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Valsamos, Charalampos Michail (author)
Nowadays with the growth of social media, users upload millions of photos in different platforms online. Researchers in the field of computer vision devote their time and effort to analyze images in order to gain valuable insight. Data<br/>analysis and classification can be impeded by different factors. One of which is the image filters that are...
master thesis 2019
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Głowacki, Piotr (author)
Metamaterials are a new class of materials where the properties crucially depend on the design of the unit cell that is periodically repeated in space. In this study a new metamaterial unit cell concept has been proposed, inspired by a class of space structures known as deployable masts. The ability of these structures to contract to a fraction...
master thesis 2019
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Scholten, Jan (author)
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems. Modelling and control design is longer required, which paves the way to numerous in- novations, such as optimal control of evermore sophisticated robotic systems, fast and efficient scheduling and logistics, effective personal drug dosing...
master thesis 2019
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Wout, Daan (author)
A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is the necessity for extensive amounts of data for a suitable control policy. For systems that concern physical application, acquiring this vast amount of data might take an extraordinary amount...
master thesis 2019
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Heyer, Stefan (author)
In recent years Adaptive Critic Designs (ACDs) have been applied to adaptive flight control of uncertain, nonlinear systems. However, these algorithms often rely on representative models as they require an offline training stage. Therefore, they have limited applicability to a system for which no accurate system model is available, nor readily...
master thesis 2019
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Echaniz Soldevila, Ignasi (author)
This master thesis aims to gain new empirical insights into longitudinal driving behavior by means of the enumeration of a new hybrid car-following (CF) model which combines parametric and non parametric formulation. On one hand, the model, which predicts the drivers acceleration given a set of variables, benefits from innovative machine...
master thesis 2017
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van Dijk, Mick (author)
Global medical use of azole antifungals and echinocandins has led to an enormous increase in resistant <i>Candida</i> species, that are most commonly associated with fungal infections. A possible mechanism causing resistance are single or simultaneous point mutations in the genes responsible for encoding antifungal target enzymes. The aim of...
master thesis 2019
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Kroezen, Dave (author)
Online Reinforcement Learning is a possible solution for adaptive nonlinear flight control. In this research an Adaptive Critic Design (ACD) based on Dual Heuristic Dynamic Programming (DHP) is developed and implemented on a simulated Cessna Citation 550 aircraft. Using an online identified system model approximation, the method is independent...
master thesis 2019
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Humala, Bontor (author)
Providing detailed appliance-level energy consumption information helps consumers to understand their usage behavior and encourages them to optimize their energy usage. Non-intrusive load monitoring (NILM) or energy disaggregation aims to estimate appliance-level energy consumption data from the aggregate consumption data of households. NILM...
master thesis 2018
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Panis, Marijke (author)
In 2008 the Red Cross Red Crescent (RCRC) started with Forecast-based Financing pilots to improve existing Early-Warning Early Action systems. Forecast-based financing is a new methodology to prepare, deliver and respond in a more effective and efficient manner, based on hazard forecasts. Actions are triggered when a forecast exceeds a danger...
master thesis 2019
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Meulman, Erik (author)
Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostly used to repeatedly optimize different instances of a single problem class, a setting in which specialized algorithms generally perform better. In this paper, we introduce the...
master thesis 2019
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Houlder, Michael (author)
Mechanical metamaterials are a new emerging class of materials which achieve properties outside the bounds of conventional materials. A metamaterial consists of a unit cell which is periodically repeated in space. In this study, a new metamaterial unit cell is proposed, derived from a class of space structures known as deployable masts. What...
master thesis 2019
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Chatzopoulos Vouzoglanis, Konstantinos (author)
Eutrophication processes in coastal waters are becoming more prominent as a result of high nutrient discharges from intensive agriculture and increased urban waste. These processes can be devastating for local ecosystems and lead to dissolved oxygen depletion, which applies considerable stress on aquatic organisms. For ecosystems to preserve...
master thesis 2019
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Daw, Karim (author)
This project seeks to investigate the multiple materialisation approaches of timber as a construction material for a residential/mixed use building sited in Berlin, Germany. Seeing as there is a major housing crisis Europe wide with the added concern of the growing environmental concern, this proposal aims to be the embodiment of applied...
master thesis 2019
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Hes, Robin (author)
This thesis explores the use of machine learning techniques in an effort to increase insurer competitiveness. It asks whether it is possible to accurately estimate the expected financial loss of a given insurance contract and how this information can be used to gain a competitive edge in the business. To answer these questions, some basic...
master thesis 2018
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Shriram, Sharad (author)
Modern web information systems use machine learning models to provide personalized user services and experiences. However, machine learning models require annotated data for training, and creating annotated data is done through crowdsourcing tasks. The content used in annotation crowdsourcing tasks like medical records and images might contain...
master thesis 2019
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Noppen, Marko (author)
Adaptive optics are widely used to correct the wavefront distortion imposed by atmospheric turbulence. Focal plane phase retrieval from intensity measurements has advantages due to the ease of implementation, potential broader application, less computations, low cost, high system bandwidth, simplified hardware and less calibration. To cope with...
master thesis 2019
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van der Valk, Daan (author)
Side-channel attacks (SCA) aim to extract a secret cryptographic key from a device, based on unintended leakage. Profiled attacks are the most powerful SCAs, as they assume the attacker has a perfect copy of the target device under his control. In recent years, machine learning (ML) and deep learning (DL) techniques have became popular as...
master thesis 2019
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Plaisant van der Wal, Renzo (author)
Machine learning methods are explored in an attempt to achieve better predictive performance than the legacy rule-based fraud detection systems that are currently used to detect fraudulent car insurance claims. There are two key principles that lead the exploration of machine learning techniques and algorithms in this thesis, namely, the...
master thesis 2018
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Reinbergen, Hugo (author)
The analysis of lawfully intercepted traffic is a key part of many investigations of criminal activity. This makes it vitally important that the intercepted data is correct and that issues with the configuration of the network or interception solution do not contain errors. A late discovery of a problem in either the network setup or the traffic...
master thesis 2019
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