Second-order parameter estimation

This work provides a general framework for the design of second-order blind estimators without adopting anyapproximation about the observation statistics or the a prioridistribution of the parameters. The proposed solution is obtainedminimizing the estimator variance subject to some constraints onthe estimator bias. The resulting optimal estimator is found todepend on the observation fourth-order moments that can be calculatedanalytically from the known signal model. Unfortunately,in most cases, the performance of this estimator is severely limitedby the residual bias inherent to nonlinear estimation problems.To overcome this limitation, the second-order minimum varianceunbiased estimator is deduced from the general solution by assumingaccurate prior information on the vector of parameters.This small-error approximation is adopted to design iterativeestimators or trackers. It is shown that the associated varianceconstitutes the lower bound for the variance of any unbiasedestimator based on the sample covariance matrix.The paper formulation is then applied to track the angle-of-arrival(AoA) of multiple digitally-modulated sources by means ofa uniform linear array. The optimal second-order tracker is comparedwith the classical maximum likelihood (ML) blind methodsthat are shown to be quadratic in the observed data as well. Simulationshave confirmed that the discrete nature of the transmittedsymbols can be exploited to improve considerably the discriminationof near sources in medium-to-high SNR scenarios.

Comparison of electrical nerve stimulation, electrical muscle stimulation and magnetic nerve stimulation to assess the neuromuscular function of the plantar flexor muscles.

INTRODUCTION: As it might lead to less discomfort, magnetic nerve stimulation (MNS) is increasingly used as an alternative to electrical stimulation methods. Yet, MNS and electrical nerve stimulation (ENS) and electrical muscle stimulation (EMS) have not been formally compared for the evaluation of plantar flexor neuromuscular function. METHODS: We quantified plantar flexor neuromuscular function with ENS, EMS and MNS in 10 volunteers in fresh and fatigued muscles. Central alterations were assessed through changes in voluntary activation level (VAL) and peripheral function through changes in M-wave, twitch and doublet (PS100) amplitudes. Discomfort associated with 100-Hz paired stimuli delivered with each method was evaluated on a 10-cm visual analog scale. RESULTS: VAL, agonist and antagonist M-wave amplitudes and PS100 were similar between the different methods in both fresh and fatigued states. Potentiated peak twitch was lower in EMS compared to ENS, whereas no difference was found between ENS and MNS for any parameter. Discomfort associated with MNS (1.5 ± 1.4 cm) was significantly less compared to ENS (5.5 ± 1.9 cm) and EMS (4.2 ± 2.6 cm) (p < 0.05). CONCLUSION: When PS100 is used to evaluate neuromuscular properties, MNS, EMS and ENS can be used interchangeably for plantar flexor neuromuscular function assessment as they provide similar evaluation of central and peripheral factors in unfatigued and fatigued states. Importantly, electrical current spread to antagonist muscles was similar between the three methods while discomfort from MNS was much less compared to ENS and EMS. MNS may be potentially employed to assess neuromuscular function of plantar flexor muscles in fragile populations.

Probabilistic electrical resistivity tomography of a CO2 sequestration analog

Electrical resistivity tomography (ERT) is a well-established method for geophysical characterization and has shown potential for monitoring geologic CO2 sequestration, due to its sensitivity to electrical resistivity contrasts generated by liquid/gas saturation variability. In contrast to deterministic inversion approaches, probabilistic inversion provides the full posterior probability density function of the saturation field and accounts for the uncertainties inherent in the petrophysical parameters relating the resistivity to saturation. In this study, the data are from benchtop ERT experiments conducted during gas injection into a quasi-2D brine-saturated sand chamber with a packing that mimics a simple anticlinal geological reservoir. The saturation fields are estimated by Markov chain Monte Carlo inversion of the measured data and compared to independent saturation measurements from light transmission through the chamber. Different model parameterizations are evaluated in terms of the recovered saturation and petrophysical parameter values. The saturation field is parameterized (1) in Cartesian coordinates, (2) by means of its discrete cosine transform coefficients, and (3) by fixed saturation values in structural elements whose shape and location is assumed known or represented by an arbitrary Gaussian Bell structure. Results show that the estimated saturation fields are in overall agreement with saturations measured by light transmission, but differ strongly in terms of parameter estimates, parameter uncertainties and computational intensity. Discretization in the frequency domain (as in the discrete cosine transform parameterization) provides more accurate models at a lower computational cost compared to spatially discretized (Cartesian) models. A priori knowledge about the expected geologic structures allows for non-discretized model descriptions with markedly reduced degrees of freedom. Constraining the solutions to the known injected gas volume improved estimates of saturation and parameter values of the petrophysical relationship. (C) 2014 Elsevier B.V. All rights reserved.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines

The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.

Pseudolikelihood equations for Potts MRF model parameter estimation on higher order neighborhood systems

This letter presents pseudolikelihood equations for the estimation of the Potts Markov random field model parameter on higher order neighborhood systems. The derived equation for second-order systems is a significantly reduced version of a recent result found in the literature (from 67 to 22 terms). Also, with the proposed method, a completely original equation for Potts model parameter estimation in third-order systems was obtained. These equations allow the modeling of less restrictive contextual systems for a large number of applications in a computationally feasible way. Experiments with both simulated and real remote sensing images provided good results.

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Optical and electrical properties of polymerizing plasmas and their correlation with DLC film properties

Diamond-like carbon (DLC) films were grown from radiofrequency plasmas of acetylene-argon mixtures, at different excitation powers, P. The effects of this parameter on the plasma potential, electron density, electron temperature, and plasma activity were investigated using a Langmuir probe. The mean electron temperature increased from about 0.5 to about 7.0 eV while the mean electron density decreased from about 1.2x10(9) to about 0.2x10(9) cm(-3) as P was increased from 25 to 150 W. Both the plasma potential and the plasma activity were found to increase with increasing P. Through actinometric optical emission spectrometry, the relative concentrations of CH, [CH], and H, [H], in the discharge were mapped as a function of the applied power. A rise in [H] and a fall in [CH] with increasing P were observed and are discussed in relation to the plasma characteristics and the subimplantation model. The optical properties of the films were calculated from ultraviolet-visible spectroscopic data; the surface resistivity was measured by the two-point probe method. The optical gap, E(G), and the surface resistivity, rho(s), fall with increasing P. E(G) and rho(s) are in the ranges of about 2.0-1.3 eV and 10(14)-10(16) Omega/square, respectively. The plasma power also influences the film self-bias, V(b), via a linear dependence, and the effect of V(b) on ion bombardment during growth is addressed together with variation in the relative densities of sp(2) and sp(3) bonds in the films as determined by Raman spectroscopy.

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.

A generalized theory of electrical characteristics of schottky barriers for amorphous materials

In the present paper, we discuss a generalized theory of electrical characteristics for amorphous semiconductor (or insulator) Schottky barriers, considering: (i) surface states, (ii) doping impurity states at a single energy level and (iii) energetically distributed bulk impurity states. We also consider a thin oxide layer (≈10 Å) between metal and semiconductor. We develop current versus applied potential characteristics considering the variation of the Fermi level very close to contact inside the semiconductor and decrease in barrier height due to the image force effect as well as potential fall on the oxide layer. Finally, we discuss the importance of each parameter, i.e. surface states, distributed impurity states, doping impurity states, thickness of oxide layer etc. on the log I versus applied potential characteristics. The present theory is also applicable for intimate contact, i.e. metal-semiconductor contact, crystalline material structures or for Schottky barriers in insulators or polymers.

Urbach energy parameter of flash evaporated amorphous gallium arsenide films

The dependence of the optical absorption edge on the deposition crucible temperature is used to investigate the electronic states in As-rich a-GaAs flash evaporated films. The Urbach energy parameter, determined from photothermal deflection spectroscopy (PDS), presents large correlated variations with crucible temperature. The optical and electrical results are consistent with the As under coordinated sites being the more important defect in the material. © 2002 Elsevier Science B.V. All rights reserved.

A simplified method for determining the high frequency induction motor equivalent electrical circuit parameters to be used in EMI effect

The aim of this paper is to present a simple method for determining the high frequency parameters of a three-phase induction motor to be used in studies involving variable speed drives with PWM three-phase inverters, in which it is necessary to check the effects caused to the motor by the electromagnetic interference, (EMI) in the differential mode, as well as in the common mode. The motor parameters determination is generally performed in adequate laboratories using accurate instruments, such as very expensive RLC bridges. The method proposed here consists in the identification of the motor equivalent electrical circuit parameters in rated frequency and in high frequency through characteristic tests in the laboratory, together with the use of characteristic equations and curves, shown in the references to be mentioned for determining the motor high frequency parasite capacitances and also through system simulations using dedicated software, like Pspice, determining the characteristic waveforms involved in the differential and common mode phenomena, comparing and validating the procedure through published papers [01].

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

Contribution of electrical parameters on the dynamical behaviour of a nonlinear electromagnetic damper

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

Theorem for series in three-parameter mittag-leffler function

The new result presented here is a theorem involving series in the three-parameter Mittag-Le er function. As a by-product, we recover some known results and discuss corollaries. As an application, we obtain the solution of a fractional di erential equation associated with a RLC electrical circuit in a closed form, in terms of the two-parameter Mittag-Le er function.