Browsing by Author "McNaught, K."
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Item Open Access Developing a Decision Analytic Framework Based on Influence Diagrams in Relation to Mass Evacuations(2013-07-03) Zagorecki, A.; McNaught, K.In this paper, we examine the role which decision analysis can play in a situation requiring a mass evacuation. In particular, we focus on the influence diagram as a tool for reasoning and supporting decision-makers under conditions of risk and uncertainty. This powerful modelling tool can help to bridge multiple specialist domains and provide a common framework for supporting decision-makers in different agencies. An influence diagram is also referred to as a decision network and can be considered as an extension of a Bayesian network. Like a Bayesian network, it contains chance nodes which represent random variables and deterministic nodes which represent deterministic functions of input variables. However, in addition, an influence diagram contains decision nodes which represent decisions under local control and utility nodes which can represent a variety of costs and benefits. These might be measured in several dimensions including casualties and monetary units. Advantages of Bayesian networks and influence diagrams over more traditional risk and safety modelling approaches such as event trees and fault trees are discussed - in particular, the ease with which they represent dependencies between many factors and the different types of reasoning supported at the same time, e.g. predictive reasoning and diagnostic reasoning. An illustrative, generic influence diagram is presented of a situation corresponding to a CBRNE attack. We then consider how this generic model can be applied to a more specific scenario such as an attack at a sporting event. A variety of potential uses of the model are identified and discussed, along with problems which are likely to be encountered in model development. We argue that this modelling approach provides a useful framework to support cost-effectiveness studies and high-level trade-offs between alternative possible security measures and other resources impacting on response and recovery operations.Item Open Access Development of an intelligent system for operator support during wireless infrastructure system testing(2009-12-02T16:36:46Z) Chan, A. K. W.; McNaught, K.Intense competition and the requirement to continually drive down costs within a mature mobile telephone infrastructure market calls for new and innovative solutions to process improvement. One particular challenge is to improve the quality and reliability of the diagnostic process for systems testing of GSM and UMTS products. In this thesis, we concentrate on a particularly important equipment type – the Base Transceiver Station (BTS). The BTS manages the radio channels and transfers signalling information to and from mobile stations (i.e. mobile phones). Most of the diagnostic processes are manually operated and rely heavily on individual operators and technicians' knowledge for their performance. Hence, there is a high cost associated with trouble-shooting in terms of time and manpower. To address this issue, we employ Bayesian networks (BNs) to model the domain knowledge that comprises the operations of the System Under Test (SUT), Automated Test Equipment (ATE) and the diagnostic skill of experienced engineers, in an attempt to enhance the efficiency and reliability of the diagnostic process. The proposed automated diagnostic tool (known as Wisdom) consists of several modules. An intelligent user interface will provide possible solutions to test operators / technicians; to capture their responses, and to activate the automated test programme. Server and client software architecture will be used to integrate Wisdom with the ATE seamlessly and to maintain Wisdom as an independent module. A local area network will provide the infrastructure for managing and deploying the multimedia and text information in real time. We describe how a diagnostic model can be developed and implemented using a Bayesian network approach. We also describe how the resulting process of diagnosis following failure, advice generation and subsequent actions by the operator are handled interactively by the prototype system.Item Open Access Prognostic Modelling with Dynamic Bayesian Networks(2009-11-04T00:00:00Z) McNaught, K.; Zagorecki, A.In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not possible and then how a simple change to the model allows us to represent different maintenance policies for repairable equipmenItem Open Access Seabed Protection Systems to prevent Scour from High-Speed Ships(2010-11-04) Evans, G.; Jolly, C. K.; McNaught, K.This document reviews the scour protection systems required around port structures where these are to be used for the berthing of vessels powered by water jet systems. The development of a scour protection system at Poole Harbour in Dorset has been documented and reviewed and a series of laboratory investigations were then undertaken. This has enabled a greater understanding of the scour mechanisms from the water jet propulsion systems of High Speed Ships. This work has shown that current design guidance on scour protection is not appropriate for use on berths used by High Speed Ships, that failure of these systems can occur rapidly and catastrophically, and secondary effects from water jets may promote the failure of quay walls. The scour protection system should comprise two individual elements, a filter layer and an armour layer. It has been found that systems involving individual isolated armour units are inappropriate and prone to failure and that shaped linked armour blocks need to be used. The loads on the armour layer were also found to be oscillatory and the materials used for both the armour and filter layers need to be designed for cyclic fatigue loading and fretting. Water jets are also capable of reducing the strength of permeable, seabed strata.