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A Framework for Virtual Knowledge Graph Construction over Time Series Data
No full textAnalyses of products during and after development are essential to improve and guarantee their quality. In the automotive industry, analyses are often based on time series data coming from numerous heterogeneous sensors equipped by vehicles. The analysis of time series and sensor data is challenging due to the high volume and high variety of the data, often leading to unstructured storage solutions. In this work, we show how these challenges can be addressed relying on data preprocessing and a Virtual Knowledge Graph (VKG) approach. The data preprocessing is used to create tables from the time series data that integrate efficiently with the VKG, and the VKG itself introduces semantics, virtualization, and a graph-representation of the data. To experiment with the setup, we preprocessed the data, developed an ontology for the virtual knowledge graph, and developed a set of mappings that connect the graph to the preprocessed data. The system is evaluated using a set of analysis questions derived from a real business use case that we have encoded as SPARQL queries. The results obtained are promising, showing that a virtual knowledge graph is a viable alternative to current analytical approaches for time series data
From Logs to Insights: Exploring User Behavior in RobotStudio
No full textUnderstanding how users interact with software is essential—not only for designing intuitive interfaces but also for building meaningful, behavior-driven test scenarios. In this study, we explore user behavior in ABB RobotStudio by analyzing a large dataset of backend log files, each recording detailed event traces from real usage sessions.
To approach this problem from multiple angles, we employ three methods. First, we apply N-gram analysis to examine what users do, what events tend to occur together, and in what order, providing us with a window into common behavioral patterns. Second, we construct first-order Markov chains to model the likelihood of transitioning from one type of event to another, capturing dynamics user actions. Third, we use clustering and N-gram to investigate whether different types of event sequences naturally emerge. This helps us uncover whether there are distinct features and recurring patterns of usage across clusters.
Together, these three methods reveal both structural and sequential aspects of how users interact with RobotStudio. We find that certain behaviors repeat consistently across users and sessions, while others are more context-dependent. These insights offer practical value: they can support user-centered UI improvements and help generate test cases that more closely mirror real-world workflows
Learning sufficiency through play: A participatory playground transformation with children in Gothenburg
No full textEnvironmental crises have become a defining
challenge of our time. While technological
efficiency is often the first solution that comes
to mind, sufficiency, the practice of consciously
reducing resource use, is crucial for achieving
true sustainability. Addressing sufficient behavior
is essential for a long-term sustainable future.
Moreover, cultivating this mindset among future
generations, particularly children, can have a
lasting impact. Experiential learning, which
emphasizes direct interaction, experimentation, and
reflection, provides an effective way for children
to internalize sufficiency as a lived practice rather
than abstract knowledge. Play, as a powerful
learning tool for children, makes it a meaningful
approach to engage them in understanding
sufficiency.Thus, This thesis explores how the
transformation of playgrounds can help children
learn sufficiency through experiential learning.
The research investigates how playgrounds
can go beyond recreation to become tools for
teaching sufficiency principles. Through different
play experiences, children can face resource
constraints and learn to make thoughtful decisions
about energy use and material consumption. The
focus is on engaging children with the concept
of sufficiency, emphasizing learning through
experience rather than formal instruction.
To understand how sufficiency is currently
incorporated into public initiatives in Gothenburg,
an interview with the municipality was conducted.
The findings highlighted the importance of focusing
on Gothenburg due to Sweden’s high consumption
rates. While there are plans addressing the technical
aspects of sufficiency, the social dimensions remain
underexplored, making this research both timely and
significant.
Literature studies on sufficiency principles,
experiential learning, and playground design formed
the thesis’s theoretical foundation.
Reference project analysis also contributed to
the data collection. The findings demonstrate
that play-based public spaces can effectively
introduce children to sufficiency principles
through low-tech, interactive features that require
physical engagement and decision-making. The
thesis argues that embedding sufficiency into
playgrounds can complement formal education
by offering hands-on learning environments.
An initial round of participatory workshops with
children was conducted to gather insights into how
children of different ages perceive sufficiency. Collage
techniques and storyboards were used to communicate
with students in the 1st, 3rd, and 5th grades.
To better understand how children engage with
resources and learning, interviews with teachers
were conducted. These confirmed that children
engage more deeply when learning is hands-on,
interactive, and rooted in real-life experiences.
A second workshop was organized to co-design
the playground with the children, allowing
them to take an active role in shaping their play
environment. The goal was to explore how
sufficiency principles could be integrated into
play spaces from the children's perspectives.
Finally, this thesis develops design strategies for
transforming playgrounds through a participatory
design process grounded in sufficiency principles.
It emphasizes how involving children in the
creation of their own play environment becomes
a form of experiential learning. Rather than
offering a fixed design, the outcome is a flexible,
replicable model that allows communities to
adapt and co-create based on local materials and
needs. Ultimately, this project contributes to both
sustainability discourse and participatory design
by showing how sufficiency can be made tangible,
engaging, and transformative through play
Uncovering Hidden Links with Malicious Non-Interference
No full textEvery day, billions of searches are made on search engines such as Google. The results shown are ranked using proprietary algorithms by the search engine providers. Some of these algorithms consider the number of backlinks, links from other websites, as an indicator of popularity and relevance. This ranking mechanism is widely known and, in some cases, exploited by attackers who inject links onto other websites to improve their own search engine rankings. While normal backlinks are visible to users and search engines alike, some attackers use hidden links, links that are not meant to be seen by users but are still indexed by search engines. In an attempt to artificially boost rankings in a search engine. In this thesis, we focus on links that are invisible to users but visible to search engines. We call these "hidden links".
Because of the malicious behavior of some websites, methods for detecting these hidden links have been developed previously. The key concept for this thesis is non-interference, a semantic condition that defines when a system is well-behaved, such as ensuring that a system does not leak secrets or preserve the integrity of certain data. In our thesis, the concept of non-interference is used in reverse, and is therefore referred to as malicious non-interference. The fundamental idea of our approach for detecting hidden links is to make changes to the website’s code and make a visual inspection to see if the changes are visible.
The tool developed by applying malicious non-interference in this thesis is called Malicious Non-interference Scanner (MANIS), and it shows promising results, as it is capable of detecting hiding methods that other scanning tools, such as Sucuri SiteCheck, are unable to detect. MANIS shows promising results when tested on two different datasets: one randomly sampled from the latest Tranco domain ranking list, and a dataset collected by us during the development of MANIS with websites that we suspect to contain hidden links. From these datasets, MANIS is capable of detecting hidden links with accuracies of 86% and 97% respectively. With the majority of the false positives coming from an inability to interact with the website
Moriska Paviljongen - Concept model
No full textMoriska Paviljongen, located in Malmö, Sweden, was originally built as a palace of entertainment for the peoples movement. It has over the years been extended multiple times but the function remains similar, hosting night clubs and concerts. The building has a neo-moorish design, popular in europe at the time, and features horseshoe arches and onion domes
Sorting Simulations - Preparing fission experiments through simulations
No full textThis master thesis focuses on development and tests of the analysis software to include
newly added detectors before the start of a novel fission experiment to study
Ac 230 scheduled for July 2025 at the ISOLDE facility at CERN. By converting
simulated data to mimic the actual experimental data format it is possible to test
the analysis software in advance. Comparing data output from the software with
the same data before the conversion allows to spot bugs in the software. Thus the
software can be prepared and tested before the experiment, as it removes the need
to use measured data for development.
The experiment will study fission of Ac 230 after (d,p) reactions of Ac 229 at
8MeV/u. While the Si array for detecting emitted protons has been used in
many experiments, handling of three new detectors must be prepared. Two Si
CD detectors for fission fragments, four position sensitive Si strip detectors for
luminosity monitoring as well as 36 CeBr3 crystals for γ rays.
The work in this thesis allow the analysis software to be used as a diagnostic
tool for these new detectors already during the setup phase prior to the experiment
Toward an automatic parallelized two-qubit-gate calibration
No full textCalibrating a large quantum processor is a challenging task as it requires optimizing
many parameters for each gate. As the number of gates increases, the amount of human intervention must be reduced to a minimum and an automatic calibration must
be developed to carry out all these complex calibrations. The quantum processor
developed by the quantum computing group at Chalmers is already complex enough
to require such automation, as it contains 25 qubits. The group has developed an
application, called Tergite automatic calibration, that can perform single-qubit calibration but is not yet capable of calibrating two-qubit gates. This Master’s thesis
focuses on this crucial missing functionality; in this work, a fully automatic calibration procedure is presented that can calibrate two-qubit gates for the 25-qubit chip.
Two key aspects have been considered in this work; the possibility of reliably finding
operational points that can be used for the calibrations of the desired two-qubit gate
and the possibility do this for multiple couplers in parallel. Robustness measurements are presented to show that the autocalibration procedure can reliably find the
calibration parameters in different conditions. Crucially, the algorithm developed
is not only capable of calibrating multiple two-qubit gates, but it can do so in a
relatively fast enough time to allow reliable operations on the quantum processor
Predictive Modelling of Electrical Loads for Grid Infrastructure Planning - Segmentation and Stochastic Simulation of Residential and Commercial Energy Demand with Machine Learning
No full textAbstract
The transition towards a more sustainable energy system has increased the need for more precise and interpretable models for predicting future electricity demand. This thesis proposes a machine-learning based framework for predictive modelling of electrical loads, with the goal of enhancing grid infrastructure planning, particularly at the local level. The framework encompasses three components: (1) a load decomposition model that separates historical load into base load, temperaturedependent load and a solar generation component using only basic information such as temperature and solar radiation, (2) a building-informed segmentation model that estimates the coefficients for these load categories based on aggregated local building data, and (3) a residual process model that is used to model the stochastic variations that are not accounted for in the previous models. The models implement statistical
methods, including machine learning with neural networks, modelling of stochastic processes and Monte Carlo simulations. The proposed framework can be used to provide several insights, such as peak demand, load duration curves and how these are affected by changing climate patterns or construction of new buildings. In addition to estimating extreme values,
the framework can be used to model daily demand patterns, valuable when evaluating adoption of sustainable energy sources which are often less controllable. By explicitly linking electricity usage to observable variables such as temperature and solar radiation, the models allow energy and utility companies to make data-driven decisions for grid planning without sacrificing interpretability or operational transparency
Design and characterisation of terahertz planar hybrids
No full textPower splitting and combining are essential functions in electronic and optical instruments. These functions are typically implemented using a directional coupler, which is a passive component found in various forms across the electromagnetic spectrum. These couplers are used to combine power sources, distribute signals for balanced and sideband-separating mixers, or redirect portions of signals for monitoring. At terahertz frequencies, the multi-section branch guide coupler, implemented in E-plane split block waveguide technology, is the most common version. However, the fabrication of these couplers is constrained by tight tolerances and high aspect ratio features, making them scale poorly at higher frequencies. There is a need to revisit this simple component and explore alternative implementations for integration with semiconductor devices that are rapidly being developed at terahertz frequencies. In this work, three different planar hybrids operating in the 750 GHz to 1100 GHz frequency range are presented. Specifically, the branch-guide, broadside coupled line, and Lange couplers are evaluated through electromagnetic simulations. The 3-dB branch-guide coupler is fabricated and demonstrated on a 3-μm-thin silicon substrate using micro-fabrication and SOI technology. Gold beam leads are used to mechanically support the planar circuits in an E-plane split waveguide block. The branch-guide coupler, after de-embedding from the 18 mm long access waveguides, showed a measured bandwidth of 17% over which the isolation was better than 15 dB, with over 40 dB of isolation at the centre frequency of 934 GHz. Over this band, the through and coupled signals remained over -4 dB, with amplitude balance better than 0.25 dB. The measurements agree well with simulations. The broadside coupled line and Lange coupler designs, from simulations, promise a flatter amplitude response across the band and improved phase balance. These results demonstrate the promising potential of planar couplers and hybrids at terahertz frequencies, for their use in more advanced receiver and transmitter circuits