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).  IEEE.  ISBN 9781-7281-2927-3.  Conference paper.
• 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.
• 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; 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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