SYSTEMS-OF-SYSTEMS ENGINEERING

This course deals with model-based testing, a class of technologies shown to be effective and efficient in assessing the quality and correctness of large software systems. Throughout the course the participants will learn how to design and use model-based testing tools, how to create realistic models and how to use these models to automate the testing process in their organisation.

In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.

The aim of this course is to give students insight about certification and about what it means to certify/self-assess safety- critical systems with focus on software system and to create a safety case, including a multi-concern perspective when needed and reuse opportunities, when appropriate.

The important part of the course will be focused on addressing real industrial and environmental challenges by employing state-of-the-art spectroscopic material characterization methods for process monitoring, control and optimization.

This course, Digitally-enabled production, is offered in a collaboration between Mälardalens högskola (MDH), Högskolan Väst (HV) and Linnéuniversitetet (LNU). The course aims to support and facilitate our partner manufacturing companies to become competitive in digitally-enabled production. During the course, we address the potential prerequisites and capabilities required for implementing industry 4.0 in the context of an overall production system. More specifically, we increase the competence base of companies in three areas—internal logistics systems, virtual factory, and sensor and signal processing—which can holistically interconnect the key components for the successful implementation of industry 4.0. This course consists of three interconnected course modules at an advanced level and it is offered between September and November 2020: Internal Logistics in Industry 4.0, 2.5 credits, MDH, https://www.mdh.se/en/malardalen-university/education/course-syllabus?id=29594 Virtual Factory and Robot Cell Simulation, 2.5 credits, HV, https://admin.hv.se/samverka-med-oss/kompetensutveckling/teknik/kompetensutveckling-inom-produktionsteknik/virtual-factory-and-robot-cell-simulation/ Data Acquisition and Monitoring, 2.5 credits, LNU, https://kursplan.lnu.se/kursplaner/kursplan-4MT017-1.pdf More information about the course is available on the below link: https://www.mdh.se/en/malardalen-university/education/further-training/smart-production/digitally-enabled-production-7.5-hp Apply to the course in the below link:  https://www.mdh.se/en/malardalen-university/education/further-training/smart-production/digitally-enabled-production-7.5-hp/application-form-digitally-enabeled-production-7.5-hp

The emergence of artificial intelligence has created a new opportunity to apply machine learning (ML) in industry 4.0. In this era of the Internet of Things and Big data, processing of a large amount of data would not be possible without ML. Thus, ML made industrial production smarter than ever before.  So, learn the course “Machine learning for Industry 4.0” to bring in new business models in your company and boost productivity using ML.  The course provides knowledge about basics of ML and data, describes ML algorithms and tools and also explains the concept of Industry 4.0 and digitalization in industry 4.0.   The individual processes can be better understood and optimized with the help of the knowledge from the course. Also, it could have an important contribution in analysing the industrial data set, improving results, and making  decision and/or predictions for failure, demand, sales, and production. This course is a collaboration between Högskolan Väst, Linnéuniversitetet and Mälardalens Högskola. It consist of three course modules: Introduction to Machine Learning (MDH – 2 credits, Basic level), Industry Digitalization – Industry 4.0 (HV – 2,5 credits, Advanced level), Applied Machine Learning (LNU – 3 credits, Advanced level). Students who pass all three courses will receive a diploma from Learning for Professionals.