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Exploring and testing of orthopedic exoskeletons
No full textExoskeletons are a growing technology in both industry and healthcare. With the increasing
number of cases of musculoskeletal disorders, exoskeletons can be a potential solution for the
rehabilitation of people with such disorders.
The department of electrical engineering at Chalmers University of Technology, working with
Sahlgrenska University Hospital, wants to develop the understanding and technology of
exoskeletons. A previous student group of Chalmers developed an elbow exoskeleton for this
task, which used the pre-contracted exoskeleton EduExo lite 2 from AUXIVO.
The goal of this project is to improve the movement of the previous student group’s elbow
exoskeleton, as its movement system was too unnatural and static. This was done by improving
and developing a more dynamic movement system for the elbow exoskeleton, to further
develop understanding and research of orthopedic exoskeletons for rehabilitation, without
introducing new hardware components or replacing the existing hardware components provided
by the group.
Developing the movement system required first testing the learning the response time between
the microcontroller and feedback servomotor, as well as the rotation speed and resolution of
the feedback servomotor. Spline and polynomial functions were used to better simulate the
natural movement of a human arm, as it gives motion a gradual increase and decrease in
movement velocity.
The resulting code made it a dynamic movement control system, where movement from any
start and end point, results in a smoother and softer motion, where the duration of the movement
can also be altered.
The result of this project provides good grounds for potential further development, which can
lead to a dynamic modular movement system to provide ways easily adapted to a patient’s
specific needs
Elektrifiering av regionala lastbilstransporter Drivkrafter, hinder och möjligheter
No full textKlimatförändringar beskrivs ofta som en av vår tids största globala utmaningar. För att skapa
en grönare framtid behöver vi anpassa och förändra de industrier som skadar vår planet. För
att bekämpa klimatförändringarna har Europeiska unionen introducerat paketet ”Fit for 55”,
med målet att minska nettoutsläppen med minst 55% till år 2030. Transportsektorn är en av de
största bovarna bakom den globala uppvärmningen och står för en stor andel av
växthusgasutsläppen. Detta innebär att sättet vi transporterar varor över världen på måste
förändras på flera sätt. Genom intressentanalys och kvalitativa intervjuer syftar denna studie
till att ge en bild av hur olika intressenter är beroende av varandra. Studien fokuserar på
problemen, för- och nackdelarna med elektrifiering av regionala tunga lastbilstransporter.
Höga investeringskostnader, brist på laddinfrastruktur och osäkerhet kring framtiden leder till
en ovilja att investera i ellastbilar. Resultaten visar att även om intresset för elektrifiering av
lastbilar är stort och tekniken har utvecklats långt, saknas i dagsläget incitament för de
enskilda aktörerna att investera
Updating Paulings rules using a machine learning approach
No full textOxides are an important family of materials that have an extremely wide range of
applications in for example semiconductors, pigments and catalysis. It is therefore
important to have a solid understanding of these ubiquitous materials. In 1929
Linus Pauling proposed five rules for oxide stability that are widely used. These
rules are however not good enough to describe oxide stability as only a fraction of
stable oxides fulfil them. In this project a machine learning approach was used to
attempt to find better rules based on the composition of oxides. This was done
by training a set of autoencoders and analysing the latent spaces of these models
by sampling new compositions from the models. Three different autoencoders were
trained and based on the results, three new rules of thumb are proposed; Oxides
containing only reactive non-metals are in general unstable, metals favour stability
and heavier cations favour stability
Thermal Runaway Propagation and Fire Safety Modeling of Large-Scale Marine Battery Rooms
No full textThe transition to fully electric ships is an essential step toward reducing greenhouse
gas emissions in the maritime industry. This change requires significant modifications
to the operations and construction of ships. This thesis makes a contribution
to the study of the thermal runaway (TR) behavior of lithium iron phosphate
(LiFePO2 or LFP) battery systems. The objective is to gain an understanding of how
fire, heat, and gas spread throughout a marine battery room during a TR event.
To investigate the process by which a fire spreads from a single cell to an entire
battery room, a thermal runaway simulation model was developed in Spreadsheet.
The model is based on experimental heat release rate (HRR) and total heat release
(THR) data from recent fire tests. It employs a hierarchical structure that extends
from the cell to the module to the rack to the room. In addition, it incorporates
flame propagation that is based on the direction of the flame, variable state-ofcharge
levels ranging from 0% to 100%, and battery room sizes. For each scenario,
the tool computes the peak HRR, the total energy release, combustion duration,
and the structural heat load on the steel in the room.
The analysis show that state of charge (SOC) is the strongest lever for safety: 100%
SOC gives the fastest roof failure, 50% SOC extends survival by about 30-50%, and
at 0% SOC many layouts do not reach roof melting at all. Rack spacing controls
room density and creates a practical trade-off more spacing lowers heat concentration
and increases melt time, but also increases steel mass and ship space. A moderate
spacing band (≈0.5-0.9 m) offers the best balance for design, especially for
15-25 MWh rooms. The seawater flooding analysis further showed that early flooding
can completely prevent roof failure, while late flooding has limited effect and
may even increase explosion risks. The tool therefore gives designers a simple, validated
way to compare layouts, operating modes and to set safe design limits for
marine battery rooms. In doing so, it directly supports compliance with DNV’s 5
MWh threshold rule for single spaces, while offering quantified methods to justify
larger 10-25 MWh installations where compensatory measures are required
Analyzing Order-to-Cash Using Process Mining A Case Study in Collaboration with Paulig
No full textOrganizations increasingly rely on digital data to understand and improve their
business processes. Process Mining is a data-driven approach that uses event logs
from information systems to visualize actual process behavior and identify inefficiencies. This thesis investigates how Process Mining can be applied in practice to analyze the Order-to-Cash process, with a particular focus on the use of pre-defined
reference process models and backward-looking analytical techniques.
The study is conducted as a case study in collaboration with Paulig, using Infor’s
Process Mining solution integrated with the ERP system M3. Through a combination of Process Mining analysis, interviews, workshops and shadowing sessions, the thesis evaluates how well a pre-defined industry-specific process model reflects an
organization’s actual Order-to-Cash process and how inefficiencies and bottlenecks
can be identified. The reference process model proved to be a strong baseline for
understanding the overall process structure, while the analysis revealed bottlenecks
related to master data issues that cause unnecessary manual interventions and longer
cycle times.
The results demonstrate that Process Mining can support improvements in both
administrative processes and physical logistics flows by revealing systematic issues
that are difficult to detect through traditional qualitative methods alone. The study
also highlights the importance of combining Process Mining insights with domain
knowledge and stakeholder involvement to correctly interpret results
Self-Supervised Fixed-Scene Adaptation for Object-Detection in Real-Time Surveillance: A Comparative Study of YOLO11 and RF-DETR
No full textThis thesis investigates self-supervised fixed-scene adaptation for real-time objectdetectors
in an edge-computing surveillance context. While modern object-detectors
achieve strong results on general-purpose benchmarks, deployment in static camera
scenes introduces distinct challenges: domain shift to a specific viewpoint, limited
availability of scene-specific labels, and stringent compute and memory budgets ondevice.
At the same time, the stationary background of surveillance footage provides
exploitable structures, as do their temporal dependencies of video-frames. This study
conducts a comparative analysis of two state-of-the-art object detection architectures:
the Transformer-dominant RF-DETR and the convolutional neural network (CNN)-
dominant YOLO11. The thesis employs the 100Scenes dataset to represent a broad
range of surveillance environments. Experimental results demonstrate that RF-DETR
consistently achieves higher accuracy, smoother convergence, and greater robustness
than YOLO11, albeit with higher hardware demands. In contrast, YOLO11 variants
(with a frozen backbone) leverage the larger trainable capacity of the neck and head
to enable high scene-specific adaptability. While this yields significant gains under
quality labeling, it tends to increase sensitivity to imperfect pseudo-labels and the
risk of overfitting. Furthermore, by systematically varying model scales, adaptation
strategies and environmental conditions the experimental design yields more than 3400
distinct runs. First the work examines the extent to which smaller, specialized models
can match the approach of substantially larger models. The experimental results show
that a small specialised model can compete with larger general models. Secondly, the
study evaluated a proposed on-device self-supervised labeling strategy that integrates
SAHI with a bidirectional implementation of ByteTrack. The proposed self-supervised
labeling strategy provided reliable performance gains across all architectures and
configurations, by recovering hard negatives, more specifically small, occluded and
low confidence instances. Thirdly, the study investigated background-context fusion
(BF). It proved to be consistently improving the performance in general for RFDETR,
while it proved inconsistent for YOLO11 and failed to increase robustness
against seasonality, suggesting it induced background-dependent overfitting. Finally,
the study shows that all models being trained on a summer scene exhibit a decrease
in relative performance compared with the non-adapted models during a seasonal
domain shift to a winter scene
A WGAN Based Method for Stochastic Filtering
No full textThe problem of extracting information about a state from incomplete noisy measurement
is knowns as a ”filtration problem” in the field of stochastic processes.
In this thesis the information extracted corresponds to an estimate of the posterior
conditional distribution of a stochastic process. Recent development in generative
adversarial networks allows for such filtering problems to be solved using a network
class called the WGAN. In this thesis a method of implementing the WGAN for
filtration is investigated. The method is tested for a linear SDE scaled in dimensions
and on a non-linear SDE of singular dimension. both examples were observed
linearly with additive Gaussian noise.
The method was benchmarked against filtering methods not based on machine learning.
In summary it can be stated the the results indicated that some merit to the
method could be deducted. It was however the result that the method was outperformed
by most of its peers. An investigation into where the method could be
improved was conducted
Designing a Collaboration and Communication Process for a Garment Renewal Service
No full textThe textile industry has a significant environmental impact and is one of the largest contributors
to land and water use, greenhouse gas emissions, and raw material consumption. Transitioning
the industry toward a circular economy is therefore essential, and one way to support this shift
is by extending the lifespan of garments through repair and refresh. RecoMended is a company
that provides industrial scale garment renewal services for other companies, enabling the resale
or continued use of existing products. However, as industrial scale garment renewal services
are new to the market, established collaboration and communication processes for developing
service specifications are limited. As a result, the collaboration and communication process
currently used by RecoMended is time-consuming, resource-intensive, and highly customerspecific,
making it difficult to scale.
This study aims to investigate how the collaboration and communication process between
RecoMended and its customers can be improved to support more efficient development of
service specifications for garment renewal and allow RecoMended to scale its production. The
research was conducted through interviews, workshops, and contextual inquiries with
RecoMended and several of its customer companies. The collected data were analyzed and
synthesized, resulting in the design of a proposed future collaboration and communication
process together with a service toolkit.
The proposed design outlines a structured, step-based collaboration and communication process
that is intended to support RecoMended and its customers in developing service specifications
in a more informed and efficient way. The process is envisioned to be supported by a service
toolkit consisting of standardized service packages intended to enable scalability in the
production workshop, a reference library with visual and material examples of available
renewal procedures, and a set of guiding questions designed to ensure that key decisions are
addressed. While further development and testing would be required, the proposed process and
service toolkit are intended to provide a foundation for clearer communication, more informed
decision-making, and improved collaboration, thereby supporting RecoMended’s ability to
scale its garment renewal services