Fenômenos não-lineares, incluindo-se os não-ideias, em captura de energia utilizando-se dispositivos piezoelétricos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Vacuum birefringence in strong inhomogeneous electromagnetic fields

Birefringence is one of the fascinating properties of the vacuum of quantum electrodynamics (QED) in strong electromagnetic fields. The scattering of linearly polarized incident probe photons into a perpendicularly polarized mode provides a distinct signature of the optical activity of the quantum vacuum and thus offers an excellent opportunity for a precision test of nonlinear QED. Precision tests require accurate predictions and thus a theoretical framework that is capable of taking the detailed experimental geometry into account. We derive analytical solutions for vacuum birefringence which include the spatio-temporal field structure of a strong optical pump laser field and an x-ray probe. We show that the angular distribution of the scattered photons depends strongly on the interaction geometry and find that scattering of the perpendicularly polarized scattered photons out of the cone of the incident probe x-ray beam is the key to making the phenomenon experimentally accessible with the current generation of FEL/high-field laser facilities.

Validation of a novel ELISA for measurement of electronegative low-density lipoprotein

Background: Oxidative modification of low-density lipoprotein (LDL) plays a key role in the pathogenesis of atherosclerosis. LDL(-) is present in blood plasma of healthy subjects and at higher concentrations in diseases with high cardiovascular risk, such as familial hypercholesterolemia or diabetes. Methods: We developed and validated a sandwich ELISA for LDL(-) in human plasma using two monoclonal antibodies against LDL(-) that do not bind to native LDL, extensively copper-oxidized LDL or malondialdehyde-modified LDL. The characteristics of assay performance, such as limits of detection and quantification, accuracy, inter- and intra-assay precision were evaluated. The linearity, interferences and stability tests were also performed. Results: The calibration range of the assay is 0.625-20.0 mU/L at 1: 2000 sample dilution. ELISA validation showed intra- and inter- assay precision and recovery within the required limits for immunoassays. The limits of detection and quantification were 0.423 mU/L and 0.517 mU/L LDL(-), respectively. The intra- and inter- assay coefficient of variation ranged from 9.5% to 11.5% and from 11.3% to 18.9%, respectively. Recovery of LDL(-) ranged from 92.8% to 105.1%. Conclusions: This ELISA represents a very practical tool for measuring LDL(-) in human blood for widespread research and clinical sample use. Clin Chem Lab Med 2008; 46: 1769-75.

Nonlinear effects generation in suspended core chalcogenide fibre

In this work we report our achievements in the elaboration and optical characterizations of low-losses suspended core optical fibers elaborated from As2S3 glass. For preforms elaboration, alternatively to other processes like the stack and draw or extrusion, we use a process based on mechanical drilling. The drawing of these drilled performs into fibers allows reaching a suspended core geometry, in which a 2 μm diameter core is linked to the fiber clad region by three supporting struts. The different fibers that have been drawn show losses close to 0.9 dB/m at 1.55 μm. The suspended core waveguide geometry has also an efficient influence on the chromatic dispersion and allows its management. Indeed, the zero dispersion wavelength, which is around 5 μm in the bulk glass, is calculated to be shifted towards around 2μm in our suspended core fibers. In order to qualify their nonlinearity we have pumped them at 1.995 μm with the help of a fibered ns source. We have observed a strong non linear response with evidence of spontaneous Raman scattering and strong spectral broadening. © 2011 SPIE.

Transformer Operation at Deep Saturation: Model and Parameter Determination

This paper proposes a model that adequately describes the operation of the transformer at deep saturation, suitable for power-electronics applications, and a method for determining its parameters. Simulation and experimental results are presented to confirm the validity of the model and the method.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

超参数控制下的波阻抗约束反演

The seismic survey is the most effective geophysical method during exploration and development of oil/gas. As a main means in processing and interpreting seismic data, impedance inversion takes up a special position in seismic survey. This is because the impedance parameter is a ligament which connects seismic data with well-logging and geological information, while it is also essential in predicting reservoir properties and sand-body. In fact, the result of traditional impedance inversion is not ideal. This is because the mathematical inverse problem of impedance is poor-pose so that the inverse result has instability and multi-result, so it is necessary to introduce regularization. Most simple regularizations are presented in existent literature, there is a premise that the image(or model) is globally smooth. In fact, as an actual geological model, it not only has made of smooth region but also be separated by the obvious edge, the edge is very important attribute of geological model. It's difficult to preserve these characteristics of the model and to avoid an edge too smooth to clear. Thereby, in this paper, we propose a impedance inverse method controlled by hyperparameters with edge-preserving regularization, the inverse convergence speed and result would be improved. In order to preserve the edge, the potential function of regularization should satisfy nine conditions such as basic assumptions edge preservation and convergence assumptions etc. Eventually, a model with clear background and edge-abnormity can be acquired. The several potential functions and the corresponding weight functions are presented in this paper. The potential functionφLφHL andφGM can meet the need of inverse precision by calculating the models. For the local constant planar and quadric models, we respectively present the neighborhood system of Markov random field corresponding to the regularization term. We linearity nonlinear regularization by using half-quadratic regularization, it not only preserve the edge, and but also simplify the inversion, and can use some linear methods. We introduced two regularization parameters (or hyperparameters) λ2 and δ in the regularization term. λ2 is used to balance the influence between the data term and the transcendental term; δ is a calibrating parameter used to adjust the gradient value at the discontinuous position(or formation interface). Meanwhile, in the inverse procedure, it is important to select the initial value of hyperparameters and to change hyperparameters, these will then have influence on convergence speed and inverse effect. In this paper, we roughly give the initial value of hyperparameters by using a trend- curve of φ-(λ2, δ) and by a method of calculating the upper limit value of hyperparameters. At one time, we change hyperparameters by using a certain coefficient or Maximum Likelihood method, this can be simultaneously fulfilled with the inverse procedure. Actually, we used the Fast Simulated Annealing algorithm in the inverse procedure. This method overcame restrictions from the local extremum without depending on the initial value, and got a global optimal result. Meanwhile, we expound in detail the convergence condition of FSA, the metropolis receiving probability form Metropolis-Hasting, the thermal procession based on the Gibbs sample and other methods integrated with FSA. These content can help us to understand and improve FSA. Through calculating in the theoretic model and applying it to the field data, it is proved that the impedance inverse method in this paper has the advantage of high precision practicability and obvious effect.

Integrated modelling of cost-effective siting and operation of flow-control infrastructure for river ecosystem conservation

Wetland and floodplain ecosystems along many regulated rivers are highly stressed, primarily due to a lack of environmental flows of appropriate magnitude, frequency, duration, and timing to support ecological functions. In the absence of increased environmental flows, the ecological health of river ecosystems can be enhanced by the operation of existing and new flow-control infrastructure (weirs and regulators) to return more natural environmental flow regimes to specific areas. However, determining the optimal investment and operation strategies over time is a complex task due to several factors including the multiple environmental values attached to wetlands, spatial and temporal heterogeneity and dependencies, nonlinearity, and time-dependent decisions. This makes for a very large number of decision variables over a long planning horizon. The focus of this paper is the development of a nonlinear integer programming model that accommodates these complexities. The mathematical objective aims to return the natural flow regime of key components of river ecosystems in terms of flood timing, flood duration, and interflood period. We applied a 2-stage recursive heuristic using tabu search to solve the model and tested it on the entire South Australian River Murray floodplain. We conclude that modern meta-heuristics can be used to solve the very complex nonlinear problems with spatial and temporal dependencies typical of environmental flow allocation in regulated river ecosystems. The model has been used to inform the investment in, and operation of, flow-control infrastructure in the South Australian River Murray.

Some Robust Exact Results on Sample Autocorrelations and Tests of Randomness

Ce Texte Presente Plusieurs Resultats Exacts Sur les Seconds Moments des Autocorrelations Echantillonnales, Pour des Series Gaussiennes Ou Non-Gaussiennes. Nous Donnons D'abord des Formules Generales Pour la Moyenne, la Variance et les Covariances des Autocorrelations Echantillonnales, Dans le Cas Ou les Variables de la Serie Sont Interchangeables. Nous Deduisons de Celles-Ci des Bornes Pour les Variances et les Covariances des Autocorrelations Echantillonnales. Ces Bornes Sont Utilisees Pour Obtenir des Limites Exactes Sur les Points Critiques Lorsqu'on Teste le Caractere Aleatoire D'une Serie Chronologique, Sans Qu'aucune Hypothese Soit Necessaire Sur la Forme de la Distribution Sous-Jacente. Nous Donnons des Formules Exactes et Explicites Pour les Variances et Covariances des Autocorrelations Dans le Cas Ou la Serie Est un Bruit Blanc Gaussien. Nous Montrons Que Ces Resultats Sont Aussi Valides Lorsque la Distribution de la Serie Est Spheriquement Symetrique. Nous Presentons les Resultats D'une Simulation Qui Indiquent Clairement Qu'on Approxime Beaucoup Mieux la Distribution des Autocorrelations Echantillonnales En Normalisant Celles-Ci Avec la Moyenne et la Variance Exactes et En Utilisant la Loi N(0,1) Asymptotique, Plutot Qu'en Employant les Seconds Moments Approximatifs Couramment En Usage. Nous Etudions Aussi les Variances et Covariances Exactes D'autocorrelations Basees Sur les Rangs des Observations.

Heteroscedasticity robust panel unit root tests

This article proposes new unit root tests for panels where the errors may be not only serial and/or crosscorrelated,but also unconditionally heteroscedastic. Despite their generality, the test statistics are shown tobe very simple to implement, requiring only minimal corrections and still the limiting distributions underthe null hypothesis are completely free from nuisance parameters. Monte Carlo evidence is also providedto suggest that the new tests perform well in small samples, also when compared to some of the existingtests. Supplementary materials for this article are available online.

Purchasing power parity and the unit root tests: a robust analysis

Empirical evidence suggests that real exchange rate is characterized by the presence of near-unity and additive outliers. Recent studeis have found evidence on favor PPP reversion by using the quasi-differencing (Elliott et al., 1996) unit root tests (ERS), which is more efficient against local alternatives but is still based on least squares estimation. Unit root tests basead on least saquares method usually tend to bias inference towards stationarity when additive out liers are present. In this paper, we incorporate quasi-differencing into M-estimation to construct a unit root test that is robust not only against near-unity root but also against nonGaussian behavior provoked by assitive outliers. We re-visit the PPP hypothesis and found less evidemce in favor PPP reversion when non-Gaussian behavior in real exchange rates is taken into account.

A virtual species set for robust and reproducible Species Distribution Modelling tests

Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation. In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700.000 random observations at the extent of Europe. The dataset includes spatial attributes such as, distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide spread species.

Influence analysis of robust Wald-type tests

We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum likelihood estimators. An extensive study of their robustness properties is given though the influence functions as well as the chi-square inflation factors. It is theoretically established that the level and power of these robust tests are stable against outliers, whereas the classical Wald test breaks down. Some numerical examples confirm the validity of the theoretical results.