p-brane superalgebras via integrability

It has long been appreciated that superalgebras with bosonic and fermionic generators additional to those in the super-Poincare algebra underlie p-brane and D-brane actions in superstring theory. These algebras have been revealed via "bottom up" approaches, involving consideration of Noether charges, and by "top down" approaches, involving the construction of manifestly supersymmetry invariant Wess-Zumino actions. In this paper, we give an alternative derivation of these algebras based on integrability of supersymmetry transformations assigned to fields in order to solve a cohomology problem related to the construction of Wess-Zumino terms for p-brane and D-brane actions.Comment: 22 pages, typo corrected, reference adde

Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring

As the nuclear power plants within the UK age, there is an increased requirement for condition monitoring to ensure that the plants are still be able to operate safely. This paper describes the novel application of Intelligent Systems (IS) techniques to provide decision support to the condition monitoring of Nuclear Power Plant (NPP) reactor cores within the UK. The resulting system, BETA (British Energy Trace Analysis) is deployed within the UK’s nuclear operator and provides automated decision support for the analysis of refuelling data, a lead indicator of the health of AGR (Advanced Gas-cooled Reactor) nuclear power plant cores. The key contribution of this work is the improvement of existing manual, labour-intensive analysis through the application of IS techniques to provide decision support to NPP reactor core condition monitoring. This enables an existing source of condition monitoring data to be analysed in a rapid and repeatable manner, providing additional information relating to core health on a more regular basis than routine inspection data allows. The application of IS techniques addresses two issues with the existing manual interpretation of the data, namely the limited availability of expertise and the variability of assessment between different experts. Decision support is provided by four applications of intelligent systems techniques. Two instances of a rule-based expert system are deployed, the first to automatically identify key features within the refuelling data and the second to classify specific types of anomaly. Clustering techniques are applied to support the definition of benchmark behaviour, which is used to detect the presence of anomalies within the refuelling data. Finally data mining techniques are used to track the evolution of the normal benchmark behaviour over time. This results in a system that not only provides support for analysing new refuelling events but also provides the platform to allow future events to be analysed. The BETA system has been deployed within the nuclear operator in the UK and is used at both the engineering offices and on station to support the analysis of refuelling events from two AGR stations, with a view to expanding it to the rest of the fleet in the near future

Deriving all p-brane superalgebras via integrability

In previous work we demonstrated that the enlarged super-Poincare algebras which underlie p-brane and D-brane actions in superstring theory can be directly determined based on the integrability of supersymmetry transformations assigned to fields appearing in Wess-Zumino terms. In that work we derived p-brane superalgebras for p = 2 and 3. Here we extend our previous results and give a compact expression for superalgebras for all valid p.Comment: 26 pages, table added, typos corrected, a few remarks added for clarificatio

Exploiting multi-agent system technology within an autonomous regional active network management system

This paper describes the proposed application of multi-agent system (MAS) technology within AuRA-NMS, an autonomous regional network management system currently being developed in the UK through a partnership between several UK universities, distribution network operators (DNO) and a major equipment manufacturer. The paper begins by describing the challenges facing utilities and why those challenges have led the utilities, a major manufacturer and the UK government to invest in the development of a flexible and extensible active network management system. The requirements the utilities have for a network automation system they wish to deploy on their distribution networks are discussed in detail. With those requirements in mind the rationale behind the use of multi-agent systems (MAS) within AuRA-NMS is presented and the inherent research and design challenges highlighted including: the issues associated with robustness of distributed MAS platforms; the arbitration of different control functions; and the relationship between the ontological requirements of Foundation for Intelligent Physical Agent (FIPA) compliant multi-agent systems, legacy protocols and standards such as IEC 61850 and the common information model (CIM)

Practical applications of data mining in plant monitoring and diagnostics

Using available expert knowledge in conjunction with a structured process of data mining, characteristics observed in captured condition monitoring data, representing characteristics of plant operation may be understood, explained and quantified. Knowledge and understanding of satisfactory and unsatisfactory plant condition can be gained and made explicit from the analysis of data observations and subsequently used to form the basis of condition assessment and diagnostic rules/models implemented in decision support systems supporting plant maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of in-service distribution circuit breakers and empirical UHF data captured from laboratory experiments simulating partial discharge defects typically found in HV transformers. This discovered knowledge then forms the basis of two separate decision support systems for the condition assessment/defect clasification of these respective plant items

Comparative Analysis of Ledoit\u27s Covariance Matrix and Comparative Adjustment Liability Model (CALM) Within the Markowitz Framework

Estimation of the covariance matrix of asset returns is a key component of portfolio optimization. Inherent in any estimation technique is the capacity to inaccurately reflect current market conditions. Typical of Markowitz portfolio optimization theory, which we use as the basis for our analysis, is to assume that asset returns are stationary. This assumption inevitably causes an optimized portfolio to fail during a market crash since estimates of covariance matrices of asset returns no longer reflect current conditions. We use the market crash of 2008 to exemplify this fact. A current industry-standard benchmark for estimation is the Ledoit covariance matrix, which attempts to adjust a portfolio’s aggressiveness during varying market conditions. We test this technique against the CALM (Covariance Adjustment for Liability Management Method), which incorporates forward-looking signals for market volatility to reduce portfolio variance, and assess under certain criteria how well each model performs during recent market crash. We show that CALM should be preferred against the sample convariance matrix and Ledoit covariance matrix under some reasonable weight constraints

Learning models of plant behavior for anomaly detection and condition monitoring

Providing engineers and asset managers with a too] which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a Hidden Markov Model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible

Knowledge and model based reasoning for power system protection performance analysis

Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available.Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available

Coherent control of plasmons in nanoparticles with nonlocal response

We discuss a scheme for the coherent control of light and plasmons in nanoparticles that have nonlocal dielectric permittivity and contain nonlinear impurities or color centers. We consider particles which have a response to light that is strongly influenced by plasmons over a broad range of frequencies. Our coherent control method enables the reduction of absorption and/or suppression of scattering