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Ozdemir, Durmus, Matt Mosley, and Ron Williams. "Hybrid Calibration Models: An Alternative to Calibration Transfer." Applied Spectroscopy 52, no. 4 (1998): 599–603. http://dx.doi.org/10.1366/0003702981943932.
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A new procedure for calibrating multiple instruments is presented in which spectra from each are used simultaneously during the construction of multivariate calibration models. The application of partial least-squares (PLS) and genetic regression (GR) to the problem of generating these hybrid calibrations is presented. Spectra of ternary mixtures of methylene chloride, ethyl acetate, and methanol were collected on a dispersive and a Fourier transform spectrometer. Calibration models were generated by using differing numbers of spectra from each instrument simultaneously in the calibration and prediction sets, and then validated by using a set of spectra from each instrument separately. Calibration models were found that perform well on both instruments, even when only a single spectrum from the second instrument was used during the calibration process. As a benchmark, comparison with PLS showed that GR is more effective than PLS in building these hybrid calibration models.
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Zhang, Bao Long, Shao Jing Zhang, Wei Qi Ding, and Hui Shuang Shi. "Fisheye Lens Distortion Calibration Based on the Lens Charactetic Curves." Applied Mechanics and Materials 519-520 (February 2014): 636–39. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.636.
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The fisheye lens is a kind of ultra wide angle lens, which can produce a big super-wide-angle lens distortion. In order to cover a large scope of light, barrel distortion is artificially added to the optical system. However, in some cases this distortion is not allowed, then it requires calibrations of those distortions. Most of the traditional distortion calibration method uses target plane calibration to do it. This paper discusses the way of design fisheye lens, through which we can know the forming process of distortion clearly. Based on this paper, a simple and effective calibration method can be understood. Different from common camera calibration method, the proposed calibration method can avoid the error occurring in the process of calibrating test, that directly use the lens’ characteristic curve. Through multiple sets of experimental verifications, this method is effective and feasible.
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Alsam, Ali, and Graham Finlayson. "Metamer sets without spectral calibration." Journal of the Optical Society of America A 24, no. 9 (2007): 2505. http://dx.doi.org/10.1364/josaa.24.002505.
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Fearn, Tom. "Flat Calibration Sets: At What Price?" NIR news 18, no. 8 (2007): 16–17. http://dx.doi.org/10.1255/nirn.1055.
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Alarid-Escudero, Fernando, Richard F. MacLehose, Yadira Peralta, Karen M. Kuntz, and Eva A. Enns. "Nonidentifiability in Model Calibration and Implications for Medical Decision Making." Medical Decision Making 38, no. 7 (2018): 810–21. http://dx.doi.org/10.1177/0272989x18792283.
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Background. Calibration is the process of estimating parameters of a mathematical model by matching model outputs to calibration targets. In the presence of nonidentifiability, multiple parameter sets solve the calibration problem, which may have important implications for decision making. We evaluate the implications of nonidentifiability on the optimal strategy and provide methods to check for nonidentifiability. Methods. We illustrate nonidentifiability by calibrating a 3-state Markov model of cancer relative survival (RS). We performed 2 different calibration exercises: 1) only including RS as a calibration target and 2) adding the ratio between the 2 nondeath states over time as an additional target. We used the Nelder-Mead (NM) algorithm to identify parameter sets that best matched the calibration targets. We used collinearity and likelihood profile analyses to check for nonidentifiability. We then estimated the benefit of a hypothetical treatment in terms of life expectancy gains using different, but equally good-fitting, parameter sets. We also applied collinearity analysis to a realistic model of the natural history of colorectal cancer. Results. When only RS is used as the calibration target, 2 different parameter sets yield similar maximum likelihood values. The high collinearity index and the bimodal likelihood profile on both parameters demonstrated the presence of nonidentifiability. These different, equally good-fitting parameter sets produce different estimates of the treatment effectiveness (0.67 v. 0.31 years), which could influence the optimal decision. By incorporating the additional target, the model becomes identifiable with a collinearity index of 3.5 and a unimodal likelihood profile. Conclusions. In the presence of nonidentifiability, equally likely parameter estimates might yield different conclusions. Checking for the existence of nonidentifiability and its implications should be incorporated into standard model calibration procedures.
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Dinku, Tufa, and Emmanouil N. Anagnostou. "Investigating Seasonal PR–TMI Calibration Differences." Journal of Atmospheric and Oceanic Technology 25, no. 7 (2008): 1228–37. http://dx.doi.org/10.1175/2007jtecha977.1.
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Abstract Seasonal differences in the calibration of overland passive microwave rain retrieval are investigated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR). Four geographic regions from southern Africa, South Asia, the Amazon basin, and the southeastern United States are selected. Three seasons are compared for each region. Two scenarios of algorithm calibration are considered. In the first, the parameter sets are derived by calibrating the TMI algorithm with PR in each season. In the second scenario, common parameter sets are derived from the combined dataset of all three seasons. The parameter sets from both scenarios are then applied to the validation dataset of each season to determine the effect of seasonal calibration. Furthermore, calibration parameters from one season are also applied to another season, and results are compared against those derived using the season’s own parameters. Appreciable seasonal differences are observed for the U.S. region, while there are no significant differences between using individual seasonal calibration and the all-season calibration for the other regions. However, using one season’s parameter set to retrieve rainfall for another season is associated with increased uncertainty. It is also shown that the performance of the retrieval varies by season.
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Peiris, K. H. S., G. G. Dull, R. G. Leffler, and S. J. Kays. "Near-infrared Spectrometric Method for Nondestructive Determination of Soluble Solids Content of Peaches." Journal of the American Society for Horticultural Science 123, no. 5 (1998): 898–905. http://dx.doi.org/10.21273/jashs.123.5.898.
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A nondestructive method for measuring the soluble solids (SS) content of peaches [Prunus persica (L.) Batsch] was developed using near-infrared (NIR) spectrometry. NIR transmittance in the 800 to 1050 nm region was measured for four cultivars of peaches (`Blake', `Encore', `Red Haven', and `Winblo'), over a period of three seasons (1993 through 1995). Each fruit was scanned on both halves keeping the suture away from the incident light beam. Soluble solids contents of flesh samples taken from corresponding scanned areas were determined using a refractometer. Multiple linear regression models using two wavelengths were developed with second derivative spectral data and laboratory measurements of SS content. Multiple correlation coefficients (R) for individual cultivar calibrations within a single season ranged from 0.76 to 0.98 with standard error of calibration (SEC) values from 0.35% to 1.22%. Selected spectra and corresponding SS data in individual cultivar calibration data sets were combined to create season and cultivar calibration data sets to cover the entire range of SS contents within the season or within the cultivar. These combined calibrations resulted in R values of 0.92 to 0.97 with SEC values ranging from 0.37% to 0.79%. Simple correlations of validations (r) ranged from 0.20 to 0.94 and the standard error of prediction (SEP) ranged from 0.49% to 1.63% while the bias varied from -0.01% to -2.62%. Lower r values and higher SEP and bias values resulted when individual cultivar calibrations were used to predict SS levels in other cultivar validation data sets. Cultivar calibrations, season calibrations and the overall calibration predicted SS content of all validation data sets with a smaller bias and SEP and with higher r values. These results indicate that NIR spectrometry is suitable for rapid nondestructive determination of SS in peaches. Feasible applications of the method include packinghouse sorting of peaches for sweetness and parent and progeny fruit quality assessment in peach breeding programs. Using this technique fruit may be sorted into two or three sweetness classes. The technique may also potentially be extended to other fruit.
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Park, Byung-Seo, Woosuk Kim, Jin-Kyum Kim, Eui Seok Hwang, Dong-Wook Kim, and Young-Ho Seo. "3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview." Sensors 22, no. 3 (2022): 1097. http://dx.doi.org/10.3390/s22031097.
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This paper proposes a new technique for performing 3D static-point cloud registration after calibrating a multi-view RGB-D camera using a 3D (dimensional) joint set. Consistent feature points are required to calibrate a multi-view camera, and accurate feature points are necessary to obtain high-accuracy calibration results. In general, a special tool, such as a chessboard, is used to calibrate a multi-view camera. However, this paper uses joints on a human skeleton as feature points for calibrating a multi-view camera to perform calibration efficiently without special tools. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D joint set obtained through pose estimation as feature points. Since human body information captured by the multi-view camera may be incomplete, a joint set predicted based on image information obtained through this may be incomplete. After efficiently integrating a plurality of incomplete joint sets into one joint set, multi-view cameras can be calibrated by using the combined joint set to obtain extrinsic matrices. To increase the accuracy of calibration, multiple joint sets are used for optimization through temporal iteration. We prove through experiments that it is possible to calibrate a multi-view camera using a large number of incomplete joint sets.
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Wetherill, G. Z., and I. Murray. "The spread of the calibration set in near-infrared reflectance spectroscopy." Journal of Agricultural Science 109, no. 3 (1987): 539–44. http://dx.doi.org/10.1017/s0021859600081752.
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SummaryFrequently in near-infrared reflectance spectroscopy, a calibration is developed using very restricted data sets, e.g. material from one season, a small area or of a limited type: consequently, the predictions may have limited validity. This paper describes the use of both restricted and wide calibration sets for the prediction of crude protein in grass, silage and hay. Results show that predictions from the wider calibration sets are often as good as or better than predictions from restricted calibration sets. Therefore the use of wide calibration sets should be considered much more frequently in near-infrared reflectance.
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Moody, Jordan N., Reid Redden, Faron A. Pfeiffer, Ronald Pope, and John W. Walker. "PSV-15 Using near infrared reflectance spectroscopy to predict lab scoured yield in Rambouillet sheep." Journal of Animal Science 99, Supplement_3 (2021): 303. http://dx.doi.org/10.1093/jas/skab235.558.
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Abstract Lab scoured yield (LSY) is a major indicator of wool quality. LSY is used for the valuation of wool in commercial settings and can be used by growers as selection criteria for breeding stock. Current laboratory methods for LSY are costly and labor intensive. Evaluation of fleece core samples using Near-Infrared Reflectance Spectroscopy (NIR) may present an efficient, cost-effective alternative to predict LSY. Lamb and yearling fleece core samples from flocks originating from Texas were scanned on a FOSS 6500 spectrometer. Constituent data were obtained from the Bill Sims Wool and Mohair Laboratory using ASTM methodology. LSY ranged from 48–68%. Spectral data were pretreated with a 14 nm moving average and Savitsky-Golay 2nd derivative. Eight outlier spectra were removed. Samples were parsed from the center of the distribution to minimize the Dunn effect creating calibration (n = 108) and test (n = 41) sets. Calibrations were executed using a partial least squares regression on spectra from 1100 to 2492 nm. Test set calibration statistics for LSY were: r2=0.64, RMSE=3.39, and slope=0.91. Independent validation statistics for LSY using spectra for different years were: r2=0.33, RMSE=3.69, and slope=0.29. RMSE for independent validation and lab methods on side samples are similar. Between flock independent validations were less promising. Accuracy of laboratory methods for estimating yield is 2 percentage units. NIRS calibrations can be improved by developing calibration sets with a uniform distribution, which can be difficult within flocks because of the small number of fleeces in the tails of the distribution. These data demonstrate that when calibration and test sets are developed such that test samples are drawn from the calibration population, NIR is a reliable predictor of LSY. However, when test samples are drawn from populations dissimilar to the calibration set, reliability of NIR predictions are greatly reduced.
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Glick, Mark, and Gary M. Hieftje. "Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra." Applied Spectroscopy 45, no. 10 (1991): 1706–16. http://dx.doi.org/10.1366/0003702914335238.
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Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.
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Centner, Vítézslav, Jorge Verdú-Andrés, Beata Walczak, et al. "Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets." Applied Spectroscopy 54, no. 4 (2000): 608–23. http://dx.doi.org/10.1366/0003702001949816.
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The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recommendations are derived from the comparison, which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data. A flexible methodology is proposed to allow selection of an appropriate calibration technique for a given calibration problem.
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Казарян, A. Kazaryan, Ланкин, et al. "CALIBRATOR RFID IDENTIFY." Alternative energy sources in the transport-technological complex: problems and prospects of rational use of 2, no. 2 (2015): 455–58. http://dx.doi.org/10.12737/19309.
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The paper proposes a device for calibration devices of measuring AC and DC wide range. A distinctive feature of this device is the use of RFID - tags for automatic detection of the calibration parameters. This method consists in that the readable RFID-tag signal is received by the processor of the calibrator, which in turn sets require calibration mode. Application of the developed device will allow considerably increase the speed of measurement operations while maintaining high accuracy
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Schimleck, Laurence R., P. David Jones, Gary F. Peter, Richard F. Daniels, and Alexander Clark. "Success in Using near Infrared Spectroscopy to Estimate Wood Properties of Pinus Taeda Radial Strips Not Due to Autocorrelation." Journal of Near Infrared Spectroscopy 13, no. 1 (2005): 47–51. http://dx.doi.org/10.1255/jnirs.456.
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Near infrared (NIR) spectroscopy provides a rapid method for estimating several important wood properties of 10 mm sections of radial wooden strips. Successful calibrations have been obtained with NIR spectra collected from 3 to 16 consecutive 10 mm sections of the same wood core. The success of these calibrations might be due to an autocorrelation that exists between the adjacent sections of a core. In this study, we compared calibrations with spectra collected from consecutive 10 mm sections to calibrations obtained with spectra collected from unrelated 10 mm sections. Very similar calibration statistics were obtained with both sets of spectra, demonstrating that existing calibration success is not due to an autocorrelation.
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VALDES, E. V., G. E. JONES, and G. J. HOEKSTRA. "EFFECT OF GROWING YEAR AND APPLICATION OF A MULTI-YEAR CALIBRATION FOR PREDICTING QUALITY PARAMETERS BY NEAR INFRARED REFLECTANCE SPECTROSCOPY IN WHOLE-PLANT CORN FORAGE." Canadian Journal of Plant Science 70, no. 3 (1990): 747–55. http://dx.doi.org/10.4141/cjps90-092.
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Near infrared reflectance spectroscopy analysis (NIRA) to predict quality parameters in whole-plant corn forage was investigated. Quality parameters studied included acid detergent fiber (ADF), in vitro dry matter digestibility (IVDMD) and crude protein (CP). Samples of whole-plant corn forage were collected during three growing years (1984, 1985, 1986) across six geographical locations in Ontario, Canada and were harvested at an average 35% dry matter content. Samples were randomly divided into two sets: a calibration (CAL) set to develop NIRA equations and a testing (TEST) set to validate these equations. NIRA calibrations for ADF, IVDMD and CP percent were performed for each growing season across locations. A multi-year calibration was also developed with samples drawn from the three growing years. The accuracy of the NIRA predictions was assessed by the standard error of the estimate (SEE), bias or the mean difference between laboratory and NIRA data, the coefficient of determination (r2) and the slope (b) of the regressions between laboratory and NIRA data. The single year calibrations showed good predictions for all quality parameters in samples drawn within the year. The SEE values in the TEST sets for single year calibrations varied from 1.6 to 1.9% for ADF, 1.5 to 2.3% for IVDMD and 0.3 to 0.5% for CP, respectively. However, recalibration was necessary every year because calibrations based on a single year failed to account for variances introduced by samples drawn from other years. The multi-year calibration predicted ADF, IVDMD and CP percent accurately regardless of year. The SEE and bias for ADF, IVDMD and CP percent for the TEST set were 1.6 and 0.1, 2.2 and 0.3, and 0.5 and 0.1, respectively. The success of the multi-year calibration encourage the possibility to develop a standard calibration for whole-plant corn.Key words: Near infrared reflectance analysis, calibrations, whole-plant corn forage, year, multi-year quality parameters
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Chen, Hsuan-Yu, and Chiachung Chen. "Evaluation of Calibration Equations by Using Regression Analysis: An Example of Chemical Analysis." Sensors 22, no. 2 (2022): 447. http://dx.doi.org/10.3390/s22020447.
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A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established using regression analysis. An adequate calibration equation ensures the performance of these instruments. Most studies use linear and polynomial equations. This study uses data sets from previous studies. Four types of calibration equations are proposed: linear, higher-order polynomial, exponential rise to maximum and power equations. A constant variance test was performed to assess the suitability of calibration equations for this dataset. Suspected outliers in the data sets are verified. The standard error of the estimate errors, s, was used as criteria to determine the fitting performance. The Prediction Sum of Squares (PRESS) statistic is used to compare the prediction ability. Residual plots are used as quantitative criteria. Suspected outliers in the data sets are checked. The results of this study show that linear and higher order polynomial equations do not allow accurate calibration equations for many data sets. Nonlinear equations are suited to most of the data sets. Different forms of calibration equations are proposed. The logarithmic transformation of the response is used to stabilize non-constant variance in the response data. When outliers are removed, this calibration equation’s fit and prediction ability is significantly increased. The adequate calibration equations with the data sets obtained with the same equipment and laboratory indicated that the adequate calibration equations differed. No universe calibration equation could be found for these data sets. The method for this study can be used for other chemical instruments to establish an adequate calibration equation and ensure the best performance.
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Fisch, Roland D., and Gunther A. Strehlau. "A Simplified Approach to Calibration Confidence Sets." American Statistician 47, no. 3 (1993): 168. http://dx.doi.org/10.2307/2684969.
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Fisch, Roland D., and Günther A. Strehlau. "A Simplified Approach to Calibration Confidence Sets." American Statistician 47, no. 3 (1993): 168–71. http://dx.doi.org/10.1080/00031305.1993.10475969.
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Lee, Jeong Eun, Geoff K. Nicholls, and Robin J. Ryder. "Calibration Procedures for Approximate Bayesian Credible Sets." Bayesian Analysis 14, no. 4 (2019): 1245–69. http://dx.doi.org/10.1214/19-ba1175.
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Dickow, A., and G. Feiertag. "A systematic MEMS sensor calibration framework." Journal of Sensors and Sensor Systems 4, no. 1 (2015): 97–102. http://dx.doi.org/10.5194/jsss-4-97-2015.
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Abstract. In this paper we present a systematic method to determine sets of close to optimal sensor calibration points for a polynomial approximation. For each set of calibration points a polynomial is used to fit the nonlinear sensor response to the calibration reference. The polynomial parameters are calculated using ordinary least square fit. To determine the quality of each calibration, reference sensor data is measured at discrete test conditions. As an error indicator for the quality of a calibration the root mean square deviation between the calibration polynomial and the reference measurement is calculated. The calibration polynomials and the error indicators are calculated for all possible calibration point sets. To find close to optimal calibration point sets, the worst 99% of the calibration options are dismissed. This results in a multi-dimensional probability distribution of the probably best calibration point sets. In an experiment, barometric MEMS (micro-electromechanical systems) pressure sensors are calibrated using the proposed calibration method at several temperatures and pressures. The framework is applied to a batch of six of each of the following sensor types: Bosch BMP085, Bosch BMP180, and EPCOS T5400. Results indicate which set of calibration points should be chosen to achieve good calibration results.
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Ma, Zhenling, Xu Zhong, Hong Xie, Yongjun Zhou, Yuan Chen, and Jiali Wang. "A Combined Physical and Mathematical Calibration Method for Low-Cost Cameras in the Air and Underwater Environment." Sensors 23, no. 4 (2023): 2041. http://dx.doi.org/10.3390/s23042041.
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Low-cost camera calibration is vital in air and underwater photogrammetric applications. However, various lens distortions and the underwater environment influence are difficult to be covered by a universal distortion compensation model, and the residual distortions may still remain after conventional calibration. In this paper, we propose a combined physical and mathematical camera calibration method for low-cost cameras, which can adapt to both in-air and underwater environments. The commonly used physical distortion models are integrated to describe the image distortions. The combination is a high-order polynomial, which can be considered as basis functions to successively approximate the image deformation from the point of view of mathematical approximation. The calibration process is repeated until certain criteria are met and the distortions are reduced to a minimum. At the end, several sets of distortion parameters and stable camera interior orientation (IO) parameters act as the final camera calibration results. The Canon and GoPro in-air calibration experiments show that GoPro owns distortions seven times larger than Canon. Most Canon distortions have been described with the Australis model, while most decentering distortions for GoPro still exist. Using the proposed method, all the Canon and GoPro distortions are decreased to close to 0 after four calibrations. Meanwhile, the stable camera IO parameters are obtained. The GoPro Hero 5 Black underwater calibration indicates that four sets of distortion parameters and stable camera IO parameters are obtained after four calibrations. The camera calibration results show a difference between the underwater environment and air owing to the refractive and asymmetric environment effects. In summary, the proposed method improves the accuracy compared with the conventional method, which could be a flexible way to calibrate low-cost cameras for high accurate in-air and underwater measurement and 3D modeling applications.
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Jessen, W., S. Wilbert, B. Nouri, N. Geuder, and H. Fritz. "Calibration methods for rotating shadowband irradiometers and evaluation of calibration duration." Atmospheric Measurement Techniques Discussions 8, no. 10 (2015): 10249–82. http://dx.doi.org/10.5194/amtd-8-10249-2015.
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Abstract. Resource assessment for Concentrated Solar Power (CSP) needs accurate Direct Normal Irradiance (DNI) measurements. An option for such measurement campaigns are Rotating Shadowband Irradiometers (RSIs) with a thorough calibration. Calibration of RSIs and Si-sensors in general is complex because of the inhomogeneous spectral response of such sensors and incorporates the use of several correction functions. A calibration for a given atmospheric condition and air mass might not work well for a different condition. This paper covers procedures and requirements for two calibration methods for the calibration of Rotating Shadowband Irradiometers. The necessary duration of acquisition of test measurements is examined in regard to the site specific conditions at Plataforma Solar de Almeria (PSA) in Spain. Data sets of several long-term calibration periods from PSA are used to evaluate the deviation of results from calibrations with varying duration from the long-term result. The findings show that seasonal changes of environmental conditions are causing small but noticeable fluctuation of calibration results. Certain periods (i.e. November to January and April to May) show a higher likelihood of particularly adverse calibration results. These effects can partially be compensated by increasing the inclusions of measurements from outside these periods. Consequently, the duration of calibrations at PSA can now be selected depending on the time of the year in which measurements are commenced.
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Fang, Quanxiao, L. Ma, R. D. Harmel, et al. "Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm." Agronomy 9, no. 5 (2019): 241. http://dx.doi.org/10.3390/agronomy9050241.
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An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals.
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Beltrán, Berta. "Early commissioning calibration data sets for DEAP-3600." Journal of Physics: Conference Series 718 (May 2016): 042004. http://dx.doi.org/10.1088/1742-6596/718/4/042004.
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Conlin, A. K., E. B. Martin, and A. J. Morris. "Industrial Spectral Data Calibration from Minimal Data Sets." IFAC Proceedings Volumes 31, no. 11 (1998): 363–68. http://dx.doi.org/10.1016/s1474-6670(17)44954-8.
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Tellinghuisen, J. "Inverse vs. classical calibration for small data sets." Fresenius' Journal of Analytical Chemistry 368, no. 6 (2000): 585–88. http://dx.doi.org/10.1007/s002160000556.
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Ramirez-Lopez, Leonardo, Karsten Schmidt, Thorsten Behrens, Bas van Wesemael, Jose A. M. Demattê, and Thomas Scholten. "Sampling optimal calibration sets in soil infrared spectroscopy." Geoderma 226-227 (August 2014): 140–50. http://dx.doi.org/10.1016/j.geoderma.2014.02.002.
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Gharari, S., M. Hrachowitz, F. Fenicia, and H. H. G. Savenije. "Moving beyond traditional model calibration or how to better identify realistic model parameters: sub-period calibration." Hydrology and Earth System Sciences Discussions 9, no. 2 (2012): 1885–918. http://dx.doi.org/10.5194/hessd-9-1885-2012.
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Abstract. Conceptual hydrological models often rely on calibration for the identification of their parameters. As these models are typically designed to reflect real catchment processes, a key objective of an appropriate calibration strategy is the determination of parameter sets that reflect a "realistic" model behavior. Previous studies have shown that parameter estimates for different calibration periods can be significantly different. This questions model transposability in time, which is one of the key conditions for the set-up of a "realistic" model. This paper presents a new approach that selects parameter sets that provide a consistent model performance in time. The approach consists of confronting model performance in different periods, and selecting parameter sets that are as close as possible to the optimum of each individual sub-period. While aiding model calibration, the approach is also useful as a diagnostic tool, illustrating tradeoffs in the identification of time consistent parameter sets. The approach is demonstrated in a case study where we illustrate the multi-objective calibration of the HyMod hydrological model to a Luxembourgish catchment.
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Melfsen, Andreas, Eberhard Hartung, and Angelika Haeussermann. "Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process." Journal of Dairy Research 80, no. 1 (2012): 103–12. http://dx.doi.org/10.1017/s0022029912000672.
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The robustness of in-line raw milk analysis with near-infrared spectroscopy (NIRS) was tested with respect to the prediction of the raw milk contents fat, protein and lactose. Near-infrared (NIR) spectra of raw milk (n = 3119) were acquired on three different farms during the milking process of 354 milkings over a period of six months. Calibration models were calculated for: a random data set of each farm (fully random internal calibration); first two thirds of the visits per farm (internal calibration); whole datasets of two of the three farms (external calibration), and combinations of external and internal datasets. Validation was done either on the remaining data set per farm (internal validation) or on data of the remaining farms (external validation). Excellent calibration results were obtained when fully randomised internal calibration sets were used for milk analysis. In this case, RPD values of around ten, five and three for the prediction of fat, protein and lactose content, respectively, were achieved. Farm internal calibrations achieved much poorer prediction results especially for the prediction of protein and lactose with RPD values of around two and one respectively. The prediction accuracy improved when validation was done on spectra of an external farm, mainly due to the higher sample variation in external calibration sets in terms of feeding diets and individual cow effects. The results showed that further improvements were achieved when additional farm information was added to the calibration set. One of the main requirements towards a robust calibration model is the ability to predict milk constituents in unknown future milk samples. The robustness and quality of prediction increases with increasing variation of, e.g., feeding and cow individual milk composition in the calibration model.
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Tolleson, D. R., and J. P. Angerer. "The application of near infrared spectroscopy to predict faecal nitrogen and phosphorus in multiple ruminant herbivore species." Rangeland Journal 42, no. 6 (2020): 415. http://dx.doi.org/10.1071/rj20071.
Texte intégralRésumé :
Near infrared spectroscopy (NIRS) was applied to determine faecal nitrogen and phosphorus using a temporo-spatially diverse dataset derived from multiple ruminant herbivore species (i.e. cattle, bison, deer, elk, goats, and sheep). Single-species NIRS calibrations have previously been developed to predict faecal constituents. Multi-species NIRS calibrations have previously been developed for herbivore faecal nitrogen but not for faecal phosphorus. Faecal samples representing a herd or flock composite were analysed via NIRS (400–2498nm). Calibration sets for faecal nitrogen and phosphorus were developed from: (1) all samples from all six species, (2) all cattle samples only, (3) all samples except those from bison, (4) all samples except those from deer, (5) all samples except those from elk, (6) all samples except those from goats, and (7) all samples except those from sheep. Validation sample sets included: (1) each of the individual species (predicted with a cattle only-derived calibration), and (2) each of the individual species (other than cattle) predicted with a multi-species calibration constructed from all cattle samples plus those samples from the remaining four species (i.e. ‘leave-one-out’). All multiple coefficient of determination (R2) values for faecal nitrogen calibrations were ≥0.97. Corresponding standard error of cross validation (SECV) values were ≤0.13. Validation simple coefficient of determination (r2) and standard error of prediction (SEP) of each alternate species using the cattle-derived calibration ranged from 0.76 to 0.84, and 0.28 to 0.5 respectively. Similar values for the sequential species leave-one-out validation for faecal nitrogen were 0.67 to 0.89, and 0.17 to 0.47 respectively. All R2 values for faecal phosphorus calibrations were ≥0.79; corresponding SECV were ≤0.14. Validation r2 and SEP of each alternate species using the cattle-derived phosphorus calibration were ≤0.63 and ≥0.13 respectively. Similar values for the sequential species leave-one-out validation were ≤0.66 and ≥0.22 respectively for faecal phosphorus. Multi-species faecal NIRS calibrations can be developed for monitoring applications in which determination of faecal nitrogen is appropriate, e.g. free-ranging herbivore nutrition, nitrogen deposition from animal faeces on rangelands with declining forage quality, or runoff from confined animal feeding operations. Similar calibrations for faecal phosphorus require additional research to ascertain their applicability.
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Hogg, Alan, Christopher Bronk Ramsey, Chris Turney, and Jonathan Palmer. "Bayesian Evaluation of the Southern Hemisphere Radiocarbon Offset during the Holocene." Radiocarbon 51, no. 4 (2009): 1165–76. http://dx.doi.org/10.1017/s0033822200034226.
Texte intégralRésumé :
While an interhemispheric offset in atmospheric radiocarbon levels from AD 1950–950 is now well established, its existence earlier in the Holocene is less clear, with some studies reporting globally uniform 14C levels while others finding Southern Hemisphere samples older by a few decades. In this paper, we present a method for wiggle-matching Southern Hemisphere data sets against Northern Hemisphere curves, using the Bayesian calibration program OxCal 4.1 with the Reservoir Offset function accommodating a potential interhemispheric offset. The accuracy and robustness of this approach is confirmed by wiggle-matching known-calendar age sequences of the Southern Hemisphere calibration curve SHCal04 against the Northern Hemisphere curve IntCal04. We also show that 5 of 9 Holocene Southern Hemisphere data sets are capable of yielding reliable offset information. Those data sets that are accurate and precise show that interhemispheric offset levels in the Early Holocene are similar to modern levels, confirming SHCal04 as the curve of choice for calibrating Southern Hemisphere samples.
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Cosenza, Diogo N., Luísa Gomes Pereira, Juan Guerra-Hernández, Adrián Pascual, Paula Soares, and Margarida Tomé. "Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach." Remote Sensing 12, no. 6 (2020): 918. http://dx.doi.org/10.3390/rs12060918.
Texte intégralRésumé :
Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height.
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Sudarno, Divo D. Silalahi, Tauvik Risman, et al. "Rapid determination of oil content in dried-ground oil palm mesocarp and kernel using near infrared spectroscopy." Journal of Near Infrared Spectroscopy 25, no. 5 (2017): 338–47. http://dx.doi.org/10.1177/0967033517732679.
Texte intégralRésumé :
Near infrared spectroscopy calibrations for rapid oil content determination of dried-ground oil palm mesocarp and kernel were developed. Samples were analyzed, one set using the Soxhlet extraction method for reference analysis and the other set scanned by near infrared spectroscopy instrument for calibration. Successful calibrations were obtained with good accuracy and precision for mesocarp and kernel, based on statistical models. Math treatment and scatter correction had significant effects on the fitting of the calibration model. The best obtained calibration models were demonstrated by multiple correlation coefficient (R2), standard error of calibration, standard error of cross validation, coefficient of determination in cross validation (1-VR) and relative predictive deviation of calibration, which respectively were 0.997, 1.21%, 1.23%, 0.997 and 17.89 for mesocarp and 0.952, 0.47%, 0.53%, 0.94 and 4.00 for kernel. The correlations between reference and predicted values for samples in the validation sets were in agreement with high linearity, high ratio performance to deviation of prediction (≥4.00) and low standard error of prediction samples for both samples. The results demonstrated that near infrared spectroscopy can be used as an alternative and reliable technique to estimate the mesocarp and kernel oil contents in dry matter basis accurately and rapidly.
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Jessen, Wilko, Stefan Wilbert, Bijan Nouri, Norbert Geuder, and Holger Fritz. "Calibration methods for rotating shadowband irradiometers and optimizing the calibration duration." Atmospheric Measurement Techniques 9, no. 4 (2016): 1601–12. http://dx.doi.org/10.5194/amt-9-1601-2016.
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Abstract. Resource assessment for concentrated solar power (CSP) needs accurate direct normal irradiance (DNI) measurements. An option for such measurement campaigns is the use of thoroughly calibrated rotating shadowband irradiometers (RSIs). Calibration of RSIs and Si-sensors is complex because of the inhomogeneous spectral response of these sensors and incorporates the use of several correction functions. One calibration for a given atmospheric condition and air mass might not be suitable under different conditions. This paper covers procedures and requirements of two calibration methods for the calibration of rotating shadowband irradiometers. The necessary duration of acquisition of test measurements is examined with regard to the site-specific conditions at Plataforma Solar de Almería (PSA) in Spain. Seven data sets of long-term test measurements were collected. For each data set, calibration results of varying durations were compared to its respective long-term result. Our findings show that seasonal changes of environmental conditions are causing small but noticeable fluctuation of calibration results. Calibration results within certain periods (i.e. November to January and April to May) show a higher likelihood of deviation. These effects can partially be attenuated by including more measurements from outside these periods. Consequently, the duration of calibrations at PSA can now be selected depending on the time of year in which measurements commence.
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Nogales-Bueno, Julio, Francisco José Rodríguez-Pulido, Berta Baca-Bocanegra, et al. "Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models." Foods 10, no. 2 (2021): 233. http://dx.doi.org/10.3390/foods10020233.
Texte intégralRésumé :
Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally representative samples within a large population set. In this study, principal component and hierarchical clustering analyses have been compared for their ability to provide different representative calibration sets. The calibration sets generated have been used to control the technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars. Finally, the accuracy and precision of the models obtained with these calibration sets resulted from the application of the selection algorithms studied have been compared with each other and with the whole set of samples using an external validation set. Most of the standard errors of prediction (SEP) in external validation obtained from the reduced data sets were not significantly different from those obtained using the whole data set. Moreover, sample subsets resulting from hierarchical clustering analysis appear to produce slightly better results.
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Czarnik-Matusewicz, Henryk W., and Adolf Korniewicz. "Determination of Capsaicin in the Antirheumatical Plasters by near Infrared Reflectance Spectroscopy: A Comparison of Statistical Methods." Journal of Near Infrared Spectroscopy 6, A (1998): A181—A184. http://dx.doi.org/10.1255/jnirs.191.
Texte intégralRésumé :
The evaluation of near infrared (NIR) reflectance spectroscopy as a method for the determination of capsaicin (8-methyl-N-vanillyl-6-nonenamide)—an active ingredient in the antirheumatical plasters was examined. The analytical procedure for determining the capsaicin was carried out by conventional, time-consuming colorimetric method. Spectra of the 76 plaster samples were recorded in reflectance mode at 2 nm intervals in the range 1100–2500 nm using InfraAlyzer 500 (Bran+Luebbe GmbH). A comparison is made between two regression methods, stepwise multiple linear regression (MLR) and partial least squares regression (PLS). MLR and PLS regression were used for calibrations, with the aid of the software SESAME ver. 2.10 (Bran+Luebbe GmbH). The PLS method showed consistently lower standard error of calibration and higher R values with first and second difference equations. The first difference PLS regression equation resulted in standard error of calibration of 0.018 %, with an R of 0.95. Generalizability of both methods for prediction of capsaicin contents on independent data sets is discussed. Prediction accuracy for independent data sets was increased using PLS regression, but was poor for sample sets with laboratory-measured concentration ranges beyond those of the calibration set. The results in this study indicate that NIR technique has a high applicability to quantitative analysis of capsaicin content in antirheumatical plasters.
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He, Zhong Hai, Xin Pan Wang, and Zhen He Ma. "Found Robust Calibration Model in Fermentation Process by Combining Different Sample Sets." Applied Mechanics and Materials 667 (October 2014): 372–75. http://dx.doi.org/10.4028/www.scientific.net/amm.667.372.
Texte intégralRésumé :
Robust calibration model is the key factor for spectroscopic measurement. The commonly used way is build calibration model by crude samples collected from process. There are two deficiencies in fermentation processes in that: matrix effects variation always existed for different batch of fermentation and, chance correlation is sure existed which spoil robust of calibration model. In the research being presented, method of overcoming the weakness is examined. By combing the standard analyte and fermentation broth and spiking solvend, the calibration model can correlate specific spectral features only with the measured analyte concentrations so have good robustness.
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Gharari, S., M. Hrachowitz, F. Fenicia, and H. H. G. Savenije. "An approach to identify time consistent model parameters: sub-period calibration." Hydrology and Earth System Sciences 17, no. 1 (2013): 149–61. http://dx.doi.org/10.5194/hess-17-149-2013.
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Abstract. Conceptual hydrological models rely on calibration for the identification of their parameters. As these models are typically designed to reflect real catchment processes, a key objective of an appropriate calibration strategy is the determination of parameter sets that reflect a "realistic" model behavior. Previous studies have shown that parameter estimates for different calibration periods can be significantly different. This questions model transposability in time, which is one of the key conditions for the set-up of a "realistic" model. This paper presents a new approach that selects parameter sets that provide a consistent model performance in time. The approach consists of testing model performance in different periods, and selecting parameter sets that are as close as possible to the optimum of each individual sub-period. While aiding model calibration, the approach is also useful as a diagnostic tool, illustrating tradeoffs in the identification of time-consistent parameter sets. The approach is applied to a case study in Luxembourg using the HyMod hydrological model as an example.
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Hua, Quan, and Mike Barbetti. "Review of Tropospheric Bomb 14C Data for Carbon Cycle Modeling and Age Calibration Purposes." Radiocarbon 46, no. 3 (2004): 1273–98. http://dx.doi.org/10.1017/s0033822200033142.
Texte intégralRésumé :
Comprehensive published radiocarbon data from selected atmospheric records, tree rings, and recent organic matter were analyzed and grouped into 4 different zones (three for the Northern Hemisphere and one for the whole Southern Hemisphere). These 14C data for the summer season of each hemisphere were employed to construct zonal, hemispheric, and global data sets for use in regional and global carbon model calculations including calibrating and comparing carbon cycle models. In addition, extended monthly atmospheric 14C data sets for 4 different zones were compiled for age calibration purposes. This is the first time these data sets were constructed to facilitate the dating of recent organic material using the bomb 14C curves. The distribution of bomb 14C reflects the major zones of atmospheric circulation.
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Steharnik, Mirjana, Marija Todorovic, Dragan Manojlovic, Dalibor Stankovic, Jelena Mutic, and Vlastimir Trujic. "Determination of trace elements in refined gold samples by inductively coupled plasma atomic emission spectrometry." Journal of the Serbian Chemical Society 78, no. 4 (2013): 565–77. http://dx.doi.org/10.2298/jsc120505135s.
Texte intégralRésumé :
This paper presents a method for determination the trace contents of silver, copper, iron, palladium, zinc and platinum in refined gold samples. Simultaneous inductively coupled plasma atomic emission spectrometer with radial torch position and cross flow nebulizer was used for determination. In order to compare the different calibration strategies, two sets of calibration standards were prepared. The first set was based on matrix matched calibration standards and the second was prepared without the addition of matrix material. Detection limits for matrix matching calibrations were higher for some elements than those without matrix matching. In addition, the internal standardization method was applied and experiments indicated that indium was the best option as internal standard. The obtained results for gold sample by matrix matching and matrix free calibrations were compared with the obtained results by standard addition method. The accuracy of the methods was tested performing recovery test. Recoveries for spiked sample were in the range of 90-115 %. The accuracy of the methods was also tested by analysis of certified reference material of high pure goldAuGHP1. The best results were achieved by matrix free calibration and standard addition method using indium as internal standard at wavelength of 230 nm.
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Henclewood, Dwayne, Wonho Suh, Michael O. Rodgers, Richard Fujimoto, and Michael P. Hunter. "A calibration procedure for increasing the accuracy of microscopic traffic simulation models." SIMULATION 93, no. 1 (2016): 35–47. http://dx.doi.org/10.1177/0037549716673723.
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Efforts to address operational issues in transportation have been the focus of many research efforts. A number of these efforts were geared toward developing microscopic traffic simulation models to accurately represent the complex and dynamic operation of a transportation network. One of the challenges with such models is that they do not always adequately reflect field conditions—particularly when representing traffic operations across different time periods. This paper presents a robust calibration procedure that aims to increase the accuracy of calibrated microscopic traffic simulation models. This procedure is based on a Monte Carlo approach to generate candidate parameter sets, which are aimed to produce calibrated simulation models. Model runs of these parameter sets are evaluated against robust calibration criteria, including startup and saturation flow characteristics and travel time distributions. The parameter sets that satisfy these criteria are considered as adequately calibrated to accurately reflect field performance measures. In applying this procedure, the results suggest that this approach offers a robust and effective method of calibrating simulation models where disaggregate-level vehicle data are available—which is becoming more prevalent with further advancements in mobile sensor and connected vehicle technologies.
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Andraka, Dariusz, Iwona Piszczatowska, Jacek Dawidowicz, and Wojciech Kruszyński. "Calibration of Activated Sludge Model with Scarce Data Sets." Journal of Ecological Engineering 19, no. 6 (2018): 182–90. http://dx.doi.org/10.12911/22998993/93793.
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Vasconcelos, Francisco, Joao P. Barreto, and Edmond Boyer. "Automatic Camera Calibration Using Multiple Sets of Pairwise Correspondences." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 4 (2018): 791–803. http://dx.doi.org/10.1109/tpami.2017.2699648.
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Pedrycz, W., R. R. Gudwin, and F. A. C. Gomide. "Nonlinear context adaptation in the calibration of fuzzy sets." Fuzzy Sets and Systems 88, no. 1 (1997): 91–97. http://dx.doi.org/10.1016/s0165-0114(96)00057-7.
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Söhl, Jakob. "Confidence sets in nonparametric calibration of exponential Lévy models." Finance and Stochastics 18, no. 3 (2014): 617–49. http://dx.doi.org/10.1007/s00780-014-0228-9.
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Tang, Yongquan, Martin J. Turner, Johnny S. Yem, and A. Barry Baker. "Calibration of pneumotachographs using a calibrated syringe." Journal of Applied Physiology 95, no. 2 (2003): 571–76. http://dx.doi.org/10.1152/japplphysiol.00196.2003.
Texte intégralRésumé :
Pneumotachograph require frequent calibration. Constant-flow methods allow polynomial calibration curves to be derived but are time consuming. The iterative syringe stroke technique is moderately efficient but results in discontinuous conductance arrays. This study investigated the derivation of first-, second-, and third-order polynomial calibration curves from 6 to 50 strokes of a calibration syringe. We used multiple linear regression to derive first-, second-, and third-order polynomial coefficients from two sets of 6–50 syringe strokes. In part A, peak flows did not exceed the specified linear range of the pneumotachograph, whereas flows in part B peaked at 160% of the maximum linear range. Conductance arrays were derived from the same data sets by using a published algorithm. Volume errors of the calibration strokes and of separate sets of 70 validation strokes ( part A) and 140 validation strokes ( part B) were calculated by using the polynomials and conductance arrays. Second- and third-order polynomials derived from 10 calibration strokes achieved volume variability equal to or better than conductance arrays derived from 50 strokes. We found that evaluation of conductance arrays using the calibration syringe strokes yields falsely low volume variances. We conclude that accurate polynomial curves can be derived from as few as 10 syringe strokes, and the new polynomial calibration method is substantially more time efficient than previously published conductance methods.
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Sun, Wenchao, Xiaolei Yao, Na Cao, Zongxue Xu, and Jingshan Yu. "Integration of soil hydraulic characteristics derived from pedotransfer functions into hydrological models: evaluation of its effects on simulation uncertainty." Hydrology Research 47, no. 5 (2016): 964–78. http://dx.doi.org/10.2166/nh.2016.150.
Texte intégralRésumé :
Aimed at reducing simulation uncertainty of hydrological models in data-sparse basins where soil hydraulic data are unavailable, a method of estimating soil water parameters of soil and water assessment tool (SWAT) from readily available soil information using pedotransfer functions was introduced. The method was evaluated through a case study of Jinjiang Basin, China and was performed based on comparison between two model calibrations: (1) soil parameters estimated from pedotransfer functions and other parameters obtained from calibration; and (2) all parameters derived from calibration. The generalized likelihood uncertainty estimation (GLUE) was used as a model calibration and uncertainty analysis tool. The results show that information contained in streamflow data is insufficient to derive physically reasonable soil parameter values via calibration. The proposed method can reduce simulation uncertainty, resulting from greater average performance of behavioral parameter sets identified by GLUE. Exploring the parameter space reveals that the means of estimating soil parameters has little influence on other parameters. These facts indicate the decrease in uncertainty most likely results from a more realistic description of soil water characteristics than calibration. Thus, the proposed method is superior to calibration for estimating soil parameters of SWAT model when basin data are sparse.
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Tabasi, Ali, Maria Lazzaroni, Niels P. Brouwer, et al. "Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control." Sensors 22, no. 1 (2021): 87. http://dx.doi.org/10.3390/s22010087.
Texte intégralRésumé :
The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 nm (9% of peak moment) and increased up to 35.0 nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 nm (10% of peak moment), and not substantially different than that of the extensive calibration.
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Auinger, Hans-Jürgen, Christina Lehermeier, Daniel Gianola, et al. "Calibration and validation of predicted genomic breeding values in an advanced cycle maize population." Theoretical and Applied Genetics 134, no. 9 (2021): 3069–81. http://dx.doi.org/10.1007/s00122-021-03880-5.
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Abstract Key message Model training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets. Abstract The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.
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Muschalla, D., S. Schneider, K. Schröter, V. Gamerith, and G. Gruber. "Sewer modelling based on highly distributed calibration data sets and multi-objective auto-calibration schemes." Water Science and Technology 57, no. 10 (2008): 1547–54. http://dx.doi.org/10.2166/wst.2008.305.
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Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002. In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two build-up wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. However, until now, convective storms have not been convincingly represented. The build-up wash-off approach using the basic wash-off equation shows the best correlations between measured data and simulation results. As one of the chosen objectives for the multi-objective optimisation reacted highly sensitively to measurement errors, additional improvements can be expected after refining the criteria used in this algorithm.