Investigating different models that can be used to define the characteristics that influence customer behaviour in the online grocery sector

In the Netherlands, online groceries are becoming increasingly popular, as are the challenges grocery companies face in meeting customers' rising demand for smaller and cheaper time slots while maintaining thin profit margins due to a highly competitive market. Customer choice modelling will be used to identify customers' behaviour and control...

On leveraging physical knowledge to augment neural network-based surrogate models for simulations

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...

Fatigue Load Emulation of Offshore Wind Turbines Using Advanced Surrogate Models

Understanding the fatigue load history of wind turbines is critical for taking decisions regarding the lifetime of a project. However, direct measurement of fatigue loads at each turbine in a wind farm is unfeasible. For this reason, surrogate models offer a useful alternative. In this thesis, a methodology for creating surrogate models for...

Finding a rich lower dimensional representation

Artificial neural networks are the key driver of progress in various semantic computer vision tasks such as age prediction and digit classification. For the successful application of neural network algorithms, the representation of the data is an important factor. A good representation can significantly simplify a regression or prediction task....

Surrogate Constitutive Models with Multi-fidelity Gaussian Processes for Composite Micromodels

Various engineering applications rely on efficient, high performance materials to overcome design challenges. This high performance can be achieved by engineering micro-heterogenous materials also known as composites. Since the behavior of composites relies heavily on micro-scale interactions between different components, modeling...

SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach

With the constantly evolving range of applications for technology the quality and amount of data constantly increases as well. In this growing data environment, there is a constant search to provide more value to all data that is available for as little effort as possible. Our research tries to add such additional value by diving into the...

The application of Deep Learning to improving low count SPECT imaging

Preclinical SPECT systems such as the U-SPECT have been able to achieve sub-half-millimetre spatial resolution with the use of cylindrical pinhole collimators. Utilising this type of collimator comes at the cost of a reduction in the total number of detection events that take place. In order to compensate for this either the activity of the...

Interpreting Information of Deep Neural Networks for Profiled Side Channel Analysis

Security has become ever more important in today's quickly growing digital world as the number of digital assets has quickly grown. Our thesis focuses on devices that compute a secure cryptographic operation such that information can be communicated or authenticated. The attack vector utilized is known as Profiled Side-Channel Analysis (SCA)...

Towards Understanding of Deep Learning in Profiled Side-Channel Analysis: Similarity of predictors measured and explained

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...