Kernel Density–Based Algorithm for Despiking ADV Data

AbstractAcoustic doppler velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter difficulties in despiking ADV data from a turbulent jet flow. An iteration-free despiking algorithm was developed for highly contaminated ADV data b...

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Published inJournal of hydraulic engineering (New York, N.Y.) Vol. 139; no. 7; pp. 785 - 793
Main Authors Islam, Md Rashedul, Zhu, David Z
Format Journal Article
LanguageEnglish
Published Reston, VA American Society of Civil Engineers 01.07.2013
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ISSN0733-9429
1943-7900
DOI10.1061/(ASCE)HY.1943-7900.0000734

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Abstract AbstractAcoustic doppler velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter difficulties in despiking ADV data from a turbulent jet flow. An iteration-free despiking algorithm was developed for highly contaminated ADV data by applying a bivariate kernel density function and its gradient to separate the data cluster from the spike clusters. It is shown that the new method overcomes some of the deficiencies of the existing despiking methods.
AbstractList Acoustic Doppier Velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter difficulty in despiking ADV data from a turbulent jet flow. An iteration-free despiking algorithm was developed for highly contaminated ADV data by applying a bivariate kernel density function and its gradient to separate the data cluster from the spike clusters. It is shown that the new method overcomes some of the deficiencies of the existing despiking methods.
AbstractAcoustic doppler velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter difficulties in despiking ADV data from a turbulent jet flow. An iteration-free despiking algorithm was developed for highly contaminated ADV data by applying a bivariate kernel density function and its gradient to separate the data cluster from the spike clusters. It is shown that the new method overcomes some of the deficiencies of the existing despiking methods.
Author Islam, Md Rashedul
Zhu, David Z
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  organization: Univ. of Alberta Postdoctoral Researcher, Dept. of Civil and Environmental Engineering, , Edmonton, AB, T6G 2W2 (corresponding author). E-mail
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  givenname: David Z
  surname: Zhu
  fullname: Zhu, David Z
  email: david.zhu@ualberta.ca
  organization: Univ. of Alberta Professor, Dept. of Civil and Environmental Engineering, , Edmonton, AB, T6G 2W2; and Dept. of Civil Engineering, Zhejiang Univ., 310058, China. E-mail
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10.1061/40655(2002)101
10.1061/(ASCE)0733-9429(1998)124:6(630)
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Issue 7
Keywords Data analysis
Turbulent flow
Hydraulics
Acoustic techniques
Despiking
Doppler effect
Acoustic method
Outlier
Acoustic doppler velocimeter (ADV)
Algorithms
Kernel density
Jet
Jets
Velocimeters
Velocity measurement
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Snippet AbstractAcoustic doppler velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter...
Acoustic Doppier Velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter difficulty in...
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SubjectTerms Algorithms
Clusters
Contamination
Density
Exact sciences and technology
Fluid dynamics
Fundamental areas of phenomenology (including applications)
Instrumentation for fluid dynamics
Kernels
Physics
Spikes
Technical Note
Technical Notes
Turbulence
Velocimetry
Title Kernel Density–Based Algorithm for Despiking ADV Data
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