With the ongoing digitalization of the industry towards "Industry 4.0" i.e. smart factories with more efficient production, shorter lead times, higher quality, etc. increase the need to measure different quantities and associated processing of this measurement data. This includes the collection of signals via different types of sensors and signal processing of these signals in order to e.g. provide "smart industrial cyber-physical systems" (SICPS) with information for the purpose of monitoring, maintaining, controlling activities, etc. in order to achieve smart factories.
The course will focus on sensors, analog-to-digital converters, quality of measured data and signal processing where we, among other things, highlights the choice of sensors and data collection systems and introduces some robust signal processing methods in the light of the digitization of the industry.
This course is for professionals who work professionally with programming and development - regardless of industry or sector. You may be in the industry, the IT sector or a larger company in a completely different industry, you may be working in a development department within a company or as an IT consultant or.
The course contains the following:
Sensors for measuring vibrations, force, elongation, speed and associated signal conditioning
Analog-to-digital converter and "Effective Number Of Bits (ENOB)"
Folding when sampling signals and anti-folding filters
Fourier transform - Discrete Fourier transform
Stochastic processes and relevant statistical concepts
Power density spectrum, Power spectrum and associated systematic and random errors
In this course we will discuss the need for experimental measurements in fluid mechanics and heat transfer, the challenges involved, and how to choose the best method for each application. You will learn about commonly used methods to measure the flow of gases and liquids. You will also learn about methods to measure heat transfer, which is relevant for cooling of high temperature parts in small and large engines for power generation.
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:
Apply to the course in the below link:
Örebro University is offering an introduction course in artificial intelligence. The course will address the basic concepts within classical artificial intelligence (other than machine learning). Traditional artificial intelligence is characterized by the so-called declarative approach to problem solving. The course deals with a selection of different intelligent problem-solving methods, both in theory and practice.
After completing the course, the student will be able to model and use appropriate generic solution algorithms to solve problems in an intelligent system. The course is part of the education initiative Smarter at Örebro University.
Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. The course is part of the education initiative Smarter at Örebro University.
This is course requires completion of course Reinforcement Learning part 1.
Berör några av de grundläggande koncepten och teorierna kring strategisk teknologihantering och granskar viktiga strategiska beslut i centrum av teknik- och innovationshantering utifrån företagets konkurrenssituation. Dessa kan till exempel beröra valet av teknologi, disruptiva innovationer, tidpunkten för teknikutvecklingsinitiativ, implementeringsstrategier, modulär design, skapande av strategiska partnerskap eller anpassning till snabb teknisk förändring.
The manufacturing industry collects increasingly large volumes of big data, that is, data at high speed, generated from a wide range of sources in different formats and quality levels. But what is data without insight? This course will help you master the fundamental concepts of big data, cloud computing and smart decision-making for industrial analytics.
Designed specifically for manufacturing sector professionals, this Master’s course provides knowledge and insights in handling and processing data, using machine learning and data analytics in the cloud environment. You will learn machine learning-based solutions for industrial applications, such as smart decision-making and predictive maintenance, using state of the art cloud platform tools.