Event tracking for real-time unaware sensitivity analysis (EventTracker)

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modelling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10% in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5% of that required when using the comparable Entropy based method.EPSR

Neighbourhoods and oral health:Agent-based modelling of tooth decay

This research used proof of concept agent-based models to test various theoretical mechanisms by which neighbourhoods may influence tooth decay in adults. Theoretical pathways were constructed using existing literature and tested in two study areas in Sheffield, UK. The models found a pathway between shops and sugar consumption had the most influence on adult tooth decay scores, revealing that similar mechanisms influence this outcome in different populations. This highlighted the importance of the interactions between neighbourhood features and individual level variables in influencing outcomes in tooth decay. Further work is required to improve the accuracy and reliability of the models

Spacings and pair correlations for finite Bernoulli convolutions

We consider finite Bernoulli convolutions with a parameter $1/2 < r < 1$ supported on a discrete point set, generically of size $2^N$. These sequences are uniformly distributed with respect to the infinite Bernoulli convolution measure $\nu_r$, as $N$ tends to infinity. Numerical evidence suggests that for a generic $r$, the distribution of spacings between appropriately rescaled points is Poissonian. We obtain some partial results in this direction; for instance, we show that, on average, the pair correlations do not exhibit attraction or repulsion in the limit. On the other hand, for certain algebraic $r$ the behavior is totally different.Comment: 17 pages, 6 figure

Oral health, sugary drink consumption and the soft drink industry levy: using spatial microsimulation to understand tooth decay

Spatial microsimulation is a powerful tool for creating large-scale population datasets that can be used to assess spatial phenomena in health-related outcomes. Despite this, it remains underutilized within dental public health. This paper outlines the development of an oral health focused microsimulation model for Sheffield (UK, SimSheffield), and how this can be used to assess potential socio-spatial impacts of a sugar tax which was introduced in the United Kingdom in 2016 and is known as the Soft Drink Industry Levy (SDIL). Exploratory analysis showed areas paying more SDIL were not those with the highest tooth decay or deprivation scores as might be hoped (in the first case) and expected from the literature (in the second)

Probing Bottom-up Processing with Multistable Images

The selection of fixation targets involves a combination of top-down and bottom-up processing. The role of bottom-up processing can be enhanced by using multistable stimuli because their constantly changing appearance seems to depend predominantly on stimulusdriven factors. We used this approach to investigate whether visual processing models based on V1 need to be extended to incorporate specific computations attributed to V4. Eye movements of 8 subjects were recorded during free viewing of the Marroquin pattern in which illusory circles appear and disappear. Fixations were concentrated on features arranged in concentric rings within the pattern. Comparison with simulated fixation data demonstrated that the saliency of these features can be predicted with appropriate weighting of lateral connections in existing V1 models

A dynamical approach to the spatiotemporal aspects of the Portevin-Le Chatelier effect: Chaos,turbulence and band propagation

Experimental time series obtained from single and poly-crystals subjected to a constant strain rate tests report an intriguing dynamical crossover from a low dimensional chaotic state at medium strain rates to an infinite dimensional power law state of stress drops at high strain rates. We present results of an extensive study of all aspects of the PLC effect within the context a model that reproduces this crossover. A study of the distribution of the Lyapunov exponents as a function of strain rate shows that it changes from a small set of positive exponents in the chaotic regime to a dense set of null exponents in the scaling regime. As the latter feature is similar to the GOY shell model for turbulence, we compare our results with the GOY model. Interestingly, the null exponents in our model themselves obey a power law. The configuration of dislocations is visualized through the slow manifold analysis. This shows that while a large proportion of dislocations are in the pinned state in the chaotic regime, most of them are at the threshold of unpinning in the scaling regime. The model qualitatively reproduces the different types of deformation bands seen in experiments. At high strain rates where propagating bands are seen, the model equations are reduced to the Fisher-Kolmogorov equation for propagative fronts. This shows that the velocity of the bands varies linearly with the strain rate and inversely with the dislocation density, consistent with the known experimental results. Thus, this simple dynamical model captures the complex spatio-temporal features of the PLC effect.Comment: 17 pages, 18 figure

Radial Basis Functions Network for Defect Sizing

An important aspect of non-destructive testing is the interpretation and classification of signal obtained by NDT methods such as eddy current and ultrasound. These signals are typically complex, non-stationary waveforms, with signals corresponding to a particular class of defect in a specimen having similar form and shape. However, distortions and noise introduced by the measurement system make the manual classification of these signals a time-consuming and unreliable process, with the results affected by operator fatigue and measurement quality. The design of traditional classifiers for this task also poses many difficulties, due to a number of parameters that influence measurement, and the limited understanding of the effect of these parameters on the signal. Recently, artificial neural networks have been applied to a variety of NDT problems, including signal classification, with encouraging results. Artificial neural networks consist of a dense interconnection of simple computational elements, whose interconnection strengths are determined using a predefined learning algorithm, specific to the network. These networks do not require an explicit mathematical modeling of the data they have to process, and are robust even in the presence of noisy data and data generated by strongly non-linear processes [1]. An example of a neural network that has been extensively used in NDT applications is the multilayer perception. However, the error backpropagation algorithm used for training the multilayer perceptron has several disadvantages, such as long training times and susceptibility to local minima. This paper presents a novel approach to defect sizing that involves the use of a radial basis functions network. The network has the advantages of having shorter training times and a parametric nature that allows network optimization on an analytic basis. The application of such a network in the inversion of ultrasonic data to obtain flaw sizing is described. Results from the sizing of defects in aluminium blocks are presented