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Lecturer(s)
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Schliesing Pavel, Doc. PhD.
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Course content
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Main topics of the course organized into weekly blocks: - Introduction to modeling and simulation of complex systems - Fundamentals of discrete event-driven systems - Algorithmic composition of finite automata - Admissible languages and their compositions - Supervisor theory and design algorithms - Basics of Markov chains - Calculation of expected values and methods of conditioning - Statistical simulation and the Monte Carlo method - Extension to Markov Chain Monte Carlo - Statistical validation methods - Advanced validation methods - Presentation of simulation results
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Learning activities and teaching methods
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Lecture supplemented with a discussion, Lecture with practical applications, One-to-One tutorial, Students' self-study, Self-study of literature, Practicum
- Contact hours
- 39 hours per semester
- Presentation preparation (report) (1-10)
- 10 hours per semester
- Preparation for an examination (30-60)
- 50 hours per semester
- Preparation for comprehensive test (10-40)
- 35 hours per semester
- Practical training (number of hours)
- 26 hours per semester
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| prerequisite |
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| Knowledge |
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| Students are expected to have elementary knowledge in system theory, algorithm development and programming, all on level of basic university courses. |
| Skills |
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| student has basic programming knowledge in Matlab |
| Competences |
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| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| understand systems analysis methods and their application to the analysis of cybernetic systems |
| understand and apply the general principles of systems analysis |
| analyze systems using systematic methods |
| analyze systems using object-oriented methods |
| effective use of computing systems |
| Skills |
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| the student is able to translate the discrete behavior of the system into the generated language |
| the student is able to schematically describe the language using a finite state machine |
| the student is able to use automated tools to verify the correctness of the finite state machine |
| the student is able to create compositions of finite automata |
| the student is able to propose admissible rules of a finite state machine to verify their feasibility |
| the student is able to design supervisory machines |
| the student is able to design a simulation according to the Monte Carlo method |
| the student is able to design and implement a simulation using the Markov Chain Monte Carlo method |
| the student is able to validate the simulation program |
| the student is able to validate the simulation results against the measured values |
| Competences |
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| N/A |
| N/A |
| N/A |
| N/A |
| teaching methods |
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| Knowledge |
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| Lecture supplemented with a discussion |
| Practicum |
| Self-study of literature |
| One-to-One tutorial |
| Interactive lecture |
| Skills |
|---|
| Lecture |
| Practicum |
| Multimedia supported teaching |
| Textual studies |
| Collaborative instruction |
| Self-study of literature |
| One-to-One tutorial |
| Discussion |
| Competences |
|---|
| Lecture |
| Practicum |
| Multimedia supported teaching |
| Textual studies |
| Collaborative instruction |
| Self-study of literature |
| One-to-One tutorial |
| Discussion |
| assessment methods |
|---|
| Knowledge |
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| Combined exam |
| Individual presentation at a seminar |
| Skills |
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| Project |
| Individual presentation at a seminar |
| Written exam |
| Competences |
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| Skills demonstration during practicum |
| Individual presentation at a seminar |
| Seminar work |
| Written exam |
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Recommended literature
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C. G. Cassandras. Introduction to Discrete Event Systems. Kluwer Academic Publishers, 1999.
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