![]() ![]() This was probably not the correct way to do that, since I am unable to get the coordinates of the created point (or rather do anything else with it), since the resulting feature object saved to sample_point does not directly provide coordinates and is instead defined in relation to the edge that it was created from: > print sample_point` This is creating an entity that is visible in the GUI, but not as a vertex. I started by creating datum points at the required locations along the edge, with the idea of using those points to partition the edge and apply loads on the resulting vertices: sample_point = plateAssembly.DatumPointByEdgeParam(Įdge=loading_edge, parameter=fraction_of_length Tectonophysics, doi: 10.1016/0040-1951(83)90155-5.I want to create vertices on the edge of a solid model to apply loads. Quasi-stable adjustment of monitoring networks. Code for engineering survey of high speed railway. Zhu Y, Lu J, Cheng A, Yan H, Liu C, and Wang G, 2010. Robust estimator for correlated observations based on bifactor equivalent weights. ![]() ![]() Gross error detectability and identifiability analysis in track control network for high-speed railway based on GEJE. Acta Geodaetica et Cartographica Sinica, 48(11), pp. 1430–1438 (in Chinese with English abstract). The identification method of gross error detection failpoint in L 1-norm estimation. ![]() Springer International Publishing, Switzerland. Survey control points: compatibility and verification. Weiss G, Weiss R, Bartoš K, et al., 2016. Delft University of Technology, Netherlands. Statistical theory and method of survey data processing. L 1-norm pre-analysis measures for geodetic networks. Surveying network design and adjustment for ballastless track HSR: case study with the first HSR in China. Transformation model selection by multiple hypotheses testing. Testing the compatibility of constraints for parameters of a geodetic adjustment model. Parameter estimation and hypothesis testing in linear models. Deutsche Geodätsche Kommission Reihe C, München. On the theory and application of model misspecification tests in geodesy. Wtusm Bulletin of Science and Technology, 2, pp. 1–6 (in Chinese with English abstract). The compatibility analysis of common points for GPS network. Robust estimation for correlated observations: two local sensitivity-based downweighting strategies. Quasi-Accurate detection of outliers for correlated observations. An introduction to the variance- covariancecomponent estimation of Helmert type. Grafarend E, Kleusberg A, Schaffrin B, 1980. Journal of the China Railway Society, 33(8), 99–102 (in Chinese with English abstract). Comparison of measuring dada compression methods of CPIII control networks. Gross error diagnostics before least squares adjustment of observations. Neth Geod Comm Publ on Geodesy, 2(5), pp. 27–55. A testing procedure for use in geodetic networks. Journal of Surveying Engineering, 129(1), pp. 37–43. Formulation of L 1 norm minimization in Gauss-Markov models. With a benchmark along route control network and a CPIII vertical control network as the example, the performance of the proposed method is demonstrated, and showing the importance and necessity of the identification of incompatible datum points in vertical control network for HSR. In this paper, the likelihood ratio (LR) test with the highest power among all the competitors for simple hypotheses problem is extended to solve the problem of composite hypothesis, resulting in a new multiple incompatible datum points identification strategy (ICDPI-LR). Before that, in addition to the gross errors checking of observations, the compatibility diagnosis of datum points should also be an important step, which is often neglected in practice. Constrained adjustment is the essential link of surveying data processing of HSR vertical control network to obtain the height of new control points. However, as an important part of surveying system, the study on the data quality control of vertical control network has not attracted as much attention as that of plane control network. The effective control of the surveying data quality in each stage and each link is a complex but important issue in the processing of surveying data. The high-speed railway (HSR) surveying system in China has developed into a modern large-scale precision surveying system with multi-stage structure, large scale, and high precision and reliability requirements. ![]()
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