Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. ©2010 IEEE.

Individual grain boundary properties and overall performance of metal-organic-deposition coated conductors

We have investigated single grain boundaries (GBs) isolated in coated conductors produced by Metal-Organic Deposition (MOD). When a magnetic field is swept in the film plane, an angle-dependent crossover from boundary to grain limited critical current density Jc is found. In the force-free orientation, even at fields as high as 8 T, the GBs still limit Jc. We deduce that this effect is a direct consequence of GB meandering. We have employed these single GB results to explain the dependence of Jc of polycrystalline tracks on their width: in-plane measurements become flatter as the tracks are narrowed down. This result is consistent with the stronger GB limitation at field configurations close to force-free found from the isolated boundaries. Our study shows that for certain geometries even at high fields the effect of GBs cannot be neglected.

Model based learning of sigma points in unscented Kalman filtering

The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L. © 2011 Elsevier B.V.

Phototransistors utilizing individual WS2 nanotubes

We report on the photoresponse characteristics of tungsten disulfide (WS2) nanotubes. Field effect transistors (FETs) were fabricated by using individual WS2 multiwall nanotubes. Photo-sensitivity to visible light is clearly observed, with enhancement of the channel conductivity, carrier mobility and carrier concentration upon illumination in the visible regime. Polarization-sensitive measurements reveal a strong anisotropy of the photocurrent on the polarization angle of the incident light with respect to the WS2 nanotube axis. This nano-scale transistor capable of detecting visible light would have a wide range of applications in medical and consumer electronics. © 2008 IEEE. Crown Copyright.

SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.

The horizontal response of framed buildings on individual footings to excavation-induced movements

Deep excavations and tunnelling can cause ground movements that affect buildings within their influence zone. The current approach for building damage assessment is based on tensile strains estimated from the deflection ratio and the horizontal strains at the building foundation. This paper examines the significance of horizontal strains in buildings on individual footings. The first part of the paper presents a case study of a framed building in Singapore which was subjected to the effects of bored tunnelling, where significant horizontal strains were observed. The second part of the paper suggests a method to relate the horizontal strains induced in a building to the stiffness of the frame structure. Using a combination of simplified structural analysis and finite element models, design guidance is proposed to estimate excavation-induced horizontal strains in frame buildings on individual footings. © 2012 Taylor & Francis Group.