Publikationer från Mälardalens högskola
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Samverkan i stödundervisning i matematik : Speciallärares perspektiv på samordnat specialpedagogiskt arbete
No full textHur samverkan mellan specialläraren, specialpedagogen, och undervisande lärare förstås och organiseras påverkar utformningen av stödundervisningen i matematik. Trots arbete mot samma mål är det inte självklart att speciallärare och specialpedagoger samverkar kring hur arbetet ska utformas och drygt 10 % av niondeklassarna fick betyget F i matematik vårterminen 2024. Av dessa anledningar är det relevant att undersöka hur speciallärare upplever samverkan med specialpedagoger och matematiklärare samt hur samverkan kan främja elevers matematikutveckling. Syftet med studien var att få en fördjupad kunskap om hur speciallärare beskrev sin samverkan mellan specialpedagog och matematiklärare och om samverkan beskrevs påverka hur stödundervisningen i matematik utformades för elever i behov av stöd. För att fånga speciallärares egna perspektiv och erfarenheter användes en kvalitativ forskningsansats med semistrukturerade intervjuer i denna studie. I studiens resultat framkom att speciallärare ansåg att samverkan med specialpedagog och matematiklärare var viktig för att organisera specialpedagogiska insatser samt för att utveckla matematikundervisningen så att den blev tillgänglig för fler elever. Hur samverkan såg ut skilde sig mycket på olika skolor beroende på att enskilda professionellas tolkning och intressen styrde om samverkan skedde eller inte. Detta innebar att även stödinsatserna såg olika ut, utifrån i vilken utsträckning specialläraren arbetade förebyggande med att utveckla matematikundervisningen eller med åtgärdande riktade stödinsatser. Slutsatsen indikerade att skilda tolkningar av uppdrag försvårade samverkan kring stödinsatser, vilket kunde motverkas genom en ledning med insyn i det specialpedagogiska arbetet som skapade förutsättningar för en fungerande samverkan.
Expanding PPGFeat for Multi-Dataset PPG Analysis,Comprehensive Biomarker Extraction and Machine learningfor Cardiovascular Risk Prediction
No full textThe number one cause of mortality worldwide is cardiovascular disease and early detection of cardiovascular risk factors can greatly reduce the risk of severe complications. Photoplethysmography (PPG) is a method to non-invasively measure changes in blood volume that can readily be implemented into wearable medical devices. This is beneficial as PPG can be used to estimate numerous cardiovascular parameters, however the underlying physiological mechanisms behind it are still not fully understood. PPGFeat is a MATLAB toolbox designed to support researchers in analysing raw PPG data by providing signal preprocessing and fiducial point extraction with a graphical user interface. This thesis presents the development of an updated version of PPGFeat that expands on the old toolbox by adding support for more diverse datasets, automatic signal quality assessment and expanding the number of PPG derived biomarkers. The methods for loading data were successfully extended to accommodate a larger number of datasets without negatively affecting the fiducial point extraction. Furthermore, the increased number of biomarkers were used to train two classifiers to determine hypertension status with over 55% accuracy across four classes. Finally, while automatic signal quality assessment was implemented in the toolbox, the resulting index is only marginally more accurate over using the mean signal quality of the dataset which limits its potential use
When Stories Open Doors : Intersectional Analysis of the Textbook Ord och äventyr
No full textStudien analyserar hur identitetskategorier som genus, etnicitet, klass, funktionsnedsättningar och familjekonstellationer framställs i läsläran Ord och Äventyr för årskurs ett till tre. Genom en kvalitativ innehållsanalys, med inslag av kvantitativa moment, undersöks både text och bild med stöd av ett intersektionellt perspektiv. Resultatet visar att läsläran innehåller normbrytande inslag. Vi har exempelvis funnit inslag av HBTQIA+, funktionsnedsättning och olika familjekonstellationer. Däremot finns en stark avsaknad av mångkulturalitet, vilket inte överensstämmer med de riktlinjer som finns i läroplanen kopplat till mångfald.
Affective Factors in Foreign Language Learning : A Quantitative Study of the English Classroom in a Swedish Context
No full textThis study investigates affective factors with particular focus on foreign language enjoyment and foreign language classroom anxiety in the context of Swedish upper secondary school.There is a research gap in the existing research that takes on a holistic perspective on which affective factors could contribute to foreign language enjoyment and foreign language classroom anxiety. To address this gap, the study has used a questionnaire completed by 43 pupils at a Swedish upper secondary school. The findings suggest that foreign language enjoyment and foreign language classroom anxiety are distinct sets of emotions that Swedish English language learners experience simultaneously. Specifically, the findings indicate that pupils experience more enjoyment than classroom anxiety on an individual level. As for the classroom atmosphere, they experience very little emotion, suggesting that they feel safe and secure among their classmates and at school. However, the educational context that aids learning triggers the most emotional response among the participants of this study. More specifically, this study provides insights into the complex relationship a learner experiences with the teacher, peers, and learning activities during English class. These results align with earlier research and underscore the need for educators to pay closer attention to how teacher relationships, peer interactions, and classroom activities shape learners’ emotional experiencesin the English language classroom. This suggests that educators and teachers should be encouraged to create supportive, well-designed learning environments, as it may increase foreign language enjoyment and support English language learning.
Lärares uppfattningar och hantering av Artificiell Intelligens i gymnasieskolan
No full text Användning av Artificiell Intelligens (AI) har ökat inom utbildningsssektorn, både bland elever och lärare. Lärarnas direkta kontakt med elevers AI-användning kan anses utgöra en grund för deras attityder och medför en betydelsefull aspekt i sammanhanget. Syftet med studien var att undersöka gymnasielärares attityder till, samt hantering av generativ AI i yrkesutövningen. Technology Acceptance Model (TAM) kombinerades med copingteori för att analysera hur gymnasielärares uppfattningar och attityder tillämpas i praktiken. Studien genomfördes med en kvalitativ ansats där semistrukturerade intervjuer med sex gymnasielärare som analyserades tematiskt. Resultaten visade en övervägande positiv beteendeavsikt gentemot AI. Upplevd belastning verkade främst kopplas till avsaknad av riktlinjer från ledningsnivå, snarare än i attityderna mot AI. Samtidigt framkom problematik avseende ökade krav och belastning på lärare. För att hantera dessa utmaningar tillämpar lärarna problem- och emotionsfokuserade copingstrategier. Studien indikerar behov av strategiska planeringar av implementering av AI i utbildningssyften för att stödja lärare i sin profession
DIGITALISERING AV SAMVERKANSPROCESSER : En socio-teknisk analys av designkrav för kompetensmatchning mellan offentlig och privat sektor
No full textCollaboration between academia, the public sector, and industry is crucial for innovation but is often hindered by organizational friction, unclear entry points, and inefficient processes. The purpose of this study is to use Design Science Research to identify the socio-technical design requirements necessary for a digital platform to support a coherent collaboration process.Through a qualitative interview study with five strategic key stakeholders and a supplementary survey with 22 respondents, the study mapped needs and barriers across two phases, namely initiation through matching and maintenance via a digital workspace.The results indicate that the main challenge is not technical functionality, but a lack of trust and integration. The study identifies that the initiation phase requires mechanisms for calculus-based trust, such as strong authentication via BankID and verified track records, to reduce transaction costs. For the subsequent workspace, service quality and structural assurance are critical. The platform must integrate seamlessly with existing ecosystems, such as Microsoft Teams, and technically support regulatory compliance like GDPR and LOU. The conclusion is that a successful platform must be designed to balance technical simplicity with organizational security, acting as a bridge between the formal requirements of the public sector and the industry's need for speed
Advanced Power Management Strategies for Complex Hybrid-Electric Aircraft
No full textAircraft electrification for propulsion is a promising way to alleviate the negative environmental impact of conventional carbon-powered aviation. Inclusion of the electrical powertrain aims to enhance design freedom, allowing for more efficient power systems and operational schemes. In this work, a design space exploration is performed, aiming to derive power management guidelines based on aircraft environmental performance. A 19-passenger commuter aircraft employing the series/parallel partial hybrid-electric architecture is examined. Two underwing-mounted turboprop engines are combined with a boundary layer ingestion fan mounted in the aft of the aircraft and powered by an electrical drive. The primary electrical energy source is a battery system. A multidisciplinary framework is utilized, comprising modeling approaches for multipoint thermal engine design, physics-based electrical component sizing and performance, aircraft sizing, mission design, and environmental assessment. The investigation revealed that the reference designed hybrid-electric configuration with entry-into-service (EIS) 2035 assumed technologies yields roughly 18% improvement in block consumption and emissions, but an 8% increase in maximum takeoff weight (MTOW), compared to its 2014 conventional counterpart. The design space exploration for an optimal power management scheme indicated a minimum average ratio of 1:1.35 between cruise and design point hybridization power. However, even the optimally operated hybrid aircraft showcases worse environmental performance compared to the conventional design of same entry-into-service date. The investigation has revealed that the complex powertrain and hybrid architecture selected may be more suitable for larger class aircraft, where aircraft requirements can be relaxed and higher degrees of electrification are not penalized or confined by set constraints
Enhancing Perception System Robustness Against Attacks and Natural Perturbations
No full textDeep learning has led to major progress in computer vision, but modern Deep Neural Networks (DNNs) are still highly vulnerable to input perturbations, which limits their robustness in safety-critical applications. This challenge becomes even more critical in real-world industrial environments, such as autonomous machinery operating on construction sites, where visual data is influenced by unpredictable weather conditions, variable lighting, and physical wear and degradation. In addition, data scarcity, privacy constraints, and domain shift prevent the direct application of conventional large-scale training pipelines. This thesis addresses these challenges by proposing a comprehensive, multi-level framework that strengthens model-level robustness against adversarial attacks, enhances data-level robustness to natural environmental perturbations, and improves adaptive learning under distributed and data-constrained conditions, enabling reliable deployment of visual perception models in complex, safety-critical environments. The first contribution focuses on the robustness of model-level attacks against adversarial attacks. A meta-heuristic search method is proposed to automatically discover activation functions that increase resistance to adversarial perturbations without requiring adversarial training. A hybrid search strategy further improves convergence efficiency, yielding Convolutional Neural Networks (CNNs) that outperform standard architectures under adversarial attacks while maintaining competitive clean-data accuracy. The second contribution introduces ConstScene, a large-scale semantic segmentation dataset representing real and synthetic construction-site imagery under diverse weather and sensor degradation conditions. Experiments reveal significant performance drops when models trained on clean data are exposed to perturbed inputs, demonstrating the need for environment-specific robustness benchmarks. The third contribution introduces an integrated framework that combines Federated Learning (FL) for decentralized collaborative training with Few-Shot Learning (FSL) for sample-efficient domain adaptation, supported by server-side Hyperparameter Optimization (HPO). The proposed approach enables effective model adaptation across distributed construction sites without sharing raw data, significantly improving robustness across heterogeneous client datasets. In general, this thesis proposes three contributions to enhance robustness in perception systems: model-level robustness against adversarial attacks, introducing the ConstScene dataset for benchmarking performance under real-world degradations and data-level robustness against natural perturbations, and an integrated framework enabling decentralized, sample-efficient model adaptation across heterogeneous environments
Direct Data-Driven and Model-Based Control Design for an Autonomous Bicycle
No full textAutonomous bicycles constitute challenging benchmark systems for control, due to their nonlinear, non-holonomic, and in general, underactuated, open-loop unstable dynamics. Traditional model-based controllers such as proportional-integral-derivative (PID) controllers and linear quadratic regulators (LQRs) can stabilize the bicycle, but rely on simplified models that may not capture unmodelled and time-varying effects. In contrast, recent direct data-driven control methods based on Willems’ fundamental lemma bypass explicit modelling, yet typically assume linear time-invariant dynamic systems and require persistently exciting inputs that are difficult to apply safely on unstable systems. This thesis investigates how traditional model-based and direct data-driven control methods can be used, and combined, to balance and guide an autonomous bicycle using mainly steering actuation as input. First, PID, LQR, fuzzy controller, feedback linearization (FL), and direct data-driven controllers are designed and compared in high-fidelity simulations and experiments on an autonomous bicycle. The results show that classical model-based controllers provide strong baselines, while direct data-driven controllers can enhance performance when combined with classical controllers. Second, a unified framework is proposed in which an inner-loop FL controller stabilizes and partially linearizes the bicycle, and an outer-loop direct data-driven controller operates on the FL-stabilized system. Two different types of direct data-driven methods are evaluated in this setting: a static, nonlinear controller and the Data-enabled Policy Optimization (DeePO) algorithm. Third, the DeePO algorithm is analysed and modified to mitigate state perturbations, leading to a perturbation-free variant studied on LTI systems. Finally, a model-based PID–MPC trajectory tracking scheme is compared with a data-driven framework relying on Data-enabled Predictive Control (DeePC) for trajectory tracking, combined in a cascade architecture with the FL-DeePO setup. Simulations show that while PID–MPC achieves better tracking accuracy, the data-driven cascade attains successful trajectory tracking without relying on an explicit dynamic model.Autonoma cyklar utgör utmanande riktmärkessystem för reglering, på grund av sin icke-linjära, icke-holonoma och i allmänhet underaktuerade, öppen-slinga-instabila dynamik. Traditionella modellbaserade regulatorer, såsom proportional–integral–derivata-regulatorer (PID) och linjärt kvadratiska regulatorer (LQR), kan stabilisera cykeln, men bygger på förenklade modeller som inte nödvändigtvis fångar omodellerade och tidsvarierande effekter. I kontrast till detta kringgår nyare direkta data-drivna regleringsmetoder baserade på Willems fundamentallemma explicit modellering, men antar typiskt linjära tidsinvarianta dynamiska system och kräver ständigt exciterande insignaler som är svåra att tillämpa på ett säkert sätt på instabila system. Denna avhandling undersöker hur traditionella modellbaserade och direkta data-drivna regleringsmetoder kan användas, och kombineras, för att balansera och guida en autonom cykel med huvudsakligen styraktuering som indata. För det första konstrueras PID-, LQR-, fuzzyregulatorer, feedbacklineariseringsregulatorer (FL) samt direkta data-drivna regulatorer, vilka jämförs i högfidelitets-simuleringar och experiment på en autonom cykel. Resultaten visar att klassiska modellbaserade regulatorer utgör starka riktmärken, medan direkta data-drivna regulatorer kan förbättra prestandan när de kombineras med klassiska regulatorer. För det andra föreslås ett enhetligt ramverk där en innerloop-FL-regulator stabiliserar och delvis lineariserar cykeln, medan en ytterloop-regulator baserad på direkt data-driven reglering verkar på det FL-stabiliserade systemet. Två olika typer av direkta data-drivna metoder utvärderas i denna konfiguration: en statisk, icke-linjär regulator och algoritmen Data-enabled Policy Optimization (DeePO). För det tredje analyseras DeePO-algoritmen och modifieras för att motverka tillståndsstörningar, vilket leder till en perturbationsfri variant som studeras på linjära tidsinvarianta (LTI) system. Slutligen jämförs ett modellbaserat PID–MPC-upplägg för banföljning med ett data-drivet ramverk som bygger på Data-enabled Predictive Control (DeePC) för banföljning, kombinerat i en kaskadarkitektur med FL–DeePO-strukturen. Simuleringar visar att PID–MPC uppnår bättre uppföljningsnoggrannhet, medan den data-drivna kaskaden uppnår lyckad banföljning utan att förlita sig på en explicit dynamisk modell
Påverkan av turbindrift vid införing av en större hetvattenackumulator : Analys av en turbins förändrade driftmönster efter införandet av större hetvattenackumulator för värmelagring
No full textThis thesis investigates how the operation of Turbine 6 at Mälarenergi has changed after a large underground hot-water storage system was put into operation. The storage makes it possible to store heat when the demand is low and use it later, which reduces the need to adjust the turbine’s load in real time. The purpose of the study is to examine how the turbine’s operating patterns, average output, efficiency, and role in the electricity and reserve markets have been affected by the introduction of the heat storage system. The work is based on a comparison of operating data from before and after the storage was activated. The analysis includes start and stop frequency, electricity and heat production, efficiency, and how much of the available energy was not used. The results show that the turbine now runs more steadily and needs fewer starts and stops. Both electric and heat output have increased, and the turbine’s efficiency has improved. The amount of unused energy has also decreased, which means the turbine is used more effectively. Overall, the study shows that the heat storage has made the system more flexible and efficient. Turbine 6 can operate closer to its optimal load, while Mälarenergi gains better opportunities to adjust production according to changes in electricity prices and heat demand.INT