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Course info
KGM / VP1
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Course description
Department/Unit / Abbreviation
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KGM
/
VP1
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Adjustment Calculus 1
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
3
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
2
[Hours/Week]
Tutorial
1
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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1 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter + Summer
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Semester taught
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Winter + Summer
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Minimum (B + C) students
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1
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
Yes
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Fundamental course |
No
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Fundamental theoretical course |
Yes
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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KMA/VP1
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
,
XLS
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Course objectives:
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The main task of this course is to study and understand a relationship between the accuracy of estimated parameters computed from observations and errors of these observations. Participants learn how to process observations using the least squares adjustment (estimate unknown parameters and their errors). Participants are able to handle existing software tools for adjustment of standard geodetic observables. They can also design and implement their own programs for special adjustment models.
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Requirements on student
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Participants has to collect a specified minimum number of points from two tests written during the term. They are also required to submit on time a seminar report elaborating a given topic.
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Content
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Probability distribution functions of observation errors. Error propagation formulas and weights. Adjustment of observation equations, observation equations with conditions and their combination. Applications of adjustment calculus in geodesy: determination of coordinates from measured lengths and angles, adjustment of trigonometric, leveling and gravimetric networks, estimation of parameters for transformation of coordinates. Statistical properties of adjusted parameters, errors, error ellipses. Numerical techniques for solutions of normal equations in geodetic applications. Software for adjustment of geodetic data.
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Activities
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Fields of study
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Guarantors and lecturers
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-
Guarantors:
Prof. Ing. Pavel Novák, PhD (100%),
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Lecturer:
Prof. Ing. Pavel Novák, PhD (100%),
Doc. Ing. Martin Pitoňák, PhD. (100%),
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Tutorial lecturer:
Doc. Ing. Martin Pitoňák, PhD. (100%),
Doc. Ing. Michal Šprlák, PhD. (100%),
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Literature
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Basic:
Kabeláč, J. Geodetické metody vyrovnání : metoda nejmenších čtverců. 1. vyd. Plzeň : Západočeská univerzita, 2003. ISBN 80-7043-260-8.
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Basic:
Hampacher, M., Radouch, V. Teorie chyb a vyrovnávací počet : příklady a návody ke cvičení. dot. 3. přeprac. vyd. Praha : ČVUT, 1998. ISBN 80-01-01327-8.
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Recommended:
Reif, J. Metody matematické statistiky. Plzeň : Západočeská univerzita, 2004. ISBN 80-7043-302-7.
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Recommended:
Přikryl, P. Numerické metody : aproximace funkcí a matematická analýza. Plzeň : Západočeská univerzita, 1994.
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Recommended:
Míka, S. Numerické metody : lineární algebra. 3., upr. vyd. Plzeň : Západočeská univerzita, 1994.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Preparation for comprehensive test (10-40)
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15
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Practical training (number of hours)
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13
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Contact hours
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26
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Preparation for an examination (30-60)
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35
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Total
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89
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
aplikovat základní poznatky lineární algebry, diferenciálního a integrálního počtu, statistiky a teorie pravděpodobnosti |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
uživatelským způsobem využívat prostředky výpočetní techniky |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
popsat základní charakteristiky chyb získaných statistickým hodnocením výsledků vyrovnání |
vysvětlit princip, vlastnosti a použití metody nejmenších čtverců |
vysvětlit základy zákonů hromadění chyb |
Skills - skills resulting from the course: |
kriticky posoudit výsledky vyrovnání |
použít metodu nejmenších čtverců a interpretovat výsledky včetně odhadů chyb |
spočítat přesnost výsledků měření |
určit nutnou přesnost měření pro dosažení stanovené přesnosti výsledků |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Seminar work |
Test |
Combined exam |
Skills - skills achieved by taking this course are verified by the following means: |
Seminar work |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture with visual aids |
Practicum |
Skills - the following training methods are used to achieve the required skills: |
Individual study |
One-to-One tutorial |
Practicum |
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