SYSTEMS-OF-SYSTEMS ENGINEERING

This course teaches you how to build convolutional neural networks (CNN). You will learn how to design intelligent systems using deep learning for classification, annotation, and object recognition. It includes three modules: Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques Deep learning with convolutional neural network: Overview of neural network as classifiers, introduction of convolutional neural network and Deep learning architecture. Deep learning tools: Implementation of Deep learning for Image classification and object recognition, e.g. using Keras.

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.

The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.

The aim of this course is to provide participants with the principles behind model-driven development of software systems and the application of such a methodology in practice. Modelling is an effective solution to reduce problem complexity and, as a consequence, to enhance time-to-market and properties of the final product.

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.

Fiber-optic sensing technologies are fast evolving and have entered in a large domain of our industry. Today all geothermal fields, water dams, railroads and to some lesser extent mines are equipped with fiber-optic cables to allow not only digital data transmission but also to interrogate fiber cables for information such as temperature changes or values (leakage issues or fractured rocks) but also strain measurements that can be indicators of soil failure or movements. When conducted in a controlled manner, artificial signals can be generated to help image the subsurface for mineral exploration, mine tailing characterization and for geothermal field development work by mapping faults and thermal fluids. There are other applications such as traffic monitoring that can also be done using this technology. Given its vast applications in the green transition, fiber-optic sensing is one of the most advanced technologies to be implemented in a wide range of fossil-free energy systems, hence, of a great importance to learn about their pros and cons and possibilities. Course content The course will have the following content: Introduction to DAS DAS Interrogators for temperature and strain measurements Fiber optic cables and their health conditions (hands-on with fiber-cable microscopes and fusion splicers) Design of a fiber-optic survey (surface and borehole) Parameter testing such as gauge length, laser pulse and width Field trials at a mine tailing test site or a mineral exploration borehole Work with the data and reporting Course design Hybrid and blended including hands-on practices.  This course takes about 30 hours of study to complete. You will learn By taking the course the participants are intended to learn about: Fiber-optic cables and their specifications including how to check their health and splice them DAS interrogators and their interior designs for fiber-optic sensing applications Design surface and borehole experiments Read and work with the data (hands-on) Who is the course for? The course will be given to a broad range of participants from engineering to geoscience backgrounds including university students but also participants from the industry. Participants can be from construction industry, road administration, energy sector (e.g., water dams), mining and defence workers. The course will be run within the newly established Smart Exploration Research Center involving tech companies such as BitSimNow Part of Prevas who are also expert in PFGA and fiber-related technologies. A prerequisite to the course is prior knowledge on different problems in the energy sector but some knowledge with Matlab and/or Python programming. The course can continue as an industry offer through the SERC-center as a multidisciplinary course at Uppsala University and for industry participants.