A simulation tool for transient stability analysis on vector computers

This paper deals with transient stability analysis based on time domain simulation on vector processing. This approach requires the solution of a set of differential equations in conjunction of another set of algebraic equations. The solution of the algebraic equations has presented a scalar as sequential set of tasks, and the solution of these equations, on vector computers, has required much more investigations to speedup the simulations. Therefore, the main objective of this paper has been to present methods to solve the algebraic equations using vector processing. The results, using a GRAY computer, have shown that on-line transient stability assessment is feasible.

Métodos Semi-Empíricos: princípios básicos e aplicações

Pós-graduação em Física - IGCE

First-principles simulation of elastic constants and electronic properties of GaN

The electronic and structural properties and elastic constants of the wurtzite phase of GaN, was investigated by computer simulation at Density Functional Theory level, with B3LYP and B3PW hybrid functional. The electronic properties were investigated through the analysis of the band structures and density of states, and the mechanical properties were studied through the calculus of the elastic constants: C11, C33, C44, C12, and C13. The results show that the maximum of the valence band and the minimum of the conduction band are both located at the Γ point, indicating that GaN is a direct band gap semiconductor. The following constants were obtained for B3LYP and B3PW (in brackets): C11 = 366.9 [372.4], C33 = 390.9 [393.4], C44 = 99.1 [96.9], C12 = 143.6 [155.2], and C13 = 107.6 [121.4].

Effect of target bias on magnetic field enhanced plasma immersion ion implantation

Recent studies have demonstrated that sheath dynamics in plasma immersion ion implantation (PIII) is significantly affected by an external magnetic field, especially in the case when the magnetic field is parallel to the workpiece surface or intersects it at small angles. In this work we report the results from two-dimensional, particle-in-cell (PIC) computer simulations of magnetic field enhanced plasma immersion implantation system at different bias voltages. The simulations begin with initial low-density nitrogen plasma, which extends with uniform density through a grounded cylindrical chamber. Negative bias voltage is applied to a cylindrical target located on the axis of the vacuum chamber. An axial magnetic field is created by a solenoid installed inside the target holder. A set of simulations at a fixed magnetic field of 0.0025 T at the target surface is performed. Secondary electron emission from the target subjected to ion bombardment is also included. It is found that the plasma density around the cylindrical target increases because of intense background gas ionization by the electrons drifting in the crossed E x B fields. Suppression of the sheath expansion and increase of the implantation current density in front of the high-density plasma region are observed. The effect of target bias on the sheath dynamics and implantation current of the magnetic field enhanced PIII is discussed. (C) 2007 Elsevier B.V. All rights reserved.

Effect of electron magnetic trapping in a plasma immersion ion implantation system

In this work we describe a two-dimensional computer simulation of magnetic field enhanced plasma immersion implantation system. Negative bias voltage of 10.0 kV is applied to a cylindrical target located on the axis of a grounded vacuum chamber filled with uniform nitrogen plasma. A pair of external coils creates a static magnetic field with main vector component along the axial direction. Thus, a system of crossed ExB field is generated inside the vessel forcing plasma electrons to rotate in azimuthal direction. In addition, the axial variation of the magnetic field intensity produces magnetic mirror effect that enables axial particle confinement. It is found that high-density plasma regions are formed around the target due to intense background gas ionization by the trapped electrons. Effect of the magnetic field on the sheath dynamics and the implantation current density of the PIII system is investigated. By changing the magnetic field axial profile (varying coils separation) an enhancement of about 30% of the retained dose can be achieved. The results of the simulation show that the magnetic mirror configuration brings additional benefits to the PIII process, permitting more precise control of the implanted dose.

The Impact on Geographic Location Accuracy due to Different Satellite Orbit Ephemerides

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A planar flexible robotic manipulator

Presents the dynamic modelling of a flexible robotic manipulator with two flexible links and two revolute joints, which rotates in the horizontal plane. The dynamic equations are derived using the Newton-Euler formulation and the finite element method, based on elementary beam theory, which is used to discretize the displacements such that the small motion is represented in terms of nodal displacements. Computer simulation results are presented to illustrate this study. The dynamic model becomes necessary for use in future design and control applications.

New approach to ideal inductors stored energy nature

Nonlinear load compensation required the definition of new concepts of electric power. With basis on these new concepts the nature of the stored energy stored in ideal inductors is theoreticaly characterized in this work. Computer simulation and theory agree when applied to an isolated alternator.

Propriedades eletrônicas, estruturais e constantes elásticas do ZnO

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

Jogo da Minoria: um modelo baseado em agentes aplicado ao mercado financeiro

Nos últimos anos houve uma contribuição significativa dos físicos para a construção de um tipo de modelo baseado em agentes que busca reproduzir, em simulação computacional, o comportamento do mercado financeiro. Esse modelo, chamado Jogo da Minoria consiste de um grupo de agentes que vão ao mercado comprar ou vender ativos. Eles tomam decisões com base em estratégias e, por meio delas, os agentes estabelecem um intrincado jogo de competição e coordenação pela distribuição da riqueza. O modelo tem demonstrado resultados bastante ricos e surpreendentes, tanto na dinâmica do sistema como na capacidade de reproduzir características estatísticas e comportamentais do mercado financeiro. Neste artigo, são apresentadas a estrutura e a dinâmica do Jogo da Minoria, bem como as contribuições recentes relacionadas ao Jogo da Minoria denominado de Grande Canônico, que é um modelo mais bem ajustado às características do mercado financeiro e reproduz as regularidades estatísticas do preço dos ativos chamadas fatos estilizados.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Static converter: didactic support software

Teaching a course of special electric loads in a continuing education program to power engineers is a difficult task because they are not familiarized with switching topology circuits. Normally, in a typical program, many hours are dedicated to explain the thyristors switching sequence and to draw the converter currents and terminal voltages waveforms for different operative conditions. This work presents teaching support software in order to optimize the time spent in this task and, mainly to benefit the assimilation of the proposed subjects, studying the static converter under different non-ideal operative conditions.

Modelling of the influence of the layer pollution thickness in high voltage polluted insulators

An experimental model and a mathematical model with the introduction of a ramp in the channel of Obenaus model are presented. The aim is to present a better reproduction of the real layer pollution deposited on the HV insulators. This better reproduction is obtained from two types of thickness variation: the introduction of a ramp (soft variation) and the introduction of a step (sudden variation). The computational simulations and the experimental data suggest that the introduction of the ramp is the better reproduction of the layer pollution. The ramp approximates to the real layer pollution more than the step.

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Another prefiltering algorithm for adaptive IIR filtering

An algorithm for adaptive IIR filtering that uses prefiltering structure in direct form is presented. This structure has an estimation error that is a linear function of the coefficients. This property greatly simplifies the derivation of gradient-based algorithms. Computer simulations show that the proposed structure improves convergence speed.