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Machine Learning Concepts for Service Data Interoperability
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4881-8971
2022 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Maskininlärningskoncept för datainteroperabilitet mellan digitala tjänster (Swedish)
Abstract [en]

Industrial automation is transforming by ongoing digitalization efforts to create a flexible industrial internet of things, turning manufacturing facilities into large-scale systems of cyber-physical systems. This development requires addressing the challenging issue of making heterogeneous systems, data models, and standards interoperable, a core problem in designing sustainable service-oriented automation frameworks. This thesis reviews the problem of automatically establishing the interoperability of services exchanging heterogeneous message data. A machine-learning architecture is developed, where the optimization of message transcoders and the system of systems utility are mechanisms for establishing interoperability. By optimizing the transcoders using both service- and metadata, the aim is to ground the learned latent representations in the physical environment to improve generalization. Two physical simulation experiments were performed to investigate and evaluate the architecture by generating heterogeneous JSON messages from multiple heating and air conditioning services. The first experiment focuses on unsupervised learning via back-translation for transcoding engineered features of service messages, reaching a maximum translation accuracy of 49%. The second experiment focuses on supervised learning and a modular neural network (JSON2Vec) for automated encoding of the heterogeneous JSON messages, which enables correct message interpretation in terms of the expected system of systems behavior. These results suggest that machine learning is a viable direction in interoperability automation research which can benefit from both symbolic metadata and message data for reliable generalization and adaptation. Appropriate open datasets are required to consolidate the envisioned development and the migration of solutions to automation systems.

Place, publisher, year, edition, pages
Luleå University of Technology, 2022.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords [en]
system of systems, system of cyber-physical systems, subsymbolic representations, semantic interoperability, dynamic interoperability
National Category
Communication Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Research subject
Cyber-Physical Systems
Identifiers
URN: urn:nbn:se:ltu:diva-87850ISBN: 978-91-7790-983-5 (print)ISBN: 978-91-7790-984-2 (electronic)OAI: oai:DiVA.org:ltu-87850DiVA, id: diva2:1612250
Public defence
2022-05-04, E632, Luleå, 10:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 826452EU, Horizon 2020, 737459Available from: 2021-11-22 Created: 2021-11-17 Last updated: 2023-09-04Bibliographically approved
List of papers
1. Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering
Open this publication in new window or tab >>Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering
2020 (English)In: IEEE 24th International Conference on Intelligent Engineering Systems: Proceedings, IEEE, 2020, p. 139-144Conference paper, Published paper (Other academic)
Abstract [en]

We formulate the challenging problem to establish information interoperability within a system of systems (SoS) as a machine-learning task, where autoencoder embeddings are aligned using message data and metadata to automate message translation. An SoS requires communication and collaboration between otherwise independently operating systems, which are subject to different standards, changing conditions, and hidden assumptions. Thus, interoperability approaches that are based on standardization and symbolic inference will have limited generalization and scalability in the SoS engineering domain. We present simulation experiments performed with message data generated using heating and ventilation system simulations. While the unsupervised learning approach proposed here remains unsolved in general, we obtained up to 75% translation accuracy with autoencoders aligned by back-translation after investigating seven different models with different training protocols and hyperparameters. For comparison, we obtain 100% translation accuracy on the same task with supervised learning, but the need for a labeled dataset makes that approach less interesting. We discuss possibilities to extend the proposed unsupervised learning approach to reach higher translation accuracy.

Place, publisher, year, edition, pages
IEEE, 2020
Series
IEEE International Conference on Intelligent Engineering Systems (INES), ISSN 1543-9259
Keywords
Adapter design pattern, Autoencoder alignment, Embeddings, M2M-Communication, System of systems
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electronic systems
Identifiers
urn:nbn:se:ltu:diva-80561 (URN)10.1109/INES49302.2020.9147168 (DOI)000618845500022 ()2-s2.0-85092665559 (Scopus ID)
Conference
2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES), 8-10 July, 2020, Reykjavík, Iceland
Note

ISBN för värdpublikation: 978-1-7281-1059-2, 978-1-7281-1060-8

Available from: 2020-08-26 Created: 2020-08-26 Last updated: 2023-09-04Bibliographically approved
2. AI Concepts for System of Systems Dynamic Interoperability
Open this publication in new window or tab >>AI Concepts for System of Systems Dynamic Interoperability
Show others...
2021 (English)Article in journal (Refereed) Submitted
Abstract [en]

Interoperability is a central problem in digitization and System of Systems (SoS)engineering which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cyber-physical systems (CPSs) at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as SoS, neural translation and agent based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish SoSs with purposeful CPS communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code;concept-learning based reasoning; and emergent communication. We find that there is a recent interest for deep learning approaches to establish communication under different assumptions about the environment, language and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with artificial intelligence (AI)-enabled solutions in relation to SoS interoperability requirements. While these developments open new avenues for research there are still no examples bridging the necessary concepts required to establish dynamic interoperability in complex SoSs, and realistic testbeds are needed.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems; Machine Learning
Identifiers
urn:nbn:se:ltu:diva-87246 (URN)
Available from: 2021-09-28 Created: 2021-09-28 Last updated: 2023-09-04
3. Semantic Interoperability in Industry 4.0: Survey of Recent Developments and Outlook
Open this publication in new window or tab >>Semantic Interoperability in Industry 4.0: Survey of Recent Developments and Outlook
2018 (English)In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), IEEE, 2018, p. 127-132, article id 8471971Conference paper, Published paper (Refereed)
Abstract [en]

Semantic interoperability is the ability of systems to exchange information with unambiguous meaning. This is an outstanding challenge in the development of Industry 4.0 due to the trend towards dynamic re-configurable production processes with increasingly complex automation systems and a diversity of standards, components, tools and services. The cost of making systems interoperable is a major limiting factor in the adoption of new technology and the envisioned development of production industry. Therefore, methods and concepts enabling efficient interoperation of heterogeneous systems are investigated to understand how the interoperability problem should be addressed. To support this development, we survey the literature on interoperability to identify automation approaches that address semantic interoperability, in particular in dynamic cyber-physical systems at large scale. We find that different aspects of the interoperability problem are investigated, some based on a conventional bottom-up standardization approach, while others consider a goal-driven computational approach; and that the different directions explored are related to open questions that motivates further research. We argue that a goaldriven machine learning approach to semantic interoperability can result in solutions that are applicable across standardization domains and thus is a promising direction of research in this era of the industrial internet of things.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Interoperability, Semantics, Ontologies, Industries, Standards, Automation, Tools
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-71466 (URN)10.1109/INDIN.2018.8471971 (DOI)000450180200018 ()2-s2.0-85055539075 (Scopus ID)9781538648292 (ISBN)
Conference
16th IEEE International Conference on Industrial Informatics, INDIN 2018; Porto; Portugal; 18-20 July 2018.
Available from: 2018-11-06 Created: 2018-11-06 Last updated: 2023-09-04Bibliographically approved
4. Interoperability and machine-to-machine translation model with mappings to machine learning tasks
Open this publication in new window or tab >>Interoperability and machine-to-machine translation model with mappings to machine learning tasks
2019 (English)In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), IEEE, 2019, p. 284-289Conference paper, Published paper (Other academic)
Abstract [en]

Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE International Conference on Industrial Informatics (INDIN), ISSN 1935-4576, E-ISSN 2378-363X
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electronic systems
Identifiers
urn:nbn:se:ltu:diva-73562 (URN)10.1109/INDIN41052.2019.8972085 (DOI)000529510400042 ()2-s2.0-85079039767 (Scopus ID)
Conference
2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 22-25 July, 2019, Helsinki, Finland
Funder
EU, Horizon 2020, 737459
Note

ISBN för värdpublikation: 978-1-7281-2928-0, 978-1-7281-2927-3

Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2023-09-04Bibliographically approved
5. Machine Learning based System–of–Systems Interoperability: A SenML–JSON Case Study
Open this publication in new window or tab >>Machine Learning based System–of–Systems Interoperability: A SenML–JSON Case Study
(English)Manuscript (preprint) (Other academic)
National Category
Computer Sciences Communication Systems Embedded Systems
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-87849 (URN)
Projects
Arrowhead Tools
Funder
EU, Horizon 2020, 826452
Available from: 2021-11-09 Created: 2021-11-09 Last updated: 2023-09-04

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