Generating VRS data using atmospheric models: How far can we go?

Nowadays, with the expansion of the reference stations networks, several positioning techniques have been developed and/or improved. Among them, the VRS (Virtual Reference Station) concept has been very used. In this paper the goal is to generate VRS data in a modified technique. In the proposed methodology the DD (double difference) ambiguities are not computed. The network correction terms are obtained using only atmospheric (ionospheric and tropospheric) models. In order to carry out the experiments it was used data of five reference stations from the GPS Active Network of West of São Paulo State and an extra station. To evaluate the VRS data quality it was used three different strategies: PPP (Precise Point Positioning) and Relative Positioning in static and kinematic modes, and DGPS (Differential GPS). Furthermore, the VRS data were generated in the position of a real reference station. The results provided by the VRS data agree quite well with those of the real file data.

Wireless mesh networks planning based on parameters of quality of service

The use of QoS parameters to evaluate the quality of service in a mesh network is essential mainly when providing multimedia services. This paper proposes an algorithm for planning wireless mesh networks in order to satisfy some QoS parameters, given a set of test points (TPs) and potential access points (APs). Examples of QoS parameters include: probability of packet loss and mean delay in responding to a request. The proposed algorithm uses a Mathematical Programming model to determine an adequate topology for the network and Monte Carlo simulation to verify whether the QoS parameters are being satisfied. The results obtained show that the proposed algorithm is able to find satisfactory solutions.

A proposal for reliability evaluation of components on electric power distribution system integrating probabilistic models and fuzzy inference systems

The system reliability depends on the reliability of its components itself. Therefore, it is necessary a methodology capable of inferring the state of functionality of these components to establish reliable indices of quality. Allocation models for maintenance and protective devices, among others, have been used in order to improve the quality and availability of services on electric power distribution systems. This paper proposes a methodology for assessing the reliability of distribution system components in an integrated way, using probabilistic models and fuzzy inference systems to infer about the operation probability of each component. © 2012 IEEE.

Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

Artificial neural network model of discharge lamps in the power quality context

This paper presents a methodology for modeling high intensity discharge lamps based on artificial neural networks. The methodology provides a model which is able to represent the device operating in the frequency of distribution systems, facing events related to power quality. With the aid of a data acquisition system to monitor the laboratory experiment, and using $$\text{ MATLAB }^{\textregistered }$$ software, data was obtained for the training of two neural networks. These neural networks, working together, were able to represent with high fidelity the behavior of a discharge lamp. The excellent performance obtained by these models allowed the simulation of a group of lamps in a distribution system with shorter simulation time when compared to mathematical models. This fact justified the application of this family of loads in electric power systems. The representation of the device facing power quality disturbances also proved to be a useful tool for more complex studies in distribution systems. © 2013 Brazilian Society for Automatics - SBA.

Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Drying kinetics and quality of natural coffee

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The influence of bone quality on the biomechanical behavior of full-arch implant-supported fixed prostheses

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

A Time-domain Harmonic Power-Flow Analysis in Electrical Energy Distribution Networks, Using Norton Models for Non-linear Loading

The purpose of this paper is to present the application of a three-phase harmonic propagation analysis time-domain tool, using the Norton model to approach the modeling of non-linear loads, making the harmonics currents flow more appropriate to the operation analysis and to the influence of mitigation elements analysis. This software makes it possible to obtain results closer to the real distribution network, considering voltages unbalances, currents imbalances and the application of mitigation elements for harmonic distortions. In this scenario, a real case study with network data and equipments connected to the network will be presented, as well as the modeling of non-linear loads based on real data obtained from some PCCs (Points of Common Coupling) of interests for a distribution company.

Fast determination of beef quality parameters with time-domain nuclear magnetic resonance spectroscopy and chemometrics

The noteworthy of this study is to predict seven quality parameters for beef samples using time-domain nuclear magnetic resonance (TD-NMR) relaxometry data and multivariate models. Samples from 61 Bonsmara heifers were separated into five groups based on genetic (breeding composition) and feed system (grain and grass feed). Seven sample parameters were analyzed by reference methods; among them, three sensorial parameters, flavor, juiciness and tenderness and four physicochemical parameters, cooking loss, fat and moisture content and instrumental tenderness using Warner Bratzler shear force (WBSF). The raw beef samples of the same animals were analyzed by TD-NMR relaxometry using Carr-Purcell-Meiboom-Gill (CPMG) and Continuous Wave-Free Precession (CWFP) sequences. Regression models computed by partial least squares (PLS) chemometric technique using CPMG and CWFP data and the results of the classical analysis were constructed. The results allowed for the prediction of aforementioned seven properties. The predictive ability of the method was evaluated using the root mean square error (RMSE) for the calibration (RMSEC) and validation (RMSEP) data sets. The reference and predicted values showed no significant differences at a 95% confidence level.

A critical comparison of different approaches to sediment-quality assessments in the Santos estuarine system in Brazil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Determination of quality parameters for mustard sauces in sealed packets using time-domain nuclear magnetic resonance spectroscopy and chemometrics

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq)