Course detail
Modern Means in Automation
FEKT-KMPAAcad. 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 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,
- 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 (EN)
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Teacher / Lecturer
Syllabus
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