Wyniki wyszukiwania - BazTech

1

Study on detection of gross error in geodetic network adjustment

Kim J. H., Kim C. J., Li R. J.

Geodesy and Cartography

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2018

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Vol. 67, no. 1

57--69

EN

Generally, gross errors exist in observations, and they affect the accuracy of results. We review methods to detect the gross errors by Robust estimation method based on L1-estimation theory and their validity in adjustment of geodetic networks with different condition. In order to detect the gross errors, we transform the weight of accidental model into equivalent one using not standardized residual but residual of observation, and apply this method to adjustment computation of triangulation network, traverse network, satellite geodetic network and so on. In triangulation network, we use a method of transforming into equivalent weight by residual and detect gross error in parameter adjustment without and with condition. The result from proposed method is compared with the one from using standardized residual as equivalent weight. In traverse network, we decide the weight by Helmert variance component estimation, and then detect gross errors and compare by the same way with triangulation network In satellite geodetic network in which observations are correlated, we detect gross errors transforming into equivalent correlation matrix by residual and variance inflation factor and the result is also compared with the result from using standardized residual. The results of detection are shown that it is more convenient and effective to detect gross errors by residual in geodetic network adjustment of various forms than detection by standardized residual.

2

Comparison of robust estimators for leveling networks in Monte Carlo simulations

Pokarowska M.

Reports on Geodesy and Geoinformatics

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2016

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Vol. 101

70--81

EN

We compared the method of least squares (LS), Pope’s iterative data snooping (IDS) and Huber’s M-estimator (HU) in realistic leveling networks, for which the heights or the vertical displacements of points are known. The study was conducted using the Monte Carlo simulation, in which one repeatedly generates sets of observations related to the measurement data, then calculates values of the estimators and, finally, assesses it with respect to the real coordinates. To simulate outliers we used popular mixture models with two or more normal distributions. It is shown that for small, strong networks robust methods IDS and HU are more accurate than LS, but for large, weak networks occurring in practice there is no significant difference between the considered methods in the accuracy of the solution.

3

Wykorzystanie analizy błędów grubych oraz krzywych Johnsona w metodzie Six Sigma

Czabak-Górska I. D.

Zeszyty Naukowe. Mechanika / Politechnika Opolska

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2015

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z. 105

37-40

EN

The paper describe the importance of Gross Error eliminating in process stability assessment. The analysis carried out at the manufacturing enterprise indicated that Gross Error might be cause of false signals in Control Chart. Additionally, the author, proposed using Johnson curves transformation to applying in statistical tools (including Control Chart, which are used in Six Sigma methodology) when the measurement data aren’t normally distributed.

4

DiSTFAG method robust to gross errors in monitoring displacements and strains in unstable reference systems

Kamiński W.

Geodesy and Cartography

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2011

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Vol. 60, no 1

21-33

EN

The DiSTFA method (Displacements and Strains using Transformation and Free Adjustment) was presented in Kaminski (2009). The method has been developed for the determination of displacements and strains of engineering objects in unstable reference systems, as well as for examining the stability of reference points. The DiSTFAG (Gross errors) method presented in the paper is the extension of the DiSTFA method making it robust to gross errors. Theoretical considerations have been supplemented with an example of a practical application on a simulated 3D surveying network.

PL

W niniejszej pracy zaproponowano uodpornienie metody DiSTFA (Displacements and Strains Rusing Transformation and Free Adjustment) na błędy grube. Metodę DiSTFA opracowano do wyznaczania przemieszczeń i odkształceń obiektów inżynierskich w niestabilnych układach odniesienia jak również badania stałości punktów dostosowania. Metoda DiSTFAG jest rozwinięciem metody DiSTFA uwzględniającym w rozwiązaniu obserwacje obarczone błędami grubymi. Teoretyczne rozważania uzupełniono przykładem praktycznego zastosowania na symulowanej, trójwymiarowej osnowie geodezyjnej.

5

Przedwyrównawcze wykrywanie błędów grubych w pomiarze środków rzutu dla aerotriangulacji

Ziobro J.

Prace Instytutu Geodezji i Kartografii

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2006

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T. 52, z. 110

89-102

PL

W artykule przedstawiono metodę przedwyrównawczego wykrywania błędów grubych w pomiarze środków rzutu oraz wyniki badań nad skutecznością metody. Metoda opiera się na analizie różnic wyników dwóch niezależnych wyznaczeń: wyniku otrzymanego z wyrównania aerotriangulacji bez uwzględnienia pomiaru środków rzutu i wyniku pomiaru środków rzutu wykonanego podczas nalotu fotogrametrycznego. Metoda została przetestowana na 26 blokach, które opracowano w kraju w ciągu kilku ostatnich lat.

EN

Author presents in the article a method for pre-adjustment detection of gross errors in the measured projection centers. The method is based on analyzing differences of the results of two independent measurements: one obtained from adjustment of aerial triangulation without determining projection center and the second, which considers measurement of projection centers during photogrammetric mission. The technique of measuring projection centers for aerial triangulation with the use of GPS method exists since 1993 and is still improved, as far as precision and reliability is concerned. In standard work real verification of quality of measurement is done only at the stage of adjustment of aerial triangulation. The main aim of adjustment is to obtain the result with the highest probability, and it depends on removing gross errors from calculations. As it can be seen from practice, this condition is difficult to fulfilI; the procedure is time-consuming and not fully efficient. Detection and location of gross errors is difficult due to improper division of GPS measurements into profiles, multiple gross errors, mistakes in GPS measurement, or insufficient reliability level of network. In the proposed method distances between neighboring points of profile are compared, obtained from two independent determinations. In addition, increments of coordinates between neighboring projection centers are also compared. These differences, which prove to be higher than triple mean error, are considered as gross errors. The method has been tested on 26 blocks, which were prepared during last years in Poland. The aim of testing was to verify magnitude and number of gross errors of projection centers, which remain in the network after applying the method. Analysis of non-detected gross errors was done using W. Baarda data snooping method, i.e. the method of standardized residuals. In the test blocks at a, scale of 1 : 13 000 level of detectability of gross errors in the measured projections centers was ca. 6 times mean error of coordinate of projection center, while for 1: 26 000 photographs it was 12 times mean error, respectively. The method enables to detect in one calculation step all mistakes and most of gross errors, which results in decreasing number of adjustment cycles in cases, when many data errors exist.