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Senast inlagda
Data-driven Development Framework for ADAS and Automation for Marine Applications
(2025) Ramesh, Adithyaa; Pollachi Malaiyalaswamy, Prasanth Balaji
The maritime industry is increasingly adopting Advanced Driver Assistance Systems
(ADAS) and automation, drawing inspiration from progress in the automotive
sector. Applying these technologies to marine environments, however, introduces
unique challenges such as sensor limitations, unpredictable conditions, and the lack
of standardized validation methods.
This thesis presents a data-driven framework to support the testing and validation of
marine ADAS/AD (Autonomous Driving) systems. The framework focuses on data
logging, structured data handling, and the use of Key Performance Indicators (KPIs)
as objective measures of performance. In collaboration with Volvo Penta and Volvo
GTT (Group Trucks Technology), a proof of concept was developed around two
representative features, with relevant KPIs defined. The framework was partially
implemented on a test vessel equipped with LiDAR (Light Detection and Ranging)
and cameras for perception, a Dynamic Positioning System (DPS) for positioning,
and a high-bandwidth logger to capture raw sensor data during real operations.
While post-test data ingestion, KPI calculation, and KPI-driven refinement were
not completed within the scope of this thesis, these stages are outlined as future
extensions. The work provides a foundation for a systematic, data-driven development
methodology in the marine ADAS/AD domain, bridging the gap between
conceptual design and a fully operational validation pipeline.
Model Model-Based Construction Understanding and implementation challenges - A Case study
(2025) Ahmad, Meqdad; Nasr, Khalid
This study explores the challenges experienced in the implementation of Model Based Construction (MBC) and examines how different stakeholders understand
and approach the concept. The empirical work draws on a literature review and
a case study of Kaj 16, an ongoing project in Sweden that is implementing model based construction. Using a qualitative approach with semi-structured
interviews and document analysis, the research investigates how stakeholders
interpret MBC, the organizational and technical barriers they face, and the
strategies developed to overcome them. The findings show that implementing
MBC represents a significant shift in the construction sector, as it extends the use
of BIM from design into site practices. The study highlights differences in how
MBC is understood and reveals that full implementation has not yet been
achieved due to multiple barriers spanning organizational, technical, legal, and
financial domains. Addressing these challenges—supported by early
standardization and strong client leadership—is essential to enable successful
and complete adoption of MBC.
Dynamic Control of HVAC Attributes. Improving Energy Efficiency of HVAC System Using Machine Learning and Computational Fluid Dynamics
(2025) Subramani Venkatachalam, Raj Gopalakrishna
Heating, Ventilation and Air-Conditioning (HVAC) accounts for a major share of building energy use. This thesis develops a data-driven HVAC control framework that couples high-fidelity Computational Fluid Dynamics (CFD) with machine learning. Three-dimensional CFD simulation is performed to model the flow field in the room using Star-CCM+. 973 steady state CFD simulations representing realistic boundary conditions were performed to form a comprehensive dataset of temperature and velocity fields. Fourier Neural Operator (FNO) was trained as a surrogate model to CFD. This model reproduces the temperature and velocity flow
fields with adequate resolution, with over 90% of predicted temperature and velocity fields across unseen samples deviating within a 5% error, making it suitable for closed-loop use. The error distribution analysis shows that the median temperature error is at 0.0169 °C and median velocity error is 0.0034 m/s, indicating that the surrogate model is reliable to predict temperature and velocity fields.
The surrogate model coupled with a Soft Actor-Critic (SAC) controller, which was designed to regulate inlet air temperature, inlet mass-flow rate, and radiator surface temperature to maximize the reward, that provides an optimal control solution to reduce the energy cost.
The controller was evaluated for a year of weather data with a 20-minute control step and benchmarked against a PID controller. Results show that the SAC consumed 4,836 kWh compared to 7,460 kWh for PID, which corresponds to approximately 35% in energy cost reduction. SAC occasionally produces larger deviations than PID, leading to a higher median temperature error (0.487 °C vs 0.273 °C), but fluctuations beyond ±2.5 °C occurred only 2.3% of the time, indicating that comfort violations remained rare. Seasonal analysis shows SAC controller’s energy savings persist across the year and strengthen in the late-year window (36% vs 34% earlier), reflecting adaptive use of outdoor conditions and smoother control.
Overall, the work demonstrates that combining CFD data trained surrogate model with entropy-regularized reinforcement learning can deliver substantial energy savings with acceptable comfort tracking, and provides a practical route to incorporate detailed physics (e.g. radiative gains, occupancy, humidity/CO2) and more advanced control designs in future studies.
Route Planning for Mobile Care Teams using Digital Twin Technology
(2025) Andersson, Wilma; Johansson, Alexandra; Thedin Olsson, Tove; You, Sophie
The ageing global population that we are facing is significantly increasing the strain on health-
care systems worldwide. Mobile care teams, a key component of the Hospitals at Home initiative
in Sweden, Västra Götalandsregionen, offer a promising solution by delivering hospital-level care
directly in patients’ homes. This report explores the potential of integrating Digital Twin (DT)
technology to optimize the routing and coordination of these teams. By creating a real-time
virtual replica of the mobile care workflow, DT technology can dynamically adjust routes based
on patient status, team locations, and potential emergency calls, improving resource efficiency
and healthcare in general. The proposed solution utilizes existing healthcare data systems,
algorithms like TOA*, and real-time data integration to enhance care delivery. The solution
also addresses challenges like scalability and workflow efficiency of today’s operations of the
mobile care team unit.
Sahlgrenska at Home: Improving Communication and Collaboration for Effective Program Development
(2025) Al-Bazi, Sogeta; Hübner, Klara Johanna; Nielsen, Cornelia; Sterne, Sara
This study investigates how various areas at Sahlgrenska University Hospital collaborate and
communicate in relation to the Sahlgrenska at Home model. The aim is to provide an overview
of how these factors operate both within individual departments and across departments. Addi-
tionally, the study evaluates how Sahlgrenska can learn from the practices of other international
hospitals that offer similar forms of care. Through interviews with people from the different areas
of Sahlgrenska, the main problems related to collaboration and communication around the service
were identified. Furthermore, inspiration and guidelines could be obtained through interviews
with external people from the Northern Ireland Hospital and Medtronic. The findings and rec-
ommendations that address the problems focus mainly on strategy, structure, processes, rewards,
and people. In order to achieve improved cooperation between different departments, motivation,
trust, and commitment are required from the employees. In addition, a clear structure is needed
where roles and responsibilities are defined. Improved communication can possibly be achieved
through a centralized communication platform. But also through regular meetings, where ongoing
feedback between teams and employees will help continuously refine the service. The study also
provides guidelines for continued work with Sahlgrenska at Home.