Probabilistic fault identification using vibration data and neural networks

Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.

Complex wavelets and shift invariance

Recently we have developed a new form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. This introduces limited redundancy (2 m:1 for m-dimensional signals) and allows the transform to provide approximate shift invariance and directionally selective filters (properties lacking in the traditional wavelet transform) while preserving the usual properties of perfect reconstruction and computational efficiency with good well-balanced frequency responses. In this paper we analyse why the new transform can be designed to be shift invariant, and describe how to estimate the accuracy of this approximation and design suitable filters to achieve this.

Feasibility of damage detection using vibration data in a population of cylinders

The feasibility of vibration data to identify damage in a population of cylindrical shells is assessed. Vibration data from a population of cylinders were measured and modal analysis was employed to obtain natural frequencies and mode shapes. The mode shapes were transformed into the Coordinate Modal Assurance Criterion (COMAC). The natural frequencies and the COMAC before and after damage for a population of structures show that modal analysis is a viable route to damage identification in a population of nominally identical cylinders. Modal energies, which are defined as the integrals of the real and imaginary components of the frequency response functions over various frequency ranges, were extracted and transformed into the Coordinate Modal Energy Assurance Criterion (COMEAC). The COMEAC before and after damage show that using modal energies is a viable approach to damage identification in a population of cylinders.

Sparse recovery of complex phase-encoded velocity images using iterative thresholding

In this paper we propose a new algorithm for reconstructing phase-encoded velocity images of catalytic reactors from undersampled NMR acquisitions. Previous work on this application has employed total variation and nonlinear conjugate gradients which, although promising, yields unsatisfactory, unphysical visual results. Our approach leverages prior knowledge about the piecewise-smoothness of the phase map and physical constraints imposed by the system under study. We show how iteratively regularizing the real and imaginary parts of the acquired complex image separately in a shift-invariant wavelet domain works to produce a piecewise-smooth velocity map, in general. Using appropriately defined metrics we demonstrate higher fidelity to the ground truth and physical system constraints than previous methods for this specific application. © 2013 IEEE.

Step synchronization and third person speed perception in virtual environment locomotion simulators

Human locomotion is known to be influenced by observation of another person's gait. For example, athletes often synchronize their step in long distance races. However, how interaction with a virtual runner affects the gait of a real runner has not been studied. We investigated this by creating an illusion of running behind a virtual model (VM) using a treadmill and large screen virtual environment showing a video of a VM. We looked at step synchronization between the real and virtual runner and at the role of the step frequency (SF) in the real runner's perception of VM speed. We found that subjects match VM SF when asked to match VM speed with their own (Figure 1). This indicates step synchronization may be a strategy of speed matching or speed perception. Subjects chose higher speeds when VMSF was higher (though VM was 12km/h in all videos). This effect was more pronounced when the speed estimate was rated verbally while standing still. (Figure 2). This may due to correlated physical activity affecting the perception of VM speed [Jacobs et al. 2005]; or step synchronization altering the subjects' perception of self speed [Durgin et al. 2007]. Our findings indicate that third person activity in a collaborative virtual locomotive environment can have a pronounced effect on an observer's gait activity and their perceptual judgments of the activity of others: the SF of others (virtual or real) can potentially influence one's perception of self speed and lead to changes in speed and SF. A better understanding of the underlying mechanisms would support the design of more compelling virtual trainers and may be instructive for competitive athletics in the real world. © 2009 ACM.

How much do we spend to save? Calculating the embodied carbon costs of retrofit

The drive to reduce carbon emissions from domestic housing has led to a recent shift of focus from new-­‐build to retrofit. However there are two significant differences. Firstly more work is needed to retrofit existing housing to the same energy efficiency standards as new-­‐build. Secondly the remaining length of service life is potentially shorter. This implies that the capital expenditure – both financial and carbon -­‐ of retrofit may be disproportionate to the savings gained over the remaining life. However the Government’s definition of low and zero carbon continues to exclude the capital (embodied) carbon costs of construction, which has resulted in a lack of data for comparison. The paper addresses this gap by reporting the embodied carbon costs of retrofitting four individual pilot properties in Rampton Drift, part of an Eco-­‐Town Demonstrator Project in Cambridgeshire. Through collecting details of the materials used and their journeys from manufacturer to site, the paper conducts a ‘cradle-­‐to-­‐gate’ life cycle carbon assessment for each property. The embodied carbon figures are calculated using a software tool being developed by the Centre for Sustainable Development at the University of Cambridge. The key aims are to assess the real embodied carbon costs of retrofit of domestic properties, and to test the new tool; it is hoped that the methodology, the tool and the specific findings will be transferable to other projects. Initial changes in operational energy as a result of the retrofit works will be reported and compared with the embodied carbon costs when presenting this paper.

A new distance for scale-invariant 3D shape recognition and registration

This paper presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transforms for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a real and challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2011 IEEE.

Scale-invariant vote-based 3D recognition and registration from point clouds

This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this spacethe SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach. © 2013 Springer-Verlag Berlin Heidelberg.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.