Course detail

Modern Means in Automation

FEKT-BMPAAcad. year: 2017/2018

The course is focused on the use of knowledge systems in automation. In this context, explains the concepts of data, information and knowledge. The lectures are focused on the issue of expert systems, artificial neural networks, machine learning, creation and solution innovation award and computer vision.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Learning outcomes of the course unit

Course graduate should be able to:
- explain the differences between the concepts of data, information and knowledge,
- explain the architecture and functionality of expert systems,
- create a base of knowledge for expert system NPS32,
- choose the field of application of expert systems,
- explain the paradigm of multilayer neural networks with backpropagation learning,
- discuss the settings for a parameter, the neural network,
- apply features multi-layered neural network backpropagation learning,
- design own solution of optimization task based on genetic algorithms,
- explain and use analysis of an object, formulation of innovative tasks to be solved, formulation of inventive tasks and use recommendation how to find new solutions,
- apply optical information in technical equipment.

Prerequisites

The subject knowledge on the secondary school level is required.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Techning methods include lectures and computer laboratories. Students have to write a single project during the course.

Assesment methods and criteria linked to learning outcomes

Condition of petting full credit is absolut (100%) attendance in obligatory parts of lessons - the computer exercises. Students are tested continuously and i tis possible to get maximum 20 points. The final written exam is rated by 70 points at maximum and the oral exam is rated by 10 points at maximum.

Course curriculum

1. Automation - relevance, resources, engineering cybernetics.
2. Data, information, knowledge - definition, examples.
3. Expert systems - definitiv, architectural engeneering, theoretical sources, characteristics, inference engine, creation of knowledge base, acguirement of knowledges, proces sof consultation, aplications.
4. Artificial neural networks - definition, neuron, topology, paradigm, multilayer neural network, backpropagation algorithm, activation, characteristics.
5. Industry 4.0 - introdduction to problems.
6.Theory of Inventive Problem Solving - analysis of the object to be improved and formulation of innovative task to be solved, then solving of inventive tasks supported by expert system and information from world patent databases.
7. Computer vision - preprocessing, segmentation, objects description, classification.

Work placements

Not applicable.

Aims

The aim of the course is to acquaint the students with modern methods and means in automation. To get knowledge and experience of pattern recognition and using neural networks, expert systems in automation.

Specification of controlled education, way of implementation and compensation for absences

The computer exercises are compulsory, the properly excused missed computer exercises can be compensate.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Hlaváč V.- Šonka M.: Počítačové vidění. Grada 1992,Praha,ISBN 80-85424-67-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 1. ACADEMIA 1993,Praha,ISBN 80-200-0496-3 (CS)
Mařík V.-Štěpánková O.-Lažanský J.:Umělá inteligence 2. ACADEMIA 1997,Praha,ISBN 80-200-0504-8 (CS)
Berka P. a kol.: Expertní systémy. Skripta, VŠE Praha, 1998. (CS)
Šíma J., Neruda R.: Teoretické otázky neuronových sítí. Matfyzpress, Praha 1996 (CS)

Recommended reading

Kasabov,N.K.: Foundations of Neural Networks, Fuzzy systems and Knowledge Engineering.The MIT Press,1996,ISBN 0-262-11212-4 (EN)
Schalkoff,R.J.:Artificial Neural Networks. The MIT Press,1997,ISBN 0-07-115554-6 (EN)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson, 2008, ISBN 978-0-495-08252-1 (EN)

Classification of course in study plans

  • Programme EECC Bc. Bachelor's

    branch B-AMT , 2 year of study, summer semester, elective specialised

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, summer semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Syllabus

Knowledge systems in automation
Data and knowledge, acquirement process of knowledge
Automatic acquirement of knowledge
Computer vision, introduction, image capturing, digitizing
Image preprocessing, filtering, thickening of edge
Image segmentation, thresholding, region growing, region merge
Image description
Pattern recognition and classification
Artificial neural networks
Multilayer perceptrons and backpropagation algorithm
Simulation dynamic systems of neural networks
Expert systems, structure, action
Application expert systems in automation

Exercise in computer lab

39 hod., compulsory

Teacher / Lecturer

Syllabus

Scientific image analyzer DIPS
Scientific image analyzer DIPS
Image preprocessing of DIPS
Image preprocessing of DIPS
Image segmentation of DIPS
Image segmentation of DIPS
Image description and Pattern recognition
Image description and Pattern recognition
Matlab with Simulink
Matlab,multilayer perceptrons and backpropagation algorithm
MAtlab,multilayer perceptrons and backpropagation algorithm
Matlab,simulation dynamic systems of neural networks
Credit