Data Processing (FSI-GSZ)

Academic year 2022/2023
Supervisor: Ing. Daniel Zuth, Ph.D.  
Supervising institute: ÚVSSR all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
The aim of the course is to organize the knowledge and methods used in data processing in the production process.
Learning outcomes and competences:
Obtaining general principles in data collection and processing. Overview of modern methods in data processing with a focus on Industry 4.0
Prerequisites:
Theoretical knowledge of physics, fundamentals of electronics and algorithmization.
Course contents:
The course acquaints students with the problems of data processing in the production process. The basic methods of data collection, industrial buses, methods of data transmission including security, data analysis and processing and last but not least the recording in the database system will be described. Emphasis is placed on current methods that meet the requirements for Industry 4.0.
Teaching methods and criteria:
The course is taught in the form of lectures that have the character of explanation of basic principles and theory of the given discipline. Teaching is complemented by laboratory exercises.
Assesment methods and criteria linked to learning outcomes:
The course consists of exercises and lectures. Exercise is completed by credit (awarded in the 13th week). To obtain it is required 100% participation in exercises and activity in exercises. Students will work out the individual work in the prescribed range and quality. Based on the quality of the work in the exercise, the student earns up to 30 points for the exam The work must be submitted in writing and checked and recognized by the teacher. The test is realized by written test, student can get up to 70 points from this test, where 30 points from exercises. Evaluation of the test result is given by the ECTS grading scale.
Controlled participation in lessons:
Attendance at lectures is recommended, participation in laboratories is controlled. A maximum of two absences in the laboratories can be compensated by the independent elaboration of missing protocols.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Laboratory exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture

1. Information and data, basic concepts, types and methods of data acquisition.
2. Buses and sensors used in industry
3. Data transmission, protocols, compression, encryption
4. IoT and cloud systems
5. Fundamentals of data processing in Matlab/Simulink
6. Fundamentals of data processing in LabVIEW
7. Databases - SQL language - query creation, relational databases
8. Text editors, spreadsheets, graphics
9. Advanced data processing methods - data classification
10. Advanced data processing methods - evolutionary algorithms
11. Advanced data processing methods - fuzzy sets
12. Practical examples of the topics covered.
13. Credit  
    Laboratory exercise 1. Data acquisition in Matlab environment, basic information
2. Data acquisition in Matlab environment, data acquisition from sensor
3. Data processing in Matlab (Octave)
4. Data collection in LabVIEW environment, basic information
5. Data acquisition in LabVIEW environment, data acquisition from sensor
6. Compression and encryption of acquired data
7. Spreadsheet processors, data processing
8. Spreadsheets, extended functions
9. MS Access, tables, search queries
10. MS Access, relational DB
11. SQL queries, relational DB
12. Inspection and completion of protocols
13. Credit
Literature - fundamental:
1. DANIŠ, Stanislav, 2009. Základy programování v prostředí Octave a Matlab. Praha: Matfyzpress. ISBN 978-80-7378-082-1.
2. BURDA, Michal, 2012. Databázové systémy. Hradec Králové: Gaudeamus. ISBN 978-80-7435-203-4.
2. Frank Lamb: Industrial Automation: Hands-On, McGraw-Hill Education, 2013
3. LabVIEW Measurements Manual, National Instruments, April 2003 Edition, Part Number 322661B-01, dostupné z www.ni.com
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
CŽV full-time study CZV Bases of Mechanical Engineering -- Cr,Ex 4 Compulsory 1 1 S
N-KSB-P full-time study --- no specialisation -- Cr,Ex 4 Compulsory 2 1 S