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001 | 978-3-658-27742-0 | ||
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_a10.1007/978-3-658-27742-0 _2doi |
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_a006.3 _223 |
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_aHoffmann, Max. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aSmart Agents for the Industry 4.0 _h[electronic resource] : _bEnabling Machine Learning in Industrial Production / _cby Max Hoffmann. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aWiesbaden : _bSpringer Fachmedien Wiesbaden : _bImprint: Springer Vieweg, _c2019. |
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300 |
_aXXXIV, 318 p. 111 illus. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aAgent OPC UA – Semantic Scalability and Interoperability Architecture for MAS through OPC UA -- Management System Integration of OPC UA Based MAS -- Flexible Manufacturing Based on Autonomous, Decentralized Systems -- Use Cases for Industrial Automation. | |
520 | _aMax 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. Contents Agent OPC UA – Semantic Scalability and Interoperability Architecture for MAS through OPC UA Management System Integration of OPC UA Based MAS Flexible Manufacturing Based on Autonomous, Decentralized Systems Use Cases for Industrial Automation Target Groups Scientists and students in automation technology, production technology, mechanical engineering, process control, factory planning Practitioners in these fields 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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aIndustrial engineering. | |
650 | 0 | _aProduction engineering. | |
650 | 0 | _aTelecommunication. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aIndustrial and Production Engineering. |
650 | 2 | 4 | _aCommunications Engineering, Networks. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783658277413 |
776 | 0 | 8 |
_iPrinted edition: _z9783658277437 |
776 | 0 | 8 |
_iPrinted edition: _z9783658277444 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-658-27742-0 |
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