Gaussian Process training with input noise

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.

Outage probability of the gaussian MIMO free-space optical channel with PPM

Atmospheric effects can significantly degrade the reliability of free-space optical communications. One such effect is scintillation, caused by atmospheric turbulence, refers to random fluctuations in the irradiance and phase of the received laser beam. In this paper we inv stigate the use of multiple lasers and multiple apertures to mitigate scintillation. Since the scintillation process is slow, we adopt a block fading channel model and study the outage probability under the assumptions of orthogonal pulse-position modulation and non-ideal photodetection. Assuming perfect receiver channel state information (CSI), we derive the signal-to-noise ratio (SNR) exponents for the cases when the scintillation is lognormal, exponential and gammagamma distributed, which cover a wide range of atmospheric turbulence conditions. Furthermore, when CSI is also available at the transmitter, we illustrate very large gains in SNR are possible (in some cases larger than 15 dB) by adapting the transmitted power. Under a long-term power constraint, we outline fundamental design criteria via a simple expression that relates the required number of lasers and apertures for a given code rate and number of codeword blocks to completely remove system outages. Copyright © 2009 IEEE.

State-space inference and learning with Gaussian processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors.

Symmetrical low voltage ride-through of the Brushless Doubly-Fed Induction Generator

The Brushless Doubly-Fed Induction Generator (BDFIG) shows commercial promise for wind power generation due to its lower cost and higher reliability compared to the Doubly-Fed Induction Generator (DFIG). For the purposes of commercialisation, the BDFIG must meet grid codes at all times. Nowadays, all new wind generators have to ride through certain grid faults, and the Low-Voltage Ride Through (LVRT) capability has become one of the most important points on which to assess the performance a generator. This paper, for the first time, proposes a control scheme to enable the the BDFIG to ride through symmetrical voltage dips. Simulation results and experimental results on a prototype BDFIG show that the proposed scheme gives the capability to ride through low voltage faults. © 2011 IEEE.

700V smart trench IGBT with monolithic over-voltage and over-current protecting functions

An advanced 700V Smart Trench IGBT with monolithically integrated over-voltage and over-current protecting circuits is presented in this paper. The proposed Smart IGBT comprises a sense IGBT, a low voltage lateral n-channel MOSFET (M 1), an avalanche diode (D av), and poly-crystalline Zener diodes (ZD) and resistor (R poly). Mix-mode transient simulations with MEDICI have proven the functionalities of the protecting circuits when the device is operating under abnormal conditions, such as Unclamped Inductive Switching (UIS) and Short Circuit (SC) condition. A Trench IGBT process is used to fabricate this device with total 11 masks including one metal mask only. The characterizations of the fabricated device exhibit the clamping capability of the avalanche diode and voltage pull-down ability of the MOSFET. © 2012 IEEE.

The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.

A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models

In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application. © 2012 IFAC.

Linear gaussian computations for near-exact Bayesian Monte Carlo inference in skewed alpha-stable time series models

In this paper we study parameter estimation for time series with asymmetric α-stable innovations. The proposed methods use a Poisson sum series representation (PSSR) for the asymmetric α-stable noise to express the process in a conditionally Gaussian framework. That allows us to implement Bayesian parameter estimation using Markov chain Monte Carlo (MCMC) methods. We further enhance the series representation by introducing a novel approximation of the series residual terms in which we are able to characterise the mean and variance of the approximation. Simulations illustrate the proposed framework applied to linear time series, estimating the model parameter values and model order P for an autoregressive (AR(P)) model driven by asymmetric α-stable innovations. © 2012 IEEE.

Sensor data scheduling for linear quadratic Gaussian control with full state feedback

Networked control systems (NCSs) have attracted much attention in the past decade due to their many advantages and growing number of applications. Different than classic control systems, resources in NCSs, such as network bandwidth and communication energy, are often limited, which degrade the closed-loop system performance and may even cause the system to become unstable. Seeking a desired trade-off between the closed-loop system performance and the limited resources is thus one heated area of research. In this paper, we analyze the trade-off between the sensor-to-controller communication rate and the closed-loop system performance indexed by the conventional LQG control cost. We present and compare several sensor data schedules, and demonstrate that two event-based sensor data schedules provide better trade-off than an optimal offline schedule. Simulation examples are provided to illustrate the theories developed in the paper. © 2012 AACC American Automatic Control Council).

Droplet Generator

A method and device for periodically perturbing the flow field within a microfluidic device to provide regular droplet formation at high speed.

Bayesian non-parametrics and the probabilistic approach to modelling.

Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.

Comparison of bleed and micro-vortex generator effects on supersonic boundary-layers

An experimental study of bleed and vortex generators in supersonic ow has been conducted. Methods were developed to analyze and directly compare the two systems' effects on turbulent boundary layers to better understand their potential to mitigate ow separation. LDA was used to measure two components of velocity in the boundary-layer for three cases|baseline, with bleed, or with a VG|at Mach numbers of 1.3, 1.5 and 1.8. The bleed system was comprised of a series of 2mm diameter normal holes operated at different suction rates, removing up to 10% of the incoming boundary layer. Three VG shapes were tested only at Mach 1.5 and 1.8. Measurements of the evolution of Hi and Cf downstream of each device indicate that Hi is not an appropriate parameter to gauge the effectiveness of vortex generators due to boundary layer wake distortion. The skin friction coeficient Cf may be a more appropriate measure. Similar increases in Cf were generated by VGs and bleed. The recovery to baseline conditions downstream of bleed was sensitive to Mach number, and more investigation of that effect will be required. Copyright © 2012 by University of Cambridge.