Calibration and data reduction for X-hotwires using cross validation

2021 ◽  
pp. 095441002110403
Author(s):  
Mohan Vijaya Anoop ◽  
Budda Thiagarajan Kannan
Keyword(s):  
Cross Validation ◽  
Low Flow ◽  
Calibration Data ◽  
Inversion Algorithm ◽  
Present Scheme ◽  
Wire Probe ◽  
Data Inversion ◽  
Turbulent Jet Flow ◽  
High Reynolds ◽  
Yaw Angle

A strategy for calibration of X-wire probes and data inversion is described in this article. The approach used has elements of full velocity vs yaw-angle calibration with robust curve fitting. The responses of an X-wire probe placed in a calibration jet are recorded for a set of velocity and yaw inputs followed by fitting cross-validated splines. These spline functions trained from calibration data are evaluated for the probe responses during measurement. X-wire probes are calibrated for low to moderate velocities (0.65 m/s to 32 m/s) and yaw angles in the range −40° to 40° and comparisons with conventional interpolation schemes are made. The proposed algorithm can be extended to calibration of other multiple wire probes and for higher velocities. Some measurements in a single round turbulent jet flow at high Reynolds number using the proposed inversion algorithm are also presented. The present scheme is found to perform better particularly at low flow magnitudes and/or extreme flow angles than the schemes used previously.

scholarly journals Supplemental Material: Contrasting northern and southern European winter climate trends during the Last Interglacial

2021 ◽  
Author(s):  
Sakari Salonen ◽  
et al.
Keyword(s):  
Cross Validation ◽  
Calibration Data ◽  
Climate Trends ◽  
Last Interglacial ◽  
Winter Climate ◽  
Paleoclimate Reconstructions

Paleoclimate reconstructions, pollen–climate calibration data, and cross-validation results.

A new data inversion algorithm for DMPS and TDMA measurements

1999 ◽  
Vol 30 ◽  
pp. S775-S776 ◽  
Author(s):  
A. Voutilainen ◽  
F. Stratmann ◽  
J.P. Kaipio
Keyword(s):  
Inversion Algorithm ◽  
Data Inversion

Statistical considerations on the Raman inversion algorithm: data inversion and error assessment

2004 ◽  
Author(s):  
Francesc Rocadenbosch ◽  
Michael Sicard ◽  
Albert Ansmann ◽  
Ulla Wandinger ◽  
Volker Matthias ◽  
...  
Keyword(s):  
Error Assessment ◽  
Inversion Algorithm ◽  
Data Inversion

A compressed implicit Jacobian scheme for 3D electromagnetic data inversion

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. F173-F183 ◽  
Author(s):  
Maokun Li ◽  
Aria Abubakar ◽  
Jianguo Liu ◽  
Guangdong Pan ◽  
Tarek M. Habashy
Keyword(s):  
Electric Fields ◽  
Jacobian Matrix ◽  
Iterative Solvers ◽  
Computational Time ◽  
Memory Usage ◽  
Inversion Algorithm ◽  
Linear System Of Equations ◽  
Electromagnetic Data ◽  
Data Inversion ◽  
Computational Overhead

We developed a compressed implicit Jacobian scheme for the regularized Gauss-Newton inversion algorithm for reconstructing 3D conductivity distributions from electromagnetic data. In this algorithm, the Jacobian matrix, whose storage usually requires a large amount of memory, is decomposed in terms of electric fields excited by sources located and oriented identically to the physical sources and receivers. As a result, the memory usage for the Jacobian matrix reduces from O(NFNSNRNP) to O[NF(NS + NR)NP], where NF is the number of frequencies, NS is the number of sources, NR is the number of receivers, and NP is the number of conductivity cells to be inverted. When solving the Gauss-Newton linear system of equations using iterative solvers, the multiplication of the Jacobian matrix with a vector is converted to matrix-vector operations between the matrices of the electric fields and the vector. In order to mitigate the additional computational overhead of this scheme, these fields are further compressed using the adaptive cross approximation (ACA) method. The compressed implicit Jacobian scheme provides a good balance between memory usage and computational time and renders the Gauss-Newton algorithm more efficient. We demonstrated the benefits of this scheme using numerical examples including both synthetic and field data for both crosswell and controlled-source electromagnetic (CSEM) applications.

scholarly journals Geostatistical prediction of flow–duration curves in an index-flow framework

2014 ◽  
Vol 18 (9) ◽  
pp. 3801-3816 ◽  
Author(s):  
A. Pugliese ◽  
A. Castellarin ◽  
A. Brath
Keyword(s):  
Cross Validation ◽  
Central Italy ◽  
Weighted Average ◽  
Low Flow ◽  
Mean Annual Precipitation ◽  
Historical Period ◽  
Negative Deviation ◽  
Time Duration ◽  
Study Region ◽  
Flow Duration

Abstract. An empirical period-of-record flow–duration curve (FDC) describes the percentage of time (duration) in which a given streamflow was equaled or exceeded over an historical period of time. In many practical applications one has to construct FDCs in basins that are ungauged or where very few observations are available. We present an application strategy of top-kriging, which makes the geostatistical procedure capable of predicting FDCs in ungauged catchments. Previous applications of top-kriging mainly focused on the prediction of point streamflow indices (e.g. flood quantiles, low-flow indices, etc.); here the procedure is used to predict the entire curve in ungauged sites as a weighted average of standardised empirical FDCs through the traditional linear-weighting scheme of kriging methods. In particular, we propose to standardise empirical FDCs by a reference index-flow value (i.e. mean annual flow, or mean annual precipitation × the drainage area) and to compute the overall negative deviation of the curves from this reference value. We then propose to use these values, which we term total negative deviation (TND), for expressing the hydrological similarity between catchments and for deriving the geostatistical weights. We focus on the prediction of FDCs for 18 unregulated catchments located in central Italy, and we quantify the accuracy of the proposed technique under various operational conditions through an extensive cross-validation and sensitivity analysis. The cross-validation points out that top-kriging is a reliable approach for predicting FDCs with Nash–Sutcliffe efficiency measures ranging from 0.85 to 0.96 (depending on the model settings) very low biases over the entire duration range, and an enhanced representation of the low-flow regime relative to other regionalisation models that were recently developed for the same study region.

scholarly journals Airborne multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US

2014 ◽  
Vol 7 (2) ◽  
pp. 1059-1073 ◽  
Author(s):  
D. Müller ◽  
C. A. Hostetler ◽  
R. A. Ferrare ◽  
S. P. Burton ◽  
E. Chemyakin ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Phase 1 ◽  
Department Of Energy ◽  
High Spectral Resolution ◽  
Inversion Algorithm ◽  
Data Inversion ◽  
Microphysical Properties ◽  
The Us ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar

Abstract. We present measurements acquired by the world's first airborne multiwavelength High Spectral Resolution Lidar (HSRL-2), developed by NASA Langley Research Center. The instrument was operated during Phase 1 of the Department of Energy (DOE) Two-Column Aerosol Project (TCAP)in July 2012. We observed pollution outflow from the northeast coast of the US out over the West Atlantic Ocean. Lidar ratios were 50–60 sr at 355 nm and 60–70 sr at 532 nm. Extinction-related Ångström exponents were on average 1.2–1.7 indicating comparably small particles. Our novel automated, unsupervised data inversion algorithm retrieves particle effective radii of approximately 0.2 μm, which is in agreement with the large Ångström exponents. We find good agreement with particle size parameters obtained from coincident in situ measurements carried out with the DOE Gulfstream-1 aircraft.

scholarly journals CHEMOMETRIC ASSISTED QUANTITATIVE DETERMINATION OF VITEXIN IN ETHANOLIC EXTRACT OF BINAHONG (ANREDERA CORDIFOLIA (TEN.) STEENIS) LEAVES

2020 ◽  
pp. 69-75
Author(s):  
OCTAVIANUS BUDI SANTOSA ◽  
MICHAEL RAHARJA GANI ◽  
SRI HARTATI YULIANI
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Multivariate Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Mean Square ◽  
Calibration Models ◽  
Pcr Method

Objective: The objective of this study was to develop a UV spectroscopy method in combination with multivariate analysis for determining vitexin in binahong (Anredera cordifolia (Ten.) Steenis) leaves extract. Methods: The partial least square (PLS) regression and the principal component regression (PCR) was performed in this study to evaluate several statistical performances such as coefficient of determination (R2), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and relative error of prediction (REP). Cross-validation in this study was performed using leave one out technique. Results: The R2 values of calibration data sets resulted from PLS ​​and PCR method were 0.9675 and 0.9648, respectively. The low values of RMSEC and RMSECV both for PLS ​​and PCR method indicated the minimum error of the calibration models. The R2 values of validation data sets resulted from PLS ​​and PCR method were 0.9778 and 0.9820, respectively. The low values of RMSEP both for PLS ​​and PCR method indicated the minimum error of prediction generated from the calibration data sets. Multivariate calibration techniques were applied to determine the content of vitexin in binahong leaves extract. Predicted values from the multivariate calibration models were compared to the actual values determined from a validated HPLC method. It was found that PLS models resulted in the lowest REP values compared to the PCR models. Conclusion: The chemometrics technique can be applied as an alternative method for determining vitexin levels in the ethanol solution of binahong leaves extract.

scholarly journals Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5518 ◽  
Author(s):  
Tomislav Hengl ◽  
Madlene Nussbaum ◽  
Marvin N. Wright ◽  
Gerard B.M. Heuvelink ◽  
Benedikt Gräler
Keyword(s):  
Random Forest ◽  
Data Quality ◽  
Cross Validation ◽  
Prediction Models ◽  
Proximity Effects ◽  
Training Data ◽  
Calibration Data ◽  
Temporal Prediction ◽  
Spatio Temporal

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as “knowledge engines” in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality—especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

True Accuracy of near Infrared Spectroscopy and its Dependence on Precision of Reference Data

2002 ◽  
Vol 10 (1) ◽  
pp. 15-25 ◽  
Author(s):  
L.K. Sørensen
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Reference Data ◽  
Nir Spectroscopy ◽  
Limited Resources ◽  
Standard Errors ◽  
Calibration Data ◽  
Pls Regression ◽  
Calibration Equations ◽  
Very High

A more precise estimate of the accuracy of near infrared (NIR) spectroscopy is obtained when the measured standard errors of cross validation ( SECV) and prediction ( SEP) are corrected for imprecision of the reference data. The significance of correction increases with increasing imprecision of reference data. Very high precision of reference data obtained through replicate analyses under reproducibility conditions may not be the optimal goal for the development of calibration equations. In a situation of limited resources, the precision of the reference data should be related to the obtainable accuracy of the spectroscopic system. Investigation of several routine applications based on the partial least-squares (PLS) regression technique showed that increased precision of calibration data only resulted in marginal improvements in true accuracy if the total standard error of reference results from the beginning was less than the estimated true accuracy of the corresponding NIR calibration.

Capability of crop canopy sensing to predict crop parameters of cut grass swards aiming at early season variable rate nitrogen top dressings

2017 ◽  
Vol 8 (2) ◽  
pp. 792-795 ◽  
Author(s):  
G. Portz ◽  
M. L. Gnyp ◽  
J. Jasper
Keyword(s):  
Cross Validation ◽  
N Uptake ◽  
Calibration Data ◽  
Variable Rate ◽  
Nitrogen Application ◽  
Crop Canopy ◽  
Validation Data ◽  
Intensively Managed ◽  
Independent Calibration ◽  
Crop Parameters

This study aims to evaluate actual biomass and N-uptake estimates with the Yara N-Sensor in intensively managed grass swards across several trial sites in Europe. The dataset was split by location into an independent calibration data (UK and Finland) and a validation data (Germany) for the first two cuts. Yara N-Sensor readings were better correlated with N-uptake (R2=0.71) than actual biomass (R2=0.53) for the 1st cut. At the 2nd cut, the R2 values for both parameters were higher (0.80 and 0.56). A cross-validation with a German grass trial indicated the potential for predicting N-uptake (R2>0.8). It can be concluded that the technology has the potential to guide management decisions and variable rate nitrogen application on European grass swards.

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