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Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)
TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model
University of Edinburgh, Scotland, United Kingdom
August 05-August 08
ISBN: 0-7695-2866-X
ASCII Text
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Gareth Howells, Bob Howlett, Klaus McDonald-Maier, "TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model," Adaptive Hardware and Systems, NASA/ESA Conference on, pp. 647-651, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/AHS.2007.107,
author = {Gareth Howells and Bob Howlett and Klaus McDonald-Maier},
title = {TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model},
journal ={Adaptive Hardware and Systems, NASA/ESA Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2866-X},
pages = {647-651},
doi = {http://doi.ieeecomputersociety.org/10.1109/AHS.2007.107},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Adaptive Hardware and Systems, NASA/ESA Conference on
TI - TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model
SN - 0-7695-2866-X
SP647
EP651
A1 - Gareth Howells,
A1 - Bob Howlett,
A1 - Klaus McDonald-Maier,
PY - 2007
KW - null
VL - 0
JA - Adaptive Hardware and Systems, NASA/ESA Conference on
ER -
 
Gareth Howells, University of Kent
Bob Howlett, University of Brighton
Klaus McDonald-Maier, University of Essex
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.
Citation:
Gareth Howells, Bob Howlett, Klaus McDonald-Maier, "TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model," ahs, pp.647-651, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007), 2007
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