Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

Diet-induced obesity impairs AKT signalling in the retina and causes retinal degeneration

Retinopathy, a common complication of diabetes, is characterized by an unbalanced production of nitric oxide (NO), a process regulated by nitric oxide synthase (NOS). We hypothesized that retinopathy might stem from changes in the insulin receptor substrate (IRS)/PI3K/AKT pathway and/or expression of NOS isoforms. Thus, we analysed the morphology and apoptosis index in retinas of obese rats in whom insulin resistance had been induced by a high-fat diet (HFD). Immunoblotting analysis revealed that the retinal tissue of HFD rats had lower levels of AKT1, eNOS and nNOS protein than those of samples taken from control animals. Furthermore, immunohistochemical analyses indicated higher levels of iNOS and 4-hydroxynonenal and a larger number of apoptotic nuclei in HFD rats. Finally, both the inner and outer retinal layers of HFD rats were thinner than those in their control counterparts. When considered alongside previous results, these patterns suggest two major ways in which HFD might impact animals: direct activity of ingested fatty acids and/or via insulin-resistance-induced changes in intracellular pathways. We discuss these possibilities in further detail and advocate the use of this animal model for further understanding relationships between retinopathy, metabolic syndrome and type 2 diabetes. © 2012 John Wiley & Sons, Ltd.

Rede Neuro-Fuzzy-Wavelet para detecção e classificação de anomalias de tensão em sistemas elétricos de potência

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modelos computacionais fuzzy e neuro-fuzzy para avaliarem os efeitos da poluição do ar

Pós-graduação em Engenharia Mecânica - FEG

Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia

Pós-graduação em Engenharia Elétrica - FEIS

Desenvolvimento de aplicações em medicina e agronomia utilizando lógica fuzzy e neuro fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Estimativa da produtividade de trigo em função da adubação nitrogenada utilizando modelagem neuro fuzzy

Currently new techniques for data processing, such as neural networks, fuzzy logic and hybrid systems are used to develop predictive models of complex systems and to estimate the desired parameters. In this article the use of an adaptive neuro fuzzy inference system was investigated to estimate the productivity of wheat, using a database of combination of the following treatments: five N doses (0, 50, 100, 150 and 200 kg ha(-1)), three sources (Entec, ammonium sulfate and urea), two application times of N (at sowing or at side-dressing) and two wheat cultivars (IAC 370 and E21), that were evaluated during two years in Selviria, Mato Grosso do Sul, Brazil. Through the input and output data, the system of adaptive neuro fuzzy inference learns, and then can estimate a new value of wheat yield with different N doses. The productivity prediciton error of wheat in function of five N doses, using a neuro fuzzy system, was smaller than that one obtained with a quadratic approximation. The results show that the neuro fuzzy system is a viable prediction model for estimating the wheat yield in function of N doses.

Aplicação de sistemas neuro-fuzzy e espectrometria no infravermelho próximo para a identificação em tempo real do teor de nitrogênio foliar em cana-de-açúcar

Pós-graduação em Engenharia Elétrica - FEB

ChIP-seq reveals a role for CrzA in the Aspergillus fumigatus high-osmolarity glycerol response (HOG) signalling pathway

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modulation of Immune Response by RAGE and TLR4 Signalling in PBMCs of Diabetic and Non-Diabetic Patients

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

Desenvolvimento de uma rede neuro-fuzzy para a previsão da carga

Pós-graduação em Engenharia Elétrica - FEIS

Modulation of immune response by RAGE and TLR4 signalling in PBMCs of diabetic and non-diabetic patients

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

Cardiac dysfunction induced by obesity es not related to beta-adrenergic system impairment at the receptor-signalling pathway

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