Course Details
Analysis of Measuring Data
Academic Year 2023/24
IE52 course is part of 1 study plan
D-K-C-GK / GAK Winter Semester 1st year
Planning of experimentsMultidimensional quantities and their characteristics. Accuracy measures and their meaning. Testing and analysis of measurement results and values entering the adjustment process. Physical and mathematical correlation. Partial and multi-dimensional correlation. Matrix of correlation coefficients, covariance matrix. Systematical errors. Laws of error propagation. Errors of composite functions. Analysis of error limits. Adjustment of correlated measurements. Block adjustment. Collocation. Kalman filter. Robust adjustment methods. Analysis of covariance and weight matrixes. Planning of experiments, optimization methods. Design of methodology and technology for complex accuracy evaluation in doctor thesis.
Course Guarantor
Institute
Objective
Mastering of methods for analysis and testing of measuring results. Understanding of correlations and their treatment in adjustment process. Understanding of principles of collocation, Kalman data filtering, and robust adjustment methods. Getting an overview of methods of experimental planning and optimization.
Syllabus
1. Multidimensional quantities. Accuracy measures.
2. Testing and analysis of measurements results. Physical and mathematical correlation. Testing of inputs of adjustment process.
3. Systematical errors. Laws of error propagation. Errors of composite functions.
4. Matrix of correlation coefficients, covariance matrix. Adjustment of correlated measurements. Block adjustment.
5. Collocation. Kalman filter. Robust adjustment. Analysis of covariance and weight matrixes.
6. Planning of experiments. Optimization methods.
2. Testing and analysis of measurements results. Physical and mathematical correlation. Testing of inputs of adjustment process.
3. Systematical errors. Laws of error propagation. Errors of composite functions.
4. Matrix of correlation coefficients, covariance matrix. Adjustment of correlated measurements. Block adjustment.
5. Collocation. Kalman filter. Robust adjustment. Analysis of covariance and weight matrixes.
6. Planning of experiments. Optimization methods.
Prerequisites
Methods of mathematical and statistical analysis. Fundamentals of data analysis and adjustment.
Language of instruction
Czech
Credits
8 credits
Semester
winter
Forms and criteria of assessment
examination
Specification of controlled instruction, the form of instruction, and the form of compensation of the absences
Extent and forms are specified by guarantor’s regulation updated for every academic year.
Offered to foreign students
Not to offer
Course on BUT site
Lecture
13 weeks, 3 hours/week, elective
Syllabus
1. Multidimensional quantities. Accuracy measures.
2. Testing and analysis of measurements results. Physical and mathematical correlation. Testing of inputs of adjustment process.
3. Systematical errors. Laws of error propagation. Errors of composite functions.
4. Matrix of correlation coefficients, covariance matrix. Adjustment of correlated measurements. Block adjustment.
5. Collocation. Kalman filter. Robust adjustment. Analysis of covariance and weight matrixes.
6. Planning of experiments. Optimization methods.