Course detail
Modern Means in Automation
FEKT-BMPAAcad. year: 2016/2017
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
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
- 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
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
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
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
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)
Šíma J., Neruda R.: Teoretické otázky neuronových sítí. Matfyzpress, Praha 1996 (CS)
Recommended reading
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
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Expertní systémy (doc. Ing. Václav Jirsík, CSc.)
3. Expertní systémy (doc. Ing. Václav Jirsík, CSc.)
4. Expertní systémy (doc. Ing. Václav Jirsík, CSc.)
5. Umělé neuronové sítě (doc. Ing. Václav Jirsík, CSc.)
6. Umělé neuronové sítě (doc. Ing. Václav Jirsík, CSc.)
7. Umělé neuronové sítě (doc. Ing. Václav Jirsík, CSc.)
8. Průmysl 4.0 (Ing. Jan Pásek, CSc.)
9. Tvorba a řešení inovačních zadání (doc. Ing. Bohuslav Bušov, CSc.)
10. Tvorba a řešení inovačních zadání (doc. Ing. Bohuslav Bušov, CSc.)
11. Tvorba a řešení inovačních zadání (doc. Ing. Bohuslav Bušov, CSc.)
12. Počítačové vidění (Ing. Karel Horák, Ph.D.)
13. Počítačové vidění (Ing. Karel Horák, Ph.D.)
Exercise in computer lab
Teacher / Lecturer
Syllabus
2. Expert system NPS32
3. Expert system - AI Tool
4. Expert system - project
5. Artificial neural network
6. Artificial neural network
7. Artificial neural network
8. Project
9. TRIZ
10. - (Easter)
11. TRIZ
12. TRIZ
13. 4. Expert system - project