The purpose is to give the students an overview of issues and methods for development and assurance of safety-critical software, including details of selected technologies, methods and tools.
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.
Robot system 1 deals with industrial robots and their use in industry. The course addresses the structure and properties of robots, as well as principles for use in industry based on different principles. Based on the requirements placed on a robot system, these can be configured based on different technical points of departure, how they are to be used and methods for creating efficiency. Common equipment is taken up as adaptations to different work processes and applications. Here grippers and sensors come in as well as process equipment of various kinds. The course covers factors for investment, as well as laboratory work on how security and programming can be handled. This course is for professionals who work with production systems, automation and robotics at various levels as responsible for individual production lines, departments, or role as production manager or production development. The course will mainly focus on the manufacturing industry in application examples, but principles that will be addressed will be applicable to a number of industries, including consulting companies working towards the manufacturing industry. The course contains the following: Industrial robots as a concept; structure and properties; robots in relation to concepts such as Industry 4.0 and Smart Industry Principles of use; configuration of robot system Laboratory work with safety and programming of industrial robots Development trends, global perspective
This course looks at where important materials in products we use every day come from and how these materials can be used more efficiently, longer, and in closed loops. This is the aim of the Circular Economy, but it doesn’t happen on its own. It is the result of choices and strategies by suppliers, designers, businesses, policymakers and all of us as consumers. In addition to providing many cases of managing materials for sustainability, the course also teaches skills and tools for analyzing circular business models and promotes development of your own ideas to become more involved in the transition to a Circular Economy. You will learn from expert researchers and practitioners from around Europe as they explain core elements and challenges in the transition to a circular economy over the course of 5 modules: Module 1: Materials. This module explores where materials come from, and builds a rationale for why society needs more circularity. Module 2: Circular Business Models. In this module circular business models are explored in-depth and a range of ways for business to create economic and social value are discussed. Module 3: Circular Design, Innovation and Assessment. This module presents topics like functional materials and eco-design as well as methods to assess environmental impacts. Module 4: Policies and Networks. This module explores the role of governments and networks and how policies and sharing best practices can enable the circular economy. Module 5: Circular Societies. This module examines new norms, forms of engagement, social systems, and institutions, needed by the circular economy and how we, as individuals, can help society become more circular.
The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling
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.
This course will teach you how to build convolutional neural networks. You will learn to design intelligent systems using deep learning for classification, annotation, and object recognition.