban environments. In this work, a new Coverage Path Planning approach handling this task was compared with previous well-performing algorithms. The proposed approach combine the strengths of existing algorithms and demonstrate state-of-the-art performance on three large-scale 3D environments. To improve the performance of the algorithms and remove bias from manual tuning, the parameters were automatically tuned using Bayesian Optimization. This makes the evaluation more robust and the results stronger.
  
  
• 2021-12-03 kl 08:15 i Alan Turing, IDA
  
  Undersökning om hur machine learning kan användas för att förutspå fel i en databas
  
  Författare: Jonatan Vilhelmsson
  Opponent: Oscar Falk
  Handledare: Zeinab Ganjei
  Examinator: Mikael Asplund
  Nivå: Grundnivå (16hp)
  
  I en värld som digitaliseras allt mer blir databaser mer komplexa än någonsin. För att kunna lita att information är korrekt uppdaterad i system behöver man kunna lita på underliggande integrationer. Det sker fel i många databaser och detta kan resultera i problem i framtiden då fel kan orsaka skador både för kunden och ett företags rykte. Det är viktigt att en databas är säker och att det uppstår så lite fel som möjligt. I detta examensarbete undersöks det hur machine learning kan användas för att förutspå fel i en databas för att enkelt kunna förebygga dessa. Olika typer av inlärningsalgoritmer analyseras för att finna den som passar bäst in för arbetet. Fyra olika algoritmer som ML.NETbidrar med analyseras sedan för att presentera vilken algoritm som är mest passande till problemet och som har bäst prestanda med hänsyn till noggrannhet och körtid.
  
  
• 2021-12-09 kl 10:00 i https://liu-se.zoom.us/j/68829190682?pwd=a3JkTm8yTFpST3BmdlV3M09kZ0hOdz09
  
  Finding co-workers with similar competencies through data clustering.
  
  Författare: Oskar Skoglund
  Opponent: Diba Rezaie
  Handledare: George Osipov
  Examinator: Peter Jonsson
  Nivå: Avancerad (30hp)
  
  In this thesis, data clustering techniques are applied to a competence database from the company Combitech. The goal with the clustering is to connect co-workers with similar competencies and competence areas in order to enable more skill sharing. This is accomplished by implementing and evaluating three clustering algorithms, k-modes, DBSCAN, and ROCK. The clustering algorithms are fine-tuned with the use of three internal validity indices, the Dunn, Silhouette, and Davies-Bouldin score. Finally, a form regarding the clustering of the three algorithms is sent out to the co-workers, which the clustering is based on, in order to obtain external validation by calculating the clustering accuracy. The results from the internal validity indices show that ROCK and DBSCAN creates the most separated and dense clusters. The results from the form show that ROCK is the most accurate of the three algorithms, with an accuracy of 94\%. Followed by k-modes at 58\% and DBSCAN at 40\% accuracy. However, the visualization of the clusters show that both ROCK and DBSCAN creates one very big cluster, which is not desirable. This was not the case for k-modes, where the clusters are more evenly sized while still being fairly well-separated. In general, the results show that it is possible to use data clustering techniques to connect people with similar competencies, and that the predicted clusters agree fairly well with the gold-standard data from the co-workers. However, the results are very dependent on the choice of algorithm and parametric values, and thus has to be chosen carefully.
  
  

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Last updated: 2020-06-11