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)

Comparative infrastructural modalities: Examining spatial strategies for Melbourne, Auckland and Vancouver

Infrastructure systems are critical to support sustainable and equitable urbanisation, and infrastructure is becoming more prominent within urban spatial strategies. However, the fragmented governance and delivery of spatial plans and infrastructure projects create a challenging environment to embed planning goals across the planning, delivery and operation of infrastructure systems. There is significant uncertainty around future needs and the complex ways that infrastructures influence socio-spatial relations and political-economic processes. Additionally, fragmented knowledge of infrastructure across different disciplines undermines the development of robust planning strategies. Comparative analysis of strategic spatial plans from Auckland, Melbourne and Vancouver examines how infrastructures are instrumentalised to support planning goals. Across the three cases, the analysis identified four common infrastructural modalities: rescaling socio-spatial relations through targeted intensification, intra-urban mobility upgrades and containment boundaries; re-localising socio-spatial relations to the suburban scale with ‘complete communities’; protection of ‘gateway’ precincts; and local planning provisions to support housing affordability. By examining infrastructure through a theoretical framework for suburban infrastructures, this analysis revealed how infrastructures exert agency as artefacts shaping socio-spatial relations and through the internalisation of political-economic processes. Each modality mobilised infrastructure to support goals of global competitiveness, economic growth and ‘liveability’. Findings suggest that spatial strategies should take a user-focused approach to infrastructure to meet the needs of diverse urban populations, and engage directly with the modes of infrastructure project delivery to embed planning goals across design, delivery and operations stages. Stronger institutional mandates to control land-use and provide affordable housing would improve outcomes in these city-regions

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

Data mining reactor fuel grab load trace data to support nuclear core condition monitoring

A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described

NASA Ares I Crew Launch Vehicle Upper Stage Overview

By incorporating rigorous engineering practices, innovative manufacturing processes and test techniques, a unique multi-center government/contractor partnership, and a clean-sheet design developed around the primary requirements for the International Space Station (ISS) and Lunar missions, the Upper Stage Element of NASA's Crew Launch Vehicle (CLV), the "Ares I," is a vital part of the Constellation Program's transportation system. Constellation's exploration missions will include Ares I and Ares V launch vehicles required to place crew and cargo in low-Earth orbit (LEO), crew and cargo transportation systems required for human space travel, and transportation systems and scientific equipment required for human exploration of the Moon and Mars. Early Ares I configurations will support ISS re-supply missions. A self-supporting cylindrical structure, the Ares I Upper Stage will be approximately 84' long and 18' in diameter. The Upper Stage Element is being designed for increased supportability and increased reliability to meet human-rating requirements imposed by NASA standards. The design also incorporates state-of-the-art materials, hardware, design, and integrated logistics planning, thus facilitating a supportable, reliable, and operable system. With NASA retiring the Space Shuttle fleet in 2010, the success of the Ares I Project is essential to America's continued leadership in space. The first Ares I test flight, called Ares I-X, is scheduled for 2009. Subsequent test flights will continue thereafter, with the first crewed flight of the Crew Exploration Vehicle (CEV), "Orion," planned for no later than 2015. Crew transportation to the ISS will follow within the same decade, and the first Lunar excursion is scheduled for the 2020 timeframe

Ares I Crew Launch Vehicle Upper Stage Element Overview

This viewgraph presentation gives an overview of NASA's Ares I Crew Launch Vehicle Upper Stage Element. The topics include: 1) What is NASA s Mission?; 2) NASA s Exploration Roadmap What is our time line?; 3) Building on a Foundation of Proven Technologies Launch Vehicle Comparisons; 4) Ares I Upper Stage; 5) Upper Stage Primary Products; 6) Ares I Upper Stage Development Approach; 7) What progress have we made?; 8) Upper Stage Subsystem Highlights; 9) Structural Testing; 10) Common Bulkhead Processing; 11) Stage Installation at Stennis Space Center; 12) Boeing Producibility Team; 13) Upper Stage Low Cost Strategy; 14) Ares I and V Production at Michoud Assembly Facility (MAF); 15) Merged Manufacturing Flow; and 16) Manufacturing and Assembly Weld Tools

The Internal Proper Motions Of Stars In The Open Cluster M35

Relative proper motions, based on 108 orbits of Hubble Space Telescope Fine Guidance Sensor data extending from 1992 to 2006, are reported for 74 stars in the open cluster M35 (NGC 2168). A subset of 22 of these objects are then used to compute the cluster's internal proper motion dispersions in both right ascension and declination. We find that these dispersions are equal to within their measurement errors. The average one-dimensional dispersion is 0.018 +/- 0.002 arcsec century(-1). When combined with the M35 radial velocity dispersion of 0.65 +/- 0.10 km s(-1) found by Geller et al., this produces a cluster distance of 762 +/- 145 pc. Using isochrone fits to the cluster main sequence, this distance suggests that M35 has an age of about 133 Myr. Although this age is consistent with that typically found for M35, the formal error in the dynamical distance of +/- 19% can accommodate ages between 65 Myr and 201 Myr.McDonald Observator

B-SMART: A Reference Architecture for Artificially Intelligent Autonomic Smart Buildings

The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building