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Publications (5 of 5) Show all publications
Nilsson, J., Sandin, F. & Delsing, J. (2019). Interoperability automation considered as machine learning tasks. In: : . Paper presented at 2nd Productive4.0 Consortium Conference, Budapest, March 12-14, 2019.
Open this publication in new window or tab >>Interoperability automation considered as machine learning tasks
2019 (English)Conference paper, Poster (with or without abstract) (Other academic)
Keywords
Interoperability, machine learning, optimization, translation, semantics
National Category
Other Computer and Information Science
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-73578 (URN)
Conference
2nd Productive4.0 Consortium Conference, Budapest, March 12-14, 2019
Funder
EU, Horizon 2020, 737459
Available from: 2019-04-11 Created: 2019-04-11 Last updated: 2019-09-06
Nilsson, J. (2019). System of Systems Interoperability Machine Learning Model. (Licentiate dissertation). Luleå University of Technology
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: 2019-10-30Bibliographically approved
Nilsson, J. & Sandin, F. (2018). Semantic Interoperability in Industry 4.0: Survey of Recent Developments and Outlook. In: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN): . Paper presented at 16th IEEE International Conference on Industrial Informatics, INDIN 2018; Porto; Portugal; 18-20 July 2018. (pp. 127-132). IEEE, Article ID 8471971.
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: 2019-10-03Bibliographically approved
Nilsson, J. (2017). Improving the Security of the Android Pattern Lock using Biometrics and Machine Learning. (Student paper). Luleå tekniska universitet
Open this publication in new window or tab >>Improving the Security of the Android Pattern Lock using Biometrics and Machine Learning
2017 (English)Student thesis
Abstract [en]

With the increased use of Android smartphones, the Android Pattern Lock graphical password has become commonplace. The Android Pattern Lock is advantageous in that it is easier to remember and is more complex than a five digit numeric code. However, it is susceptible to a number of attacks, both direct and indirect. This fact shows that the Android Pattern Lock by itself is not enough to protect personal devices. Other means of protection are needed as well.

In this thesis I have investigated five methods for the analysis of biometric data as an unnoticable second verification step of the Android Pattern Lock. The methods investigated are the euclidean barycentric anomaly detector, the dynamic time warping barycentric anomaly detector, a one-class support vector machine, the local outlier factor anomaly detector and a normal distribution based anomaly detector. The models were trained using an online training strategy to enable adaptation to changes in the user input behaviour. The model hyperparameters were fitted using a data set with 85 users. The models are then tested with other data sets to illustrate how different phone models and patterns affect the results.       

The euclidean barycentric anomaly detector and dynamic time warping (DTW) barycentric anomaly detector have a sub 10 \% equal error rate in both mean and median, while the other three methods have an equal error rate between 15 \% and 20 \% in mean and median. The higher performance of the euclidean and DTW barycentric anomaly detector is likely because they account for the time series nature of the data, while the other methods do not. Each user in the data set have provided each pattern at most 50 times, meaning that the long-term effects of user adaptation could not be studied.

Publisher
p. 41
Keywords
Android Pattern Lock, Dynamic Time Warping, Anomaly Detection, Time Series, Behavioral Biometrics
National Category
Other Computer and Information Science Human Computer Interaction
Identifiers
urn:nbn:se:ltu:diva-65439 (URN)
External cooperation:
BehavioSec
Thesis level
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis, at least 30 credits
Examiners
Available from: 2017-09-07 Created: 2017-08-31 Last updated: 2019-03-22Bibliographically approved
Nilsson, J., Sandin, F. & Delsing, J.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
(English)Manuscript (preprint) (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.

National Category
Other Computer and Information Science
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-73562 (URN)
Funder
EU, Horizon 2020, 737459
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-10-03
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4881-8971

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