Some aspects of the dynamics of fictitious Earth's satellites

In this work we study the dynamics of fictitious satellites of the Earth. In the first part we do not consider the effect of the Moon and study the dynamics in the restrict three-body model, i.e., a massless satellite under the effect of the gravitational force of an oblate Earth and that of the Sun. We show that a satellite starting with an almost circular orbit suffers very large variations of eccentricity, depending on the initial inclination of the orbit with respect to the reference plane. As the eccentricity may be driven to very large values (approximate to0.9) mutual collisions between satellites or collisions with the planet may occur. In the second part, we include the gravitational effect of the Moon. In this case, we find two regions with large variations of eccentricity due to the presence of the Moon. Consequently, in both scenarios, we find some large regions of the phase space where the long-term stability of some fictitious Earth's satellites is not possible. (C) 2001 Elsevier B.V. Ltd. All rights reserved.

Fractal character of the SAXS correlation volume in poly(ethylene glycol)/silica hybrid wet gels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Dynamics of some fictitious satellites of Venus and Mars

The dynamics of some fictitious satellites of Venus and Mars are studied considering only solar perturbation and the oblateness of the planet, as disturbing forces. Several numerical integrations of the averaged system, taking different values of the obliquity of ecliptic (a), show the existence of strong chaotic motion, provided that the semi major axis is near a critical value. As a consequence, large increase of eccentricities occur and the satellites may collide with the planet or cross possible internal orbits. Even starting from almost circular and equatorial orbits, most satellites can easily reach prohibitive values. The extension of the chaotic zone depends clearly on the value ε, so that, previous regular regions may become chaotic, provided ε increases sufficiently. © 1999 Elsevier Science Ltd. All rights reserved.

Steel bars identification in reinforced concrete structures by using ANN and magnetic fields

This work proposes a methodology for non destructive testing (NDT) of reinforced concrete structures, using superficial magnetic fields and artificial neural networks, in order to identify the size and position of steel bars, embedded into the concrete. For the purposes of this paper, magnetic induction curves were obtained by using a finite element program. Perceptron Multilayered (PML) ANNs, with Levemberg-Marquardt training algorithm were used. The results presented very good agreement with the expect ones, encouraging the development of real systems based upon the proposed methodology.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

The embodied embedded character of system 1 processing

In the last thirty years, a relatively large group of cognitive scientists have begun characterising the mind in terms of two distinct, relatively autonomous systems. To account for paradoxes in empirical results of studies mainly on reasoning, Dual Process Theories were developed. Such Dual Process Theories generally agree that System 1 is rapid, automatic, parallel, and heuristic-based and System 2 is slow, capacity-demanding, sequential, and related to consciousness. While System 2 can still be decently understood from a traditional cognitivist approach, I will argue that it is essential for System 1 processing to be comprehended in an Embodied Embedded approach to Cognition.© MSM 2013.

Contribuições na modelagem dinâmica e no controle neural de um braço robótico com elos flexíveis

Em geral, estruturas espaciais e manipuladores robóticos leves têm uma característica similar e inerente que é a flexibilidade. Esta característica torna a dinâmica do sistema muito mais complexa e com maiores dificuldades para a análise de estabilidade e controle. Então, braços robóticos bastantes leves, com velocidade elevada e potencia limitada devem considerar o controle de vibração causada pela flexibilidade. Por este motivo, uma estratégia de controle é desejada não somente para o controle do modo rígido mas também que seja capaz de controlar os modos de vibração do braço robótico flexível. Também, redes neurais artificiais (RNA) são identificadas como uma subespecialidade de inteligência artificial. Constituem atualmente uma teoria para o estudo de fenômenos complexos e representam uma nova ferramenta na tecnologia de processamento de informação, por possuírem características como processamento paralelo, capacidade de aprendizagem, mapeamento não-linear e capacidade de generalização. Assim, neste estudo utilizam-se RNA na identificação e controle do braço robótico com elos flexíveis. Esta tese apresenta a modelagem dinâmica de braços robóticos com elos flexíveis, 1D no plano horizontal e 2D no plano vertical com ação da gravidade, respectivamente. Modelos dinâmicos reduzidos são obtidos pelo formalismo de Newton-Euler, e utiliza-se o método dos elementos finitos (MEF) na discretização dos deslocamentos elásticos baseado na teoria elementar da viga. Além disso, duas estratégias de controle têm sido desenvolvidas com a finalidade de eliminar as vibrações devido à flexibilidade do braço robótico com elos flexíveis. Primeiro, utilizase um controlador neural feedforward (NFF) na obtenção da dinâmica inversa do braço robótico flexível e o calculo do torque da junta. E segundo, para obter precisão no posicionamento... (Resumo completo, clicar acesso eletrônico abaixo)

Local structure and hybridization states in Ba0.9Ca0.1Ti1-xZrxO3 ceramic compounds: Correlation with a normal or relaxor ferroelectric character

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Globalização, Estado e crise estrutural do capital

Pós-graduação em Economia - FCLAR

Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling

In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L-1, 2.8 g L-1, 4.2 g L-1), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency of biodegradation.

Solvation in aqueous binary mixtures: consequences of the hydrophobic character of the ionic liquids and the solvatochromic probes

Information on the solvation in mixtures of water, W, and the ionic liquids, ILs, 1-allyl-3-R-imidazolium chlorides; R = methyl, 1-butyl, and 1-hexyl, has been obtained from the responses of the following solvatochromic probes: 2,6-dibromo-4-[(E)-2-(1-R-pyridinium-4-yl)ethenyl] phenolate, R = methyl, MePMBr2; 1-octyl, OcPMBr(2), and the corresponding quinolinium derivative, MeQMBr(2). A model developed for solvation in binary mixtures of W and molecular solvents has been extended to the present mixtures. Our objective is to assess the relevance to solvation of hydrogen-bonding and the hydrophobic character of the IL and the solvatochromic probe. Plots of the medium empirical polarity, E-T(probe) versus its composition revealed non-ideal behavior, attributed to preferential solvation by the IL and, more efficiently, by the IL-W hydrogen-bonded complex. The deviation from linearity increases as a function of increasing number of carbon atoms in the alkyl group of the IL, and is larger than that observed for solvation by W plus molecular solvents (1-propanol and 2-(1-butoxy)ethanol) that are more hydrophobic than the ILs investigated. This enhanced deviation is attributed to the more organized structure of the ILs proper, which persists in their aqueous solutions. MeQMBr(2) is more susceptible to solvent lipophilicity than OcPMBr(2), although the former probe is less lipophilic. This enhanced susceptibility agrees with the important effect of annelation on the contributions of the quinonoid and zwitterionic limiting structures to the ground and excited states of the probe, hence on its response to both medium composition and lipophilicity of the IL.

Niobium Effects on the Recrystallization Behavior on a Full Austenitic 15Cr-15Ni Stainless Steel Investigated Using Adaptive Neural Networks (ANN)

A new series of austenitic stainless steels-Nb stabilized, without Mo additions, non-susceptible to delta ferrite formation and devoid of intemetallic phases (sigma and chi), without deformation induced martensite is being developed, aiming at high temperature applications as well as for corrosive environments. The base steel composition is a 15Cr-15Ni with normal additions of Nb of 0.5, 1.0 and 2 wt%. Mechanical properties, oxidation and corrosion resistance already have been invetigated in previous papers. In this paper, the effects of Nb on the SFE, strain hardening and recrystallization resistance are evaluated with the help of Adaptive Neural Networks (ANN).

Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.