Academic year 2020 - 2021

There is no implementations attached to this degree programme.

Academic year 2021 - 2022

•   Dynamic systems - modeling, simulation and control TKV21AT-02-3001 06.09.2021-31.12.2021  5 credits  (AT21HP-V) +-
  Competence objectives of the study unit

  Systems thinking is the holistic approach to a field of science, where cause and effect are entangled in a feedback network. The field of automation relies on systems thinking in the way it examines the feedback system and process dynamics in general. Ways of describing these dynamic systems are through modeling and identification. The course aims at introducing the above to the students to achieve a common platform of knowledge.
  Knowledge and understanding
  After completing the course, the student should be able to:
  - recognize and describe common dynamic process systems
  - explain the difference between various systems
  - describe the practical usage of some process models
  
  Skills and abilities
  After completing the course, the student should be able to:
  - create and simulate process models
  - experiment with different process models in automation and control
  - perform basic identification of processes
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - justify the choice of a process model
  - evaluate and report on the choice of a model

  Prerequisites

  No pre-requirements.

  Content of the study unit

  The contents of the course are concept and principles of dynamic systems, system modeling and simulation, techniques for system identification, optimization methods and feedback control.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in systems thinking, dynamic systems, modeling, identification, optimization and control.

  Assessment criteria – good (3-4)

  Good skills in systems thinking, dynamic systems, modeling, identification, optimization and control.

  Assessment criteria – excellent (5)

  Excellent skills in systems thinking, dynamic systems, modeling, identification, optimization and control.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Teaching language

  English

  Timing

  06.09.2021 - 31.12.2021

  Enrollment date range

  11.06.2021 - 06.09.2021

  • 5 credits

  Group(s)

  • AT21HP-V

  Teachers and responsibilities

  Joachim Böling, Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Machine learning methods in automation TKV21AT-03-3001 04.10.2021-19.12.2021  5 credits  (AT21HP-V) +-
  Competence objectives of the study unit

  Machine learning methods have become an important tool for automation, control and data-driven decision making in an industrial context. The course aims at introducing and practicing several of these tools for system identification, classification and predictive analytics.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - identify and recognize industrial problems that are solvable through machine learning
  - plan and apply the machine learning workflow to classification, regression and identification problems
  - understand and explain the difference between different machine learning tasks and techniques
  
  Skills and abilities
  After completing the course, the student should be able to:
  - develop and build machine learning models for different tasks
  - validate and test the performance of a model
  - develop hands-on experience with machine learning techniques
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - interpret the result of a machine learning experiment
  - report on the result of a machine learning experiment
  - assess the outcome of a machine learning experiment

  Prerequisites

  No prerequisites

  Content of the study unit

  The availability of big amounts of data for various systems makes it possible to apply both linear and nonlinear models to fit the data. The course focuses on different techniques that can be used for identification, classification and predictive purposes in the area of automation and control.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in understanding and using machine learning tools for classification, prediction and identification.

  Assessment criteria – good (3-4)

  Good skills in understanding and using machine learning tools for classification, prediction and identification.

  Assessment criteria – excellent (5)

  Excellent skills in understanding and using machine learning tools for classification, prediction and identification.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Teaching language

  English

  Timing

  04.10.2021 - 19.12.2021

  Enrollment date range

  11.06.2021 - 04.10.2021

  • 5 credits

  Group(s)

  • AT21HP-V

  Teachers and responsibilities

  Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5

•   Research methods and scientific writing TKV21AT-01-3001 01.09.2021-31.12.2021  5 credits  (AT21HP-V) +-
  Competence objectives of the study unit

  Reading and comprehending reliable, scientifically and technically high-level written articles and documentation is a prerequisite for successful work in technologically developing fields. The course introduces the students to the methodology of writing scientific technical documentation and articles. The course also aims at developing student’s written and presentation skills and serve as support for the individual master thesis work.
  
  Knowledge and understanding
  After completing the course, the student should be able to:
  - review and comprehend scientific and technical documentation
  - understand the basic principles of different research methods in engineering
  - understand the principles of writing for academic purposes
  
  Skills and abilities
  After completing the course, the student should be able to:
  - communicate technical and scientific material
  - plan and realize a research study
  
  Evaluation ability and approach
  After completing the course, the student should be able to:
  - perform relevant choices of methodology for a research study,
  - demonstrate their ability to write for an academic audience

  Prerequisites

  No pre-requirements.

  Content of the study unit

  The contents of the course are reading and summarizing scientific articles and documentation, writing and presentation of technical material and work. Overview of qualitative and quantitative research methods in engineering.

  Assessment criteria

  Assessment criteria – satisfactory (1-2)

  Satisfactory skills in research methods, scientific writing and presentation skills.

  Assessment criteria – good (3-4)

  Good skills in research methods, scientific writing and presentation skills.

  Assessment criteria – excellent (5)

  Excellent skills in research methods, scientific writing and presentation skills.

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  Person(s) in charge

  Ray Pörn

  Mode of delivery

  Face-to-face

  Teaching language

  English

  Timing

  01.09.2021 - 31.12.2021

  Enrollment date range

  11.06.2021 - 05.09.2021

  • 5 credits

  Group(s)

  • AT21HP-V

  Teachers and responsibilities

  Hanna Kuusisto, Ray Pörn

  Responsible unit

  Faculty of Technology and Seafaring

  Degree Programme(s)

  Degree Programme in Automation Technology

  Campus

  Vasa, Wolffskavägen 33

  Assessment scale

  H-5