H.F. de Campos Velho

Entropy- and tikhonov-based regularization techniques applied to the backwards heat equation☆

Abstract The goal of this paper is to analyze the performance of different regularization techniques for an inverse heat conduction problem (IHCP): the estimation of the initial condition. The inverse problem is formulated as a nonlinear constrained optimization problem, and a regularization term is added to the objective function with the help of a regularization parameter. Three classes of regularization methods have been considered: Tikhonov regularization, maximum entropy principle, and truncated singular value decomposition. Concerning the entropic methodology, two new techniques are introduced and good results were obtained using synthetic data corrupted with noise. The Morozovs discrepancy principle is used to find out the regularization parameter.

The Aster Project: Flight to a Near-Earth Asteroid

The information on the project being developed in Brazil for a flight to binary or triple near-Earth asteroid is presented. The project plans to launch a spacecraft into an orbit around the asteroid and to study the asteroid and its satellite within six months. Main attention is concentrated on the analysis of trajectories of flight to asteroids with both impulsive and low thrust in the period 2013-2020. For comparison, the characteristics of flights to the (45) Eugenia triple asteroid of the Main Belt are also given.

Fuzzy ant colony optimization for estimating chlorophyll concentration profile in offshore sea water

The determination of some inherent optical properties can be addressed by estimating the ocean chlorophyll concentration, if bio-optical models can be applied – such as for the offshore sea water. This inverse problem can be formulated as an optimization problem and iteratively solved, where the radiative transfer equation is the direct model. An objective function is given by the square difference between computed and in situ experimental radiances at every iteration. In the standard ant colony optimization (ACO), the pheromone is reinforced only on the best ant of the population. The fuzzy strategy consists in including additional pheromone quantity on the best ant, but a small pheromone quantity is also spread over the other solutions close to the best one. Test results show that the fuzzy-ACO produces better inverse solutions.

A parallel implementation of the LTSn method for a radiative transfer problem

A radiative transfer solver that implements the LTSn method was optimized and parallelized using the MPI message passing communication library. Timing and profiling information was obtained for the sequential code in order to identify performance bottlenecks. Performance tests were executed in a distributed memory parallel machine, a multicomputer based on IA-32 architecture. The radiative transfer equation was solved for a cloud test case to evaluate the parallel performance of the LTSn method. The LTSn code includes spatial discretization of the domain and Fourier decomposition of the radiances leading to independent azimuthal modes. This yields an independent radiative transfer equation for each mode that can be executed by a different processor in a parallel implementation. Speed-up results show that the parallel implementation is suitable for the used architecture.

Parallel implementation of a Lagrangian stochastic model for pollution dispersion

Pollutant dispersion models in the atmosphere can describe by Eulerian or Lagrangian approaches. Lagrangian models belong to the class of Monte Carlo methods. This type of method is very flexible, solving more complex problems, however this computational cost is greater than Eulerian models, as it is well established in the atmospheric pollutant and nuclear engineering communities. A parallel version of the Lagrangian particle model - LAMBDA - is developed using the MPI message passing communication library. Performance tests were executed in a distributed memory parallel machine, a multicomputer based on IA-32 architecture. Portions of the pollutant in the air are considered particles emitted from a pollutant source, evolving under stochastic forcing. This yields independent evolution equations for each particle of the model that can be executed by a different processor in a parallel implementation. Speed-up results show that the parallel implementation is suitable for the used architecture.

Atmospheric temperature retrieval using a Radial Basis Function Neural Network

Vertical temperature profiles are obtained from measured satellite radiance data by using a Radial Basis Function Neural Network (RBF-NN). The RBF-NN is trained with data provided by the direct model, characterised by the Radiative Transfer Equation. The results are compared with regularisation-based inverse solutions. The approach is tested using satellite radiances, and the inversion temperature profile is compared with radiosonde temperature measurements. Analysis reveals that the generated profiles are closely approximate to previous results, showing the methodology adequacy. ANNs are useful because of the parallelism and implementation simplicity, turn hardware implementation possible, that may imply in on-board and real-time systems.

A nonmodal approach for time-integration of a barotropic limited area model

Abstract In this work, we integrate a semidiscrete nonlinear matrix differential system arising from a barotropic limited area model with periodic boundary conditions on the β-plane. We use a new formulation for the state transition matrix, the nonmodal matrix, in order to carry out the time-integration. The Helmholtz wind field reconstruction is performed by singular value decomposition (SVD).

Counter-Gradient Term Applied to the Turbulence Parameterization in the BRAMS

The numerical weather prediction is a routine in operational meteorological centers, where sophisticated computer models are executed. The atmospheric dynamics is simulated by solving the Navier-Stokes equations, considering several physical phenomena. One parameterization applied to this dynamical system is to represent the turbulence. A counter-gradient flow can be described for higher order closure turbulence approaches. Here, a first order parameterization for the turbulent flow is coupled with an explicit counter-gradient term. Both latter schemes are based on the Taylors statistical theory of turbulence. The parameterization schemes are applied to the BRAMS, a mesoscale meteorological model. The simulation is compared with experimental data measured in the Brazilian Amazon region.

Severe Precipitation in Brazil: Data Mining Approach

Data mining approach is applied to evaluate extreme rainfall events in the Brazil. Statistical analysis is combined with an artificial intelligence technique to identify the most relevant meteorological variables for a local severe precipitation in the Rio de Janeiro state (Brazil): Rio de Janeiro and Nova Friburgo cities. The p-value statistical technique is employed to select a much smaller subset of climatic variables, preserving the information associated with extreme meteorological events. A decision tree algorithm is used as a model to identify the precipitation severity. The method is tested with the events at Apr/2010 (Rio de Janeiro city) and at Jan/2011 (Nova Friburgo city). In both cases, our results show a good local analysis for extreme precipitation episodes.

Image matching applied to autonomous navigation of unmanned aerial vehicles

This paper brings results of an image matching approach applied to the estimation of position for autonomous navigation of unmanned aerial vehicles UAVs. The main idea is to replace the global positioning system GPS signal by matching the onboard video to a georeferenced satellite image. Image processing techniques and edge descriptors are considered in order to achieve robustness to different sensors and luminosity conditions. The UAV position is then calculated by finding the pixel in the georeferenced image providing a higher spatial correlation to the image captured at flight time. The evaluation of our vision system takes into account different kinds of terrains, such as in forests, roads and urban areas and its capacity to follow real routes specified in a flight simulator. The results obtained are promising and indicate that our methodology can be used in substitution for GPS on real flights.