A hybrid Laplace transform/finite difference boundary element method for diffusion problems

The solution process for diffusion problems usually involves the time development separately from the space solution. A finite difference algorithm in time requires a sequential time development in which all previous values must be determined prior to the current value. The Stehfest Laplace transform algorithm, however, allows time solutions without the knowledge of prior values. It is of interest to be able to develop a time-domain decomposition suitable for implementation in a parallel environment. One such possibility is to use the Laplace transform to develop coarse-grained solutions which act as the initial values for a set of fine-grained solutions. The independence of the Laplace transform solutions means that we do indeed have a time-domain decomposition process. Any suitable time solver can be used for the fine-grained solution. To illustrate the technique we shall use an Euler solver in time together with the dual reciprocity boundary element method for the space solution

A hybrid training method for B-spline neural networks

Current and past research has brought up new views related to the optimization of neural networks. For a fixed structure, second order methods are seen as the most promising. From previous works we have shown how second order methods are of easy applicability to a neural network. Namely, we have proved how the Levenberg-Marquard possesses not only better convergence but how it can assure the convergence to a local minima. However, as any gradient-based method, the results obtained depend on the startup point. In this work, a reformulated Evolutionary algorithm - the Bacterial Programming for Levenberg-Marquardt is proposed, as an heuristic which can be used to determine the most suitable starting points, therefore achieving, in most cases, the global optimum.

Hybrid special finite element method for bi-material crack

In the paper, two novel 2-D hybrid special finite elements each containing an interfacial edge crack, which lies along or vertical to the interface between two materials, are developed. These proposed elements can assure the high precision especially in the vicinity of crack tip and provide a better description of its singularity with only one hybrid element surrounding one interfacial crack, thus, the numerical modeling of fracture analysis on bi-material crack can be greatly simplified. Numerical examples are provided to demonstrate the validity and versatility of the proposed method.

Direct method for optimal power management in hybrid electric vehicles

Hybrid electric vehicles are powered by an electric system and an internal combustion engine. The components of a hybrid electric vehicle need to be coordinated in an optimal manner to deliver the desired performance. This paper presents an approach based on direct method for optimal power management in hybrid electric vehicles with inequality constraints. The approach consists of reducing the optimal control problem to a set of algebraic equations by approximating the state variable which is the energy of electric storage, and the control variable which is the power of fuel consumption. This approximation uses orthogonal functions with unknown coefficients. In addition, the inequality constraints are converted to equal constraints. The advantage of the developed method is that its computational complexity is less than that of dynamic and non-linear programming approaches. Also, to use dynamic or non-linear programming, the problem should be discretized resulting in the loss of optimization accuracy. The propsed method, on the other hand, does not require the discretization of the problem producing more accurate results. An example is solved to demonstrate the accuracy of the proposed approach. The results of Haar wavelets, and Chebyshev and Legendre polynomials are presented and discussed. © 2011 The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.

K-Complex Detection Using a Hybrid-Synergic Machine Learning Method

Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection.

An attack-resistant hybrid data-privatization method with low information loss

We examine a recent proposal for data-privatization by testing it against well-known attacks, we show that all of these attacks successfully retrieve a relatively large (and unacceptable) portion of the original data. We then indicate how the data-privatization method examined can be modified to assist it to withstand these attacks and compare the performance of the two approaches. We also show that the new method has better privacy and lower information loss than the former method.

A hybrid weighted aggregation method based on consistency and consensus in group decision making

A recent study in Science indicated that the confidence of a decision maker played an essential role in group decision making problems. In order to make use of the information of each individual's confidence of the current decision problem, a new hybrid weighted aggregation method to solve a group decision making peoblem is proposed in this paper. Specifically, the hybrid weight of each expert is generated by a convex combination of his/her subjective experience-based weight and objective problem-domain-based weight. The experience-based weight is derived from the expert's historical experiences and the problem-domain-based weight is characterized by the confidence degree and consensus degree of each expert's opinions in the current decision making process. Based on the hybrid weighted aggregation method, all the experts' opinions which are expressed in the form of fuzzy preference relations are consequently aggregated to obtain a collective group opinion. Some valuable properities of the proposed method are discussed. A nurse manager hiring problem in a hospital is employed to illustrate that the proposed method provides a rational and valid solution for the group decision making problem when the experts are not willing to change their initial preferences, or the cost of change is high due to time limitation.

Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method

In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.

Leakage Detection And Localization Method Based On A Hybrid Inverse/Direct Modelling Approach Suitable For Handling Multiple-Leak Scenarios

When an accurate hydraulic network model is available, direct modeling techniques are very straightforward and reliable for on-line leakage detection and localization applied to large class of water distribution networks. In general, this type of techniques based on analytical models can be seen as an application of the well-known fault detection and isolation theory for complex industrial systems. Nonetheless, the assumption of single leak scenarios is usually made considering a certain leak size pattern which may not hold in real applications. Upgrading a leak detection and localization method based on a direct modeling approach to handle multiple-leak scenarios can be, on one hand, quite straightforward but, on the other hand, highly computational demanding for large class of water distribution networks given the huge number of potential water loss hotspots. This paper presents a leakage detection and localization method suitable for multiple-leak scenarios and large class of water distribution networks. This method can be seen as an upgrade of the above mentioned method based on a direct modeling approach in which a global search method based on genetic algorithms has been integrated in order to estimate those network water loss hotspots and the size of the leaks. This is an inverse / direct modeling method which tries to take benefit from both approaches: on one hand, the exploration capability of genetic algorithms to estimate network water loss hotspots and the size of the leaks and on the other hand, the straightforwardness and reliability offered by the availability of an accurate hydraulic model to assess those close network areas around the estimated hotspots. The application of the resulting method in a DMA of the Barcelona water distribution network is provided and discussed. The obtained results show that leakage detection and localization under multiple-leak scenarios may be performed efficiently following an easy procedure.

Structure and microstructure of PbTiO3 thin films obtained from hybrid chemical method

PbTiO3 thin films were deposited on Si(100) via hybrid chemical method and crystallized between 400 and 700 degreesC to study the effect of the crystallization kinetics on structure and microstructure of these materials. X-ray diffraction (XRD) technique was used to study the structure of the crystallized films. In the temperature range investigated, the lattice strain (c/a) presented a maximum value (c/a = 1.056) for film crystallized at 600 degreesC for I h. Atomic force microscopy (AFM) was used in investigation of the microstructure of the films. The rms roughness of the films linearly increases with temperature and ranged from 1.25 to 9.04 nm while the grain sizes ranged from 130.6 to 213.6 nm. Greater grain size was observed for film crystallized at 600 degreesC for 1 h. (C) 2002 Elsevier B.V. S.A. All rights reserved.

Luminescent properties of hybrid materials prepared by the polymeric precursor method

Rare earth complexes (RE) can be incorporated in silica matrixes, originating organic/inorganic hybrid materials with good thermal stability and high rare earth emission lines. In this work, the hybrid material was obtained by the polymeric precursor method and ultrasonic dispersed with spherical silica particles prepared by the Stöber Method. The Raman spectra indicated that the Eu3+ ions are involved in a polymeric structure formed as consequence of the chelation and polyesterification reactions of this ion with citric acid and ethylene glycol. After the ultrasonic stirring, 2-hydroxynicotinic ligand will also compose this polymeric rigid structure. The TGA/DTA analysis showed that this polymeric material was thermal decomposed at 300 °C. Moreover, this process allows the chelating process of the 2-hydroxynicotinic acid ligand to the Eu3+ ions. The 29Si NMR showed that the ultrasonic dispersion of the reactants was not able to promote the functionalization of the silica particles with the 2-hydroxynicotinic acid ligand. Moreover, heat treatment promotes the [Eu(HnicO2)3] complex particles incorporation into silica pores. At this temperature, the TGA curve showed that only the thermal degradation of ethylene glycol and citric acid used during the experimental procedure occurs. The silica and hybrid materials are composed by spherical and aggregated particles with particle size of approximately 450 nm, which can be influenced by the heat treatment. These materials also present an absorption band located at 337 nm. The photoluminescent study showed that when the hybrid samples were excited at 337 nm wavelength, the ligand absorbs the excitation light. Part of this energy is transferred to the Eu3+ ion, which main emission, 5D0→ 7F2, is observed in the emission spectrum at 612 nm. As the heating temperature increases to 300 C, the energy transfer is more favorable. The lifetime values showed that the Eu3+ emission is enhanced due to the energy transfer process in the powders. © 2013 Elsevier B.V. All rights reserved.

GENERALIZED FINITE ELEMENT METHOD ON NONCONVENTIONAL HYBRID-MIXED FORMULATION

The generalized finite element method (GFEM) is applied to a nonconventional hybrid-mixed stress formulation (HMSF) for plane analysis. In the HMSF, three approximation fields are involved: stresses and displacements in the domain and displacement fields on the static boundary. The GFEM-HMSF shape functions are then generated by the product of a partition of unity associated to each field and the polynomials enrichment functions. In principle, the enrichment can be conducted independently over each of the HMSF approximation fields. However, stability and convergence features of the resulting numerical method can be affected mainly by spurious modes generated when enrichment is arbitrarily applied to the displacement fields. With the aim to efficiently explore the enrichment possibilities, an extension to GFEM-HMSF of the conventional Zienkiewicz-Patch-Test is proposed as a necessary condition to ensure numerical stability. Finally, once the extended Patch-Test is satisfied, some numerical analyses focusing on the selective enrichment over distorted meshes formed by bilinear quadrilateral finite elements are presented, thus showing the performance of the GFEM-HMSF combination.