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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.

WExUpp - kommande framläggningar
2020-02-27 - HCS
Performance Evaluation of JavaScript Rendering Frameworks
Adam Lindberg
Avancerad (30hp)
kl 15:15, Alan Turing (På svenska)
[Abstract]
When developing interactive web applications a number of different technologies and frameworks could be used. This thesis is set to evaluate a number of popular frameworks that are using different native web rendering techniques. More specifically, the goal of this study is to find what JavaScript visualization framework is best suited for developing a visualization module capable of handling up to 1000 continuously moving nodes with retained frame rate. In this case, retained frame rate refers to keeping the average frame rate above 20 FPS. The frameworks investigated in this study are D3.js using SVG and Canvas, and PixiJS using WebGL 2D rendering. The evaluation was conducted by first developing a visualization module containing a force-directed graph. This was done three times over, once with each rendering technique. Next, the average frame rate was measured during the first 10 seconds of loading a fixed size data set. Data sets of increasing volume were then loaded to examine how the different modules handle data sets of various sizes. The results showed that the SVG module was far behind the other two in terms of retained FPS on larger data sets. The Canvas and WebGL modules were closer in the level of performance, where WebGL outperformed the Canvas implementation in the base case. However, when a Gaussian blur filter was activated in both modules, the Canvas module prevailed. This blur filter was a requested feature for the final product, which led to the choice of using D3.js with Canvas rendering for further development.
2020-02-28 - SaS
Product Line Engineering for large-scale simulators: An exploratory case study
Felix Härnström
Avancerad (30hp)
kl 10:15, John Von Neumann (In English)
[Abstract]
This thesis takes a process-centric approach to Product Line Engineering (PLE) with
the purpose of evaluating the suitability of PLE practices and processes in the context of
large-scale industrial simulator products. This human-centered approach sets itself apart
from previous research on the subject which has been mostly focused on architectural
and technical aspects of PLE. The study took place at Saab, a Swedish aerospace and
defense company whose primary product is the Saab 39 Gripen fighter aircraft. The
study was conducted as a series of interviews with participants across three product lines,
each responsible for a different line of simulators. By investigating their current working
processes using the Family Evaluation Framework a maturity rating was derived for each
product line. This maturity rating was then considered alongside commonly reported
issues and experiences in order to evaluate the usefulness of PLE practices for each product
line. It was found that the studied organization could likely benefit from implementing
PLE. PLE and the Family Evaluation Framework promotes practices that would alleviate
some of the major issues found in the studied organization such as unclear requirements,
issues with product integration and external dependencies, and a lack of quantitative
data. Due to the relative immaturity of PLE processes in the studied organization, these
conclusions are based on a review of existing literature and the stated goals and practices
of PLE applied to the context of the studied organization.
2020-02-28 - SaS
Underhållbara webbapplikationer med React - En evaluering av arkitekturmönster utförd på lärplattformen Canvas
David Johansson
Avancerad (30hp)
kl 13:15, Alan Turing (På svenska)
[Abstract]
Maintainability for web applications is increasingly important due to increasing demands
for advanced functionality as well as a short time-to-market. Fixing errors, reusing
functionality and adding new features efficiently is crucial for making the application profitable
for the software organization as well as valuable for the end-user. Modern frameworks
and libraries as React assists web engineers in building sophisticated applications
using high-quality solutions called architectural patterns. In this thesis architectural patterns
has been evaluated by performing static code analysis using well-established metrics.
The evaluation was conducted using a Design Science Research approach on the Learning
Management System Canvas. The results showed large variations in maintainability depending
on the architectural pattern used.
2020-02-28 - SaS
Machine Learning for Predictive Maintenance on Wind Turbines (using SCADA Data and the Apache Hadoop Ecosystem)
John Eriksson
Avancerad (30hp)
kl 15:15, Allan Newell (In English)
[Abstract]
This thesis explores how to implement a predictive maintenance system for wind turbines in Apache Spark using SCADA data. How to balance and scale the data set is evaluated, together with the effects of applying the algorithms available in Spark mllib to the given problem. These algorithms include Multilayer Perceptron (MLP), Linear Regression (LR), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM) and Gradient Boosted Tree (GBT). This thesis also evaluates the effects of applying stacking and bagging algorithms in an attempt to decrease the variance and improve the metrics of the model. It is found that the MLP produces the most promising model for predicting failures on the given data set and that stacking multiple MLP models is a good way of producing a model with a lower variance than the individual base models. In addition to this, a function that creates a savings estimation is developed. Using this function, a time window function that explores the decisiveness of a model is created. The conclusion is made that a model is more decisive if the failure it predicts occurs in a turbine where it has been trained on failure data from that same component, indicating that there are unknown variables that affect the sensor data.

Page responsible: Ola Leifler
Last updated: 2017-04-27