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Vermeer, Jort (author)
The Random Finite Element Method (RFEM) is a robust stochastic method for slope reliability analysis that incorporates the spatial variability of soil properties. However, the extensive computational time associated with the direct Monte Carlo simulation limits its practical application. To overcome this problem, this study investigates the use...
master thesis 2024
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Kappé, Jeroen (author)
The city of Amsterdam faces the challenge of monitoring and assessing 200 kilometers of historic quay walls, of which much is deemed to be in poor condition. A key monitoring technique used is photogrammetry resulting in deformation testing. The fundamental data source forming the basis of this deformation analysis is a collection of overlapping...
master thesis 2024
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Yin, Junzhe (author)
The thesis explores an innovative technique for enhancing the precision of short-term weather forecasts, particularly in predicting extreme weather phenomena, which present a notable challenge for existing models such as PySTEPS due to their volatile behavior. Leveraging precipitation and meteorological data sourced from the Royal Netherlands...
master thesis 2024
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Graauw, Mitchel (author)
The dose computation algorithm, or dose engine, is one of the fundamental parts of radiotherapy treatment planning. These algorithms predict how the dose will be distributed inside the patient.<br/>Current dose engines are mainly based on either Monte Carlo simulations (MC) or pencil beam algorithms (PBA). MC being very precise, but relatively...
master thesis 2024
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Roy, Ankush (author)
Extreme precipitation, like floods and landslides, poses major risks to safety and the economy, underscoring the need for sophisticated weather forecasting to predict these events accurately, enhancing readiness and resilience. Nowcasting, which uses real-time atmospheric data to predict short-term weather, is key in addressing this challenge....
master thesis 2024
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Mertzanis, Nick (author)
This thesis investigates the integration of algorithm unrolling and genetic algorithms (GA) for optimizing pump scheduling in water distribution systems (WDS), a critical component for ensuring energy-efficient water delivery. In the context of modern civilization’s reliance on clean, affordable water for diverse uses, the operation of a WDS,...
master thesis 2024
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Rijpkema, Gerben (author)
Forensic microtrace investigation relies on a time- and labour-intensive process of manually analysing samples via microscopy. To aid forensic experts in their investigations, an image recognition model for microtrace localisation and classification is needed. This work investigates the trace recognition accuracy that can be achieved by...
master thesis 2024
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Lee, Y. (author)
E-learning has shifted the traditional learning paradigms in higher education, offering more flexible, ubiquitous, and personalized learning experiences. The previous years COVID-19 pandemic required a re-calibration of education to accommodate virtual learning environments from the traditional classroom-based education. Widespread learning...
doctoral thesis 2024
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Niemantsverdriet, Ruben (author)
The periconceptional period, encompassing the embryonic phase, is a critical window where a majority of reproductive failures, pregnancy complications, and adverse pregnancy outcomes arise. The Carnegie staging system comprises 23 stages which are based on embryonic morphological development. This allows for the assessment of normal and abnormal...
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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Doornbos, Marie-Claire (author)
Objective: This study introduces a novel deep-learning-based orientation recognition approach for detecting intraoperative lung orientation during robot-assisted anatomical resections, including lobectomy and segmentectomy. This method can potentially aid in anatomical structure identification, facilitate training and education, improve...
master 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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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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Ahmad, Bashar I. (author), Rogers, Colin (author), Harman, Stephen (author), Dale, Holly (author), Jahangir, Mohammed (author), Antoniou, Michael (author), Baker, Chris (author), Newman, Mike (author), Fioranelli, F. (author)
Automatic target classification or recognition is a critical capability in noncooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognizing targets, including miniature unmanned air systems or drones (i.e., small, mini, micro, and nano...
journal article 2024
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Lu, C.J. (author)
Real-time nonintrusive occupancy estimation can maximize the use of existing sensors to infer occupant information in buildings with the advantages of fewer privacy concerns and fewer extra device costs. Recently, many deep learning architectures have proven effective in estimating occupancy directly from raw sensor data. However, some...
journal article 2024
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Martinez Lopez, V.A. (author), van Urk, G.A. (author), Doodkorte, P.J.F. (author), Zeman, M. (author), Isabella, O. (author), Ziar, H. (author)
Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short...
journal article 2024
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Tollenaar, Veronica (author), Zekollari, Harry (author), Pattyn, Frank (author), Rußwurm, Marc (author), Kellenberger, Benjamin (author), Lhermitte, S.L.M. (author), Izeboud, M. (author), Tuia, Devis (author)
In some areas of Antarctica, blue-colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi-sensor remote sensing data (MODIS, RADARSAT-2, and TanDEM-X) in a deep learning framework, we map blue ice across the continent at 200...
journal article 2024
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Sadrtdinova, Renata (author), Perez, Gerald Augusto Corzo (author), Solomatine, D.P. (author)
Kazakhstan is recently experiencing an increase in drought trends. However, low-capacity probabilistic drought forecasts and poor dissemination have led to a drought crisis in 2021 that resulted in the loss of thousands of livestock. To improve drought forecasting accuracy, this study applies Machine Learning and Deep Learning (ML and DL)...
journal article 2024
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Hu, M. (author), Yue, N. (author), Groves, R.M. (author)
With the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due...
journal article 2024
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van Nuland, T.D.H. (author)
The universal approximation theorem is generalised to uniform convergence on the (noncompact) input space R<sup>n</sup>. All continuous functions that vanish at infinity can be uniformly approximated by neural networks with one hidden layer, for all activation functions φ that are continuous, nonpolynomial, and asymptotically polynomial at ±∞...
journal article 2024
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