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Senast inlagda
Validating a Sea Ice Drift Retrieval Algorithm Through Remote Sensing and In-Situ Buoy Comparisons
(2025) Desjonqueres , Thibault
This Master’s thesis validates a sea ice drift retrieval algorithm developed at Chalmers
(referred to as the Chalmers algorithm) using in situ buoy data collected during the
ARTofMELT 2023 cruise in the Fram Strait from May to June 2023.
The ARTofMELT 2023 expedition aimed to document melt onset in the Arctic
Ocean, collecting extensive data on sea ice dynamics near the Marginal Ice Zone
(MIZ) with seven OpenMETbuoys and three SIMBA buoys. These buoys were
deployed on first-year ice floes, with detailed measurements of horizontal motion,
rotation, deformation, wave action, and temperature. This deployment enabled
analysis of ice floe movement, convergence, divergence, and the interaction between
these dynamics and SAR imagery.
Low-noise Radarsat Constellation Mission (RCM) and Sentinel-1A SAR images,
captured in dual polarization (HH+VV or HH+HV), were acquired to match the
study area both spatially and temporally. Integrating in situ weather data, wave
measurements, sea ice concentration, and weather reanalysis with satellite imagery
observations of ice conditions around the buoys enabled a thorough interpretation
of drift patterns, floe decay, and wave signatures associated with the buoys.
The Chalmers drift algorithm was successfully applied to two pairs of RCM images,
each with a 10 m resolution, out of eight total image pairs. These pairs were cho sen based on criteria including a time gap of 1-24 hours between images, identical
resolution, and a maximum 5-minute time gap between the buoy and the images.
The remaining six pairs contained Sentinel-1A images with a resolution of 100-150 m.
Although the drift estimates from the Chalmers algorithm were not statistically
significant, they demonstrated the algorithm’s capability to produce coherent and
realistic drift estimates, evidenced by the alignment of drift vector azimuths with ac tual buoy drift and realistic sea ice drift distances. To better assess the algorithm’s
performance, future work should test it on a larger dataset, including the six re maining Sentinel-1A image pairs. Additionally, exploring the effects of increased
time gaps between buoy and satellite images, and between the two satellite images,
would provide further insights into the algorithm’s capabilities
Machine Learning for Wind Power Prediction. A Comparative Analysis of Traditional Machine Learning Models and Graph Neural Network for Wind Power Prediction and Forecasting in Wind Farms
(2025) Antonsson, Emma; Vind, Lisa
This thesis presents a comparative study of machine learning (ML) models and the deep learning (DL) model graph neural network (GNN) for wind power prediction and short- to medium-term forecasting in wind farms. Using high-resolution SCADA data from a 16-turbine onshore wind farm in Sweden, along with re-analysis and forecast weather datasets, various models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), k-nearest neighbours (kNN), Multi-Layer Perceptron (MLP), and GNN were trained and evaluated. Two baseline approaches, the farm’s
theoretical power curve and FLORIS wake model, were used as references. Results show that ML models outperform baseline models in predicting wind power output, with GNN achieving the best overall performance, although all ML models perform similarly. The ability of the models to generalize from wind power prediction to forecasting is however limited. The findings indicate that re-analysis data with low spatial resolution fails to adequately capture local weather conditions necessary for accurate power prediction. The study also investigates the effects of input feature selection, temporal resolution, and multi-task learning on model performance.
Furthermore, it identifies challenges related to input data quality, particularly in the estimation of global wind conditions from SCADA-based measurements. These results underscore the potential of ML methods for wind power applications and highlight the critical importance of accurately representing global weather data, as well as accounting for discrepancies between training data and forecast data.
Steering behavior-based fatigue detection: Evaluation and implementation for drowsy driver warning system
(2025) Hassan Ananda Kumar, Sanjana
This research was conducted to analyze steering behavior as an alternative approach
to detecting driver drowsiness. The thesis examined standard metrics such as steering wheel angle, steering wheel angle rate, yaw rate, and lane position—variables
directly and indirectly related to steering behavior—to assess how much information
they bear about drowsiness. A derived metric, the steering reversal rate, was also
analyzed to further explain the effects of drowsiness. These metrics demonstrated
a strong correlation with driver drowsiness, which was subjectively measured using
the Karolinska Sleepiness Scale (KSS).
Based on this analysis, two parameters derived from the steering reversal rate—micro
and macro corrections—were used to develop two methods for detecting drowsiness.
These parameters were significant because, as a driver becomes drowsier, the frequency of micro-corrections tends to decrease, while macro-corrections increase.
Two methods have been developed to detect a drowsy driver based on the above
analysis. The first method employed a logistic regression model, using the absolute
values of micro and macro corrections to directly correlate with the KSS ratings.
This approach did not account for temporal patterns and treated the data independently of its time-series nature. In contrast, the second method incorporated a
time-series perspective by evaluating changes in micro and macro correction rates
over time rather than relying on their absolute values. During development, it
was observed that vehicle speed significantly influenced steering behavior. At lower
speeds, even non-drowsy drivers exhibited more macro corrections and fewer microcorrections. However, at speeds above 65 km/h, non-drowsy drivers typically made
more micro-corrections and fewer macro-corrections. This insight enhanced the robustness of the second method, wherein vehicle speed was considered one of the
contributing factors in analyzing driver steering behavior. Additionally, the second
method involved a learning phase for each individual driver, allowing for personalized threshold values. This driver-specific calibration improved adaptability.
Overall, the second method of real-time analysis of changes in micro and macro
correction rates proved to be a more effective and reliable approach, yielding better
results than the current system(based on lane distance) for detecting driver drowsiness.
Enhancing Gas Adsorption In Sensors：Au–Pt Nanoparticles and Methylated Coordination Cages
(2025) Advand, Marzyeh
This thesis investigates two nanostructured materials, bimetallic gold platinum (AuPt) nanoparticles and a methylated cobalt(II)/iron(II) coordination cage, for their potential use in gas sensors designed to detect acetone, a key biomarker for
non-invasive glucose monitoring. The AuPt nanoparticles were synthesized using a modified co-reduction method based on Britto’s procedure. The synthesis was optimized to achieve uniform and well-dispersed nanoparticles through precise control of reaction time and washing steps. The nanoparticles were characterized using SEM and, EDX, to confirm their morphology, composition, and crystal structure. They were immobilized on quartz
substrates through silane functionalization, and UV–Vis spectroscopy was used to verify both immobilization and acetone vapor adsorption. A clear spectral shift observed after acetone exposure confirmed successful adsorption on the nanoparticle surface, indicating their suitability for gas sensing applications.
In parallel, methylated Co(II) and Fe(II) coordination cages were synthesized through subcomponent self-assembly and characterized using NMR and UV–Vis spectroscopy. The immobilization of Co(II) cages on glass and silicon substrates was verified using UV–Vis and TOF-SIMS. The data is consistent with successful immobilization of the cages on both glass and silicon surfaces. Notably, this study demonstrates for the first time that this particular cage has been successfully adsorbed onto glass and silicon substrates. Gas exposure experiments were performed on the Fe(II) cage solutions and analyzed by NMR; however, no significant spectral changes were detected, likely due to gas dissipation or weak interactions at ambient conditions.
Overall, the study presents promising results for the AuPt nanoparticles, while preliminary findings for the coordination cages indicate the need for further optimization. These results contribute to the development of nanostructured mate-
rials for more sensitive and selective gas-sensing technologies aimed at advancing non-invasive diagnostic methods.
Reproducible Performance Variability Mitigation of OpenMP and SYCL Applications
(2025) Persson, Christoffer; Prétot, Mathias
Performance variability caused by unpredictable system noise remains a persistent challenge in high-performance and parallel computing. This thesis presents a methodology for characterising such variability through reproducible noise injection, using three representative benchmarks implemented with OpenMP and SYCL. A custom noise injector was developed to capture real system traces, isolate average and outlier behaviours, and reinject the delta as controlled, reproducible noise. We evaluate and compare multiple mitigation strategies, such as thread pinning, use of housekeeping cores, and simultaneous multithreading (SMT) toggling, under both default and noise-injected conditions. Our experimental study spans three benchmarks (N-body, Babelstream, and MiniFE) executed on local Intel and AMD desktop processors, enabling a comprehensive analysis of mitigation effectiveness across platforms and workloads. Results indicate that while OpenMP consistently delivers higher raw performance, SYCL tends to be more resilient to noisy environments. The proposed noise injection framework facilitates more rigorous and repeatable assessment of parallel program behaviour under controlled perturbations. Although the effectiveness of mitigation strategies varies with workload characteristics, system configuration, and noise intensity, certain techniques, such as isolating housekeeping cores, show clear benefits, particularly in high-noise scenarios.