Estimation procedures affect the center of pressure frequency analysis

Even though frequency analysis of body sway is widely applied in clinical studies, the lack of standardized procedures concerning power spectrum estimation may provide unreliable descriptors. Stabilometric tests were applied to 35 subjects (20-51 years, 54-95 kg, 1.6-1.9 m) and the power spectral density function was estimated for the anterior-posterior center of pressure time series. The median frequency was compared between power spectra estimated according to signal partitioning, sampling rate, test duration, and detrending methods. The median frequency reliability for different test durations was assessed using the intraclass correlation coefficient. When increasing number of segments, shortening test duration or applying linear detrending, the median frequency values increased significantly up to 137%. Even the shortest test duration provided reliable estimates as observed with the intraclass coefficient (0.74-0.89 confidence interval for a single 20-s test). Clinical assessment of balance may benefit from a standardized protocol for center of pressure spectral analysis that provides an adequate relationship between resolution and variance. An algorithm to estimate center of pressure power density spectrum is also proposed.

Electromagnetic and thermal design of a multilevel converter with high power density and reliability

Electric energy demand has been growing constantly as the global population increases. To avoid electric energy shortage, renewable energy sources and energy conservation are emphasized all over the world. The role of power electronics in energy saving and development of renewable energy systems is significant. Power electronics is applied in wind, solar, fuel cell, and micro turbine energy systems for the energy conversion and control. The use of power electronics introduces an energy saving potential in such applications as motors, lighting, home appliances, and consumer electronics. Despite the advantages of power converters, their penetration into the market requires that they have a set of characteristics such as high reliability and power density, cost effectiveness, and low weight, which are dictated by the emerging applications. In association with the increasing requirements, the design of the power converter is becoming more complicated, and thus, a multidisciplinary approach to the modelling of the converter is required. In this doctoral dissertation, methods and models are developed for the design of a multilevel power converter and the analysis of the related electromagnetic, thermal, and reliability issues. The focus is on the design of the main circuit. The electromagnetic model of the laminated busbar system and the IGBT modules is established with the aim of minimizing the stray inductance of the commutation loops that degrade the converter power capability. The circular busbar system is proposed to achieve equal current sharing among parallel-connected devices and implemented in the non-destructive test set-up. In addition to the electromagnetic model, a thermal model of the laminated busbar system is developed based on a lumped parameter thermal model. The temperature and temperature-dependent power losses of the busbars are estimated by the proposed algorithm. The Joule losses produced by non-sinusoidal currents flowing through the busbars in the converter are estimated taking into account the skin and proximity effects, which have a strong influence on the AC resistance of the busbars. The lifetime estimation algorithm was implemented to investigate the influence of the cooling solution on the reliability of the IGBT modules. As efficient cooling solutions have a low thermal inertia, they cause excessive temperature cycling of the IGBTs. Thus, a reliability analysis is required when selecting the cooling solutions for a particular application. The control of the cooling solution based on the use of a heat flux sensor is proposed to reduce the amplitude of the temperature cycles. The developed methods and models are verified experimentally by a laboratory prototype.

The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purching Power Parity.

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors. By adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by feasible generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. Simulation results suggest that this new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of purchasing power parity (PPP) among the G-7 countries. The test based on the efficient estimates rejects the PPP hypothesis for most countries.

Le progiciel PoweR : un outil de recherche reproductible pour faciliter les calculs de puissance de certains tests d'hypothèses au moyen de simulations de Monte Carlo

Notre progiciel PoweR vise à faciliter l'obtention ou la vérification des études empiriques de puissance pour les tests d'ajustement. En tant que tel, il peut être considéré comme un outil de calcul de recherche reproductible, car il devient très facile à reproduire (ou détecter les erreurs) des résultats de simulation déjà publiés dans la littérature. En utilisant notre progiciel, il devient facile de concevoir de nouvelles études de simulation. Les valeurs critiques et puissances de nombreuses statistiques de tests sous une grande variété de distributions alternatives sont obtenues très rapidement et avec précision en utilisant un C/C++ et R environnement. On peut même compter sur le progiciel snow de R pour le calcul parallèle, en utilisant un processeur multicœur. Les résultats peuvent être affichés en utilisant des tables latex ou des graphiques spécialisés, qui peuvent être incorporés directement dans vos publications. Ce document donne un aperçu des principaux objectifs et les principes de conception ainsi que les stratégies d'adaptation et d'extension.

Learning-Based Approach to Estimation of Morphable Model Parameters

We describe the key role played by partial evaluation in the Supercomputing Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputing Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at MIT, and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.

Estimation of zero-sequence impedance of undergrounds cables for single-phase fault location in distribution systems with electric arc

This paper focus on the problem of locating single-phase faults in mixed distribution electric systems, with overhead lines and underground cables, using voltage and current measurements at the sending-end and sequence model of the network. Since calculating series impedance for underground cables is not as simple as in the case of overhead lines, the paper proposes a methodology to obtain an estimation of zero-sequence impedance of underground cables starting from previous single-faults occurred in the system, in which an electric arc occurred at the fault location. For this reason, the signal is previously pretreated to eliminate its peaks voltage and the analysis can be done working with a signal as close as a sinus wave as possible

A novel parallel approach to the likelihood-based estimation of admixture in population genetics

Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its message passing interface (MPI)-based C implementation are reported.

Computer modelling and estimation of recruitment patterns of non-branching coral colonies at three sites in Wakatobi Marine Park, S.E. Sulawesi, Indonesia; implications for coral reef conservation

We have studied growth and estimated recruitment of massive coral colonies at three sites, Kaledupa, Hoga and Sampela, separated by about 1.5 km in the Wakatobi Marine National Park, S.E. Sulawesi, Indonesia. There was significantly higher species richness (P<0.05), coral cover (P<0.05) and rugosity (P<0.01) at Kaledupa than at Sampela. A model for coral reef growth has been developed based on a rational polynomial function, where dx/dt is an index of coral growth with time; W is the variable (for example, coral weight, coral length or coral area), up to the power of n in the numerator and m in the denominator; a1……an and b1…bm are constants. The values for n and m represent the degree of the polynomial, and can relate to the morphology of the coral. The model was used to simulate typical coral growth curves, and tested using published data obtained by weighing coral colonies underwater in reefs on the south-west coast of Curaçao [‘Neth. J. Sea Res. 10 (1976) 285’]. The model proved an accurate fit to the data, and parameters were obtained for a number of coral species. Surface area data was obtained on over 1200 massive corals at three different sites in the Wakatobi Marine National Park, S.E. Sulawesi, Indonesia. The year of an individual's recruitment was calculated from knowledge of the growth rate modified by application of the rational polynomial model. The estimated pattern of recruitment was variable, with little numbers of massive corals settling and growing before 1950 at the heavily used site, Sampela, relative to the reef site with little or no human use, Kaledupa, and the intermediate site, Hoga. There was a significantly greater sedimentation rate at Sampela than at either Kaledupa (P<0.0001) or Hoga (P<0.0005). The relative mean abundance of fish families present at the reef crests at the three sites, determined using digital video photography, did not correlate with sedimentation rates, underwater visibility or lack of large non-branching coral colonies. Radial growth rates of three genera of non-branching corals were significantly lower at Sampela than at Kaledupa or at Hoga, and there was a high correlation (r=0.89) between radial growth rates and underwater visibility. Porites spp. was the most abundant coral over all the sites and at all depths followed by Favites (P<0.04) and Favia spp. (P<0.03). Colony ages of Porites corals were significantly lower at the 5 m reef flat on the Sampela reef than at the same depth on both other reefs (P<0.005). At Sampela, only 2.8% of corals on the 5 m reef crest are of a size to have survived from before 1950. The Scleractinian coral community of Sampela is severely impacted by depositing sediments which can lead to the suffocation of corals, whilst also decreasing light penetration resulting in decreased growth and calcification rates. The net loss of material from Sampela, if not checked, could result in the loss of this protective barrier which would be to the detriment of the sublittoral sand flats and hence the Sampela village.

Improved channel estimation in TDMA by reproduction with applications in GSM/GPRS under co-channel interference

Finding an estimate of the channel impulse response (CIR) by correlating a received known (training) sequence with the sent training sequence is commonplace. Where required, it is also common to truncate the longer correlation to a sub-set of correlation coefficients by finding the set of N sequential correlation coefficients with the maximum power. This paper presents a new approach to selecting the optimal set of N CIR coefficients from the correlation rather than relying on power. The algorithm reconstructs a set of predicted symbols using the training sequence and various sub-sets of the correlation to find the sub-set that results in the minimum mean squared error between the actual received symbols and the reconstructed symbols. The application of the algorithm is presented in the context of the TDMA based GSM/GPRS system to demonstrate an improvement in the system performance with the new algorithm and the results are presented in the paper. However, the application lends itself to any training sequence based communication system often found within wireless consumer electronic device(1).

Estimation of distances in virtual environments using size constancy

It is reported in the literature that distances from the observer are underestimated more in virtual environments (VEs) than in physical world conditions. On the other hand estimation of size in VEs is quite accurate and follows a size-constancy law when rich cues are present. This study investigates how estimation of distance in a CAVETM environment is affected by poor and rich cue conditions, subject experience, and environmental learning when the position of the objects is estimated using an experimental paradigm that exploits size constancy. A group of 18 healthy participants was asked to move a virtual sphere controlled using the wand joystick to the position where they thought a previously-displayed virtual cube (stimulus) had appeared. Real-size physical models of the virtual objects were also presented to the participants as a reference of real physical distance during the trials. An accurate estimation of distance implied that the participants assessed the relative size of sphere and cube correctly. The cube appeared at depths between 0.6 m and 3 m, measured along the depth direction of the CAVE. The task was carried out in two environments: a poor cue one with limited background cues, and a rich cue one with textured background surfaces. It was found that distances were underestimated in both poor and rich cue conditions, with greater underestimation in the poor cue environment. The analysis also indicated that factors such as subject experience and environmental learning were not influential. However, least square fitting of Stevens’ power law indicated a high degree of accuracy during the estimation of object locations. This accuracy was higher than in other studies which were not based on a size-estimation paradigm. Thus as indirect result, this study appears to show that accuracy when estimating egocentric distances may be increased using an experimental method that provides information on the relative size of the objects used.

Neural networks for power systems alarm handling

A multi-layered architecture of self-organizing neural networks is being developed as part of an intelligent alarm processor to analyse a stream of power grid fault messages and provide a suggested diagnosis of the fault location. Feedback concerning the accuracy of the diagnosis is provided by an object-oriented grid simulator which acts as an external supervisor to the learning system. The utilization of artificial neural networks within this environment should result in a powerful generic alarm processor which will not require extensive training by a human expert to produce accurate results.

A Wiener model for memory high power amplifiers using B-spline function approximation

In this paper we introduce a new Wiener system modeling approach for memory high power amplifiers in communication systems using observational input/output data. By assuming that the nonlinearity in the Wiener model is mainly dependent on the input signal amplitude, the complex valued nonlinear static function is represented by two real valued B-spline curves, one for the amplitude distortion and another for the phase shift, respectively. The Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first order derivatives recursion. An illustrative example is utilized to demonstrate the efficacy of the proposed approach.

Use of the ratio plot in capture-recapture estimation

Statistical graphics are a fundamental, yet often overlooked, set of components in the repertoire of data analytic tools. Graphs are quick and efficient, yet simple instruments of preliminary exploration of a dataset to understand its structure and to provide insight into influential aspects of inference such as departures from assumptions and latent patterns. In this paper, we present and assess a graphical device for choosing a method for estimating population size in capture-recapture studies of closed populations. The basic concept is derived from a homogeneous Poisson distribution where the ratios of neighboring Poisson probabilities multiplied by the value of the larger neighbor count are constant. This property extends to the zero-truncated Poisson distribution which is of fundamental importance in capture–recapture studies. In practice however, this distributional property is often violated. The graphical device developed here, the ratio plot, can be used for assessing specific departures from a Poisson distribution. For example, simple contaminations of an otherwise homogeneous Poisson model can be easily detected and a robust estimator for the population size can be suggested. Several robust estimators are developed and a simulation study is provided to give some guidance on which should be used in practice. More systematic departures can also easily be detected using the ratio plot. In this paper, the focus is on Gamma mixtures of the Poisson distribution which leads to a linear pattern (called structured heterogeneity) in the ratio plot. More generally, the paper shows that the ratio plot is monotone for arbitrary mixtures of power series densities.

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-toaverage power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA’s nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.