Series-connected IGBTs using active voltage control technique

With series insulated-gate bipolar transistor (IGBT) operation, well-matched gate drives will not ensure balanced dynamic voltage sharing between the switching devices. Rather, it is IGBT parasitic capacitances, mainly gate-to-collector capacitance Cgc, that dominate transient voltage sharing. As Cgc is collector voltage dependant and is significantly larger during the initial turn-off transition, it dominates IGBT dynamic voltage sharing. This paper presents an active control technique for series-connected IGBTs that allows their dynamic voltage transition dV\ce/dt to adaptively vary. Both switch ON and OFF transitions are controlled to follow a predefined dVce/dt. Switching losses associated with this technique are minimized by the adaptive dv /dt control technique incorporated into the design. A detailed description of the control circuits is presented in this paper. Experimental results with up to three series devices in a single-ended dc chopper circuit, operating at various low voltage and current levels, are used to illustrate the performance of the proposed technique. © 2012 IEEE.

IGBT series connection under Active Voltage Control with temporary clamp

This paper presents the use of an Active Voltage Control (AVC) technique for balancing the voltages in a series connection of Insulated Gate Bipolar Transistors (IGBTs). The AVC technique can control the switching trajectory of an IGBT according to a pre-set reference signal. In series connections, every series connected IGBT follows the reference and so that the dynamic voltage sharing is achieved. For the static voltage balancing, a temporary clamp technique is introduced. The temporary clamp technique clamps the collector-emitter voltage of all the series connected IGBTs at the ideal voltage so that the IGBTs will share the voltage evenly. © 2012 IEEE.

Two problems with variational expectation maximisation for time-series models

Variational methods are a key component of the approximate inference and learning toolbox. These methods fill an important middle ground, retaining distributional information about uncertainty in latent variables, unlike maximum a posteriori methods (MAP), and yet generally requiring less computational time than Monte Carlo Markov Chain methods. In particular the variational Expectation Maximisation (vEM) and variational Bayes algorithms, both involving variational optimisation of a free-energy, are widely used in time-series modelling. Here, we investigate the success of vEM in simple probabilistic time-series models. First we consider the inference step of vEM, and show that a consequence of the well-known compactness property of variational inference is a failure to propagate uncertainty in time, thus limiting the usefulness of the retained distributional information. In particular, the uncertainty may appear to be smallest precisely when the approximation is poorest. Second, we consider parameter learning and analytically reveal systematic biases in the parameters found by vEM. Surprisingly, simpler variational approximations (such a mean-field) can lead to less bias than more complicated structured approximations.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

Durability and photo-electric characteristics of a mille-feuille structured amorphous selenium (a-Se)-arsenic selenide (As2Se3) multi-layered thin film

A mille-feuille structured amorphous selenium (a-Se)-arsenic selenide (As2Se3) multi-layered thin film and a mixed amorphous Se-As2Se3 film is compared from a durability perspective and photo-electric perspective. The former is durable to incident laser induced degradation after numerous laser scans and does not crystallise till 105 of annealing, both of which are improved properties from the mixed evaporated film. In terms of photo-electric properties, the ratio between the photocurrent and the dark current improved whereas the increase of the dark current was higher than that of As2Se3 due to the unique current path developed within the mille-feuille structure. Implementing this structure into various amorphous semiconductors may open up a new possibility towards structure-sensitive amorphous photoconductors. © 2013 Elsevier B.V.

Flow field measurements of a series of turbulent premixed and stratified methane/air flames

This paper presents flow field measurements for the turbulent stratified burner introduced in two previous publications in which high resolution scalar measurements were made by Sweeney et al. [1,2] for model validation. The flow fields of the series of premixed and stratified methane/air flames are investigated under turbulent, globally lean conditions (φg=0.75). Velocity data acquired with laser Doppler anemometry (LDA) and particle image velocimetry (PIV) are presented and discussed. Pairwise 2-component LDA measurements provide profiles of axial velocity, radial velocity, tangential velocity and corresponding fluctuating velocities. The LDA measurements of axial and tangential velocities enable the swirl number to be evaluated and the degree of swirl characterized. Power spectral density and autocorrelation functions derived from the LDA data acquired at 10kHz are optimized to calculate the integral time scales. Flow patterns are obtained using a 2-component PIV system operated at 7Hz. Velocity profiles and spatial correlations derived from the PIV and LDA measurements are shown to be in very good agreement, thus offering 3D mapping of the velocities. A strong correlation was observed between the shape of the recirculation zones above the central bluff body and the effects of heat release, stoichiometry and swirl. Detailed analyses of the LDA data further demonstrate that the flow behavior changes significantly with the levels of swirl and stratification, which combines the contributions of dilatation, recirculation and swirl. Key turbulence parameters are derived from the total velocity components, combining axial, radial and tangential velocities. © 2013 The Combustion Institute.

An analysis of competing toughening mechanisms in layered and particulate solids

The relative potency of common toughening mechanisms is explored for layered solids and particulate solids, with an emphasis on crack multiplication and plasticity. First, the enhancement in toughness due to a parallel array of cracks in an elastic solid is explored, and the stability of co-operative cracking is quantified. Second, the degree of synergistic toughening is determined for combined crack penetration and crack kinking at the tip of a macroscopic, mode I crack; specifically, the asymptotic problem of self-similar crack advance (penetration mode) versus 90 ° symmetric kinking is considered for an isotropic, homogeneous solid with weak interfaces. Each interface is treated as a cohesive zone of finite strength and toughness. Third, the degree of toughening associated with crack multiplication is assessed for a particulate solid comprising isotropic elastic grains of hexagonal shape, bonded by cohesive zones of finite strength and toughness. The study concludes with the prediction of R-curves for a mode I crack in a multi-layer stack of elastic and elastic-plastic solids. A detailed comparison of the potency of the above mechanisms and their practical application are given. In broad terms, crack tip kinking can be highly potent, whereas multiple cracking is difficult to activate under quasi-static conditions. Plastic dissipation can give a significant toughening in multi-layers especially at the nanoscale. © 2013 Springer Science+Business Media Dordrecht.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.

In situ intercalation dynamics in inorganic-organic layered perovskite thin films.

The properties of layered inorganic semiconductors can be manipulated by the insertion of foreign molecular species via a process known as intercalation. In the present study, we investigate the phenomenon of organic moiety (R-NH3I) intercalation in layered metal-halide (PbI2)-based inorganic semiconductors, leading to the formation of inorganic-organic (IO) perovskites [(R-NH3)2PbI4]. During this intercalation strong resonant exciton optical transitions are created, enabling study of the dynamics of this process. Simultaneous in situ photoluminescence (PL) and transmission measurements are used to track the structural and exciton evolution. On the basis of the experimental observations, a model is proposed which explains the process of IO perovskite formation during intercalation of the organic moiety through the inorganic semiconductor layers. The interplay between precursor film thickness and organic solution concentration/solvent highlights the role of van der Waals interactions between the layers, as well as the need for maintaining stoichiometry during intercalation. Nucleation and growth occurring during intercalation matches a Johnson-Mehl-Avrami-Kolmogorov model, with results fitting both ideal and nonideal cases.