Hybrid Algorithms for Routing and Assignment Wavelengths in Optical Networks

This paper presents a strategy for the solution of the WDM optical networks planning. Specifically, the problem of Routing and Wavelength Allocation (RWA) in order to minimize the amount of wavelengths used. In this case, the problem is known as the Min-RWA. Two meta-heuristics (Tabu Search and Simulated Annealing) are applied to take solutions of good quality and high performance. The key point is the degradation of the maximum load on the virtual links in favor of minimization of number of wavelengths used; the objective is to find a good compromise between the metrics of virtual topology (load in Gb/s) and of the physical topology (quantity of wavelengths). The simulations suggest good results when compared to some existing in the literature.

Hybrid fault diagnosis scheme implementation for power distribution systems automation

Power distribution automation and control are import-ant tools in the current restructured electricity markets. Unfortunately, due to its stochastic nature, distribution systems faults are hardly avoidable. This paper proposes a novel fault diagnosis scheme for power distribution systems, composed by three different processes: fault detection and classification, fault location, and fault section determination. The fault detection and classification technique is wavelet based. The fault-location technique is impedance based and uses local voltage and current fundamental phasors. The fault section determination method is artificial neural network based and uses the local current and voltage signals to estimate the faulted section. The proposed hybrid scheme was validated through Alternate Transient Program/Electromagentic Transients Program simulations and was implemented as embedded software. It is currently used as a fault diagnosis tool in a Southern Brazilian power distribution company.

Hybrid Reinforcement of Sisal and Polypropylene Fibers in Cement-Based Composites

Several studies using vegetable fibers as the exclusive reinforcement in fiber-cement composites have shown acceptable mechanical performance at the first ages. However, after the exposure to accelerated aging tests, these composites have shown significant reduction in the toughness or increase in embrittlement. This was mainly attributed to the improved fiber-matrix adhesion and fiber mineralization after aging process. The objective of the present research was to evaluate composites produced by the slurry dewatering technique followed by pressing and air curing, reinforced with combinations of polypropylene fibers and sisal kraft pulp at different pulp freeness. The physical properties, mechanical performance, and microstructural characteristics of the composites were evaluated before and after accelerated and natural aging. Results showed the great contribution of pulp refinement on the improvement of the mechanical strength in the composites. Higher intensities of refinement resulted in higher modulus of rupture for the composites with hybrid reinforcement after accelerated and natural aging. The more compact microstructure was due to the improved packing of the mineral particles with refined sisal pulp. The toughness of the composites after aging was maintained in relation to the composites at 28 days of cure.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Hybrid Method to Assess Sensitive Process Interruption Costs Due to Faults in Electric Power Distribution Networks

This paper shows a new hybrid method for risk assessment regarding interruptions in sensitive processes due to faults in electric power distribution systems. This method determines indices related to long duration interruptions and short duration voltage variations (SDVV), such as voltage sags and swells in each customer supplied by the distribution network. Frequency of such occurrences and their impact on customer processes are determined for each bus and classified according to their corresponding magnitude and duration. The method is based on information regarding network configuration, system parameters and protective devices. It randomly generates a number of fault scenarios in order to assess risk areas regarding long duration interruptions and voltage sags and swells in an especially inventive way, including frequency of events according to their magnitude and duration. Based on sensitivity curves, the method determines frequency indices regarding disruption in customer processes that represent equipment malfunction and possible process interruptions due to voltage sags and swells. Such approach allows for the assessment of the annual costs associated with each one of the evaluated power quality indices.

A hybrid-mixed finite element formulation for the geometrically exact analysis of three-dimensional framed structures

This paper addresses the development of a hybrid-mixed finite element formulation for the quasi-static geometrically exact analysis of three-dimensional framed structures with linear elastic behavior. The formulation is based on a modified principle of stationary total complementary energy, involving, as independent variables, the generalized vectors of stress-resultants and displacements and, in addition, a set of Lagrange multipliers defined on the element boundaries. The finite element discretization scheme adopted within the framework of the proposed formulation leads to numerical solutions that strongly satisfy the equilibrium differential equations in the elements, as well as the equilibrium boundary conditions. This formulation consists, therefore, in a true equilibrium formulation for large displacements and rotations in space. Furthermore, this formulation is objective, as it ensures invariance of the strain measures under superposed rigid body rotations, and is not affected by the so-called shear-locking phenomenon. Also, the proposed formulation produces numerical solutions which are independent of the path of deformation. To validate and assess the accuracy of the proposed formulation, some benchmark problems are analyzed and their solutions compared with those obtained using the standard two-node displacement/ rotation-based formulation.

Hybrid and multi-field variational principles for geometrically exact three-dimensional beams

This paper addresses the development of several alternative novel hybrid/multi-field variational formulations of the geometrically exact three-dimensional elastostatic beam boundary-value problem. In the framework of the complementary energy-based formulations, a Legendre transformation is used to introduce the complementary energy density in the variational statements as a function of stresses only. The corresponding variational principles are shown to feature stationarity within the framework of the boundary-value problem. Both weak and linearized weak forms of the principles are presented. The main features of the principles are highlighted, giving special emphasis to their relationships from both theoretical and computational standpoints. (C) 2010 Elsevier Ltd. All rights reserved.

Toward Optimal Design of Piezoelectric Transducers Based on Multifunctional and Smoothly Graded Hybrid Material Systems

This work explores the design of piezoelectric transducers based on functional material gradation, here named functionally graded piezoelectric transducer (FGPT). Depending on the applications, FGPTs must achieve several goals, which are essentially related to the transducer resonance frequency, vibration modes, and excitation strength at specific resonance frequencies. Several approaches can be used to achieve these goals; however, this work focuses on finding the optimal material gradation of FGPTs by means of topology optimization. Three objective functions are proposed: (i) to obtain the FGPT optimal material gradation for maximizing specified resonance frequencies; (ii) to design piezoelectric resonators, thus, the optimal material gradation is found for achieving desirable eigenvalues and eigenmodes; and (iii) to find the optimal material distribution of FGPTs, which maximizes specified excitation strength. To track the desirable vibration mode, a mode-tracking method utilizing the `modal assurance criterion` is applied. The continuous change of piezoelectric, dielectric, and elastic properties is achieved by using the graded finite element concept. The optimization algorithm is constructed based on sequential linear programming, and the concept of continuum approximation of material distribution. To illustrate the method, 2D FGPTs are designed for each objective function. In addition, the FGPT performance is compared with the non-FGPT one.

Electrochemical impedance spectroscopy investigation of the electrochemical behaviour of copper coated with artificial patina layers and submitted to wet and dry cycles

Due to rain events historical monuments exposed to the atmosphere are frequently submitted to wet and dry cycles. During drying periods wetness is maintained in some confined regions and the corrosion product layer, generally denominated patinas, builds up and gets thicker. The aim of this study is to use electrochemical impedance spectroscopy (EIS) to investigate the electrochemical behaviour of pure copper coated with two artificial patina layers and submitted either to continuous or to intermittent immersion tests, this latter aiming to simulate wet and dry cycles. The experiments were performed in 0.1 mol dm(-3) NaCl solution and in artificial rainwater containing the most significant pollutants of the city of Sao Paulo. The results of the continuous immersion tests in the NaCl solution have shown that the coated samples behave like a porous electrode with finite pore length. On the other hand, in the intermittent tests a porous electrode response with semi-infinite pore length can be developed. The results were interpreted based on the model of de Levie and a critical comparison with previous interpretations reported in the literature for similar systems is presented. (C) 2011 Elsevier Ltd. All rights reserved.

Electrochemical study of modified non-functional bis-silane layers on Al alloy 2024-T3

In the last few years great efforts have been made in order to find and to develop environmentally friendly substitutes for Cr6+ pre-treatments applied on aluminium alloys used in the aircraft industry. Among the potential substitutes, silane layers have attracted considerable interest from researchers and from the industry. The present work investigates the anti-corrosion behaviour of (bis-1, 2-(triethoxysilyl) ethane (BTSE)) silane layers modified with Ce ions and/or silica nanoparticles applied on Al alloy 2024-T3 substrates. The corrosion behaviour was investigated in 0.1 M NaCl solution via d.c. polarization and electrochemical impedance spectroscopy (EIS). Contact angle measurements and XPS were used to assess information on the chemistry of the silane pre-treated surfaces. The results have shown that the introduction of additives improves the corrosion protection properties of the silane layer. (c) 2008 Elsevier Ltd. All rights reserved.

A sensor array based on mass and capacitance transducers for the detection of adulterated gasolines

The simultaneous use of different sensors technologies is an efficient method to increase the performance of chemical sensors systems. Among the available technologies, mass and capacitance transducers are particularly interesting because they can take advantage also from non-conductive sensing layers, such as most of the more interesting molecular recognition systems. In this paper, an array of quartz microbalance sensors is complemented by an array of capacitors obtained from a commercial biometrics fingerprints detector. The two sets of transducers, properly functionalized by sensitive molecular and polymeric films, are utilized for the estimation of adulteration in gasolines, and in particular to quantify the content of ethanol in gasolines, an application of importance for Brazilian market. Results indicate that the hybrid system outperforms the individual sensor arrays even if the quantification of ethanol in gasoline, due to the variability of gasolines formulation, is affected by a barely acceptable error. (C) 2009 Elsevier B.V. All rights reserved.

Ultra-thin films of alternating semi-interpenetrating layers of a conducting polymer with thermosetting phenolic resins for sensor application

In this work a new method for crosslinking ultra-thin films with potential applications in sensor systems is proposed. The films were produced by layer-by-layer (LbL) assembly using a conducting polymer, poly(o-ethoxyaniline) (POEA), alternated with a thermosetting resin, novolac-type phenolformaldehyde (PF), crosslinked by a simple thermal treatment. The PF resin served as both alternating and crosslinking agents. The films were characterized by Fourier transform infrared (FTIR) and ultraviolet-visible (UV-Vis) spectroscopy, thermogravimetry (TG), desorption, doping/dedoping cycling and electrical measurements. The results showed that film architecture and crosslinking degree can be controlled by the conditions used for film deposition (number of bilayers, polymer concentration, pH, and deposition time), and crosslinking time. Moreover, this approach offers several advantages such as fast curing time and low cost, indicating that these films can be used to produce sensors with improved stability.

Evaluation of Characterization and Performance Modeling of Cementitiously Stabilized Layers in the Mechanistic-Empirical Pavement Design Guide

Cementitious stabilization of aggregates and soils is an effective technique to increase the stiffness of base and subbase layers. Furthermore, cementitious bases can improve the fatigue behavior of asphalt surface layers and subgrade rutting over the short and long term. However, it can lead to additional distresses such as shrinkage and fatigue in the stabilized layers. Extensive research has tested these materials experimentally and characterized them; however, very little of this research attempts to correlate the mechanical properties of the stabilized layers with their performance. The Mechanistic Empirical Pavement Design Guide (MEPDG) provides a promising theoretical framework for the modeling of pavements containing cementitiously stabilized materials (CSMs). However, significant improvements are needed to bring the modeling of semirigid pavements in MEPDG to the same level as that of flexible and rigid pavements. Furthermore, the MEPDG does not model CSMs in a manner similar to those for hot-mix asphalt or portland cement concrete materials. As a result, performance gains from stabilized layers are difficult to assess using the MEPDG. The current characterization of CSMs was evaluated and issues with CSM modeling and characterization in the MEPDG were discussed. Addressing these issues will help designers quantify the benefits of stabilization for pavement service life.

Single Axis Controlled Hybrid Magnetic Bearing for Left Ventricular Assist Device: Hybrid Core and Closed Magnetic Circuit

In previous studies, we presented main strategies for suspending the rotor of a mixed-flow type (centrifugal and axial) ventricular assist device (VAD), originally presented by the Institute Dante Pazzanese of Cardiology (IDPC), Brazil. Magnetic suspension is achieved by the use of a magnetic bearing architecture in which the active control is executed in only one degree of freedom, in the axial direction of the rotor. Remaining degrees of freedom, excepting the rotation, are restricted only by the attraction force between pairs of permanent magnets. This study is part of a joint project in development by IDPC and Escola Politecnica of Sao Paulo University, Brazil. This article shows advances in that project, presenting two promising solutions for magnetic bearings. One solution uses hybrid cores as electromagnetic actuators, that is, cores that combine iron and permanent magnets. The other solution uses actuators, also of hybrid type, but with the magnetic circuit closed by an iron core. After preliminary analysis, a pump prototype has been developed for each solution and has been tested. For each prototype, a brushless DC motor has been developed as the rotor driver. Each solution was evaluated by in vitro experiments and guidelines are extracted for future improvements. Tests have shown good results and demonstrated that one solution is not isolated from the other. One complements the other for the development of a single-axis-controlled, hybrid-type magnetic bearing for a mixed-flow type VAD.

A hybrid commodity price-forecasting model applied to the sugar-alcohol sector

Accurate price forecasting for agricultural commodities can have significant decision-making implications for suppliers, especially those of biofuels, where the agriculture and energy sectors intersect. Environmental pressures and high oil prices affect demand for biofuels and have reignited the discussion about effects on food prices. Suppliers in the sugar-alcohol sector need to decide the ideal proportion of ethanol and sugar to optimise their financial strategy. Prices can be affected by exogenous factors, such as exchange rates and interest rates, as well as non-observable variables like the convenience yield, which is related to supply shortages. The literature generally uses two approaches: artificial neural networks (ANNs), which are recognised as being in the forefront of exogenous-variable analysis, and stochastic models such as the Kalman filter, which is able to account for non-observable variables. This article proposes a hybrid model for forecasting the prices of agricultural commodities that is built upon both approaches and is applied to forecast the price of sugar. The Kalman filter considers the structure of the stochastic process that describes the evolution of prices. Neural networks allow variables that can impact asset prices in an indirect, nonlinear way, what cannot be incorporated easily into traditional econometric models.