Cellular neural network to detect spurious vectors in PIV data

Liang, Dongfang; Jiang, Chunbo; Li, Y. L.

doi:10.1007/s00348-002-0530-8

Cited by 44 publications

(25 citation statements)

References 13 publications

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“…ANNs are increasingly being applied to pattern-recognition problems, and their application has been extended to include particle-image velocimetry and other, similar techniques [17,24,20]. The authors in [6] applied ANNs to generate wave equations from hydraulic data, whereas [8,25] used ANNs for turbulent eddy classification and the detection of eddy patterns.…”

Section: Introductionmentioning

confidence: 98%

A Turbulent-Wake Estimation Using Radial Basis Function Neural Networks

Hočevar

Širok

Grabec

2005

Flow Turbulence Combust

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An estimation of the fluctuations in the passive-tracer concentration for the turbulent wake behind an airfoil is presented. The estimation is based on experimental modelling using Radial Basis Function Neural Networks. For the experiment the fluctuations of the concentration in the turbulent wake were recorded with a visualization method. The records of the concentration in the selected regions of the turbulent wake were used as the input and output regions for the training and estimation with neural networks. The uncertainty of the estimation increased with increasing distance between the input and the output regions. The power spectra, the spatial correlation functions and the profiles of the concentration were calculated from the measured and estimated fluctuations of the concentration. The measured and estimated concentration power spectra were in reasonable agreement. The measured and estimated spatial correlation functions and the profiles of the concentration showed a similar agreement.

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Section: Introductionmentioning

confidence: 98%

A Turbulent-Wake Estimation Using Radial Basis Function Neural Networks

Hočevar

Širok

Grabec

2005

Flow Turbulence Combust

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“…This method focuses on patterns (triangles or tetrahedrons) rather than individual particles, and becomes time consuming as particle number becomes large. A recent alternative is the cellular neural network (CNN) method based on the cross-correlation algorithm (Liang et al 2003). This technique, by simulating human brain procedure, is only suitable for measured vectors distributed on evenly spaced, structurally discrete points.…”

Section: Introductionmentioning

confidence: 99%

Relaxation algorithm-based PTV with dual calculation method and its application in addressing particle saltation

Jia

Wang

Zhang

et al. 2014

J Vis

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This paper proposes a dual calculation method (DCM) that in velocity fields obtained from particle tracking velocimetry detects spurious velocity vectors. The synthetic images were used to test the performance of the DCM. By adding random erasing to the genuine matching data, the influence of noise effect was simulated and analyzed. A comparison with the minimum net-flux method indicates that the DCM is more effective in identifying erroneous vectors as particles having no genuine candidates are more readily identified, thereby, ensuring strong consistent match. The DCM is also found to be more effective in addressing particle saltation, based on the accurately abstracted trajectories, the kinetic parameters of the sand grains are calculated.

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“…detection of the outliers, 2) replacement of the incorrect and missing values, and 3) data smoothing. Outlier identification is the most critical procedure and has been the subject matter of several recent papers (Hart 2000;Liang et al 2003;Liu et al 2008;Pun et al 2007;Shinneeb et al 2004;Westerweel et al 2005). The most common validation techniques proposed in standard commercial software are the global histogram filter, the dynamic mean value operator and the normalized median test (Raffel et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…The most common validation techniques proposed in standard commercial software are the global histogram filter, the dynamic mean value operator and the normalized median test (Raffel et al 2007). Alternative processes based on cellular neural network or bootstrapping have also been shown to provide satisfactory detections but they are relatively time-consuming (Liang et al 2003;Pun et al 2007) and rarely used in practice. Most of the current outlier detectors require the use of thresholding parameters whose selection remains somewhat arbitrary.…”

Section: Introductionmentioning

confidence: 99%

A fast all-in-one method for automated post-processing of PIV data

2010

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Post-processing of PIV (particle image velocimetry) data typically contains three following stages: validation of the raw data, replacement of spurious and missing vectors, and some smoothing. A robust post-processing technique that carries out these steps simultaneously is proposed. The new all-in-one method (DCT-PLS), based on a penalized least squares approach (PLS), combines the use of the discrete cosine transform (DCT) and the generalized cross-validation, thus allowing fast unsupervised smoothing of PIV data. The DCT-PLS was compared with conventional methods, including the normalized median test, for post-processing of simulated and experimental raw PIV velocity fields. The DCT-PLS was shown to be more efficient than the usual methods, especially in the presence of clustered outliers. It was also demonstrated that the DCT-PLS can easily deal with a large amount of missing data. Because the proposed algorithm works in any dimension, the DCT-PLS is also suitable for post-processing of volumetric three-component PIV data.

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Cellular neural network to detect spurious vectors in PIV data

Cited by 44 publications

References 13 publications

A Turbulent-Wake Estimation Using Radial Basis Function Neural Networks

A Turbulent-Wake Estimation Using Radial Basis Function Neural Networks

Relaxation algorithm-based PTV with dual calculation method and its application in addressing particle saltation

A fast all-in-one method for automated post-processing of PIV data

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