This article investigates the use of hybrid machine learning (HML) for the detection of
anomalous multivariate time-series (MVTS). Focusing on a specific industrial use-case from …

This article presents a new approach of quality control to vibro ground improvement
techniques based on hybrid machine learning (ML), ie, a combination of classical analysis …

This thesis investigates the optimization of autoencoders for the classification of multivariate
real-time measurement data emanating from production machinery. The objective is to …

Yield estimation is an important step in the analysis of the performance of the IC
manufacturing process and relies heavily on the modelling of multivariate distributions. In …

This thesis is an exploratory work of unsupervised anomaly detection in multi-variate time-
series machine data with methods originating from natural language processing. The …

Spatial structures are widely used in large buildings and bridges. These structures are
usually composed of thousands of members and joints. The damage of members and joints …

This paper presents a system that combines KPI with autoencoders to implement a hybrid
machine learning system. The goal here is to investigate workflows which permit the site …