document

Affective Active Inference and Precision Estimation: A representation of affective feelings in active inference contsex

Timmer, Sebastiaan (author)

<div>As Neuroscience progresses, there is an increasing amount of research that endorses predictions and reducing of prediction errors as one of the main functions of the brain. active inference is a brain-inspired, mathematical framework that successfully implements this idea both in simulations as well as in robotics. The predictive nature of...

master thesis 2024

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Label synchronization for Hybrid Federated Learning in manufacturing and predictive maintenance

Llasag Rosero, Raúl (author), Silva, Catarina (author), Ribeiro, Bernardete (author), Santos, Bruno F. (author)

Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information....

journal article 2024

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Trajectory optimization with gradient descent for a variable-volume float

Snijders, Hanne (author)

In the realm of fluid dynamics and particle transport, the control of particle trajectories represents a formidable challenge. It would be useful to be able to optimally navigate an oceanographic float from one pre-set location to another by solely changing its buoyancy. In this thesis, a first step in discovering whether this is possible and...

bachelor thesis 2023

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Bayesian deep learning: Insights in the Bayesian paradigm for deep learning

Schipper, Wieger (author)

In this thesis, we study a particle method for Bayesian deep learning. In particular, we look at the estimation of the parameters of an ensemble of Bayesian neural networks by means of this particle method, called Stein variational gradient descent (SVGD). This method iteratively updates a collection of parameters and it has the property that...

master thesis 2023

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”How Much Data is Enough?” Learning curves for machine learning: Investigating alternatives to the Levenberg-Marquardt algorithm for learning curve extrapolation

Negru, Lucian (author)

The conducted research explores fitting algorithms for learning curves. Learning curves describe how the performance of a machine learning model changes with the size of the training input. Therefore, fitting these learning curves and extrapolating them can help determine the required data set size for any desired performance. <br/><br/>The...

bachelor thesis 2023

document

Deep Generative Design: A Deep Learning Framework for Optimized Spatial Truss Structures with Stock Constraints

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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Mini-Batching, Gradient-Clipping, First-versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression'

Harrison, Joe (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)

The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at...

conference paper 2023

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Neuro-GOMEA: Using Modern Evolutionary Algorithms to Train Neural Networks

Everse, Luc (author)

Neural networks (NNs) have, in recent years, become a major part of modern pattern recognition, and both theoretical and applied research evolve at an astounding pace. NNs are usually trained via gradient descent (GD), but research has shown that GD is not always capable of training very small networks. As a result, networks trained via GD are...

master thesis 2022

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Safety Performance Boundary Identification of Highly Automated Vehicles: A Surrogate Model-Based Gradient Descent Searching Approach

Wang, Y. (author), Yu, Rongjie (author), Qiu, Shuhan (author), SUN, J. (author), Farah, H. (author)

Highly automated vehicles (HAVs) have been introduced to the transportation system for the purpose of providing safer mobility. Considering the expected long co-existence period of HAVs and human-driven vehicles (HDVs), the safety operation of HAVs interacting with HDVs needs to be verified. To achieve this, HAVs' Operational Design Domain ...

journal article 2022

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Efficient Bayesian Uncertainty Estimation for nnU-Net

Zhao, Y. (author), Yang, C. (author), Schweidtmann, A.M. (author), Tao, Q. (author)

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its extraordinary performance, nnU-Net does not supply a measure of uncertainty to indicate its possible...

conference paper 2022

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First-order methods to recover the measurement operators and states of bipartite quantum correlations

Goverse, Vincent (author)

In this thesis, we start with giving a mathematical description of bipartite quantum correlations and how they are built up in the Tensor model. This is needed because we want to recover the state and the operators when only the bipartite quantum correlation is known. In the literature, there are see-saw algorithms to...

bachelor thesis 2021

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David and the vegetable factory: A Two-stage Optimization Model for Generating a Vegetable Factory's Facility Layout Using the Gradient Descent Approach

den Ouden, D. (author)

In this thesis a two-stage model is proposed, combining the idea of a two-stage model from Anjos and Vieira [1] with a gradient descent approach, much like proposed in Sikaroudi and Shahanaghi [42], in order to solve the facility layout problem for problems consisting of 8 and 12 departments . This method was chosen after analyzing the criteria...

master thesis 2020

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Predictive Modelling of Asphalt Concrete Functional Properties Using Multiple Linear Regression and Gradient Boosting

Martini, Giulia (author)

Despite therelevance of the road infrastructure, the mechanisms governing the mechanicalproperties of asphalt concrete pavements, are currently not sufficientlyunderstood. Many empirical models of different complexity are proposed in theliterature; however, (i) they all have below high (R2 =0.85) predictiveaccuracy; (ii)...

master thesis 2019

document

Measurement Light Bulb: Control

Damsteegt, Rob (author), van Dunné, Jippe (author)

This document describes the design process the prototype of a measurement light bulb which is able to project spherical harmonics of orders 0-1-2. Photographs of these projections can be combined to represent many light distributions. The measurement light bulb can be used in the field of research focused on the computation of the illumination...

bachelor thesis 2019

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In-flight model parameter and state estimation using gradient descent for high-speed flight

Li, S. (author), de Wagter, C. (author), de Visser, C.C. (author), Chu, Q. P. (author), de Croon, G.C.H.E. (author)

High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of micro air vehicles. Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. These properties hamper...

journal article 2019

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Comparison of bounds for optimal PMU placement for state estimation in distribution grids

Picallo, Miguel (author), Anta, Adolfo (author), De Schutter, B.H.K. (author)

The lack of measurements in distribution grids poses a severe challenge for their monitoring: Since there may not be enough sensors to achieve numerical observability, so load forecasts (pseudo-measurements) are typically used, and thus an accurate state estimation is not guaranteed. However, estimation is required to control distribution...

journal article 2019

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Modelling epidemic spreading phenomena processes on networks

Hamad, Abdul Aziz (author)

Spreading processes are ubiquitous in nature and societies, e.g. spreading of diseases and computer virus, propagation of messages, and activation of neurons. Computer viruses cause an enormous economic loss. Moreover, many illnesses/diseases still causing a serious threat to public health. For example, the outbreaks of circulating influenza...

master thesis 2018

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Membrane mediated interactions and pattern formation of conical inclusions on a flat membrane

Vos, Roel (author)

In this thesis, we investigate interactions between conical inclusions in a lipid bilayer membrane and make predictions about the patterns they form. To find these patterns, we derive an expression for the energy of a membrane as a function of the inclusion locations and search numerically for the pattern<br/>that gives minimum energy.<br/><br/...

bachelor thesis 2018

document

Design of an Aberration Correction System for a Deployable Space Telescope

van Marrewijk, Gijsbert (author)

Launch costs for high-resolution space telescopes for Earth observation can be reduced when the telescope mirrors are made deployable. However, such a system is subject to optical aberrations that decreases image quality. To counter these aberrations, an Aberration Correction System (ACS) is proposed that uses a deformable mirror (DM) which is...

master thesis 2018

document

GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications

Bhosale, P.S. (author), Staring, M. (author), Al-Ars, Z. (author), Berendsen, Floris F. (author)

Currently, non-rigid image registration algorithms are too computationally intensive to use in time-critical applications. Existing implementations that focus on speed typically address this by either parallelization on GPU-hardware, or by introducing methodically novel techniques into CPU-oriented algorithms. Stochastic gradient descent (SGD...

conference paper 2018