Searched for: subject%3A%22Neural%255C+networks%22
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Xu, J. (author)
Deep Neural Networks (DNNs) have found extensive applications across diverse fields, such as image classification, speech recognition, and natural language processing. However, their susceptibility to various adversarial attacks, notably the backdoor attack, has repeatedly been demonstrated in recent years. <br/>The backdoor attack aims to...
doctoral thesis 2025
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Vos, Geert (author)
This research has aimed to investigate the possibility of applying a neural network algorithm into the structural design process of bascule bridge leaves, by creating a workflow in Grasshopper. The demand for this tool, originates from the fact that the current design process is experienced as linear and slow, and does not fit the dynamic design...
master thesis 2024
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Keulen, D. (author)
Vaccination is a strong and effective way to prevent spreading of infectious diseases and promotes global health. In the future, the importance of vaccines is expected only to increase, driven by factors such as increased international traveling, higher healthcare expenditures, and a growing population. To meet the growing demands, it is...
doctoral thesis 2024
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Determan, Brendan (author)
The inspection of extensive and hard-to-access sewer systems is a challenging and expensive task. As these networks age and need to comply with stricter health and environmental regulations, the demand for effective inspection solutions has increased. The introduction of technologies like CCTV (closed-circuit television) and SSET (sewer scanner...
master thesis 2024
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Krijgsman, Floris (author)
Supercritical CO<sub>2</sub> (SCO<sub>2</sub>) is a promising alternative to traditional working fluids in heat pumps and power cycles due to its high density, thermal efficiency, and stability. These properties allow for the design of more compact and efficient equipment. However, accurately modeling supercritical heat transfer, especially near...
master thesis 2024
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Usa, Lyana (author)
Olfactory learning in <i>Drosophila </i>larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory...
master thesis 2024
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Corvi, Giovanni (author)
The rapidly growing volume of parcel shipments is straining transportation and logistics sectors, highlighting the need for innovative solutions to optimize packing and loading processes. The online bin packing problem (BPP), an NP-hard computational problem, finds practical applications in numerous sectors, including modern packaging and...
master thesis 2024
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Neagu, Alex (author)
As the power system grows more complex and active, equivalent models have become a solution for modelling parts of the network that have limited observability or are confidential or too complex to simulate otherwise. In the past decade, this topic has also made its way to distribution networks because of its transition towards an active network,...
master thesis 2024
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Andringa, Jilles (author)
Machine learning models have improved Prognostics and Health Management (PHM) in aviation, notably in estimating the Remaining Useful Life (RUL) of aircraft engines. However, their 'black-box' nature limits transparency, critical in safety-sensitive aviation maintenance. Explainable AI (XAI), particularly Counterfactual (CF) explanations, offers...
master thesis 2024
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ten Voorde, Maarten (author)
The use of machine learning (ML), especially neural networks, in modeling control systems has shown promise, particularly for systems with complex physics. However, applying these models in safety-critical areas requires reliable verification and control synthesis methods due to their inherent complexity. Formal methods, using stochastic finite...
master thesis 2024
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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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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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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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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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Versteeg, Rogier (author), Pool, D.M. (author), Mulder, Max (author)
This article discusses a long short-term memory (LSTM) recurrent neural network that uses raw time-domain data obtained in compensatory tracking tasks as input features for classifying (the adaptation of) human manual control with single- and double-integrator controlled element dynamics. Data from two different experiments were used to train...
journal article 2024
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Schweidtmann, A.M. (author), Zhang, Dongda (author), von Stosch, Moritz (author)
The term hybrid modeling refers to the combination of parametric models (typically derived from knowledge about the system) and nonparametric models (typically deduced from data). Despite more than 20 years of research, over 150 scientific publications (Agharafeie et al., 2023), and some recent industrial applications on this topic, the...
review 2024
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