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Communities worldwide face significant threats from Explosive Remnants of War (ERW), which endanger lives and restrict land usage. From forest fires due to ERWs or in ERW-contaminated areas (e.g., in Jüterbog, Germany) to broader global challenges (e.g., the Ukrainian conflict), the need for efficient detection and removal of these remnants, especially for humanitarian demining, is paramount. Traditional methods, like manual demining, have severe limitations in safety and efficiency. Here, we introduce an innovative solution to these challenges: “Chemosensing Smart Dust.” This technology uses chemoselective dyes that change their fluorescence properties when exposed to explosives like 2,4,6-trinitrotoluene (TNT). Fluorescence-based detection offers superior sensitivity, reduced likelihood of false positives, and enhanced accuracy of explosive detection. Drones, equipped with excitation lasers or LEDs, deploy the Chemosensing Smart Dust over areas of interest and actively detect the fluorescence changes using high-resolution cameras, offering a rapid, safe, and adaptable detection method. Beyond demining, this innovative approach has potential applications in monitoring polluted areas, homeland security, and emergency response.
The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities.
Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations.
In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence.
Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems.
Die Thermografie ist trotz ihrer ausgereiften wissenschaftlichen und technologischen Grundlagen ein noch relativ junges Mitglied in der Familie der zerstörungsfreien Prüfverfahren. Sie erschließt sich aufgrund einer Reihe von Vorzügen eine wachsende Anwendungsgemeinde. Für eine weitere Verbreitung insbesondere im industriellen Kontext spielen Normen, Standards und technische Regeln eine wichtige Rolle. In diesem Beitrag wird der aktuelle Stand der Normung in Deutschland vorgestellt. Wir zeigen, welche Normen und technischen Regeln es für die Thermografie in Deutschland und international gibt und wir wagen einen Blick in die Zukunft. Darüber hinaus lebt auch die Normierungsarbeit von der Beteiligung durch interessierte Kreise. Dies können industrielle und akademische Anwender*innen, Hersteller*innen von Geräten, Forschungseinrichtungen oder Dienstleistungsunternehmen sein. Sie können gern Ihre Bedarfe bezüglich Normierungsprojekten mitbringen und/oder direkt an die Autoren senden.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). By fully utilizing these KETs, advanced and sustainable economies will be created. It is considered that Metrology is a key enabler for the advancement of these KETs. EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network for Advanced Manufacturing. The EMN is made up of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The EMN aims to provide a high-level coordination of European metrology activities for the Advanced Materials and Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing and Advanced Materials (Large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider metrology community (including TCs) to provide input for the preparation of a Strategic Research Agenda (SRA) for Metrology for Advanced Manufacturing.
This presentation will describe the progress in the development of the SRA by the EMN for Advanced Manufacturing. The metrology challenges identified across the various key industrial sectors, which utilise Advanced Materials and Advanced Manufacturing will be presented.
The EMN for Advanced Manufacturing is supported by the project JNP 19NET01 AdvManuNet.
The transfer of data for the metrological traceability of measurement results is currently taking place in the form of paper-based calibration certificates, mostly. Especially accredited metrology services providers are often bound to paper certificates by regulatory constrictions and sometimes lack of IT-infrastructure. This analog form of metrology data transfer, however, implies hands-on work, in particular for the certificate receiver, as its key values need to be translated into the device- and quality management (QM) system, manually. With thousands of certificates transferred from accredited calibration labs until the industrial regime, this binds a significant amount of human resources and is a well-known source of errors, thus constricting cost-effective and secure workflows.
This contribution highlights the status of the advancing digital transformation in metrology, on the example of force calibration. Although this appealing initiative is to a large extend driven by German stakeholders, it currently evolves to extend across all Europe and far beyond.
Virtual CT with aRTist
(2023)
The software aRTist is a simulation tool for the generation of realistic radiographs of virtual radiographic superstructures.
With radiographic simulations, virtual component models can be scanned as in a computer tomograph.
Industrial X-ray computed tomography (CT) enables the non-destructive detection of internal and external surfaces as well as inhomogeneities of technical objects. Virtual CT offers new possibilities for the investigation of parameter influences of this complex testing and measuring technique. In addition to the option of switching physical effects on and off, scanning movements can also be tested before their technical realization.
The virtual CT generates projection images from different directions for the subsequent reconstruction of a volume model of the examined object. The reconstruction of the simulated scans is carried out with the algorithms and programs for real scans. Tomographic scans consist of a large number of projections, which practically cannot be generated individually by the user of a simulation. The software offers various options for the automated simulation of tomographic scans. These range from standard CT to scans on free trajectories or with individual projection matrices.
Robotic Measurement System Concept for Greenhouse Gas Tomography in Wastewater Treatment Plants
(2023)
This paper introduces a conceptual framework for a greenhouse gas (GHG) measurement system designed for wastewater treatment plants (WWTPs). The emphasis is on accurate gas tomography measurements of emissions, crucial for national emission assessments. The proposed system is movable, semi-autonomous, and employs a combination of ground and air robots equipped with integral remote gas sensors and in-situ sensors. The proposed two/two configuration ensures comprehensive coverage of WWTPs, overcoming obstacles. The system concept lays the foundation for future implementation and evaluation, addressing the need for enhanced understanding of GHG emissions from WWTPs.
This poster reports an improved outdoor gas source localization algorithm, showing promising results under simulations with a realistic gas plume. In the scenario assumed in this work, an unmanned ground vehicle searches for the location of a gas source in an open outdoor field. The simulated gas plume in this work consists of frequent and large meandering as a real outdoor plume. The source location is estimated using gas and wind measurements obtained from sensors fixed on a mobile platform. When a gas puff is detected, its source is likely to be in the upwind direction. Particle filter-based algorithms have been proposed in previous work to estimate the most likely source location from multiple gas detection events. In this work, the particle weight update function has been modified by adopting a 2D Gaussian plume model, to improve the accuracy in estimating the likelihood of the source location. We have evaluated the performance of this algorithm combined with an estimation-based route planning algorithm. The simulator uses wind data recorded outdoors to calculate transport of gas puffs allowing a formation of a gas plume containing large meandering, due to real fluctuations of outdoor wind. Simulations of the improved particle filter with the estimation-based route planning algorithm have yielded more accurate, stable and time-efficient results than the pre-modified version.