Electromagnetic-thermal design optimization of the brushless doubly fed induction generator

In view of its special features, the brushless doubly fed induction generator (BDFIG) shows high potentials to be employed as a variable-speed drive or wind generator. However, the machine suffers from low efficiency and power factor and also high level of noise and vibration due to spatial harmonics. These harmonics arise mainly from rotor winding configuration, slotting effects, and saturation. In this paper, analytical equations are derived for spatial harmonics and their effects on leakage flux, additional loss, noise, and vibration. Using the derived equations and an electromagnetic-thermal model, a simple design procedure is presented, while the design variables are selected based on sensitivity analyses. A multiobjective optimization method using an imperialist competitive algorithm as the solver is established to maximize efficiency, power factor, and power-to-weight ratio, as well as to reduce rotor spatial harmonic distortion and voltage regulation simultaneously. Several constraints on dimensions, magnetic flux densities, temperatures, vibration level, and converter voltage and rating are imposed to ensure feasibility of the designed machine. The results show a significant improvement in the objective function. Finally, the analytical results of the optimized structure are validated using finite-element method and are compared to the experimental results of the D180 frame size prototype BDFIG. © 1982-2012 IEEE.

Performance analysis and testing of a 250 kW medium-speed brushless doubly-fed induction generator

This study presents the performance analysis and testing of a 250 kW medium-speed brushless doubly-fed induction generator (DFIG), and its associated power electronics and control systems. The experimental tests confirm the design, and showthe system's steady-state and dynamic performance and grid low-voltage ride- through capability. The medium-speed brushless DFIG in combination with a simplified two-stage gearbox promises a low-cost low-maintenance and reliable drivetrain for wind turbine applications. © The Institution of Engineering and Technology 2013.

Computational analysis of liquid crystalline elastomer membranes: Changing Gaussian curvature without stretch energy

Liquid crystalline elastomers (LCEs) can undergo extremely large reversible shape changes when exposed to external stimuli, such as mechanical deformations, heating or illumination. The deformation of LCEs result from a combination of directional reorientation of the nematic director and entropic elasticity. In this paper, we study the energetics of initially flat, thin LCE membranes by stress driven reorientation of the nematic director. The energy functional used in the variational formulation includes contributions depending on the deformation gradient and the second gradient of the deformation. The deformation gradient models the in-plane stretching of the membrane. The second gradient regularises the non-convex membrane energy functional so that infinitely fine in-plane microstructures and infinitely fine out-of-plane membrane wrinkling are penalised. For a specific example, our computational results show that a non-developable surface can be generated from an initially flat sheet at cost of only energy terms resulting from the second gradients. That is, Gaussian curvature can be generated in LCE membranes without the cost of stretch energy in contrast to conventional materials. © 2013 Elsevier Ltd. All rights reserved.

Free energy surface reconstruction from umbrella samples using Gaussian process regression

We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By making a prior assumption of smoothness and taking account of the sampling noise in a consistent fashion, we achieve a significant improvement in accuracy over the state of the art in two or more dimensions or, equivalently, a significant cost reduction to obtain the free energy surface within a prescribed tolerance in both regimes of spatially sparse data and short sampling trajectories. Stemming from its Bayesian interpretation the method provides meaningful error bars without significant additional computation. A software implementation is made available on www.libatoms.org.

A novel modeling approach for design studies of brushless doubly fed induction generator based on magnetic equivalent circuit

Brushless doubly fed induction generator (BDFIG) has substantial benefits, which make it an attractive alternative as a wind turbine generator. However, it suffers from lower efficiency and larger dimensions in comparison to DFIG. Hence, optimizing the BDFIG structure is necessary for enhancing its situation commercially. In previous studies, a simple model has been used in BDFIG design procedure that is insufficiently accurate. Furthermore, magnetic saturation and iron loss are not considered because of difficulties in determination of flux density distributions. The aim of this paper is to establish an accurate yet computationally fast model suitable for BDFIG design studies. The proposed approach combines three equivalent circuits including electric, magnetic and thermal models. Utilizing electric equivalent circuit makes it possible to apply static form of magnetic equivalent circuit, because the elapsed time to reach steady-state results in the dynamic form is too long for using in population-based design studies. The operating characteristics, which are necessary for evaluating the objective function and constraints values of the optimization problem, can be calculated using the presented approach considering iron loss, saturation, and geometrical details. The simulation results of a D-180 prototype BDFIG are compared with measured data in order to validate the developed model. © 1986-2012 IEEE.

Gaussian process volatility model

The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to over-fitting. To address these problems we introduce GP-Vol, a novel non-parametric model for time-changing variances based on Gaussian Processes. This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce a new online algorithm for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly better than current standard alternatives.

Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Calibrating the Gaussian multi-target tracking model

We present novel batch and online (sequential) versions of the expectation-maximisation (EM) algorithm for inferring the static parameters of a multiple target tracking (MTT) model. Online EM is of particular interest as it is a more practical method for long data sets since in batch EM, or a full Bayesian approach, a complete browse of the data is required between successive parameter updates. Online EM is also suited to MTT applications that demand real-time processing of the data. Performance is assessed in numerical examples using simulated data for various scenarios. For batch estimation our method significantly outperforms an existing gradient based maximum likelihood technique, which we show to be significantly biased. © 2014 Springer Science+Business Media New York.

Symmetrical 2-D hermite-gaussian square launch for high bit rate transmission in multimode fiber links

A 2-D Hermite-Gaussian square launch is demonstrated to show improved systems capacity over multimode fiber links. It shows a bandwidth improvement over both center and offset launches and exhibits ±5 ìm misalignment tolerance. © OSA/OFC/NFOEC 2011.

Avoiding pathologies in very deep networks

Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

Fault tolerant control using Gaussian processes and model predictive control

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control. © 2013 IEEE.

First principles interatomic potential for tungsten based on Gaussian process regression

An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However, the computational complexity associated with modern schemes explicitly based on quantum mechanics limits their applications to systems of a few hundreds of atoms at most. This thesis investigates the application of the Gaussian Approximation Potential (GAP) scheme to atomistic modelling of tungsten - a bcc transition metal which exhibits a brittle-to-ductile transition and whose plasticity behaviour is controlled by the properties of $\frac{1}{2} \langle 111 \rangle$ screw dislocations. We apply Gaussian process regression to interpolate the quantum-mechanical (QM) potential energy surface from a set of points in atomic configuration space. Our training data is based on QM information that is computed directly using density functional theory (DFT). To perform the fitting, we represent atomic environments using a set of rotationally, permutationally and reflection invariant parameters which act as the independent variables in our equations of non-parametric, non-linear regression. We develop a protocol for generating GAP models capable of describing lattice defects in metals by building a series of interatomic potentials for tungsten. We then demonstrate that a GAP potential based on a Smooth Overlap of Atomic Positions (SOAP) covariance function provides a description of the $\frac{1}{2} \langle 111 \rangle$ screw dislocation that is in agreement with the DFT model. We use this potential to simulate the mobility of $\frac{1}{2} \langle 111 \rangle$ screw dislocations by computing the Peierls barrier and model dislocation-vacancy interactions to QM accuracy in a system containing more than 100,000 atoms.

Gaussian processes for POMDP-based dialogue manager optimization

A partially observable Markov decision process (POMDP) has been proposed as a dialog model that enables automatic optimization of the dialog policy and provides robustness to speech understanding errors. Various approximations allow such a model to be used for building real-world dialog systems. However, they require a large number of dialogs to train the dialog policy and hence they typically rely on the availability of a user simulator. They also require significant designer effort to hand-craft the policy representation. We investigate the use of Gaussian processes (GPs) in policy modeling to overcome these problems. We show that GP policy optimization can be implemented for a real world POMDP dialog manager, and in particular: 1) we examine different formulations of a GP policy to minimize variability in the learning process; 2) we find that the use of GP increases the learning rate by an order of magnitude thereby allowing learning by direct interaction with human users; and 3) we demonstrate that designer effort can be substantially reduced by basing the policy directly on the full belief space thereby avoiding ad hoc feature space modeling. Overall, the GP approach represents an important step forward towards fully automatic dialog policy optimization in real world systems. © 2013 IEEE.

20 Gb/s mode-group-division multiplexing employing Hermite-Gaussian launches over worst-case multimode fiber links

For the first time, mode group division multiplexing is achieved in a multimode fiber link using a 2-D Hermite-Gaussian mode launch. 20 Gb/s error-free transmission is achieved over a 250 m worst-case OM1 multimode fiber link. © OSA 2014.

Effects of the "all-fish" growth hormone transgene expression on non-specific immune functions of common carp, Cyprinus carpio L.

This study investigated non-specific immune functions of the F-2 generation of "all-fish" growth hormone transgenic carp, Cyprinus carpio L. Lysozyme activity was 145.0 (+/- 30.7) U ml(-1) in the transgenic fish serum and 105.0 (+/- 38.7) U ml(-1) in age-matched non-transgenic control fish serum, a significant difference (P < 0.01). The serum bactericidal activity in the transgenics was significantly higher than that in the controls (P < 0.05), with the percentage serum killing of 59.5% (6.83%) and 50.8% (8.67%), respectively. Values for leukocrit and phagocytic percent of macrophages in head kidney were higher in transgenics than controls (P < 0.05). However, the phagocytic indices in the transgenics and the controls were not different. In addition, the mean body weight of the transgenics was 63.4 (6.65) g, much higher than that of the controls [39.2 (+/- 3.30) g, P < 0.01]. The absolute weight of spleen of the transgenics [0.13 (+/- 0.03) g] was higher than that of the controls [0.08 (+/- 0.02) g, P < 0.01]. However, there was no difference in the relative weight of spleen between the transgenics and the controls, with the spleen mass index being 0.21% (+/- 0.02%) and 0.20% (+/- 0.03%), respectively. This study suggests that the "all-fish" growth hormone transgene expression could stimulate not only the growth but also the non-specific immune functions of carp. (c) 2006 Published by Elsevier B.V.