Course detail

Modern means in automation

FEKT-BMPAAcad. year: 2010/2011

Using of knowledge systems in automation. Data and knowledge. Acquirement process of knowledges. Automatic acquirement of knowledges. Expert systems, structure, action. Application expert systems in automation. Artificial neural networks, paradigm, multilayer perceptrons, simulation dynamic systems of neural networks. Computer vision, image preprocessing, image segmentation.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

doc. Ing. Václav Jirsík, CSc.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

Student gets acquirement of theoretical and practice knowledge of pattern recognition, artificial neural networks, expert systems application of automation.

Prerequisites

The subject knowledge on the secondary school level is required.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Work of students is evaluated during study by tests in exercises. They can obtain maximum 20 points by these tests during semester.
Final examination is evaluated by 80 points at maximum.

Course curriculum

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

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 content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

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

doc. Ing. Václav Jirsík, CSc.

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

doc. Ing. Václav Jirsík, CSc.

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