An Ngoc Lam

• Lam, An Ngoc & Haugen, Øystein (2019). Applying semantics into Service-oriented IoT Framework, In  The 17th IEEE International Conference on Industrial Informatics (IEEE-INDIN 2019): Proceedings.  IEEE.  ISBN 9781-7281-2927-3.  Conference paper.  s 206 - 213 Show summary

  Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Semantic technologies have been used to enable interoperability as well as deal with the heterogeneity, massive scale, and dynamic nature of IoT resources. With the emergence of Industry 4.0, semantic technologies arise as a potential approach toward information modeling and dynamic reconfiguration of highly complex automation systems with high diversity of domains, protocols, tools or hardware platforms. Applying semantics into existing IoT frameworks requires a thorough understanding of the framework architectures as well as careful considerations of different semantic technologies. To support this process, we survey the literature on the contributions and usage of semantics in IoT. We find that semantics are mainly used to handle interoperable systems and heterogeneous standards. In this paper, we also propose procedures for applying semantics into IoT frameworks. Further, we present our idea of using semantics to enable dynamical orchestration of services within the Arrowhead Framework - an IoT framework that supports the development of industrial automation systems.

• Lam, An Ngoc & Haugen, Øystein (2019). Implementing OPC-UA services for Industrial Cyber-Physical Systems in Service-Oriented Architecture, In  Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019.  IEEE conference proceedings.  ISBN 978-1-7281-4878-6.  Conference Paper.  s 5486 - 5492 Show summary

  Industrial cyber-physical systems are advancing rapidly along with the emergence of the fourth industrial revolution. It is, therefore, necessary to design and develop appropriate tools and frameworks that allow the migration/integration of legacy systems into the new Industry 4.0 environment. OPC-UA is one of the recommended technologies to enable communication and interoperability of digitalized assets in Industry 4.0. This paper proposes a solution to develop an OPC-UA interface for industrial systems for service-oriented architecture. With the use of the Arrowhead Framework - a cloud-based framework facilitating interoperability and integrability of Industrial Internet of Things - and Industry 4.0-compliant technologies, the authors describe the procedure of developing different application systems which dynamically produce and consume OPC-UA services within a local automation cloud.

• Lam, An Ngoc & Haugen, Øystein (2018). Supporting IoT semantic interoperability with autonomic computing, In  Proceeding of the 1st IEEE international conference on industrial cyber-physical systems (ICPS 2018)..  IEEE.  ISBN 9781538665305.  Article.  s 761 - 767 Show summary

  Recent advances in the Internet of Things (IoT) has kindled the possibility of a lot of smart industrial systems. With the evolution of these IoT systems in the form of size and complexity, there is a growing need for a high level of interoperability. Autonomic Computing, with the vision of equipping software systems with self-management capabilities, emerges as a potential catalyst to support interoperability. In this paper, we present an approach which exploits Autonomic Computing to facilitate the development of interoperable IoT systems at semantic level. Our approach extends state-of-the-art IoT ontologies as well as Semantic Web Technologies to fit the MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) paradigm in Autonomic Computing. By using a Smart Home Use Case, the approach is also evaluated under different performance criteria.

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• Garcia-Ceja, Enrique; Hugo, Åsmund Pedersen; Morin, Brice; Hansen, Per Olav; Martinsen, Espen; Lam, An Ngoc & Haugen, Øystein (2019). Towards the Automation of a Chemical Sulphonation Process with Machine Learning. Show summary

  Nowadays, the continuous improvement of industrial processes has become a key factor in many fields, and the chemical industry is no exception. This translates into a more efficient use of resources, reduced production time, output of higher quality and reduced waste. Given the complexity of industrial processes today, it becomes infeasible to monitor and optimize them without the use of information technologies and analytics. In recent years, machine learning methods have been used to optimize processes and provide decision support. All of this, based on analyzing large amounts of data generated in a continuous manner. In this paper we present the results of applying machine learning methods during a chemical sulphonation process with the aim of optimizing the process. We used data from process parameters to train different models including Random Forest, Neural Network and linear regression in order to predict product quality values. Our experiments showed that it is possible to predict those product quality values with good accuracy. Specifically, the best results were obtained with Random Forest with a mean absolute error of 0.089 and a correlation of 0.978.

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