Moderate energy impact analysis combining phenomenological contact law with localised damage and integral equation method

A computational impact analysis methodology has been developed, based on modal analysis and a local contact force-deflection model. The contact law is based on Hertz contact theory while contact stresses are elastic, defines a modified contact theory to take account of local permanent indentation, and considers elastic recovery during unloading. The model was validated experimentally through impact testing of glass-carbon hybrid braided composite panels. Specimens were mounted in a support frame and the contact force was inferred from the deceleration of the impactor, measured by high-speed photography. A Finite Element analysis of the panel and support frame assembly was performed to compute the modal responses. The new contact model performed well in predicting the peak forces and impact durations for moderate energy impacts (15 J), where contact stresses locally exceed the linear elastic limit and damage may be deemed to have occurred. C-scan measurements revealed substantial damage for impact energies in the range of 30-50 J. For this regime the new model predictions might be improved by characterisation of the contact law hysteresis during the unloading phase, and a modification of the elastic vibration response in line with damage levels acquired during the impact. © 2011 Elsevier Ltd. All rights reserved.

Enhancement of Construction Equipment Detection in Video Frames by Combining with Tracking

Vision-based object detection has been introduced in construction for recognizing and locating construction entities in on-site camera views. It can provide spatial locations of a large number of entities, which is beneficial in large-scale, congested construction sites. However, even a few false detections prevent its practical applications. In resolving this issue, this paper presents a novel hybrid method for locating construction equipment that fuses the function of detection and tracking algorithms. This method detects construction equipment in the video view by taking advantage of entities' motion, shape, and color distribution. Background subtraction, Haar-like features, and eigen-images are used for motion, shape, and color information, respectively. A tracking algorithm steps in the process to make up for the false detections. False detections are identified by catching drastic changes in object size and appearance. The identified false detections are replaced with tracking results. Preliminary experiments show that the combination with tracking has the potential to enhance the detection performance.

Combining information sources for confidence estimation with CRF models

Obtaining accurate confidence measures for automatic speech recognition (ASR) transcriptions is an important task which stands to benefit from the use of multiple information sources. This paper investigates the application of conditional random field (CRF) models as a principled technique for combining multiple features from such sources. A novel method for combining suitably defined features is presented, allowing for confidence annotation using lattice-based features of hypotheses other than the lattice 1-best. The resulting framework is applied to different stages of a state-of-the-art large vocabulary speech recognition pipeline, and consistent improvements are shown over a sophisticated baseline system. Copyright © 2011 ISCA.

Achieving robust face recognition from video by combining a weak photometric model and a learnt generic face invariant

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. The central contribution is an illumination invariant, which we show to be suitable for recognition from video of loosely constrained head motion. In particular there are three contributions: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation to exploit the proposed invariant and generalize in the presence of extreme illumination changes; (ii) we introduce a video sequence re-illumination algorithm to achieve fine alignment of two video sequences; and (iii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve robustness to unseen head poses. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 323 individuals and 1474 video sequences with extreme illumination, pose and head motion variation. Our system consistently achieved a nearly perfect recognition rate (over 99.7% on all four databases). © 2012 Elsevier Ltd All rights reserved.

Increased gas production from hydrates by combining depressurization with heating of the wellbore

To extract gas from hydrate reservoirs, it has to be dissociated in situ. This endothermic dissociation process absorbs heat energy from the formation and pore fluid. The heat transfer governs the dissociation rate, which is proportional to the difference between the actual temperature and the equilibrium temperature. This study compares three potential gas production schemes from hydrate-bearing soil, where the radial heat transfer is governing. Cylindrical samples with 40% pore-filling hydrate saturation were tested. The production tests were carried out over 90 min by dissociating the hydrate from a centered miniature wellbore, by either lowering the pressure to 6, 4, or 6 MPa with simultaneous heating of the wellbore to 288 K. All tests were replicated by a numerical simulation. With additional heating at the same wellbore pressure, the gas production from hydrates could, on average, be increased by 1.8 and 3.6 times in the simulation and experiments, respectively. If the heat influx from the outer boundary is limited, a simulation showed that the specific heat of the formation is rapidly used up when the wellbore is only depressurized and not heated. © 2012 American Chemical Society.

Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

Magnetic shielding properties of high- Tc superconducting hollow cylinders: Model combining experimental data for axial and transverse magnetic field configurations

Magnetic shielding efficiency was measured on high- Tc superconducting hollow cylinders subjected to either an axial or a transverse magnetic field in a large range of field sweep rates, dBapp/dt. The behaviour of the superconductor was modelled in order to reproduce the main features of the field penetration curves by using a minimum number of free parameters suitable for both magnetic field orientations. The field penetration measurements were carried out on Pb-doped Bi-2223 tubes at 77K by applying linearly increasing magnetic fields with a constant sweep rate ranging between 10νTs-1 and 10mTs-1 for both directions of the applied magnetic field. The experimental curves of the internal field versus the applied field, Bin(Bapp), show that, at a given sweep rate, the magnetic field for which the penetration occurs, Blim, is lower for the transverse configuration than for the axial configuration. A power law dependence with large exponent, n′, is found between Blim and dBapp/dt. The values of n′ are nearly the same for both configurations. We show that the main features of the curves B in(Bapp) can be reproduced using a simple 2D model, based on the method of Brandt, involving a E(J) power law with an n-exponent and a field-dependent critical current density, Jc(B), (following the Kim model: Jc = Jc0(1+B/B1)-1). In particular, a linear relationship between the measured n′-exponents and the n-exponent of the E(J) power law is suggested by taking into account the field dependence of the critical current density. Differences between the axial and the transverse shielding properties can be simply attributed to demagnetizing fields. © 2009 IOP Publishing Ltd.

Mechanics of capillary forming of aligned carbon nanotube assemblies.

Elastocapillary self-assembly is emerging as a versatile technique to manufacture three-dimensional (3D) microstructures and complex surface textures from arrangements of micro- and nanoscale filaments. Understanding the mechanics of capillary self-assembly is essential to engineering of properties such as shape-directed actuation, anisotropic wetting and adhesion, and mechanical energy transfer and dissipation. We study elastocapillary self-assembly (herein called "capillary forming") of carbon nanotube (CNT) microstructures, combining in situ optical imaging, micromechanical testing, and finite element modeling. By imaging, we identify sequential stages of liquid infiltration, evaporation, and solid shrinkage, whose kinetics relate to the size and shape of the CNT microstructure. We couple these observations with measurements of the orthotropic elastic moduli of CNT forests to understand how the dynamic of shrinkage of the vapor-liquid interface is coupled to the compression of the forest. We compare the kinetics of shrinkage to the rate of evporation from liquid droplets having the same size and geometry. Moreover, we show that the amount of shrinkage during evaporation is governed by the ability of the CNTs to slip against one another, which can be manipulated by the deposition of thin conformal coatings on the CNTs by atomic layer deposition (ALD). This insight is confirmed by finite element modeling of pairs of CNTs as corrugated beams in contact and highlights the coupled role of elasticity and friction in shrinkage and stability of nanoporous solids. Overall, this study shows that nanoscale porosity can be tailored via the filament density and adhesion at contact points, which is important to the development of lightweight multifunctional materials.