Searched for: subject%3A%22network%22
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Yuan, Zibo (author)
Implied volatility is critical in financial markets, especially for option pricing. Traditional methods for its calculation sometimes are not well suited to some scenarios. Recent developments in neural networks have provided more efficient alternatives.<br/><br/>Leveraging advances in quantum computing, our research introduces quantum neural...
master thesis 2024
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Boersma, Femke (author)
The consequences of climate change are becoming more and more visible. A significant cause of this is CO2 emissions; the shipping sector is responsible for 3% of global CO2 emissions. As a result, the Fourth IMO GHG Study 2020 presents pathways to reduce the GHG emission of the shipping industry by 50% by 2050. Recent IMO goals have overtaken...
master thesis 2024
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Nelemans, Peter (author)
Fully distributed hydrological models take into account the spatial variability of a catchment, and allow for assessing its hydrological response at virtually any location. However, these models can be time-consuming when it comes to model runtime and calibration, especially for large-scale catchments. Meanwhile, deep learning models have shown...
master thesis 2024
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Kouwenhoven, Robin (author)
Foremost among the challenges of the Bitcoin blockchain is the scalability bottleneck. To address this issue, the Lightning Network, a payment channel network, was created. Lightning is a payment channel network that is source-routed and uses onion routing, like Tor. However, unlike Tor, the routing path is determined by optimizing a cost...
master thesis 2024
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Kljaić, Korina (author)
The thesis focuses on creating a solution aimed at reducing suffering in earthquake prone regions. The research done resulted in the development of Embrace, a wearable communication device designed to alleviate human suffering during seismic events in seismically active regions like Zagreb. Commencing with an exploration of challenges faced by...
master thesis 2024
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Verhagen, Alexandra (author)
Accurate capacity planning is essential to ensure uninterrupted services and network stability through peak hours for the transport core network of KPN. This involves a trade-off between minimizing the risks of capacity shortages and costs of capacity expansions. High network loads are occurring more frequently and their magnitude is increasing....
master thesis 2024
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Spitzbarth, B. (author)
Nature has inspired countless researchers in their quest to understand the phenomena we observe and utilise their findings to develop new technologies. This becomes especially apparent in systems chemistry, which heavily draws inspiration from natural systems in its pursuit for the understanding and development of chemical reaction networks ...
doctoral thesis 2024
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Hettema, Bart (author)
Neuromorphic computing can be used to efficiently implement spiking neural networks.<br/>Such spiking neural networks can be used in edge AI applications, where low power consumption is paramount.<br/>The use of analog components allows for extremely low power implementations.<br/>This thesis contributes the designs of an analog spike generator,...
master thesis 2024
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Kiste, Amund (author)
Solving Partial Differential Equations (PDEs) in engineering such as Navier-Stokes is incredibly computationally expensive and complex. Without analytical solutions, numerical solutions can take ages to simulate at great expense. In order to reduce this cost, neural networks may be used to compute approximations of the solution for use during...
bachelor thesis 2024
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Taklimi, Sam (author)
The objective of this project is to train a model that transforms a tree with its foliage into only its branch structure. This is achieved by employing machine-learning techniques, specifically Generative Adverserial Networks (GANs). By utilizing the proposed method, a predictive model is built that automatically minimizes its own error function...
bachelor thesis 2024
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Lacombe, Pablo (author)
This paper presents a comprehensive exploration of a novel method combining Principal Component Analysis (PCA) and Neural Networks (NN) to efficiently solve Partial Differential Equations (PDEs), a fundamental challenge in modeling a wide range of real-world phenomena. Our research extends the work of Bhattacharya et al. by focusing on PCA for...
bachelor thesis 2024
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Hueber, Paul (author)
Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics...
master thesis 2024
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Hashemi, L. (author)
The demand for sustainable and clean energy sources has become increasingly vital in addressing the challenges of climate change and energy security. Hydrogen, with its high energy density and potential for carbon-free energy conversion, has emerged as a promising candidate for future energy systems. Efficient storage and retrieval of hydrogen...
doctoral thesis 2024
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Qin, Xusen (author)
Neural networks have made significant progress in domains like image recognition and natural language processing. However, they encounter the challenge of catastrophic forgetting in continual learning tasks, where they sequentially learn from distinct datasets. Learning a new task can lead to forgetting important information from previous tasks,...
master thesis 2024
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Papenhuijzen, Daan (author)
The importance of climate change is getting increasingly more attention from all stakeholders in urban development. This results in the energy network operator being increasingly involved in the partnerships needed to realize new urban area development projects. Current projects have been experiencing delays due to the electricity grid not being...
master thesis 2024
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Sebus, Siert (author)
The Deep Neural Network (DNN) has become a widely popular machine learning architecture thanks to its ability to learn complex behaviors from data. Standard learning strategies for DNNs however rely on the availability of large, labeled datasets. Self-Supervised Learning (SSL) is a style of learning that allows models to also use unlabeled data...
master thesis 2024
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Achterberg, M.A. (author)
The COVID-19 pandemic has had a disruptive impact on healthcare systems and everyday life of the majority of the people around the globe. Despite many years of research on network epidemiology, many key aspects of disease transmission and in particular the response of people to the spread of a disease, remain poorly understood. On the basis of...
doctoral thesis 2024
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Fu, Peng (author)
Keyword spotting (KWS) is an essential component of voice recognition services on smart devices. Its always-on characteristic requires high accuracy and real-time response. Also, low power consumption is another key demand for KWS devices. In previous research, neural networks have become popular for KWS tasks for their accuracy compared to...
master thesis 2024
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Tognan, A. (author), Patanè, Andrea (author), Laurenti, L. (author), Salvati, Enrico (author)
Accurate fatigue assessment of material plagued by defects is of utmost importance to guarantee safety and service continuity in engineering components. This study shows how state-of-the-art semi-empirical models can be endowed with additional defect descriptors to probabilistically predict the occurrence of fatigue failures by exploiting...
journal article 2024
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Huber, Katharina T. (author), van Iersel, L.J.J. (author), Janssen, R. (author), Jones, M.E.L. (author), Moulton, V.L. (author), Murakami, Yukihiro (author), Semple, Charles (author)
This paper studies the relationship between undirected (unrooted) and directed (rooted) phylogenetic networks. We describe a polynomial-time algorithm for deciding whether an undirected nonbinary phylogenetic network, given the locations of the root and reticulation vertices, can be oriented as a directed nonbinary phylogenetic network....
journal article 2024
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