Investigation into IGBT dV/dt during turn-off and its temperature dependence

In many power converter applications, particularly those with high variable loads, such as traction and wind power, condition monitoring of the power semiconductor devices in the converter is considered desirable. Monitoring the device junction temperature in such converters is an essential part of this process. In this paper, a method for measuring the insulated gate bipolar transistor (IGBT) junction temperature using the collector voltage dV/dt at turn-OFF is outlined. A theoretical closed-form expression for the dV/dt at turn-OFF is derived, closely agreeing with experimental measurements. The role of dV/dt in dynamic avalanche in high-voltage IGBTs is also discussed. Finally, the implications of the temperature dependence of the dV/dt are discussed, including implementation of such a temperature measurement technique. © 2006 IEEE.

Photo-assisted hydrosilylation of silicon nanoparticles: Dependence of particle size on grafting chemistry

Silicon nanoparticles between 2.5 nm and 30 nm in diameter were functionalized by means of photoassisted hydrosilylation reactions in the aerosol phase with terminal alkenes of varying chain length. Using infrared spectroscopy and nuclear magnetic resonance, the chemical composition of the alkyl layer was determined for each combination of particle size and alkyl chain length. The spectroscopic techniques were used to determine that smaller particles functionalized with short chains in the aerosol phase tend to attach to the interior (β) alkenyl carbon atom, whereas particles >10 nm in diameter exhibit attachment primarily with the exterior (α) alkenyl carbon atom, regardless of chain length. © 2011 American Chemical Society.

Sensor graphs for guaranteed cooperative localization performance

A group of mobile robots can localize cooperatively, using relative position and absolute orientation measurements, fused through an extended Kalman filter (ekf). The topology of the graph of relative measurements is known to affect the steady-state value of the position error covariance matrix. Classes of sensor graphs are identified, for which tight bounds for the trace of the covariance matrix can be obtained based on the algebraic properties of the underlying relative measurement graph. The string and the star graph topologies are considered, and the explicit form of the eigenvalues of error covariance matrix is given. More general sensor graph topologies are considered as combinations of the string and star topologies, when additional edges are added. It is demonstrated how the addition of edges increases the trace of the steady-state value of the position error covariance matrix, and the theoretical predictions are verified through simulation analysis.

The most persistent soft-clique in a set of sampled graphs

When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques. We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations. Copyright 2012 by the author(s)/owner(s).

Random function priors for exchangeable arrays with applications to graphs and relational data

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Concentration dependence of the Er3+ visible and infrared luminescence in Y2-x Erx O3 thin films on Si

Y2-x Erx O3 thin films, with x varying between 0 and 0.72, have been successfully grown on crystalline silicon (c-Si) substrates by radio-frequency magnetron cosputtering of Y2 O 3 and Er2 O3 targets. As-deposited films are polycrystalline, showing the body-centered cubic structure of Y2 O3, and show only a slight lattice parameter contraction when x is increased, owing to the insertion of Er ions. All the films exhibit intense Er-related optical emission at room temperature both in the visible and infrared regions. By studying the optical properties for different excitation conditions and for different Er contents, all the mechanisms (i.e., cross relaxations, up-conversions, and energy transfers to impurities) responsible for the photoluminescence (PL) emission have been identified, and the existence of two different well-defined Er concentration regimes has been demonstrated. In the low concentration regime (x up to 0.05, Er-doped regime), the visible PL emission reaches its highest intensity, owing to the influence of up-conversions, thus giving the possibility of using Y2-x Er x O3 films as an up-converting layer in the rear of silicon solar cells. However, most of the excited Er ions populate the first two excited levels 4I11/2 and 4I13/2, and above a certain excitation flux a population inversion condition between the former and the latter is achieved, opening the route for the realization of amplifiers at 2.75 μm. Instead, in the high concentration regime (Er-compound regime), an increase in the nonradiative decay rates is observed, owing to the occurrence of cross relaxations or energy transfers to impurities. As a consequence, the PL emission at 1.54 μm becomes the most intense, thus determining possible applications for Y2-x Erx O 3 as an infrared emitting material. © 2009 American Institute of Physics.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

Beyond 2D-grids: A dependence maximization view on image browsing

Ideally, one would like to perform image search using an intuitive and friendly approach. Many existing image search engines, however, present users with sets of images arranged in some default order on the screen, typically the relevance to a query, only. While this certainly has its advantages, arguably, a more flexible and intuitive way would be to sort images into arbitrary structures such as grids, hierarchies, or spheres so that images that are visually or semantically alike are placed together. This paper focuses on designing such a navigation system for image browsers. This is a challenging task because arbitrary layout structure makes it difficult - if not impossible - to compute cross-similarities between images and structure coordinates, the main ingredient of traditional layouting approaches. For this reason, we resort to a recently developed machine learning technique: kernelized sorting. It is a general technique for matching pairs of objects from different domains without requiring cross-domain similarity measures and hence elegantly allows sorting images into arbitrary structures. Moreover, we extend it so that some images can be preselected for instance forming the tip of the hierarchy allowing to subsequently navigate through the search results in the lower levels in an intuitive way. Copyright 2010 ACM.

Model reductions for inference: generality of pairwise, binary, and planar factor graphs.

We offer a solution to the problem of efficiently translating algorithms between different types of discrete statistical model. We investigate the expressive power of three classes of model-those with binary variables, with pairwise factors, and with planar topology-as well as their four intersections. We formalize a notion of "simple reduction" for the problem of inferring marginal probabilities and consider whether it is possible to "simply reduce" marginal inference from general discrete factor graphs to factor graphs in each of these seven subclasses. We characterize the reducibility of each class, showing in particular that the class of binary pairwise factor graphs is able to simply reduce only positive models. We also exhibit a continuous "spectral reduction" based on polynomial interpolation, which overcomes this limitation. Experiments assess the performance of standard approximate inference algorithms on the outputs of our reductions.

Anisotropic behaviour in the magnetic field dependence of the low temperature electrical resistance of calcium-doped lanthanum manganate thin films grown by RF magnetron sputtering

We report about the magnetoresistive properties of calcium-doped lanthanum manganate thin films grown by RF magnetron sputtering on single crystalline LaAlO3 and MgO substrates. Two orientations of the magnetic field with respect to the electrical current have been studied: (i) magnetic field in the plane of the film and parallel to the electrical current, and (ii) magnetic field perpendicular to the plane of the film. The film grown on LaAlO 3 is characterised by an unusual magnetoresistive behaviour when the magnetic field is applied perpendicular to the film plane: the appearance of two bumps in the field dependence of the resistance is shown to be related to the occurrence of anisotropic magnetoresistive effects in manganate films. © 2004 Elsevier B.V. All rights reserved.