openHSU – Research Showcase

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales—from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Structural health monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an “input–output” and an “output-only” method.

Automated damage detection is an integral component of each structural health monitoring (SHM) system. Typically, measurements from various sensors are collected and reduced to damage-sensitive features, and diagnostic values are generated by statistically evaluating the features. Since changes in data do not only result from damage, it is necessary to determine the confounding factors (environmental or operational variables) and to remove their effects from the measurements or features. Many existing methods for correcting confounding effects are based on different types of mean regression. This neglects potential changes in higher-order statistical moments, but in particular, the output covariances are essential for generating reliable diagnostics for damage detection. This article presents an approach to explicitly quantify the changes in the covariance, using conditional covariance matrices based on a non-parametric, kernel-based estimator. The method is applied to the Munich Test Bridge and the KW51 Railway Bridge in Leuven, covering both raw sensor measurements (acceleration, strain, inclination) and extracted damage-sensitive features (natural frequencies). The results show that covariances between different vibration or inclination sensors can significantly change due to temperature changes, and the same is true for natural frequencies. To highlight the advantages, it is explained how conditional covariances can be combined with standard approaches for damage detection, such as the Mahalanobis distance and principal component analysis. As a result, more reliable diagnostic values can be generated with fewer false alarms.

Die Modernisierung der industriellen Wertschöpfung hin zur Industrie 4.0 erfordert eine Digitalisierung der Produktionssysteme und ihrer Produktionskomponenten. Digitale Zwillinge nehmen als digitale Abbildung der Komponenten eine besondere Rolle ein und bieten eine hohe Anwendungsbreite für unterschiedliche industrielle Produkte. Ihre Dienstleistungen und ihre Anwendungen sind auf die Produkte, die sie abbilden, zugeschnitten, so dass im Zuge des Entwicklungsvorgehens der Anwendungskontextes des Digitalen Zwillings in der Form von Use Cases festzulegen und aus diesem Kontext die Anforderungen zu erheben sind. Diese Arbeit zielt darauf ab, eine Methode für eine zielorientierte Erhebung der Informationsanforderungen an den Digitalen Zwilling eines industriellen Produktes zu entwickeln. Eine erste Teilmethode zur zielgerichteten Auswahl ermöglicht es, Use Cases für die eigene Zielumgebung zu spezifizieren. Zugleich wird auch eine Sammlung produktunabhängiger Beschreibungen hergeleitet, um bestehende Entwicklungsergebnisse als Informationsquelle erneut zu verwenden. Mit der zweiten Teilmethode zur modellbasierten Identifikation der Informationsanforderungen werden die ausgewählten Use Cases als UML/SysML-Modelle abgebildet und schlussendlich die Informationsanforderungen an den Digitalen Zwilling erhoben.