Article published in Sensors
The following article on explainable machine learning under the leadership of Sercan Alipek has been accepted for publication in Sensors journal. Abstract: This paper evaluates a data-driven classification approach of operational wind turbine blades based on consecutive tower-radar measurements that are each compressed in a two-dimensional slow-time to range representation (radargram). Like many real-world machine...Read More
Article published in PIER C
A journal article demonstrating recent developments on a reversible damage model for the qualification of radar-based SHM systems has been accepted for publication in PIER C. Abstract: In this work, a delamination model for millimeter-wave inspections of glass fiber reinforced polymer (GFRP) is proposed that replicates the scattering characteristics of a real delamination. The model...Read More
DFG project selected for funding
A joint proposal with Prof. Dr. Steffen Freitag (KIT, Karlsruhe) and supported by Dr. Vittorio Memmolo (University of Naples, Italy) has been selected for funding. Project title: Quantification and minimization of uncertainty for guided wave-based structural health monitoring with artificial neural network approaches Project number: 566880366 Project description: The focus of the research project is...Read More
Guest Editor of Special Issue in Sensors
Dr. Vittorio Memmolo (University of Naples) and I proposed the following special issue in Sensors journal: “Advancements in the Use of Distributed Sensing and Edge/Cloud Diagnostics in Structural Health Monitoring Systems”. More information can be found here: https://www.mdpi.com/journal/sensors/special_issues/A2A0XI301Q We are looking forward to your contribution to this special issue!Read More
Article published in Applied Sciences
A joint work of researchers from different countries (Spain, Italy, Germany) has been accepted for publication in Applied Sciences. Abstract: This paper introduces a novel structural health monitoring (SHM) approach based on guided electromagnetic waves propagating in a dielectric waveguide in the frequency range from 23.5 to 26 GHz. This approach enables the detection of...Read More
Article published in Sensors
Machine Learning in the context of structural health monitoring becomes more and more important. A paper has been accepted for publication in Sensors journal describing radar-based damage detection under fatigue loading using convolutional neural networks (CNNs). Abstract: This paper reports on a convolutional neural network (CNN) based damage detection approach for radar-based structural health monitoring...Read More