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Dondera, Alin (author)Masked Autoencoders (MAEs) represent a significant shift in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative) pairs as in contrastive frameworks. Their masking and reconstruction strategy also aligns well with SSL approaches in natural language processing. Most MAEs...master thesis 2024
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Buriani, Gioele (author)This work introduces a novel methodology for the development of interpretable reduced-order dynamic models specifically tailored for jumping quadruped robots. Leveraging Symbolic Regression combined with autoencoder neural networks, the framework autonomously derives symbolic equations from data and fundamental physics principles capturing the...master thesis 2024
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Alwani, Neil (author)This study investigates the application of generative models for synthetic data generation in pathway optimization experiments within the field of metabolic engineering. Conditional Variational Autoencoders (CVAEs) use neural networks and latent variable distributions to generate new, plausible data samples. We adapt this model by conditioning...bachelor thesis 2024
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Lepri, M. (author), Bacciu, Davide (author), Della Santina, C. (author)This letter concerns control-oriented and structure-preserving learning of low-dimensional approximations of high-dimensional physical systems, with a focus on mechanical systems. We investigate the integration of neural autoencoders in model order reduction, while at the same time preserving Hamiltonian or Lagrangian structures. We focus on...journal article 2024
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Veerman, Jochem (author)Chaotic systems are widespread and can be found everywhere, from small scale processes inside the human body to the large scale dynamics of the entire atmosphere. However, modelling these high dimensional chaotic systems is a difficult task due to the intrinsic nonlinear nature of chaos as well as the accompanied computational cost. Therefore,...master thesis 2023
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Rashad, Mohamed (author)Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm...master thesis 2023
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Wigmans, Bram (author)This paper examines whether complex high-dimensional data that describes the dynamics of a cantilever beam can be transformed into a less complex system. In particular, the transformation that is examined is the reduction of the dimension. An essential aspect of this study involves the implementation of a linear autoencoder, which is a type of...bachelor thesis 2023
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Sitaram, Arnish (author)Unsteady numerical simulation has been proven to be an essential tool for research. The quality of the results can be improved by using mesh adaptation. Mesh adaptation uses error indicators to refine the mesh in regions with high errors. The error indicators used are output errors with the most accurate output error estimation method being...master thesis 2023
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de Ruijter, Pim (author)The detection of anomalous behaviour is fundamental to component health analysis techniques. However, detecting anomalies is a difficult and time consuming task if their form, location, and frequency are unknown. This research introduces an innovative unsupervised predictive maintenance pipeline that requires minimal domain knowledge and time to...master thesis 2023
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van Amerongen, Maximilian (author)Artificial Neural Networks (ANNs) have emerged as a powerful tool for classification tasks due to their ability to outperform traditional methods. Nevertheless, their effectiveness relies heavily on the availability of large, varied, and labeled datasets, which are often not available. To counter this constraint, data augmentation techniques...master thesis 2023
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LIU, Xinjie (author)Many autonomous navigation tasks require mobile robots to operate in dynamic environments involving interactions between agents. Developing interaction-aware motion planning algorithms that enable safe and intelligent interactions remains challenging. Dynamic game theory renders a powerful mathematical framework to model these interactions...master thesis 2023
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Yümlü, Ege (author)Smartwatches are equipped with sensors that allow continuous monitoring of physiological and physical activities, making them ideal sources of data for data analysis. However, accurately identifying individuals based on smartwatch data can be challenging due to the presence of outliers. Hence, outlier detection techniques play a crucial part in...bachelor thesis 2023
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Biliński, Filip (author)Indoor localization is an actively researched field due to there not being a universal solution found yet. Applications of such systems include but are not limited to indoor wayfinding and automated tour guides. In previous years multiple solutions were proposed. This work looks into the performance of an indoor location sensing system in the...bachelor thesis 2023
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Sterrenberg, Amy (author)Energy use, CO2 emissions, and waste production are all significant causes of environmental issues. The building sector is a major contributor to these problems, specifically the manufacturing of (structural) steel elements. Application of reuse and/or remanufacturing, as done in a circular economy, will reduce these effects. Therefore, these...master thesis 2023
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- Pastor Serrano, O. (author) doctoral thesis 2023
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Wang, C. (author)The scale of the power system has been significantly expanded in recent decades. To gain real-time insights into the power system, an increasing number of sensors have been deployed tomonitor grid states, resulting in a rapidly growing number of measurement points. Simultaneously, there has also been a rise in the penetration of renewable energy...doctoral thesis 2023
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Lenferink, Luc (author)The ability to model other agents can be of great value in multi-agent sequential decision making problems and has become more accessible due to the introduction of deep learning into reinforcement learning. In this study, the aim is to investigate the usefulness of modelling other agents using variational autoencoder based models in partially...master thesis 2023
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de Pater, I.I. (author), Mitici, M.A. (author)Most Remaining Useful Life (RUL) prognostics are obtained using supervised learning models trained with many labelled data samples (i.e., the true RUL is known). In aviation, however, aircraft systems are often preventively replaced before failure. There are thus very few labelled data samples available. We therefore propose a Long Short-Term...journal article 2023
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Ghorbani, R. (author), Reinders, M.J.T. (author), Tax, D.M.J. (author)With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict different tasks such as activity recognition, sleep stage detection, or more general health status. However, supervised...conference paper 2023
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Krcek, M. (author), Perin, G. (author)Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different...journal article 2023