Design and analysis of kinetic energy recovery system for automobiles: a case study for commuters in Edinburgh.

Transport and its energetic and environmental impacts affect our daily lives. The transport sector is the backbone of the United Kingdom’s economy with 2.3 million people being employed in this sector. With a high dependency on transport for passengers and freight and with the knowledge that oil reserves are rapidly decreasing a solution has to be identified for conserving fuel. Passenger vehicles account for 61% of the transport fuel consumed in the U.K. and should be seen as a key area to tackle. Despite the introduction and development of electric powered cars, the widespread infrastructure that is required is not in place and has attributed to their slow uptake, as well as the fact that the electric car’s performance is not yet comparable with the conventional internal combustion engine. The benefits of the introduction of kinetic energy recovery systems to be used in conjunction with internal combustion engines and designed such that the system could easily be fitted into future passenger vehicles are examined. In this article, a review of automobile kinetic energy recovery system is presented. It has been argued that the ultracapacitor technology offers a sustainable solution. An optimum design for the urban driving cycle experienced in the city of Edinburgh has been introduced. The potential for fuel savings is also presented

Tactile Sensing for Mechatronics: A State of the Art Survey

Lee M.H. and Nicholls H.R., Tactile Sensing for Mechatronics: A State of the Art Survey, Mechatronics, 9, Jan 1999, pp1-31.

ART: An ontology based tool for the translation of papers into Semantic Web format

To be presented at SIG/ISMB07 ontology workshop: http://bio-ontologies.org.uk/index.php To be published in BMC Bioinformatics. Sponsorship: JISC

From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain

Sexton, J. (2008). From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain. In L. Mulvey and J. Sexton (Eds.), Experimental British Television (pp.89-105). Manchester: Manchester University Press. RAE2008

Kinetic Analysis of Ester Hydrolysis

A two-week multi-step experiment that introduces students to mechanistic organic chemistry and substituent effects. A simple preparation of differentially substituted para-nitrophenyl benzoates is followed by ester hydrolysis with monitoring by UV-Vis spectroscopy to provide rate data for the reaction.

Bring ART into the ACT

ACT is compared with a particular type of connectionist model that cannot handle symbols and use non-biological operations that cannot learn in real time. This focus continues an unfortunate trend of straw man "debates" in cognitive science. Adaptive Resonance Theory, or ART, neural models of cognition can handle both symbols and sub-symbolic representations, and meets the Newell criteria at least as well as these models.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines.