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Max Hoffmann describes the realization of a framework that enables
autonomous decision-making in industrial manufacturing processes by
means of multi-agent systems and the OPC UA meta-modeling standard.
The integration of communication patterns and SOA with grown
manufacturing systems enables an upgrade of legacy environments in
terms of Industry 4.0 related technologies. The added value of the
derived solutions are validated through an industrial use case and
verified by the development of a demonstrator that includes
elements of self-optimization through Machine Learning and
communication with high-level planning systems such as ERP. About
the Author: Dr.-Ing. Max Hoffmann is a scientific researcher at the
Institute of Information Management in Mechanical Engineering, RWTH
Aachen University, Germany, and leads the group "Industrial Big
Data". His research emphasizes on production optimization by means
of data integration through interoperability and communication
standards for industrial manufacturing and integrated analysis by
using Machine Learning and stream-based information processing.
Max Hoffmann describes the realization of a framework that enables
autonomous decision-making in industrial manufacturing processes by
means of multi-agent systems and the OPC UA meta-modeling standard.
The integration of communication patterns and SOA with grown
manufacturing systems enables an upgrade of legacy environments in
terms of Industry 4.0 related technologies. The added value of the
derived solutions are validated through an industrial use case and
verified by the development of a demonstrator that includes
elements of self-optimization through Machine Learning and
communication with high-level planning systems such as ERP. About
the Author: Dr.-Ing. Max Hoffmann is a scientific researcher at the
Institute of Information Management in Mechanical Engineering, RWTH
Aachen University, Germany, and leads the group "Industrial Big
Data". His research emphasizes on production optimization by means
of data integration through interoperability and communication
standards for industrial manufacturing and integrated analysis by
using Machine Learning and stream-based information processing.
Sie studieren Mathematik im ersten oder zweiten Semester? Das
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grundlegende Inhalte und Studiertechniken im Mathematikstudium
helfen. Die Autoren greifen tief in die Know-How-Kiste und zeigen,
wie Mathematik erfolgreich studiert werden kann. Basierend auf
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(allesamt mit Loesungen) finden Sie konkrete Hinweise und Konzepte
zum Lesen mathematischer Texte, zum Verfassen dieser und zum
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AEquivalenzrelationen Beweistechniken Gruppen Ringe, Koerper von
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