Title:

Intelligent Systems

Code:SIN
Ac.Year:2019/2020
Sem:Winter
Curriculums:
ProgrammeField/
Specialization
YearDuty
IT-MSC-2MBI-Compulsory-Elective - group I
IT-MSC-2MBS-Elective
IT-MSC-2MGM-Compulsory-Elective - group M
IT-MSC-2MIN2ndCompulsory
IT-MSC-2MIS-Compulsory-Elective - group S
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Compulsory-Elective - group B
IT-MSC-2MSK2ndCompulsory-Elective - group M
MITAINADE-Elective
MITAINBIO-Elective
MITAINCPS-Elective
MITAINEMB-Elective
MITAINGRI-Elective
MITAINHPC-Elective
MITAINIDE-Elective
MITAINISD-Elective
MITAINISY2ndCompulsory
MITAINMAL-Elective
MITAINMAT-Elective
MITAINNET-Elective
MITAINSEC-Elective
MITAINSEN-Elective
MITAINSPE-Elective
MITAINVER-Elective
MITAINVIZ-Elective
Language of Instruction:Czech
Public info:http://www.fit.vutbr.cz/study/courses/SIN/public/
Credits:5
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2640022
 ExamsTestsExercisesLaboratoriesOther
Points:70150015
Guarantor:Janoušek Vladimír, doc. Ing., Ph.D. (DITS)
Deputy guarantor:Zbořil František V., doc. Ing., CSc. (DITS)
Lecturer:Janoušek Vladimír, doc. Ing., Ph.D. (DITS)
Instructor:Janoušek Vladimír, doc. Ing., Ph.D. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Substitute for:
Artificial Intelligence (UIN), DITS
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
MonlecturelecturesD105 18:0019:501MIT 2MIT MIN xx
MonexerciselecturesD105 20:0020:501MIT 2MIT MIN xx
 
Learning objectives:
  To acquaint students with principles, architectures, and methods of design of intelligent systems of various kinds.
The course is suitable for students of all specializations taught at FIT.
Description:
  Intelligent systems, mechatronic, sociotechnical and cyber-physical systems. Artificial Intelligence Methods in Systems Design and Implementation. Discrete event systems. Control Systems Architectures. Internet of things, communication infrastructure. Smart Building, Smart Home. Smart City, Traffic Telematics, Intelligent Vehicle. Industry 4.0.
Knowledge and skills required for the course:
  Basics of systems theory, simulation.
Students can use any other special knowledge to implement an individual project.
Subject specific learning outcomes and competencies:
  Ability to model and design intelligent (smart) systems and their control using current methods and technologies.
Generic learning outcomes and competencies:
  Students acquire knowledge of principles, architectures and design of intelligent systems of various kinds.
Why is the course taught:
  The course combines theoretical knowledge of modelling systems and methods of artificial intelligence with technologies used in the practical implementation of smart systems.
Syllabus of lectures:
 
  1. Introduction. Motivation and goals of the course. 
  2. Mechatronic, sociotechnical and cyber-physical systems.
  3. Discrete event systems in control systems design.
  4. Softcomputing and expert systems in system design.
  5. Control system architectures and components.
  6. Agent paradigm. Learning and adaptive control systems.
  7. Markov decision process and learning controller.
  8. SCADA systems and distributed control systems. 
  9. Internet of Things (IoT), IoT Architecture, Communication Protocols.
  10. Intelligent buildings - sensors, networks, actuators, intelligent control.
  11. Smart Home. Smart City. Smart Grid.
  12. Intelligent transportation systems - telematic systems, traffic management, intelligent vehicle.
  13. Smart manufacturing, Industry 4.0.
Syllabus of numerical exercises:
 
  1. Application of soft computing in intelligent systems.
  2. Intelligent systems design methods.
Syllabus - others, projects and individual work of students:
 
  • Individual project - implementation of intelligent control in a simulated environment. The application area can be Smart Home, Transportation Systems Telematics, Smart Manufacturing, etc.
Fundamental literature:
 
  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Zeigler, B.P.: Theory of Modeling and Simulation, Academic Press; 2 edition (March 15, 2000), ISBN 978-0127784557
  3. Cassandras, C. G.,  Lafortune, S.: Introduction to discrete event systems, Springer, 2008.
  4. David, R., Alla, H.: Petri Nets and Grafcet: Tools for Modelling Discrete Event Systems, Prentice Hall, 1992, ISBN-10: 013327537X, ISBN-13: 978-0133275377
  5. Mehta, B.R., Reddy, Y.J.: Industrial Process Automation Systems: Design and Implementation, Elsevier, 2015, ISBN 978-0-12-800939-0
  6. Valeš, M.: Inteligentní dům. Brno, Vydavatelství ERA, 2006.
  7. Přibyl, P., Svítek, M.: Inteligentní dopravní systémy, Nakladatelství BEN, Praha 2001, ISBN 80-7300-029-6
  8. Automatizace. http://www.automatizace.cz/
Study literature:
 
  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Zeigler, B.P.: Theory of Modeling and Simulation, Academic Press; 2 edition (March 15, 2000), ISBN 978-0127784557
  3. Cassandras, C. G.,  Lafortune, S.: Introduction to discrete event systems, Springer, 2008.
  4. David, R., Alla, H.: Petri Nets and Grafcet: Tools for Modelling Discrete Event Systems, Prentice Hall, 1992, ISBN-10: 013327537X, ISBN-13: 978-0133275377
  5. Mehta, B.R., Reddy, Y.J.: Industrial Process Automation Systems: Design and Implementation, Elsevier, 2015, ISBN 978-0-12-800939-0
  6. Valeš, M.: Inteligentní dům. Brno, Vydavatelství ERA, 2006.
  7. Přibyl, P., Svítek, M.: Inteligentní dopravní systémy, Nakladatelství BEN, Praha 2001, ISBN 80-7300-029-6
  8. Automatizace. http://www.automatizace.cz/
Progress assessment:
  
  • Mid-term written test
  • Individual project
 

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