Neural network classification of diesel spray images

Walters, S.D., Lee, S.H., Crua, C. and Howlett, R.J. (2006) Neural network classification of diesel spray images In: KES2006 10th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems, 9-11 October 2006, Bournemouth, United Kingdom.

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Abstract

Abstract. 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 semiautomated 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.

Item Type: Contribution to conference proceedings in the public domain ( Full Paper)
Subjects: G000 Computing and Mathematical Sciences > G600 Software Engineering
H000 Engineering > H300 Mechanical Engineering > H330 Automotive Engineering
G000 Computing and Mathematical Sciences > G700 Artificial Intelligence
Faculties: Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Engineering and Product Design Research > Smart Systems and Materials
Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Engineering and Product Design Research > Automotive Engineering
Depositing User: Dr Cyril Crua
Date Deposited: 11 Mar 2010
Last Modified: 21 May 2014 11:01
URI: http://eprints.brighton.ac.uk/id/eprint/3013

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