2020 Introduction to Measurement Engineering

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Academic unit or major
Undergraduate major in Systems and Control Engineering
Instructor(s)
Hachiya Hiroyuki 
Course component(s)
Lecture
Mode of instruction
ZOOM
Day/Period(Room No.)
Tue3-4(W611)  Fri3-4(W611)  
Group
-
Course number
SCE.I201
Credits
2
Academic year
2020
Offered quarter
2Q
Syllabus updated
2020/3/24
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

All measurement has a limit of precision and accuracy, and this must be taken into account when evaluating experimental results. This course focuses on the fundamental principles of measurement and uncertainty. Topics include the international system of units, statistical description of data, representation of experimental error, data analysis, sensor application, and case study of measurement system.

Student learning outcomes

At the end of this course, students will be able to
1) Explain the International System of Units.
2) Have an understanding of the basic concepts of probability, random variables, probability distribution, and joint probability distribution.
3) Explain the fundamental principles of measurement and uncertainty.
4) Have an understanding how measurement systems are designed, calibrated, characterized, and analyzed.

Keywords

Measurement, Instrumentation, Metrology, International System of Units, Uncertainty, Propagation of uncertainty, Least squares method, Measurement uncertainties evaluation, Probability and statistics

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

At the beginning of each class, solutions to exercise problems assigned during the previous class are reviewed. Towards the end of class, students are given exercise problems related to what is taught on that day to solve.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Fundamentals of measurements, Units and standards, International System of Units, Standards for the SI Base Units Understand International System of Units.
Class 2 Base units and derived units, Dimensional analysis, Measurand and error Explain SI base units and derived units.
Class 3 Measurement and error, Uncertainty - Meaning and Evaluation -, Significant figures, Approximation formula Understand the concept of measurement uncertainty.
Class 4 Direct and indirect measurements Fundamentals of probability and statistics(1): Probability variables Understand probability variables.
Class 5 Fundamentals of probability and statistics(2) Expected value and deviation, Propability, Propability density function Understand what expectation and variance mean and be able to compute them.
Class 6 Fundamentals of probability and statistics(3) Joint probability distribution, marginal probability distribution Understand joint probability and marginal probability
Class 7 Fundamentals of probability and statistics(4) Binomial distribution, Gaussian distribution Understand typical distribution functions.
Class 8 Propagation law of uncertainty Use propagation law of uncertainty.
Class 9 Least squares method, Interporation Analyse measured data using least squares method.
Class 10 Least squares method in matrix notation Analyse multidimensional data using least squares method in matrix notarion.
Class 11 Fundamentals of measurement circuit Measurement of voltage, current and resistance(1) Understand basic measurement circuit. Explain measurement methods of resistance.
Class 12 Measurement of voltage, current and resistance(2) Explain measurement methods of voltage, current and resistance.
Class 13 Reliablilty and Validity, Evaluating uncertainty components Understand random errors and systematic errors.
Class 14 Signal processing for measurement system Explain signal processing techniques in measurement system.
Class 15 Summarization of the course. Review the course contents.

Textbook(s)

Not specified.

Reference books, course materials, etc.

An Introduction to Uncertainty in Measurement (Cambridge University Press)

Assessment criteria and methods

Students' course scores are based on exercise problems (20%) and term examination (80%).

Related courses

  • SCE.I202 : Random Signal Processing
  • SCE.I203 : Digital Signal Processing

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None.

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