TRICODA - Complex Data Analysis and Condition Monitoring based onv Neural Network Model

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.

[1]  R. J. Howlett,et al.  Determination of Air Fuel Ratio in Automotive Ignition System Using Neural Networks , 2003 .

[2]  Atsushi Ueda,et al.  Application of Vehicle Equipped with Ionic Current Detection System for the Engine Management System , 1997 .

[3]  P. A. Howson,et al.  Air-Fuel Ratio Measurement in an Internal Combustion Engine using a Neural Network , 1998 .

[4]  Stavros Paschalakis,et al.  A functional neural network prototype for multidimensional data analysis , 1999 .

[5]  Christopher M. Atkinson,et al.  Virtual sensors for spark ignition engines using neural networks , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[6]  Giorgio Rizzoni,et al.  Combustion Diagnostics in Methane-Fueled SI Engines Using the Spark Plug as an Ionization Probe , 1997 .

[7]  R. J. Howlett.  Monitoring and Control of an Internal Combustion Engine Air-Fuel Ratio using Neural and Fuzzy Techniques , 2003 .

[8]  Klaus D. McDonald-Maier,et al.  Debug support strategy for systems-on-chips with multiple processor cores , 2006, IEEE Transactions on Computers.

[9]  N. Ikoma,et al.  SENSOR TECHNOLOGY UPDATE , 1996 .

[10]  E. R. Davies.  Image processing and its applications , 2000 .

[11]  Harry Siebert,et al.  Debug support, calibration and emulation for multiple processor and powertrain control SoCs [automotive applications] , 2005, Design, Automation and Test in Europe.

[12]  M. Majors,et al.  Neural network control of automotive fuel-injection systems , 1994, IEEE Control Systems.

[13]  A.B.T. Hopkins,et al.  Instrumentation of Real-Time Embedded Systems for Performance Analysis , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[14]  Mark Longley,et al.  Specification and Verification Using Dependent Types , 1990, IEEE Trans. Software Eng..

[15]  Simon Thompson,et al.  Type theory and functional programming , 1991, International computer science series.