Leak detection in petroleum pipelines using a fuzzy system

A methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.

An adaptive system based on artificial neural networks for NPK fluid fertilizer application

The application process of fluid fertilizers through variable rates implemented by classical techniques with feedback and conventional equipments can be inefficient or unstable. This paper proposes an open-loop control system based on artificial neural network of the type multilayer perceptron for the identification and control of the fertilizer flow rate. The network training is made by the algorithm of Levenberg-Marquardt with training data obtained from measurements. Preliminary results indicate a fast, stable and low cost control system for precision fanning. Copyright (C) 2000 IFAC.

Magnetic susceptibility of an exactly solvable anisotropic spin ladder system

We investigate the thermodynamics of an integrable spin ladder model which possesses a free parameter besides rung and leg couplings. The model is exactly solvable by means of the Bethe ansatz and exhibits a phase transition between a gapped and a gapless spin excitation spectrum. The magnetic susceptibility is obtained numerically and its dependence on the anisotropy parameter is determined. The spin gap obtained from the susceptibility curve and the one obtained from the Bethe ansatz equations are in very good agreement. Our results for the magnetic susceptibility fit well the experimental data for the organometallic compounds (5IAP)(2)CuBr4 . 2H(2)O (Landee C. P. et al., Phys. Rev. B, 63 (2001) 100402(R)) Cu-2(C5H12N2)(2)Cl-4 (Hayward C. A., Poilblanc D. and Levy L. P., Phys. Rev. B, 54 (1996) R12649, Chaboussant G. et al., Phys. Rev. Lett., 19 ( 1997) 925; Phys. Rev. B, 55 ( 1997) 3046.) and (C5H12N)(2)CuBr4 (Watson B. C. et al., Phys. Rev. Lett., 86 ( 2001) 5168) in the strong-coupling regime.

Using artificial neural networks for identification of electrical losses in transformers during the manufacturing phase

The paper describes a novel neural model to estimate electrical losses in transformer during the manufacturing phase. The network acts as an identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core losses, copper losses, resistance, current and temperature. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to manufacturing process. Thus, this research has led to an improvement on the rational use of energy.

Nonlinear parameter estimation in laminar forced convection within a circular sector tube

In this article we examine an inverse heat convection problem of estimating unknown parameters of a parameterized variable boundary heat flux. The physical problem is a hydrodynamically developed, thermally developing, three-dimensional steady state laminar flow of a Newtonian fluid inside a circular sector duct, insulated in the flat walls and subject to unknown wall heat flux at the curved wall. Results are presented for polynomial and sinusoidal trial functions, and the unknown parameters as well as surface heat fluxes are determined. Depending on the nature of the flow, on the position of experimental points the inverse problem sometimes could not be solved. Therefore, an identification condition is defined to specify a condition under which the inverse problem can be solved. Once the parameters have been computed it is possible to obtain the statistical significance of the inverse problem solution. Therefore, approximate confidence bounds based on standard statistical linear procedure, for the estimated parameters, are analyzed and presented.

New glasses in the system InF3-BaF2-ErPO4

New glasses have been obtained in the system InF3-BaF2-ErPO4. Glass compositions with up to 30% mol Er3+ were shown to exist and characterized by thermal analysis, x-ray: diffraction, IR absorption and electronic spectroscopy. The systems with high Er3+ content were studied recording IR and visible emission spectral characteristics. A specially elaborated technique allowed the preparation of a high purity phosphate precursor ErPO4. X-ray identification of the crystalline phases appearing during thermal treatment have been carried out and parameters of a mixed fluoride Ba4In3-nErnF17 calculated.

Cognitive Deficits in Machado-Joseph Disease Correlate with Hypoperfusion of Visual System Areas

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

Harmonic identification using parallel neural networks in single-phase systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Quantum and semiclassical Husimi distributions for a one-dimensional resonant system

We compare exact and semiclassical Husimi distributions for the single eigenstates of a one-dimensional resonant Hamiltonian. We find that both distributions concentrate near the unstable fixed points even when these points are made complex by suitably varying a parameter. © 1992 The American Physical Society.

Synchronous machines parameters identification using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. The quadrature axis parameters are obtained with a rejection under an arbitrary reference, reducing the present difficulties.

Immunological alterations in patients with primary tumors in central nervous system

Natural killer (NK) cells play an important role in immune surveillance against tumors. The present work aimed to study the cytotoxic activity of NK cells and T cell subsets in peripheral blood of 13 patients with primary tumors in central nervous system (CNS). As controls 29 healthy subjects with the age range equivalent to the patients were studied. The methods employed were: a) determination of cytotoxic activity of NK cells towards K562 target cells, evaluated by single cell-assay; b) enumeration of CD3+ lymphocytes and their CD4+ and CD8+ subsets defined by monoclonal antibodies; c) the identification of tumors were done by histologic and immunochemistry studies. The results indicated that adults and children with tumor in CNS display reduced percentage of total T cells, helper/inducer subset and low helper/suppressor ratio. The cytotoxic activity of NK cells was decreased in patients with CNS tumors due mainly to a decrease in the proportion of target-binding lymphocytes. These results suggest that cytotoxic activity of NK cells may be affected by the immunoregulatory disturbances observed in patients with primary tumors in CNS.

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Constraining the free parameter of the high parton density effects

The high parton density effects are strongly dependent of the spatial gluon distribution within the proton, with radius R, which cannot be derived from perturbative QCD. In this paper we assume that the unitarity corrections are present in the HERA kinematical region and constrain the value of R using the data for the proton structure function and its slope. We obtain that the gluons are not distributed uniformly in the whole proton disc, but behave as concentrated in smaller regions. (C) 2000 Elsevier Science B.V.

Application of HPLC-NMR coupling using C30 phase in the separation and identification of flavonoids of taxonomic relevance

The on-line separation and identification of two important taxonomic markers for plant species of the Paepalanthus genus, the flavonoids 6-methoxykaempferol-3-O-β-D-glucopyranoside and 6-methoxykaempferol-3-O-β-D-6″(p-coumaroyl)glucopyranoside, has been performed with an HPLC-NMR coupling using C30 phase. 1D spectra have been recorded in the stopped-flow mode for the two predominant chromatographic peaks. This is the first application of HPLC-NMR coupling using C30 phase to a taxonomic problem. The technique drastically reduces the required amount of sampling for structure determination. © Springer-Verlag 2000.

A barrier method for constrained nonlinear optimization using a modified Hopfield network

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.