TRICODA: Complex Data Analysis and Condition Monitoring based on Neural Network Models

Howells, Gareth and Howlett, R.J. and McDonald-Maier, Klaus D. (2007) TRICODA: Complex Data Analysis and Condition Monitoring based on Neural Network Models. In: NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2007), 2007 August, Edinburgh, UK. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/AHS.2007.107

Abstract

The increasing availability of advanced computer equipment and sensory systems often results in large volumes of data, with subsequent difficulties in efficient analysis and real-time processing. The Tricoda initiative focuses on tools and techniques to aid in the automated analysis of large, complex systems and the data sets they generate. A novel general-purpose modelling system is employed based on the combination of a number of artificial intelligence based and conventional techniques, all integrated with a novel formal framework based on Constructive Type Theory. The tool is evaluated for the solution of a data analysis and condition monitoring case study focusing on an automotive application, specifically the automotive sector for engine control.

Item Type: Conference or workshop item (Paper)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications)
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Yiqing Liang
Date Deposited: 14 Aug 2008 15:30
Last Modified: 28 May 2014 15:54
Resource URI: http://kar.kent.ac.uk/id/eprint/6457 (The current URI for this page, for reference purposes)
  • Depositors only (login required):