SylabUZ
| Course name | Modelling of measurement transducers |
| Course ID | 06.0-WE-ELEKTD-ModofMeasTrans-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | Electrical Engineering |
| Education profile | academic |
| Level of studies | Second-cycle Erasmus programme |
| Beginning semester | winter term 2022/2023 |
| Semester | 2 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
Aacquaint students with the basic principles of construction of mathematical models of measurement transducers.
Shaping of basic skills in analyzing of sources error primary function blocks measuring transducers.
Shaping of basic skills for conducting simulation research and experimental research measuring transducers.
Measurement transducers
General characteristic of measurement transducers. Characteristic of basic functional measurement transducers blocks. Features distinguishing measurement transducers from previous generation transducers.
General notes about designing and role of mathematical model. The aim and stages of the design process. Sequential-iteration design algorithm. Limitations in the process of designing. Essence and scope of the mathematical modeling.
Fundamentals of models building. Stages of modeling. Analogies between physical phenomena. Methods of creation of mathematical models and neural models. Examples of building models of sensor and hardware analog-to-digital.
Basic elements of transducers and their mathematical models. Mathematical models of input circuits, analogue function modules, S/H and A/D converters. Application of artificial intelligence methods in modeling of measuring transducers.
Application of modeling in selected error correction methods of measuring transducers.
Modeling of reverse processing processes in measuring transducers.
Selected examples of the realization of modern measuring transducers.
Lecture, laboratory exercises.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – obtaining a positive grade from exam.
Laboratory – the passing condition is to obtain positive marks from all laboratory exercises to be planned during the semester.
Calculation of the final grade: lecture 50% + laboratory 50%
1. Bolikowski J. (red): Essentials of designing of smart measurement transducers of electrical quantities, Monograph Nr 68, WSI, Zielona Gora 1993 (in Polish).
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4. Kesler W.: The Data Conversion Handbook, Analog Devices Inc., Newnes, 2004.
5. Rutkowski L.: Computational Intelligence. Methods and Techniques. Springer-Verlag, Berlin, 2008.
1. Miczulski W., Sobolewski Ł., Algorithm for Predicting [UTC-UTC(k)] by Means of Neural Networks// IEEE Transactions on Instrumentation and Measurement .- 2017, Vol. 66, no. 8, s. 2136--2142, ISSN: 0018-9456.
2. Miczulski W., Powroźnik P., A new elastic scheduling task model in the node of a control and measurement system// Metrology and Measurement Systems .- 2013, Vol. 20, no 1, s. 87--98, ISSN: 0860-8229.
3. Miczulski W., Sobolewski Ł., Croonenbroeck C., Neural model of a wind turbine// W: XXI IMEKO World Congress Measurement in Research and Industry. Prague, Czechy, 2015 .- Prague,- ISBN: 9788001057933.
4. Proakis J.G, Manolakis D.G.: Digital Signal Processing. Principles, Algorithms and Aplications, Prentice Hall, 2007.
5. Gajda J., Szyper M.: Modeling and simulation research of measurement systems, Jartek s.c. Krakow 1998 (in Polish).
Modified by dr hab. inż. Paweł Szcześniak, prof. UZ (last modification: 06-04-2022 22:33)
