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AI Concepts for System of Systems Dynamic Interoperability
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: 0000-0002-2123-8187
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-5052-9629
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-4133-3317
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2024 (English)In: Sensors, E-ISSN 1424-8220, Vol. 24, no 9, article id 2921Article in journal (Refereed) Published
Abstract [en]

Interoperability is a central problem in digitization and sos engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cps 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 sos 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 has been a recent interest in deep learning approaches to establishing 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 ai-enabled solutions in relation to sos interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex sos, and realistic testbeds are needed.

Place, publisher, year, edition, pages
MDPI, 2024. Vol. 24, no 9, article id 2921
Keywords [en]
system of systems, dynamic interoperability, AI for cyber-physical systems, representation learning
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Cyber-Physical Systems; Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-87246DOI: 10.3390/s24092921OAI: oai:DiVA.org:ltu-87246DiVA, id: diva2:1597832
Note

Validerad;2024;Nivå 2;2024-05-03 (joosat);

Funder: European Commission and Arrowhead Tools project (ECSEL JU grant agreement No. 826452);

Full text: CC BY License

Available from: 2021-09-28 Created: 2021-09-28 Last updated: 2024-05-03Bibliographically approved
In thesis
1. Machine Learning in High-Energy Physics: Displaced Event Detection and Developments in ROOT/TMVA
Open this publication in new window or tab >>Machine Learning in High-Energy Physics: Displaced Event Detection and Developments in ROOT/TMVA
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many proposed extensions to the Standard Model of particle physics predict long-lived particles, which can decay at a significant distance from the primary interaction point. Such events produce displaced vertices with distinct detector signatures when compared to standard model processes. The Large Hadron Collider (LHC) operates at a collision rate where it is not feasible to record all generated data—a problem that will be exac-erbated in the coming high-luminosity upgrade—necessitating an online trigger system to decide which events to keep based on partial information. However, the trigger is not directly sensitive to signatures with displaced vertices from Long-lived particles (LLPs). Current LLP detection approaches require a computationally expensive reconstruction step, or rely on auxiliary signatures such as energetic particles or missing energy. An improved trigger sensitivity increases the reach of searches for extensions to the standard model.This thesis explores the possibility to apply machine learning methods directly on low-level tracking features, such as detector hits and hit-pairs to identify displaced high-mass decays while avoiding a full vertex and track reconstruction step.A dataset is developed where modelled displaced signatures from novel and known physics processes are mixed in a custom simulation environment, which models the in-ner detector of a general purpose particle detector. Two machine learning models are evaluated using the dataset: a multi-layer dense Artificial Neural Network (ANN), and a Graph Neural Network (GNN). Two case studies suggest that dense ANNs have difficulty capturing relational information in low-level data, while GNNs can feasibily discriminate heavy displaced decay signatures from a Standard Model background. Furthermore it was found that GNNs can perform at a background rejection factor of 103 and a signal efficiency of 20% in collision environments with moderate levels of pile-up interactions, i.e. low-energy particle collisions simultaneous with the primary hard scatter. Further work is required to integrate the approach into a trigger environment. In particular, detector material and measurement resolution effects should be included in the simulation, which should be scaled to model the High-Luminosity Large Hadron Collider (HL-LHC) with its more complicated geometry and its high levels of pile-up.In parallel, the machine learning landscape is quickly evolving and concentrating into large software frameworks with expanding scope, while the High-Energy Physics (HEP) community maintains its own set of tools and frameworks, one example being the Toolkit for Multivariate Analysis (TMVA) which is part of the ROOT framework. This thesis discusses the long- and short-term evolution of these tools, both current trends and some relations to parallel developments in Industry 4.0.

Place, publisher, year, edition, pages
Luleå University of Technology, 2021. p. 161
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Accelerator Physics and Instrumentation
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-87247 (URN)978-91-7790-934-7 (ISBN)978-91-7790-935-4 (ISBN)
Public defence
2021-10-29, E632, Luleå Tekniska Universitet, Luleå, 14:00 (English)
Opponent
Supervisors
Available from: 2021-09-28 Created: 2021-09-28 Last updated: 2023-09-04Bibliographically approved
2. Machine Learning Concepts for Service Data Interoperability
Open this publication in new window or tab >>Machine Learning Concepts for Service Data Interoperability
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Maskininlärningskoncept för datainteroperabilitet mellan digitala tjänster
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
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:nbn:se:ltu:diva-87850 (URN)978-91-7790-983-5 (ISBN)978-91-7790-984-2 (ISBN)
Public defence
2022-05-04, E632, Luleå, 10:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 826452EU, Horizon 2020, 737459
Available from: 2021-11-22 Created: 2021-11-17 Last updated: 2023-09-04Bibliographically approved
3. Towards Digitization and Machine learning Automation for Cyber-Physical System of Systems
Open this publication in new window or tab >>Towards Digitization and Machine learning Automation for Cyber-Physical System of Systems
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Cyber-physical systems (CPS) connect the physical and digital domains and are often realized as spatially distributed. CPS is built on the Internet of Things (IoT) and Internet of Services, which use cloud architecture to link a swarm of devices over a decentralized network. Modern CPSs are undergoing a foundational shift as Industry 4.0 is continually expanding its boundaries of digitization. From automating the industrial manufacturing process to interconnecting sensor devices within buildings, Industry 4.0 is about developing solutions for the digitized industry. An extensive amount of engineering efforts are put to design dynamically scalable and robust automation solutions that have the capacity to integrate heterogeneous CPS. Such heterogeneous systems must be able to communicate and exchange information with each other in real-time even if they are based on different underlying technologies, protocols, or semantic definitions in the form of ontologies. This development is subject to interoperability challenges and knowledge gaps that are addressed by engineers and researchers, in particular, machine learning approaches are considered to automate costly engineering processes. For example, challenges related to predictive maintenance operations and automatic translation of messages transmitted between heterogeneous devices are investigated using supervised and unsupervised machine learning approaches.

In this thesis, a machine learning-based collaboration and automation-oriented IIoT framework named Cloud-based Collaborative Learning (CCL) is developed. CCL is based on a service-oriented architecture (SOA) offering a scalable CPS framework that provides machine learning-as-a-Service (MLaaS). Furthermore, interoperability in the context of the IIoT is investigated. I consider the ontology of an IoT device to be its language, and the structure of that ontology to be its grammar. In particular, the use of aggregated language and structural encoders is investigated to improve the alignment of entities in heterogeneous ontologies. Existing techniques of entity alignment are based on different approaches to integrating structural information, which overlook the fact that even if a node pair has similar entity labels, they may not belong to the same ontological context, and vice versa. To address these challenges, a model based on a modification of the BERT_INT model on graph triples is developed. The developed model is an iterative model for alignment of heterogeneous IIoT ontologies enabling alignments within nodes as well as relations. When compared to the state-of-the-art BERT_INT, on DBPK15 language dataset the developed model exceeds the baseline model by (HR@1/10, MRR) of 2.1%. This motivated the development of a proof-of-concept for conducting an empirical investigation of the developed model for alignment between heterogeneous IIoT ontologies. For this purpose, a dataset was generated from smart building systems and SOSA and SSN ontologies graphs. Experiments and analysis including an ablation study on the proposed language and structural encoders demonstrate the effectiveness of the model.

The suggested approach, on the other hand, highlights prospective future studies that may extend beyond the scope of a single thesis. For instance, to strengthen the ablation study, a generalized IIoT ontology that is designed for any type of IoT devices (beyond sensors), such as SAREF can be tested for ontology alignment. Next potential future work is to conduct a crowdsourcing process for generating a validation dataset for IIoT ontology alignment and annotations. Lastly, this work can be considered as a step towards enabling translation between heterogeneous IoT sensor devices, therefore, the proposed model can be extended to a translation module in which based on the ontology graphs of any device, the model can interpret the messages transmitted from that device. This idea is at an abstract level as of now and needs extensive efforts and empirical study for full maturity.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022. p. 47
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Digitization, Automation, Industry 4.0, Machine-to-Machine Translation, Ontology Alignment Eclipse Arrowhead Framework, Machine Learning, Unsupervised Learning, Condition Monitoring, Ontology Alignment
National Category
Computer Sciences
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-90196 (URN)978-91-8048-069-7 (ISBN)978-91-8048-070-3 (ISBN)
Presentation
2022-05-18, E632, Luleå tekniska universitet, Luleå, 08:30 (English)
Supervisors
Available from: 2022-04-13 Created: 2022-04-13 Last updated: 2023-09-05Bibliographically approved

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Nilsson, JacobJaved, SalehaAlbertsson, KimDelsing, JerkerLiwicki, MarcusSandin, Fredrik

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