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Innovation Cluster for Systems Metrology

Innovation Cluster for Systems Metrology

Large amounts of intricately connected (measurement) data are coming into being within the scope of digitalization. Whether we are dealing with smart energy grids, autonomous driving or artificial intelligence in medicine – one thing is true of them all: Not only do we have to measure and evaluate individual values, we also have to understand and characterize complex systems of measurement procedures and sensors. Thinking holistically and cross-contextually is crucial too. And this is also something that will be required by metrology and the testing systems that have to guarantee trust, reliability and security in the future. Just as the international prototypes of the meter and the kilogram served as the standards for metrology in the past, so should the quality standards for networked data and AI be traceable to quantities which can be measured. This research will initially be advanced within the scope of three thematic fields: connected mobility, the smart city and digital medicine.

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Systems Metrology: Research at PTBSystems Metrology: Research at PTB

Connected Mobility
Vehicles equipped with new technologies such as automated driving represent complex connected systems that consist, for example, of sensors and artificial intelligence. With a strong focus on this systems-level aspect, PTB is developing and investigating new measurement techniques and new test procedures.

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Smart City
The city of the future is characterized by measurement networks that ensure the sustainable supply of water, heat and energy. Within the scope of interdisciplinary projects, PTB is conducting research on how these networks can be integrated into the urban habitat and on what new issues may arise with respect to the handling of this measurement data.

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Digital Medicine
Due to the large amounts of data involved, the medical field is also increasingly relying on artificial intelligence (AI) in the form of machine learning. In this context, the data has to be interpreted at the systems level. To evaluate the quality of this data, PTB is working to provide the necessary basis to ensure that AI is trustworthy.

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