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.

Measurement of semileptonic branching fractions of B mesons to narrow D-** states - art. no. 1711803

Using the data accumulated in 2002-2004 with the D0 detector in proton-antiproton collisions at the Fermilab Tevatron collider with a center-of-mass energy of 1.96 TeV, the branching fractions of the decays B ->(D) over bar (0)(1)(2420)mu(+)nu(mu)X and B ->(D) over bar (*0)(2)(2460)mu(+)nu(mu)X and their ratio have been measured: B (b) over bar -> B)xB(B -> (D) over bar (0)(1)mu(+)nu(mu)X)xB((D) over bar (0)(1)-> D(*-)pi(+))=[0.087 +/- 0.007(stat)+/- 0.014(syst)]%; B((b) over bar -> B)xB(B ->(D) over bar (*0)(2)mu(+)nu(mu)X)xB((D) over bar (*0)(2)-> D(*-)pi(+))=[0.035 +/- 0.007(stat)+/- 0.008(syst)]% and [B(B ->(D) over bar (*0)(2)mu(+)nu(mu)X)xB((D) over bar (*0)(2)-> D(*-)pi(+))]/[B(B ->(D) over bar (0)(1)mu(+)nu(mu)X)xB((D) over bar (0)(1)-> D(*-)pi(+))]=0.39 +/- 0.09(stat)+/- 0.12(syst), where the charge conjugated states are always implied.

Surface roughness of glass ionomer cements indicated for atraumatic restorative treatment (ART)

The purpose of this study was to evaluate the surface roughness of four conventional chemically cured glass ionomer cements (Fuji IX, Ketac Molar, Vidrion R and Vitromolar) commonly used in atraumatic restorative treatment (ART) immediately after material preparation. Twenty specimens of each glass ionomer cement were fabricated and surface roughness was measured after material setting. The specimens were further examined under scanning electron microscopy. Data were analyzed statistically by Kruskal-Wallis test and Mann-Whitney test at 5% significance level. Two-by-two comparisons showed statistically significant difference (p<0.05) between all materials, except for Ketac Molar and Vidrion R, which had statically similar results (p>0.05). Regarding their results of surface roughness, the materials can be presented in a crescent order, as follows: Ketac Molar < Vidrion R < Fuji IX < Vitromolar. In conclusion, from the tested glass ionomer cements, Fuji IX, Ketac Molar and Vidrion R presented acceptable surface roughness after setting reaction while Vitromolar showed remarkably higher surface roughness.

Microhardness of glass ionomer cements indicated for the ART technique according to surface protection treatment and storage time

The aim of this study was to assess the microhardness of 5 glass ionomer cements (GIC) - Vidrion R (V, SS White), Fuji IX (F, GC Corp.), Magic Glass ART (MG, Vigodent), Maxxion R (MR, FGM) and ChemFlex (CF, Dentsply) - in the presence or absence of a surface protection treatment, and after different storage periods. For each GIC, 36 test specimens were made, divided into 3 groups according to the surface protection treatment applied - no protection, varnish or nail varnish. The specimens were stored in distilled water for 24 h, 7 and 30 days and the microhardness tests were performed at these times. The data obtained were submitted to the ANOVA for repeated measures and Tukey tests (α = 5%). The results revealed that the mean microhardness values of the GICs were, in decreasing order, as follows: F > CF = MR > MG > V; that surface protection was significant for MR, at 24 h, without protection (64.2 ± 3.6a), protected with GIC varnish (59.6 ± 3.4b) and protected with nail varnish (62.7 ± 2.8ab); for F, at 7 days, without protection (97.8 ± 3.7ab), protected with varnish (95.9 ± 3.2b) and protected with nail varnish (100.8 ± 3.4a); and at 30 days, for F, without protection (98.8 ± 2.6b), protected with varnish (103.3 ± 4.4a) and protected with nail varnish (101 ± 4.1ab) and, for V, without protection (46 ± 1.3b), protected with varnish (49.6 ± 1.7ab) and protected with nail varnish (51.1 ± 2.6a). The increase in storage time produced an increase in microhardness. It was concluded that the different GICs, surface protection treatments and storage times could alter the microhardness values.

Previsão de cargas elétricas através de uma Rede Neural Híbrida Back-ART Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Análise da estabilidade transitória via rede neural Art-Artmap fuzzy Euclidiana modificada com treinamento continuado

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Previsão de carga multinodal usando a rede neural ART-ARTMAP fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Microdureza de cimentos de ionômeros de vidro indicados para a técnica do ART, variando-se a proteção superficial e o tempo de armazenamento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Clinical evaluation of multiple-surface ART restorations: three-year follow-up

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