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Industrie 4.0

Data analytics for the fourth industrial revolution, such as proactive service and maintenance of production resources or finding anomalies in production processes.

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Medicine

Data-driven aspects of medicine are explored, such as the need-driven care of patients or IT controlled medical technology.

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Smart Infrastructure

Untersuchung datengetriebener Aspekter städtischen Lebens, bspw. der Verkehrssteuerung, der Müllentsorgung oder der Katastrophenbewältigung, bedarfsgesteuerte Optimierung von Verbrauchsmodellen, basierend auf Daten intelligenter Stromzähler.

Featured Projects

  • Analytics for a Smart Air Quality Network

    The project “Smart Air Qualitiy Network” (SmartAQnet) is based on a pragmatic, data driven approach since the existing data treasures of mcloud.de are combined for the first time and linked with a networked mobile measurement strategy.

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    Ports as Intelligent Logistics Hubs

    This project is part of the Transforming Transport EU lighthouse project. The TransformingTransport project will demonstrate, in a realistic, measurable, and replicable way the transformative effects that Big Data will have to the mobility and logistics market.

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    Optimierung der Produktqualität bei OSRAM Schwabmünchen

    An important goal in the implementation of an industry 4.0 strategy is the optimization of production to further increase the quality of the produced product. Using data analysis of the production parameters, sensor data, test protocols and their interdependencies forecasts and rules for the production can be created.

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  • Trelleborg_SDIL

    Condition monitoring and prediction of sealing systems

    Trelleborg Sealing Solutions carries out numerous, fully instrumented, tests of these seals and sealing systems. Measured variables, such as pressures, temperatures and velocities, are measured at various points in a very high frequency. The tests carried out and planned represent an extensive data base, which offers great potential for the application of big data analytics.

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    Enhancing Traffic Flow Forecasting with Environmental Models

    In diesem Projekt wird eine Verkehrsflussvorhersage mit Umgebungsmodellen für Verkehrsnetze vorgeschlagen. Heutzutage berücksichtigt die Verkehrsflussvorhersage vor allem Informationen von einzelnen, spezifischen Sensoren. Allerdings könnten Informationen von Nachbar-Sensoren und anderen Sensoren im Verkehrs-Subnetz genutzt werden, um moderne Prognosemodelle zu verbessern.

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