Spelling suggestions: "subject:"time variable"" "subject:"lime variable""
1 |
GPS radio occultation and the role of atmospheric pressure on spaceborne gravity estimation over AntarcticaGe, Shengjie 08 August 2006 (has links)
No description available.
|
2 |
Coseismic Deformation Detection and Quantification for Great Earthquakes Using Spaceborne GravimetryWang, Lei 19 June 2012 (has links)
No description available.
|
3 |
Using regularization for error reduction in GRACE gravity estimationSave, Himanshu Vijay 02 June 2010 (has links)
The Gravity Recovery and Climate Experiment (GRACE) is a joint
National Aeronautics and Space Administration / Deutsches Zentrum für Luftund
Raumfahrt (NASA/DLR) mission to map the time-variable and mean
gravity field of the Earth, and was launched on March 17, 2002. The nature
of the gravity field inverse problem amplifies the noise in the data that creeps
into the mid and high degree and order harmonic coefficients of the earth's
gravity fields for monthly variability, making the GRACE estimation problem
ill-posed. These errors, due to the use of imperfect models and data noise, are
manifested as peculiar errors in the gravity estimates as north-south striping
in the monthly global maps of equivalent water heights.
In order to reduce these errors, this study develops a methodology
based on Tikhonov regularization technique using the L-curve method in combination
with orthogonal transformation method. L-curve is a popular aid for determining a suitable value of the regularization parameter when solving
linear discrete ill-posed problems using Tikhonov regularization. However, the
computational effort required to determine the L-curve can be prohibitive for
a large scale problem like GRACE. This study implements a parameter-choice
method, using Lanczos bidiagonalization that is a computationally inexpensive
approximation to L-curve called L-ribbon. This method projects a large
estimation problem on a problem of size of about two orders of magnitude
smaller. Using the knowledge of the characteristics of the systematic errors in
the GRACE solutions, this study designs a new regularization matrix that reduces
the systematic errors without attenuating the signal. The regularization
matrix provides a constraint on the geopotential coefficients as a function of its
degree and order. The regularization algorithms are implemented in a parallel
computing environment for this study. A five year time-series of the candidate
regularized solutions show markedly reduced systematic errors without any
reduction in the variability signal compared to the unconstrained solutions.
The variability signals in the regularized series show good agreement with the
hydrological models in the small and medium sized river basins and also show
non-seasonal signals in the oceans without the need for post-processing. / text
|
4 |
Temporary Variables for Predicting Electricity Consumption Through Data MiningSilva, Jesús, Senior Naveda, Alexa, Hernández Palma, Hugo, Niebles Núẽz, William, Niebles Núẽz, Leonardo 07 January 2020 (has links)
In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.
|
5 |
Adungované soustavy diferenciálních rovnic / Adjoint Differential EquationsKmenta, Karel January 2007 (has links)
This project deals with solving differential equations. The aim is find the correct algorithm transforming differential equations of higher order with time variable coefficients to equivalent systems of differential equations of first order. Subsequently verify its functionality for equations containing the involutioin goniometrical functions and finally implement this algorithm. The reason for this transformation is requirement to solve these differential equations by programme TKSL (Taylor Kunovský simulation language).
|
Page generated in 0.0553 seconds