AI-Assisted Design & Optimization for Predictive Maintenance: A Case Study using Deep Learning and Search Metaheuristics for Structural Health Monitoring in Aviation

One of the classical solutions to maintain the aircraft structural integrity is to rely on the analysis of non-destructive testing (NDT) inspector with various inspection methods. However, it is relatively expensive in matter of time and costs to train human resources until the certification is reached. Further, in majority of the cases of...

In silico exploration of post-burn contraction using uncertainty quantification

Burns can make patients’ lives quite miserable. Apart from prominent and thickened, or hypertrophic, scars, the skin may be characterized by contraction. When this contraction is so severe that the patient loses joint mobility, it is called contracture. Then a patient may have difficulties with sports or other daily activities. The consequence...

Machine learning and randomness in mechanical metamaterials

On Safety in Machine Learning

This dissertation focuses on safety in machine learning. Our adopted safety notion is related to robustness of learning algorithms. Related to this concept, we touch upon three topics: explainability, active learning and learning curves.<br/><br/>Complex models can often achieve better performance compared to simpler ones. Such larger models are...

Improving satellite remote sensing methodologies for analyzing landscape dynamics in arid environments with focus on Egypt

Anomaly Detection and Synthetic Data Generation for Power Systems Using Autoencoder Neural Networks

The scale of the power system has been significantly expanded in recent decades. To gain real-time insights into the power system, an increasing number of sensors have been deployed tomonitor grid states, resulting in a rapidly growing number of measurement points. Simultaneously, there has also been a rise in the penetration of renewable energy...

Kinematics Computing for Soft Robots: Method based on Geometric Computing and Machine Learning

Soft robots that are built from materials with mechanical properties similar to those of living tissues can achieve tasks like never before in comparison to conventional rigid robots. Powered by the compliance of soft materials and novel structure designs, complex motion (e.g., bending, twisting, and extension) can be accomplished in robotic...

Sensors, algorithms, and representations for efficient environment perception

that do not require any action from the drivers for a short period of time. Although these systems are still limited and only reliable in certain situations, it shows the general trend: cars will become more and more autonomous. The reasons why people and companies are eagerly anticipating fully autonomous cars are manifold: self-driving...

Influence and Detection of Vacuum Bag Leakages in Composites Manufacturing

Composites are increasingly used in the aerospace industry due to their lightweight potential and flexible design options. The most widespread manufacturing process for large components made of fibre composites is still the open mould process. In this process, a composite component is placed on a mould and hermetically sealed with a vacuum bag...

Explainable Artificial Intelligence for human-AI collaboration

As a society, we have come to notice the influence and impact Artificially Intelligent (AI) agents have on the way we live our lives. For these AI agents to support us both effectively and responsibly, we require an understanding on how they make decisions and what the consequences are of these decisions. The research _field of Explainable...

On Fine-grained Temporal Emotion Recognition in Video: How to Trade off Recognition Accuracy with Annotation Complexity?

Fine-grained emotion recognition is the process of automatically identifying the emotions of users at a fine granularity level, typically in the time intervals of 0.5s to 4s according to the expected duration of emotions. Previous work mainly focused on developing algorithms to recognize only one emotion for a video based on the user feedback...

Automated Defect Analysis using Optical Sensing and Explainable Artificial Intelligence for Fibre Layup Processes in Composite Manufacturing

In modern aircraft, structural lightweight composite components are increasingly<br/>used. To manufacture these components in a costeffective way, robust production systems for the manufacturing of complex fibre composite components are necessary. A rather novel but already established process for fibre material deposition is the Automated Fibre...

Machine Learning and Counter-Terrorism: Ethics, Efficacy, and Meaningful Human Control

Machine Learning (ML) is reaching the peak of a hype cycle. If you can think of a personal or grand societal challenge – then ML is being proposed to solve it. For example, ML is purported to be able to assist in the current global pandemic by predicting COVID-19 outbreaks and identifying carriers (see, e.g., Ardabili et al. 2020). ML can make...

Automatic synthesis of supervisory control systems

Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. Sequential composition focuses on the interaction between a collection of pre-designed...

Quantifying Pathology in Diffusion Weighted MRI

In this thesis algorithms are proposed for quantification of pathology in Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) data. Functional evidence for brain diseases can be explained by specific structural loss in the white matter of the brain. That is, certain biomarkers may exist where the disease inhibits improper functioning. Axonal...