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Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.
Research led by Professor Roger Fletcher has resulted in the development of a suite of algorithms that are now widely used throughout industry. An algorithm of fundamental importance constructed by Fletcher and co-workers is the filter method — a radically different approach to solving large and complex nonlinear optimization problems typical of those faced by industry. This algorithm was developed with the principal aim of providing a computationally reliable and effective method for solving such problems. The filter method is now utilised by a variety of high-profile industry end-users including IBM, Schlumberger, Lucent, EXXON, Boeing, The Ford Motor Company, QuantiSci and Thomson CSF. The use of the filter method has had a significant economic and developmental impact in these companies through enhanced business performance and cost savings.
This study demonstrates how Bayes linear methodologies developed at Durham University have impacted on industrial practice. Two examples are given. The approach has been applied by London Underground Ltd. to the management of bridges, stations and other civil engineering assets, enabling a whole-life strategic approach to maintenance and renewal to reduce costs and increase safety. The approach has won a major award for innovation in engineering and technology. The methodology has also been applied by Unilever and Fera to improve methods of assessing product safety and in particular the risk of chemical ingredients in products causing allergic skin reactions.
This case study demonstrates the benefits achieved when the mathematical and computational aspects of a computational fluid dynamics (CFD) problem were brought together to work on real-world aerodynamic applications. While earlier insight on the solution reconstruction problem was purely based on empirical intuition, research in the School of Mathematics at the University of Birmingham by Dr Natalia Petrovskaya has resulted in the development of the necessary synthetic judgement in which the importance of accurate reconstruction on unstructured grids has been fully recognised by the CFD researchers at the Boeing Company. Boeing has confirmed that the research has led to substantial resultant improvements in their products as well as gains in engineering productivity. For instance, wing body fairing and winglets optimization for the Boeing 787 has been done by means of CFD only. Implementation of CFD in the design of their new aircraft allowed Boeing to reduce the testing time in the wind tunnel for the 787 aircraft by 30% in comparison with testing carried out for Boeing 777. Efficient use of CFD in the design of new aircrafts has helped the Boeing Company to further strengthen their core operations, improve their execution and competitiveness and leverage their international advantage.
A new hybrid analysis method, arising from research at the University of Cambridge Department of Engineering (DoEng), unites Statistical Energy Analysis (SEA) with Finite Element Analysis (FEA) to enable full-spectrum vibro-acoustic analysis of large and complex structures with modest computing resources for the first time. The method also allows for uncertainties in the manufacturing process. This research breakthrough has been exploited by ESI Group (ESI), which is a company that provides virtual prototyping solutions, in commercial software licensed to more than 600 companies across a wide range of industrial sectors to improve product design and performance with regard to vibrations and noise. Typical applications include the prediction and reduction of interior noise in automotive and aerospace structures, and the assessment of launch- induced vibration levels in satellite structures.
In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst — Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao's research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.
Our research team has developed new approaches to classifying demand series as `intermittent' and `lumpy', and devised new variants of the standard Croston's method for intermittent demand forecasting, which improve forecast accuracy and stock performance. These approaches have impacted the forecasting software of Syncron and Manugistics, through the team's consultancy advice and knowledge transfer. Subsequently, this impact has extended to Syncron International and JDA Software, which took over Manugistics. These companies' forecasting software packages have a combined client base turnover of over £200 billion per annum, and their clients benefit from substantial inventory savings from the new approaches adopted.
This impact case is based on economic impact through improved forecasting technology. It shows how research in pattern recognition by Professor Henry Wu at the School of Electrical Engineering and Computer Science led to significantly improved accuracy of daily national gas demand forecasting by National Grid plc. The underpinning research on predicting non-linear time series began around 2002 and the resulting new prediction methodology is applied on a daily basis by National Grid plc since December 2011. The main beneficiaries from the improved accuracy (by 0.5 to 1 million cubic meters per day) are UK gas shippers, who by conservative estimates save approximately £3.5M per year. Savings made by gas shippers benefit the whole economy since they reduce the energy bills of end users.
A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.
Bath research has substantially improved the estimation and formulation of GAMs and hence
This improved statistical infrastructure has resulted in improved data analysis by practitioners in fields such as natural resource management, energy load prediction, environmental impact assessment, climate policy, epidemiology, finance and economics. In REF impact terms, such changes in practice by practitioners leads ultimately to direct economic and societal benefits, health benefits and policy changes. Below, these impacts are illustrated via two specific examples: (1) use of the methods by the energy company EDF for electricity load forecasting and (2) their use in environmental management. The statistical methods are implemented in R via the software package mgcv, largely written at Bath. As a `recommended' R package mgcv has also contributed to the global growth of R, which currently has an estimated 1.2M business users worldwide [A].
Short Term Conflict Alert systems are used by NATS to alert air traffic controllers to the risk of aircraft becoming dangerously close. This research has provided the means to enhance international air traffic safety by automatically optimising STCA systems so as to simultaneously maximise the number of alerts raised in response to truly dangerous situations, while, at the same time, minimising the number of false alerts. This has been achieved by developing multi-objective evolutionary algorithms to automatically locate the Pareto front describing the optimal trade-off between the numbers of true and false positives. The optimiser is described by NATS as "an outstanding improvement to our safety" [KTP-1395, final report].