Chalmers Open Digital Repository
Välkommen till Chalmers öppna digitala arkiv!
Här hittar du:
- Studentarbeten utgivna på lärosätet, såväl kandidatarbeten som examensarbeten på grund- och masternivå
- Digitala specialsamlingar, som t ex Chalmers modellkammare
- Utvalda projektrapporter
Forskningspublikationer, rapporter och avhandlingar hittar du i research.chalmers.se
Enheter i Chalmers ODR
Välj en enhet för att se alla samlingar.
Senast inlagda
Pd-Pt nanoparticles for plasmonic sensing of hydrogen in humid environments
Colliander, William
Using hydrogen as energy storage and as fuel in fuel cells are attractive ideas that
have been investigated for decades. One of the big problems with using hydrogen
for this purpose is that hydrogen, due to its small size, can easily escape from its
containers, and only low concentrations are required for it to be explosive when
mixed with oxygen. To help detect leaks and prevent accidents, a fast and reliable
method for the detection of hydrogen gas is necessary. One of the most commonly
used materials for this purpose is Pd, due to its ability to absorb H into interstitial
sites in its lattice. The problem with Pd is that it is vulnerable to deactivation
by several different compounds, one of which being water. This thesis aims to
investigate a solution to this by incorporating Pt into the system. Several different
compositions were fabricated (Pd70Pt30, Pd85Pt15, Pd90Pt10 and Pd95Pt5), and
tested both as a bi-layered structure and alloyed together, in a humidity reactor.
All systems show potential as hydrogen sensors in humid environments. However,
how to clearly translate the complex signal output into readings of H2 concentration
remains unclear.
Automated Height Adjustment for Truck-Trailer Coupling
(2025) Chen, Hongrui; Wall, Joel
Coupling a truck to a trailer is a common but non-trivial task in logistics, typically requiring manual height adjustment to align the truck’s fifth wheel with the trailer. This thesis explores an automated approach using computer vision to estimate the relative height between the truck’s fifth wheel and the trailer, and automatically adjust the height accordingly. The inputs available for estimating this height is the camera feed from a singular backup camera mounted at the rear of the truck, and sensor data from the trucks air suspension.
The proposed system performs keypoint detection, locating corners, utilizing the YOLOv11 pose estimation model. We explore two different methods for calculating distance and then utilize this information to calculate the height difference between the trailer and the fifth wheel of the truck. The first method is based on assuming the width of the trailer and counts the number of pixels between the two bottom
corners. The second method assumes both the width and the height of the trailer and solves the perspective-n-point problem to obtain the distance.
The model was tested in real-world scenarios and achieved high keypoint precision and recall. The distance calculation remained within 10% of the ground truth most of the time, which is adequate for this application. Furthermore, the error in height estimation was within 7-15 cm of margin of error which was sufficient to enable automatic height adjustment for the truck-trailer coupling.
The results indicate that the full system is successful on most of the trailers tested. This confirms the practical feasibility of using keypoint detection to understand the geometrical relationships between the camera and the trailer it is observing, which could also be useful for other assisted driving applications. Our results indicate that more training data are needed to obtain robust height and distance estimates when
approaching a trailer from the side. The constraints our model must place on the shape of the trailer it is detecting also highlight the need for research into other methods with weaker assumptions about the shape of the trailer.
Judgement & Decision-Making in High-Risk Maritime Search and Rescue Operations
(2025) Sondell, Jessica
In maritime Search and Rescue (SAR) operations, decisions must often be made quickly, under pressure, and with incomplete information. This study investigates how experienced maritime
professionals, specifically On-Scene Coordinators (OSCs), make decisions and judgments during simulated SAR scenarios. By applying psychological theories such as dual process
theory, naturalistic decision-making (NDM), and ecological rationality, the research explores the interplay between intuition, analytical reasoning, experience, and heuristics in high-risk
environments.
Data were collected through full-mission bridge simulations, post-simulation interviews, and observation notes, and analysed using thematic coding and an abductive approach. The findings
show that intuitive strategies were not only prevalent but often highly effective. Heuristics, recognition-based judgments, and shared mental models shaped the decision-making more than
formal analysis. At the same time, analytical processes were also engaged. The study challenges the notion that intuitive decision-making is inherently flawed and emphasises the
importance of structured experience and reflective training in developing safe and adaptive expertise.
This research contributes to maritime safety by highlighting the cognitive foundations of realtime decision-making and offers practical insights for the design of training environments,
leadership development, and future revisions of operational guidelines.
Production Plan Forecasting on Limited Dataset
(2025) Kjellström, Pontus; Lundblad, Niklas
Forecasting production plans with limited data is a significant challenge, especially
for smaller firms or new products with short production histories. This
thesis aims to predict actual production outcomes based on historical data and
to understand the relationships between and connect planned and executed
production volumes. By exploring various forecasting approaches, including
ARIMA and Long Short-Term Memory (LSTM) networks, the study focuses
on methods designed to perform well with smaller datasets. The research employs
a hierarchical model architecture that decomposes the forecasting task
into three components: production forecasting, quarterly mapping, and unfolding
to a monthly plan estimate. The models are evaluated against baseline
strategies, including naive predictions and basic LSTM and ARIMA models.
Results show that the proposed hierarchical models outperform baseline models,
capturing the general behavior of the data more effectively. The deep
learning-based model excels at capturing extremes in the time series, while
the regression-based model provides stable and accurate forecasts. However,
the models struggle with highly erratic production plan patterns, indicating
the need for further refinement. This thesis contributes to more robust and
scalable production planning solutions for data-constrained environments, offering
valuable insights for both academic research and practical applications.
Frysseparation av slam och sediment: En studie av Effektivitet och Materialoptimering
(2025) Hussein, Redir; Johansson, Victor
This report investigates and compares three different methods for sludge drying: passive air drying, active air drying, and freeze separation using Siccum’s technology. The aim was to
evaluate each method based on energy consumption, drying efficiency, processing capacity, and potential environmental benefits. The evaluation was conducted through a comparative
analysis of each method’s technical and operational performance under controlled conditions.
The results show that passive air drying has the lowest energy demand but requires extensive space and time. Active air drying offers faster results at the cost of higher energy usage.
Siccum’s freeze separation technology demonstrated the highest energy consumption in the study, but also the greatest ability to process large material volumes through a closed and
automated system.
Although energy-intensive, freeze separation may be advantageous in large-scale operations where high throughput, controlled processing, and reduced waste volumes are prioritized.
The choice of method should be based on specific operational needs, considering factors such as energy costs, available space, processing time, and environmental impact.