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 currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (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: https://kar.kent.ac.uk/id/eprint/6457 (The current URI for this page, for reference purposes)
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