Elektroteknik (E2) // Electrical Engineering (E2)

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https://odr.chalmers.se/handle/20.500.12380/12

Arbetar med hållbara och smarta lösningar på samhällsutmaningar, såsom energieffektivitet och elektrifiering inom områden från transport- och produktionssystem till kommunikationslösningar och medicinteknik.

Läs mer om våra utbildningar

För forskning och forskningspublikationer, se https://research.chalmers.se/organisation/elektroteknik/

At the department of Electrical Engineering research and education are performed in the areas of Communication and Antenna systems, Systems and Control, Signal processing and Biomedical engineering, and Electric Power Engineering.

We work with challenges for a sustainable future in society of today, for example in the growing demands concerning efficient systems for communications and electrification. Our knowledge is of use everywhere where there is advanced technology with integrated electronics, no matter if it involves electricity, electrical signals, optical signals or microwaves.

Studying at the Department of Electrical Engineering at Chalmers

For research and research output, please visit https://research.chalmers.se/en/organization/electrical-engineering/

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Visar 1 - 20 av 84

3D scenario generation using generative models
(2019) Ramazzina, Andrea; Chalmers tekniska högskola / Institutionen för elektroteknik; Kahl, Fredrik
A lane departure detection system based on uncertainty aware machine learning
(2020) Larsson, Jesper; Sjöstedt, Mattias; Chalmers tekniska högskola / Institutionen för elektroteknik; Svensson, Lennart
Acoustic impulse response function estimation using machine learning
(2019) Larsson, Erik; Viberg, Felix; Chalmers tekniska högskola / Institutionen för elektroteknik; Kahl, Fredrik
Active learning of neural network from weak and strong oracles
(2017) Mattsson, Björn; Chalmers tekniska högskola / Institutionen för signaler och system; Chalmers University of Technology / Department of Signals and Systems
Ageing-Aware Fast-Charging Strategy for Electric Vehicle Batteries
(2023) Olsson, William; Lind Stefansson, Olof; Chalmers tekniska högskola / Institutionen för elektroteknik; Zou, Changfu
Anti-Money Laundering with Unreliable Labels
(2024) Bergquist, Jesper; Chalmers tekniska högskola / Institutionen för elektroteknik; Graell I Amat, Alexandre; Östman, Johan
This report examines the effectiveness of Graph Neural Networks (GNNs) in detecting money laundering activities using transaction data with unreliable labels. It explores how weakly supervised learning, specifically with GNNs, manages the challenges posed by missing and inaccurate labels in anti-money laundering (AML) systems. The study utilizes simulated transaction datasets to compare the performance of GNNs against traditional statistical models. Findings indicate that GNNs, due to their ability to process relational data structures, demonstrate superior adaptability and accuracy in scenarios with label deficiencies. This research provides effective strategies for enhancing anti-money laundering systems by employing GNNs to more effectively manage data challenges.
Anti-spoofing for facial recognition-based identification system
(2020) Liljestrand, Simon; Chalmers tekniska högskola / Institutionen för elektroteknik; Furdek Prekratic, Marija
Artificial intelligence applied to routing and energy prediction of electric vehicles
(2021) Guiladi, Ella; Eriksson, Josefine; Chalmers tekniska högskola / Institutionen för elektroteknik; Kulcsar, Balazs
Augmenting semantic segmentation data using generative adversarial networks
(2018) Lindqvist, Jakob; Nilsson Lundberg, Per; Chalmers tekniska högskola / Institutionen för elektroteknik; Chalmers University of Technology / Department of Electrical Engineering
Autoencoders for Anomaly Detection in Commercial Truck Gearshifts
(2024) Imsirovic, Anes; Rokni, Arvin; Chalmers tekniska högskola / Institutionen för elektroteknik; Brännström, Fredrik; Olesen, Oscar; Lillskog, Simon; Bordbar, Alireza
Abstract The following study revolves around implementing a machine learning model that supports engineers in analyzing vehicle sensor data at Volvo Trucks. The aim is to classify different gear shift types and to automate the detection of abnormal gear shifts making decisions more efficient and data-driven. By applying clustering- and classification algorithms, the model demonstrates how unlabeled field test data can be classified in diverse gear shift types. Furthermore, through evaluation and comparision of dense, convolutional and long short-term memory (LSTM) autoencoder (AE) neural networks, this study exhibits how abnormal sensor data within specific gear shift types can be detected. Obtained results indicate adequate gear shift classification with an overall accuracy of 98%. Furthermore, a comparison of the autoencoders in regard to the performance metrics accuracy, precision and recall concluded that the convolutional autoencoder outperformed the other architectures with scores above 95%. Although further fine-tuning of the implemented model is possible, findings indicate that it is feasible to develop and use machine learning models for classification and anomaly detection of gear shift data within heavy-duty trucks.
Autoencoding as regularization
(2019) Mericle, Lukas; Chalmers tekniska högskola / Institutionen för elektroteknik; Chalmers University of Technology / Department of Electrical Engineering
Automated construction of statistical deformation models for non-rigid registrations of the pericardium and hippocampus
(2015) Larsson, Måns; Chalmers tekniska högskola / Institutionen för signaler och system; Chalmers University of Technology / Department of Signals and Systems
Automatic Reconstruction of Indoor Spaces from 3D Point Clouds
(2022) Norman, Eric; Landgren, Lovisa; Chalmers tekniska högskola / Institutionen för elektroteknik; Zach, Christopher
We present a new take on the unresolved challenge of automating indoor environment reconstruction from LiDAR point clouds. Utilizing point clouds as a basis for creation of BIM models yields highly accurate results and simplifies the task substantially as compared to gathering and using manual measuring methods. It is however still a time-consuming and labor intensive process for a human to draw the model using the point cloud as a mere blueprint. We therefore attempt to automate a key part of the process end to end, namely the reconstruction of polygonal room spaces. With the goal of reaching human accuracy, if not above, we attempt to automate the steps from point cloud to a 2D room-level polygonal model of a building floor. To this end research was conducted to combine some promising methods from different studies that have previously been done in the field into a modular data pipeline. The prototype uses multiple high-performing algorithms to denoise the point cloud, accurately identify planar room dividing components and finally define the room spaces as simple polygons. Our work shows that end-to-end automation of room space classification is indeed possible, although lack of objective measure of room divisions poses a yet unresolved challenge. For the purpose of full BIM model reconstruction, reliable room classification is a necessity. Our work shows a promising way of combining available methods into an automatic and robust indoor environment space reconstruction process with a high level of accuracy.
Camera based localization and autonomous navigation for a flying drone
(2019) Kjelltoft, Mattias; Chalmers tekniska högskola / Institutionen för elektroteknik; Kahl, Fredrik
Characterization of radio link reliability
(2017) Magnusson, Andreas; Chalmers tekniska högskola / Institutionen för elektroteknik; Chalmers University of Technology / Department of Electrical Engineering
Comparing numerical and machine learning algorithms for optimized operation points of an electrical machine
(2023) Ren, Yijie; Chalmers tekniska högskola / Institutionen för elektroteknik; Fabian, Martin
Abstract This work compares the Lookup Table (LuT) based numerical method with neural network (NN) based, and reinforcement learning (RL) based methods, for finding the optimal operating point of an Interior Permanent Magnet Synchronous Machine. Commonly, numerical methods are used to search for Maximum Torque Per Ampere (MTPA) points, which, although relatively accurate, often require significant computation time and generate large amounts of output data to obtain precise operating points. In this thesis, a simple approach was employed to establish a three-dimensional LuT based on nonlinear data, which is used as a baseline for comparing machine learning models. By comparing multiple metrics, it was verified that the presented NN-based method can quickly, efficiently, and accurately fit the LuT data, making it suitable for data reduction and addressing the issue of large output data of LuT. The RL-based method offers a simple model that is not dependent on data and can essentially achieve MTPA control, providing new inspiration for finding operating points. Finally, based on the comparative results, the advantages and challenges of the proposed different models are presented
Computationally Efficient Real Time Embedded Computer Battery Simulator
(2022) Sandgren, John; Chalmers tekniska högskola / Institutionen för elektroteknik; Ehnberg, Jimmy
Consequences on Ringhals Operation from Introduction of Adjacent Offshore Wind Power
(2023) Johansson, Erik; Fransson, Mattias; Chalmers tekniska högskola / Institutionen för elektroteknik; Chen, Peiyuan; Johansson, Sofia; Knutsson, Magnus; Nilsson, Mikael
Abstract As of the 16th of May, two offshore wind power plants (WPPs) located outside the coast of Halland was granted approval from the Swedish government. The combined installed capacity of the WPPs is expected to be over 1500 MW, and both prospectors were planning to connect to the same transmission lines that Ringhals power plant are utilizing, however when the approval came from the Swedish government only one of the projects were approved a large enough capacity to connect to the transmission grid. This has not been taken into account throughout the project so instead both WPPs are aggregated into a single power plant in this project. This report investigates possible electrical phenomena inherent to large scale WPPs that may affect the operation of a close by thermal power plant. A literature review was conducted which indicated subjects of interest, four key subjects were chosen to study further, namely harmonic emission, sub-synchronous oscillations, behavior during fault and abnormal grid states, and finally, inter-area oscillations. Through further literature analysis, the findings showed that harmonic emission should be manageable, while sub-synchronous oscillations could pose an issue given a situa tion of weakened grid and poor damping techniques. It was also found that there is a minor risk that an introduction of a WPP could increase the risk of inter-area oscillation. Dynamic phenomena was studied though extensive simulations in PSS/E, the simulations indicate that the grid, in its current condition should be able to sustain the impact of a sudden disconnection of a WPP without serious voltage deviations, and that the critical fault clearing times of the generators at Ringhals could either increase or decrease from an introduction of a WPP, depending on if the WPP rides through the fault or if it disconnects from the grid and if it injects any fault current or not. For cases were the WPP disconnects from the grid during fault the critical fault clearing time at Ringhals decreases noticeably whereas strong fault current injection from the WPP can increase the critical fault clearing times for the closest generator.
Control and Camera-based State Estimation using Machine Vision and Machine Learning - A Comparison Study in IMU-replacing Neural Networks on a Wheeled Inverted Pendulum System
(2023) Kötz, Lasse; Almgren, Jonathan; Chalmers tekniska högskola / Institutionen för elektroteknik; Sjöberg, Jonas
Crest factor reduction based on artificial neural networks
(2017) Sesma Caselles, Eduardo; Chalmers tekniska högskola / Institutionen för elektroteknik; Chalmers University of Technology / Department of Electrical Engineering

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