Projects and products of ILT department

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  • KASTEL

    Competence Center for Applied Security Technology

    The Competence Center for Applied Security Technology (KASTEL) is one of three competence centers for cyber security in Germany, which were initiated in March 2011.

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  • Our extensive experience in vulnerability detection and hardening of industrial control systems flows into a new holistic resilience monitoring approach.

    Cyber resilience is seen as the next step in IT security and focuses primarily on incident response and restoring process capability. Appropriate monitoring of process-carrying automation systems and infrastructures is necessary to achieve cyber resilience.

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  • © Fraunhofer IOSB (mit Material von stock.adobe.com)

    Machine-Learning-Anwendungen im operativen Dauereinsatz erfordern besondere Tools für (Weiter-)Entwicklung und Pflege.

    A project of the same name aims to harness our experience across a whole host of AI application domains to realize MLOps with maximum effectiveness and automate it as far as possible. To that end a software suite was developed that incorporates a wide range of tools for creating machine learning applications and training the ML models.

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  • AutoInspect: Inspection of complex objects – multisensory, modular, continuously digitized

    Technical infrastructure and digital twin for quality inspection and comprehensive evaluation

    © Fraunhofer IOSB

    “AutoInspect” is a system for continuous object assessment (in real time) by multimodal inspection along the production cycle. Both a direct reaction to the results and their long-term observation are possible. The system is supported by a digital quality twin with open I4.0 standards.

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  • If you want to implement reactive Digital Twins in an interoperable and transparent way, then FA³ST Service should be a key component in your IT stack.

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  • In the Fraunhofer ML4P lead project, several Fraunhofer Institutes are pooling their application experience and machine learning skills to develop solutions for industry. The aim is to develop a tool-supported process model that paves the way for flexible, fast-learning machines and systems.

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