Retrievals of a size parameter for phytoplankton and spectral light absorption by colored detrital matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil

Many efforts are currently oriented toward extracting more information from ocean color than the chlorophyll a concentration. Among biological parameters potentially accessible from space, estimates of phytoplankton cell size and light absorption by colored detrital matter (CDM) would lead to an indirect assessment of major components of the organic carbon pool in the ocean, which would benefit oceanic carbon budget models. We present here 2 procedures to retrieve simultaneously from ocean color measurements in a limited number of bands, magnitudes, and spectral shapes for both light absorption by CDM and phytoplankton, along with a size parameter for phytoplankton. The performance of the 2 procedures was evaluated using different data sets that correspond to increasing uncertainties: ( 1) measured absorption coefficients of phytoplankton, particulate detritus, and colored dissolved organic matter ( CDOM) and measured chlorophyll a concentrations and ( 2) SeaWiFS upwelling radiance measurements and chlorophyll a concentrations estimated from global algorithms. In situ data were acquired during 3 cruises, differing by their relative proportions in CDM and phytoplankton, over a continental shelf off Brazil. No local information was introduced in either procedure, to make them more generally applicable. Over the study area, the absorption coefficient of CDM at 443 nm was retrieved from SeaWiFS radiances with a relative root mean square error (RMSE) of 33%, and phytoplankton light absorption coefficients in SeaWiFS bands ( from 412 to 510 nm) were retrieved with RMSEs between 28% and 33%. These results are comparable to or better than those obtained by 3 published models. In addition, a size parameter of phytoplankton and the spectral slope of CDM absorption were retrieved with RMSEs of 17% and 22%, respectively. If these methods are applied at a regional scale, the performances could be substantially improved by locally tuning some empirical relationships.

Compressão auto-adaptativa de imagens coloridas

Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors

Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a novel approach to solve robust parameter estimation problem for nonlinear model with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods. (C) 1999 IMACS/Elsevier B.V. B.V. All rights reserved.

Age constraints and fine tuning in variable-mass particle models

VAMP (variable-mass particle) scenarios, in which the mass of the cold dark matter particles is a function of the scalar field responsible for the present acceleration of the Universe, have been proposed as a solution to the cosmic coincidence problem, since in the attractor regime both dark energy and dark matter scale in the same way. We find that only a narrow region in parameter space leads to models with viable values for the Hubble constant and dark energy density today. In the allowed region, the dark energy density starts to dominate around the present epoch and consequently such models cannot solve the coincidence problem. We show that the age of the Universe in this scenario is considerably higher than the age for noncoupled dark energy models, and conclude that more precise independent measurements of the age of the Universe would be useful in distinguishing between coupled and noncoupled dark energy models.

Swarm Control Designs applied to a Mechanical Oscillator

This paper presented the particle swarm optimization approach for nonlinear system identification and for reducing the oscillatory movement of the nonlinear systems to periodic orbits. We analyzes the non-linear dynamics in an oscillator mechanical and demonstrated that this model has a chaotic behavior. Chaos control problems consist of attempts to stabilize a chaotic system to an equilibrium point, a periodic orbit, or more general, about a given reference trajectory. This approaches is applied in analyzes the nonlinear dynamics in an oscillator mechanical. The simulation results show the identification by particle swarm optimization is very effective.

Método da histerese por auto-ajuste para minimização de esforço de controle no separador submarino VASPS

Neste trabalho apresenta-se o estudo do desenvolvimento de um controlador não-tradicional baseado em um mecanismo de histerese com auto-ajuste para o controle de nível de líquido de um sistema de separação e bombeio submarino conhecido como VASPS. O controlador desenvolvido gera sinais enviados para a bomba centrífuga submersa para controlar o nível de líquido no tanque do separador, evitando que ele atinja valores muito baixos que poderiam danificar a bomba ou valores muito altos que reduziriam a eficiência da separação líquido/gás. Os sinais de controle gerados pelo controlador visam solicitar a bomba o mínimo possível de modo a evitar o seu desgaste e falhas prematuras. Nas simulações, o controlador desenvolvido foi testado sob grandes variações nas condições de operação, tais como golfadas, produzindo resultados bastante satisfatórios e promissores.

Fuzzy distributed artificial intelligence systems

In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

Fine tuning in quintessence models with exponential potentials

We explore regions of parameter space in a simple exponential model of the form V = V0 e-λ(Q/Mp) that are allowed by observational constraints. We find that the level of fine tuning in these models is not different from more sophisticated models of dark energy. We study a transient regime where the parameter λ has to be less than √3 and the fixed point ΩQ = 1 has not been reached. All values of the parameter λ that lead to this transient regime are permitted. We also point out that this model can accelerate the universe today even for λ > √2, leading to a halt of the present acceleration of the universe in the future thus avoiding the horizon problem. We conclude that this model can not be discarded by current observations. © SISSA/ISAS 2002.

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Swarm control designs applied to a micro-electro-mechanical gyroscope system (MEMS)

This paper analyzes the non-linear dynamics of a MEMS Gyroscope system, modeled with a proof mass constrained to move in a plane with two resonant modes, which are nominally orthogonal. The two modes are ideally coupled only by the rotation of the gyro about the plane's normal vector. We demonstrated that this model has an unstable behavior. Control problems consist of attempts to stabilize a system to an equilibrium point, a periodic orbit, or more general, about a given reference trajectory. We also developed a particle swarm optimization technique for reducing the oscillatory movement of the nonlinear system to a periodic orbit. © 2010 Springer-Verlag.

As funções do grupo de renormalização no modelo com auto-interação quártica fermiônica

Modelos com interações quárticas fermiônicas tem sido estudadas para clarificar aspectos conceituais e possíveis aplicações em teoria quântica de campos. Neste trabalho apresentamos a estrutura do grupo de renormalização no modelo de Nambu-Jona-Lasinio até a ordem de 1-loop. O modelo é não renormalizável perturbativamente, no sentido usual de contagem de potência, mas é tratado como uma teoria efetiva, válida numa escala de energia onde p << ^, sendo p o momento externo do loop e ^ um parâmetro de escala de massa que caracteriza o acoplamento do vértice não renormalizável. Esclarecemos a estrutura tensorial dos vértices de interação e calculamos as funções do grupo de renormalização. A análise dos pontos fixos da teoria também é apresentada e discutida usando o formalismo de redução das constantes de acoplamento proposto por Zimmermann. Encontramos a baixas eneergias a origem como ponto fixo infravermelho estável e um ponto fixo não trivial ultravioleta estável, indicando a consistência perturbativa se o momento é pequeno.