Bayes Linear Variance Learning for Mixed Linear Temporal Models

Modelling of complex corroding industrial systems is ritical to effective inspection and maintenance for ssurance of system integrity. Wall thickness and corrosion rate are modelled for multiple dependent corroding omponents, given observations of minimum wall thickness per component. At each inspection, partial observations of the system are considered. A Bayes Linear approach is adopted simplifying parameter estimation and avoiding often unrealistic distributional assumptions. Key system variances are modelled, making exchangeability assumptions to facilitate analysis for sparse inspection time-series. A utility based criterion is used to assess quality of inspection design and aid decision making. The model is applied to inspection data from pipework networks on a full-scale offshore platform

Analysing the familiar : reasoning about space and time in the everyday world

The development of suitable explicit representations of knowledge that can be manipulated by general purpose inference mechanisms has always been central to Artificial Intelligence (AI). However, there has been a distinct lack of rigorous formalisms in the literature that can be used to model domain knowledge associated with the everyday physical world. If AI is to succeed in building automata that can function reasonably well in unstructured physical domains, the development and utility of such formalisms must be secured. This thesis describes a first order axiomatic theory that can be used to encode much topological and metrical information that arises in our everyday dealings with the physical world. The formalism is notable for the minimal assumptions required in order to lift up a very general framework that can cover the representation of much intuitive spatial and temporal knowledge. The basic ontology assumes regions that can be either spatial or temporal and over which a set of relations and functions are defined. The resulting partitioning of these abstract spaces, allow complex relationships between objects and the description of processes to be formally represented. This also provides a useful foundation to control the proliferation of inference commonly associated with mechanised logics. Empirical information extracted from the domain is added and mapped to these basic structures showing how further control of inference can be secured. The representational power of the formalism and computational tractability of the general methodology proposed is substantiated using two non-trivial domain problems - modelling phagocytosis and exocytosis of uni-cellular organisms, and modelling processes arising during the cycle of operations of a force pump

Locating and quantifying gas emission sources using remotely obtained concentration data

We describe a method for detecting, locating and quantifying sources of gas emissions to the atmosphere using remotely obtained gas concentration data; the method is applicable to gases of environmental concern. We demonstrate its performance using methane data collected from aircraft. Atmospheric point concentration measurements are modelled as the sum of a spatially and temporally smooth atmospheric background concentration, augmented by concentrations due to local sources. We model source emission rates with a Gaussian mixture model and use a Markov random field to represent the atmospheric background concentration component of the measurements. A Gaussian plume atmospheric eddy dispersion model represents gas dispersion between sources and measurement locations. Initial point estimates of background concentrations and source emission rates are obtained using mixed L2-L1 optimisation over a discretised grid of potential source locations. Subsequent reversible jump Markov chain Monte Carlo inference provides estimated values and uncertainties for the number, emission rates and locations of sources unconstrained by a grid. Source area, atmospheric background concentrations and other model parameters are also estimated. We investigate the performance of the approach first using a synthetic problem, then apply the method to real data collected from an aircraft flying over: a 1600 km^2 area containing two landfills, then a 225 km^2 area containing a gas flare stack

The role of pre-existing Precambrian structures in the development of Rukwa Rift Basin, southwest Tanzania

This study has been supported by BG Group Tanzania (now Shell) under the initiative of the University of Dar es Salaam (Tanzania) - University of Aberdeen (United Kingdom) - BG Group Tanzania (now Shell). Tanzania Petroleum Development Corporation (TPDC) provided aeromagnetic data used in this study at no cost. We are grateful to the editors and anonymous reviewers for detailed and constructive reviews that improved the manuscript.Peer reviewedPostprin

Bayes linear analysis for ordinary differential equations

Differential equation models are used in a wide variety of scientific fields to describe the behaviour of physical systems. Commonly, solutions to given systems of differential equations are not available in closed-form; in such situations, the solution to the system is generally approximated numerically. The numerical solution obtained will be systematically different from the (unknown) true solution implicitly defined by the differential equations. Even if it were known, this true solution would be an imperfect representation of the behaviour of the real physical system that it was designed to represent. A Bayesian framework is proposed which handles all sources of numerical and structural uncertainty encountered when using ordinary differential equation (ODE) models to represent real-world processes. The model is represented graphically, and the graph proves to be useful tool, both for deriving a full prior belief specification and for inferring model components given observations of the real system. A general strategy for modelling the numerical discrepancy induced through choice of a particular solver is outlined, in which the variability of the numerical discrepancy is fixed to be proportional to the length of the solver time-step and a grid-refinement strategy is used to study its structure in detail. A Bayes linear adjustment procedure is presented, which uses a junction tree derived from the originally specified directed graphical model to propagate information efficiently between model components, lessening the computational demands associated with the inference. The proposed framework is illustrated through application to two examples: a model for the trajectory of an airborne projectile moving subject to gravity and air resistance, and a model for the coupled motion of a set of ringing bells and the tower which houses them

Numerical techniques for the American put

This dissertation considers an American put option written on a single underlying which does not pay dividends, for which no closed form solution exists. As a conse- quence, numerical techniques have been developed to estimate the value of the Amer- ican put option. These include analytical approximations, tree or lattice methods, ¯nite di®erence methods, Monte Carlo simulation and integral representations. We ¯rst present the mathematical descriptions underlying these numerical techniques. We then provide an examination of a selection of algorithms from each technique, including implementation details, possible enhancements and a description of the convergence behaviour. Finally, we compare the estimates and the execution times of each of the algorithms considered

Heads Up! A Biomechanical Pilot Investigation of Soccer Heading Using Instrumented Mouthguards (iMGs)

Soccer players purposefully head the ball, raising concerns about reduced tolerance to concussion and potential long-term brain health. By combining qualitative video analysis with custom-fit instrumented mouthguards (iMGs), we aimed to categorize header kinematics (peak linear acceleration (PLA) and peak angular acceleration (PAA)) by header type and ball delivery method. iMGs were fitted to 10 male collegiate players for twelve matches. A total of 133 headers were verified and contextualized via video review. The most common header type (38.7%), as well as the preceding ball delivery method (47.4%), was found to be a pass. Approximately one-quarter of header impacts (27.0%) occurred below 10 g. For header type, there were no significant differences in kinematics, with shot attempts having the highest median PLA and PAA. For ball delivery methods, goal kicks had significantly greater PAA than long balls and pass attempts. The current study highlights the utility of qualitative video analysis in combination with real-time head kinematic data from iMGs to understand the mechanism and severity of header impacts. The pilot findings indicate that high-speed ball delivery methods result in higher head kinematics and should be a focus of future mitigation strategies

Hill of Banchory Geothermal Energy Project Feasibility Study Report

This feasibility study explored the potential for a deep geothermal heat project at Hill of Banchory, Aberdeenshire. The geology of the Hill of Fare, to the north of Banchory, gives cause to believe it has good geothermal potential, while the Hill of Banchory heat network, situated on the northern side of the town, offers a ready-made heat customer. The partners in the consortium consisted of academics and developers with relevant expertise in deep geothermal energy, heat networks, and financial analysis, together with representatives of local Government. They conducted geological fieldwork around the Hill of Fare, engaged with local residents to establish their attitudes to geothermal energy, and built business models to predict the conditions under which the heat network at Hill of Banchory would be commercial if it utilised heat from the proposed geothermal well. They also estimated the potential carbon emission reductions that could be achieved by using deep geothermal energy, both at Hill of Banchory and more widely