Course detail

Modern means in automation

FEKT-KMPAAcad. year: 2012/2013

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

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

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

Condition of petting full credit is absolut (100%) attendance in obligatory parts of lessons. 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

Automation - relevance, resources, engineering cybernetics.
Data, information, knowledge - definition, examples.
Expert systems - definitiv, architectural engeneering, theoretical sources, characteristics, inference engine, creation of knowledge base, acguirement of kbnowledges, proces sof consultation, aplications.
Artificial neural networks - definition, neuron, topology, paradigm, multilayer perceptrons neural network, backpropagation algorithm, activation, characteristics.
Machine learning - definitions, preprocessing, supervised learning, unsupervised learning, meta-learning, optimisation algorithms.
Computer vision - introduction, digital image processing, image preprocessing, image segmentation.

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

Berka P. a kol.: Expertní systémy. Skripta, VŠE Praha, 1998. (CS)
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)
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 (EN)

Classification of course in study plans

  • Programme EECC Bc. Bachelor's

    branch BK-AMT , 3 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