Towards forecasting and mitigating ionospheric scintillation effects on GNSS

The effect of the ionosphere on the signals of Global Navigation Satellite Systems (GNSS), such as the Global Positionig System (GPS) and the proposed European Galileo, is dependent on the ionospheric electron density, given by its Total Electron Content (TEC). Ionospheric time-varying density irregularities may cause scintillations, which are fluctuations in phase and amplitude of the signals. Scintillations occur more often at equatorial and high latitudes. They can degrade navigation and positioning accuracy and may cause loss of signal tracking, disrupting safety-critical applications, such as marine navigation and civil aviation. This paper addresses the results of initial research carried out on two fronts that are relevant to GNSS users if they are to counter ionospheric scintillations, i.e. forecasting and mitigating their effects. On the forecasting front, the dynamics of scintillation occurrence were analysed during the severe ionospheric storm that took place on the evening of 30 October 2003, using data from a network of GPS Ionospheric Scintillation and TEC Monitor (GISTM) receivers set up in Northern Europe. Previous results [1] indicated that GPS scintillations in that region can originate from ionospheric plasma structures from the American sector. In this paper we describe experiments that enabled confirmation of those findings. On the mitigation front we used the variance of the output error of the GPS receiver DLL (Delay Locked Loop) to modify the least squares stochastic model applied by an ordinary receiver to compute position. This error was modelled according to [2], as a function of the S4 amplitude scintillation index measured by the GISTM receivers. An improvement of up to 21% in relative positioning accuracy was achieved with this technnique.

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle

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

The Accuracy of National Body Fat Cutoff Levels in the Prediction of Elevated Blood Pressure among Brazilian Male Adolescents

Objective: To assess viability of the development of percentage body fat cutoffs based on blood pressure values in Brazilian adolescents.Methods: A cross-sectional study was conducted with a sample of 358 male subjects from 8 to 18 years old. Blood pressure was measured by the oscilometric method, and body composition was measured by dual-energy X-ray absorptiometry (DXA).Results: For the identification of elevated blood pressure, these nationally developed body fat cutoffs presented relative accuracy. The cutoffs were significantly associated with elevated blood pressure [odds ratio = 5.91 (95% confidence interval: 3.54-9.86)].Conclusions: Development of national body fat cutoffs is viable, because presence of high accuracy is an indication of elevated blood pressure.

Step Counting and Energy Expenditure Estimation in Patients With Chronic Obstructive Pulmonary Disease and Healthy Elderly: Accuracy of 2 Motion Sensors

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks

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

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

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Electrical load forecasting formulation by a fast neural network

The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Accuracy of six minute walk test, stair test and spirometry using maximal oxygen uptake as gold standard

OBJETIVO: Determinar a acurácia das variáveis: tempo de escada (tTE), potência de escada (PTE), teste de caminhada (TC6) e volume expiratório forçado (VEF1) utilizando o consumo máximo de oxigênio (VO2máx) como padrão-ouro. MÉTODOS: Os testes foram realizados em 51 pacientes. O VEF1 foi obtido através da espirometria. O TC6 foi realizado em corredor plano de 120m. O TE foi realizado em escada de 6 lances obtendo-se tTE e PTE. O VO2máx foi obtido por ergoespirometria, utilizando o protocolo de Balke. Foram calculados a correlação linear de Pearson (r) e os valores de p, entre VO2máx e variáveis. Para o cálculo da acurácia, foram obtidos os pontos de corte, através da curva característica operacional (ROC). A estatística Kappa (k) foi utilizada para cálculo da concordância. RESULTADOS: Obteve-se as acurácias: tTE - 86%, TC6 - 80%, PTE - 71%, VEF1(L) - 67%, VEF1% - 63%. Para o tTE e TC6 combinados em paralelo, obteve-se sensibilidade de 93,5% e em série, especificidade de 96,4%. CONCLUSÃO: O tTE foi a variável que apresentou a melhor acurácia. Quando combinados o tTE e TC6 podem ter especificidade e sensibilidade próxima de 100%. Estes testes deveriam ser mais usados rotineiramente, especialmente quando a ergoespirometria para a medida de VO2máx não é disponível.

Accuracy of Tc-99m-sestamibi scintimammography for breast cancer diagnosis

Scintimammography using Tc-99m-sestamibi is a noninvasive and painless diagnostic imaging method that is used to detect breast cancer when mammography is inconclusive Because of the advantages of labeling v '7,ith Tc-99m-sestamibi and its high efficiency in detecting carcinomas, it is the most widespread agent for this purpose Its accumulation in the tumor has multifactorial causes and does not depend on the presence of architectural distortion or local or diffuse density variation in the breast The objective of tfiis study was to evaluate the accuracy of scintimammography 1 for detecting breast cancer One hundred and fifty-seven patients presenting 158 palpable and non-palpable breast nodules were evaluated Three patients were male and 154 were female, aged between 14 and 81 years All patients underwent scintimammography, and the nodule was subjected i to cytological or histological study, i e, the gold standard for diagnosing cancer One hundred and eleven malignant and 47 benign nodules were detected, with predominance of ductal carcinomas (n=94) and fibroadenoma/fibrocysiic condition (n=11/n=11), respectively The mean size was 3 11 cm (7-10 cm) among the malignant nodules and 2 07 cm among the benign nodules (0 5-10 cm) The sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 89, 89, 95, 78 and 89%, respectively Analysis on the histological types showed that the technique was more effective on tumors that were more aggressive, such as ductal carcinoma In this study, Tc-99m-sestamibi scintim immography was shown to be an important tool for diagnosing breast cancer when mammography was inconclusive