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Towards Autoencoder Based Adapters for Run-Time Interoperability in System of Systems Engineering
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4881-8971
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825x
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-4133-3317
(English)Manuscript (preprint) (Other academic)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
URN: urn:nbn:se:ltu:diva-76224OAI: oai:DiVA.org:ltu-76224DiVA, id: diva2:1357217
Projects
Productive 4.0
Funder
EU, Horizon 2020, 737459Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-03
In thesis
1. System of Systems Interoperability Machine Learning Model
Open this publication in new window or tab >>System of Systems Interoperability Machine Learning Model
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Increasingly flexible and efficient industrial processes and automation systems are developed by integrating computational systems and physical processes, thereby forming large heterogeneous systems of cyber-physical systems. Such systems depend on particular data models and payload formats for communication, and making different entities interoperable is a challenging problem that drives the engineering costs and time to deployment. Interoperability is typically established and maintained manually using domain knowledge and tools for processing and visualization of symbolic metadata, which limits the scalability of the present approach. The vision of next generation automation frameworks, like the Arrowhead Framework, is to provide autonomous interoperability solutions. In this thesis the problem to automatically establish interoperability between cyber-physical systems is reviewed and formulated as a mathematical optimisation problem, where symbolic metadata and message payloads are combined with machine learning methods to enable message translation and improve system of systems utility. An autoencoder based implementation of the model is investigated and simulation results for a heating and ventilation system are presented, where messages are partially translated correctly by semantic interpolation and generalisation of the latent representations. A maximum translation accuracy of 49% is obtained using this unsupervised learning approach. Further work is required to improve the translation accuracy, in particular by further exploiting metadata in the model architecture and autoencoder training protocol, and by considering more advanced regularization methods and utility optimization.

Place, publisher, year, edition, pages
Luleå University of Technology, 2019
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
system of systems interoperability, machine learning, message translation, information interoperability, autoencoder, cyber-physical systems
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-76229 (URN)978-91-7790-458-8 (ISBN)978-91-7790-459-5 (ISBN)
Presentation
2019-11-28, E632, Regnbågsallén E7, Luleå, 10:00 (English)
Opponent
Supervisors
Projects
Productive 4.0
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-03Bibliographically approved

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Sandin, FredrikDelsing, Jerker

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Citation style
  • apa
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