Performance of a Boundary Layer Ingesting (BLI) propulsion system

This paper presents an assessment of the performance of an embedded propulsion system in the presence of distortion associated with boundary layer ingestion. For fan pressure ratios of interest for civil transports, the benefits of boundary layer ingestion are shown to be very sensitive to the magnitude of fan and duct losses. The distortion transfer across the fan, basically the comparison of the stagnation pressure non-uniformity downstream of the fan to that upstream of the fan, has a major role in determining the impact of boundary layer ingestion on overall fuel burn. This, in turn, puts requirements on the fidelity with which one needs to assess the distortion transfer, and thus the type of models that need to be used in such assessment. For the three-dimensional distortions associated with fuselage boundary layers ingested into a subsonic diffusing inlet, it is found that boundary layer ingestion can provide decreases in fuel burn of several per cent. It is also shown that a promising avenue for mitigating the risks (aerodynamic as well as aeromechanical) in boundary layer ingestion is to mix out the flow before it reaches the engine face.

Modelling within-host spatiotemporal dynamics of invasive bacterial disease

Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS]) in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics. © 2008 Grant et al.

Probabilistic graphical models for semi-supervised traffic classification

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.

Streaming covariance selection with applications to adaptive querying in sensor networks

Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

Development of selection methods for steels and their heat treatments

Designers who want to manufacture a hardenable steel component need to select both the steel and its heat treatment. This project aims to develop a selection methodology for steels and process routes as an aid to designers. Three studies were conducted: - production of software to calculate the "equivalent diameter" and "equivalent Jominy distance" for simple shapes of a steel component; - prediction of semi-empirical Jominy curves (as-cooled) using CCT diagrams and process modelling methods, which were validated by experiment on plain carbon steels; - investigation of tempering of Jominy bars to explore the potential for semi-empirical models for the hardness after tempering.

Human muscle spindles act as forward sensory models.

Modern theories of motor control incorporate forward models that combine sensory information and motor commands to predict future sensory states. Such models circumvent unavoidable neural delays associated with on-line feedback control. Here we show that signals in human muscle spindle afferents during unconstrained wrist and finger movements predict future kinematic states of their parent muscle. Specifically, we show that the discharges of type Ia afferents are best correlated with the velocity of length changes in their parent muscles approximately 100-160 ms in the future and that their discharges vary depending on motor sequences in a way that cannot be explained by the state of their parent muscle alone. We therefore conclude that muscle spindles can act as "forward sensory models": they are affected both by the current state of their parent muscle and by efferent (fusimotor) control, and their discharges represent future kinematic states. If this conjecture is correct, then sensorimotor learning implies learning how to control not only the skeletal muscles but also the fusimotor system.

Cross-Lingual spoken language understanding from unaligned data using discriminative classification models and machine translation

This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.

Design and process selection for joints in flexible materials

The application of high performance textiles has grown significantly in the last 10 to 15 years. Various research groups throughout the United Kingdom, such as the Department of Trade and Industry, have identified technical textiles as a field for future development. There is little design guidance for joining of flexible materials or general property models that can be applied to theses materials. This lack is due to the large diversity of properties, structures and resulting behaviours of the materials that are classified as "Flexible Materials". This dissertation explores the issues that are involved in characterising the materials at the fibre, bulk and textile levels. Different units of measurement are used for each stage of the manufacturing process of flexible materials and this disparity creates problems when trying to make general comparisons (e.g. comparing textiles to polymer films). Thus, a possible solution to this is to create selection charts that allow designers to compare the strength of materials for a given mass per unit area. A design tool was created using the Cambridge Engineering Selector (CES) software to enable the selection of joining processes for material. The tool is effective in selecting a reduced number of viable joining processes. Through case studies it was shown that designers are required to examine the selected processes (identified by the software) in greater detail - in particular the economics and geometry of the joint - in order to identify the optimum joining process.

Perceptual thresholds for acoustical guitar models

Synthesised acoustic guitar sounds based on a detailed physical model are used to provide input for psychoacoustical testing. Thresholds of perception are found for changes in the main parameters of the model. Using a three-alternative forced-choice procedure, just-noticeable differences are presented for changes in frequency and damping of the modes of the guitar body, and also for changes in the tension, bending stiffness and damping parameters of the strings. These are compared with measured data on the range of variation of these parameters in a selection of guitars. © S. Hirzel Verlag © EAA.