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PublicationCognitive User Modeling for Adaptivity in Serious Games( 2024)Accurate user models that capture information such as needs and knowledge levels are a central part of adaptive e-learning systems, which is all the more important in a post-pandemic world with more individualized learning. In this article, we report on the application of a Bayesian cognitive state modeling approach to adaptive educational serious games. Adaptivity needs information on the users as control variables, e.g., high or low cognitive load. Typically, this information is encoded in user models. One approach to building user models is to use tools from cognitive sciences such as Bayesian cognitive state modeling. However, cognitive modeling tools for adaptivity are sparse and can be difficult to implement. The main research question of this work is how to apply cognitive modeling tools to serious games to control adaptivity. The contribution of this article is the concept of how to implement cognitive modeling for adaptive serious games. Our approach makes use of standardized Experience API (xAPI) tracking data to facilitate applicability. We investigate how to compute quantitative measures of user performance to control adaptive responses. The implemented system has been evaluated in a user study with a serious game for image interpretation. The study results show a moderate correlation between self-assessed and computed variables.
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PublicationSensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning( 2024)Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the basis for sorting. To enable economical use, it is necessary to ensure robust detection of individual objects even with high material throughput. Conventional algorithms struggle with resulting high occupancy densities and object overlap, making deep learning object detection methods more promising. In this study, different deep learning architectures for object detection (Region-based CNN/Region-based Convolutional Neural Network (Faster R-CNN), You only look once (YOLOv3), Single Shot MultiBox Detector (SSD)) are investigated with respect to their suitability for CDW characterization. A mixture of brick and sand-lime brick is considered as an exemplary waste stream. Particular attention is paid to detection performance with increasing occupancy density and particle overlap. A method for the generation of synthetic training images is presented, which avoids time-consuming manual labelling. By testing the models trained on synthetic data on real images, the success of the method is demonstrated. Requirements for synthetic training data composition, potential improvements and simplifications of different architecture approaches are discussed based on the characteristic of the detection task. In addition, the required inference time of the presented models is investigated to ensure their suitability for use under real-time conditions.
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PublicationFortschrittsbericht zur Digitalisierung des Energiesystems(Fraunhofer CINES, 2024)
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PublicationPotential of Deep Learning methods for image processing in sensor-based sorting: data generation, training strategies and model architectures( 2024)The main component of a sensor-based sorting system is an imaging sensor and the associated data processing unit for detecting and classifying bulk material objects. High occupancy densities and objects with similar appearance lead to increasing problems for conventional image processing algorithms in object and class separation. Therefore, in this article, specialized Deep Learning approaches were applied to two datasets for instance segmentation. Due to the need for a large amount of training data for such models, a method for synthetic training data generation has been developed. Subsequently, established model architectures as well as an own approach specialized for the problem characteristics is presented and compared regarding their detection performance. Finally, the models are evaluated in terms of their speed and therefore their potential use in a sorting system. Our approach more than halves the inference time of the fastest model while achieving the best detection performance.
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PublicationBridging the Gap Between IDS and Industry 4.0 - Lessons Learned and Recommendations for the Future( 2024)The Plattform Industrie 4.0 (PI4.0) and the International Data Spaces Association (IDSA) are two independent, parallel initiatives with clear focuses. While PI4.0 addresses communication and interaction between networked assets in a smart factory and/or supply chain across an asset or product lifecycle, IDSA is about a secure, sovereign system of data sharing in which all stakeholders can realize the full value of their data. Since data sharing between companies requires both interoperability and data sovereignty, the question emerges regarding the feasibility and rationality of integrating the expertise of PI4.0 and IDSA. The IDS-Industrial Community (IDS-I) is an extension of IDSA whose goal is to strengthen the cooperation between IDSA and PI4.0. Two fields of expertise could be combined: The Platform's know-how in the area of Industrie 4.0 (I4.0) and the IDSA's expertise in the areas of data sharing ecosystems and data sovereignty. In order to realize this vision, many aspects have to be taken into account, as there are discrepancies on multiple levels. Specifically, at the reference architecture level, we have the RAMI4.0 model on the PI4.0 side and the IDS Reference Architecture Model (IDS-RAM) on the IDSA side. While the existing I4.0 and IDS specifications are incompatible e.g. in terms of models (i.e., the AAS metamodel and the IDS information model) and APIs, there is also the issue of interoperability between I4.0 and IDS solutions. This position paper aims to bridge the gap between IDS and PI4.0 by not only analyzing how their existing concepts, tools, etc. have been "connected" in different contexts. Rather, this position paper makes recommendations on how different technologies could be combined in a generic way, independent of the concrete implementation of IDS and/or I4.0 relevant technology components. This paper could be used by both the IDS and I4.0 communities to further improve their specifications, which are still under development. The lessons learned and feedback from the initial joint use of technology components from both areas could provide concrete guidance on necessary improvements that could further strengthen or extend the specifications. Furthermore, it could help to promote the IDS architecture and specifications in the industrial production and smart manufacturing community and extend typical PI4.0 use cases to include data sovereignty by incorporating IDS aspects.
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PublicationA communication concept using 5G for the automated driving monorail vehicle MONOCAB( 2023)The MONOCAB is an innovative monorail vehicle designed to operate in two directions simultaneously on a single rail track. To ensure smooth operations and efficient fleet management, various communi- cation needs arise. This paper outlines four common use cases and identifies nine communication requirements for the MONOCAB. Based on this, it presents a communication concept utilizing 5G technology, covering Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, as well as time critical communication to an edge application in a central control centre and non-time critical communication for fleet management and provision of information for the MONOCAB users.
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PublicationGet ahead of the Situation: Simulation of Cross-Sectoral Cascading Effects during a Crisis( 2023)In recent years, the frequency and severity of extreme weather events has been steadily increasing. Due to the often-exposed location of critical infrastructures, extreme weather events pose a serious threat to them. As supply networks become increasingly interconnected with multiple critical infrastructures, the occurrence of cascading effects does not only lead to further failures within a specific sector but can introduce severe consequences to adjacent sectors as well. In parallel, critical infrastructures must be regarded as socio-technical systems which not only supply people but are also operated by people. During stressful situations like extreme weather events, it is therefore mandatory to obtain a comprehensive assessment for enabling quick and coordinated countermeasures to minimize damage to critical infrastructure, establish a rapid emergency supply and prepare for reconstruction. This paper presents a concept to reproduce cross-sectoral cascading effects through coupled simulations of individual supply networks. The results of the individual simulations of the different sectors are bundled and interlinked in an overall platform able to trigger subsequent simulations. To represent both anticipated and unforeseen cascading effects, the interfaces between the simulations must be carefully defined and implemented. The results of the overall platform will be incorporated into a demonstrator that will provide a training environment for emergency forces and network operators in which communication during the crisis can be practiced, security measures can be pre-thought, and vulnerable nodes can be identified.
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PublicationPiloten 2022 der Initiative Digitale Standards (IDIS) - Praktische Nutzung von Smart Standards( 2023)Nach Laufzeiten von 12 bis 18 Monaten wurden vier Piloten der Initiative Digitale Standards (IDiS) erfolgreich abgeschlossen und haben ihre Projektergebnisse und Zwischenstände überreicht. Anhand identischer Fragen wurden die Projektteilnehmer gebeten, in einer Retrospektive ihre Einschätzungen zu den Erkenntnissen und Herausforderungen in den Piloten zu übermitteln. Ziel ist einerseits, weitere Stakeholder mit den Chancen und Möglichkeiten von digitalen Standards in Kontakt zu bringen, die nicht unmittelbar die Möglichkeit haben, in IDiS aktiv mitzuarbeiten. Andererseits sollen die Erfahrungen mit weiteren Partnern und Normungsorganisationen geteilt werden, wie z.B. auf europäischer Ebene, wo sie Einzug in das bei CEN/CENELEC gehostete Backlog zur Erfassung von europäischen Anwendungsszenarien digitaler Normen und deren Assets erhalten haben.
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PublicationIT support for climate resilient cultural heritage - examples from the KERES project( 2023)Not only ecosystems are particularly sensitive to extreme weather as a result of climate change. Historical buildings, museum's collections and historical gardens can also be affected by extreme weather conditions. Assessing the extent to which cultural assets are endangered by such weather and climate events is an interdisciplinary task that requires the collaboration of climate scientists together with cultural heritage managers, monument conservators, restorers and engineers. However, this discussion is currently hardly taking place in Germany, both on a scientific and on policy levels.
