A Methodology for Damage Detection Using Unsupervised Learning in the Field of Structural Health Monitoring: Based on Gaussian Mixture Modeling

This thesis aims to investigate the feasibility of developing a successful unsupervised Structural Health Monitoring (SHM) methodology to detect damage in structures, specifically bridges. Detecting damage, especially in its earliest stages, is challenging, thus prompting the need for robust and effective methods. The success of such a...

Machine learning-based identification of precursors of extreme events in chaotic systems

Abrupt and rapid high-amplitude changes in a dynamical system’s states known as extreme events appear in many processes occurring in nature, such as drastic climate patterns, rogue waves, or avalanches. These events often entail catastrophic effects, therefore their description and prediction is of great importance. Nevertheless, because of...

MalPaCa Feature Combination: Which packet header features and combination thereof are the most generalizable, private and easy to extract to cluster malicious behavior?

MalPaCa is an unsupervised clustering tool, which the main purpose is to cluster unidirectional network connections based on network behavior. The clustering is only based on non-intrusive (private) packet features such as transport and network header fields, and thus it has a strong potential use-case. This paper focuses on feature extraction...

Global shoreline forecasting with a clustering approach

Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior...

Learning State Machines in Real-Time on a Small Dedicated Hardware Device

The Internet is a technology that was invented in the 1960s and was used only by a few users to do simple communications between computers. Fast forward to 2020, the Internet has become a technology that is being used by billions of users. It allows users to communicate with each other across the world and even allows users to access data...

Investigating the MetOp ASCAT vegetation parameters

In this study, an unsupervised classification approach is used to investigate and characterize the spatial and temporal variability of MetOp-A ASCAT backscatter (σ◦) and the TUW SMR vegetation parameters across mainland France between 2007 and 2017. Currently, soil moisture data is retrieved from ASCAT backscatter measurements using the TU Wien...

Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques,...

Unsupervised approach to bunching swings phenomenon analysis

We perform analysis of public transport data from March 2015 from The<br/>Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location (AVL) and automated fare collection (AFC) data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised...