document

Dependent Type-Checking Modulo Associativity and Commutativity

Holten, Lucas (author)

Writing software that follows its specification is important for many applications. One approach to guarantee this is formal verification in a dependently-typed programming language. Formal verification in these dependently-typed languages is based on proof writing. Sadly, while proofs are easy to check for computers, writing proofs can be...

master thesis 2023

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One model, denoise them all!: A Comprehensive Investigation of Denoising Transfer Learning

Mullaj, Dajt (author)

Deep convolutional neural networks (CNNs) have achieved current state-of-the-art in image denoising, but require large datasets for training. Their performance remains limited on smaller real-noise datasets. In this paper, we investigate robust deep learning denoising using transfer learning. We explore the impact of dataset sizes, CNN parameter...

master thesis 2023

document

Applying QMIX to Active Wake Control: Multi-Agent Reinforcement Learning

van Selm, Jasper (author)

When multiple wind turbines are positioned close to one another, such as in a wind farm, wind turbines located downwind of other turbines are not 100% efficient due to wakes, negatively affecting the total power output of the wind farm. A way to mitigate the loss of power is to steer the wake away from the next turbine, which lowers the current...

bachelor thesis 2023

document

Aggregation and Prediction of Energy Consumption Data: What is the Aggregatino Level at which a Graph Neural Network Performs Optimally?

Timp, Lennard (author)

Electrical load forecasting, namely short-term load forecasting, is essential to power grids’ safe and efficient operations. The need for accurate short-term load forecasting becomes increasingly pressing with increased renewable energy sources, which are stochastic in their power supply. Most forecasting models are focused on the temporal...

bachelor thesis 2023

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Disaggregation of Community Level Energy Data to Individual Households: Using sequence-to-point learning

Verlaan, Stan (author)

Non-intrusive load monitoring (NILM) is a well-researched concept that aims to provide insights into individual appliance energy usage without the need for dedicated meters. This paper explores the possibility of applying the NILM concept to disaggregate energy data from a community level to a household level. By doing so, it addresses privacy...

bachelor thesis 2023

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Improving the Generalisability of Deep Learning NILM Algorithms using One-Shot Transfer Learning: Can one-shot transfer learning be leveraged to enhance the performance of a CNN-based NILM algorithm on unseen data?

Verschuren, Wim (author)

Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one...

bachelor thesis 2023

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Partial Hierarchy Appliance Modelling In Household Energy Consumption: Utilizing ARMA based methods to improve the prediction of household energy consumption

Kienhuis, Maurits (author)

The ever-evolving power grid is becoming smarter and smarter. Modern houses come with smart meters and energy conscious consumers will buy additional smart meters to place in their home to help monitor their energy consumption. This new smart technology also opens the door to more accurate power consumption forecasting. In this study we look at...

bachelor thesis 2023

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Aggregation of energy consumption forecasts across spatial levels: Using CNN-LSTM forecasts of lower spatial levels to forecast on higher spatial levels

Borst, Twan (author)

Bottom up load forecasting, is a technique where energy consumption forecasts are made on lower spatial levels, after which the resulting forecasts are aggregated to form forecasts of higher spatial levels. With the current move to renewable energy sources and the importance of reducing the strain on an already congested electricity grid,...

bachelor thesis 2023

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Treewidth based algorithms for Tree Containment in phylogenetics

Huijsman, Robbert (author)

TreeContainment is a well-known problem within phylogenetics, which asks whether a binary phylogenetic tree is embedded in a binary phylogenetic network. For this problem, Jones, Weller and van Iersel (2022) have created an algorithm that uses dynamic programming on tree-decompositions to achieve a running time that is exponential in the tree...

master thesis 2023

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Non-destructive Infield Quality Estimation of Strawberries using Deep Architectures

Jol, Cees (author)

Strawberries have a short shelf-life time and thus need to be harvested at the right time to reduce waste. To this end, information about quality attributes is useful. Recently, many computer vision methods have been proposed. Most literature analyzes postharvest, which means that strawberries can only be analyzed after harvesting. As a result,...

master thesis 2022

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TrustVault: A privacy-first data wallet for the European Blockchain Services Infrastructure

Jacobino, Sharif (author)

The European Union is on course for introducing a European Digital Identity that will be available to all EU citizens and businesses. This will have a huge impact on how citizens and businesses interact online. Big Tech companies currently dictate how digital identities are used. As a result they have amassed huge amounts of private user data....

master thesis 2022

document

Efficient Shapley Value Approximation Methods: for Cost Redistribution in Energy Communities

Cremers, Sho (author)

With the emergence of energy communities, where a number of prosumers (consumers with their own energy generation) invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value, a solution concept in cooperative game theory initially...

master thesis 2022

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Optimising BESS Control Strategies for Congestion and Price Arbitrage

Rietveld, Jip (author)

Renewable energy generation projects are often measured by their peak capacity. A wind farm rated at 25 MW will generate 25 MW of power under the right circumstances. This peak capacity is reached very little in practice. However, these generators are forced to purchase grid operation infrastructure that can handle this peak generation event....

master thesis 2022

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Robust Attack Graphs

Mouwen, Dennis (author)

Every day, Intrusion Detection Systems around the world generate huge amounts of data. This data can be used to learn attacker behaviour, such as Techniques, Tactics, and Procedures (TTPs). Attack Graphs (AGs) provide a visual way of describing these attack patterns. They can be generated without expert knowledge and vulnerability reports. The...

master thesis 2022

document

Learned equivariance in Convolutional Neural Networks

Motyka, Tomasz (author)

Aside from developing methods to embed the equivariant priors into the architectures, one can also study how the networks learn equivariant properties. In this work, we conduct a study on the influence of different factors on learned equivariance. We propose a method to quantify equivariance and argue why using the correlation to compare...

master thesis 2022

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Algorithmic study of 2-interval graphs

Simon, Alexandre (author)

Interval graphs play an important role in graph theory and have intensively been studied for over sixty years due to their wide range of applications and because most NP-hard problems on general graphs become solvable in linear time on interval graphs. However, the class of interval graphs is restricted and does not even contain a cycle of...

master thesis 2021

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Comparing Model-Free Deep Reinforcement Learning Algorithms on Stock Market

Meral, Murat Kaan Meral (author)

Automated asset trading is a crucial method used by financial entities such as investment firms or hedge funds. It allows them to allocate their capital in order to maximize their rate of returns. In scientific literature, there are multiple models suggested to solve this problem. However, these models either lack the complexity to understand...

bachelor thesis 2021

document

Comparing Deep Reinforcement Learning Approaches for Sparse Reward Settings with Discrete State-Action Spaces

Çapanoğlu, Alp (author)

One of the most challenging types of environments for a Deep Reinforcement Learning agent to learn in are those with sparse reward functions. There exist algorithms that are designed to perform well in settings with sparse rewards, but they are often applied to continuous state-action spaces, since economically relevant problems like robotic...

bachelor thesis 2021

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Short-Term Traffic Forecasting on Highways Using Neural Networks: A Congestion Pattern-Based Assessment

Simons, Koen (author)

As traffic demands are ever increasing and building new infrastructure poses challenges in densely populated areas, it is important to optimally utilise existing infrastructure. Short-term traffic forecasting can help with this task, as its predictions can help to prevent congestion by rerouting vehicles. Recently, neural networks developed for...

master thesis 2021

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Collaborative Private Decision-Tree Evaluation using (Multi-Key) Fully Homomorphic Encryption: with applications to Risk-Adaptive Access Control

van der Ende, Dieuwke (author)

Decision-tree evaluation is a widely-used classification approach known for its simplicity and effectiveness. Decision-tree models are shown to be helpful in classifying instances of fraud, malware, or diseases and can be used to make dynamic, flexible access decisions within an access-control system. These applications often require sensitive...

master thesis 2021