Neural Network Classification of Diesel Spray Images

  • S. D. Walters
  • S. H. Lee
  • C. Crua
  • R. J. Howlett
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


This paper describes an evaluation of a neural network technique for modelling fuel spray penetration in the cylinder of a diesel internal combustion engine. The model was implemented using a multi-layer perceptron neural network. Two engine operating parameters were used as inputs to the model, namely injection pressure and in-cylinder pressure. Spray penetration length were modelled on the basis of these two inputs. The model was validated using test data that had not been used during training, and it was shown that semi-automated classification of complex diesel spray data is possible. The work lays the foundations for the establishment of an improved neural network paradigm for totally automatic, fast, accurate analysis of such complex data, thus saving many man-hours of tedious manual data analysis.


Neural Network Hide Node Diesel Spray Spray Penetration Neural Network Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. D. Walters
    • 1
  • S. H. Lee
    • 1
  • C. Crua
    • 1
  • R. J. Howlett
    • 1
  1. 1.Engineering Research Centre, School of EngineeringThe University of BrightonBrightonU.K.

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