scholarly journals RANSAC ART Tomography

2019 ◽  
Author(s):  
Владимир Афанасьев ◽  
Vladimir Afanasyev ◽  
Алексей Волобой ◽  
Alexey Voloboy
Keyword(s):  
Input Data ◽  
Optical Tomography ◽  
Data Errors

This paper describes using of per-voxel RANSAC approach in ART tomography. The method works as an addition to any ART and does not depend on its internal details. Firstly, the histograms of voxel map corrections are constructed in each voxel during usual pass of ART. Then, they are used to refine the absorption map. It allows to improve resulting voxel absorption map, reducing ghost effects caused by input data errors and inconsistency. This method was demonstrated with optical tomography algorithm as it has certain difficulties with input data. The proposed algorithm was implemented to run on GPU.

Методика практического восстановления параметров формы поверхностных двухмерных дефектов с учетом нелинейных свойств ферромагнетика

2021 ◽  
pp. 46-55
Author(s):  
А.В. Никитин ◽  
А.В. Михайлов ◽  
А.С. Петров ◽  
С.Э. Попов
Keyword(s):  
Magnetic Field ◽  
Free Surface ◽  
Numerical Experiments ◽  
Input Data ◽  
Metal Plate ◽  
Two Dimensional ◽  
Nonlinear Properties ◽  
Data Errors ◽  
The Magnetic Field

A technique for determining the depth and opening of a surface two-dimensional defect in a ferromagnet is presented, that is resistant to input data errors. Defects and magnetic transducers are located on opposite sides of the metal plate. The nonlinear properties of the ferromagnet are taken into account. The components of the magnetic field in the metal were reconstructed from the measured components of the magnetic field above the defect-free surface of the metal. As a result of numerical experiments, the limits of applicability of the method were obtained. The results of the technique have been verified experimentally.

The Realization of a Highly Modulized Open-Ended Distribution System Comprehensive Planning Platform Compatible with CIM Model

2013 ◽  
Vol 805-806 ◽  
pp. 1065-1072
Author(s):  
Kang Ma ◽  
Jun Liu ◽  
Hai Tao Liu ◽  
Tao Wei ◽  
Jian Su
Keyword(s):  
Distribution System ◽  
Input Data ◽  
Poor Quality ◽  
Comprehensive Planning ◽  
Urban Network ◽  
Reliability Indices ◽  
Data Errors ◽  
Corrected Data ◽  
Key Features ◽  
Cloud Computation

This paper introduces key features and the structure of a distribution network comprehensive planning platform as well as the functions and algorithms of core modules. The platform has a number of features, some of which are innovation points: 1) it provides a unified data interface compatible with IEC61968-based CIM model; 2) it adopts an effective algorithm that diagnoses and corrects input data of a relatively poor quality; 3) It converts CIM-based data into data based on calculation models, so that the calculation modules can analyze them. 4) Its reliability analysis module uses an algorithm based on zones. 5) The highly modulized platform has the provision for cloud computation in future. Furthermore, this paper has defined a number of reliability indices based on zones and feeders. The application of the platform in Tianjin urban network has desmonstrated its effectiveness: it successfully corrected data errors with no need for human intervention and yielded satisfactory results.

scholarly journals Formation of the Convergence Functions of Errors of Input Data of Measurement Systems Computing Components on the Basis of the Finite Automatics Theory

2019 ◽  
pp. 37-40
Author(s):  
O. Krychevets
Keyword(s):  
Measurement Error ◽  
Measurement Errors ◽  
Input Data ◽  
Transformation Function ◽  
Measurement Systems ◽  
Data Errors ◽  
Measurement Results ◽  
Data Error ◽  
Iterative Procedures ◽  
Number Of Iterations

This paper presents the results of an investigation into the behavior of the functions of transforming the input data errors for different types of measurement systems’ computing components in order to use their generalized models developed on the basis of the finite automata theory. It is shown that, depending on the kind and value of an input data error transformation function (metrological condition of computing components), the errors of measurement results obtained with the systems’ measuring channels have a determinate character of changes in both static and dynamic regimes of computing components. Determined are the basic dependences of the errors of measurement results upon the input data errors, and upon the types of input data transformation functions; given are the results of their calculation. The investigation results demonstrate a linear character of the dependence of measurement result errors upon the input data errors ΔХ{(tn). In addition, the transformation function calculation f = ΔY{(tn)/ΔХ{(tn) gives its steady state value f = 1,0, i.e. a computing component does not transform the input data error, and does not reverse its sign. For the iterative procedures, the input data errors do not affect the final measurement result, and its accuracy. The measurement error values Δуn depend on the iteration number, and decrease with the increasing number. Of particular interest is the behavior of the function of transforming the input data errors: first, its values are dependent upon the number of iterations; second, f < 1, which clearly shows that the input data errors decrease with the increa­sing number of iterations; and third, the availability of values f = 0 indicates that the function of transforming the input data errors is able to «swallow up» the input data error at the end of the computational procedure. For the linear-chain structures, data have been obtained for a predominantly linear dependence of the measurement error Δs on the input data error Δх, and for the absence of the chain’s transformation function f dependence on the input data errors Δх. For the computing components having a cyclic structure, typi­cal is the same dependence of measurement errors Δt on the input data errors and on the behavior of transformation function ft/x which are specific to the above mentioned computing components that rea­lize iterative procedures. The difference is that the computing components having a cyclic structure realize the so-called (sub)space iteration as opposed to the time iteration specific to the computing components considered. The computing components having a complicated structure (e.g. serial-cyclic, serial-parallel, etc.) demonstrate the dependence of measurement errors on the input data errors which is specific to the linear link that, with such a structure, is determinative for eva­luating the measurement error. Also the function of transforming the input data errors behaves similarly.

scholarly journals Evaluation of Uncertainties in Input Data and Parameters of a Hydrological Model Using a Bayesian Framework: A Case Study of a Snowmelt–Precipitation-Driven Watershed

2016 ◽  
Vol 17 (8) ◽  
pp. 2333-2350 ◽  
Author(s):  
J. L. Zhang ◽  
Y. P. Li ◽  
G. H. Huang ◽  
C. X. Wang ◽  
G. H. Cheng
Keyword(s):  
Input Data ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Bayesian Framework ◽  
Model Parameters ◽  
Precipitation Data ◽  
Snow Melting ◽  
Simulation Performance ◽  
Data Errors ◽  
Temperature And Precipitation

Abstract In this study, a Bayesian framework is proposed for investigating uncertainties in input data (i.e., temperature and precipitation) and parameters in a distributed hydrological model as well as their effects on the runoff response in the Kaidu watershed (a snowmelt–precipitation-driven watershed). In the Bayesian framework, the Soil and Water Assessment Tool (SWAT) is used for providing the basic hydrologic protocols. The Delayed Rejection Adaptive Metropolis (DRAM) algorithm is employed for the inference of uncertainties in input data and model parameters with global and local adaptive strategies. The advanced Bayesian framework can help facilitate the exploration of variation of model parameters due to input data errors, as well as propagation from uncertainties in data and parameters to model outputs in both snow-melting and nonmelting periods. A series of calibration cases corresponding to data errors under different periods are examined. Results show that 1) input data errors can affect the distributions of model parameters as well as parameters’ correlation, implying that data errors could influence the related hydrologic processes as well as their relations; 2) considering input data errors could improve the hydrologic simulation ability for peak streamflows; 3) considering errors of temperature and precipitation data as well as uncertainties of model parameters can provide the best modeling simulation performance in the snow-melting period; and 4) accounting for uncertainties in precipitation data and model parameters can provide the best modeling performance during the nonmelting period. The findings will help enhance hydrological model’s capability for simulating/predicting water resources during different seasons for snowmelt–precipitation-driven watersheds.

scholarly journals Estimating Monthly Precipitation Reconstruction Uncertainty Beginning in 1900

2013 ◽  
Vol 30 (6) ◽  
pp. 1107-1122 ◽  
Author(s):  
Thomas M. Smith ◽  
Samuel S. P. Shen ◽  
Li Ren ◽  
Phillip A. Arkin
Keyword(s):  
Input Data ◽  
Spatial Scales ◽  
Total Error ◽  
Correlated Data ◽  
Reconstruction Method ◽  
Monthly Precipitation ◽  
Spatial Correlations ◽  
Related Data ◽  
Data Errors ◽  
Uncertainty Estimates

Abstract Uncertainty estimates are computed for a statistical reconstruction of global monthly precipitation that was developed in an earlier publication. The reconstruction combined the use of spatial correlations with gauge precipitation and correlations between precipitation and related data beginning in 1900. Several types of errors contribute to uncertainty, including errors associated with the reconstruction method and input data errors. This reconstruction includes the use of correlated data for the ocean-area first guess, which contributes to much of the uncertainty over those regions. Errors associated with the input data include random, sampling, and bias errors. Random and bias data errors are mostly filtered out of the reconstruction analysis and are the smallest components of the total error. The largest errors are associated with sampling and the method, which together dominate the total error. The uncertainty estimates in this study indicate that (i) over oceans the reconstruction is most reliable in the tropics, especially the Pacific, because of the large spatial scales of ENSO; (ii) over the high-latitude oceans multidecadal variations are fairly reliable, but many month-to-month variations are not; and (iii) over- and near-land errors are much smaller because of local gauge. The reconstruction indicates that the average precipitation increases early in the twentieth century, followed by several decades of multidecadal variations with little trend until near the end of the century, when precipitation again appears to systematically increase. The uncertainty estimates indicate that the average changes over land are most reliable, while over oceans the average change over the reconstruction period is slightly larger than the uncertainty.

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