A DNA algorithm for feature selection in condition monitoring of manufacturing processes

This paper proposes a DNA based algorithm for signal processing in condition monitoring of manufacturing processes. Its goal is to find the 'DNA' (feature) of monitoring signal through DNA encoding and analysis. Furthermore, features selection is implemented for vibration signal collecting...

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Published in2005 International Symposium on Intelligent Signal Processing and Communications Systems pp. 773 - 776
Main Authors Quan-Fang Wang, Ruxu Du
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
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ISBN0780392663
9780780392663
DOI10.1109/ISPACS.2005.1595524

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Abstract This paper proposes a DNA based algorithm for signal processing in condition monitoring of manufacturing processes. Its goal is to find the 'DNA' (feature) of monitoring signal through DNA encoding and analysis. Furthermore, features selection is implemented for vibration signal collecting from the injection molding machine to verify the DNA algorithm.
AbstractList This paper proposes a DNA based algorithm for signal processing in condition monitoring of manufacturing processes. Its goal is to find the 'DNA' (feature) of monitoring signal through DNA encoding and analysis. Furthermore, features selection is implemented for vibration signal collecting from the injection molding machine to verify the DNA algorithm.
Author Quan-Fang Wang
Ruxu Du
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  surname: Ruxu Du
  fullname: Ruxu Du
  organization: Dept. of Autom. & Comput. Aided Eng., Chinese Univ. of Hong Kong, Shatin, China
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Snippet This paper proposes a DNA based algorithm for signal processing in condition monitoring of manufacturing processes. Its goal is to find the 'DNA' (feature) of...
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StartPage 773
SubjectTerms Artificial neural networks
Biomedical signal processing
Computer aided engineering
Condition monitoring
Discrete Fourier transforms
DNA
Genetic mutations
Manufacturing automation
Manufacturing processes
Signal processing algorithms
Title A DNA algorithm for feature selection in condition monitoring of manufacturing processes
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