A Comparison of Data-Reduction Methods for a Seven-Hole Probe

2002 ◽  
Vol 124 (2) ◽  
pp. 523-527 ◽  
Author(s):  
David Sumner
Keyword(s):  
Data Reduction ◽  
Reduction Method ◽  
Interpolation Method ◽  
Low Flow ◽  
Leeward Side ◽  
Pressure Probe ◽  
Calibration Data ◽  
Grid Spacing ◽  
Reduction Methods ◽  
Data Reduction Method

Two data-reduction methods were compared for the calibration of a seven-hole conical pressure probe in incompressible flow. The polynomial curve-fit method of Gallington and the direct-interpolation method of Zilliac were applied to the same set of calibration data, for a range of calibration grid spacings. The results showed that the choice of data-reduction method and the choice of calibration grid spacing each have an influence on the measurement uncertainty. At high flow angles, greater than 30 deg, where flow may separate from the leeward side of the probe, the direct-interpolation method was preferable. At low flow angles, less than 30 deg, where flow remains attached about the probe, neither data-reduction method had any advantage. For both methods, a calibration grid with a maximum interval of 10 deg was recommended. The Reynolds-number sensitivity of the probe began at Re=5000, based on probe diameter, and was independent of the data-reduction method or calibration grid spacing.

Calibration of Triple-Wire Probes Using an Artificial Neural Network

2010 ◽  
Author(s):  
Noorallah Rostamy ◽  
Soheil Akbari ◽  
David Sumner ◽  
Donald J. Bergstrom
Keyword(s):  
Neural Network ◽  
Data Reduction ◽  
Velocity Vector ◽  
Reduction Method ◽  
Calibration Data ◽  
Hot Wire ◽  
Data Set ◽  
Wire Probe ◽  
The Mean ◽  
Data Reduction Method

Hot-wire anemometry is an established technique for velocity measurements in turbulent flows. Calibration of hot-wire probes is challenging due to the nonlinear relationship between the probe output voltage and the velocity, and the sensitivity to the temperature difference between the heated wire and the ambient flow. A triple-wire probe contains three mutually orthogonal wires that permit the three components of the local instantaneous velocity vector to be measured simultaneously. Calibration data reduction methods for multi-wire probes, based on variable-angle calibration techniques, may include curve-fits and direct-interpolation schemes. In the present study, a novel calibration data reduction method for a triple-wire probe is reported which uses an artificial neural network. Such a method has been successfully applied by other researchers for the calibration of seven-hole pressure probes. For the triple-wire probe, the neural network is used to produce a calibration relation between the three probe output voltages and the three components of the local velocity vector. Variable-angle calibration data were obtained for a triple-wire probe for velocity magnitudes from 5 to 40 m/s, yaw angles from −35° to +35°, and roll angles from 0° to 345°. A three-layer perceptron feed-forward network, using a Levenberg-Marquardt training algorithm, was applied to the calibration data, to map the mean voltages to the mean velocity components. The network was tested using an independent data set. The present results yielded standard errors of approximately ±0.38 m/s, ±0.25 m/s and ±0.26 m/s in the magnitudes of the streamwise, vertical, and cross-flow velocity components, respectively. The results showed that the present neural network model is not significantly sensitive to the size of the calibration data set, suggesting it may be a more convenient calibration data reduction method compared to other methods.

scholarly journals Improving learning vector quantization using data reduction

2019 ◽  
Vol 5 (3) ◽  
pp. 218 ◽  
Author(s):  
Pande Nyoman Ariyuda Semadi ◽  
Reza Pulungan
Keyword(s):  
Vector Quantization ◽  
Learning Process ◽  
Data Reduction ◽  
Reduction Method ◽  
Learning Algorithm ◽  
Learning Vector Quantization ◽  
Reduction Methods ◽  
Using Data ◽  
Data Reduction Method ◽  
Bank Marketing

Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of data involved in the learning process can be reduced by using data reduction methods. In this paper, we propose a data reduction method that uses geometrical proximity of the data. The basic idea is to drop sets of data that have many similarities and keep one representation for each set. By certain adjustments, the data reduction methods can decrease the amount of data involved in the learning process while still maintain the existing accuracy. The amount of data involved in the learning process can be reduced down to 33.22% for the abalone dataset and 55.02% for the bank marketing dataset, respectively.

In situ isotopic analysis of uranium using a new data acquisition protocol for 1013 ohm Faraday amplifiers

2021 ◽  
Author(s):  
Kota Yamamoto ◽  
Hisashi Asanuma ◽  
Hiroaki Takahashi ◽  
Takafumi Hirata
Keyword(s):  
Data Acquisition ◽  
Data Reduction ◽  
Reduction Method ◽  
Isotopic Analysis ◽  
High Gain ◽  
Transient Signals ◽  
Acquisition Protocol ◽  
Data Reduction Method ◽  
Isotopic Measurements

New data reduction method for isotopic measurements using high-gain Faraday amplifiers enables precise uranium isotopic analysis even from transient signals.

scholarly journals An efficient data reduction method and its application to cluster analysis

2017 ◽  
Vol 238 ◽  
pp. 234-244 ◽  
Author(s):  
Jianpei Wang ◽  
Shihong Yue ◽  
Xiao Yu ◽  
Yaru Wang
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Efficient Data ◽  
Data Reduction Method

Finite Element Simulation of the Sachs Boring Method of Measuring Residual Stresses in Thick-Walled Cylinders

2003 ◽  
Vol 125 (3) ◽  
pp. 274-276 ◽  
Author(s):  
R. R. de Swardt
Keyword(s):  
Finite Element ◽  
Residual Stresses ◽  
Data Reduction ◽  
High Speed ◽  
High Strength ◽  
Residual Stress Field ◽  
Element Analysis ◽  
Element Mesh ◽  
Reduction Methods ◽  
Data Reduction Method

During a recent study the residual strain/stress states through the walls of autofrettaged thick-walled high-strength steel cylinders were measured with neutron diffraction, Sachs boring and the compliance methods (Venter et al., 2000, J. Strain Anal. Eng. Des., 35, pp. 459–469). The Sachs boring method was developed prior to the advent of high speed computers. A new method for the data reduction was proposed. In order to verify the proposed procedure, the Sachs boring experimental method was simulated using finite element modeling. A residual stress field was introduced in the finite element method by elasto-plastic finite element analysis. The physical process of material removal by means of boring was simulated by step-by-step removal of elements from the finite element mesh. Both the traditional and newly proposed data reduction methods were used to calculate the residual stresses. The new data reduction method compares favorably with the traditional method.

A corpus study of variation in written Chinese

2012 ◽  
Vol 8 (1) ◽  
pp. 209-240 ◽  
Author(s):  
Zheng-sheng Zhang,
Keyword(s):  
Feature Selection ◽  
Correspondence Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Three Dimensions ◽  
Linguistic Variation ◽  
Corpus Study ◽  
Distributional Patterns ◽  
Preliminary Study ◽  
Data Reduction Method

AbstractThe present paper reports on the findings of a preliminary study of written Chinese, using the Lancaster Corpus of Mandarin Chinese (LCMC, McEnery & Xiao 2004). The first part of the paper introduces the stylistic features, and briefly describes the distributional patterns of these features across the selected written registers. Then, using a multi-feature, multi-dimensional framework (Biber 1988) and the data reduction method of correspondence analysis, three dimensions are identified and interpreted. The study reveals extensive linguistic variation across written Chinese registers, thus complementing previous observations about stylistic differences between spoken and written Chinese. Finally, issues concerning feature selection and dimension interpretation are discussed.

Sign in / Sign up

Export Citation Format

Share Document