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| JDZ5060 | Optimization of Geodetic Network | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Emine TANIR KAYIKÇI | | Co-Lecturer | Prof. Dr. Aslan DİLAVER | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Optimization of geodetic networks with precision and confidence criterions. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | learn test statistics and apply on geodetic problems. | 5 | 1,3 | | PO - 2 : | learn accuracy and confidence criterions of geodetic networks and make interpretations on. | 7 | 1,3 | | PO - 3 : | make optimization according to accuracy criterions. | 7 | 1,3,5 | | PO - 4 : | make optimization according to confidence criterions. | 7 | 1,3,5 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Optimization possibilities on the geodetic networks. Test statistics and hypothesis tests. Accuracy and confidence criterions. Aim functions. Optimization of datum parameters. Network optimization with respect to precision. Optimization of geometrical shape. Optimization of measurement plan. Optimization of geometrical shape by adding new points into networks. Optimization with criterion matrixes. Optimization with confidence criterions. Optimization with external confidence criterions. Optimization with internal confidence criterions. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Optimization concept. | | | Week 2 | Optimization possibilities on the geodetic networks. | | | Week 3 | Test statistics and hypothesis tests. | | | Week 4 | Accuracy and confidence criterions. | | | Week 5 | Aim functions. | | | Week 6 | Optimization of datum parameters. | | | Week 7 | Network optimization with respect to precision. | | | Week 8 | Mid-term exam | | | Week 9 | Optimization of geometrical shape. | | | Week 10 | Optimization of measurement plan. | | | Week 11 | Optimization of geometrical shape by adding new points into networks. | | | Week 12 | Optimization with criterion matrixes. | | | Week 13 | Optimization with confidence criterions. | | | Week 14 | Optimization with external and internal confidence criterions. | | | Week 15 | Duties presentation. | | | Week 16 | End-of-term exam | | | |
| 1 | Graferend, E.W. ve Sanso, F. 1985; Optimization and Desing of Geodetic Networks, Springer-Verlag, New York.
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| 1 | Pelzer, H. 1985; Geodatische Netze in Landes-und İngenieurvermessung II, Kondrad Wittwer, Stutgart.
| | | 2 | Koch, K.R. 1999; Parameter Estimation and Hypothesis Testing in Linear Models, Springer-Verlag, Berlin.
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| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Presentation | 14 | | 1 | 10 | | Homework/Assignment/Term-paper | 5
6
7
8
9
10
11
12 | | 8 | 10 | | End-of-term exam | 15 | | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 3 | 10 | 30 | | Arasınav için hazırlık | 10 | 1 | 10 | | Arasınav | 2 | 1 | 2 | | Ödev | 8 | 8 | 64 | | Dönem sonu sınavı için hazırlık | 8 | 1 | 8 | | Dönem sonu sınavı | 2 | 1 | 2 | | Diğer 1 | 10 | 3 | 30 | | Total work load | | | 188 |
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