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Senast publicerade

Physics-Informed Two-Stage Learning Framework for Engine Ignition Frequency and RPM Estimation
(2026) KIm, Nuree
Accurate estimation of the ignition frequency (f0) of internal combustion engines is essential for non-invasive rotational speed (RPM) monitoring and condition diagnosis. In practice, domain shifts caused by sensor placement, vehicle-specific resonances, and environmental noise can distort the harmonic structure of f0, leading to multiple plausible spectral peaks within short analysis windows. A physics-informed, two-stage machine-learning framework is developed to estimate the ignition frequency of four-stroke engines using synchronized sound and vibration measurements. A nonlinear product signal (xprod(t) = xsound(t) xvib(t)) is introduced to emphasize ignition events jointly detected by both sensors, providing an additional representation for joint analysis of ignition-related components. Features are extracted from multiple signal representations, including the FFT, Envelope FFT, Cepstrum, Autocorrelation (ACF), and Envelope–ACF, and combined into a unified feature space capturing harmonic consistency, periodicity, and peak morphology. The framework consists of two learning stages. Stage 1 classifies global engine characteristics, including the cylinder count and ignition-frequency class. Stage 2 ranks local frequency candidates using a LightGBM-based LambdaMART ranker to identify the ignition frequency f0. Generalization performance is evaluated using Leave-One-Source-Out (LOSO) and Leave- One-Vehicle-Out (LOVO) protocols. Results show a mean LOSO accuracy of 95.5% for cylinder classification and a Top-1 accuracy of 84% within a ±5 Hz tolerance for candidate ranking, with mean frequency errors below 2 Hz (≈60 rpm). The model demonstrates consistent generalization behavior on unseen vehicles, indicating robustness to domain shifts across sensors and operating conditions. The proposed approach therefore provides an interpretable and practically applicable solution for non-invasive RPM estimation and establishes a foundation for real-time diagnostic applications on embedded automotive systems.
BESS och dess påverkan i lågspänningsnätet
(2026) Josefsson, Oskar; Johansson Kostovski, Felix
Likt solceller är batterienergilagringssystem (BESS) på väg att slå igenom bland husägare och väcker stort intresse. Det är en relativt ny teknik som möjliggör till exempel elhandel via arbitrage samt bidrar till att hålla elnätets frekvens stabil vid 50 Hz. Trots detta är kunskapen begränsad kring hur användningen av BESS påverkar lågspänningsnätet i extremfall. Denna rapport undersöker just sådana extremfall och drar slutsatser om vilka konsekvenser som kan uppstå i elnätet. Arbetet utfördes på uppdrag av Härryda Energi AB som vill undersöka hur BESS påverkar sitt elnät då efterfrågan är hög hos elnätskunderna. Samtliga simuleringar genomfördes i dpPower vilket är det nätanalysverktyg som Härryda Energi använder för att modellera och övervaka sitt elnät. I dpPower kan nätägare bland annat hantera kundanläggningar, felsöka, analysera data och simulera framtida scenarier. Inför varje simulering justerades kundanläggningarna genom att stegvis lägga till BESS-enheter till befintliga kundanslutningar inom kundanslutningens abonnemangsstorlek. Resultaten visade att urladdning av BESS gav relativt liten påverkan på matarkablar men spänningen i hushållen blev orimligt hög. Dock sker detta inte i praktiken eftersom strömmen i verkligheten går via en växelriktare som reglerar utspänningen genom att strypa/begränsa uteffekten. Vid uppladdning av BESS var effekten omvänd och spänningen i hushållen blev låg, men belastningen på matarkabeln ökade kraftigt. Simuleringarna utfördes på tre olika nätstyrkor. Det starka nätet definieras som ett kabelnät med nominell area på 240 mm², det medelstarka 150 mm² och det svaga 50 mm². Det svaga nätet blev snabbt överbelastat medan det medelstarka och starka nätet klarade av den ökade effekten från tiotals batterier. Studien fokuserade på extrema scenarier med simultan aktivering av många batterier inom ett lågspänningsnät där varje batteri antogs verka inom kundens befintliga abonnemang. För att isolera effekterna av batterisystemen inkluderades inte simultan drift av solcellsanläggningar i simuleringarna.
Copper-Doped Strontium Apatite
(2026) Ericstam, Pontus
In this Master’s thesis project, strontium apatite has been doped with copper ions using solid state synthesis methods. Different annealing temperatures and dwelling times were tested to see which synthesis conditions yielded the most phase pure sample. The composition and structure of the synthesised samples have been inves tigated using synchrotron powder X-ray diffraction with complementary Rietveld refinement. Infrared spectroscopy was used in order to investigate presence of OH bonds in the structures. The results suggest that by annealing in air at 1400 °C for 6 hours, followed by air quenching, it is possible to achieve a purple-coloured copper-doped strontium apatite, with strontium phosphate being created as a mi nor side-phase. The infrared spectroscopy suggests no OH-bonds are present in the synthesised structures, and that some form of oxocuprate ions are present in the one dimensional channels of the apatite structure. These results offer a relatively simple synthesis route, to be further optimised in order to achieve a phase pure sample of copper-doped strontium apatite. If achieved, the copper-doped strontium apatite could be used as a potential reference to copper-doped lead apatite, to see how the electron interactions differ between the two. The synthesis method could also be used as a platform for future investigations of how the structure and properties of apatites can be changed upon doping with copper, and potentially with other types of ions as well.
Stabilare materialflöde som grund för ökad produktionseffektivitet
(2026) Mohamad, Nader; Qiyam, Omid
Variations in material flow constitute a common challenge in industrial production systems and can lead to minor stoppages, reduced production rates, and inefficient utilization of existing capacity. This bachelor's thesis addresses issues related to uneven material flow in an industrial bagging process for powder-based material, where the actual production rate varies despite unchanged machine settings. The core message of the study is that variation in material flow, especially during the handling of the powder material, can limit the production rate even though the technical capacity of the packing line is higher. By improving the design of the material connection and working in a more standardized way during startup, the flow can be stabilized and the buffer level maintained more evenly. This can contribute to fewer minor stoppage, more stable production, and better utilization of the line’s capacity. For the company, this represents an opportunity to increase production efficiency and achieve more robust operations when handling different materials. The purpose of the study is to map the current state of the material flow, identify possible causes of flow variations and predictable production. The work has been conducted as a case study in an industrial production environment combining qualitative methods such as process observation and interviews with operating technicians with quantitative elements in the form of time studies and analysis of available production data. The analysis has been carried out using tools from Lean and Six Sigma, including the DMAIC structure and cause and effect analysis. The results show that variations in material flow can be linked to both technical factors in the transition between container and piping system as well as differences in routines and machine settings during start up and container changes. The study results in improvement proposals at a conceptual level such as adjustments to design solutions, standardization of machine settings, and improved data collection. These proposals aim to stabilize the material flow, reduce minor stoppages, and improve the utilization rate of the bagging machines capacity. The results are intended to serve as a design decision making basis for further technical and organizational actions within the production process.
Deep Learning-Assisted Differential Cryptanalysis on Round-reduced Block Ciphers: What are the advantages?
(2026) Flink, Lucas
This thesis investigates and evaluates the application of deep learning techniques in differential cryptanalysis of lightweight block ciphers. The main two focuses are on comparison between a hybrid attack pipeline and a classical attack pipeline, and on transfer learning compared to training a model from scratch. The study considers both SPN ciphers and the SIMON32/64 cipher, which is part of the ISO standard for RFID systems. Neural distinguishers based on convolutional neural networks are trained to classify pairs of ciphertexts, as either random noise or ciphertexts produced by the cipher. For the SPN cipher it is integrated into a key recovery attack pipeline. For SIMON32/64 it is compared to the outcome of transfer learning of a pre-trained network. The results show that a hybrid approach is comparable to a classical approach in terms of key recovery attack. The transfer learning enables faster convergence, but does not reach the same accuracy in classification compared to training the model from scratch. These findings contribute to the understanding of the role of machine learning in cryptanalysis, and how it can be further studied to potentially be more useful in the future, and what the security impacts might be for real-world use in especially supply chains using RFID technology.