Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Standing in front of bulldozers? Explaining policy stability in land use planning

Policy towards planning presents scholars of politics and public policy with a significant puzzle. Since 1947, there has been a surprising level of stability in the system used to plan the use of land. On the other hand, there has been growing evidence that insufficient land has been released for development. The paper considers the question why, in spite of the planning system demonstrably failing to allocate sufficient land, fundamental reform of the system has not been achieved. In answering the question, the paper considers in particular attempts at reform under the Labour governments from 1997 to 2010. It argues that there is an interplay of interests, ideas and institutions: public attitudes, the interests of certain sections of the population, and institutions which are responsive to these attitudes and interests combined to stymie policy reform. As a consequence, radical reform was not achieved, and the paper concludes that attempt to find a technical “fix” to the planning system are unlikely to succeed. A diagnosis recognising the political and distributive nature of the problem will be required.

Creating and sustaining competitive advantage in collaborative systems:the what and the how

Initially this paper asks two questions: In order to create and sustain competitive advantage through collaborative systems WHAT should be managed? and HOW should it be managed? It introduces the competitive business structure and reviews some of the global trends in manufacturing and business, which leads to focus on manage processes, value propositions and extended business processes. It then goes on to develop a model of the collaborative architecture for extended enterprises and demonstrates the validity of this architecture through a case study. It concludes that, in order to create and sustain competitive advantage, collaborative systems should facilitate the management of: the collaborative architecture of the extended enterprise; the extended business processes and the value proposition for each extended enterprise through a meta level management process. It also identifies areas for further research, such as better understanding of: the exact nature and interaction of multiple strategies within an enterprise; how to manage people/teams working along extended business processes; and the nature and prerequisites of the manage processes.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Simulation of guiding-centre soliton transmission system stability in the presence of polarisation mode dispersion

Results of full numerical simulations of a guiding-centre soliton system with randomly birefringent SMF fibre are shown and analysed. It emerges that the soliton system becomes unstable even for small amounts of PMD.

Symptoms of hyperactivity and inattention can mediate deficits of postural stability in developmental dyslexia

DDevelopmental dyslexia is a reading disorder associated with impaired postural control. However, such deficits are also found in attention deficit hyperactivity disorder (ADHD), which is present in a substantial subset of dyslexia diagnoses. Very few studies of balance in dyslexia have assessed ADHD symptoms, thereby motivating the hypothesis that such measures can account for the group differences observed. In this study, we assessed adults with dyslexia and similarly aged controls on a battery of cognitive, literacy and attention measures, alongside tasks of postural stability. Displacements of centre of mass to perturbations of posture were measured in four experimental conditions using digital optical motion capture. The largest group differences were obtained in conditions where cues to the support surface were reduced. Between-group differences in postural sway and in sway variability were largely accounted for by co-varying hyperactivity and inattention ratings, however. These results therefore suggest that postural instability in dyslexia is more strongly associated with symptoms of ADHD than to those specific to reading impairment.

Reliability:its implications in production systems design

Research on production systems design has in recent years tended to concentrate on ‘software’ factors such as organisational aspects, work design, and the planning of the production operations. In contrast, relatively little attention has been paid to maximising the contributions made by fixed assets, particularly machines and equipment. However, as the cost of unproductive machine time has increased, reliability, particularly of machine tools, has become ever more important. Reliability theory and research has traditionally been based in the main on electrical and electronic equipment whereas mechanical devices, especially machine tools, have not received sufficiently objective treatment. A recently completed research project has considered the reliability of machine tools by taking sample surveys of purchasers, maintainers and manufacturers. Breakdown data were also collected from a number of engineering companies and analysed using both manual and computer techniques. Results obtained have provided an indication of those factors most likely to influence reliability and which in turn could lead to improved design and selection of machine tool systems. Statistical analysis of long-term field data has revealed patterns of trends of failure which could help in the design of more meaningful maintenance schemes.