Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Szkoła Doktorska - ZUT Doctoral School
specjalność: IT, ELECTRICAL ENGINEERING AND MECHANICAL ENGINEERING BLOCK

Sylabus przedmiotu Estimation of measurement uncertainty:

Informacje podstawowe

Kierunek studiów ZUT Doctoral School
Forma studiów studia stacjonarne Poziom
Stopnień naukowy absolwenta doktor
Obszary studiów charakterystyki PRK
Profil
Moduł
Przedmiot Estimation of measurement uncertainty
Specjalność IT, ELECTRICAL ENGINEERING AND MECHANICAL ENGINEERING BLOCK
Jednostka prowadząca Środowiskowe Laboratorium Miernictwa
Nauczyciel odpowiedzialny Paweł Majda <Pawel.Majda@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 0,5 ECTS (formy) 0,5
Forma zaliczenia zaliczenie Język angielski
Blok obieralny 6 Grupa obieralna 2

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW3 8 0,51,00zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Differential calculus, algebra
W-2Knowledge of the basics of mathematical statistics, such as: the concept of a random variable, variance and standard deviation, testing statistical hypotheses, estimating distribution parameters probabilities.

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Developing the ability to classify errors and their sources.
C-2Developing the ability to estimate measurement uncertainty according to the international standard "JCGM 100 - Evaluation of measurement data - Guide to the expression of uncertainty in measurement", i.e. the so-called "GUM Guide".

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
wykłady
T-W-1Basic concepts of mathematical statistics (probability distribution of dispersion, correlation of random variables, central limit theorem of probability theory, etc.). Errors add up” – what does it mean and how do you understand it? Concepts: error, uncertainty, accuracy, correction, maximum error limit. Sources of measurement errors (metrological equipment, methods, impact of environmental conditions, personnel) and measures of their uncertainty. Basic concepts in the field of estimation of measurement uncertainty (standard, complex and expanded uncertainty, coverage factor, estimation using the A and B method, uncertainty budget). "Short recipe" for estimating measurement uncertainty, special cases. Rules for assessing product compliance with specifications. The law of propagation in the analysis of correlated variables. Computer simulations of measurement uncertainty estimation using the Monte Carlo method. Monte Carlo extension for multi-input and output models. Estimation of uncertainty using the effective number of degrees of freedom; Welch-Sattertwaite formula. A method for rigorously estimating measurement uncertainty. Practical exercises in estimating measurement uncertainty.8
8

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
wykłady
A-W-1participation in classes8
A-W-2studying the subject4
A-W-3participation in the final examination1
A-W-4consultations2
15

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Informative lecture
M-2Problem lecture

Sposoby oceny

KODSposób oceny
S-1Ocena podsumowująca: Written assessment

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
ISDE_4-_IEM04.2_W01
The result of education will be extended, theoretically anchored knowledge related to the represented field and scientific discipline, as well as advanced knowledge regarding conducting scientific research, methodology of scientific work, preparing publications and presenting results. The graduate will be aware of the rules for disseminating the results of scientific work, including in the context of open access. They will also have a full understanding of the fundamental dilemmas of modern civilization, especially in relation to the latest scientific achievements in the field and discipline represented. This comprehensive knowledge will also include aspects of mathematical statistics and the ability to practically apply concepts related to measurement errors, uncertainties, estimation, correlated variable analysis and Monte Carlo simulation techniques. Thanks to this, the graduate will be ready not only to immerse himself in the latest scientific achievements, but also to effectively contribute to the development of the field in the area he is considering, presenting his results in a way that is accessible and understandable to society.
ISDE_4-_W02, ISDE_4-_W03C-1, C-2T-W-1M-1S-1

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
ISDE_4-_IEM04.2_K01
The result of education will be the acquisition of competences enabling understanding and critical analysis of scientific achievements and one's own contribution to the development of the field and discipline. The graduate will be aware of the need for a creative approach to solving civilization challenges, both social, research and creative. They will have the ability to initiate activities in the public interest, while being an entrepreneurial thinker. They will also acquire practical skills in the practical applications of mathematical statistics, understanding key concepts for measurements, errors, uncertainties, and being able to effectively estimate and analyze measurement uncertainties according to an international standard. Thanks to the acquired skills, the graduate will be ready to effectively contribute to scientific, social and creative development, and to practically apply knowledge in practice, while achieving high standards of analysis and understanding of problems in the field and discipline represented.
ISDE_4-_K01, ISDE_4-_K02C-1, C-2T-W-1M-1, M-2S-1

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
ISDE_4-_IEM04.2_W01
The result of education will be extended, theoretically anchored knowledge related to the represented field and scientific discipline, as well as advanced knowledge regarding conducting scientific research, methodology of scientific work, preparing publications and presenting results. The graduate will be aware of the rules for disseminating the results of scientific work, including in the context of open access. They will also have a full understanding of the fundamental dilemmas of modern civilization, especially in relation to the latest scientific achievements in the field and discipline represented. This comprehensive knowledge will also include aspects of mathematical statistics and the ability to practically apply concepts related to measurement errors, uncertainties, estimation, correlated variable analysis and Monte Carlo simulation techniques. Thanks to this, the graduate will be ready not only to immerse himself in the latest scientific achievements, but also to effectively contribute to the development of the field in the area he is considering, presenting his results in a way that is accessible and understandable to society.
2,0
3,0providing more than 50% correct answers in the written assessment
3,5
4,0
4,5
5,0

Kryterium oceny - inne kompetencje społeczne i personalne

Efekt uczenia sięOcenaKryterium oceny
ISDE_4-_IEM04.2_K01
The result of education will be the acquisition of competences enabling understanding and critical analysis of scientific achievements and one's own contribution to the development of the field and discipline. The graduate will be aware of the need for a creative approach to solving civilization challenges, both social, research and creative. They will have the ability to initiate activities in the public interest, while being an entrepreneurial thinker. They will also acquire practical skills in the practical applications of mathematical statistics, understanding key concepts for measurements, errors, uncertainties, and being able to effectively estimate and analyze measurement uncertainties according to an international standard. Thanks to the acquired skills, the graduate will be ready to effectively contribute to scientific, social and creative development, and to practically apply knowledge in practice, while achieving high standards of analysis and understanding of problems in the field and discipline represented.
2,0
3,0providing more than 50% correct answers in the written assessment
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. JCGM 100: Evaluation of measurement data − Guide to the expression of uncertainty in measurement., Geneva, 2008
  2. JCGM 101: Evaluation of measurement data − Supplement 1 to the “Guide to the expression of uncertainty in measurement” − Propagation of distributions using a Monte Carlo method., 2008
  3. JCGM 102: Evaluation of measurement data − Supplement 2 to the “Guide to the expression of uncertainty in measurement” − Extension to any number of output quantities., 2011

Literatura dodatkowa

  1. JCGM 103: Evaluation of measurement data – Supplement 3 to the "Guide to the expression of uncertainty in measurement" – Modelling (ISO/IEC Guide 98-3-3)
  2. JCGM 104: Evaluation of measurement data − An introduction to the “Guide to the expression of uncertainty in measurement” and related documents., 2009
  3. JCGM 105: Evaluation of measurement data – Concepts and basic principles (ISO/IEC Guide 98-2)
  4. JCGM 106: Evaluation of measurement data - The role of measurement uncertainty in conformity assessment., 2012
  5. JCGM 107 – Evaluation of measurement data – Applications of the least-squares method (ISO/IEC Guide 98-5)
  6. JCGM GUM-6:2020 Guide to the expression of uncertainty in measurement - Part 6: Developing and using measurement models., 2020
  7. PKN-ISO/IEC Guide 99. Międzynarodowy słownik metrologii. Pojęcia podstawowe i ogólne oraz terminy z nimi związane (VIM).

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Basic concepts of mathematical statistics (probability distribution of dispersion, correlation of random variables, central limit theorem of probability theory, etc.). Errors add up” – what does it mean and how do you understand it? Concepts: error, uncertainty, accuracy, correction, maximum error limit. Sources of measurement errors (metrological equipment, methods, impact of environmental conditions, personnel) and measures of their uncertainty. Basic concepts in the field of estimation of measurement uncertainty (standard, complex and expanded uncertainty, coverage factor, estimation using the A and B method, uncertainty budget). "Short recipe" for estimating measurement uncertainty, special cases. Rules for assessing product compliance with specifications. The law of propagation in the analysis of correlated variables. Computer simulations of measurement uncertainty estimation using the Monte Carlo method. Monte Carlo extension for multi-input and output models. Estimation of uncertainty using the effective number of degrees of freedom; Welch-Sattertwaite formula. A method for rigorously estimating measurement uncertainty. Practical exercises in estimating measurement uncertainty.8
8

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1participation in classes8
A-W-2studying the subject4
A-W-3participation in the final examination1
A-W-4consultations2
15
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_IEM04.2_W01The result of education will be extended, theoretically anchored knowledge related to the represented field and scientific discipline, as well as advanced knowledge regarding conducting scientific research, methodology of scientific work, preparing publications and presenting results. The graduate will be aware of the rules for disseminating the results of scientific work, including in the context of open access. They will also have a full understanding of the fundamental dilemmas of modern civilization, especially in relation to the latest scientific achievements in the field and discipline represented. This comprehensive knowledge will also include aspects of mathematical statistics and the ability to practically apply concepts related to measurement errors, uncertainties, estimation, correlated variable analysis and Monte Carlo simulation techniques. Thanks to this, the graduate will be ready not only to immerse himself in the latest scientific achievements, but also to effectively contribute to the development of the field in the area he is considering, presenting his results in a way that is accessible and understandable to society.
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_W02They have extended, theory-based knowledge relating to the represented field and discipline and detailed knowledge at an advanced level in the area of scientific research ,methodology of scientific work, preparation of publications and presentations of research results and the principle of dissemination of the results of scientific work, including open access mode.
ISDE_4-_W03They know and understand fundamental dilemmas of modern civilisation, also in relation to the recent scientific developments in the represented field and discipline.
Cel przedmiotuC-1Developing the ability to classify errors and their sources.
C-2Developing the ability to estimate measurement uncertainty according to the international standard "JCGM 100 - Evaluation of measurement data - Guide to the expression of uncertainty in measurement", i.e. the so-called "GUM Guide".
Treści programoweT-W-1Basic concepts of mathematical statistics (probability distribution of dispersion, correlation of random variables, central limit theorem of probability theory, etc.). Errors add up” – what does it mean and how do you understand it? Concepts: error, uncertainty, accuracy, correction, maximum error limit. Sources of measurement errors (metrological equipment, methods, impact of environmental conditions, personnel) and measures of their uncertainty. Basic concepts in the field of estimation of measurement uncertainty (standard, complex and expanded uncertainty, coverage factor, estimation using the A and B method, uncertainty budget). "Short recipe" for estimating measurement uncertainty, special cases. Rules for assessing product compliance with specifications. The law of propagation in the analysis of correlated variables. Computer simulations of measurement uncertainty estimation using the Monte Carlo method. Monte Carlo extension for multi-input and output models. Estimation of uncertainty using the effective number of degrees of freedom; Welch-Sattertwaite formula. A method for rigorously estimating measurement uncertainty. Practical exercises in estimating measurement uncertainty.
Metody nauczaniaM-1Informative lecture
Sposób ocenyS-1Ocena podsumowująca: Written assessment
Kryteria ocenyOcenaKryterium oceny
2,0
3,0providing more than 50% correct answers in the written assessment
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_IEM04.2_K01The result of education will be the acquisition of competences enabling understanding and critical analysis of scientific achievements and one's own contribution to the development of the field and discipline. The graduate will be aware of the need for a creative approach to solving civilization challenges, both social, research and creative. They will have the ability to initiate activities in the public interest, while being an entrepreneurial thinker. They will also acquire practical skills in the practical applications of mathematical statistics, understanding key concepts for measurements, errors, uncertainties, and being able to effectively estimate and analyze measurement uncertainties according to an international standard. Thanks to the acquired skills, the graduate will be ready to effectively contribute to scientific, social and creative development, and to practically apply knowledge in practice, while achieving high standards of analysis and understanding of problems in the field and discipline represented.
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_K01They understand the necessity and are prepared to critically analyse the achieved scientific output and the contribution of the results of their own research activity to the development of the represented field and discipline.
ISDE_4-_K02They understand the obligation to seek creative solutions to the challenges of civilisation, in particular to social, research and creative commitments, are aware of the need to initiate actions in the public interest, to think in the entrepreneurial manner and the need for scientific development for new phenomena and problems in the represented field and discipline.
Cel przedmiotuC-1Developing the ability to classify errors and their sources.
C-2Developing the ability to estimate measurement uncertainty according to the international standard "JCGM 100 - Evaluation of measurement data - Guide to the expression of uncertainty in measurement", i.e. the so-called "GUM Guide".
Treści programoweT-W-1Basic concepts of mathematical statistics (probability distribution of dispersion, correlation of random variables, central limit theorem of probability theory, etc.). Errors add up” – what does it mean and how do you understand it? Concepts: error, uncertainty, accuracy, correction, maximum error limit. Sources of measurement errors (metrological equipment, methods, impact of environmental conditions, personnel) and measures of their uncertainty. Basic concepts in the field of estimation of measurement uncertainty (standard, complex and expanded uncertainty, coverage factor, estimation using the A and B method, uncertainty budget). "Short recipe" for estimating measurement uncertainty, special cases. Rules for assessing product compliance with specifications. The law of propagation in the analysis of correlated variables. Computer simulations of measurement uncertainty estimation using the Monte Carlo method. Monte Carlo extension for multi-input and output models. Estimation of uncertainty using the effective number of degrees of freedom; Welch-Sattertwaite formula. A method for rigorously estimating measurement uncertainty. Practical exercises in estimating measurement uncertainty.
Metody nauczaniaM-1Informative lecture
M-2Problem lecture
Sposób ocenyS-1Ocena podsumowująca: Written assessment
Kryteria ocenyOcenaKryterium oceny
2,0
3,0providing more than 50% correct answers in the written assessment
3,5
4,0
4,5
5,0