Multivariate calibration applied to the quantitative analysis of infrared spectra
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
NASA Astrophysics Data System (ADS)
Long, C. L.
1991-02-01
Multivariate calibration techniques can reduce the time required for routine testing and can provide new methods of analysis. Multivariate calibration is commonly used with near infrared reflectance analysis (NIRA) and Fourier transform infrared (FTIR) spectroscopy. Two feasibility studies were performed to determine the capability of NIRA, using multivariate calibration techniques, to perform analyses on the types of samples that are routinely analyzed at this laboratory. The first study performed included a variety of samples and indicated that NIRA would be well-suited to perform analyses on selected materials properties such as water content and hydroxyl number on polyol samples, epoxy content on epoxy resins, water content of desiccants, and the amine values of various amine cure agents. A second study was performed to assess the capability of NIRA to perform quantitative analysis of hydroxyl numbers and water contents of hydroxyl-containing materials. Hydroxyl number and water content were selected for determination because these tests are frequently run on polyol materials and the hydroxyl number determination is time consuming. This study pointed out the necessity of obtaining calibration standards identical to the samples being analyzed for each type of polyol or other material being analyzed. Multivariate calibration techniques are frequently used with FTIR data to determine the composition of a large variety of complex mixtures. A literature search indicated many applications of multivariate calibration to FTIR data. Areas identified where quantitation by FTIR would provide a new capability are quantitation of components in epoxy and silicone resins, polychlorinated biphenyls (PCBs) in oils, and additives to polymers.
NASA Astrophysics Data System (ADS)
Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.
2013-09-01
Four simple, accurate and specific methods were developed and validated for the simultaneous estimation of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in commercial tablets. The derivative spectrophotometric methods include Derivative Ratio Zero Crossing (DRZC) and Double Divisor Ratio Spectra-Derivative Spectrophotometry (DDRS-DS) methods, while the multivariate calibrations used are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods were applied successfully in the determination of the drugs in laboratory-prepared mixtures and in commercial pharmaceutical preparations. The validity of the proposed methods was assessed using the standard addition technique. The linearity of the proposed methods is investigated in the range of 2-32, 4-44 and 2-20 μg/mL for AML, VAL and HCT, respectively.
Darwish, Hany W; Hassan, Said A; Salem, Maissa Y; El-Zeany, Badr A
2013-09-01
Four simple, accurate and specific methods were developed and validated for the simultaneous estimation of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in commercial tablets. The derivative spectrophotometric methods include Derivative Ratio Zero Crossing (DRZC) and Double Divisor Ratio Spectra-Derivative Spectrophotometry (DDRS-DS) methods, while the multivariate calibrations used are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods were applied successfully in the determination of the drugs in laboratory-prepared mixtures and in commercial pharmaceutical preparations. The validity of the proposed methods was assessed using the standard addition technique. The linearity of the proposed methods is investigated in the range of 2-32, 4-44 and 2-20 μg/mL for AML, VAL and HCT, respectively.
Adaptable Multivariate Calibration Models for Spectral Applications
THOMAS,EDWARD V.
1999-12-20
Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.
Exploration of new multivariate spectral calibration algorithms.
Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J.; Guenard, Robert D.
2004-03-01
A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery∗
Liu, Han; Wang, Lie; Zhao, Tuo
2016-01-01
We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O(1/ϵ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/. PMID:28316509
Multivariate Seismic Calibration for the Novaya Zemlya Test Site
1992-09-30
every multivariate magnitude combination. A classical confidence interval is presented to estimate future yields, based on estimates of the unknown...multivariate calibration parameters. A test of TTBT compliance and a definition of the F-number, based on the confidence interval , are also provided. F
Calibrated predictions for multivariate competing risks models.
Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni
2014-04-01
Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.
Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration
Chang, Haitao; Zhu, Lianqing; Lou, Xiaoping; Meng, Xiaochen; Guo, Yangkuan; Wang, Zhongyu
2016-01-01
One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20–200 µg/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity. PMID:27271636
Ultrasonic sensor for predicting sugar concentration using multivariate calibration.
Krause, D; Hussein, W B; Hussein, M A; Becker, T
2014-08-01
This paper presents a multivariate regression method for the prediction of maltose concentration in aqueous solutions. For this purpose, time and frequency domain of ultrasonic signals are analyzed. It is shown, that the prediction of concentration at different temperatures is possible by using several multivariate regression models for individual temperature points. Combining these models by a linear approximation of each coefficient over temperature results in a unified solution, which takes temperature effects into account. The benefit of the proposed method is the low processing time required for analyzing online signals as well as the non-invasive sensor setup which can be used in pipelines. Also the ultrasonic signal sections used in the presented investigation were extracted out of buffer reflections which remain primarily unaffected by bubble and particle interferences. Model calibration was performed in order to investigate the feasibility of online monitoring in fermentation processes. The temperature range investigated was from 10 °C to 21 °C. This range fits to fermentation processes used in the brewing industry. This paper describes the processing of ultrasonic signals for regression, the model evaluation as well as the input variable selection. The statistical approach used for creating the final prediction solution was partial least squares (PLS) regression validated by cross validation. The overall minimum root mean squared error achieved was 0.64 g/100 g.
Multivariate analysis applied to tomato hybrid production.
Balasch, S; Nuez, F; Palomares, G; Cuartero, J
1984-11-01
Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.
Pan, Wenxiu; Zhao, Jiewen; Chen, Quansheng
2015-01-01
An optical sensor system, namely NIR laser scatter imaging system, was developed for rapid and noninvasive classification of foodborne pathogens. This developed system was used for images acquisition. The current study is focused on exploring the potential of this system combined with multivariate calibrations in classifying three categories of popular bacteria. Initially, normalization and Zernike moments extraction were performed, and the resultant translation, scale and rotation invariances were applied as the characteristic variables for subsequent discriminant analysis. Both linear (LDA, KNN and PLSDA) and nonlinear (BPANN, SVM and OSELM) pattern recognition methods were employed comparatively for modeling, and optimized by cross validation. Experimental results showed that the performances of nonlinear tools were superior to those of linear tools, especially for OSELM model with 95% discrimination rate in the prediction set. The overall results showed that it is extremely feasible for rapid and noninvasive classifying foodborne pathogens using this developed system combined with appropriate multivariate calibration. PMID:25860918
Silva Spinacé, M A; Lucato, M U; Ferrão, M F; Davanzo, C U; De Paoli, M-A
2006-05-15
A methodology was developed to determine the intrinsic viscosity of poly(ethylene terephthalate) (PET) using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and multivariate calibration (MVC) methods. Multivariate partial least squares calibration was applied to the spectra using mean centering and cross validation. The results were correlated to the intrinsic viscosities determined by the standard chemical method (ASTM D 4603-01) and a very good correlation for values in the range from 0.346 to 0.780dLg(-1) (relative viscosity values ca. 1.185-1.449) was observed. The spectrophotometer detector sensitivity and the humidity of the samples did not influence the results. The methodology developed is interesting because it does not produce hazardous wastes, avoids the use of time-consuming chemical methods and can rapidly predict the intrinsic viscosity of PET samples over a large range of values, which includes those of recycled materials.
A Bayesian, multivariate calibration for Globigerinoides ruber Mg/Ca
NASA Astrophysics Data System (ADS)
Khider, D.; Huerta, G.; Jackson, C.; Stott, L. D.; Emile-Geay, J.
2015-09-01
The use of Mg/Ca in marine carbonates as a paleothermometer has been challenged by observations that implicate salinity as a contributing influence on Mg incorporation into biotic calcite and that dissolution at the sea-floor alters the original Mg/Ca. Yet, these factors have not yet been incorporated into a single calibration model. We introduce a new Bayesian calibration for Globigerinoides ruber Mg/Ca based on 186 globally distributed core top samples, which explicitly takes into account the effect of temperature, salinity, and dissolution on this proxy. Our reported temperature, salinity, and dissolution (here expressed as deep-water ΔCO32-) sensitivities are (±2σ) 8.7±0.9%/°C, 3.9±1.2%/psu, and 3.3±1.3%/μmol.kg-1 below a critical threshold of 21 μmol/kg in good agreement with previous culturing and core-top studies. We then perform a sensitivity experiment on a published record from the western tropical Pacific to investigate the bias introduced by these secondary influences on the interpretation of past temperature variability. This experiment highlights the potential for misinterpretations of past oceanographic changes when the secondary influences of salinity and dissolution are not accounted for. Multiproxy approaches could potentially help deconvolve the contributing influences but this awaits better characterization of the spatio-temporal relationship between salinity and δ18Osw over millennial and orbital timescales.
Poláček, Roman; Májek, Pavel; Hroboňová, Katarína; Sádecká, Jana
2016-04-01
Fluoxetine is the most prescribed antidepressant chiral drug worldwide. Its enantiomers have a different duration of serotonin inhibition. A novel simple and rapid method for determination of the enantiomeric composition of fluoxetine in pharmaceutical pills is presented. Specifically, emission, excitation, and synchronous fluorescence techniques were employed to obtain the spectral data, which with multivariate calibration methods, namely, principal component regression (PCR) and partial least square (PLS), were investigated. The chiral recognition of fluoxetine enantiomers in the presence of β-cyclodextrin was based on diastereomeric complexes. The results of the multivariate calibration modeling indicated good prediction abilities. The obtained results for tablets were compared with those from chiral HPLC and no significant differences are shown by Fisher's (F) test and Student's t-test. The smallest residuals between reference or nominal values and predicted values were achieved by multivariate calibration of synchronous fluorescence spectral data. This conclusion is supported by calculated values of the figure of merit.
Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy
Wolfrum, E.; Payne, C.; Stefaniak, T.; Rooney, W.; Dighe, N.; Bean, B.; Dahlberg, J.
2013-03-01
NREL developed calibration models based on near-infrared (NIR) spectroscopy coupled with multivariate statistics to predict compositional properties relevant to cellulosic biofuels production for a variety of sorghum cultivars. A robust calibration population was developed in an iterative fashion. The quality of models developed using the same sample geometry on two different types of NIR spectrometers and two different sample geometries on the same spectrometer did not vary greatly.
Dai, J; Yaylayan, V A; Raghavan, G S; Parè, J R; Liu, Z
2001-03-01
Two-component and multivariate calibration techniques were developed for the simultaneous quantification of total azadirachtin-related limonoids (AZRL) and simple terpenoids (ST) in neem extracts using vanillin assay. A mathematical modeling method was also developed to aid in the analysis of the spectra and to simplify the calculations. The mathematical models were used in a two-component calibration (using azadirachtin and limonene as standards) for samples containing mainly limonoids and terpenoids (such as neem seed kernel extracts). However, for the extracts from other parts of neem, such as neem leaf, a multivariate calibration was necessary to eliminate the possible interference from phenolics and other components in order to obtain the accurate content of AZRL and ST. It was demonstrated that the accuracy of the vanillin assay in predicting the content of azadirachtin in a model mixture containing limonene (25% w/w) can be improved from 50% overestimation to 95% accuracy using the two-component calibration, while predicting the content of limonene with 98% accuracy. Both calibration techniques were applied to estimate the content of AZRL and ST in different parts of the neem plant. The results of this study indicated that the relative content of limonoids was much higher than that of the terpenoids in all parts of the neem plant studied.
NASA Astrophysics Data System (ADS)
Singh, Veena D.; Daharwal, Sanjay J.
2017-01-01
Three multivariate calibration spectrophotometric methods were developed for simultaneous estimation of Paracetamol (PARA), Enalapril maleate (ENM) and Hydrochlorothiazide (HCTZ) in tablet dosage form; namely multi-linear regression calibration (MLRC), trilinear regression calibration method (TLRC) and classical least square (CLS) method. The selectivity of the proposed methods were studied by analyzing the laboratory prepared ternary mixture and successfully applied in their combined dosage form. The proposed methods were validated as per ICH guidelines and good accuracy; precision and specificity were confirmed within the concentration range of 5-35 μg mL- 1, 5-40 μg mL- 1 and 5-40 μg mL- 1of PARA, HCTZ and ENM, respectively. The results were statistically compared with reported HPLC method. Thus, the proposed methods can be effectively useful for the routine quality control analysis of these drugs in commercial tablet dosage form.
Fuentes, Edwar; Cid, Camila; Báez, María E
2015-03-01
This paper presents a new method for the determination of imidacloprid in water samples; one of the most widely used neonicotinoid pesticides in the farming industry. The method is based on the measurement of excitation-emission spectra of photo-induced fluorescence (PIF-EEMs) associated with second-order multivariate calibration with a parallel factor analysis (PARAFAC) and unfolded partial least squares coupled to residual bilinearization (U-PLS/RBL). The second order advantage permitted the determination of imidacloprid in the presence of potential interferences, which also shows photo-induced fluorescence (other pesticides and/or unexpected compounds of the real samples). The photoreaction was performed in 100-μl disposable micropipettes. As a preliminary step, solid phase extraction on C18 (SPE-C18) was applied to concentrate the analyte and diminish the limit of detection. The LOD was approximately 1 ng mL(-1), which is suitable for detecting imidacloprid in water according to the guidelines established in North America and Europe. The PIF-EEMs coupled to PARAFAC or U-PLS/RBL was successfully applied for the determination of imidacloprid in different real water samples, with an average recovery of 101±10%.
Abdolmaleki, Azizeh; Ghasemi, Jahan B; Shiri, Fereshteh; Pirhadi, Somayeh
2015-01-01
Data manipulation and maximum efficient extraction of useful information need a range of searching, modeling, mathematical, and statistical approaches. Hence, an adequate multivariate characterization is the first necessary step in investigation and the results are interpreted after multivariate analysis. Multivariate data analysis is capable of not only large dataset management but also interpret them surely and rapidly. Application of chemometrics and cheminformatics methods may be useful for design and discovery of new drug compounds. In this review, we present a variety of information sources on chemometrics, which we consider useful in different fields of drug design. This review describes exploratory analysis (PCA), classification and multivariate calibration (PCR, PLS) methods to data analysis. It summarizes the main facts of linear and nonlinear multivariate data analysis in drug discovery and provides an introduction to manipulation of data in this field. It handles the fundamental aspects of basic concepts of multivariate methods, principles of projections (PCA and PLS) and introduces the popular modeling and classification techniques. Enough theory behind these methods, more particularly concerning the chemometrics tools is included for those with little experience in multivariate data analysis techniques such as PCA, PLS, SIMCA, etc. We describe each method by avoiding unnecessary equations, and details of calculation algorithms. It provides a synopsis of the method followed by cases of applications in drug design (i.e., QSAR) and some of the features for each method.
de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.
2014-01-01
Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625
Kay, D; McDonald, A
1983-01-01
This paper reports on the calibration and use of a multiple regression model designed to predict concentrations of Escherichia coli and total coliforms in two upland British impoundments. The multivariate approach has improved predictive capability over previous univariate linear models because it includes predictor variables for the timing and magnitude of hydrological input to the reservoirs and physiochemical parameters of water quality. The significance of these results for catchment management research is considered. PMID:6639016
Hernandez, Silvia R; Kergaravat, Silvina V; Pividori, Maria Isabel
2013-03-15
An approach based on the electrochemical detection of the horseradish peroxidase enzymatic reaction by means of square wave voltammetry was developed for the determination of phenolic compounds in environmental samples. First, a systematic optimization procedure of three factors involved in the enzymatic reaction was carried out using response surface methodology through a central composite design. Second, the enzymatic electrochemical detection coupled with a multivariate calibration method based in the partial least-squares technique was optimized for the determination of a mixture of five phenolic compounds, i.e. phenol, p-aminophenol, p-chlorophenol, hydroquinone and pyrocatechol. The calibration and validation sets were built and assessed. In the calibration model, the LODs for phenolic compounds oscillated from 0.6 to 1.4 × 10(-6) mol L(-1). Recoveries for prediction samples were higher than 85%. These compounds were analyzed simultaneously in spiked samples and in water samples collected close to tanneries and landfills.
Chotimah, Chusnul; Sudjadi; Riyanto, Sugeng; Rohman, Abdul
2015-01-01
Purpose: Analysis of drugs in multicomponent system officially is carried out using chromatographic technique, however, this technique is too laborious and involving sophisticated instrument. Therefore, UV-VIS spectrophotometry coupled with multivariate calibration of partial least square (PLS) for quantitative analysis of metamizole, thiamin and pyridoxin is developed in the presence of cyanocobalamine without any separation step. Methods: The calibration and validation samples are prepared. The calibration model is prepared by developing a series of sample mixture consisting these drugs in certain proportion. Cross validation of calibration sample using leave one out technique is used to identify the smaller set of components that provide the greatest predictive ability. The evaluation of calibration model was based on the coefficient of determination (R2) and root mean square error of calibration (RMSEC). Results: The results showed that the coefficient of determination (R2) for the relationship between actual values and predicted values for all studied drugs was higher than 0.99 indicating good accuracy. The RMSEC values obtained were relatively low, indicating good precision. The accuracy and presision results of developed method showed no significant difference compared to those obtained by official method of HPLC. Conclusion: The developed method (UV-VIS spectrophotometry in combination with PLS) was succesfully used for analysis of metamizole, thiamin and pyridoxin in tablet dosage form. PMID:26819934
Hegazy, M A; Yehia, A M; Moustafa, A A
2013-05-01
The ability of bivariate and multivariate spectrophotometric methods was demonstrated in the resolution of a quaternary mixture of mosapride, pantoprazole and their degradation products. The bivariate calibrations include bivariate spectrophotometric method (BSM) and H-point standard addition method (HPSAM), which were able to determine the two drugs, simultaneously, but not in the presence of their degradation products, the results showed that simultaneous determinations could be performed in the concentration ranges of 5.0-50.0 microg/ml for mosapride and 10.0-40.0 microg/ml for pantoprazole by bivariate spectrophotometric method and in the concentration ranges of 5.0-45.0 microg/ml for both drugs by H-point standard addition method. Moreover, the applied multivariate calibration methods were able for the determination of mosapride, pantoprazole and their degradation products using concentration residuals augmented classical least squares (CRACLS) and partial least squares (PLS). The proposed multivariate methods were applied to 17 synthetic samples in the concentration ranges of 3.0-12.0 microg/ml mosapride, 8.0-32.0 microg/ml pantoprazole, 1.5-6.0 microg/ml mosapride degradation products and 2.0-8.0 microg/ml pantoprazole degradation products. The proposed bivariate and multivariate calibration methods were successfully applied to the determination of mosapride and pantoprazole in their pharmaceutical preparations.
Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration.
Godinho, Robson B; Santos, Mauricio C; Poppi, Ronei J
2016-03-15
An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies.
Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration
NASA Astrophysics Data System (ADS)
Godinho, Robson B.; Santos, Mauricio C.; Poppi, Ronei J.
2016-03-01
An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies.
Sikorska, Ewa; Gliszczyńska-Swigło, Anna; Insińska-Rak, Małgorzata; Khmelinskii, Igor; De Keukeleire, Denis; Sikorski, Marek
2008-04-21
The study demonstrates an application of the front-face fluorescence spectroscopy combined with multivariate regression methods to the analysis of fluorescent beer components. Partial least-squares regressions (PLS1, PLS2, and N-way PLS) were utilized to develop calibration models between synchronous fluorescence spectra and excitation-emission matrices of beers, on one hand, and analytical concentrations of riboflavin and aromatic amino acids, on the other hand. The best results were obtained in the analysis of excitation-emission matrices using the N-way PLS2 method. The respective correlation coefficients, and the values of the root mean-square error of cross-validation (RMSECV), expressed as percentages of the respective mean analytic concentrations, were: 0.963 and 14% for riboflavin, 0.974 and 4% for tryptophan, 0.980 and 4% for tyrosine, and 0.982 and 19% for phenylalanine.
Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration.
de Carvalho, Bruna Mara Aparecida; de Carvalho, Lorendane Millena; dos Reis Coimbra, Jane Sélia; Minim, Luis Antônio; de Souza Barcellos, Edilton; da Silva Júnior, Willer Ferreira; Detmann, Edenio; de Carvalho, Gleidson Giordano Pinto
2015-05-01
A rapid method for the detection and quantification of the adulteration of milk powder by the addition of whey was assessed by measuring glycomacropeptide protein using mid-infrared spectroscopy (MIR). Fluid milk samples were dried and then spiked with different concentrations of GMP and whey. Calibration models were developed using multivariate techniques, from spectral data. For the principal component analysis and discriminant analysis, excellent percentages of correct classification were achieved in accordance with the increase in the proportion of whey samples. For partial least squares regression analysis, the correlation coefficient (r) and root mean square error of prediction (RMSEP) in the best model were 0.9885 and 1.17, respectively. The rapid analysis, low cost monitoring and high throughput number of samples tested per unit time indicate that MIR spectroscopy may hold potential as a rapid and reliable method for detecting milk powder frauds using cheese whey.
NASA Astrophysics Data System (ADS)
Tan, Chao; Wang, Jinyue; Wu, Tong; Qin, Xin; Li, Menglong
2010-12-01
Based on the combination of uninformative variable elimination (UVE), bootstrap and mutual information (MI), a simple ensemble algorithm, named ESPLS, is proposed for spectral multivariate calibration (MVC). In ESPLS, those uninformative variables are first removed; and then a preparatory training set is produced by bootstrap, on which a MI spectrum of retained variables is calculated. The variables that exhibit higher MI than a defined threshold form a subspace on which a candidate partial least-squares (PLS) model is constructed. This process is repeated. After a number of candidate models are obtained, a small part of models is picked out to construct an ensemble model by simple/weighted average. Four near/mid-infrared (NIR/MIR) spectral datasets concerning the determination of six components are used to verify the proposed ESPLS. The results indicate that ESPLS is superior to UVEPLS and its combination with MI-based variable selection (SPLS) in terms of both the accuracy and robustness. Besides, from the perspective of end-users, ESPLS does not increase the complexity of a calibration when enhancing its performance.
Felipe-Sotelo, M; Cal-Prieto, M J; Gómez-Carracedo, M P; Andrade, J M; Carlosena, A; Prada, D
2006-07-07
Analysis of solid samples by slurry-sampling-electrothermal atomic absorption spectrometry (SS-ETAAS) can imply spectral and chemical interferences caused by the large amount of concomitants introduced into the graphite furnace. Sometimes they cannot be solved using stabilized temperature platform furnace (STPF) conditions or typical approaches (previous sample ashing, use of chemical modifiers, etc.), which are time consuming and quite expensive. A new approach to handle interferences using multivariate calibrations (partial least squares, PLS, and artificial neural networks, ANN) is presented and exemplified with a real problem consisting on determining Sb in several solid matrices (soils, sediments and coal fly ash) as slurries by ETAAS. Experimental designs were implemented at different levels of Sb to develop the calibration matrix and assess which concomitants (seven ions were considered) modified the atomic signal mostly. They were Na+ and Ca2+ and they induced simultaneous displacement, depletion (enhancement) and broadening of the atomic peak. Here it is shown that these complex effects can be handled in a reliable, fast and cost-effective way to predict the concentration of Sb in slurry samples of several solid matrices. The method was validated predicting the concentrations of five certified reference materials (CRMs) and studying its robustness to current ETAAS problems. It is also shown that linear PLS can handle eventual non-linearities and that its results are comparable to more complex (non-linear) models, as those from ANNs.
Gryniewicz-Ruzicka, Connie M; Arzhantsev, Sergey; Pelster, Lindsey N; Westenberger, Benjamin J; Buhse, Lucinda F; Kauffman, John F
2011-03-01
The transfer of a multivariate calibration model for quantitative determination of diethylene glycol (DEG) contaminant in pharmaceutical-grade glycerin between five portable Raman spectrometers was accomplished using piecewise direct standardization (PDS). The calibration set was developed using a multi-range ternary mixture design with successively reduced impurity concentration ranges. It was found that optimal selection of calibration transfer standards using the Kennard-Stone algorithm also required application of the algorithm to multiple successively reduced impurity concentration ranges. Partial least squares (PLS) calibration models were developed using the calibration set measured independently on each of the five spectrometers. The performance of the models was evaluated based on the root mean square error of prediction (RMSEP), calculated using independent validation samples. An F-test showed that no statistical differences in the variances were observed between models developed on different instruments. Direct cross-instrument prediction without standardization was performed between a single primary instrument and each of the four secondary instruments to evaluate the robustness of the primary instrument calibration model. Significant increases in the RMSEP values for the secondary instruments were observed due to instrument variability. Application of piecewise direct standardization using the optimal calibration transfer subset resulted in the lowest values of RMSEP for the secondary instruments. Using the optimal calibration transfer subset, an optimized calibration model was developed using a subset of the original calibration set, resulting in a DEG detection limit of 0.32% across all five instruments.
Tonello, Natalia; Moressi, Marcela Beatriz; Robledo, Sebastián Noel; D'Eramo, Fabiana; Marioli, Juan Miguel
2016-09-01
The simultaneous determination of eugenol (EU), thymol (Ty) and carvacrol (CA) in honey samples, employing square wave voltammetry (SWV) and chemometrics tools, is informed for the first time. For this purpose, a glassy carbon electrode (GCE) was used as working electrode. The operating conditions and influencing parameters (involving several chemical and instrumental parameters) were first optimized by cyclic voltammetry (CV). Thus, the effects of the scan rate, pH and analyte concentration on the electrochemical response of the above mentioned molecules were studied. The results show that the electrochemical responses of the three compounds are very similar and that the voltammetric traces present a high degree of overlap under all the experimental conditions used in this study. Therefore, two chemometric tools were tested to obtain the multivariate calibration model. One method was the partial least squares regression (PLS-1), which assumes a linear behaviour. The other nonlinear method was an artificial neural network (ANN). In this last case we used a supervised, feed-forward network with Levenberg-Marquardt back propagation training. From the accuracies and precisions analysis between nominal and estimated concentrations calculated by using both methods, it was inferred that the ANN method was a good model to quantify EU, Ty and CA in honey samples. Recovery percentages were between 87% and 104%, except for two samples whose values were 136% and 72%. The analytical methodology was simple, fast and accurate.
Yun, Yong-Huan; Wang, Wei-Ting; Tan, Min-Li; Liang, Yi-Zeng; Li, Hong-Dong; Cao, Dong-Sheng; Lu, Hong-Mei; Xu, Qing-Song
2014-01-07
Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.
Multivariate calibration modeling of liver oxygen saturation using near-infrared spectroscopy
NASA Astrophysics Data System (ADS)
Cingo, Ndumiso A.; Soller, Babs R.; Puyana, Juan C.
2000-05-01
The liver has been identified as an ideal site to spectroscopically monitor for changes in oxygen saturation during liver transplantation and shock because it is susceptible to reduced blood flow and oxygen transport. Near-IR spectroscopy, combined with multivariate calibration techniques, has been shown to be a viable technique for monitoring oxygen saturation changes in various organs in a minimally invasive manner. The liver has a dual system circulation. Blood enters the liver through the portal vein and hepatic artery, and leaves through the hepatic vein. Therefore, it is of utmost importance to determine how the liver NIR spectroscopic information correlates with the different regions of the hepatic lobule as the dual circulation flows from the presinusoidal space into the post sinusoidal region of the central vein. For NIR spectroscopic information to reliably represent the status of liver oxygenation, the NIR oxygen saturation should best correlate with the post-sinusoidal region. In a series of six pigs undergoing induced hemorrhagic chock, NIR spectra collected from the liver were used together with oxygen saturation reference data from the hepatic and portal veins, and an average of the two to build partial least-squares regression models. Results obtained from these models show that the hepatic vein and an average of the hepatic and portal veins provide information that is best correlate with NIR spectral information, while the portal vein reference measurement provides poorer correlation and accuracy. These results indicate that NIR determination of oxygen saturation in the liver can provide an assessment of liver oxygen utilization.
NASA Astrophysics Data System (ADS)
Samadi-Maybodi, Abdolraouf; Hassani Nejad-Darzi, Seyed Karim
2010-04-01
Resolution of binary mixtures of paracetamol, phenylephrine hydrochloride and chlorpheniramine maleate with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable, principal component regression and hybrid linear analysis. Data of analysis were obtained from UV-vis spectra of the above compounds. The method of central composite design was used in the ranges of 1-15 mg L -1 for both calibration and validation sets. The models refinement procedure and their validation were performed by cross-validation. Figures of merit such as selectivity, sensitivity, analytical sensitivity and limit of detection were determined for all three compounds. The procedure was successfully applied to simultaneous determination of the above compounds in pharmaceutical tablets.
Samadi-Maybodi, Abdolraouf; Hassani Nejad-Darzi, Seyed Karim
2010-04-01
Resolution of binary mixtures of paracetamol, phenylephrine hydrochloride and chlorpheniramine maleate with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable, principal component regression and hybrid linear analysis. Data of analysis were obtained from UV-vis spectra of the above compounds. The method of central composite design was used in the ranges of 1-15 mg L(-1) for both calibration and validation sets. The models refinement procedure and their validation were performed by cross-validation. Figures of merit such as selectivity, sensitivity, analytical sensitivity and limit of detection were determined for all three compounds. The procedure was successfully applied to simultaneous determination of the above compounds in pharmaceutical tablets.
Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM
ERIC Educational Resources Information Center
Warner, Rebecca M.
2007-01-01
This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…
NASA Astrophysics Data System (ADS)
Han, Tongshuai; Zhang, Ziyang; Sun, Cuiying; Guo, Chao; Sun, Di; Liu, Jin
2016-10-01
The measurement accuracy of non-invasive blood glucose concentration (BGC) sensing with near-infrared spectroscopy is easily affected by the temperature variation in tissue because it would induce an unacceptable spectrum variation and the consequent prediction deviation. We use a multivariable correction method based on external parameter orthogonalization (EPO) to calibrate the spectral data recorded at different temperature values to reduce the spectral variation. The tested medium is a kind of tissue phantom, the Intralipid aqueous solution. The calibration uses a projection matrix to get the orthogonal spectral space to the variable of external parameter, i.e. temperature, and then the useful spectral information relative to glucose concentration has been reserved. Even more, training the projection matrix can be separated to building the calibration matrix for the prediction of glucose concentration as it only uses the representative samples' data with temperature variation. The method presents a lower complexity than modeling a robust prediction matrix, which can be built from comprehensive spectral data involved the all variables both of BGC and temperature. In our test, the calibrated spectra with the same glucose concentration but different temperature values show a significantly improved repeatability. And then the glucose concentration prediction results show a lower root mean squared error of prediction (RMSEP) than that using the robust calibration model, which has considered the two variables. We also discuss the rationality of the representative samples chosen by EPO. This research may be referenced to the temperature calibration for in vivo BGC sensing.
Stenlund, Hans; Johansson, Erik; Gottfries, Johan; Trygg, Johan
2009-01-01
Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood's anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in
A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil
NASA Technical Reports Server (NTRS)
Negri, A. J.; Xu, L.; Adler, R. F.; Einaudi, Franco (Technical Monitor)
2000-01-01
The development of a satellite infrared technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall in Amazonia are presented. The Convective-Stratiform. Technique, calibrated by coincident, physically retrieved rain rates from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI), is applied during January to April 1999 over northern South America. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall is presented. Results compare well (a one-hour lag) with the diurnal cycle derived from Tropical Ocean-Global Atmosphere (TOGA) radar-estimated rainfall in Rondonia. The satellite estimates reveal that the convective rain constitutes, in the mean, 24% of the rain area while accounting for 67% of the rain volume. The effects of geography (rivers, lakes, coasts) and topography on the diurnal cycle of convection are examined. In particular, the Amazon River, downstream of Manaus, is shown to both enhance early morning rainfall and inhibit afternoon convection. Monthly estimates from this technique, dubbed CST/TMI, are verified over a dense rain gage network in the state of Ceara, in northeast Brazil. The CST/TMI showed a high bias equal to +33% of the gage mean, indicating that possibly the TMI estimates alone are also high. The root mean square difference (after removal of the bias) equaled 36.6% of the gage mean. The correlation coefficient was 0.77 based on 72 station-months.
A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil
NASA Technical Reports Server (NTRS)
Negri, Andrew J.; Xu, L.; Adler, R. F.; Einaudi, Franco (Technical Monitor)
2001-01-01
A satellite infrared (IR) technique for estimating rainfall over northern South America is presented. The objectives are to examine the diurnal variability of rainfall and to investigate the relative contributions from the convective and stratiform components. In this study, we apply the Convective-Stratiform Technique (CST) of Adler and Negri (1988). The parameters of the original technique were re-calibrated using coincident rainfall estimates (Olson et W., 2000) derived from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI) and GOES IR (11 micrometer) observations. Local circulations were found to play a major role in modulating the rainfall and its diurnal cycle. These included land/sea circulations (notably along the northeast Brazilian coast and in the Gulf of Panama), mountain/valley circulations (along the Andes Mountains), and circulations associated with the presence of rivers. This last category was examined in detail along the Amazon R. east of Manaus. There we found an early morning rainfall maximum along the river (5 LT at 58W, 3 LT at 56W). Rainfall avoids the river in the afternoon (12 LT and later), notably at 56 W. The width of the river seems to be generating a land/river circulation which enhances early morning rainfall but inhibits afternoon rainfall. Results are compared to ground-based radar data collected during the Large-Scale Biosphere-Atmosphere (LBA) experiment in southwest Brazil, to monthly raingages in northeastern Brazil, and to data from the TRMM Precipitation Radar.
Li, Yan; Wang, Jun-de; Chen, Zuo-ru; Zhou, Xue-tie; Huang, Zhong-hua
2002-10-01
The concentration determination abilities of four multivariate calibration methods--classical least squares (CLS), partial least squares (PLS), kalman filter method (KFM) and artificial neural network (ANN) were compared in this paper. Five air toxic organic compounds--1,3-butadiene, benzene, o-xylen, chlorobenzene, and acrolein--whose FTIR spectra seriously overlap each other were selected to compose the analytical objects. The evaluation criterion was according to the mean prediction error (MPE) and mean relative error (MRE). Results showed that PLS was superior to other methods when treating multicomponent analysis problem, while there was no comparable difference between CLS, KFM and ANN.
Collado, M S; Mantovani, V E; Goicoechea, H C; Olivieri, A C
2000-08-16
The use of multivariate spectrophotometric calibration for the simultaneous determination of several active components and excipients in ophthalmic solutions is presented. The resolution of five-component mixtures of phenylephrine, chloramphenicol, antipyrine, methylparaben and thimerosal has been accomplished by using partial least-squares (PLS-1) and a variant of the so-called hybrid linear analysis (HLA). Notwithstanding the presence of a large number of components and their high degree of spectral overlap, they have been determined simultaneously with high accuracy and precision, with no interference, rapidly and without resorting to extraction procedures using non aqueous solvents. A simple and fast method for wavelength selection in the calibration step is presented, based on the minimisation of the predicted error sum of squares (PRESS) calculated as a function of a moving spectral window.
Collado, M S; Robles, J C; De Zan, M; Cámara, M S; Mantovani, V E; Goicoechea, H C
2001-10-23
The use of multivariate spectrophotometric calibration for the simultaneous determination of dexamethasone and two typical excipients (creatinine and propylparaben) in injections is presented. The resolution of the three-component mixture in a matrix of excipients has been accomplished by using partial least-squares (PLS-1). Notwithstanding the elevated degree of spectral overlap, they have been rapidly and simultaneously determined with high accuracy and precision (comparable to the HPLC pharmacopeial method), with no interference, and without resorting to extraction procedures using non-aqueous solvents. A simple and fast method for wavelength selection in the calibration step is used, based on the minimisation of the predicted error sum of squares (PRESS) calculated as a function of a moving spectral window.
Guenard, Robert D; Wehlburg, Christine M; Pell, Randy J; Haaland, David M
2007-07-01
This paper reports on the transfer of calibration models between Fourier transform near-infrared (FT-NIR) instruments from four different manufacturers. The piecewise direct standardization (PDS) method is compared with the new hybrid calibration method known as prediction augmented classical least squares/partial least squares (PACLS/PLS). The success of a calibration transfer experiment is judged by prediction error and by the number of samples that are flagged as outliers that would not have been flagged as such if a complete recalibration were performed. Prediction results must be acceptable and the outlier diagnostics capabilities must be preserved for the transfer to be deemed successful. Previous studies have measured the success of a calibration transfer method by comparing only the prediction performance (e.g., the root mean square error of prediction, RMSEP). However, our study emphasizes the need to consider outlier detection performance as well. As our study illustrates, the RMSEP values for a calibration transfer can be within acceptable range; however, statistical analysis of the spectral residuals can show that differences in outlier performance can vary significantly between competing transfer methods. There was no statistically significant difference in the prediction error between the PDS and PACLS/PLS methods when the same subset sample selection method was used for both methods. However, the PACLS/PLS method was better at preserving the outlier detection capabilities and therefore was judged to have performed better than the PDS algorithm when transferring calibrations with the use of a subset of samples to define the transfer function. The method of sample subset selection was found to make a significant difference in the calibration transfer results using the PDS algorithm, while the transfer results were less sensitive to subset selection when the PACLS/PLS method was used.
NASA Astrophysics Data System (ADS)
Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen
2014-10-01
To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.
Applying the multivariate time-rescaling theorem to neural population models
Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon
2011-01-01
Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436
Gao, J B; Hu, X Y; Hu, D C
2001-12-01
In order to solve the problems of feature extraction and calibration modelling in the area of quantitatively infrared spectra analysis, an input layer self-constructive neural network (ILSC-NN) is proposed. Before the NN training process, the training data is firstly analyzed and some prior knowledge about the problem is obtained. During the training process, the number of the input neurons is determined adaptively based on the prior knowledge. Meantime, the network parameters are also determined. This algorithm of the NN model helps to increase the efficiency of calibration modelling. The test experiment of quantitative analysis using simulated spectral data showed that this modelling method could not only achieve efficient wavelength selection, but also remarkably reduce the random and non-linear noises.
Applying Hierarchical Model Calibration to Automatically Generated Items.
ERIC Educational Resources Information Center
Williamson, David M.; Johnson, Matthew S.; Sinharay, Sandip; Bejar, Isaac I.
This study explored the application of hierarchical model calibration as a means of reducing, if not eliminating, the need for pretesting of automatically generated items from a common item model prior to operational use. Ultimately the successful development of automatic item generation (AIG) systems capable of producing items with highly similar…
Calibration and investigation of infrared camera systems applying blackbody radiation
NASA Astrophysics Data System (ADS)
Hartmann, Juergen; Fischer, Joachim
2001-03-01
An experimental facility is presented, which allows calibration and detailed investigation of infrared camera systems. Various blackbodies operating in the temperature range from -60 degree(s)C up to 3000 degree(s)C serve as standard radiation sources, enabling calibration of camera systems in a wide temperature and spectral range with highest accuracy. Quantitative results and precise long-term investigations, especially in detecting climatic trends, require accurate traceability to the International Temperature Scale of 1990 (ITS-90). For the used blackbodies the traceability to ITS- 90 is either realized by standard platinum resistance thermometers (in the temperature range below 962 degree(s)C) or by absolute and relative radiometry (in the temperature range above 962 degree(s)C). This traceability is fundamental for implementation of quality assurance systems and realization of different standardizations, for example according ISO 9000. For investigation of the angular and the temperature resolution our set-up enables minimum resolvable (MRTD) and minimum detectable temperature difference (MDTD) measurements in the various temperature ranges. A collimator system may be used to image the MRTD and MDTD targets to infinity. As internal calibration of infrared camera systems critically depends on the temperature of the surrounding, the calibration and investigation of the cameras is performed in a climate box, which allows a detailed controlling of the environmental parameters like humidity and temperature. Experimental results obtained for different camera systems are presented and discussed.
Meira, Marilena; Quintella, Cristina M; Tanajura, Alessandra Dos Santos; da Silva, Humbervânia Reis Gonçalves; Fernando, Jaques D'Erasmo Santos; da Costa Neto, Pedro R; Pepe, Iuri M; Santos, Mariana Andrade; Nascimento, Luciana Lordelo
2011-07-15
Oxidation stability is an important quality parameter for biodiesel. In general, the methods used to evaluate the oxidation stability of oils and biodiesels are time-consuming. This work reports the use of spectrofluorimetry, a fast analytical technique, associated with multivariate data analysis as a powerful analytical tool to prediction of the oxidation stability. The prediction of the oxidation stability showed a good agreement with the results obtained by the EN14112 reference method Rancimat. The models presented high correlation (0.99276 and 0.97951) between real and predicted values. The R(2) values of 0.98557 and 0.95943 indicated the accuracy of the models to predict the oxidation stability of soy oil and soy biodiesel, respectively. The residual distribution does not follow a trend with respect to the predicted variables indicating the good quality of the fits.
Tawakkol, Shereen M; Farouk, M; Elaziz, Omar Abd; Hemdan, A; Shehata, Mostafa A
2014-12-10
Three simple, accurate, reproducible, and selective methods have been developed and subsequently validated for the simultaneous determination of Moexipril (MOX) and Hydrochlorothiazide (HCTZ) in pharmaceutical dosage form. The first method is the new extended ratio subtraction method (EXRSM) coupled to ratio subtraction method (RSM) for determination of both drugs in commercial dosage form. The second and third methods are multivariate calibration which include Principal Component Regression (PCR) and Partial Least Squares (PLSs). A detailed validation of the methods was performed following the ICH guidelines and the standard curves were found to be linear in the range of 10-60 and 2-30 for MOX and HCTZ in EXRSM method, respectively, with well accepted mean correlation coefficient for each analyte. The intra-day and inter-day precision and accuracy results were well within the acceptable limits.
NASA Astrophysics Data System (ADS)
Tawakkol, Shereen M.; Farouk, M.; Elaziz, Omar Abd; Hemdan, A.; Shehata, Mostafa A.
2014-12-01
Three simple, accurate, reproducible, and selective methods have been developed and subsequently validated for the simultaneous determination of Moexipril (MOX) and Hydrochlorothiazide (HCTZ) in pharmaceutical dosage form. The first method is the new extended ratio subtraction method (EXRSM) coupled to ratio subtraction method (RSM) for determination of both drugs in commercial dosage form. The second and third methods are multivariate calibration which include Principal Component Regression (PCR) and Partial Least Squares (PLSs). A detailed validation of the methods was performed following the ICH guidelines and the standard curves were found to be linear in the range of 10-60 and 2-30 for MOX and HCTZ in EXRSM method, respectively, with well accepted mean correlation coefficient for each analyte. The intra-day and inter-day precision and accuracy results were well within the acceptable limits.
Liu, Xianhua; Wang, Lili
2015-01-01
A series of ultraviolet-visible (UV-Vis) spectra from seawater samples collected from sites along the coastline of Tianjin Bohai Bay in China were subjected to multivariate partial least squares (PLS) regression analysis. Calibration models were developed for monitoring chemical oxygen demand (COD) and concentrations of total organic carbon (TOC). Three different PLS models were developed using the spectra from raw samples (Model-1), diluted samples (Model-2), and diluted and raw samples combined (Model-3). Experimental results showed that: (i) possible nonlinearities in the signal concentration relationships were well accounted for by the multivariate PLS model; (ii) the predicted values of COD and TOC fit the analytical values well; the high correlation coefficients and small root mean squared error of cross-validation (RMSECV) showed that this method can be used for seawater quality monitoring; and (iii) compared with Model-1 and Model-2, Model-3 had the highest coefficient of determination (R2) and the lowest number of latent variables. This latter finding suggests that only large data sets that include data representing different combinations of conditions (i.e., various seawater matrices) will produce stable site-specific regressions. The results of this study illustrate the effectiveness of the proposed method and its potential for use as a seawater quality monitoring technique.
Zhang, Jie; Stonnington, Cynthia; Li, Qingyang; Shi, Jie; Bauer, Robert J.; Gutman, Boris A.; Chen, Kewei; Reiman, Eric M.; Thompson, Paul M.; Ye, Jieping; Wang, Yalin
2016-01-01
Alzheimer’s disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD risk presymptomatically. Here, we applied a recently developed multivariate tensor-based morphometry (mTBM) method to extract features from hippocampal surfaces, derived from anatomical brain MRI. For such surface-based features, the feature dimension is usually much larger than the number of subjects. We used dictionary learning and sparse coding to effectively reduce the feature dimensions. With the new features, an Adaboost classifier was employed for binary group classification. In tests on publicly available data from the Alzheimers Disease Neuroimaging Initiative, the new framework outperformed several standard imaging measures in classifying different stages of AD. The new approach combines the efficiency of sparse coding with the sensitivity of surface mTBM, and boosts classification performance. PMID:27499829
Silva, Maurício A M; Ferreira, Marcus H; Braga, Jez W B; Sena, Marcelo M
2012-01-30
This paper proposes a new method for determination of amoxicillin in pharmaceutical suspension formulations, based on transflectance near infrared (NIR) measurements and partial least squares (PLS) multivariate calibration. A complete methodology was implemented for developing the proposed method, including an experimental design, data preprocessing by using multiple scatter correction (MSC) and outlier detection based on high values of leverage, and X and Y residuals. The best PLS model was obtained with seven latent variables in the range from 40.0 to 65.0 mg mL(-1) of amoxicillin, providing a root mean square error of prediction (RMSEP) of 1.6 mg mL(-1). The method was validated in accordance with Brazilian and international guidelines, through the estimate of figures of merit, such as linearity, precision, accuracy, robustness, selectivity, analytical sensitivity, limits of detection and quantitation, and bias. The results for determinations in four commercial pharmaceutical formulations were in agreement with the official high performance liquid chromatographic (HPLC) method at the 99% confidence level. A pseudo-univariate calibration curve was also obtained based on the net analyte signal (NAS). The proposed chemometric method presented the advantages of rapidity, simplicity, low cost, and no use of solvents, compared to the principal alternative methods based on HPLC.
Waskitho, Dri; Lukitaningsih, Endang; Sudjadi; Rohman, Abdul
2016-01-01
Analysis of lard extracted from lipstick formulation containing castor oil has been performed using FTIR spectroscopic method combined with multivariate calibration. Three different extraction methods were compared, namely saponification method followed by liquid/liquid extraction with hexane/dichlorometane/ethanol/water, saponification method followed by liquid/liquid extraction with dichloromethane/ethanol/water, and Bligh & Dyer method using chloroform/methanol/water as extracting solvent. Qualitative and quantitative analysis of lard were performed using principle component (PCA) and partial least square (PLS) analysis, respectively. The results showed that, in all samples prepared by the three extraction methods, PCA was capable of identifying lard at wavelength region of 1200-800 cm(-1) with the best result was obtained by Bligh & Dyer method. Furthermore, PLS analysis at the same wavelength region used for qualification showed that Bligh and Dyer was the most suitable extraction method with the highest determination coefficient (R(2)) and the lowest root mean square error of calibration (RMSEC) as well as root mean square error of prediction (RMSEP) values.
Hassaninejad-Darzi, Seyed Karim; Samadi-Maybodi, Abdolraouf; Nikou, Seyed Mohsen
2016-01-01
Resolution of binary mixtures of theophylline (THEO), montelukast (MKST) and loratadine (LORA) with minimum sample pre-treatment and without analyte separation has been successfully achieved by multivariate spectrophotometric calibration, together with partial least-squares (PLS-1), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV-Vis spectra of three compounds. The method of central composite design was used in the ranges of 2-14 and 3-11 mg L(-1) for calibration and validation sets, respectively. The models refinement procedure and their validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 0.173 mg L(-1) for THEO with PCR, 0.187 mg L(-1) for MKST with PLS1 and 0.251 mg L(-1) for LORA with HLA techniques. The limit of detection was obtained 0.03, 0.05 and 0.05 mg L(-1) by PCR model for THEO, MKST and LORA, respectively. The procedure was successfully applied for simultaneous determination of the above compounds in pharmaceutical tablets and human plasma. Notwithstanding the spectral overlapping among three drugs, as well as the intrinsic variability of the latter in unknown samples, the recoveries are excellent.
Hassaninejad-Darzi, Seyed Karim; Samadi-Maybodi, Abdolraouf; Nikou, Seyed Mohsen
2016-01-01
Resolution of binary mixtures of theophylline (THEO), montelukast (MKST) and loratadine (LORA) with minimum sample pre-treatment and without analyte separation has been successfully achieved by multivariate spectrophotometric calibration, together with partial least-squares (PLS-1), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV–Vis spectra of three compounds. The method of central composite design was used in the ranges of 2–14 and 3–11 mg L–1 for calibration and validation sets, respectively. The models refinement procedure and their validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 0.173 mg L−1 for THEO with PCR, 0.187 mg L–1 for MKST with PLS1 and 0.251 mg L–1 for LORA with HLA techniques. The limit of detection was obtained 0.03, 0.05 and 0.05 mg L−1 by PCR model for THEO, MKST and LORA, respectively. The procedure was successfully applied for simultaneous determination of the above compounds in pharmaceutical tablets and human plasma. Notwithstanding the spectral overlapping among three drugs, as well as the intrinsic variability of the latter in unknown samples, the recoveries are excellent. PMID:27980573
Bürck, J; Wiegand, G; Roth, S; Mathieu, H; Krämer, K
2006-02-28
Metal parts and residues from machining processes are usually polluted with cutting or grinding oil and have to be cleaned before further use. Supercritical carbon dioxide can be used for extraction processes and precision cleaning of metal parts, as developed at Forschungszentrum Karlsruhe. For optimizing and efficiently conducting the extraction process, in-line analysis of oil concentration is desirable. Therefore, a monitoring method using fiber-optic NIR spectroscopy in combination with PLS calibration has been developed. In an earlier paper we have described the instrumental set-up and a calibration model using the model compound squalane in the spectral range of the CH combination bands from 4900 to 4200cm(-1). With this model only poor prediction results were obtained if applied to technical oil samples in supercritical CO(2). In this paper we describe a new calibration model, which was set up for the squalane/carbon dioxide system covering the 323-353K temperature and the 16-35.6MPa pressure range. Here, calibration data in the spectral range from 6100 to 5030cm(-1) have been used. This range includes the 5100cm(-1) CO(2) band of the Fermi triad as well as the hydrocarbon 1st overtone CH stretching bands, where spectral features of oil compounds and squalane are more similar to each other. The root mean-squared error of prediction obtained with this model is 4mgcm(-3) for carbon dioxide and 0.4mgcm(-3) for squalane, respectively. The utilizability of the newly developed PLS calibration model for predicting the oil concentration and CO(2) density of solutions of technical oils in supercritical carbon dioxide has been tested. Three types of "real world" cutting and grinding oil formulations were used in these experiments. The calibration proved to be suitable for determining the technical oil concentration with an error of 1.1mgcm(-3) and the CO(2) density with an error of 6mgcm(-3). Therefore, it seems possible to apply this in-line analytical approach on
Zhou, Chengfeng; Jiang, Wei; Cheng, Qingzheng; Via, Brian K.
2015-01-01
This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15 cm−1 from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry. PMID:26576321
Variable selection in multivariate calibration based on clustering of variable concept.
Farrokhnia, Maryam; Karimi, Sadegh
2016-01-01
Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.
NASA Astrophysics Data System (ADS)
Moustafa, Azza A.; Hegazy, Maha A.; Mohamed, Dalia; Ali, Omnia
2016-02-01
A novel approach for the resolution and quantitation of severely overlapped quaternary mixture of carbinoxamine maleate (CAR), pholcodine (PHL), ephedrine hydrochloride (EPH) and sunset yellow (SUN) in syrup was demonstrated utilizing different spectrophotometric assisted multivariate calibration methods. The applied methods have used different processing and pre-processing algorithms. The proposed methods were partial least squares (PLS), concentration residuals augmented classical least squares (CRACLS), and a novel method; continuous wavelet transforms coupled with partial least squares (CWT-PLS). These methods were applied to a training set in the concentration ranges of 40-100 μg/mL, 40-160 μg/mL, 100-500 μg/mL and 8-24 μg/mL for the four components, respectively. The utilized methods have not required any preliminary separation step or chemical pretreatment. The validity of the methods was evaluated by an external validation set. The selectivity of the developed methods was demonstrated by analyzing the drugs in their combined pharmaceutical formulation without any interference from additives. The obtained results were statistically compared with the official and reported methods where no significant difference was observed regarding both accuracy and precision.
Masoum, Saeed; Mehran, Mehdi; Ghaheri, Salehe
2015-02-01
Thyme species are used in traditional medicine throughout the world and are known for their antiseptic, antispasmodic, and antitussive properties. Also, antioxidant activity is one of the interesting properties of thyme essential oil. In this research, we aim to identify peaks potentially responsible for the antioxidant activity of thyme oil from chromatographic fingerprints. Therefore, the chemical compositions of hydrodistilled essential oil of thyme species from different regions were analyzed by gas chromatography with mass spectrometry and antioxidant activities of essential oils were measured by a 1,1-diphenyl-2-picrylhydrazyl radical scavenging test. Several linear multivariate calibration techniques with different preprocessing methods were applied to the chromatograms of thyme essential oils to indicate the peaks responsible for the antioxidant activity. These techniques were applied on data both before and after alignment of chromatograms with correlation optimized warping. In this study, orthogonal projection to latent structures model was found to be a good technique to indicate the potential antioxidant active compounds in the thyme oil due to its simplicity and repeatability.
Hemmateenejad, Bahram; Abbaspour, Abdolkarim; Maghami, Homeyra; Miri, Ramin; Panjehshahin, Mohhamad Reza
2006-08-11
The partial least squares regression method has been applied for simultaneous spectrophotometric determination of harmine, harmane, harmalol and harmaline in Peganum harmala L. (Zygophyllaceae) seeds. The effect of pH was optimized employing multivariate definition of selectivity and sensitivity and best results were obtained in basic media (pH>9). The calibration models were optimized for number of latent variables by the cross-validation procedure. Determinations were made over the concentration range of 0.15-10 microg mL(-1). The proposed method was validated by applying it to the analysis of the beta-carbolines in synthetic quaternary mixtures of media at pH 9 and 11. The relative standard errors of prediction were less than 4% in most cases. Analysis of P. harmala seeds by the proposed models for contents of the beta-carboline derivatives resulted in 1.84%, 0.16%, 0.25% and 3.90% for harmine, harmane, harmaline and harmalol, respectively. The results were validated against an existing HPLC method and it no significant differences were observed between the results of two methods.
Fire impact on forest soils evaluated using near-infrared spectroscopy and multivariate calibration.
Vergnoux, A; Dupuy, N; Guiliano, M; Vennetier, M; Théraulaz, F; Doumenq, P
2009-11-15
The assessment of physico-chemical properties in forest soils affected by fires was evaluated using near infrared reflectance (NIR) spectroscopy coupled with chemometric methods. In order to describe the soil properties, measurements were taken of the total organic carbon on solid phase, the total nitrogen content, the organic carbon and the specific absorbences at 254 and 280 nm of humic substances, organic carbon in humic and fulvic acids, concentrations of NH(4)(+), Ca(2+), Mg(2+), K(+) and phosphorus in addition to NIR spectra. Then, a fire recurrence index was defined and calculated according to the different fires extents affecting soils. This calculation includes the occurrence of fires as well as the time elapsed since the last fire. This study shows that NIR spectroscopy could be considered as a tool for soil monitoring, particularly for the quantitative prediction of the total organic carbon, total nitrogen content, organic carbon in humic substances, concentrations of phosphorus, Mg(2+), Ca(2+) and NH(4)(+) and humic substances UVSA(254). Further validation in this field is necessary however, to try and make successful predictions of K(+), organic carbon in humic and fulvic acids and the humic substances UVSA(280). Moreover, NIR coupled with PLS can also be useful to predict the fire recurrence index in order to determine the spatial variability. Also this method can be used to map more or less burned areas and possibly to apply adequate rehabilitation techniques, like soil litter reconstitution with organic enrichments (industrial composts) or reforestation. Finally, the proposed recurrence index can be considered representative of the state of the soils.
Multivariate Curve Resolution Applied to Hyperspectral Imaging Analysis of Chocolate Samples.
Zhang, Xin; de Juan, Anna; Tauler, Romà
2015-08-01
This paper shows the application of Raman and infrared hyperspectral imaging combined with multivariate curve resolution (MCR) to the analysis of the constituents of commercial chocolate samples. The combination of different spectral data pretreatment methods allowed decreasing the high fluorescent Raman signal contribution of whey in the investigated chocolate samples. Using equality constraints during MCR analysis, estimations of the pure spectra of the chocolate sample constituents were improved, as well as their relative contributions and their spatial distribution on the analyzed samples. In addition, unknown constituents could be also resolved. White chocolate constituents resolved from Raman hyperspectral image indicate that, at macro scale, sucrose, lactose, fat, and whey constituents were intermixed in particles. Infrared hyperspectral imaging did not suffer from fluorescence and could be applied for white and milk chocolate. As a conclusion of this study, micro-hyperspectral imaging coupled to the MCR method is confirmed to be an appropriate tool for the direct analysis of the constituents of chocolate samples, and by extension, it is proposed for the analysis of other mixture constituents in commercial food samples.
Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI
Levman, Jacob; Takahashi, Emi
2016-01-01
Multivariate analysis (MVA) is a class of statistical and pattern recognition techniques that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neurological medical imaging related challenges including the evaluation of healthy brain development, the automated analysis of brain tissues and structures through image segmentation, evaluating the effects of genetic and environmental factors on brain development, evaluating sensory stimulation's relationship with functional brain activity and much more. Compared to adult imaging, pediatric, neonatal and fetal imaging have attracted less attention from MVA researchers, however, recent years have seen remarkable MVA research growth in pre-adult populations. This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in brain MRI, the field is still young and significant research growth will continue into the future. PMID:26834576
Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders
Levman, Jacob; Takahashi, Emi
2015-01-01
Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us. PMID:26640765
NASA Astrophysics Data System (ADS)
Liu, Fei; He, Yong; Wang, Li
2008-02-01
The feasibility of visible and near infrared (Vis/NIR) spectroscopy, in combination with a hybrid multivariate methods of partial least squares (PLS) analysis and BP neural network (BPNN), was investigated to identify the variety of rice vinegars with different internal qualities. Five varieties of rice vinegars were prepared and 225 samples (45 for each variety) were selected randomly for the calibration set, while 75 samples (15 for each variety) for the validation set. After some pretreatments with moving average and standard normal variate (SNV), partial least squares (PLS) analysis was implemented for the extraction of principal components (PCs), which would be used as the inputs of BP neural network (BPNN) according to their accumulative reliabilities. Finally, a PLS-BPNN model with sigmoid transfer function was achieved. The performance was validated by the 75 unknown samples in validation set. The threshold error of prediction was set as +/-0.1 and an excellent precision and recognition ratio of 100% was achieved. Simultaneously, certain effective wavelengths for the identification of varieties were proposed by x-loading weights and regression coefficients. The prediction results indicated that Vis/NIR spectroscopy could be used as a rapid and high precision method for the identification of different varieties of rice vinegars.
NASA Astrophysics Data System (ADS)
Segura, Rodrigo; Cierpka, Christian; Rossi, Massimiliano; Kähler, Christian J.
2012-11-01
An experimental method to track the temperature of individual non-encapsulated thermo-liquid crystal (TLC) particles is presented. TLC thermography has been investigated for several years but the low quality of individual TLC particles, as well as the methods used to relate their color to temperature, has prevented the development of a reliable approach to track their temperature individually. In order to overcome these challenges, a Shirasu Porous Glass (SPG) membrane approach was used to produce an emulsion of stable non-encapsulated TLC micro particles, with a narrower size distribution than that of encapsulated TLC solutions which are commercially available (Segura et al., Microfluid Nanofluid, 2012). On the other hand, a multi-variable calibration approach was used, as opposed to the well known temperature-hue relationship, using the three-components of the HSI color space measured in each particle image. A third degree three-dimensional polynomial was fitted to the color data of thousands of particles to estimate their temperature individually. The method is able to measure individual temperatures over a range exceeding the nominal range of the TLC material, with lower uncertainty than any method used for individual particle thermography reported in the literature. Financial support from the German Research Foundation (DFG), under the Forschergruppe 856 grant program, is gratefully appreciated.
Ahmadvand, Mohammad; Parastar, Hadi; Sereshti, Hassan; Olivieri, Alejandro; Tauler, Roma
2017-02-01
In the present study, multivariate analytical figures of merit (AFOM) for three well-known second-order calibration algorithms, parallel factor analysis (PARAFAC), PARAFAC2 and multivariate curve resolution-alternating least squares (MCR-ALS), were investigated in simulated hyphenated chromatographic systems including different artifacts (e.g., noise and peak shifts). Different two- and three-component systems with interferences were simulated. Resolved profiles from the target components were used to build calibration curves and to calculate the multivariate AFOMs, sensitivity (SEN), analytical sensitivity (γ), selectivity (SEL) and limit of detection (LOD). The obtained AFOMs for different simulated data sets using different algorithms were used to compare the performance of the algorithms and their calibration ability. Furthermore, phenanthrene and anthracene were analyzed by GC-MS in a mixture of polycyclic aromatic hydrocarbons (PAHs) to confirm the applicability of multivariate AFOMs in real samples. It is concluded that the MCR-ALS method provided the best resolution performance among the tested methods and that more reliable AFOMs were obtained with this method for the studied chromatographic systems with various levels of noise, elution time shifts and presence of unknown interferences.
Tencate, Alister J; Kalivas, John H; White, Alexander J
2016-05-19
New multivariate calibration methods and other processes are being developed that require selection of multiple tuning parameter (penalty) values to form the final model. With one or more tuning parameters, using only one measure of model quality to select final tuning parameter values is not sufficient. Optimization of several model quality measures is challenging. Thus, three fusion ranking methods are investigated for simultaneous assessment of multiple measures of model quality for selecting tuning parameter values. One is a supervised learning fusion rule named sum of ranking differences (SRD). The other two are non-supervised learning processes based on the sum and median operations. The effect of the number of models evaluated on the three fusion rules are also evaluated using three procedures. One procedure uses all models from all possible combinations of the tuning parameters. To reduce the number of models evaluated, an iterative process (only applicable to SRD) is applied and thresholding a model quality measure before applying the fusion rules is also used. A near infrared pharmaceutical data set requiring model updating is used to evaluate the three fusion rules. In this case, calibration of the primary conditions is for the active pharmaceutical ingredient (API) of tablets produced in a laboratory. The secondary conditions for calibration updating is for tablets produced in the full batch setting. Two model updating processes requiring selection of two unique tuning parameter values are studied. One is based on Tikhonov regularization (TR) and the other is a variation of partial least squares (PLS). The three fusion methods are shown to provide equivalent and acceptable results allowing automatic selection of the tuning parameter values. Best tuning parameter values are selected when model quality measures used with the fusion rules are for the small secondary sample set used to form the updated models. In this model updating situation, evaluation of
NASA Astrophysics Data System (ADS)
Rasouli, Zolaikha; Ghavami, Raouf
2016-08-01
Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD = 0.12], 0.67-23.19 [LOD = 0.13] and 0.73-25.12 [LOD = 0.15] μg mL- 1 for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.
NASA Astrophysics Data System (ADS)
Wang, Lei; Cao, Peng; Li, Wei; Tong, Peijin; Zhang, Xiaofang; Du, Yiping
2016-04-01
Solid Phase Extraction Spectroscopy (SPES) developed in this paper is a technique to measure spectrum directly on the solid phase material where the analytes are concentrated in SPE process. Membrane enrichment and UV-Visible spectroscopy were utilized to fulfill SPES, and multivariate calibration method of partial least squares (PLS) was used to simultaneously detect the concentrations of trace cobalt (II) and zinc (II) in water samples. The proposed method is simple, sensitive and selective. The complexes of analyte ions were collected on the cellulose acetate membranes via membrane filtration after the complexation reaction with 1-2-pyridylazo 2-naphthol (PAN). The spectra of the membranes which contained the complexes of metal ions and PAN were measured directly without eluting. The analytical conditions including pH, reaction time, sample volume, the amount of PAN, and flow rates were optimized. Nonionic surfactant Brij-30 was absorbed on the membranes prior to SPES to modify the membranes for improving the enrichment and spectrum measurement. The interference from other ions to the determination was investigated. Under the optimal condition, the absorbance was linearly related to the concentration at the range of 0.1-3.0 μg/L and 0.1-2.0 μg/L, with the correlation coefficients (R2) of 0.9977 and 0.9951 for Co (II) and Zn (II), respectively. The limits of detection were 0.066 μg/L for cobalt (II) and 0.104 μg/L for zinc (II). PLS regression with leave-one-out cross-validation was utilized to build models to detect cobalt (II) and zinc (II) in drinking water samples simultaneously. The correlation coefficient between ion concentration and spectrum of calibration set and independent prediction set were 1.0000 and 0.9974 for cobalt (II) and 1.0000 and 0.9956 for zinc (II). For cobalt (II) and zinc (II), the errors of the prediction set were in the range 0.0406-0.1353 μg/L and 0.0025-0.1884 μg/L.
NASA Astrophysics Data System (ADS)
Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed
2017-01-01
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.
Möltgen, C-V; Herdling, T; Reich, G
2013-11-01
This study demonstrates an approach, using science-based calibration (SBC), for direct coating thickness determination on heart-shaped tablets in real-time. Near-Infrared (NIR) spectra were collected during four full industrial pan coating operations. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film up to a film thickness of 28 μm. The application of SBC permits the calibration of the NIR spectral data without using costly determined reference values. This is due to the fact that SBC combines classical methods to estimate the coating signal and statistical methods for the noise estimation. The approach enabled the use of NIR for the measurement of the film thickness increase from around 8 to 28 μm of four independent batches in real-time. The developed model provided a spectroscopic limit of detection for the coating thickness of 0.64 ± 0.03 μm root-mean square (RMS). In the commonly used statistical methods for calibration, such as Partial Least Squares (PLS), sufficiently varying reference values are needed for calibration. For thin non-functional coatings this is a challenge because the quality of the model depends on the accuracy of the selected calibration standards. The obvious and simple approach of SBC eliminates many of the problems associated with the conventional statistical methods and offers an alternative for multivariate calibration.
Villar, Alberto; Gorritxategi, Eneko; Otaduy, Deitze; Ciria, Jose I; Fernandez, Luis A
2011-10-31
This paper describes the calibration process of a Visible-Near Infrared sensor for the condition monitoring of a gas engine's lubricating oil correlating transmittance oil spectra with the degradation of a gas engine's oil via a regression model. Chemometric techniques were applied to determine different parameters: Base Number (BN), Acid Number (AN), insolubles in pentane and viscosity at 40 °C. A Visible-Near Infrared (400-1100 nm) sensor developed in Tekniker research center was used to obtain the spectra of artificial and real gas engine oils. In order to improve sensor's data, different preprocessing methods such as smoothing by Saviztky-Golay, moving average with Multivariate Scatter Correction or Standard Normal Variate to eliminate the scatter effect were applied. A combination of these preprocessing methods was applied to each parameter. The regression models were developed by Partial Least Squares Regression (PLSR). In the end, it was shown that only some models were valid, fulfilling a set of quality requirements. The paper shows which models achieved the established validation requirements and which preprocessing methods perform better. A discussion follows regarding the potential improvement in the robustness of the models.
Bona, M T; Andrés, J M
2008-08-22
The aim of this paper focuses on the determination of nine coal properties related to combustion power plants (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal kg(-1)), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) by mid-infrared spectroscopy. For that, a wide and diverse coal sample set has been clustered into new homogeneous coal subgroups by the use of hierarchical clustering analysis. This process was performed including property values and spectral data (scores of principal component analysis, PCA) as independent variables. Once the clusters were defined, the corresponding property calibration models were performed by partial least squares regression. Several mathematical pre-treatments were applied to the original spectral data in order to cope with some non-linearities. The accuracy and precision levels for each property were studied. The results revealed that coal properties related to organic components presented relative error values around 2% for some clusters, comparable to those provided by commercial online analysers. Finally, the discrimination level between those groups of samples was evaluated by linear discriminant analysis (LDA). The sensitivity of the system was studied accomplishing percentages close to 100% when the samples were classified attending only to their mid-infrared spectra.
A PID de-tuned method for multivariable systems, applied for HVAC plant
NASA Astrophysics Data System (ADS)
Ghazali, A. B.
2015-09-01
A simple yet effective de-tuning of PID parameters for multivariable applications has been described. Although the method is felt to have wider application it is simulated in a 3-input/ 2-output building energy management system (BEMS) with known plant dynamics. The controller performances such as the sum output squared error and total energy consumption when the system is at steady state conditions are studied. This tuning methodology can also be extended to reduce the number of PID controllers as well as the control inputs for specified output references that are necessary for effective results, i.e. with good regulation performances being maintained.
Mabood, Fazal; Al-Harrasi, Ahmed; Boqué, Ricard; Jabeen, Farah; Hussain, Javid; Hafidh, A; Hind, K; Ahmed, M A G; Manzoor, A; Hussain, Hidayat; Ur Rehman, Najeeb; Iman, S H; Said, Jahina J; Hamood, Sara A
2015-01-01
A Near Infrared (NIR) spectroscopic method combined with multivariate calibration was developed for the determination of the amount of sucrose in date fruits growing in the Sultanate of Oman. In this study two groups of samples were used: one group of 48 sucrose standard solutions in the concentration range from 0.01% to 50% (w/v) and another group of 54 date fruit samples of 18 different varieties. The sucrose standard samples were split in two sets, i.e. one training set of 31 samples and one test set of 17 samples. All samples were measured with a NIR spectrophotometer in the wavelength range from 700 to 2500 nm. The spectra collected were preprocessed using baseline correction and Savitzky-Golay 1st derivative. Partial least-squares regression (PLSR) was used to build the regression model with the training set of 31 samples. This model was then validated by using random leave-one-out cross-validation. Later, the PLS regression model was externally validated by using the test set of 17 samples of known sucrose concentration. The root mean squared error of prediction (RMSEP) was found to be of 1.5%, which shows a good prediction ability of the model. Finally, the PLS model was applied to the spectra of 54 date fruit samples to quantify their sucrose amount. It was found that the Khalas, Barnia Nizwi, Ajwa Almadina, Maan, and Khunizi varieties contain high amounts of sucrose, i.e. ranging from 36% to 60%, while Naghal, Fardh, Nashu and Qash Tabaq varieties contain the least amount of sucrose, ranging from 3.5% to 8.1%.
Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman
2011-01-01
This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626
NASA Technical Reports Server (NTRS)
Schierman, John D.; Lovell, T. A.; Schmidt, David K.
1993-01-01
Three multivariable robustness analysis methods are compared and contrasted. The focus of the analysis is on system stability and performance robustness to uncertainty in the coupling dynamics between two interacting subsystems. Of particular interest is interacting airframe and engine subsystems, and an example airframe/engine vehicle configuration is utilized in the demonstration of these approaches. The singular value (SV) and structured singular value (SSV) analysis methods are compared to a method especially well suited for analysis of robustness to uncertainties in subsystem interactions. This approach is referred to here as the interacting subsystem (IS) analysis method. This method has been used previously to analyze airframe/engine systems, emphasizing the study of stability robustness. However, performance robustness is also investigated here, and a new measure of allowable uncertainty for acceptable performance robustness is introduced. The IS methodology does not require plant uncertainty models to measure the robustness of the system, and is shown to yield valuable information regarding the effects of subsystem interactions. In contrast, the SV and SSV methods allow for the evaluation of the robustness of the system to particular models of uncertainty, and do not directly indicate how the airframe (engine) subsystem interacts with the engine (airframe) subsystem.
Kaplan, Jonas T.; Man, Kingson; Greening, Steven G.
2015-01-01
Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application. PMID:25859202
Calibration methodology for proportional counters applied to yield measurements of a neutron burst.
Tarifeño-Saldivia, Ariel; Mayer, Roberto E; Pavez, Cristian; Soto, Leopoldo
2014-01-01
This paper introduces a methodology for the yield measurement of a neutron burst using neutron proportional counters. This methodology is to be applied when single neutron events cannot be resolved in time by nuclear standard electronics, or when a continuous current cannot be measured at the output of the counter. The methodology is based on the calibration of the counter in pulse mode, and the use of a statistical model to estimate the number of detected events from the accumulated charge resulting from the detection of the burst of neutrons. The model is developed and presented in full detail. For the measurement of fast neutron yields generated from plasma focus experiments using a moderated proportional counter, the implementation of the methodology is herein discussed. An experimental verification of the accuracy of the methodology is presented. An improvement of more than one order of magnitude in the accuracy of the detection system is obtained by using this methodology with respect to previous calibration methods.
A Multivariate Randomization Text of Association Applied to Cognitive Test Results
NASA Technical Reports Server (NTRS)
Ahumada, Albert; Beard, Bettina
2009-01-01
Randomization tests provide a conceptually simple, distribution-free way to implement significance testing. We have applied this method to the problem of evaluating the significance of the association among a number (k) of variables. The randomization method was the random re-ordering of k-1 of the variables. The criterion variable was the value of the largest eigenvalue of the correlation matrix.
Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio
2016-09-01
Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%).
Liu, Yan; Cai, Wensheng; Shao, Xueguang
2016-12-05
Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses.
NASA Astrophysics Data System (ADS)
Liu, Yan; Cai, Wensheng; Shao, Xueguang
2016-12-01
Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses.
van Lankveld, J J; Grotjohann, Y; van Lokven, B M; Everaerd, W
1999-01-01
This study compared characteristics of couples with different sexual dysfunctions who were recruited for participation in a bibliotherapy program via two routes: in response to media advertisements and through their presence on a waiting list for therapist-administered treatment in an outpatient sexology clinic. Data were collected from 492 subjects (246 couples). Male sexology patients were younger than media-recruited males. However, type of sexual dysfunction accounted for a substantially larger proportion of variance in the demographic and psychometric data. An interaction effect of recruitment strategy and sexual dysfunction type was found with respect to female anorgasmia. We conclude from the absence of differences between the two study groups that the Wills and DePaulo (1991) model of help-seeking behavior for mental problems does not apply to couples with sexual dysfunctions joining a bibliotherapy program who either primarily requested professional treatment or who responded to media advertising.
NASA Technical Reports Server (NTRS)
Bosworth, John T.; Burken, John J.
1997-01-01
Safety and productivity of the initial flight test phase of a new vehicle have been enhanced by developing the ability to measure the stability margins of the combined control system and vehicle in flight. One shortcoming of performing this analysis is the long duration of the excitation signal required to provide results over a wide frequency range. For flight regimes such as high angle of attack or hypersonic flight, the ability to maintain flight condition for this time duration is difficult. Significantly reducing the required duration of the excitation input is possible by tailoring the input to excite only the frequency range where the lowest stability margin is expected. For a multiple-input/multiple-output system, the inputs can be simultaneously applied to the control effectors by creating each excitation input with a unique set of frequency components. Chirp-Z transformation algorithms can be used to match the analysis of the results to the specific frequencies used in the excitation input. This report discusses the application of a tailored excitation input to a high-fidelity X-31A linear model and nonlinear simulation. Depending on the frequency range, the results indicate the potential to significantly reduce the time required for stability measurement.
Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil
NASA Astrophysics Data System (ADS)
Brito, Thábata T.; Oliveira-Júnior, José F.; Lyra, Gustavo B.; Gois, Givanildo; Zeri, Marcelo
2016-10-01
Spatial and temporal patterns of rainfall were identified over the state of Rio de Janeiro, southeast Brazil. The proximity to the coast and the complex topography create great diversity of rainfall over space and time. The dataset consisted of time series (1967-2013) of monthly rainfall over 100 meteorological stations. Clustering analysis made it possible to divide the stations into six groups (G1, G2, G3, G4, G5 and G6) with similar rainfall spatio-temporal patterns. A linear regression model was applied to a time series and a reference. The reference series was calculated from the average rainfall within a group, using nearby stations with higher correlation (Pearson). Based on t-test (p < 0.05) all stations had a linear spatiotemporal trend. According to the clustering analysis, the first group (G1) contains stations located over the coastal lowlands and also over the ocean facing area of Serra do Mar (Sea ridge), a 1500 km long mountain range over the coastal Southeastern Brazil. The second group (G2) contains stations over all the state, from Serra da Mantiqueira (Mantiqueira Mountains) and Costa Verde (Green coast), to the south, up to stations in the Northern parts of the state. Group 3 (G3) contains stations in the highlands over the state (Serrana region), while group 4 (G4) has stations over the northern areas and the continent-facing side of Serra do Mar. The last two groups were formed with stations around Paraíba River (G5) and the metropolitan area of the city of Rio de Janeiro (G6). The driest months in all regions were June, July and August, while November, December and January were the rainiest months. Sharp transitions occurred when considering monthly accumulated rainfall: from January to February, and from February to March, likely associated with episodes of "veranicos", i.e., periods of 4-15 days of duration with no rainfall.
Hou, Siyuan; Riley, Christopher B; Mitchell, Cynthia A; Shaw, R Anthony; Bryanton, Janet; Bigsby, Kathryn; McClure, J Trenton
2015-09-01
Immunoglobulin G (IgG) is crucial for the protection of the host from invasive pathogens. Due to its importance for human health, tools that enable the monitoring of IgG levels are highly desired. Consequently there is a need for methods to determine the IgG concentration that are simple, rapid, and inexpensive. This work explored the potential of attenuated total reflectance (ATR) infrared spectroscopy as a method to determine IgG concentrations in human serum samples. Venous blood samples were collected from adults and children, and from the umbilical cord of newborns. The serum was harvested and tested using ATR infrared spectroscopy. Partial least squares (PLS) regression provided the basis to develop the new analytical methods. Three PLS calibrations were determined: one for the combined set of the venous and umbilical cord serum samples, the second for only the umbilical cord samples, and the third for only the venous samples. The number of PLS factors was chosen by critical evaluation of Monte Carlo-based cross validation results. The predictive performance for each PLS calibration was evaluated using the Pearson correlation coefficient, scatter plot and Bland-Altman plot, and percent deviations for independent prediction sets. The repeatability was evaluated by standard deviation and relative standard deviation. The results showed that ATR infrared spectroscopy is potentially a simple, quick, and inexpensive method to measure IgG concentrations in human serum samples. The results also showed that it is possible to build a united calibration curve for the umbilical cord and the venous samples.
Girard, Jean-Michel; Deschênes, Jean-Sébastien; Tremblay, Réjean; Gagnon, Jonathan
2013-09-01
The objective of this work is to develop a quick and simple method for the in situ monitoring of sugars in biological cultures. A new technology based on Attenuated Total Reflectance-Fourier Transform Infrared (FT-IR/ATR) spectroscopy in combination with an external light guiding fiber probe was tested, first to build predictive models from solutions of pure sugars, and secondly to use those models to monitor the sugars in the complex culture medium of mixotrophic microalgae. Quantification results from the univariate model were correlated with the total dissolved solids content (R(2)=0.74). A vector normalized multivariate model was used to proportionally quantify the different sugars present in the complex culture medium and showed a predictive accuracy of >90% for sugars representing >20% of the total. This method offers an alternative to conventional sugar monitoring assays and could be used at-line or on-line in commercial scale production systems.
NASA Astrophysics Data System (ADS)
Barbeira, Paulo J. S.; Paganotti, Rosilene S. N.; Ássimos, Ariane A.
2013-10-01
This study had the objective of determining the content of dry extract of commercial alcoholic extracts of bee propolis through Partial Least Squares (PLS) multivariate calibration and electronic spectroscopy. The PLS model provided a good prediction of dry extract content in commercial alcoholic extracts of bee propolis in the range of 2.7 a 16.8% (m/v), presenting the advantage of being less laborious and faster than the traditional gravimetric methodology. The PLS model was optimized with outlier detection tests according to the ASTM E 1655-05. In this study it was possible to verify that a centrifugation stage is extremely important in order to avoid the presence of waxes, resulting in a more accurate model. Around 50% of the analyzed samples presented content of dry extract lower than the value established by Brazilian legislation, in most cases, the values found were different from the values claimed in the product's label.
Pereira, Claudete Fernandes; Pasquini, Celio
2010-05-01
A flow system is proposed to produce a concentration perturbation in liquid samples, aiming at the generation of two-dimensional correlation near-infrared spectra. The system presents advantages in relation to batch systems employed for the same purpose: the experiments are accomplished in a closed system; application of perturbation is rapid and easy; and the experiments can be carried out with micro-scale volumes. The perturbation system has been evaluated in the investigation and selection of relevant variables for multivariate calibration models for the determination of quality parameters of gasoline, including ethanol content, MON (motor octane number), and RON (research octane number). The main advantage of this variable selection approach is the direct association between spectral features and chemical composition, allowing easy interpretation of the regression models.
Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia
2016-02-18
Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration.
Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia
2016-01-01
Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration. PMID:26901203
Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A
2017-04-01
Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.
Marques Junior, Jucelino Medeiros; Muller, Aline Lima Hermes; Foletto, Edson Luiz; da Costa, Adilson Ben; Bizzi, Cezar Augusto; Irineu Muller, Edson
2015-01-01
A method for determination of propranolol hydrochloride in pharmaceutical preparation using near infrared spectrometry with fiber optic probe (FTNIR/PROBE) and combined with chemometric methods was developed. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). The treatments based on the mean centered data and multiplicative scatter correction (MSC) were selected for models construction. A root mean square error of prediction (RMSEP) of 8.2 mg g−1 was achieved using siPLS (s2i20PLS) algorithm with spectra divided into 20 intervals and combination of 2 intervals (8501 to 8801 and 5201 to 5501 cm−1). Results obtained by the proposed method were compared with those using the pharmacopoeia reference method and significant difference was not observed. Therefore, proposed method allowed a fast, precise, and accurate determination of propranolol hydrochloride in pharmaceutical preparations. Furthermore, it is possible to carry out on-line analysis of this active principle in pharmaceutical formulations with use of fiber optic probe. PMID:25861516
De Almeida Brehm, Franciane; de Azevedo, Julio Cesar R; da Costa Pereira, Jorge; Burrows, Hugh D
2015-11-01
Dissolved organic carbon (DOC) is frequently used as a diagnostic parameter for the identification of environmental contamination in aqueous systems. Since this organic matter is evolving and decaying over time. If samples are collected under environmental conditions, some sample stabilization process is needed until the corresponding analysis can be made. This may affect the analysis results. This problem can be avoided using the direct determination of DOC. We report a study using in situ synchronous fluorescence spectra, with independent component analysis to retrieve relevant major spectral contributions and their respective component contributions, for the direct determination of DOC. Fluorescence spectroscopy is a very powerful and sensitive technique to evaluate vestigial organic matter dissolved in water and is thus suited for the analytical task of direct monitoring of dissolved organic matter in water, thus avoiding the need for the stabilization step. We also report the development of an accurate calibration model for dissolved organic carbon determinations using environmental samples of humic and fulvic acids. The method described opens the opportunity for a fast, in locus, DOC estimation in environmental or other field studies using a portable fluorescence spectrometer. This combines the benefits of the use of fresh samples, without the need of stabilizers, and also allows the interpretation of various additional spectral contributions based on their respective estimated properties. We show how independent component analysis may be used to describe tyrosine, tryptophan, humic acid and fulvic acid spectra and, thus, to retrieve the respective individual component contribution to the DOC.
Goodacre, R; Trew, S; Wrigley-Jones, C; Neal, M J; Maddock, J; Ottley, T W; Porter, N; Kell, D B
1994-11-20
Binary mixtures of model systems consisting of the antibiotic ampicillin with either Escherichia coli or Staphylococcus auresu were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra, so as to obtain quantitative information on the concentration of ampicilin in the mixtures, partial least squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard backpropagation algorithm, and the nodes used a sigmoidal squsahing funciton. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of ampicillin in samples on which they had not been trained. Furthermore, ANNs trained to predict the amount of ampicilin in E. coli were able to generalise so as to predict the concentration of ampicillin in a S. aureus background, illustrating the robustness of ANNs to rather substantial variations in the biological background. The PyMS of the complex mixture of ampicilin in bacteria could not be expressed simply in terms of additive combinations of the spectra describing the pure components of the mixtures and their relative concentrations. Intermolecular reactions took place in the pyrolysate, leading to a lack of superposition of the spectral components and to a dependence of the normalized mass spectrum on sample size. Samples from fermentations of a single organism in a complex production medium were also analyzed quantitatively for a drug of commercial interest. The drug could also be quantified in a variety of mutant-producing strains cultivated in the same medium. The combination of PyMS and ANNs constitutes a novel, rapid, and convenient method for exploitation in strain improvement screening programs. (c) 1994 John Wiley & Sons, Inc.
NASA Astrophysics Data System (ADS)
Samadi-Maybodi, Abdolraouf; Darzi, S. K. Hassani Nejad
2008-10-01
Resolution of binary mixtures of vitamin B12, methylcobalamin and B12 coenzyme with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable (PLS1), orthogonal signal correction/partial least squares (OSC/PLS), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV-vis spectra. The UV-vis spectra of the vitamin B12, methylcobalamin and B12 coenzyme were recorded in the same spectral conditions. The method of central composite design was used in the ranges of 10-80 mg L -1 for vitamin B12 and methylcobalamin and 20-130 mg L -1 for B12 coenzyme. The models refinement procedure and validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 2.26 mg L -1 for vitamin B12 with PLS1, 1.33 mg L -1 for methylcobalamin with OSC/PLS and 3.24 mg L -1 for B12 coenzyme with HLA techniques. Figures of merit such as selectivity, sensitivity, analytical sensitivity and LOD were determined for three compounds. The procedure was successfully applied to simultaneous determination of three compounds in synthetic mixtures and in a pharmaceutical formulation.
Farouk, M; Elaziz, Omar Abd; Tawakkol, Shereen M; Hemdan, A; Shehata, Mostafa A
2014-04-05
Four simple, accurate, reproducible, and selective methods have been developed and subsequently validated for the determination of Benazepril (BENZ) alone and in combination with Amlodipine (AML) in pharmaceutical dosage form. The first method is pH induced difference spectrophotometry, where BENZ can be measured in presence of AML as it showed maximum absorption at 237nm and 241nm in 0.1N HCl and 0.1N NaOH, respectively, while AML has no wavelength shift in both solvents. The second method is the new Extended Ratio Subtraction Method (EXRSM) coupled to Ratio Subtraction Method (RSM) for determination of both drugs in commercial dosage form. The third and fourth methods are multivariate calibration which include Principal Component Regression (PCR) and Partial Least Squares (PLSs). A detailed validation of the methods was performed following the ICH guidelines and the standard curves were found to be linear in the range of 2-30μg/mL for BENZ in difference and extended ratio subtraction spectrophotometric method, and 5-30 for AML in EXRSM method, with well accepted mean correlation coefficient for each analyte. The intra-day and inter-day precision and accuracy results were well within the acceptable limits.
Martí, M P; Boqué, R; Riu, M; Busto, O; Guasch, J
2003-06-01
The system based on coupling a headspace sampler to a mass spectrometer (HS-MS), which is considered one kind of electronic nose, is an emergent technique for ensuring and controlling quality in industry. It involves injecting the headspace of the sample into the ionization chamber of the mass spectrometer where the analytes are fragmented. The result is a complex mass spectrum for each sample analyzed. When several samples are analyzed the data matrix generated is processed with chemometric techniques to compare and classify the substances from their volatile composition, in other words, to compare and classify their flavor. So far, information from electronic nose applications has mainly been qualitative. In this paper we present a quantitative study that uses a multivariate calibration. We analyzed several white wines using HS-MS to determine 2,4,6-tricholoranisole (TCA). This is an off-flavor that is a serious problem for the wine industry. The method is simple because it does not require sample preparation, only addition of sodium chloride being necessary for sample conditioning. Also, it provides a fast screening (10 min/sample) of the quantity of TCA in wines at ultratrace (sub microg L(-1)) levels.
Zoom lens calibration with zoom- and focus-related intrinsic parameters applied to bundle adjustment
NASA Astrophysics Data System (ADS)
Zheng, Shunyi; Wang, Zheng; Huang, Rongyong
2015-04-01
A zoom lens is more flexible for photogrammetric measurements under diverse environments than a fixed lens. However, challenges in calibration of zoom-lens cameras preclude the wide use of zoom lenses in the field of close-range photogrammetry. Thus, a novel zoom lens calibration method is proposed in this study. In this method, instead of conducting modeling after monofocal calibrations, we summarize the empirical zoom/focus models of intrinsic parameters first and then incorporate these parameters into traditional collinearity equations to construct the fundamental mathematical model, i.e., collinearity equations with zoom- and focus-related intrinsic parameters. Similar to monofocal calibration, images taken at several combinations of zoom and focus settings are processed in a single self-calibration bundle adjustment. In the self-calibration bundle adjustment, three types of unknowns, namely, exterior orientation parameters, unknown space point coordinates, and model coefficients of the intrinsic parameters, are solved simultaneously. Experiments on three different digital cameras with zoom lenses support the feasibility of the proposed method, and their relative accuracies range from 1:4000 to 1:15,100. Furthermore, the nominal focal length written in the exchangeable image file header is found to lack reliability in experiments. Thereafter, the joint influence of zoom lens instability and zoom recording errors is further analyzed quantitatively. The analysis result is consistent with the experimental result and explains the reason why zoom lens calibration can never have the same accuracy as monofocal self-calibration.
Augmented classical least squares multivariate spectral analysis
Haaland, David M.; Melgaard, David K.
2004-02-03
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
Augmented Classical Least Squares Multivariate Spectral Analysis
Haaland, David M.; Melgaard, David K.
2005-07-26
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
Augmented Classical Least Squares Multivariate Spectral Analysis
Haaland, David M.; Melgaard, David K.
2005-01-11
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
A novel calibration method for an infrared thermography system applied to heat transfer experiments
NASA Astrophysics Data System (ADS)
Ochs, M.; Horbach, T.; Schulz, A.; Koch, R.; Bauer, H.-J.
2009-07-01
In heat transfer measurements with highly non-uniform wall heat fluxes, high spatial resolution of wall temperatures is required to fully capture the complex thermal situation. Infrared thermography systems provide that spatial resolution. To meet the thermal accuracy, they are usually calibrated in situ using thermocouples embedded in the test surface, which have to cover the complete temperature range of interest. However, thermocouples which are placed in regions of high temperature and heat flux gradients often cannot be used for the calibration and the overall accuracy of the calibration decreases significantly. Therefore, in the present work a novel in situ calibration method is presented which does not require thermocouples over the complete surface temperature range. The number of free parameters of the calibration function is reduced by an optimized insensitivity of the system with respect to changes in operating conditions. Reference measurements demonstrate the advantages of the new method.
El-Gindy, Alaa; Attia, Khalid Abdel-Salam; Nassar, Mohammad Wafaa; El-Abasawy, Nasr M A; Shoeib, Maisra Al-shabrawi
2012-01-01
A reflectance near-infrared (RNIR) spectroscopy method was developed for simultaneous determination of chondroitin (CH), glucosamine (GO), and ascorbic acid (AS) in capsule powder. A simple preparation of the sample was done by grinding, sieving, and compression of the powder sample for improving RNIR spectra. Partial least squares (PLS-1 and PLS-2) was successfully applied to quantify the three components in the studied mixture using information included in RNIR spectra in the 4240-9780 cm(-1) range. The calibration model was developed with the three drug concentrations ranging from 50 to 150% of the labeled amount. The calibration models using pure standards were evaluated by internal validation, cross-validation, and external validation using synthetic and pharmaceutical preparations. The proposed method was applied for analysis of two pharmaceutical products. Both pharmaceutical products had the same active principle and similar excipients, but with different nominal concentration values. The results of the proposed method were compared with the results of a pharmacopoeial method for the same pharmaceutical products. No significant differences between the results were found. The standard error of prediction was 0.004 for CH, 0.003 for GO, and 0.005 for AS. The correlation coefficient was 0.9998 for CH, 0.9999 for GO, and 0.9997 for AS. The highly accurate and precise RNIR method can be used for QC of pharmaceutical products.
Calibration and uncertainty issues of a hydrological model (SWAT) applied to West Africa
NASA Astrophysics Data System (ADS)
Schuol, J.; Abbaspour, K. C.
2006-09-01
Distributed hydrological models like SWAT (Soil and Water Assessment Tool) are often highly over-parameterized, making parameter specification and parameter estimation inevitable steps in model calibration. Manual calibration is almost infeasible due to the complexity of large-scale models with many objectives. Therefore we used a multi-site semi-automated inverse modelling routine (SUFI-2) for calibration and uncertainty analysis. Nevertheless, the question of when a model is sufficiently calibrated remains open, and requires a project dependent definition. Due to the non-uniqueness of effective parameter sets, parameter calibration and prediction uncertainty of a model are intimately related. We address some calibration and uncertainty issues using SWAT to model a four million km2 area in West Africa, including mainly the basins of the river Niger, Volta and Senegal. This model is a case study in a larger project with the goal of quantifying the amount of global country-based available freshwater. Annual and monthly simulations with the "calibrated" model for West Africa show promising results in respect of the freshwater quantification but also point out the importance of evaluating the conceptual model uncertainty as well as the parameter uncertainty.
Geist, David R. ); Brown, Richard S.; Lepla, Ken; Chandler, James P.
2001-12-01
One of the practical problems with quantifying the amount of energy used by fish implanted with electromyogram (EMG) radio transmitters is that the signals emitted by the transmitter provide only a relative index of activity unless they are calibrated to the swimming speed of the fish. Ideally calibration would be conducted for each fish before it is released, but this is often not possible and calibration curves derived from more than one fish are used to interpret EMG signals from individuals which have not been calibrated. We tested the validity of this approach by comparing EMG data within three groups of three wild juvenile white sturgeon Acipenser transmontanus implanted with the same EMG radio transmitter. We also tested an additional six fish which were implanted with separate EMG transmitters. Within each group, a single EMG radio transmitter usually did not produce similar results in different fish. Grouping EMG signals among fish produced less accurate results than having individual EMG-swim speed relationships for each fish. It is unknown whether these differences were a result of different swimming performances among individual fish or inconsistencies in the placement or function of the EMG transmitters. In either case, our results suggest that caution should be used when applying calibration curves from one group of fish to another group of uncalibrated fish.
Angles-centroids fitting calibration and the centroid algorithm applied to reverse Hartmann test
NASA Astrophysics Data System (ADS)
Zhao, Zhu; Hui, Mei; Xia, Zhengzheng; Dong, Liquan; Liu, Ming; Liu, Xiaohua; Kong, Lingqin; Zhao, Yuejin
2017-02-01
In this paper, we develop an angles-centroids fitting (ACF) system and the centroid algorithm to calibrate the reverse Hartmann test (RHT) with sufficient precision. The essence of ACF calibration is to establish the relationship between ray angles and detector coordinates. Centroids computation is used to find correspondences between the rays of datum marks and detector pixels. Here, the point spread function of RHT is classified as circle of confusion (CoC), and the fitting of a CoC spot with 2D Gaussian profile to identify the centroid forms the basis of the centroid algorithm. Theoretical and experimental results of centroids computation demonstrate that the Gaussian fitting method has a less centroid shift or the shift grows at a slower pace when the quality of the image is reduced. In ACF tests, the optical instrumental alignments reach an overall accuracy of 0.1 pixel with the application of laser spot centroids tracking program. Locating the crystal at different positions, the feasibility and accuracy of ACF calibration are further validated to 10-6-10-4 rad root-mean-square error of the calibrations differences.
Schenone, Agustina V; Culzoni, María J; Marsili, Nilda R; Goicoechea, Héctor C
2013-06-01
The performance of MCR-ALS was studied in the modeling of non-linear kinetic-spectrophotometric data acquired by a stopped-flow system for the quantitation of tartrazine in the presence of brilliant blue and sunset yellow FCF as possible interferents. In the present work, MCR-ALS and U-PCA/RBL were firstly applied to remove the contribution of unexpected components not included in the calibration set. Secondly, a polynomial function was used to model the non-linear data obtained by the implementation of the algorithms. MCR-ALS was the only strategy that allowed the determination of tartrazine in test samples accurately. Therefore, it was applied for the analysis of tartrazine in beverage samples with minimum sample preparation and short analysis time. The proposed method was validated by comparison with a chromatographic procedure published in the literature. Mean recovery values between 98% and 100% and relative errors of prediction values between 4% and 9% were indicative of the good performance of the method.
Casarrubea, M; Magnusson, M S; Roy, V; Arabo, A; Sorbera, F; Santangelo, A; Faulisi, F; Crescimanno, G
2014-08-30
Aim of this article is to illustrate the application of a multivariate approach known as t-pattern analysis in the study of rat behavior in elevated plus maze. By means of this multivariate approach, significant relationships among behavioral events in the course of time can be described. Both quantitative and t-pattern analyses were utilized to analyze data obtained from fifteen male Wistar rats following a trial 1-trial 2 protocol. In trial 2, in comparison with the initial exposure, mean occurrences of behavioral elements performed in protected zones of the maze showed a significant increase counterbalanced by a significant decrease of mean occurrences of behavioral elements in unprotected zones. Multivariate t-pattern analysis, in trial 1, revealed the presence of 134 t-patterns of different composition. In trial 2, the temporal structure of behavior become more simple, being present only 32 different t-patterns. Behavioral strings and stripes (i.e. graphical representation of each t-pattern onset) of all t-patterns were presented both for trial 1 and trial 2 as well. Finally, percent distributions in the three zones of the maze show a clear-cut increase of t-patterns in closed arm and a significant reduction in the remaining zones. Results show that previous experience deeply modifies the temporal structure of rat behavior in the elevated plus maze. In addition, this article, by highlighting several conceptual, methodological and illustrative aspects on the utilization of t-pattern analysis, could represent a useful background to employ such a refined approach in the study of rat behavior in elevated plus maze.
NASA Astrophysics Data System (ADS)
Chu, Ning; Fan, Shihua
2009-12-01
A new analytical method was developed for the simultaneous kinetic spectrophotometric determination of a quaternary carbamate pesticide mixture consisting of carbofuran, propoxur, metolcarb and fenobucarb using sequential injection analysis (SIA). The procedure was based upon the different kinetic properties between the analytes reacted with reagent in flow system in the non-stopped-flow mode, in which their hydrolysis products coupled with diazotized p-nitroaniline in an alkaline medium to form the corresponding colored complexes. The absorbance data from SIA peak time profile were recorded at 510 nm and resolved by the use of back-propagation-artificial neural network (BP-ANN) algorithms for multivariate quantitative analysis. The experimental variables and main network parameters were optimized and each of the pesticides could be determined in the concentration range of 0.5-10.0 μg mL -1, at a sampling frequency of 18 h -1. The proposed method was compared to other spectrophotometric methods for simultaneous determination of mixtures of carbamate pesticides, and it was proved to be adequately reliable and was successfully applied to the simultaneous determination of the four pesticide residues in water and fruit samples, obtaining the satisfactory results based on recovery studies (84.7-116.0%).
Lefèvre, Thomas; Rondet, Claire; Parizot, Isabelle; Chauvin, Pierre
2014-01-01
Background Cost containment policies and the need to satisfy patients’ health needs and care expectations provide major challenges to healthcare systems. Identification of homogeneous groups in terms of healthcare utilisation could lead to a better understanding of how to adjust healthcare provision to society and patient needs. Methods This study used data from the third wave of the SIRS cohort study, a representative, population-based, socio-epidemiological study set up in 2005 in the Paris metropolitan area, France. The data were analysed using a cross-sectional design. In 2010, 3000 individuals were interviewed in their homes. Non-conventional multivariate clustering techniques were used to determine homogeneous user groups in data. Multinomial models assessed a wide range of potential associations between user characteristics and their pattern of healthcare utilisation. Results We identified four distinct patterns of healthcare use. Patterns of consumption and the socio-demographic characteristics of users differed qualitatively and quantitatively between these four profiles. Extensive and intensive use by older, wealthier and unhealthier people contrasted with narrow and parsimonious use by younger, socially deprived people and immigrants. Rare, intermittent use by young healthy men contrasted with regular targeted use by healthy and wealthy women. Conclusion The use of an original technique of massive multivariate analysis allowed us to characterise different types of healthcare users, both in terms of resource utilisation and socio-demographic variables. This method would merit replication in different populations and healthcare systems. PMID:25506916
Cernuda, Carlos; Lughofer, Edwin; Klein, Helmut; Forster, Clemens; Pawliczek, Marcin; Brandstetter, Markus
2017-01-01
During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.e., samples are taken from the process and analyzed in the laboratory. This typically requires significant lab technicians efforts for only a small fraction of samples to be analyzed, which leads to significant costs for beer breweries and companies. Fourier transform mid-infrared (FT-MIR) spectroscopy was used in combination with nonlinear multivariate calibration techniques to overcome (i) the time consuming off-line analyses in beer production and (ii) already known limitations of standard linear chemometric methods, like partial least squares (PLS), for important quality parameters Speers et al. (J I Brewing. 2003;109(3):229-235), Zhang et al. (J I Brewing. 2012;118(4):361-367) such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation, or foam stability. The calibration models are established with enhanced nonlinear techniques based (i) on a new piece-wise linear version of PLS by employing fuzzy rules for local partitioning the latent variable space and (ii) on extensions of support vector regression variants (-PLSSVR and ν-PLSSVR), for overcoming high computation times in high-dimensional problems and time-intensive and inappropriate settings of the kernel parameters. Furthermore, we introduce a new model selection scheme based on bagged ensembles in order to improve robustness and thus predictive quality of the final models. The approaches are tested on real-world calibration data sets for wort and beer mix beverages, and successfully compared to
Abdel-Aziz, Omar; El Kosasy, A M; El-Sayed Okeil, S M
2014-12-10
A modified dispersive liquid-liquid extraction (DLLE) procedure coupled with spectrophotometric techniques was adopted for simultaneous determination of naphthalene, anthracene, benzo(a)pyrene, alpha-naphthol and beta-naphthol in water samples. Two different methods were used, partial least-squares (PLS) method and a new derivative ratio method, namely extended derivative ratio (EDR). A PLS-2 model was established for simultaneous determination of the studied pollutants in methanol, by using twenty mixtures as calibration set and five mixtures as validation set. Also, in methanol a novel (EDR) method was developed for determination of the studied pollutants, where each component in the mixture of the five PAHs was determined by using a mixture of the other four components as divisor. Chemometric and EDR methods could be also adopted for determination of the studied PAH in water samples after transferring them from aqueous medium to the organic one by utilizing dispersive liquid-liquid extraction technique, where different parameters were investigated using a full factorial design. Both methods were compared and the proposed method was validated according to ICH guidelines and successfully applied to determine these PAHs simultaneously in spiked water samples, where satisfactory results were obtained. All the results obtained agreed with those of published methods, where no significant difference was observed.
NASA Astrophysics Data System (ADS)
Abdel-Aziz, Omar; El Kosasy, A. M.; El-Sayed Okeil, S. M.
2014-12-01
A modified dispersive liquid-liquid extraction (DLLE) procedure coupled with spectrophotometric techniques was adopted for simultaneous determination of naphthalene, anthracene, benzo(a)pyrene, alpha-naphthol and beta-naphthol in water samples. Two different methods were used, partial least-squares (PLS) method and a new derivative ratio method, namely extended derivative ratio (EDR). A PLS-2 model was established for simultaneous determination of the studied pollutants in methanol, by using twenty mixtures as calibration set and five mixtures as validation set. Also, in methanol a novel (EDR) method was developed for determination of the studied pollutants, where each component in the mixture of the five PAHs was determined by using a mixture of the other four components as divisor. Chemometric and EDR methods could be also adopted for determination of the studied PAH in water samples after transferring them from aqueous medium to the organic one by utilizing dispersive liquid-liquid extraction technique, where different parameters were investigated using a full factorial design. Both methods were compared and the proposed method was validated according to ICH guidelines and successfully applied to determine these PAHs simultaneously in spiked water samples, where satisfactory results were obtained. All the results obtained agreed with those of published methods, where no significant difference was observed.
Technology Transfer Automated Retrieval System (TEKTRAN)
In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly ...
Teglia, Carla M; Cámara, María S; Vera-Candioti, Luciana
2017-02-08
In the previously published part of this study, we detailed a novel strategy based on dispersive liquid-liquid microextraction to extract and preconcentrate nine fluoroquinolones in porcine blood. Moreover, we presented the optimized experimental conditions to obtain complete CE separation between target analytes. Consequently, this second part reports the validation of the developed method to determine flumenique, difloxacin, enrofloxacin, marbofloxacin, ofloxacin, ciprofloxacin, through univariate calibration, and enoxacin, danofloxacin, and gatifloxacin through multivariate curve resolution analysis. The validation was performed according to FDA guidelines for bioanalytical assay procedures and the European Directive 2002/657 to demonstrate that the results are reliable. The method was applied for the determination of fluoroquinolones in real samples. Results indicated a high selectivity and excellent precision characteristics, with RSD less than 11.9% in the concentrations, in intra- and interassay precision studies. Linearity was proved for a range from 4.00 to 30.00 mg/L and the recovery has been investigated at four different fortification levels, from 89 to 113%. Several approaches found in the literature were used to determinate the LODs and LOQs. Though all strategies used were appropriate, we obtained different values when using different methods. Estimating the S/N ratio with the mean noise level in the migration time of each fluoroquinolones turned out as the best studied method for evaluating the LODs and LOQs, and the values were in a range of 1.55 to 4.55 mg/L and 5.17 to 9.62 mg/L, respectively.
Dead-blow hammer design applied to a calibration target mechanism to dampen excessive rebound
NASA Technical Reports Server (NTRS)
Lim, Brian Y.
1991-01-01
An existing rotary electromagnetic driver was specified to be used to deploy and restow a blackbody calibration target inside of a spacecraft infrared science instrument. However, this target was much more massive than any other previously inherited design applications. The target experienced unacceptable bounce when reaching its stops. Without any design modification, the momentum generated by the driver caused the target to bounce back to its starting position. Initially, elastomeric dampers were used between the driver and the target. However, this design could not prevent the bounce, and it compromised the positional accuracy of the calibration target. A design that successfully met all the requirements incorporated a sealed pocket 85 percent full of 0.75 mm diameter stainless steel balls in the back of the target to provide the effect of a dead-blow hammer. The energy dissipation resulting from the collision of balls in the pocket successfully dampened the excess momentum generated during the target deployment. The disastrous effects of new requirements on a design with a successful flight history, the modifications that were necessary to make the device work, and the tests performed to verify its functionality are described.
Brombin, Chiara; Di Serio, Clelia
2016-02-01
Multiple sclerosis is an autoimmune complex disease that affects the central nervous system. It has a multitude of symptoms that are observed in different people in many different ways. At this time, there is no definite cure for multiple sclerosis. However, therapies that slow the progression of disability, controlling symptoms and helping patients to maintain a normal quality of life, are available. We will focus on relapsing-remitting multiple sclerosis patients treated with interferons or glatiramer acetate. These treatments have been shown to be effective, but their relative effectiveness has not been well established yet. To assess the superiority of a treatment, instead of classical parametric methods, we propose a statistical approach within the permutation setting and the nonparametric combination of dependent permutation tests. In this framework, we may easily handle with hypothesis testing problems for multivariate monotonic stochastic ordering. This approach has been motivated by the analysis of a large observational Italian multicentre study on multiple sclerosis, with several continuous and categorical outcomes measured at multiple time points.
EyeSys corneal topography measurement applied to calibrated ellipsoidal convex surfaces.
Douthwaite, W A
1995-01-01
AIMS/BACKGROUND--This study was carried out to assess the accuracy of the EyeSys videokeratoscope by using convex ellipsoidal surfaces of known form. METHODS--PMMA convex ellipsoidal buttons were calibrated using Form Talysurf analysis which allowed subsequent calculation of the vertex radius and p value of the surface. The EyeSys videokeratoscope was used to examine the same ellipsoids. The tabular data provided by the instrument software were used to plot a graph of r2 versus y2 where r is the measured radius at y, the distance from the corneal point being measured to the surface vertex. The intercept on the ordinate of this graph gives the vertex radius and the slope the p value. The results arising from the Talysurf and the EyeSys techniques were compared. RESULTS--The EyeSys videokeratoscope gave readings for both vertex radius and p value that were higher than those of the Talysurf analysis. The vertex radius was around 0.1 mm greater. The p value results were similar by the two methods for p values around unity but the EyeSys results were higher and the discrepancy increased as the p value approached that of a paraboloid. CONCLUSIONS--Although the videokeratoscope may be useful in comparative studies of the cornea, there must be some doubt about the absolute values displayed. The disagreement is sufficiently large to suggest that the instrument may not be accurate enough for contact lens fitting purposes. PMID:7488595
NASA Astrophysics Data System (ADS)
Giachetti, A.; Daffara, C.; Reghelin, C.; Gobbetti, E.; Pintus, R.
2015-06-01
In this paper we analyze some problems related to the acquisition of multiple illumination images for Polynomial Texture Maps (PTM) or generic Reflectance Transform Imaging (RTI). We show that intensity and directionality nonuniformity can be a relevant issue when acquiring manual sets of images with the standard highlight-based setup both using a flash lamp and a LED light. To maintain a cheap and flexible acquisition setup that can be used on field and by non-experienced users we propose to use a dynamic calibration and correction of the lights based on multiple intensity and direction estimation around the imaged object during the acquisition. Preliminary tests on the results obtained have been performed by acquiring a specifically designed 3D printed pattern in order to see the accuracy of the acquisition obtained both for spatial discrimination of small structures and normal estimation, and on samples of different types of paper in order to evaluate material discrimination. We plan to design and build from our analysis and from the tools developed and under development a set of novel procedures and guidelines that can be used to turn the cheap and common RTI acquisition setup from a simple way to enrich object visualization into a powerful method for extracting quantitative characterization both of surface geometry and of reflective properties of different materials. These results could have relevant applications in the Cultural Heritage domain, in order to recognize different materials used in paintings or investigate the ageing status of artifacts' surface.
Ultrasonic self-calibrated method applied to monitoring of sol-gel transition.
Robin, Guillaume; Vander Meulen, François; Wilkie-Chancellier, Nicolas; Martinez, Loïc; Haumesser, Lionel; Fortineau, Jérôme; Griesmar, Pascal; Lethiecq, Marc; Feuillard, Guy
2012-07-01
In many industrial processes where online control is necessary such as in the food industry, the real time monitoring of visco-elastic properties is essential to ensure the quantity of production. Acoustic methods have shown that reliable properties could be obtained from measurements of velocity and attenuation. This paper proposes a simple, real time ultrasound method for monitoring linear medium properties (phase velocity and attenuation) that vary in time. The method is based on a pulse echo measurement and is self-calibrated. Results on a silica gel are reported and the importance of taking into account the changes of the mechanical loading on the front face of the transducer will be shown. This is done through a modification of the emission and reception transfer parameters. The simultaneous measurement of the input and output currents and voltages enables these parameters to be calculated during the reaction. The variations of the transfer parameters are in the order of 6% and predominate other effects. The evolution of the ultrasonic longitudinal wave phase velocity and attenuation as a function of time allows the characteristic times of the chemical reaction to be determined. The results are well correlated with the gelation time measured by rheological method at low frequency.
Improving and Applying the Measurement of Erodibility: Examining and Calibrating Rock Mass Indices
NASA Astrophysics Data System (ADS)
Rodriguez, R. S.; Spotila, J. A.
2011-12-01
The Rock Mass Strength index (Selby, 1980) has become a standard test in geomorphology to quantify rock erodibility. Yet, the index combines a mixture of quantitative and qualitative parameters, yielding classification disparities arising from subjective user interpretations and producing final ratings that are effectively only comparable within a single researcher's dataset. Other methods, such as the Rock Quality Designation (Deere and Deere, 1988) and the Slope Mass Rating system (Bieniawski, 1989; Romana, 1995) employ some additional quantitative methods, but do not eliminate variability in user interpretation. Still, the idea of quantifying erodibility in an easily-applied field method holds great potential for furthering the understanding of large-scale landscape evolution. Therefore, we are applying several published and unpublished erodibility indices across a suite of rock types, varying the relative weights of index parameters and calculating ratings based on various potential interpretations of the index guidelines. To evaluate these results, we regress the iterations against the mean topographic slopes, allowing us to determine which index and weighting scheme is ideal overall. Results thus far have shown discrepancies between rating and slope in rocks that are more susceptible to chemical weathering (a parameter not typically included in erodibility indices). We are therefore examining the addition of chemical composition as an index parameter, or the possibility of creating weighting schema tailored to specific rock types and erosional environments. Preliminary results also suggest that beyond a threshold fracture density, high compressive rock strength is rendered moot, requiring further modification to existing indices.
Alizadeh, Taher; Ganjali, Mohammad Reza; Nourozi, Parviz; Zare, Mashaalah
2009-04-13
In this work a parathion selective molecularly imprinted polymer was synthesized and applied as a high selective adsorber material for parathion extraction and determination in aqueous samples. The method was based on the sorption of parathion in the MIP according to simple batch procedure, followed by desorption by using methanol and measurement with square wave voltammetry. Plackett-Burman and Box-Behnken designs were used for optimizing the solid-phase extraction, in order to enhance the recovery percent and improve the pre-concentration factor. By using the screening design, the effect of six various factors on the extraction recovery was investigated. These factors were: pH, stirring rate (rpm), sample volume (V(1)), eluent volume (V(2)), organic solvent content of the sample (org%) and extraction time (t). The response surface design was carried out considering three main factors of (V(2)), (V(1)) and (org%) which were found to be main effects. The mathematical model for the recovery percent was obtained as a function of the mentioned main effects. Finally the main effects were adjusted according to the defined desirability function. It was found that the recovery percents more than 95% could be easily obtained by using the optimized method. By using the experimental conditions, obtained in the optimization step, the method allowed parathion selective determination in the linear dynamic range of 0.20-467.4 microg L(-1), with detection limit of 49.0 ng L(-1) and R.S.D. of 5.7% (n=5). Parathion content of water samples were successfully analyzed when evaluating potentialities of the developed procedure.
NASA Astrophysics Data System (ADS)
Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik
2016-04-01
Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.
Mujica Ascencio, Saul; Choe, ChunSik; Meinke, Martina C; Müller, Rainer H; Maksimov, George V; Wigger-Alberti, Walter; Lademann, Juergen; Darvin, Maxim E
2016-07-01
Propylene glycol is one of the known substances added in cosmetic formulations as a penetration enhancer. Recently, nanocrystals have been employed also to increase the skin penetration of active components. Caffeine is a component with many applications and its penetration into the epidermis is controversially discussed in the literature. In the present study, the penetration ability of two components - caffeine nanocrystals and propylene glycol, applied topically on porcine ear skin in the form of a gel, was investigated ex vivo using two confocal Raman microscopes operated at different excitation wavelengths (785nm and 633nm). Several depth profiles were acquired in the fingerprint region and different spectral ranges, i.e., 526-600cm(-1) and 810-880cm(-1) were chosen for independent analysis of caffeine and propylene glycol penetration into the skin, respectively. Multivariate statistical methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) combined with Student's t-test were employed to calculate the maximum penetration depths of each substance (caffeine and propylene glycol). The results show that propylene glycol penetrates significantly deeper than caffeine (20.7-22.0μm versus 12.3-13.0μm) without any penetration enhancement effect on caffeine. The results confirm that different substances, even if applied onto the skin as a mixture, can penetrate differently. The penetration depths of caffeine and propylene glycol obtained using two different confocal Raman microscopes are comparable showing that both types of microscopes are well suited for such investigations and that multivariate statistical PCA-LDA methods combined with Student's t-test are very useful for analyzing the penetration of different substances into the skin.
NASA Technical Reports Server (NTRS)
Crutcher, H. L.; Falls, L. W.
1976-01-01
Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.
Hamchevici, Carmen; Udrea, Ion
2013-11-01
The concept of basin-wide Joint Danube Survey (JDS) was launched by the International Commission for the Protection of the Danube River (ICPDR) as a tool for investigative monitoring under the Water Framework Directive (WFD), with a frequency of 6 years. The first JDS was carried out in 2001 and its success in providing key information for characterisation of the Danube River Basin District as required by WFD lead to the organisation of the second JDS in 2007, which was the world's biggest river research expedition in that year. The present paper presents an approach for improving the survey strategy for the next planned survey JDS3 (2013) by means of several multivariate statistical techniques. In order to design the optimum structure in terms of parameters and sampling sites, principal component analysis (PCA), factor analysis (FA) and cluster analysis were applied on JDS2 data for 13 selected physico-chemical and one biological element measured in 78 sampling sites located on the main course of the Danube. Results from PCA/FA showed that most of the dataset variance (above 75%) was explained by five varifactors loaded with 8 out of 14 variables: physical (transparency and total suspended solids), relevant nutrients (N-nitrates and P-orthophosphates), feedback effects of primary production (pH, alkalinity and dissolved oxygen) and algal biomass. Taking into account the representation of the factor scores given by FA versus sampling sites and the major groups generated by the clustering procedure, the spatial network of the next survey could be carefully tailored, leading to a decreasing of sampling sites by more than 30%. The approach of target oriented sampling strategy based on the selected multivariate statistics can provide a strong reduction in dimensionality of the original data and corresponding costs as well, without any loss of information.
Sulub, Yusuf; LoBrutto, Rosario; Vivilecchia, Richard; Wabuyele, Busolo Wa
2008-03-24
Near-infrared calibration models were developed for the determination of content uniformity of pharmaceutical tablets containing 29.4% drug load for two dosage strengths (X and Y). Both dosage strengths have a circular geometry and the only difference is the size and weight. Strength X samples weigh approximately 425 mg with a diameter of 12 mm while strength Y samples, weigh approximately 1700 mg with a diameter of 20mm. Data used in this study were acquired from five NIR instruments manufactured by two different vendors. One of these spectrometers is a dispersive-based NIR system while the other four were Fourier transform (FT) based. The transferability of the optimized partial least-squares (PLS) calibration models developed on the primary instrument (A) located in a research facility was evaluated using spectral data acquired from secondary instruments B, C, D and E. Instruments B and E were located in the same research facility as spectrometer A while instruments C and D were located in a production facility 35 miles away. The same set of tablet samples were used to acquire spectral data from all instruments. This scenario mimics the conventional pharmaceutical technology transfer from research and development to production. Direct cross-instrument prediction without standardization was performed between the primary and each secondary instrument to evaluate the robustness of the primary instrument calibration model. For the strength Y samples, this approach was successful for data acquired on instruments B, C, and D producing root mean square error of prediction (RMSEP) of 1.05, 1.05, and 1.22%, respectively. However for instrument E data, this approach was not successful producing an RMSEP value of 3.40%. A similar deterioration was observed for the strength X samples, with RMSEP values of 2.78, 5.54, 3.40, and 5.78% corresponding to spectral data acquired on instruments B, C, D, and E, respectively. To minimize the effect of instrument variability
Botelho, Bruno G; de Assis, Luciana P; Sena, Marcelo M
2014-09-15
This paper proposed a novel methodology for the quantification of an artificial dye, sunset yellow (SY), in soft beverages, using image analysis (RGB histograms) and partial least squares regression. The developed method presented many advantages if compared with alternative methodologies, such as HPLC and UV/VIS spectrophotometry. It was faster, did not require sample pretreatment steps or any kind of solvents and reagents, and used a low cost equipment, a commercial flatbed scanner. This method was able to quantify SY in isotonic drinks and orange sodas, in the range of 7.8-39.7 mg L(-1), with relative prediction errors lower than 10%. A multivariate validation was also performed according to the Brazilian and international guidelines. Linearity, accuracy, sensitivity, bias, prediction uncertainty and a recently proposed tool, the β-expectation tolerance intervals, were estimated. The application of digital images in food analysis is very promising, opening the possibility for automation.
NASA Astrophysics Data System (ADS)
Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo
2013-03-01
Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.
Guo, Canyong; Luo, Xuefang; Zhou, Xiaohua; Shi, Beijia; Wang, Juanjuan; Zhao, Jinqi; Zhang, Xiaoxia
2017-03-10
Vibrational spectroscopic techniques such as infrared, near-infrared and Raman spectroscopy have become popular in detecting and quantifying polymorphism of pharmaceutics since they are fast and non-destructive. This study assessed the ability of three vibrational spectroscopy combined with multivariate analysis to quantify a low-content undesired polymorph within a binary polymorphic mixture. Partial least squares (PLS) regression and support vector machine (SVM) regression were employed to build quantitative models. Fusidic acid, a steroidal antibiotic, was used as the model compound. It was found that PLS regression performed slightly better than SVM regression in all the three spectroscopic techniques. Root mean square errors of prediction (RMSEP) were ranging from 0.48% to 1.17% for diffuse reflectance FTIR spectroscopy and 1.60-1.93% for diffuse reflectance FT-NIR spectroscopy and 1.62-2.31% for Raman spectroscopy. The results indicate that diffuse reflectance FTIR spectroscopy offers significant advantages in providing accurate measurement of polymorphic content in the fusidic acid binary mixtures, while Raman spectroscopy is the least accurate technique for quantitative analysis of polymorphs.
Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir
2014-01-01
Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations.
Prokeš, Lubomír; Amato, Filippo; Pivetta, Tiziana; Hampl, Aleš; Havel, Josef; Vaňhara, Petr
2016-01-01
Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general. PMID:26821236
Valletta, Elisa; Kučera, Lukáš; Prokeš, Lubomír; Amato, Filippo; Pivetta, Tiziana; Hampl, Aleš; Havel, Josef; Vaňhara, Petr
2016-01-01
Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general.
Williams, D Keith; Chadwick, M Ashley; Williams, Taufika Islam; Muddiman, David C
2008-12-01
Operation of any mass spectrometer requires implementation of mass calibration laws to translate experimentally measured physical quantities into a m/z range. While internal calibration in Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) offers several attractive features, including exposure of calibrant and analyte ions to identical experimental conditions (e.g. space charge), external calibration affords simpler pulse sequences and higher throughput. The automatic gain control method used in hybrid linear trap quadrupole (LTQ) FT-ICR-MS to consistently obtain the same ion population is not readily amenable to matrix-assisted laser desorption/ionization (MALDI) FT-ICR-MS, due to the heterogeneous nature and poor spot-to-spot reproducibility of MALDI. This can be compensated for by taking external calibration laws into account that consider magnetic and electric fields, as well as relative and total ion abundances. Herein, an evaluation of external mass calibration laws applied to MALDI-FT-ICR-MS is performed to achieve higher mass measurement accuracy (MMA).
NASA Technical Reports Server (NTRS)
Lerch, F. J.; Nerem, R. S.; Chinn, D. S.; Chan, J. C.; Patel, G. B.; Klosko, S. M.
1993-01-01
A new method has been developed to provide a direct test of the error calibrations of gravity models based on actual satellite observations. The basic approach projects the error estimates of the gravity model parameters onto satellite observations, and the results of these projections are then compared with data residual computed from the orbital fits. To allow specific testing of the gravity error calibrations, subset solutions are computed based on the data set and data weighting of the gravity model. The approach is demonstrated using GEM-T3 to show that the gravity error estimates are well calibrated and that reliable predictions of orbit accuracies can be achieved for independent orbits.
NASA Astrophysics Data System (ADS)
Froes, Roberta Eliane Santos; Neto, Waldomiro Borges; Silva, Nilton Oliveira Couto e.; Naveira, Rita Lopes Pereira; Nascentes, Clésia Cristina; da Silva, José Bento Borba
2009-06-01
A method for the direct determination (without sample pre-digestion) of microelements in fruit juice by inductively coupled plasma optical emission spectrometry has been developed. The method has been optimized by a 2 3 factorial design, which evaluated the plasma conditions (nebulization gas flow rate, applied power, and sample flow rate). A 1:1 diluted juice sample with 2% HNO 3 (Tetra Packed, peach flavor) and spiked with 0.5 mg L - 1 of Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, Sn, and Zn was employed in the optimization. The results of the factorial design were evaluated by exploratory analysis (Hierarchical Cluster Analysis, HCA, and Principal Component Analysis, PCA) to determine the optimum analytical conditions for all elements. Central point condition differentiation (0.75 L min - 1 , 1.3 kW, and 1.25 mL min - 1 ) was observed for both methods, Principal Component Analysis and Hierarchical Cluster Analysis, with higher analytical signal values, suggesting that these are the optimal analytical conditions. F and t-student tests were used to compare the slopes of the calibration curves for aqueous and matrix-matched standards. No significant differences were observed at 95% confidence level. The correlation coefficient was higher than 0.99 for all the elements evaluated. The limits of quantification were: Al 253, Cu 3.6, Fe 84, Mn 0.4, Zn 71, Ni 67, Cd 69, Pb 129, Sn 206, Cr 79, Co 24, and Ba 2.1 µg L - 1 . The spiking experiments with fruit juice samples resulted in recoveries between 80 and 120%, except for Co and Sn. Al, Cd, Pb, Sn and Cr could not be quantified in any of the samples investigated. The method was applied to the determination of several elements in fruit juice samples commercialized in Brazil.
NASA Technical Reports Server (NTRS)
Prasad, C. B.; Prabhakaran, R.; Tompkins, S.
1987-01-01
The first step in the extension of the semidestructive hole-drilling technique for residual stress measurement to orthotropic composite materials is the determination of the three calibration constants. Attention is presently given to an experimental determination of these calibration constants for a highly orthotropic, unidirectionally-reinforced graphite fiber-reinforced polyimide composite. A comparison of the measured values with theoretically obtained ones shows agreement to be good, in view of the many possible sources of experimental variation.
NASA Astrophysics Data System (ADS)
Darwish, Hany W.; Elzanfaly, Eman S.; Saad, Ahmed S.; Abdelaleem, Abdelaziz El-Bayoumi
2016-12-01
Five different chemometric methods were developed for the simultaneous determination of betamethasone dipropionate (BMD), clotrimazole (CT) and benzyl alcohol (BA) in their combined dosage form (Lotriderm® cream). The applied methods included three full spectrum based chemometric techniques; namely principal component regression (PCR), Partial Least Squares (PLS) and Artificial Neural Networks (ANN), while the other two methods were PLS and ANN preceded by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. A multilevel multifactor experimental design was adopted for proper construction of the models. A validation set composed of 12 mixtures containing different ratios of the three analytes was used to evaluate the predictive power of the suggested models. All the proposed methods except ANN, were successfully applied for the analysis of their pharmaceutical formulation (Lotriderm® cream). Results demonstrated the efficiency of the four methods as quantitative tool for analysis of the three analytes without prior separation procedures and without any interference from the co-formulated excipient. Additionally, the work highlighted the effect of GA on increasing the predictive power of PLS and ANN models.
Ebrahimabadi, Ebrahim H; Ghoreishi, Sayed Mehdi; Masoum, Saeed; Ebrahimabadi, Abdolrasoul H
2016-01-01
Myrtus communis L. is an aromatic evergreen shrub and its essential oil possesses known powerful antimicrobial activity. However, the contribution of each component of the plant essential oil in observed antimicrobial ability is unclear. In this study, chemical components of the essential oil samples of the plant were identified qualitatively and quantitatively using GC/FID/Mass spectrometry system, antimicrobial activity of these samples against three microbial strains were evaluated and, these two set of data were correlated using chemometrics methods. Three chemometric methods including principal component regression (PCR), partial least squares (PLS) and orthogonal projections to latent structures (OPLS) were applied for the study. These methods showed similar results, but, OPLS was selected as preferred method due to its predictive and interpretational ability, facility, repeatability and low time-consuming. The results showed that α-pinene, 1,8 cineole, β-pinene and limonene are the highest contributors in antimicrobial properties of M. communis essential oil. Other researches have reported high antimicrobial activities for the plant essential oils rich in these compounds confirming our findings.
Feng, Yuyan; Li, Xiangling; Xu, Kailin; Zou, Huayu; Li, Hui; Liang, Bing
2015-02-01
The object of the present study was to investigate the feasibility of applying ultraviolet-visible and shortwave near-infrared diffuse reflectance spectroscopy (UV-vis-SWNIR DRS) coupled with chemometrics in qualitative and simultaneous quantitative analysis of drug polymorphs, using cimetidine as a model drug. Three polymorphic forms (A, B and D) and a mixed crystal (M1) of cimetidine, obtained by preparation under different crystallization conditions, were characterized by microscopy, X-ray powder diffraction (XRPD) and infrared spectroscopy (IR). The discriminant models of four forms (A, B, D and M1) were established by discriminant partial least squares (PLS-DA) using different pretreated spectra. The R and RMSEP of samples in the prediction set by discriminant model with original spectra were 0.9959 and 0.1004. Among the quantitative models of binary mixtures (A and D) established by partial least squares (PLS) and least squares-support vector machine (LS-SVM) with different pretreated spectra, the LS-SVM models based on original and MSC spectra had better prediction effect with a R of 1.0000 and a RMSEP of 0.0134 for form A, and a R of 1.0000 and a RMSEP of 0.0024 for form D. For ternary mixtures, the established PLS quantitative models based on normalized spectra had relatively better prediction effect for forms A, B and D with R of 0.9901, 0.9820 and 0.9794 and RMSEP of 0.0471, 0.0529 and 0.0594, respectively. This research indicated that UV-vis-SWNIR DRS can be used as a simple, rapid, nondestructive qualitative and quantitative method for the analysis of drug polymorphs.
NASA Technical Reports Server (NTRS)
Prasad, C. B.; Prabhakaran, R.; Tompkins, S.
1987-01-01
The hole-drilling technique for the measurement of residual stresses using electrical resistance strain gages has been widely used for isotropic materials and has been adopted by the ASTM as a standard method. For thin isotropic plates, with a hole drilled through the thickness, the idealized hole-drilling calibration constants are obtained by making use of the well-known Kirsch's solution. In this paper, an analogous attempt is made to theoretically determine the three idealized hole-drilling calibration constants for thin orthotropic materials by employing Savin's (1961) complex stress function approach.
Porcar, Iolanda; García-Lopera, Rosa; Abad, Concepción; Campos, Agustín
2007-08-01
The size-exclusion chromatographic (SEC) behaviour of different solvent/polymer systems in three packing sets has been analysed from fractal considerations. The three-column sets studied are specifically formed by: (i) 'pure' micro-styragel, (ii) 'mixed' TSK Gel H(HR + XL + HR) and (iii) mixed TSK Gel H(XL + HR + XL). The experimental data reveals that in most of the systems assayed the classical universal calibration (UC) is not fulfilled, denoting the existence of secondary effects accompanying the main SEC mechanism. In order to obtain an accurate characterization of different polymers eluted in solvent mixtures and/or mixed packings, the use of a reliable and trusted calibration curve is required. In this sense, two alternative procedures have been analysed: the specific (SC) and the fractal (FC) calibrations. The results have evidenced that the use of the FC instead of the classical universal method diminishes up to nine times (in the case of the micro-styragel set) the mean deviation on the calculated molar mass with respect to the value given by the supplier. In the case of TSK Gel-based sets, the mean deviation is reduced to the half. The SC curve made with standards of the sample under study also reduces the mean deviation values but needs a broad set of narrow standards, whereas the fractal approach only needs one polymeric sample to build up the calibration curve.
Calibration maintenance and transfer using Tikhonov regularization approaches.
Kalivas, John H; Siano, Gabriel G; Andries, Erik; Goicoechea, Hector C
2009-07-01
Maintaining multivariate calibrations is essential and involves keeping models developed on an instrument applicable to predicting new samples over time. Sometimes a primary instrument model is needed to predict samples measured on secondary instruments. This situation is referred to as calibration transfer. This paper reports on using a Tikhonov regularization (TR) based method in both cases. A distinction of the TR design for calibration maintenance and transfer is a defined weighting scheme for a small set of new (transfer or standardization) samples augmented to the full set of calibration samples. Because straight application of basic TR theory is not always possible with calibration maintenance and transfer, this paper develops a generic solution to always enable application of TR. Harmonious (bias/variance tradeoff) and parsimonious (effective rank) considerations for TR are compared with the same TR format applied to partial least squares (PLS), showing that both approaches are viable solutions to the calibration maintenance and transfer problems.
Almeida, Luciano F; Vale, Maria G R; Dessuy, Morgana B; Silva, Márcia M; Lima, Renato S; Santos, Vagner B; Diniz, Paulo H D; Araújo, Mário C U
2007-10-31
The increasing development of miniaturized flow systems and the continuous monitoring of chemical processes require dramatically simplified and cheap flow schemes and instrumentation with large potential for miniaturization and consequent portability. For these purposes, the development of systems based on flow and batch technologies may be a good alternative. Flow-batch analyzers (FBA) have been successfully applied to implement analytical procedures, such as: titrations, sample pre-treatment, analyte addition and screening analysis. In spite of its favourable characteristics, the previously proposed FBA uses peristaltic pumps to propel the fluids and this kind of propulsion presents high cost and large dimension, making unfeasible its miniaturization and portability. To overcome these drawbacks, a low cost, robust, compact and non-propelled by peristaltic pump FBA is proposed. It makes use of a lab-made piston coupled to a mixing chamber and a step motor controlled by a microcomputer. The piston-propelled FBA (PFBA) was applied for automatic preparation of calibration solutions for manganese determination in mineral waters by electrothermal atomic-absorption spectrometry (ET AAS). Comparing the results obtained with two sets of calibration curves (five by manual and five by PFBA preparations), no significant statistical differences at a 95% confidence level were observed by applying the paired t-test. The standard deviation of manual and PFBA procedures were always smaller than 0.2 and 0.1mugL(-1), respectively. By using PFBA it was possible to prepare about 80 calibration solutions per hour.
Uncertainty Analysis of Instrument Calibration and Application
NASA Technical Reports Server (NTRS)
Tripp, John S.; Tcheng, Ping
1999-01-01
Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.
NASA Technical Reports Server (NTRS)
Tripp, John S.; Tcheng, Ping
1999-01-01
Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.
Muñoz de la Peña, A; Durán Merás, I; Jiménez Girón, A
2006-08-01
First-, second- and third-order calibration methods were investigated for the simultaneous determination of folic acid and methotrexate. The interest in the determination of these compounds is related to the fact that methotrexate inhibits the body's absorption of folic acid and prolonged treatment with methotrexate may lead to folic acid deficiency, and to the use of folic acid to cope with toxic side effects of methotrexate. Both analytes were converted into highly fluorescent compounds by oxidation with potassium permanganate, and the kinetics of the reaction was continuously monitored by recording the kinetics curves of fluorescence emission, the evolution with time of the emission spectra and the excitation-emission matrices (EEMs) of the samples at different reaction times. Direct determination of mixtures of both drugs in urine was accomplished on the basis of the evolution of the kinetics of EEMs by fluorescence measurements and four-way parallel-factor analysis (PARAFAC) or multiway partial least squares (N-PLS) chemometric calibration. The core consistency diagnostic (CORCONDIA) was employed to determine the correct number of factors in PARAFAC and the procedure converged to a choice of three factors, attributed to folic acid, methotrexate and to the sum of fluorescent species present in the urine.
Multivariate postprocessing techniques for probabilistic hydrological forecasting
NASA Astrophysics Data System (ADS)
Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian
2016-04-01
Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power
Hein, A; Tsolakidou, A; Iliopoulos, I; Mommsen, H; Buxeda i Garrigós, J; Montana, G; Kilikoglou, V
2002-04-01
Chemical analysis is a well-established procedure for the provenancing of archaeological ceramics. Various analytical techniques are routinely used and large amounts of data have been accumulated so far in data banks. However, in order to exchange results obtained by different laboratories, the respective analytical procedures need to be tested in terms of their inter-comparability. In this study, the schemes of analysis used in four laboratories that are involved in archaeological pottery studies on a routine basis were compared. The techniques investigated were neutron activation analysis (NAA), X-ray fluorescence analysis (XRF), inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS). For this comparison series of measurements on different geological standard reference materials (SRM) were carried out and the results were statistically evaluated. An attempt was also made towards the establishment of calibration factors between pairs of analytical setups in order to smooth the systematic differences among the results.
NASA Astrophysics Data System (ADS)
Cerqueira, J. G.; Fernandez, J. H.; Hoelzemann, J. J.; Leme, N. M. P.; Sousa, C. T.
2014-10-01
Due to the high costs of commercial monitoring instruments, a portable sun photometer was developed at INPE/CRN laboratories, operating in four bands, with two bands in the visible spectrum and two in near infrared. The instrument calibration process is performed by applying the classical Langley method. Application of the Langley’s methodology requires a site with high optical stability during the measurements, which is usually found in high altitudes. However, far from being an ideal site, Harrison et al. (1994) report success with applying the Langley method to some data for a site in Boulder, Colorado. Recently, Liu et al. (2011) show that low elevation sites, far away from urban and industrial centers can provide a stable optical depth, similar to high altitudes. In this study we investigated the feasibility of applying the methodology in the semiarid region of northeastern Brazil, far away from pollution areas with low altitudes, for sun photometer calibration. We investigated optical depth stability using two periods of measurements in the year during dry season in austral summer. The first one was in December when the native vegetation naturally dries, losing all its leaves and the second one was in September in the middle of the dry season when the vegetation is still with leaves. The data were distributed during four days in December 2012 and four days in September 2013 totaling eleven half days of collections between mornings and afternoons and by means of fitted line to the data V0 values were found. Despite the high correlation between the collected data and the fitted line, the study showed a variation between the values of V0 greater than allowed for sun photometer calibration. The lowest V0 variation reached in this experiment with values lower than 3% for the bands 500, 670 and 870 nm are displayed in tables. The results indicate that the site needs to be better characterized with studies in more favorable periods, soon after the rainy season.
Fragoso, Wallace; Allegrini, Franco; Olivieri, Alejandro C
2016-08-24
Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different calibration methodologies, and helps to solve literature inconsistencies on the relationship between classical sensitivity and prediction error. In contrast to the classical plain sensitivity, γ incorporates the noise properties in its definition, and its inverse is well correlated with root mean square errors of prediction in the presence of general noise structures. The proposal is supported by studying simulated and experimental first-order multivariate calibration systems with various models, namely multiple linear regression, principal component regression (PCR) and maximum likelihood PCR (MLPCR). The simulations included instrumental noise of different types: independently and identically distributed (iid), correlated (pink) and proportional noise, while the experimental data carried noise which is clearly non-iid.
NASA Astrophysics Data System (ADS)
Burns, P. J.; Nolin, A. W.; Lettenmaier, D. P.; Clarke, G. K.; Naz, B. S.; Gleason, K. E.
2011-12-01
Glacier retreat has been well documented in the Cordillera Blanca of the Peruvian Andes. It is becoming clearer that changes in glacier area and volume will negatively affect water resources in this region, particularly during the dry season (May to September). Previous studies focusing on this issue in the Cordillera Blanca have had success modeling runoff but did so using somewhat over-simplified hydrologic models. The question driving this study is: How well does the Distributed Hydrology Soil and Vegetation Model (DHSVM) coupled with a new dynamic glacier sub-model replicate runoff in a test basin of the Cordillera Blanca, namely Llanganuco. During the 2011 dry season we collected data on stream discharge, meteorological conditions, soil, and vegetation in the basin. We installed two stage height recorders in the middle reaches of the watershed to complement a third which delineates the basin outlet. Flow data collected at these points will be used for model calibration and/or validation. For geochemical validation we collected spring and meltwater samples for use in a two component isotopic mixing model. We also mapped dominant soil and vegetation types for model input. We use satellite imagery (ASTER and Landsat) to map the change in glacier extent over approximately the last 30 years as this will be another model input. Coupled together, all of these data will be used to run, validate, and refine a model which will also be implemented in other regions of the world where glacier melt is crucial at certain times of the year.
Modular multivariable control improves hydrocracking
Chia, T.L.; Lefkowitz, I.; Tamas, P.D.
1996-10-01
Modular multivariable control (MMC), a system of interconnected, single process variable controllers, can be a user-friendly, reliable and cost-effective alternative to centralized, large-scale multivariable control packages. MMC properties and features derive directly from the properties of the coordinated controller which, in turn, is based on internal model control technology. MMC was applied to a hydrocracking unit involving two process variables and three controller outputs. The paper describes modular multivariable control, MMC properties, tuning considerations, application at the DCS level, constraints handling, and process application and results.
Callén, M S; López, J M; Mastral, A M
2010-08-15
The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R(2)=0.817, PRESS/SSY=0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (Q(CV)(2)=0.813, PRESS/SSY=0.187) and with the maximal external prediction for the 2001-2002 campaign (Q(ext)(2)=0.679 and PRESS/SSY=0.321) versus the 2001-2004 campaign (Q(ext)(2)=0.551, PRESS/SSY=0.449).
NASA Technical Reports Server (NTRS)
Schiller, Stephen; Luvall, Jeffrey C.; Rickman, Doug L.; Arnold, James E. (Technical Monitor)
2000-01-01
Detecting changes in the Earth's environment using satellite images of ocean and land surfaces must take into account atmospheric effects. As a result, major programs are underway to develop algorithms for image retrieval of atmospheric aerosol properties and atmospheric correction. However, because of the temporal and spatial variability of atmospheric transmittance it is very difficult to model atmospheric effects and implement models in an operational mode. For this reason, simultaneous in situ ground measurements of atmospheric optical properties are vital to the development of accurate atmospheric correction techniques. Presented in this paper is a spectroradiometer system that provides an optimized set of surface measurements for the calibration and validation of atmospheric correction algorithms. The Portable Ground-based Atmospheric Monitoring System (PGAMS) obtains a comprehensive series of in situ irradiance, radiance, and reflectance measurements for the calibration of atmospheric correction algorithms applied to multispectral. and hyperspectral images. The observations include: total downwelling irradiance, diffuse sky irradiance, direct solar irradiance, path radiance in the direction of the north celestial pole, path radiance in the direction of the overflying satellite, almucantar scans of path radiance, full sky radiance maps, and surface reflectance. Each of these parameters are recorded over a wavelength range from 350 to 1050 nm in 512 channels. The system is fast, with the potential to acquire the complete set of observations in only 8 to 10 minutes depending on the selected spatial resolution of the sky path radiance measurements
Berger, C.D.; Gupton, E.D.; Lane, B.H.; Miller, J.H.; Nichols, S.W.
1982-08-01
The ORNL Calibrations Facility is operated by the Instrumentation Group of the Industrial Safety and Applied Health Physics Division. Its primary purpose is to maintain radiation calibration standards for calibration of ORNL health physics instruments and personnel dosimeters. This report includes a discussion of the radioactive sources and ancillary equipment in use and a step-by-step procedure for calibration of those survey instruments and personnel dosimeters in routine use at ORNL.
Enßlin, Torsten A; Junklewitz, Henrik; Winderling, Lars; Greiner, Maksim; Selig, Marco
2014-10-01
Response calibration is the process of inferring how much the measured data depend on the signal one is interested in. It is essential for any quantitative signal estimation on the basis of the data. Here, we investigate self-calibration methods for linear signal measurements and linear dependence of the response on the calibration parameters. The common practice is to augment an external calibration solution using a known reference signal with an internal calibration on the unknown measurement signal itself. Contemporary self-calibration schemes try to find a self-consistent solution for signal and calibration by exploiting redundancies in the measurements. This can be understood in terms of maximizing the joint probability of signal and calibration. However, the full uncertainty structure of this joint probability around its maximum is thereby not taken into account by these schemes. Therefore, better schemes, in sense of minimal square error, can be designed by accounting for asymmetries in the uncertainty of signal and calibration. We argue that at least a systematic correction of the common self-calibration scheme should be applied in many measurement situations in order to properly treat uncertainties of the signal on which one calibrates. Otherwise, the calibration solutions suffer from a systematic bias, which consequently distorts the signal reconstruction. Furthermore, we argue that nonparametric, signal-to-noise filtered calibration should provide more accurate reconstructions than the common bin averages and provide a new, improved self-calibration scheme. We illustrate our findings with a simplistic numerical example.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M.
2004-03-23
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M.
2002-01-01
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Haaland, David M.
1999-07-14
The analysis precision of any multivariate calibration method will be severely degraded if unmodeled sources of spectral variation are present in the unknown sample spectra. This paper describes a synthetic method for correcting for the errors generated by the presence of unmodeled components or other sources of unmodeled spectral variation. If the spectral shape of the unmodeled component can be obtained and mathematically added to the original calibration spectra, then a new synthetic multivariate calibration model can be generated to accommodate the presence of the unmodeled source of spectral variation. This new method is demonstrated for the presence of unmodeled temperature variations in the unknown sample spectra of dilute aqueous solutions of urea, creatinine, and NaCl. When constant-temperature PLS models are applied to spectra of samples of variable temperature, the standard errors of prediction (SEP) are approximately an order of magnitude higher than that of the original cross-validated SEPs of the constant-temperature partial least squares models. Synthetic models using the classical least squares estimates of temperature from pure water or variable-temperature mixture sample spectra reduce the errors significantly for the variable temperature samples. Spectrometer drift adds additional error to the analyte determinations, but a method is demonstrated that can minimize the effect of drift on prediction errors through the measurement of the spectra of a small subset of samples during both calibration and prediction. In addition, sample temperature can be predicted with high precision with this new synthetic model without the need to recalibrate using actual variable-temperature sample data. The synthetic methods eliminate the need for expensive generation of new calibration samples and collection of their spectra. The methods are quite general and can be applied using any known source of spectral variation and can be used with any multivariate
NASA Astrophysics Data System (ADS)
Hoffman, Ross N.; Ardizzone, Joseph V.; Leidner, S. Mark; Smith, Deborah K.; Atlas, Robert M.
2013-04-01
The cross-calibrated, multi-platform (CCMP) ocean surface wind project [Atlas et al., 2011] generates high-quality, high-resolution, vector winds over the world's oceans beginning with the 1987 launch of the SSM/I F08, using Remote Sensing Systems (RSS) microwave satellite wind retrievals, as well as in situ observations from ships and buoys. The variational analysis method [VAM, Hoffman et al., 2003] is at the center of the CCMP project's analysis procedures for combining observations of the wind. The VAM was developed as a smoothing spline and so implicitly defines the background error covariance by means of several constraints with adjustable weights, and does not provide an explicit estimate of the analysis error. Here we report on our research to develop uncertainty estimates for wind speed for the VAM inputs and outputs, i.e., for the background (B), the observations (O) and the analysis (A) wind speed, based on the Desroziers et al. [2005] diagnostics (DD hereafter). The DD are applied to the CCMP ocean surface wind data sets to estimate wind speed errors of the ECMWF background, the microwave satellite observations and the resulting CCMP analysis. The DD confirm that the ECMWF operational surface wind speed error standard deviations vary with latitude in the range 0.7-1.5 m/s and that the cross-calibrated Remote Sensing Systems (RSS) wind speed retrievals standard deviations are in the range 0.5-0.8 m/s. Further the estimated CCMP analysis wind speed standard deviations are in the range 0.2-0.4 m/s. The results suggests the need to revise the parameterization of the errors due to the FGAT (first guess at the appropriate time) procedure. Errors for wind speeds < 16 m/s are homogeneous, but for the relatively rare, but critical higher wind speed situations, errors are much larger. Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, and D. Gombos, A cross-calibrated, multi-platform ocean surface wind velocity product for
2010-01-01
Background In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system. Methods The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters. Results The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data. Conclusion Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs. PMID:20092654
NASA Astrophysics Data System (ADS)
Divya, O.; Shinde, Mandakini
2013-07-01
A multivariate calibration model for the simultaneous estimation of propranolol (PRO) and amiloride (AMI) using synchronous fluorescence spectroscopic data has been presented in this paper. Two multivariate techniques, PCR (Principal Component Regression) and PLSR (Partial Least Square Regression), have been successfully applied for the simultaneous determination of AMI and PRO in synthetic binary mixtures and pharmaceutical dosage forms. The SF spectra of AMI and PRO (calibration mixtures) were recorded at several concentrations within their linear range between wavelengths of 310 and 500 nm at an interval of 1 nm. Calibration models were constructed using 32 samples and validated by varying the concentrations of AMI and PRO in the calibration range. The results indicated that the model developed was very robust and able to efficiently analyze the mixtures with low RMSEP values.
Classical least squares multivariate spectral analysis
Haaland, David M.
2002-01-01
An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.
Monakhova, Yulia B; Diehl, Bernd W K
2016-03-22
In recent years the number of spectroscopic studies utilizing multivariate techniques and involving different laboratories has been dramatically increased. In this paper the protocol for calibration transfer of partial least square regression model between high-resolution nuclear magnetic resonance (NMR) spectrometers of different frequencies and equipped with different probes was established. As the test system previously published quantitative model to predict the concentration of blended soy species in sunflower lecithin was used. For multivariate modelling piecewise direct standardization (PDS), direct standardization, and hybrid calibration were employed. PDS showed the best performance for estimating lecithin falsification regarding its vegetable origin resulting in a significant decrease in root mean square error of prediction from 5.0 to 7.3% without standardization to 2.9-3.2% for PDS. Acceptable calibration transfer model was obtained by direct standardization, but this standardization approach introduces unfavourable noise to the spectral data. Hybrid calibration is least recommended for high-resolution NMR data. The sensitivity of instrument transfer methods with respect to the type of spectrometer, the number of samples and the subset selection was also discussed. The study showed the necessity of applying a proper standardization procedure in cases when multivariate model has to be applied to the spectra recorded on a secondary NMR spectrometer even with the same magnetic field strength. Copyright © 2016 John Wiley & Sons, Ltd.
MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES
Dunson, David B.
2013-01-01
Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors. PMID:24478563
Detrended fluctuation analysis of multivariate time series
NASA Astrophysics Data System (ADS)
Xiong, Hui; Shang, P.
2017-01-01
In this work, we generalize the detrended fluctuation analysis (DFA) to the multivariate case, named multivariate DFA (MVDFA). The validity of the proposed MVDFA is illustrated by numerical simulations on synthetic multivariate processes, where the cases that initial data are generated independently from the same system and from different systems as well as the correlated variate from one system are considered. Moreover, the proposed MVDFA works well when applied to the multi-scale analysis of the returns of stock indices in Chinese and US stock markets. Generally, connections between the multivariate system and the individual variate are uncovered, showing the solid performances of MVDFA and the multi-scale MVDFA.
Multivariate meta-analysis: potential and promise.
Jackson, Dan; Riley, Richard; White, Ian R
2011-09-10
The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice.
NASA Astrophysics Data System (ADS)
Houchin, J. S.
2014-09-01
A common problem for the off-line validation of the calibration algorithms and algorithm coefficients is being able to run science data through the exact same software used for on-line calibration of that data. The Joint Polar Satellite System (JPSS) program solved part of this problem by making the Algorithm Development Library (ADL) available, which allows the operational algorithm code to be compiled and run on a desktop Linux workstation using flat file input and output. However, this solved only part of the problem, as the toolkit and methods to initiate the processing of data through the algorithms were geared specifically toward the algorithm developer, not the calibration analyst. In algorithm development mode, a limited number of sets of test data are staged for the algorithm once, and then run through the algorithm over and over as the software is developed and debugged. In calibration analyst mode, we are continually running new data sets through the algorithm, which requires significant effort to stage each of those data sets for the algorithm without additional tools. AeroADL solves this second problem by providing a set of scripts that wrap the ADL tools, providing both efficient means to stage and process an input data set, to override static calibration coefficient look-up-tables (LUT) with experimental versions of those tables, and to manage a library containing multiple versions of each of the static LUT files in such a way that the correct set of LUTs required for each algorithm are automatically provided to the algorithm without analyst effort. Using AeroADL, The Aerospace Corporation's analyst team has demonstrated the ability to quickly and efficiently perform analysis tasks for both the VIIRS and OMPS sensors with minimal training on the software tools.
Seichter, Felicia; Vogt, Josef; Radermacher, Peter; Mizaikoff, Boris
2017-01-25
The calibration of analytical systems is time-consuming and the effort for daily calibration routines should therefore be minimized, while maintaining the analytical accuracy and precision. The 'calibration transfer' approach proposes to combine calibration data already recorded with actual calibrations measurements. However, this strategy was developed for the multivariate, linear analysis of spectroscopic data, and thus, cannot be applied to sensors with a single response channel and/or a non-linear relationship between signal and desired analytical concentration. To fill this gap for a non-linear calibration equation, we assume that the coefficients for the equation, collected over several calibration runs, are normally distributed. Considering that coefficients of an actual calibration are a sample of this distribution, only a few standards are needed for a complete calibration data set. The resulting calibration transfer approach is demonstrated for a fluorescence oxygen sensor and implemented as a hierarchical Bayesian model, combined with a Lagrange Multipliers technique and Monte-Carlo Markov-Chain sampling. The latter provides realistic estimates for coefficients and prediction together with accurate error bounds by simulating known measurement errors and system fluctuations. Performance criteria for validation and optimal selection of a reduced set of calibration samples were developed and lead to a setup which maintains the analytical performance of a full calibration. Strategies for a rapid determination of problems occurring in a daily calibration routine, are proposed, thereby opening the possibility of correcting the problem just in time.
Calibration of Germanium Resistance Thermometers
NASA Technical Reports Server (NTRS)
Ladner, D.; Urban, E.; Mason, F. C.
1987-01-01
Largely completed thermometer-calibration cryostat and probe allows six germanium resistance thermometers to be calibrated at one time at superfluid-helium temperatures. In experiments involving several such thermometers, use of this calibration apparatus results in substantial cost savings. Cryostat maintains temperature less than 2.17 K through controlled evaporation and removal of liquid helium from Dewar. Probe holds thermometers to be calibrated and applies small amount of heat as needed to maintain precise temperature below 2.17 K.
Bauza, María C; Ibañez, Gabriela A; Tauler, Romà; Olivieri, Alejandro C
2012-10-16
A new equation is derived for estimating the sensitivity when the multivariate curve resolution-alternating least-squares (MCR-ALS) method is applied to second-order multivariate calibration data. The validity of the expression is substantiated by extensive Monte Carlo noise addition simulations. The multivariate selectivity can be derived from the new sensitivity expression. Other important figures of merit, such as limit of detection, limit of quantitation, and concentration uncertainty of MCR-ALS quantitative estimations can be easily estimated from the proposed sensitivity expression and the instrumental noise. An experimental example involving the determination of an analyte in the presence of uncalibrated interfering agents is described in detail, involving second-order time-decaying sensitized lanthanide luminescence excitation spectra. The estimated figures of merit are reasonably correlated with the analytical features of the analyzed experimental system.
Matula, Svatopluk; Báťková, Kamila; Legese, Wossenu Lemma
2016-11-15
Non-destructive soil water content determination is a fundamental component for many agricultural and environmental applications. The accuracy and costs of the sensors define the measurement scheme and the ability to fit the natural heterogeneous conditions. The aim of this study was to evaluate five commercially available and relatively cheap sensors usually grouped with impedance and FDR sensors. ThetaProbe ML2x (impedance) and ECH₂O EC-10, ECH₂O EC-20, ECH₂O EC-5, and ECH₂O TE (all FDR) were tested on silica sand and loess of defined characteristics under controlled laboratory conditions. The calibrations were carried out in nine consecutive soil water contents from dry to saturated conditions (pure water and saline water). The gravimetric method was used as a reference method for the statistical evaluation (ANOVA with significance level 0.05). Generally, the results showed that our own calibrations led to more accurate soil moisture estimates. Variance component analysis arranged the factors contributing to the total variation as follows: calibration (contributed 42%), sensor type (contributed 29%), material (contributed 18%), and dry bulk density (contributed 11%). All the tested sensors performed very well within the whole range of water content, especially the sensors ECH₂O EC-5 and ECH₂O TE, which also performed surprisingly well in saline conditions.
Matula, Svatopluk; Báťková, Kamila; Legese, Wossenu Lemma
2016-01-01
Non-destructive soil water content determination is a fundamental component for many agricultural and environmental applications. The accuracy and costs of the sensors define the measurement scheme and the ability to fit the natural heterogeneous conditions. The aim of this study was to evaluate five commercially available and relatively cheap sensors usually grouped with impedance and FDR sensors. ThetaProbe ML2x (impedance) and ECH2O EC-10, ECH2O EC-20, ECH2O EC-5, and ECH2O TE (all FDR) were tested on silica sand and loess of defined characteristics under controlled laboratory conditions. The calibrations were carried out in nine consecutive soil water contents from dry to saturated conditions (pure water and saline water). The gravimetric method was used as a reference method for the statistical evaluation (ANOVA with significance level 0.05). Generally, the results showed that our own calibrations led to more accurate soil moisture estimates. Variance component analysis arranged the factors contributing to the total variation as follows: calibration (contributed 42%), sensor type (contributed 29%), material (contributed 18%), and dry bulk density (contributed 11%). All the tested sensors performed very well within the whole range of water content, especially the sensors ECH2O EC-5 and ECH2O TE, which also performed surprisingly well in saline conditions. PMID:27854263
1968-01-01
one). Examples abound of systems with numerous controlled variables, and the modern tendency is toward ever greater utilization of systems and plants of this kind. We call them multivariable control systems (MCS).
Mossavar-Rahmani, Y; Sotres-Alvarez, D; Wong, W W; Loria, C M; Gellman, M D; Van Horn, L; Alderman, M H; Beasley, J M; Lora, C M; Siega-Riz, A M; Kaplan, R C; Shaw, P A
2017-02-16
Measurement error in assessment of sodium and potassium intake obscures associations with health outcomes. The level of this error in a diverse US Hispanic/Latino population is unknown. We investigated the measurement error in self-reported dietary intake of sodium and potassium and examined differences by background (Central American, Cuban, Dominican, Mexican, Puerto Rican and South American). In 2010-2012, we studied 447 participants aged 18-74 years from four communities (Miami, Bronx, Chicago and San Diego), obtaining objective 24-h urinary sodium and potassium excretion measures. Self-report was captured from two interviewer-administered 24-h dietary recalls. Twenty percent of the sample repeated the study. We examined bias in self-reported sodium and potassium from diet and the association of mismeasurement with participant characteristics. Linear regression relating self-report with objective measures was used to develop calibration equations. Self-report underestimated sodium intake by 19.8% and 20.8% and potassium intake by 1.3% and 4.6% in men and women, respectively. Sodium intake underestimation varied by Hispanic/Latino background (P<0.05) and was associated with higher body mass index (BMI). Potassium intake underestimation was associated with higher BMI, lower restaurant score (indicating lower consumption of foods prepared away from home and/or eaten outside the home) and supplement use. The R(2) was 19.7% and 25.0% for the sodium and potassium calibration models, respectively, increasing to 59.5 and 61.7% after adjusting for within-person variability in each biomarker. These calibration equations, corrected for subject-specific reporting error, have the potential to reduce bias in diet-disease associations within this largest cohort of Hispanics in the United States.Journal of Human Hypertension advance online publication, 16 February 2017; doi:10.1038/jhh.2016.98.
Multivariate residues and maximal unitarity
NASA Astrophysics Data System (ADS)
Søgaard, Mads; Zhang, Yang
2013-12-01
We extend the maximal unitarity method to amplitude contributions whose cuts define multidimensional algebraic varieties. The technique is valid to all orders and is explicitly demonstrated at three loops in gauge theories with any number of fermions and scalars in the adjoint representation. Deca-cuts realized by replacement of real slice integration contours by higher-dimensional tori encircling the global poles are used to factorize the planar triple box onto a product of trees. We apply computational algebraic geometry and multivariate complex analysis to derive unique projectors for all master integral coefficients and obtain compact analytic formulae in terms of tree-level data.
Multivariate bubbles and antibubbles
NASA Astrophysics Data System (ADS)
Fry, John
2014-08-01
In this paper we develop models for multivariate financial bubbles and antibubbles based on statistical physics. In particular, we extend a rich set of univariate models to higher dimensions. Changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. Moreover, our multivariate models are able to capture some of the contagious effects that occur during such episodes. We are able to show that declining lending quality helped fuel a bubble in the US stock market prior to 2008. Further, our approach offers interesting insights into the spatial development of UK house prices.
Multivariate Data EXplorer (MDX)
Steed, Chad Allen
2012-08-01
The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.
A variable acceleration calibration system
NASA Astrophysics Data System (ADS)
Johnson, Thomas H.
2011-12-01
A variable acceleration calibration system that applies loads using gravitational and centripetal acceleration serves as an alternative, efficient and cost effective method for calibrating internal wind tunnel force balances. Two proof-of-concept variable acceleration calibration systems are designed, fabricated and tested. The NASA UT-36 force balance served as the test balance for the calibration experiments. The variable acceleration calibration systems are shown to be capable of performing three component calibration experiments with an approximate applied load error on the order of 1% of the full scale calibration loads. Sources of error are indentified using experimental design methods and a propagation of uncertainty analysis. Three types of uncertainty are indentified for the systems and are attributed to prediction error, calibration error and pure error. Angular velocity uncertainty is shown to be the largest indentified source of prediction error. The calibration uncertainties using a production variable acceleration based system are shown to be potentially equivalent to current methods. The production quality system can be realized using lighter materials and a more precise instrumentation. Further research is needed to account for balance deflection, forcing effects due to vibration, and large tare loads. A gyroscope measurement technique is shown to be capable of resolving the balance deflection angle calculation. Long term research objectives include a demonstration of a six degree of freedom calibration, and a large capacity balance calibration.
Herman, J.R.; Hudson, R.; McPeters, R.; Stolarski, R. ); Ahmad, Z.; Gu, X.Y., Taylor, S.; Wellemeyer, C. )
1991-04-20
The currently archived (1989) total ozone mapping spectrometer (TOMS) and solar backscattered ultraviolet (SBUV) total ozone data (version 5) show a global average decrease of about 9.0% from November 1978 to November 1988. This large decrease disagrees with an approximate 3.5% decrease estimated from the ground-based Dobson network. The primary source of disagreement was found to arise from an overestimate of reflectivity change and its incorrect wavelengths dependence for the diffuser plate used when measuring solar irradiance. For total ozone measured by TOMS, a means has been found to use the measured radiance-irradiance ratio from several wavelengths pairs to construct an internally self consistent calibration. The method uses the wavelength dependence of the sensitivity to calibration errors and the requirement that albedo ratios for each wavelength pair yield the same total ozone amounts. Smaller errors in determining spacecraft attitude, synchronization problems with the photon counting electronics, and sea glint contamination of boundary reflectivity data have been corrected or minimized. New climatological low-ozone profiles have been incorporated into the TOMS algorithm that are appropriate for Antarctic ozone hole conditions and other low ozone cases. The combined corrections have led to a new determination of the global average total ozone trend (version 6) as a 2.9 {plus minus} 1.3% decrease over 11 years. Version 6 data are shown to be in agreement within error limits with the average of 39 ground-based Dobson stations and with the world standard Dobson spectrometer 83 at Mauna Loa, Hawaii.
Garrido, M; Larrechi, M S; Rius, F X
2006-02-01
This study describes the combination of multivariate curve resolution-alternating least squares with a kinetic modeling strategy for obtaining the kinetic rate constants of a curing reaction of epoxy resins. The reaction between phenyl glycidyl ether and aniline is monitored by near-infrared spectroscopy under isothermal conditions for several initial molar ratios of the reagents. The data for all experiments, arranged in a column-wise augmented data matrix, are analyzed using multivariate curve resolution-alternating least squares. The concentration profiles recovered are fitted to a chemical model proposed for the reaction. The selection of the kinetic model is assisted by the information contained in the recovered concentration profiles. The nonlinear fitting provides the kinetic rate constants. The optimized rate constants are in agreement with values reported in the literature.
NASA Technical Reports Server (NTRS)
Bate, T.; Calkins, D. E.; Price, P.; Veikins, O.
1971-01-01
Calibrator generates accurate flow velocities over wide range of gas pressure, temperature, and composition. Both pressure and flow velocity can be maintained within 0.25 percent. Instrument is essentially closed loop hydraulic system containing positive displacement drive.
NASA Technical Reports Server (NTRS)
Lessard, Wendy B.
1999-01-01
The objective of this study is to calibrate a Navier-Stokes code for the TCA (30/10) baseline configuration (partial span leading edge flaps were deflected at 30 degs. and all the trailing edge flaps were deflected at 10 degs). The computational results for several angles of attack are compared with experimental force, moments, and surface pressures. The code used in this study is CFL3D; mesh sequencing and multi-grid were used to full advantage to accelerate convergence. A multi-grid approach was used similar to that used for the Reference H configuration allowing point-to-point matching across all the trailingedge block interfaces. From past experiences with the Reference H (ie, good force, moment, and pressure comparisons were obtained), it was assumed that the mounting system would produce small effects; hence, it was not initially modeled. However, comparisons of lower surface pressures indicated the post mount significantly influenced the lower surface pressures, so the post geometry was inserted into the existing grid using Chimera (overset grids).
Transient multivariable sensor evaluation
Vilim, Richard B.; Heifetz, Alexander
2017-02-21
A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.
Multivariate Quantitative Chemical Analysis
NASA Technical Reports Server (NTRS)
Kinchen, David G.; Capezza, Mary
1995-01-01
Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.
NASA Astrophysics Data System (ADS)
Hulbert, S.; Hodge, P.; Lindler, D.; Shaw, R.; Goudfrooij, P.; Katsanis, R.; Keener, S.; McGrath, M.; Bohlin, R.; Baum, S.
1997-05-01
Routine calibration of STIS observations in the HST data pipeline is performed by the CALSTIS task. CALSTIS can: subtract the over-scan region and a bias image from CCD observations; remove cosmic ray features from CCD observations; correct global nonlinearities for MAMA observations; subtract a dark image; and, apply flat field corrections. In the case of spectral data, CALSTIS can also: assign a wavelength to each pixel; apply a heliocentric correction to the wavelengths; convert counts to absolute flux; process the automatically generated spectral calibration lamp observations to improve the wavelength solution; rectify two-dimensional (longslit) spectra; subtract interorder and sky background; and, extract one-dimensional spectra. CALSTIS differs in significant ways from the current HST calibration tasks. The new code is written in ANSI C and makes use of a new C interface to IRAF. The input data, reference data, and output calibrated data are all in FITS format, using IMAGE or BINTABLE extensions. Error estimates are computed and include contributions from the reference images. The entire calibration can be performed by one task, but many steps can also be performed individually.
Objective calibration of numerical weather prediction models
NASA Astrophysics Data System (ADS)
Voudouri, A.; Khain, P.; Carmona, I.; Bellprat, O.; Grazzini, F.; Avgoustoglou, E.; Bettems, J. M.; Kaufmann, P.
2017-07-01
Numerical weather prediction (NWP) and climate models use parameterization schemes for physical processes, which often include free or poorly confined parameters. Model developers normally calibrate the values of these parameters subjectively to improve the agreement of forecasts with available observations, a procedure referred as expert tuning. A practicable objective multi-variate calibration method build on a quadratic meta-model (MM), that has been applied for a regional climate model (RCM) has shown to be at least as good as expert tuning. Based on these results, an approach to implement the methodology to an NWP model is presented in this study. Challenges in transferring the methodology from RCM to NWP are not only restricted to the use of higher resolution and different time scales. The sensitivity of the NWP model quality with respect to the model parameter space has to be clarified, as well as optimize the overall procedure, in terms of required amount of computing resources for the calibration of an NWP model. Three free model parameters affecting mainly turbulence parameterization schemes were originally selected with respect to their influence on the variables associated to daily forecasts such as daily minimum and maximum 2 m temperature as well as 24 h accumulated precipitation. Preliminary results indicate that it is both affordable in terms of computer resources and meaningful in terms of improved forecast quality. In addition, the proposed methodology has the advantage of being a replicable procedure that can be applied when an updated model version is launched and/or customize the same model implementation over different climatological areas.
Multivariate image analysis in biomedicine.
Nattkemper, Tim W
2004-10-01
In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.
NASA Astrophysics Data System (ADS)
Wurz, Peter; Balogh, Andre; Coffey, Victoria; Dichter, Bronislaw K.; Kasprzak, Wayne T.; Lazarus, Alan J.; Lennartsson, Walter; McFadden, James P.
Calibration and characterization of particle instruments with supporting flight electronics is necessary for the correct interpretation of the returned data. Generally speaking, the instrument will always return a measurement value (typically in form of a digital number), for example a count rate, for the measurement of an external quantity, which could be an ambient neutral gas density, an ion composition (species measured and amount), or electron density. The returned values are used then to derive parameters associated with the distribution such as temperature, bulk flow speed, differential energy flux and others. With the calibration of the instrument the direct relationship between the external quantity and the returned measurement value has to be established so that the data recorded during flight can be correctly interpreted. While calibration and characterization of an instrument are usually done in ground-based laboratories prior to integration of the instrument in the spacecraft, it can also be done in space.
NASA Astrophysics Data System (ADS)
Manz, B.; Buytaert, W.; Tobón, C.; Villacis, M.; García, F.
2014-12-01
With the imminent release of GPM it is essential for the hydrological user community to improve the spatial resolution of satellite precipitation products (SPPs), also retrospectively of historical time-series. Despite the growing number of applications, to date SPPs have two major weaknesses. Firstly, geosynchronous infrared (IR) SPPs, relying exclusively on cloud elevation/ IR temperature, fail to replicate ground rainfall rates especially for convective rainfall. Secondly, composite SPPs like TRMM include microwave and active radar to overcome this, but the coarse spatial resolution (0.25°) from infrequent orbital sampling often fails to: a) characterize precipitation patterns (especially extremes) in complex topography regions, and b) allow for gauge comparisons with adequate spatial support. This is problematic for satellite-gauge merging and subsequent hydrological modelling applications. We therefore present a new re-calibration and downscaling routine that is applicable to 0.25°/ 3-hrly TRMM 3B42 and Level 3 GPM time-series to generate 1 km estimates. 16 years of instantaneous TRMM radar (TPR) images were evaluated against a unique dataset of over 100 10-min rain gauges from the tropical Andes (Colombia & Ecuador) to develop a spatially distributed error surface. Long-term statistics on occurrence frequency, convective/ stratiform fraction and extreme precipitation probability (Gamma & Generalized Pareto distributions) were computed from TPR at the 1 km scale as well as from TPR and 3B42 at the 0.25° scale. To downscale from 0.25° to 1 km a stochastic generator was used to restrict precipitation occurrence to a fraction of the 1 km pixels within the 0.25° gridcell at every time-step. Regression modelling established a relationship between probability distributions at the 0.25° scale and rainfall amounts were assigned to the retained 1 km pixels by quantile-matching to the gridcell. The approach inherently provides mass conservation of the downscaled
NASA Technical Reports Server (NTRS)
Peay, Christopher S.; Palacios, David M.
2011-01-01
Calibrate_Image calibrates images obtained from focal plane arrays so that the output image more accurately represents the observed scene. The function takes as input a degraded image along with a flat field image and a dark frame image produced by the focal plane array and outputs a corrected image. The three most prominent sources of image degradation are corrected for: dark current accumulation, gain non-uniformity across the focal plane array, and hot and/or dead pixels in the array. In the corrected output image the dark current is subtracted, the gain variation is equalized, and values for hot and dead pixels are estimated, using bicubic interpolation techniques.
Topics in Multivariate Approximation Theory.
1982-05-01
include tensor products, multivariate polynomial interpolation , esp. Kergin Interpolation , and the recent developments of multivariate B-splines. t1...AMS (MOS) Subject Classifications: 41-02, 41A05, 41A10, 41A15, 41A63, 41A65 Key Words: multivariate, B-splines, Kergin interpolation , linear projectors...splines and in multivariate polynomial interpolation . These developments may well provide the theoretical foundation for efficient methods of
Method of multivariate spectral analysis
Keenan, Michael R.; Kotula, Paul G.
2004-01-06
A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).
Introduction to multivariate discrimination
NASA Astrophysics Data System (ADS)
Kégl, Balázs
2013-07-01
Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
Crawfis, R.A.
1996-03-01
This paper presents a new technique for representing multivalued data sets defined on an integer lattice. It extends the state-of-the-art in volume rendering to include nonhomogeneous volume representations. That is, volume rendering of materials with very fine detail (e.g. translucent granite) within a voxel. Multivariate volume rendering is achieved by introducing controlled amounts of noise within the volume representation. Varying the local amount of noise within the volume is used to represent a separate scalar variable. The technique can also be used in image synthesis to create more realistic clouds and fog.
Romero-Vivas, C M E; Llinás, H; Falconar, A K I
2007-11-01
The ability of a simple sweeping method, coupled to calibration factors, to accurately estimate the total numbers of Aedes aegypti (L.) (Diptera: Culicidae) pupae in water-storage containers (20-6412-liter capacities at different water levels) throughout their main dengue virus transmission temperature range was evaluated. Using this method, one set of three calibration factors were derived that could accurately estimate the total Ae. aegypti pupae in their principal breeding sites, large water-storage containers, found throughout the world. No significant differences were obtained using the method at different altitudes (14-1630 m above sea level) that included the range of temperatures (20-30 degrees C) at which dengue virus transmission occurs in the world. In addition, no significant differences were found in the results obtained between and within the 10 different teams that applied this method; therefore, this method was extremely robust. One person could estimate the Ae. aegypti pupae in each of the large water-storage containers in only 5 min by using this method, compared with two people requiring between 45 and 90 min to collect and count the total pupae population in each of them. Because the method was both rapid to perform and did not disturb the sediment layers in these domestic water-storage containers, it was more acceptable by the residents, and, therefore, ideally suited for routine surveillance purposes and to assess the efficacy of Ae. aegypti control programs in dengue virus-endemic areas throughout the world.
mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.
Asar, Özgür; İlk, Özlem
2013-12-01
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses.
Schmidt decomposition and multivariate statistical analysis
NASA Astrophysics Data System (ADS)
Bogdanov, Yu. I.; Bogdanova, N. A.; Fastovets, D. V.; Luckichev, V. F.
2016-12-01
The new method of multivariate data analysis based on the complements of classical probability distribution to quantum state and Schmidt decomposition is presented. We considered Schmidt formalism application to problems of statistical correlation analysis. Correlation of photons in the beam splitter output channels, when input photons statistics is given by compound Poisson distribution is examined. The developed formalism allows us to analyze multidimensional systems and we have obtained analytical formulas for Schmidt decomposition of multivariate Gaussian states. It is shown that mathematical tools of quantum mechanics can significantly improve the classical statistical analysis. The presented formalism is the natural approach for the analysis of both classical and quantum multivariate systems and can be applied in various tasks associated with research of dependences.
Relationship between Multiple Regression and Selected Multivariable Methods.
ERIC Educational Resources Information Center
Schumacker, Randall E.
The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…
NASA Astrophysics Data System (ADS)
Regnault, N.
2015-08-01
The Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) is a massive imaging survey, conducted between 2003 and 2008, with the MegaCam instrument, mounted on the CFHT-3.6-m telescope. With a 1 degree wide focal plane, made of 36 2048 × 4612 sensors totalling 340 megapixels, MegaCam was at the time the largest imager on the sky. The Supernova Legacy Survey (SNLS) uses the cadenced observations of the 4 deg2 wide "DEEP" layer of the CFHTLS to search and follow-up Type Ia supernovae (SNe Ia) and study the acceleration of the cosmic expansion. The reduction and calibration of the CFHTLS/SNLS datasets has posed a series of challenges. In what follows, we give a brief account of the photometric calibration work that has been performed on the SNLS data over the last decade.
Angles of multivariable root loci
NASA Technical Reports Server (NTRS)
Thompson, P. M.; Stein, G.; Laub, A. J.
1982-01-01
A generalized eigenvalue problem is demonstrated to be useful for computing the multivariable root locus, particularly when obtaining the arrival angles to finite transmission zeros. The multivariable root loci are found for a linear, time-invariant output feedback problem. The problem is then employed to compute a closed-loop eigenstructure. The method of computing angles on the root locus is demonstrated, and the method is extended to a multivariable optimal root locus.
NASA Astrophysics Data System (ADS)
Zaconte, V.; Altea Team
The ALTEA project is aimed at studying the possible functional damages to the Central Nervous System (CNS) due to particle radiation in space environment. The project is an international and multi-disciplinary collaboration. The ALTEA facility is an helmet-shaped device that will study concurrently the passage of cosmic radiation through the brain, the functional status of the visual system and the electrophysiological dynamics of the cortical activity. The basic instrumentation is composed by six active particle telescopes, one ElectroEncephaloGraph (EEG), a visual stimulator and a pushbutton. The telescopes are able to detect the passage of each particle measuring its energy, trajectory and released energy into the brain and identifying nuclear species. The EEG and the Visual Stimulator are able to measure the functional status of the visual system, the cortical electrophysiological activity, and to look for a correlation between incident particles, brain activity and Light Flash perceptions. These basic instruments can be used separately or in any combination, permitting several different experiments. ALTEA is scheduled to fly in the International Space Station (ISS) in November, 15th 2004. In this paper the calibration of the Flight Model of the silicon telescopes (Silicon Detector Units - SDUs) will be shown. These measures have been taken at the GSI heavy ion accelerator in Darmstadt. First calibration has been taken out in November 2003 on the SDU-FM1 using C nuclei at different energies: 100, 150, 400 and 600 Mev/n. We performed a complete beam scan of the SDU-FM1 to check functionality and homogeneity of all strips of silicon detector planes, for each beam energy we collected data to achieve good statistics and finally we put two different thickness of Aluminium and Plexiglas in front of the detector in order to study fragmentations. This test has been carried out with a Test Equipment to simulate the Digital Acquisition Unit (DAU). We are scheduled to
Tarachiwin, Lucksanaporn; Masako, Osawa; Fukusaki, Eiichiro
2008-07-23
(1)H NMR spectrometry in combination with multivariate analysis was considered to provide greater information on quality assessment over an ordinary sensory testing method due to its high reliability and high accuracy. The sensory quality evaluation of watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) was carried out by means of (1)H NMR-based metabolomics. Multivariate analyses by partial least-squares projections to latent structures-discrimination analysis (PLS-DA) and PLS-regression offered extensive information for quality differentiation and quality evaluation, respectively. The impact of watermelon and rootstock cultivars on the sensory qualities of watermelon was determined on the basis of (1)H NMR metabolic fingerprinting and profiling. The significant metabolites contributing to the discrimination were also identified. A multivariate calibration model was successfully constructed by PLS-regression with extremely high reliability and accuracy. Thus, (1)H NMR-based metabolomics with multivariate analysis was considered to be one of the most suitable complementary techniques that could be applied to assess and predict the sensory quality of watermelons and other horticultural plants.
Dinç, Erdal; Ustündağ, Ozgür; Baleanu, Dumitru
2010-08-01
The sole use of pyridoxine hydrochloride during treatment of tuberculosis gives rise to pyridoxine deficiency. Therefore, a combination of pyridoxine hydrochloride and isoniazid is used in pharmaceutical dosage form in tuberculosis treatment to reduce this side effect. In this study, two chemometric methods, partial least squares (PLS) and principal component regression (PCR), were applied to the simultaneous determination of pyridoxine (PYR) and isoniazid (ISO) in their tablets. A concentration training set comprising binary mixtures of PYR and ISO consisting of 20 different combinations were randomly prepared in 0.1 M HCl. Both multivariate calibration models were constructed using the relationships between the concentration data set (concentration data matrix) and absorbance data matrix in the spectral region 200-330 nm. The accuracy and the precision of the proposed chemometric methods were validated by analyzing synthetic mixtures containing the investigated drugs. The recovery results obtained by applying PCR and PLS calibrations to the artificial mixtures were found between 100.0 and 100.7%. Satisfactory results obtained by applying the PLS and PCR methods to both artificial and commercial samples were obtained. The results obtained in this manuscript strongly encourage us to use them for the quality control and the routine analysis of the marketing tablets containing PYR and ISO drugs.
Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra.
Blanco, M; Cruz, J; Bautista, M
2008-12-01
Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert M.
2013-01-01
A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.
Wülfert, F; Kok, W T; Smilde, A K
1998-05-01
Temperature, pressure, viscosity, and other process variables fluctuate during an industrial process. When vibrational spectra are measured on- or in-line for process analytical and control purposes, the fluctuations influence the shape of the spectra in a nonlinear manner. The influence of these temperature-induced spectral variations on the predictive ability of multivariate calibration model is assessed. Short-wave NIR spectra of ethanol/water/2-propanol mixtures are taken at different temperatures, and different local and global partial least-squares calibration strategies are applied. The resulting prediction errors and sensitivity vectors of a test set are compared. For data with no temperature variation, the local models perform best with high sensitivity but the knowledge of the temperature for prediction measurements cannot aid in the improvement of local model predictions when temperature variation is introduced. The prediction errors of global models are considerably lower when temperature variation is present in the data set but at the expense of sensitivity. To be able to build temperature-stable calibration models with high sensitivity, a way of explicitly modeling the temperature should be found.
Multivariate analysis of longitudinal rates of change.
Bryan, Matthew; Heagerty, Patrick J
2016-12-10
Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd.
TIME CALIBRATED OSCILLOSCOPE SWEEP
Owren, H.M.; Johnson, B.M.; Smith, V.L.
1958-04-22
The time calibrator of an electric signal displayed on an oscilloscope is described. In contrast to the conventional technique of using time-calibrated divisions on the face of the oscilloscope, this invention provides means for directly superimposing equal time spaced markers upon a signal displayed upon an oscilloscope. More explicitly, the present invention includes generally a generator for developing a linear saw-tooth voltage and a circuit for combining a high-frequency sinusoidal voltage of a suitable amplitude and frequency with the saw-tooth voltage to produce a resultant sweep deflection voltage having a wave shape which is substantially linear with respect to time between equal time spaced incremental plateau regions occurring once each cycle of the sinusoidal voltage. The foregoing sweep voltage when applied to the horizontal deflection plates in combination with a signal to be observed applied to the vertical deflection plates of a cathode ray oscilloscope produces an image on the viewing screen which is essentially a display of the signal to be observed with respect to time. Intensified spots, or certain other conspicuous indications corresponding to the equal time spaced plateau regions of said sweep voltage, appear superimposed upon said displayed signal, which indications are therefore suitable for direct time calibration purposes.
de Godoy, Luiz Antonio Fonseca; Hantao, Leandro Wang; Pedroso, Marcio Pozzobon; Poppi, Ronei Jesus; Augusto, Fabio
2011-08-05
The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data.
NASA Astrophysics Data System (ADS)
Badocco, Denis; Lavagnini, Irma; Mondin, Andrea; Favaro, Gabriella; Pastore, Paolo
2015-12-01
The limit of quantification (LOQ) in the presence of instrumental and non-instrumental errors was proposed. It was theoretically defined combining the two-component variance regression and LOQ schemas already present in the literature and applied to the calibration of zinc by the ICP-MS technique. At low concentration levels, the two-component variance LOQ definition should be always used above all when a clean room is not available. Three LOQ definitions were accounted for. One of them in the concentration and two in the signal domain. The LOQ computed in the concentration domain, proposed by Currie, was completed by adding the third order terms in the Taylor expansion because they are of the same order of magnitude of the second ones so that they cannot be neglected. In this context, the error propagation was simplified by eliminating the correlation contributions by using independent random variables. Among the signal domain definitions, a particular attention was devoted to the recently proposed approach based on at least one significant digit in the measurement. The relative LOQ values resulted very large in preventing the quantitative analysis. It was found that the Currie schemas in the signal and concentration domains gave similar LOQ values but the former formulation is to be preferred as more easily computable.
NASA Astrophysics Data System (ADS)
Libera, D.; Arumugam, S.
2015-12-01
Water quality observations are usually not available on a continuous basis because of the expensive cost and labor requirements so calibrating and validating a mechanistic model is often difficult. Further, any model predictions inherently have bias (i.e., under/over estimation) and require techniques that preserve the long-term mean monthly attributes. This study suggests and compares two multivariate bias-correction techniques to improve the performance of the SWAT model in predicting daily streamflow, TN Loads across the southeast based on split-sample validation. The first approach is a dimension reduction technique, canonical correlation analysis that regresses the observed multivariate attributes with the SWAT model simulated values. The second approach is from signal processing, importance weighting, that applies a weight based off the ratio of the observed and model densities to the model data to shift the mean, variance, and cross-correlation towards the observed values. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are also compared with independent estimates from the USGS LOADEST model. Uncertainties in the bias-corrected estimates due to limited water quality observations are also discussed.
Rimayi, Cornelius; Odusanya, David; Mtunzi, Fanyana; Tsoka, Shepherd
2015-01-01
This paper investigates the efficiency of application of four different multivariate calibration techniques, namely matrix-matched internal standard (MMIS), matrix-matched external standard (MMES), solvent-only internal standard (SOIS) and solvent-only external standard (SOES) on the detection and quantification of 20 organochlorine compounds from high, low and blank matrix water sample matrices by Gas Chromatography-Mass Spectrometry (GC-MS) coupled to solid phase extraction (SPE). Further statistical testing, using Statistical Package for the Social Science (SPSS) by applying MANOVA, T-tests and Levene's F tests indicates that matrix composition has a more significant effect on the efficiency of the analytical method than the calibration method of choice. Matrix effects are widely described as one of the major sources of errors in GC-MS multiresidue analysis. Descriptive and inferential statistics proved that the matrix-matched internal standard calibration was the best approach to use for samples of varying matrix composition as it produced the most precise average mean recovery of 87% across all matrices tested. The use of an internal standard calibration overall produced more precise total recoveries than external standard calibration, with mean values of 77% and 64% respectively. The internal standard calibration technique produced a particularly high overall standard deviation of 38% at 95% confidence level indicating that it is less robust than the external standard calibration method which had an overall standard error of 32% at 95% confidence level. Overall, the matrix-matched external standard calibration proved to be the best calibration approach for analysis of low matrix samples which consisted of the real sample matrix as it had the most precise recovery of 98% compared to other calibration approaches for the low-matrix samples.
Multivariate Visual Explanation for High Dimensional Datasets
Barlowe, Scott; Zhang, Tianyi; Liu, Yujie; Yang, Jing; Jacobs, Donald
2010-01-01
Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach. PMID:20694164
Metwally, Fadia H
2008-02-01
The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12microgml(-1) of NIF and 2-8microgml(-1) of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.
NASA Astrophysics Data System (ADS)
Metwally, Fadia H.
2008-02-01
The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12 μg ml -1 of NIF and 2-8 μg ml -1 of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.
Muon Energy Calibration of the MINOS Detectors
Miyagawa, Paul S.
2004-01-01
MINOS is a long-baseline neutrino oscillation experiment designed to search for conclusive evidence of neutrino oscillations and to measure the oscillation parameters precisely. MINOS comprises two iron tracking calorimeters located at Fermilab and Soudan. The Calibration Detector at CERN is a third MINOS detector used as part of the detector response calibration programme. A correct energy calibration between these detectors is crucial for the accurate measurement of oscillation parameters. This thesis presents a calibration developed to produce a uniform response within a detector using cosmic muons. Reconstruction of tracks in cosmic ray data is discussed. This data is utilized to calculate calibration constants for each readout channel of the Calibration Detector. These constants have an average statistical error of 1.8%. The consistency of the constants is demonstrated both within a single run and between runs separated by a few days. Results are presented from applying the calibration to test beam particles measured by the Calibration Detector. The responses are calibrated to within 1.8% systematic error. The potential impact of the calibration on the measurement of oscillation parameters by MINOS is also investigated. Applying the calibration reduces the errors in the measured parameters by ~ 10%, which is equivalent to increasing the amount of data by 20%.
Multivariate analysis in thoracic research.
Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego
2015-03-01
Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.
Multivariate analysis in thoracic research
Mengual-Macenlle, Noemí; Marcos, Pedro J.; Golpe, Rafael
2015-01-01
Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use. PMID:25922743
Traceable Pyrgeometer Calibrations
Dooraghi, Mike; Kutchenreiter, Mark; Reda, Ibrahim; Habte, Aron; Sengupta, Manajit; Andreas, Afshin; Newman, Martina
2016-05-02
This poster presents the development, implementation, and operation of the Broadband Outdoor Radiometer Calibrations (BORCAL) Longwave (LW) system at the Southern Great Plains Radiometric Calibration Facility for the calibration of pyrgeometers that provide traceability to the World Infrared Standard Group.
Calibration of sound calibrators: an overview
NASA Astrophysics Data System (ADS)
Milhomem, T. A. B.; Soares, Z. M. D.
2016-07-01
This paper presents an overview of calibration of sound calibrators. Initially, traditional calibration methods are presented. Following, the international standard IEC 60942 is discussed emphasizing parameters, target measurement uncertainty and criteria for conformance to the requirements of the standard. Last, Regional Metrology Organizations comparisons are summarized.
Ghasemi, Jahan B; Zolfonoun, Ehsan
2013-11-01
A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.
NASA Astrophysics Data System (ADS)
Ghasemi, Jahan B.; Zolfonoun, Ehsan
2013-11-01
A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520 nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.
Bayesian Calibration of Microsimulation Models.
Rutter, Carolyn M; Miglioretti, Diana L; Savarino, James E
2009-12-01
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models.
NASA Astrophysics Data System (ADS)
Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.
2014-12-01
Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.
Multivariate Analog of Hays Omega-Squared.
ERIC Educational Resources Information Center
Sachdeva, Darshan
The multivariate analog of Hays omega-squared for estimating the strength of the relationship in the multivariate analysis of variance has been proposed in this paper. The multivariate omega-squared is obtained through the use of Wilks' lambda test criterion. Application of multivariate omega-squared to a numerical example has been provided so as…
TOF PET offset calibration from clinical data
NASA Astrophysics Data System (ADS)
Werner, M. E.; Karp, J. S.
2013-06-01
In this paper, we present a timing calibration technique for time-of-flight positron emission tomography (TOF PET) that eliminates the need for a specialized data acquisition. By eliminating the acquisition, the process becomes fully automated, and can be performed with any clinical data set and whenever computing resources are available. It also can be applied retroactively to datasets for which a TOF offset calibration is missing or suboptimal. Since the method can use an arbitrary data set to perform a calibration prior to a TOF reconstruction, possibly of the same data set, one also can view this as reconstruction from uncalibrated data. We present a performance comparison with existing calibration techniques.
Parameter Sensitivity in Multivariate Methods
ERIC Educational Resources Information Center
Green, Bert F., Jr.
1977-01-01
Interpretation of multivariate models requires knowing how much the fit of the model is impaired by changes in the parameters. The relation of parameter change to loss of goodness of fit can be called parameter sensitivity. Formulas are presented for assessing the sensitivity of multiple regression and principal component weights. (Author/JKS)
Multivariate Model of Infant Competence.
ERIC Educational Resources Information Center
Kierscht, Marcia Selland; Vietze, Peter M.
This paper describes a multivariate model of early infant competence formulated from variables representing infant-environment transaction including: birthweight, habituation index, personality ratings of infant social orientation and task orientation, ratings of maternal responsiveness to infant distress and social signals, and observational…
Multichannel hierarchical image classification using multivariate copulas
NASA Astrophysics Data System (ADS)
Voisin, Aurélie; Krylov, Vladimir A.; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane
2012-03-01
This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.
Li, Yanming; Zhu, Ji
2015-01-01
Summary We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functioning groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. PMID:25732839
Calibration of pneumotachographs using a calibrated syringe.
Tang, Yongquan; Turner, Martin J; Yem, Johnny S; Baker, A Barry
2003-08-01
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.
Multivariable PID control by decoupling
NASA Astrophysics Data System (ADS)
Garrido, Juan; Vázquez, Francisco; Morilla, Fernando
2016-04-01
This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.
Information extraction from multivariate images
NASA Technical Reports Server (NTRS)
Park, S. K.; Kegley, K. A.; Schiess, J. R.
1986-01-01
An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.
Multivariate Bioclimatic Ecosystem Change Approaches
2015-02-06
Headquarters, US Army Corps of Engineers Washington, DC 20314-1000 ERDC/CERL TR-15-2 ii Abstract Changes in climatic parameters are important in that they... climatic changes on specific installations. To support this need, the research tested and evaluated the application of six multivariate approach...techniques to predict climatic changes on a specific Army installation, Fort Benning, GA. The six approaches were tested for their ability to identify
Clines Arc through Multivariate Morphospace.
Lohman, Brian K; Berner, Daniel; Bolnick, Daniel I
2017-04-01
Evolutionary biologists typically represent clines as spatial gradients in a univariate character (or a principal-component axis) whose mean changes as a function of location along a transect spanning an environmental gradient or ecotone. This univariate approach may obscure the multivariate nature of phenotypic evolution across a landscape. Clines might instead be plotted as a series of vectors in multidimensional morphospace, connecting sequential geographic sites. We present a model showing that clines may trace nonlinear paths that arc through morphospace rather than elongating along a single major trajectory. Arcing clines arise because different characters diverge at different rates or locations along a geographic transect. We empirically confirm that some clines arc through morphospace, using morphological data from threespine stickleback sampled along eight independent transects from lakes down their respective outlet streams. In all eight clines, successive vectors of lake-stream divergence fluctuate in direction and magnitude in trait space, rather than pointing along a single phenotypic axis. Most clines exhibit surprisingly irregular directions of divergence as one moves downstream, although a few clines exhibit more directional arcs through morphospace. Our results highlight the multivariate complexity of clines that cannot be captured with the traditional graphical framework. We discuss hypotheses regarding the causes, and implications, of such arcing multivariate clines.
Analytical multicollimator camera calibration
Tayman, W.P.
1978-01-01
Calibration with the U.S. Geological survey multicollimator determines the calibrated focal length, the point of symmetry, the radial distortion referred to the point of symmetry, and the asymmetric characteristiecs of the camera lens. For this project, two cameras were calibrated, a Zeiss RMK A 15/23 and a Wild RC 8. Four test exposures were made with each camera. Results are tabulated for each exposure and averaged for each set. Copies of the standard USGS calibration reports are included. ?? 1978.
Univariate Analysis of Multivariate Outcomes in Educational Psychology.
ERIC Educational Resources Information Center
Hubble, L. M.
1984-01-01
The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…
The Optimization of Multivariate Generalizability Studies with Budget Constraints.
ERIC Educational Resources Information Center
Marcoulides, George A.; Goldstein, Zvi
1992-01-01
A method is presented for determining the optimal number of conditions to use in multivariate-multifacet generalizability designs when resource constraints are imposed. A decision maker can determine the number of observations needed to obtain the largest possible generalizability coefficient. The procedure easily applies to the univariate case.…
Multivariate classification of infrared spectra of cell and tissue samples
Haaland, David M.; Jones, Howland D. T.; Thomas, Edward V.
1997-01-01
Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.
A multivariate Bayesian model for embryonic growth.
Willemsen, Sten P; Eilers, Paul H C; Steegers-Theunissen, Régine P M; Lesaffre, Emmanuel
2015-04-15
Most longitudinal growth curve models evaluate the evolution of each of the anthropometric measurements separately. When applied to a 'reference population', this exercise leads to univariate reference curves against which new individuals can be evaluated. However, growth should be evaluated in totality, that is, by evaluating all body characteristics jointly. Recently, Cole et al. suggested the Superimposition by Translation and Rotation (SITAR) model, which expresses individual growth curves by three subject-specific parameters indicating their deviation from a flexible overall growth curve. This model allows the characterization of normal growth in a flexible though compact manner. In this paper, we generalize the SITAR model in a Bayesian way to multiple dimensions. The multivariate SITAR model allows us to create multivariate reference regions, which is advantageous for prediction. The usefulness of the model is illustrated on longitudinal measurements of embryonic growth obtained in the first semester of pregnancy, collected in the ongoing Rotterdam Predict study. Further, we demonstrate how the model can be used to find determinants of embryonic growth.
A multivariate Baltic Sea environmental index.
Dippner, Joachim W; Kornilovs, Georgs; Junker, Karin
2012-11-01
Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.
NASA Technical Reports Server (NTRS)
Robertson, G.
1982-01-01
Calibration was performed on the shuttle upper atmosphere mass spectrometer (SUMS). The results of the calibration and the as run test procedures are presented. The output data is described, and engineering data conversion factors, tables and curves, and calibration on instrument gauges are included. Static calibration results which include: instrument sensitive versus external pressure for N2 and O2, data from each scan of calibration, data plots from N2 and O2, and sensitivity of SUMS at inlet for N2 and O2, and ratios of 14/28 for nitrogen and 16/32 for oxygen are given.
1991-09-01
GRAFSTAT from IBM Research; I am grateful to Dr . Peter Welch for supplying GRAFSTAT. To P.A.W. Lewis, Thank you for your support, confidence and...34Multivariate Adaptive Regression Splines", Annals of Statistics, v. 19, no. 2, pp. 1-142, 1991. Geib , A., Applied Optimal Estimation, M.I.T. Press, Cambridge
Multivariate optimization of capillary electrophoresis methods: a critical review.
Orlandini, Serena; Gotti, Roberto; Furlanetto, Sandra
2014-01-01
In this article a review on the recent applications of multivariate techniques for optimization of electromigration methods, is presented. Papers published in the period from August 2007 to February 2013, have been taken into consideration. Upon a brief description of each of the involved CE operative modes, the characteristics of the chemometric strategies (type of design, factors and responses) applied to face a number of analytical challenges, are presented. Finally, a critical discussion, giving some practical advices and pointing out the most common issues involved in multivariate set-up of CE methods, is provided.
Steady-state decoupling and design of linear multivariable systems
NASA Technical Reports Server (NTRS)
Thaler, G. J.
1974-01-01
A constructive criterion for decoupling the steady states of a linear time-invariant multivariable system is presented. This criterion consists of a set of inequalities which, when satisfied, will cause the steady states of a system to be decoupled. Stability analysis and a new design technique for such systems are given. A new and simple connection between single-loop and multivariable cases is found. These results are then applied to the compensation design for NASA STOL C-8A aircraft. Both steady-state decoupling and stability are justified through computer simulations.
Regionalization in geology by multivariate classification
Harff, Jan; Davis, J.C.
1990-01-01
The concept of multivariate classification of "geological objects" can be combined with the concept of regionalized variables to yield a procedure for typification of geological objects, such as rock units, well records, or samples. Numerical classification is followed by subdivision of the area of investigation, and culminates in a regionalization or mapping of the classification onto the plane. Regions are subdivisions of the map area which are spatially contiguous and relatively homogeneous in their geological properties. The probability of correct classification of each point within a region as being part of that region can be assessed in terms of Bayesian probability as a space-dependent function. The procedure is applied to subsurface data from western Kansas. The geologic properties used are quantitative variables, and relationships are expressed by Mahalanobis' distances. These functions could be replaced by other metrics if qualitative or binary data derived from geological descriptions or appraisals were included in the analysis. ?? 1990 International Association for Mathematical Geology.
Design of feedforward controllers for multivariable plants
NASA Technical Reports Server (NTRS)
Seraji, H.
1987-01-01
Simple methods for the design of feedforward controllers to achieve steady-state disturbance rejection and command tracking in stable multivariable plants are developed in this paper. The controllers are represented by simple and low-order transfer functions and are not based on reconstruction of the states of the commands and disturbances. For unstable plants, it is shown that the present method can be applied directly when an additional feedback controller is employed to stabilize the plant. The feedback and feedforward controllers do not affect each other and can be designed independently based on the open-loop plant to achieve stability, disturbance rejection and command tracking, respectivley. Numerical examples are given for illustration.
Classification of adulterated honeys by multivariate analysis.
Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad
2017-06-01
In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%).
Multivariate Strategies in Functional Magnetic Resonance Imaging
ERIC Educational Resources Information Center
Hansen, Lars Kai
2007-01-01
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.
Tailored multivariate analysis for modulated enhanced diffraction
Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; ...
2015-10-21
Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less
Tailored multivariate analysis for modulated enhanced diffraction
Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; Tutuncu, Goknur; Hanson, Jonathan C.
2015-10-21
Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited for
Software For Multivariate Bayesian Classification
NASA Technical Reports Server (NTRS)
Saul, Ronald; Laird, Philip; Shelton, Robert
1996-01-01
PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.
Kong, Wenwen; Zhang, Chu; Liu, Fei; Nie, Pengcheng; He, Yong
2013-01-01
A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique. PMID:23857260
A multivariate heuristic model for fuzzy time-series forecasting.
Huarng, Kun-Huang; Yu, Tiffany Hui-Kuang; Hsu, Yu Wei
2007-08-01
Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. In addition, many such models have been shown to provide better forecasting results than their conventional counterparts. However, since most of these models require complicated matrix computations, this paper proposes the adoption of a multivariate heuristic function that can be integrated with univariate fuzzy time-series models into multivariate models. Such a multivariate heuristic function can easily be extended and integrated with various univariate models. Furthermore, the integrated model can handle multiple variables to improve forecasting results and, at the same time, avoid complicated computations due to the inclusion of multiple variables.
Multivariate image processing technique for noninvasive glucose sensing
NASA Astrophysics Data System (ADS)
Webb, Anthony J.; Cameron, Brent D.
2010-02-01
A potential noninvasive glucose sensing technique was investigated for application towards in vivo glucose monitoring for individuals afflicted with diabetes mellitus. Three dimensional ray tracing simulations using a realistic iris pattern integrated into an advanced human eye model are reported for physiological glucose concentrations ranging between 0 to 500 mg/dL. The anterior chamber of the human eye contains a clear fluid known as the aqueous humor. The optical refractive index of the aqueous humor varies on the order of 1.5x10-4 for a change in glucose concentration of 100 mg/dL. The simulation data was analyzed with a developed multivariate chemometrics procedure that utilizes iris-based images to form a calibration model. Results from these simulations show considerable potential for use of the developed method in the prediction of glucose. For further demonstration, an in vitro eye model was developed to validate the computer based modeling technique. In these experiments, a realistic iris pattern was placed in an analog eye model in which the glucose concentration within the fluid representing the aqueous humor was varied. A series of high resolution digital images were acquired using an optical imaging system. These images were then used to form an in vitro calibration model utilizing the same multivariate chemometric technique demonstrated in the 3-D optical simulations. In general, the developed method exhibits considerable applicability towards its use as an in vivo platform for the noninvasive monitoring of physiological glucose concentration.
Distance measure with improved lower bound for multivariate time series
NASA Astrophysics Data System (ADS)
Li, Hailin
2017-02-01
Lower bound function is one of the important techniques used to fast search and index time series data. Multivariate time series has two aspects of high dimensionality including the time-based dimension and the variable-based dimension. Due to the influence of variable-based dimension, a novel method is proposed to deal with the lower bound distance computation for multivariate time series. The proposed method like the traditional ones also reduces the dimensionality of time series in its first step and thus does not directly apply the lower bound function on the multivariate time series. The dimensionality reduction is that multivariate time series is reduced to univariate time series denoted as center sequences according to the principle of piecewise aggregate approximation. In addition, an extended lower bound function is designed to obtain good tightness and fast measure the distance between any two center sequences. The experimental results demonstrate that the proposed lower bound function has better tightness and improves the performance of similarity search in multivariate time series datasets.
Multicomponent seismic noise attenuation with multivariate order statistic filters
NASA Astrophysics Data System (ADS)
Wang, Chao; Wang, Yun; Wang, Xiaokai; Xun, Chao
2016-10-01
The vector relationship between multicomponent seismic data is highly important for multicomponent processing and interpretation, but this vector relationship could be damaged when each component is processed individually. To overcome the drawback of standard component-by-component filtering, multivariate order statistic filters are introduced and extended to attenuate the noise of multicomponent seismic data by treating such dataset as a vector wavefield rather than a set of scalar fields. According to the characteristics of seismic signals, we implement this type of multivariate filtering along local events. First, the optimal local events are recognized according to the similarity between the vector signals which are windowed from neighbouring seismic traces with a sliding time window along each trial trajectory. An efficient strategy is used to reduce the computational cost of similarity measurement for vector signals. Next, one vector sample each from the neighbouring traces are extracted along the optimal local event as the input data for a multivariate filter. Different multivariate filters are optimal for different noise. The multichannel modified trimmed mean (MTM) filter, as one of the multivariate order statistic filters, is applied to synthetic and field multicomponent seismic data to test its performance for attenuating white Gaussian noise. The results indicate that the multichannel MTM filter can attenuate noise while preserving the relative amplitude information of multicomponent seismic data more effectively than a single-channel filter.
SAR calibration technology review
NASA Technical Reports Server (NTRS)
Walker, J. L.; Larson, R. W.
1981-01-01
Synthetic Aperture Radar (SAR) calibration technology including a general description of the primary calibration techniques and some of the factors which affect the performance of calibrated SAR systems are reviewed. The use of reference reflectors for measurement of the total system transfer function along with an on-board calibration signal generator for monitoring the temporal variations of the receiver to processor output is a practical approach for SAR calibration. However, preliminary error analysis and previous experimental measurements indicate that reflectivity measurement accuracies of better than 3 dB will be difficult to achieve. This is not adequate for many applications and, therefore, improved end-to-end SAR calibration techniques are required.
Method of Calibrating a Force Balance
NASA Technical Reports Server (NTRS)
Parker, Peter A. (Inventor); Rhew, Ray D. (Inventor); Johnson, Thomas H. (Inventor); Landman, Drew (Inventor)
2015-01-01
A calibration system and method utilizes acceleration of a mass to generate a force on the mass. An expected value of the force is calculated based on the magnitude and acceleration of the mass. A fixture is utilized to mount the mass to a force balance, and the force balance is calibrated to provide a reading consistent with the expected force determined for a given acceleration. The acceleration can be varied to provide different expected forces, and the force balance can be calibrated for different applied forces. The acceleration may result from linear acceleration of the mass or rotational movement of the mass.
Down force calibration stand test report
BOGER, R.M.
1999-08-13
The Down Force Calibration Stand was developed to provide an improved means of calibrating equipment used to apply, display and record Core Sample Truck (CST) down force. Originally, four springs were used in parallel to provide a system of resistance that allowed increasing force over increasing displacement. This spring system, though originally deemed adequate, was eventually found to be unstable laterally. For this reason, it was determined that a new method for resisting down force was needed.
Tripathi, Markandey M.; Krishnan, Sundar R.; Srinivasan, Kalyan K.; Yueh, Fang-Yu; Singh, Jagdish P.
2011-09-07
Chemiluminescence emissions from OH*, CH*, C2, and CO2 formed within the reaction zone of premixed flames depend upon the fuel-air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH*/CH* intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane-air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using the leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of CO2 emission that is required for typical OH*/CH* intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH*/CH* intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions ( > 1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed.
Solar-Reflectance-Based Calibration of Spectral Radiometers
NASA Technical Reports Server (NTRS)
Cattrall, Christopher; Carder, Kendall L.; Thome, Kurtis J.; Gordon, Howard R.
2001-01-01
A method by which to calibrate a spectral radiometer using the sun as the illumination source is discussed. Solar-based calibrations eliminate several uncertainties associated with applying a lamp-based calibration to field measurements. The procedure requires only a calibrated reflectance panel, relatively low aerosol optical depth, and measurements of atmospheric transmittance. Further, a solar-reflectance-based calibration (SRBC), by eliminating the need for extraterrestrial irradiance spectra, reduces calibration uncertainty to approximately 2.2% across the solar-reflective spectrum, significantly reducing uncertainty in measurements used to deduce the optical properties of a system illuminated by the sun (e.g., sky radiance). The procedure is very suitable for on-site calibration of long-term field instruments, thereby reducing the logistics and costs associated with transporting a radiometer to a calibration facility.
Photogrammetric camera calibration
Tayman, W.P.; Ziemann, H.
1984-01-01
Section 2 (Calibration) of the document "Recommended Procedures for Calibrating Photogrammetric Cameras and Related Optical Tests" from the International Archives of Photogrammetry, Vol. XIII, Part 4, is reviewed in the light of recent practical work, and suggestions for changes are made. These suggestions are intended as a basis for a further discussion. ?? 1984.
NASA Technical Reports Server (NTRS)
Markham, Brian; Morfitt, Ron; Kvaran, Geir; Biggar, Stuart; Leisso, Nathan; Czapla-Myers, Jeff
2011-01-01
Goals: (1) Present an overview of the pre-launch radiance, reflectance & uniformity calibration of the Operational Land Imager (OLI) (1a) Transfer to orbit/heliostat (1b) Linearity (2) Discuss on-orbit plans for radiance, reflectance and uniformity calibration of the OLI
Calibration facility safety plan
NASA Technical Reports Server (NTRS)
Fastie, W. G.
1971-01-01
A set of requirements is presented to insure the highest practical standard of safety for the Apollo 17 Calibration Facility in terms of identifying all critical or catastrophic type hazard areas. Plans for either counteracting or eliminating these areas are presented. All functional operations in calibrating the ultraviolet spectrometer and the testing of its components are described.
Multivariate Longitudinal Analysis with Bivariate Correlation Test.
Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory
2016-01-01
In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.
Sandia WIPP calibration traceability
Schuhen, M.D.; Dean, T.A.
1996-05-01
This report summarizes the work performed to establish calibration traceability for the instrumentation used by Sandia National Laboratories at the Waste Isolation Pilot Plant (WIPP) during testing from 1980-1985. Identifying the calibration traceability is an important part of establishing a pedigree for the data and is part of the qualification of existing data. In general, the requirement states that the calibration of Measuring and Test equipment must have a valid relationship to nationally recognized standards or the basis for the calibration must be documented. Sandia recognized that just establishing calibration traceability would not necessarily mean that all QA requirements were met during the certification of test instrumentation. To address this concern, the assessment was expanded to include various activities.
Brandstätter, Christian; Laner, David; Prantl, Roman; Fellner, Johann
2014-12-01
Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed. This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables. The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills. Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.
Clegg, Samuel M; Barefield, James E; Wiens, Roger C; Sklute, Elizabeth; Dyare, Melinda D
2008-01-01
Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.
Welch, J; /SLAC
2010-11-24
indicated that the vacuum chamber was in fact in the proper position with respect to the magnet - not 19 mm off to one side - so the former possibility was discounted. Review of the Fiducial Report and an interview with Keith Caban convinced me that there was no error in the coordinate system used for magnet measurements. I went and interviewed Andrew Fischer who did the magnetic measurements of BXS. He had extensive records, including photographs of the setups and could quickly answer quite detailed questions about how the measurement was done. Before the interview, I had a suspicion there might have been a sign flip in the x coordinate which because of the wedge would result in the wrong path length and a miscalibration. Andrew was able to pin-point how this could have happened and later confirmed it by looking an measurement data from the BXG magnet done just after BXS and comparing photographs. It turned out that the sign of the horizontal stage travel that drives the measurement wire was opposite that of the x coordinate in the Traveler, and the sign difference wasn't applied to the data. The origin x = 0 was set up correctly, but the wire moved in the opposite direction to what was expected, just as if the arc had been flipped over about the origin. To quantitatively confirm that this was the cause of the observed difference in calibration I used the 'grid data', which was taken with a Hall probe on the BXS magnet originally to measure the FINT (focusing effect) term, and combined it with the Hall probe data taken on the flipped trajectory, and performed the field integral on a path that should give the same result as the design path. This is best illustrated in Figure 2. The integration path is coincident with the desired path from the pivot points (x = 0) outward. Between the pivot points the integration path is a mirror image of the design path, but because the magnet is fairly uniform, for this portion it gives the same result. Most of the calibration error
Revised landsat-5 thematic mapper radiometric calibration
Chander, G.; Markham, B.L.; Barsi, J.A.
2007-01-01
Effective April 2, 2007, the radiometric calibration of Landsat-5 (L5) Thematic Mapper (TM) data that are processed and distributed by the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) will be updated. The lifetime gain model that was implemented on May 5, 2003, for the reflective bands (1-5, 7) will be replaced by a new lifetime radiometric-calibration curve that is derived from the instrument's response to pseudoinvariant desert sites and from cross calibration with the Landsat-7 (L7) Enhanced TM Plus (ETM+). Although this calibration update applies to all archived and future L5 TM data, the principal improvements in the calibration are for the data acquired during the first eight years of the mission (1984-1991), where the changes in the instrument-gain values are as much as 15%. The radiometric scaling coefficients for bands 1 and 2 for approximately the first eight years of the mission have also been changed. Users will need to apply these new coefficients to convert the calibrated data product digital numbers to radiance. The scaling coefficients for the other bands have not changed. ?? 2007 IEEE.
Feng, Jie; Wang, Zhe; Li, Lizhi; Li, Zheng; Ni, Weidou
2013-03-01
A nonlinearized multivariate dominant factor-based partial least-squares (PLS) model was applied to coal elemental concentration measurement. For C concentration determination in bituminous coal, the intensities of multiple characteristic lines of the main elements in coal were applied to construct a comprehensive dominant factor that would provide main concentration results. A secondary PLS thereafter applied would further correct the model results by using the entire spectral information. In the dominant factor extraction, nonlinear transformation of line intensities (based on physical mechanisms) was embedded in the linear PLS to describe nonlinear self-absorption and inter-element interference more effectively and accurately. According to the empirical expression of self-absorption and Taylor expansion, nonlinear transformations of atomic and ionic line intensities of C were utilized to model self-absorption. Then, the line intensities of other elements, O and N, were taken into account for inter-element interference, considering the possible recombination of C with O and N particles. The specialty of coal analysis by using laser-induced breakdown spectroscopy (LIBS) was also discussed and considered in the multivariate dominant factor construction. The proposed model achieved a much better prediction performance than conventional PLS. Compared with our previous, already improved dominant factor-based PLS model, the present PLS model obtained the same calibration quality while decreasing the root mean square error of prediction (RMSEP) from 4.47 to 3.77%. Furthermore, with the leave-one-out cross-validation and L-curve methods, which avoid the overfitting issue in determining the number of principal components instead of minimum RMSEP criteria, the present PLS model also showed better performance for different splits of calibration and prediction samples, proving the robustness of the present PLS model.
Multivariate Global Ocean Assimilation Studies
2002-09-30
representations of the four dimensional circulation of the ocean using data assimilation methods . We intend these representations to be applied in a variety of military, academic, as well as commercial applications.
New technique for calibrating hydrocarbon gas flowmeters
NASA Technical Reports Server (NTRS)
Singh, J. J.; Puster, R. L.
1984-01-01
A technique for measuring calibration correction factors for hydrocarbon mass flowmeters is described. It is based on the Nernst theorem for matching the partial pressure of oxygen in the combustion products of the test hydrocarbon, burned in oxygen-enriched air, with that in normal air. It is applied to a widely used type of commercial thermal mass flowmeter for a number of hydrocarbons. The calibration correction factors measured using this technique are in good agreement with the values obtained by other independent procedures. The technique is successfully applied to the measurement of differences as low as one percent of the effective hydrocarbon content of the natural gas test samples.
Jet energy calibration at the LHC
Schwartzman, Ariel
2015-11-10
In this study, jets are one of the most prominent physics signatures of high energy proton–proton (p–p) collisions at the Large Hadron Collider (LHC). They are key physics objects for precision measurements and searches for new phenomena. This review provides an overview of the reconstruction and calibration of jets at the LHC during its first Run. ATLAS and CMS developed different approaches for the reconstruction of jets, but use similar methods for the energy calibration. ATLAS reconstructs jets utilizing input signals from their calorimeters and use charged particle tracks to refine their energy measurement and suppress the effects of multiple p–p interactions (pileup). CMS, instead, combines calorimeter and tracking information to build jets from particle flow objects. Jets are calibrated using Monte Carlo (MC) simulations and a residual in situ calibration derived from collision data is applied to correct for the differences in jet response between data and Monte Carlo.
Jet energy calibration at the LHC
Schwartzman, Ariel
2015-11-10
In this study, jets are one of the most prominent physics signatures of high energy proton–proton (p–p) collisions at the Large Hadron Collider (LHC). They are key physics objects for precision measurements and searches for new phenomena. This review provides an overview of the reconstruction and calibration of jets at the LHC during its first Run. ATLAS and CMS developed different approaches for the reconstruction of jets, but use similar methods for the energy calibration. ATLAS reconstructs jets utilizing input signals from their calorimeters and use charged particle tracks to refine their energy measurement and suppress the effects of multiplemore » p–p interactions (pileup). CMS, instead, combines calorimeter and tracking information to build jets from particle flow objects. Jets are calibrated using Monte Carlo (MC) simulations and a residual in situ calibration derived from collision data is applied to correct for the differences in jet response between data and Monte Carlo.« less
AUTOMATIC CALIBRATING SYSTEM FOR PRESSURE TRANSDUCERS
Amonette, E.L.; Rodgers, G.W.
1958-01-01
An automatic system for calibrating a number of pressure transducers is described. The disclosed embodiment of the invention uses a mercurial manometer to measure the air pressure applied to the transducer. A servo system follows the top of the mercury column as the pressure is changed and operates an analog- to-digital converter This converter furnishes electrical pulses, each representing an increment of pressure change, to a reversible counterThe transducer furnishes a signal at each calibration point, causing an electric typewriter and a card-punch machine to record the pressure at the instant as indicated by the counter. Another counter keeps track of the calibration points so that a number identifying each point is recorded with the corresponding pressure. A special relay control system controls the pressure trend and programs the sequential calibration of several transducers.
PACS photometer calibration block analysis
NASA Astrophysics Data System (ADS)
Moór, A.; Müller, T. G.; Kiss, C.; Balog, Z.; Billot, N.; Marton, G.
2014-07-01
The absolute stability of the PACS bolometer response over the entire mission lifetime without applying any corrections is about 0.5 % (standard deviation) or about 8 % peak-to-peak. This fantastic stability allows us to calibrate all scientific measurements by a fixed and time-independent response file, without using any information from the PACS internal calibration sources. However, the analysis of calibration block observations revealed clear correlations of the internal source signals with the evaporator temperature and a signal drift during the first half hour after the cooler recycling. These effects are small, but can be seen in repeated measurements of standard stars. From our analysis we established corrections for both effects which push the stability of the PACS bolometer response to about 0.2 % (stdev) or 2 % in the blue, 3 % in the green and 5 % in the red channel (peak-to-peak). After both corrections we still see a correlation of the signals with PACS FPU temperatures, possibly caused by parasitic heat influences via the Kevlar wires which connect the bolometers with the PACS Focal Plane Unit. No aging effect or degradation of the photometric system during the mission lifetime has been found.
Compact radiometric microwave calibrator
Fixsen, D. J.; Wollack, E. J.; Kogut, A.; Limon, M.; Mirel, P.; Singal, J.; Fixsen, S. M.
2006-06-15
The calibration methods for the ARCADE II instrument are described and the accuracy estimated. The Steelcast coated aluminum cones which comprise the calibrator have a low reflection while maintaining 94% of the absorber volume within 5 mK of the base temperature (modeled). The calibrator demonstrates an absorber with the active part less than one wavelength thick and only marginally larger than the mouth of the largest horn and yet black (less than -40 dB or 0.01% reflection) over five octaves in frequency.
Dynamic Pressure Calibration Standard
NASA Technical Reports Server (NTRS)
Schutte, P. C.; Cate, K. H.; Young, S. D.
1986-01-01
Vibrating columns of fluid used to calibrate transducers. Dynamic pressure calibration standard developed for calibrating flush diaphragm-mounted pressure transducers. Pressures up to 20 kPa (3 psi) accurately generated over frequency range of 50 to 1,800 Hz. System includes two conically shaped aluminum columns one 5 cm (2 in.) high for low pressures and another 11 cm (4.3 in.) high for higher pressures, each filled with viscous fluid. Each column mounted on armature of vibration exciter, which imparts sinusoidally varying acceleration to fluid column. Signal noise low, and waveform highly dependent on quality of drive signal in vibration exciter.
DIRBE External Calibrator (DEC)
NASA Technical Reports Server (NTRS)
Wyatt, Clair L.; Thurgood, V. Alan; Allred, Glenn D.
1987-01-01
Under NASA Contract No. NAS5-28185, the Center for Space Engineering at Utah State University has produced a calibration instrument for the Diffuse Infrared Background Experiment (DIRBE). DIRBE is one of the instruments aboard the Cosmic Background Experiment Observatory (COBE). The calibration instrument is referred to as the DEC (Dirbe External Calibrator). DEC produces a steerable, infrared beam of controlled spectral content and intensity and with selectable point source or diffuse source characteristics, that can be directed into the DIRBE to map fields and determine response characteristics. This report discusses the design of the DEC instrument, its operation and characteristics, and provides an analysis of the systems capabilities and performance.
Airdata Measurement and Calibration
NASA Technical Reports Server (NTRS)
Haering, Edward A., Jr.
1995-01-01
This memorandum provides a brief introduction to airdata measurement and calibration. Readers will learn about typical test objectives, quantities to measure, and flight maneuvers and operations for calibration. The memorandum informs readers about tower-flyby, trailing cone, pacer, radar-tracking, and dynamic airdata calibration maneuvers. Readers will also begin to understand how some data analysis considerations and special airdata cases, including high-angle-of-attack flight, high-speed flight, and nonobtrusive sensors are handled. This memorandum is not intended to be all inclusive; this paper contains extensive reference and bibliography sections.
NASA Astrophysics Data System (ADS)
Pappalardo, Gelsomina; Freudenthaler, Volker; Nicolae, Doina; Mona, Lucia; Belegante, Livio; D'Amico, Giuseppe
2016-06-01
This paper presents the newly established Lidar Calibration Centre, a distributed infrastructure in Europe, whose goal is to offer services for complete characterization and calibration of lidars and ceilometers. Mobile reference lidars, laboratories for testing and characterization of optics and electronics, facilities for inspection and debugging of instruments, as well as for training in good practices are open to users from the scientific community, operational services and private sector. The Lidar Calibration Centre offers support for trans-national access through the EC HORIZON2020 project ACTRIS-2.
Calibrating page sized Gafchromic EBT3 films
Crijns, W.; Maes, F.; Heide, U. A. van der; Van den Heuvel, F.
2013-01-15
Purpose: The purpose is the development of a novel calibration method for dosimetry with Gafchromic EBT3 films. The method should be applicable for pretreatment verification of volumetric modulated arc, and intensity modulated radiotherapy. Because the exposed area on film can be large for such treatments, lateral scan errors must be taken into account. The correction for the lateral scan effect is obtained from the calibration data itself. Methods: In this work, the film measurements were modeled using their relative scan values (Transmittance, T). Inside the transmittance domain a linear combination and a parabolic lateral scan correction described the observed transmittance values. The linear combination model, combined a monomer transmittance state (T{sub 0}) and a polymer transmittance state (T{sub {infinity}}) of the film. The dose domain was associated with the observed effects in the transmittance domain through a rational calibration function. On the calibration film only simple static fields were applied and page sized films were used for calibration and measurements (treatment verification). Four different calibration setups were considered and compared with respect to dose estimation accuracy. The first (I) used a calibration table from 32 regions of interest (ROIs) spread on 4 calibration films, the second (II) used 16 ROIs spread on 2 calibration films, the third (III), and fourth (IV) used 8 ROIs spread on a single calibration film. The calibration tables of the setups I, II, and IV contained eight dose levels delivered to different positions on the films, while for setup III only four dose levels were applied. Validation was performed by irradiating film strips with known doses at two different time points over the course of a week. Accuracy of the dose response and the lateral effect correction was estimated using the dose difference and the root mean squared error (RMSE), respectively. Results: A calibration based on two films was the optimal
Calibration Fixture For Anemometer Probes
NASA Technical Reports Server (NTRS)
Lewis, Charles R.; Nagel, Robert T.
1993-01-01
Fixture facilitates calibration of three-dimensional sideflow thermal anemometer probes. With fixture, probe oriented at number of angles throughout its design range. Readings calibrated as function of orientation in airflow. Calibration repeatable and verifiable.
On the individual calibration of hailpads
NASA Astrophysics Data System (ADS)
Palencia, Covadonga; Berthet, Claude; Massot, Marta; Castro, Amaya; Dessens, Jean; Fraile, Roberto
2007-02-01
This paper is a comparative study between the two most common hailpad calibration systems: one annual calibration of a whole consignment of material, and the individual calibration of each plate after a hailfall. Individual calibration attempts to minimize errors due to differences in sensitivity to the impact of hailstones between plates from the same consignment, or due to differences in the inking process before the actual measurement. The comparison was carried out using calibration data from the past few years in the hailpad network in south-western France, and data from an individual calibration process on material provided by the hailpad network in Lleida (Spain). The same type of material was used in the two cases. The results confirm that the error in measuring hailstone sizes is smaller in the case of an individual calibration of hailpads than when one single calibration process was carried out for a whole consignment. The former is approximately 80% of the latter. However, this error could have been higher if it had not been the same person carrying out the single calibration process and the measuring of the dents: it has been found that differences in the inking process may account for up to 20% of the error in the case of small hailstones. Calibration errors affecting other variables, e.g. energy or parameter λ of the exponential size distribution are generally higher (5% and 18%, respectively) than errors due to the spatial variability of the hailstones. However, the calibration method does not influence the maximum size, since the relative error attributed to the spatial variability is about 8 times the calibration error. In conclusion, if errors in determining energy or parameter λ are to be reduced to a minimum, it is highly advisable to be consistent in applying the measuring procedure (if possible with the same person carrying out the measurements all the time), and even to use individual calibration on each plate, always bearing in mind that
Estimating the decomposition of predictive information in multivariate systems.
Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele
2015-03-01
In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.
Synergy, redundancy, and multivariate information measures: an experimentalist's perspective.
Timme, Nicholas; Alford, Wesley; Flecker, Benjamin; Beggs, John M
2014-04-01
Information theory has long been used to quantify interactions between two variables. With the rise of complex systems research, multivariate information measures have been increasingly used to investigate interactions between groups of three or more variables, often with an emphasis on so called synergistic and redundant interactions. While bivariate information measures are commonly agreed upon, the multivariate information measures in use today have been developed by many different groups, and differ in subtle, yet significant ways. Here, we will review these multivariate information measures with special emphasis paid to their relationship to synergy and redundancy, as well as examine the differences between these measures by applying them to several simple model systems. In addition to these systems, we will illustrate the usefulness of the information measures by analyzing neural spiking data from a dissociated culture through early stages of its development. Our aim is that this work will aid other researchers as they seek the best multivariate information measure for their specific research goals and system. Finally, we have made software available online which allows the user to calculate all of the information measures discussed within this paper.
Estimating the decomposition of predictive information in multivariate systems
NASA Astrophysics Data System (ADS)
Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele
2015-03-01
In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.
Mardia's Multivariate Kurtosis with Missing Data
ERIC Educational Resources Information Center
Yuan, Ke-Hai; Lambert, Paul L.; Fouladi, Rachel T.
2004-01-01
Mardia's measure of multivariate kurtosis has been implemented in many statistical packages commonly used by social scientists. It provides important information on whether a commonly used multivariate procedure is appropriate for inference. Many statistical packages also have options for missing data. However, there is no procedure for applying…
Multivariate Density Estimation and Remote Sensing
NASA Technical Reports Server (NTRS)
Scott, D. W.
1983-01-01
Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.
Multivariate pluvial flood damage models
Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom
2015-09-15
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.
Efficient Calibration of Computationally Intensive Hydrological Models
NASA Astrophysics Data System (ADS)
Poulin, A.; Huot, P. L.; Audet, C.; Alarie, S.
2015-12-01
A new hybrid optimization algorithm for the calibration of computationally-intensive hydrological models is introduced. The calibration of hydrological models is a blackbox optimization problem where the only information available to the optimization algorithm is the objective function value. In the case of distributed hydrological models, the calibration process is often known to be hampered by computational efficiency issues. Running a single simulation may take several minutes and since the optimization process may require thousands of model evaluations, the computational time can easily expand to several hours or days. A blackbox optimization algorithm, which can substantially improve the calibration efficiency, has been developed. It merges both the convergence analysis and robust local refinement from the Mesh Adaptive Direct Search (MADS) algorithm, and the global exploration capabilities from the heuristic strategies used by the Dynamically Dimensioned Search (DDS) algorithm. The new algorithm is applied to the calibration of the distributed and computationally-intensive HYDROTEL model on three different river basins located in the province of Quebec (Canada). Two calibration problems are considered: (1) calibration of a 10-parameter version of HYDROTEL, and (2) calibration of a 19-parameter version of the same model. A previous study by the authors had shown that the original version of DDS was the most efficient method for the calibration of HYDROTEL, when compared to the MADS and the very well-known SCEUA algorithms. The computational efficiency of the hybrid DDS-MADS method is therefore compared with the efficiency of the DDS algorithm based on a 2000 model evaluations budget. Results show that the hybrid DDS-MADS method can reduce the total number of model evaluations by 70% for the 10-parameter version of HYDROTEL and by 40% for the 19-parameter version without compromising the quality of the final objective function value.
Roundness calibration standard
Burrus, Brice M.
1984-01-01
A roundness calibration standard is provided with a first arc constituting the major portion of a circle and a second arc lying between the remainder of the circle and the chord extending between the ends of said first arc.
Traceable Pyrgeometer Calibrations
Dooraghi, Mike; Kutchenreiter, Mark; Reda, Ibrahim; Habte, Aron; Sengupta, Manajit; Andreas, Afshin; Newman, Martina; Webb, Craig
2016-05-02
This presentation provides a high-level overview of the progress on the Broadband Outdoor Radiometer Calibrations for all shortwave and longwave radiometers that are deployed by the Atmospheric Radiation Measurement program.
Multivariate semiparametric spatial methods for imaging data.
Chen, Huaihou; Cao, Guanqun; Cohen, Ronald A
2017-04-01
Univariate semiparametric methods are often used in modeling nonlinear age trajectories for imaging data, which may result in efficiency loss and lower power for identifying important age-related effects that exist in the data. As observed in multiple neuroimaging studies, age trajectories show similar nonlinear patterns for the left and right corresponding regions and for the different parts of a big organ such as the corpus callosum. To incorporate the spatial similarity information without assuming spatial smoothness, we propose a multivariate semiparametric regression model with a spatial similarity penalty, which constrains the variation of the age trajectories among similar regions. The proposed method is applicable to both cross-sectional and longitudinal region-level imaging data. We show the asymptotic rates for the bias and covariance functions of the proposed estimator and its asymptotic normality. Our simulation studies demonstrate that by borrowing information from similar regions, the proposed spatial similarity method improves the efficiency remarkably. We apply the proposed method to two neuroimaging data examples. The results reveal that accounting for the spatial similarity leads to more accurate estimators and better functional clustering results for visualizing brain atrophy pattern.Functional clustering; Longitudinal magnetic resonance imaging (MRI); Penalized B-splines; Region of interest (ROI); Spatial penalty.
Multivariate sensitivity to voice during auditory categorization.
Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard
2015-09-01
Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex.
Apparatus and system for multivariate spectral analysis
Keenan, Michael R.; Kotula, Paul G.
2003-06-24
An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.
Gravitational-wave detection using multivariate analysis
NASA Astrophysics Data System (ADS)
Adams, Thomas S.; Meacher, Duncan; Clark, James; Sutton, Patrick J.; Jones, Gareth; Minot, Ariana
2013-09-01
Searches for gravitational-wave bursts (transient signals, typically of unknown waveform) require identification of weak signals in background detector noise. The sensitivity of such searches is often critically limited by non-Gaussian noise fluctuations that are difficult to distinguish from real signals, posing a key problem for transient gravitational-wave astronomy. Current noise rejection tests are based on the analysis of a relatively small number of measured properties of the candidate signal, typically correlations between detectors. Multivariate analysis (MVA) techniques probe the full space of measured properties of events in an attempt to maximize the power to accurately classify events as signal or background. This is done by taking samples of known background events and (simulated) signal events to train the MVA classifier, which can then be applied to classify events of unknown type. We apply the boosted decision tree (BDT) MVA technique to the problem of detecting gravitational-wave bursts associated with gamma-ray bursts. We find that BDTs are able to increase the sensitive distance reach of the search by as much as 50%, corresponding to a factor of ˜3 increase in sensitive volume. This improvement is robust against trigger sky position, large sky localization error, poor data quality, and the simulated signal waveforms that are used. Critically, we find that the BDT analysis is able to detect signals that have different morphologies from those used in the classifier training and that this improvement extends to false alarm probabilities beyond the 3σ significance level. These findings indicate that MVA techniques may be used for the robust detection of gravitational-wave bursts with a priori unknown waveform.
Implementation Challenges for Multivariable Control: What You Did Not Learn in School
NASA Technical Reports Server (NTRS)
Garg, Sanjay
2008-01-01
Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.
Efficient multivariate linear mixed model algorithms for genome-wide association studies.
Zhou, Xiang; Stephens, Matthew
2014-04-01
Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.
Clifford, Harry J [Los Alamos, NM
2011-03-22
A method and apparatus for mounting a calibration sphere to a calibration fixture for Coordinate Measurement Machine (CMM) calibration and qualification is described, decreasing the time required for such qualification, thus allowing the CMM to be used more productively. A number of embodiments are disclosed that allow for new and retrofit manufacture to perform as integrated calibration sphere and calibration fixture devices. This invention renders unnecessary the removal of a calibration sphere prior to CMM measurement of calibration features on calibration fixtures, thereby greatly reducing the time spent qualifying a CMM.
MIRO Continuum Calibration for Asteroid Mode
NASA Technical Reports Server (NTRS)
Lee, Seungwon
2011-01-01
MIRO (Microwave Instrument for the Rosetta Orbiter) is a lightweight, uncooled, dual-frequency heterodyne radiometer. The MIRO encountered asteroid Steins in 2008, and during the flyby, MIRO used the Asteroid Mode to measure the emission spectrum of Steins. The Asteroid Mode is one of the seven modes of the MIRO operation, and is designed to increase the length of time that a spectral line is in the MIRO pass-band during a flyby of an object. This software is used to calibrate the continuum measurement of Steins emission power during the asteroid flyby. The MIRO raw measurement data need to be calibrated in order to obtain physically meaningful data. This software calibrates the MIRO raw measurements in digital units to the brightness temperature in Kelvin. The software uses two calibration sequences that are included in the Asteroid Mode. One sequence is at the beginning of the mode, and the other at the end. The first six frames contain the measurement of a cold calibration target, while the last six frames measure a warm calibration target. The targets have known temperatures and are used to provide reference power and gain, which can be used to convert MIRO measurements into brightness temperature. The software was developed to calibrate MIRO continuum measurements from Asteroid Mode. The software determines the relationship between the raw digital unit measured by MIRO and the equivalent brightness temperature by analyzing data from calibration frames. The found relationship is applied to non-calibration frames, which are the measurements of an object of interest such as asteroids and other planetary objects that MIRO encounters during its operation. This software characterizes the gain fluctuations statistically and determines which method to estimate gain between calibration frames. For example, if the fluctuation is lower than a statistically significant level, the averaging method is used to estimate the gain between the calibration frames. If the
Multivariate normative comparisons using an aggregated database.
Agelink van Rentergem, Joost A; Murre, Jaap M J; Huizenga, Hilde M
2017-01-01
In multivariate normative comparisons, a patient's profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R.
Multivariate normative comparisons using an aggregated database
Murre, Jaap M. J.; Huizenga, Hilde M.
2017-01-01
In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796
Camera Calibration Accuracy at Different Uav Flying Heights
NASA Astrophysics Data System (ADS)
Yusoff, A. R.; Ariff, M. F. M.; Idris, K. M.; Majid, Z.; Chong, A. K.
2017-02-01
Unmanned Aerial Vehicles (UAVs) can be used to acquire highly accurate data in deformation survey, whereby low-cost digital cameras are commonly used in the UAV mapping. Thus, camera calibration is considered important in obtaining high-accuracy UAV mapping using low-cost digital cameras. The main focus of this study was to calibrate the UAV camera at different camera distances and check the measurement accuracy. The scope of this study included camera calibration in the laboratory and on the field, and the UAV image mapping accuracy assessment used calibration parameters of different camera distances. The camera distances used for the image calibration acquisition and mapping accuracy assessment were 1.5 metres in the laboratory, and 15 and 25 metres on the field using a Sony NEX6 digital camera. A large calibration field and a portable calibration frame were used as the tools for the camera calibration and for checking the accuracy of the measurement at different camera distances. Bundle adjustment concept was applied in Australis software to perform the camera calibration and accuracy assessment. The results showed that the camera distance at 25 metres is the optimum object distance as this is the best accuracy obtained from the laboratory as well as outdoor mapping. In conclusion, the camera calibration at several camera distances should be applied to acquire better accuracy in mapping and the best camera parameter for the UAV image mapping should be selected for highly accurate mapping measurement.
Multivariate Adaptive Regression Splines (Preprint)
1990-08-01
situations, but as with the previous examples, the variance of the ratio (GCV/PSE) dominates this small bias. . 4.5. Portuguese Olive Oil . For this...example MARS is applied to data from analytical chemistry. The observations consist of 417 samples of olive oil from Portugal (Forina, et al., 1983). On...extent to which olive oil from northeastern Portugal (Dour0 Valley - 90 samples) differed from that of the rest of Portugal (327 samples). One way to
Atmospheric visibility estimation and image contrast calibration
NASA Astrophysics Data System (ADS)
Hermansson, Patrik; Edstam, Klas
2016-10-01
A method, referred to as contrast calibration, has been developed for transforming digital color photos of outdoor scenes from the atmospheric conditions, illumination and visibility, prevailing at the time of capturing the image to a corresponding image for other atmospheric conditions. A photo captured on a hazy day can, for instance, be converted to resemble a photo of the same scene for good visibility conditions. Converting digital color images to specified lightning and transmission conditions is useful for image based assessment of signature suppression solutions. The method uses "calibration objects" which are photographed at about the same time as the scene of interest. The calibration objects, which (indirectly) provide information on visibility and lightning conditions, consist of two flat boards, painted in different grayscale colors, and a commercial, neutral gray, reference card. Atmospheric extinction coefficient and sky intensity can be determined, in three wavelength bands, from image pixel values on the calibration objects and using this information the image can be converted to other atmospheric conditions. The image is transformed in contrast and color. For illustration, contrast calibration is applied to sample images of a scene acquired at different times. It is shown that contrast calibration of the images to the same reference values of extinction coefficient and sky intensity results in images that are more alike than the original images. It is also exemplified how images can be transformed to various other atmospheric weather conditions. Limitations of the method are discussed and possibilities for further development are suggested.
Calibration and Validation for VIIRS Ocean Products
NASA Astrophysics Data System (ADS)
Arnone, R.; Davis, C.; May, D.
2008-12-01
Satellite data products for ocean color and SST both require precise calibration and validation to meet the continuity of present satellite ocean products. Here we outline the proposed plan for calibration and validation of VIIRS ocean data. The primary ocean color Environmental Data Records (EDRs) are Remote Sensing Reflectances (RSRs); the other EDRs such as chlorophyll are derived from the RSRs. RSRs are derived from the VIIRS Sensor Data Records (SDR) by applying an atmospheric correction that removes the gas absorptions and Rayleigh, aerosol and sea-surface reflectances. Ocean color products require highly accurate calibration and refinement of the sensor calibration using highly accurate in-situ measurements of RSRs (vicarious calibration). Similarly, the SST EDR is strongly dependent on accurate "tuning" algorithm coefficients based on large ocean match-up data sets of buoy and skin temperatures. Ocean products require both a short term and long term monitoring of the sensor "calibration" in order to provide real time ocean products for Navy and NOAA operations. Validation of EDR ocean products requires characterizing the product uncertainty based on match up ocean data from various water and atmospheric types, spanning seasonal and latitudinal variability. Product validation includes matchups with AERONET SeaPRISM above water RSRs combined with in-situ measurements of optical properties, chlorophyll, SST (bulk and skin), and other products. Ocean product validation plans are exploring using an automated network of ocean data for assessing algorithm stability and product uncertainty in order to meet the present need for real-time operational products.
A method for designing robust multivariable feedback systems
NASA Technical Reports Server (NTRS)
Milich, David Albert; Athans, Michael; Valavani, Lena; Stein, Gunter
1988-01-01
A new methodology is developed for the synthesis of linear, time-invariant (LTI) controllers for multivariable LTI systems. The aim is to achieve stability and performance robustness of the feedback system in the presence of multiple unstructured uncertainty blocks; i.e., to satisfy a frequency-domain inequality in terms of the structured singular value. The design technique is referred to as the Causality Recovery Methodology (CRM). Starting with an initial (nominally) stabilizing compensator, the CRM produces a closed-loop system whose performance-robustness is at least as good as, and hopefully superior to, that of the original design. The robustness improvement is obtained by solving an infinite-dimensional, convex optimization program. A finite-dimensional implementation of the CRM was developed, and it was applied to a multivariate design example.
NASA Astrophysics Data System (ADS)
di Cesare, M. A.; Hammersley, P. L.; Rodriguez Espinosa, J. M.
2006-06-01
We are currently developing the calibration programme for GTC using techniques similar to the ones use for the space telescope calibration (Hammersley et al. 1998, A&AS, 128, 207; Cohen et al. 1999, AJ, 117, 1864). We are planning to produce a catalogue with calibration stars which are suitable for a 10-m telescope. These sources will be not variable, non binary and do not have infrared excesses if they are to be used in the infrared. The GTC science instruments require photometric calibration between 0.35 and 2.5 microns. The instruments are: OSIRIS (Optical System for Imaging low Resolution Integrated Spectroscopy), ELMER and EMIR (Espectrógrafo Multiobjeto Infrarrojo) and the Acquisition and Guiding boxes (Di Césare, Hammersley, & Rodriguez Espinosa 2005, RevMexAA Ser. Conf., 24, 231). The catalogue will consist of 30 star fields distributed in all of North Hemisphere. We will use fields containing sources over the range 12 to 22 magnitude, and spanning a wide range of spectral types (A to M) for the visible and near infrared. In the poster we will show the method used for selecting these fields and we will present the analysis of the data on the first calibration fields observed.
Polarimetric Palsar Calibration
NASA Astrophysics Data System (ADS)
Touzi, R.; Shimada, M.
2008-11-01
Polarimetric PALSAR system parameters are assessed using data sets collected over various calibration sites. The data collected over the Amazonian forest permits validating the zero Faraday rotation hypotheses near the equator. The analysis of the Amazonian forest data and the response of the corner reflectors deployed during the PALSAR acquisitions lead to the conclusion that the antenna is highly isolated (better than -35 dB). Theses results are confirmed using data collected over the Sweden and Ottawa calibration sites. The 5-m height trihedrals deployed in the Sweden calibration site by the Chalmers University of technology permits accurate measurement of antenna parameters, and detection of 2-3 degree Faraday rotation during day acquisition, whereas no Faraday rotation was noted during night acquisition. Small Faraday rotation angles (2-3 degree) have been measured using acquisitions over the DLR Oberpfaffenhofen and the Ottawa calibration sites. The presence of small but still significant Faraday rotation (2-3 degree) induces a CR return at the cross-polarization HV and VH that should not be interpreted as the actual antenna cross-talk. PALSAR antenna is highly isolated (better than -35 dB), and diagonal antenna distortion matrices (with zero cross-talk terms) can be used for accurate calibration of PALSAR polarimetric data.
Psychophysical contrast calibration
To, Long; Woods, Russell L; Goldstein, Robert B; Peli, Eli
2013-01-01
Electronic displays and computer systems offer numerous advantages for clinical vision testing. Laboratory and clinical measurements of various functions and in particular of (letter) contrast sensitivity require accurately calibrated display contrast. In the laboratory this is achieved using expensive light meters. We developed and evaluated a novel method that uses only psychophysical responses of a person with normal vision to calibrate the luminance contrast of displays for experimental and clinical applications. Our method combines psychophysical techniques (1) for detection (and thus elimination or reduction) of display saturating nonlinearities; (2) for luminance (gamma function) estimation and linearization without use of a photometer; and (3) to measure without a photometer the luminance ratios of the display’s three color channels that are used in a bit-stealing procedure to expand the luminance resolution of the display. Using a photometer we verified that the calibration achieved with this procedure is accurate for both LCD and CRT displays enabling testing of letter contrast sensitivity to 0.5%. Our visual calibration procedure enables clinical, internet and home implementation and calibration verification of electronic contrast testing. PMID:23643843
Calibration Under Uncertainty.
Swiler, Laura Painton; Trucano, Timothy Guy
2005-03-01
This report is a white paper summarizing the literature and different approaches to the problem of calibrating computer model parameters in the face of model uncertainty. Model calibration is often formulated as finding the parameters that minimize the squared difference between the model-computed data (the predicted data) and the actual experimental data. This approach does not allow for explicit treatment of uncertainty or error in the model itself: the model is considered the %22true%22 deterministic representation of reality. While this approach does have utility, it is far from an accurate mathematical treatment of the true model calibration problem in which both the computed data and experimental data have error bars. This year, we examined methods to perform calibration accounting for the error in both the computer model and the data, as well as improving our understanding of its meaning for model predictability. We call this approach Calibration under Uncertainty (CUU). This talk presents our current thinking on CUU. We outline some current approaches in the literature, and discuss the Bayesian approach to CUU in detail.
Calculations for Calibration of a Mass Spectrometer
NASA Technical Reports Server (NTRS)
Lee, Seungwon
2008-01-01
A computer program performs calculations to calibrate a quadrupole mass spectrometer in an instrumentation system for identifying trace amounts of organic chemicals in air. In the operation of the mass spectrometer, the mass-to-charge ratio (m/z) of ions being counted at a given instant of time is a function of the instantaneous value of a repeating ramp voltage waveform applied to electrodes. The count rate as a function of time can be converted to an m/z spectrum (equivalent to a mass spectrum for singly charged ions), provided that a calibration of m/z is available. The present computer program can perform the calibration in either or both of two ways: (1) Following a data-based approach, it can utilize the count-rate peaks and the times thereof measured when fed with air containing known organic compounds. (2) It can utilize a theoretical proportionality between the instantaneous m/z and the instantaneous value of an oscillating applied voltage. The program can also estimate the error of the calibration performed by the data-based approach. If calibrations are performed in both ways, then the results can be compared to obtain further estimates of errors.
NASA Astrophysics Data System (ADS)
Coury, Charity; Dillner, Ann M.
An attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopic technique and a multivariate calibration method were developed to quantify ambient aerosol organic functional groups and inorganic compounds. These methods were applied to size-resolved particulate matter samples collected in winter and summer of 2004 at three sites: a downtown Phoenix, Arizona location, a rural site near Phoenix, and an urban fringe site between the urban and rural site. Ten organic compound classes, including four classes which contain a carbonyl functional group, and three inorganic species were identified in the ambient samples. A partial least squares calibration was developed and applied to the ambient spectra, and 13 functional groups related to organic compounds (aliphatic and aromatic CH, methylene, methyl, alkene, aldehydes/ketones, carboxylic acids, esters/lactones, acid anhydrides, carbohydrate hydroxyl and ethers, amino acids, and amines) as well as ammonium sulfate and ammonium nitrate were quantified. Comparison of the sum of the mass measured by the ATR-FTIR technique and gravimetric mass indicates that this method can quantify nearly all of the aerosol mass on sub-micrometer size-segregated samples. Analysis of sample results shows that differences in organic functional group and inorganic compound concentrations at the three sampling sites can be measured with these methods. Future work will analyze the quantified data from these three sites in detail.
A Review of Sensor Calibration Monitoring for Calibration Interval Extension in Nuclear Power Plants
Coble, Jamie B.; Meyer, Ryan M.; Ramuhalli, Pradeep; Bond, Leonard J.; Hashemian, Hash; Shumaker, Brent; Cummins, Dara
2012-08-31
Currently in the United States, periodic sensor recalibration is required for all safety-related sensors, typically occurring at every refueling outage, and it has emerged as a critical path item for shortening outage duration in some plants. Online monitoring can be employed to identify those sensors that require calibration, allowing for calibration of only those sensors that need it. International application of calibration monitoring, such as at the Sizewell B plant in United Kingdom, has shown that sensors may operate for eight years, or longer, within calibration tolerances. This issue is expected to also be important as the United States looks to the next generation of reactor designs (such as small modular reactors and advanced concepts), given the anticipated longer refueling cycles, proposed advanced sensors, and digital instrumentation and control systems. The U.S. Nuclear Regulatory Commission (NRC) accepted the general concept of online monitoring for sensor calibration monitoring in 2000, but no U.S. plants have been granted the necessary license amendment to apply it. This report presents a state-of-the-art assessment of online calibration monitoring in the nuclear power industry, including sensors, calibration practice, and online monitoring algorithms. This assessment identifies key research needs and gaps that prohibit integration of the NRC-approved online calibration monitoring system in the U.S. nuclear industry. Several needs are identified, including the quantification of uncertainty in online calibration assessment; accurate determination of calibration acceptance criteria and quantification of the effect of acceptance criteria variability on system performance; and assessment of the feasibility of using virtual sensor estimates to replace identified faulty sensors in order to extend operation to the next convenient maintenance opportunity. Understanding the degradation of sensors and the impact of this degradation on signals is key to
Calibration and Temperature Profile of a Tungsten Filament Lamp
ERIC Educational Resources Information Center
de Izarra, Charles; Gitton, Jean-Michel
2010-01-01
The goal of this work proposed for undergraduate students and teachers is the calibration of a tungsten filament lamp from electric measurements that are both simple and precise, allowing to determine the temperature of tungsten filament as a function of the current intensity. This calibration procedure was first applied to a conventional filament…
Calibration Of Airborne Visible/IR Imaging Spectrometer
NASA Technical Reports Server (NTRS)
Vane, G. A.; Chrien, T. G.; Miller, E. A.; Reimer, J. H.
1990-01-01
Paper describes laboratory spectral and radiometric calibration of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) applied to all AVIRIS science data collected in 1987. Describes instrumentation and procedures used and demonstrates that calibration accuracy achieved exceeds design requirements. Developed for use in remote-sensing studies in such disciplines as botany, geology, hydrology, and oceanography.
Multivariate Voronoi Outlier Detection for Time Series.
Zwilling, Chris E; Wang, Michelle Yongmei
2014-10-01
Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.
Multivariate Statistical Mapping of Spectroscopic Imaging Data
Young, K.; Govind, V.; Sharma, K.; Studholme, C.; Maudsley, A.A; Schuff, N.
2010-01-01
For magnetic resonance spectroscopic imaging (MRSI) studies of the brain it is important to measure the distribution of metabolites in a regionally unbiased way - that is without restrictions to apriori defined regions of interest (ROI). Since MRSI provides measures of multiple metabolites simultaneously at each voxel, there is furthermore great interest in utilizing the multidimensional nature of MRSI for gains in statistical power. Voxelwise multivariate statistical mapping is expected to address both of these issues but it has not been previously employed for SI studies of brain. The aims of this study were to: 1) develop and validate multivariate voxel based statistical mapping for MRSI and 2) demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism. Specifically, we compared multivariate to univariate tests in identifying known regional patterns in simulated data and regional patterns of metabolite alterations due to amyotrophic lateral sclerosis, a devastating brain disease of the motor neurons. PMID:19953514
BATSE spectroscopy detector calibration
NASA Technical Reports Server (NTRS)
Band, D.; Ford, L.; Matteson, J.; Lestrade, J. P.; Teegarden, B.; Schaefer, B.; Cline, T.; Briggs, M.; Paciesas, W.; Pendleton, G.
1992-01-01
We describe the channel-to-energy calibration of the Spectroscopy Detectors of the Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory (GRO). These detectors consist of NaI(TI) crystals viewed by photomultiplier tubes whose output in turn is measured by a pulse height analyzer. The calibration of these detectors has been complicated by frequent gain changes and by nonlinearities specific to the BATSE detectors. Nonlinearities in the light output from the NaI crystal and in the pulse height analyzer are shifted relative to each other by changes in the gain of the photomultiplier tube. We present the analytical model which is the basis of our calibration methodology, and outline how the empirical coefficients in this approach were determined. We also describe the complications peculiar to the Spectroscopy Detectors, and how our understanding of the detectors' operation led us to a solution to these problems.
Calibration Systems Final Report
Myers, Tanya L.; Broocks, Bryan T.; Phillips, Mark C.
2006-02-01
The Calibration Systems project at Pacific Northwest National Laboratory (PNNL) is aimed towards developing and demonstrating compact Quantum Cascade (QC) laser-based calibration systems for infrared imaging systems. These on-board systems will improve the calibration technology for passive sensors, which enable stand-off detection for the proliferation or use of weapons of mass destruction, by replacing on-board blackbodies with QC laser-based systems. This alternative technology can minimize the impact on instrument size and weight while improving the quality of instruments for a variety of missions. The potential of replacing flight blackbodies is made feasible by the high output, stability, and repeatability of the QC laser spectral radiance.
LeBlanc, R.
1987-08-01
The TA489A Calibrator, designed to operate in the MA164 Digital Data Acquisition System, is used to calibrate up to 128 analog-to-digital recording channels. The TA489A calibrates using a dc Voltage Source or any of several special calibration modes. Calibration schemes are stored in the TA489A memory and are initiated locally or remotely through a Command Link.
Iterative Magnetometer Calibration
NASA Technical Reports Server (NTRS)
Sedlak, Joseph
2006-01-01
This paper presents an iterative method for three-axis magnetometer (TAM) calibration that makes use of three existing utilities recently incorporated into the attitude ground support system used at NASA's Goddard Space Flight Center. The method combines attitude-independent and attitude-dependent calibration algorithms with a new spinning spacecraft Kalman filter to solve for biases, scale factors, nonorthogonal corrections to the alignment, and the orthogonal sensor alignment. The method is particularly well-suited to spin-stabilized spacecraft, but may also be useful for three-axis stabilized missions given sufficient data to provide observability.
In-Space Calibration of a Gyro Quadruplet
NASA Technical Reports Server (NTRS)
Bar-Itzhack, Itzhack Y.; Harman, Richard R.
2001-01-01
This work presents a new approach to gyro calibration where, in addition to being used for computing attitude that is needed in the calibration process, the gyro outputs are also used as measurements in a Kalman filter. This work also presents an algorithm for calibrating a quadruplet rather than the customary triad gyro set. In particular, a new misalignment error model is derived for this case. The new calibration algorithm is applied to the EOS-AQUA satellite gyros. The effectiveness of the new algorithm is demonstrated through simulations.
Tyler, D K; Woods, M J
2003-01-01
Before a radiopharmaceutical is administered to a patient, its activity needs to be accurately assayed. This is normally done via a radionuclide calibrator, using a glass vial as the calibration device. The radionuclide is then transferred to a syringe and it is now becoming common practice to re-measure the syringe and use this value as the activity administered to the patient. Due to elemental composition and geometrical differences, etc. between the glass vial and the syringe, the calibration factors are different for the two containers and this can lead to an incorrect activity being given to the patient unless a correction is applied for these differences. To reduce the uncertainty on syringe measurements, syringe calibration factors and volume correction factors for the NPL Secondary Standard Radionuclide Calibrator have been derived by NPL for several medically important radionuclides. It was found that the differences between the calibration factors for the syringes and glass vials depend on the energies of the photon emissions from the decay of the radionuclides; the lower the energy, the greater the difference. As expected, large differences were observed for 125I (70%) and only small differences for 131I. However, for radionuclides such as 99mTc and 67Ga, differences of up to 30% have been observed. This work has shown the need for the use of specifically derived syringe calibration factors as well as highlighting the complexity of the problem with regard to syringe types, procurement, etc.
Application of Optimal Designs to Item Calibration
Lu, Hung-Yi
2014-01-01
In computerized adaptive testing (CAT), examinees are presented with various sets of items chosen from a precalibrated item pool. Consequently, the attrition speed of the items is extremely fast, and replenishing the item pool is essential. Therefore, item calibration has become a crucial concern in maintaining item banks. In this study, a two-parameter logistic model is used. We applied optimal designs and adaptive sequential analysis to solve this item calibration problem. The results indicated that the proposed optimal designs are cost effective and time efficient. PMID:25188318
Yu, Lei; Lin, Guan-Yu; Chen, Bin
2013-01-01
The present paper studied spectral irradiation responsivities calibration method which can be applied to the far ultraviolet spectrometer for upper atmosphere remote sensing. It is difficult to realize the calibration for far ultraviolet spectrometer for many reasons. Standard instruments for far ultraviolet waveband calibration are few, the degree of the vacuum experiment system is required to be high, the stabilities of the experiment are hardly maintained, and the limitation of the far ultraviolet waveband makes traditional diffuser and the integrating sphere radiance calibration method difficult to be used. To solve these problems, a new absolute spectral irradiance calibration method was studied, which can be applied to the far ultraviolet calibration. We build a corresponding special vacuum experiment system to verify the calibration method. The light source system consists of a calibrated deuterium lamp, a vacuum ultraviolet monochromater and a collimating system. We used the calibrated detector to obtain the irradiance responsivities of it. The three instruments compose the calibration irradiance source. We used the "calibration irradiance source" to illuminate the spectrometer prototype and obtained the spectral irradiance responsivities. It realized the absolute spectral irradiance calibration for the far ultraviolet spectrometer utilizing the calibrated detector. The absolute uncertainty of the calibration is 7.7%. The method is significant for the ground irradiation calibration of the far ultraviolet spectrometer in upper atmosphere remote sensing.
Multivariate Analysis of Ladle Vibration
NASA Astrophysics Data System (ADS)
Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle
2016-08-01
The homogeneity of composition and uniformity of temperature of the steel melt before it is transferred to the tundish are crucial in making high-quality steel product. The homogenization process is performed by stirring the melt using inert gas in ladles. Continuous monitoring of this process is important to make sure the action of stirring is constant throughout the ladle. Currently, the stirring process is monitored by process operators who largely rely on visual and acoustic phenomena from the ladle. However, due to lack of measurable signals, the accuracy and suitability of this manual monitoring are problematic. The actual flow of argon gas to the ladle may not be same as the flow gage reading due to leakage along the gas line components. As a result, the actual degree of stirring may not be correctly known. Various researchers have used one-dimensional vibration, and sound and image signals measured from the ladle to predict the degree of stirring inside. They developed online sensors which are indeed to monitor the online stirring phenomena. In this investigation, triaxial vibration signals have been measured from a cold water model which is a model of an industrial ladle. Three flow rate ranges and varying bath heights were used to collect vibration signals. The Fast Fourier Transform was applied to the dataset before it has been analyzed using principal component analysis (PCA) and partial least squares (PLS). PCA was used to unveil the structure in the experimental data. PLS was mainly applied to predict the stirring from the vibration response. It was found that for each flow rate range considered in this study, the informative signals reside in different frequency ranges. The first latent variables in these frequency ranges explain more than 95 pct of the variation in the stirring process for the entire single layer and the double layer data collected from the cold model. PLS analysis in these identified frequency ranges demonstrated that the latent
Calibrating Communication Competencies
NASA Astrophysics Data System (ADS)
Surges Tatum, Donna
2016-11-01
The Many-faceted Rasch measurement model is used in the creation of a diagnostic instrument by which communication competencies can be calibrated, the severity of observers/raters can be determined, the ability of speakers measured, and comparisons made between various groups.
TWSTFT Link Calibration Report
2015-09-01
Serrano, G. Brunetti (2013) Relative Calibration of the Time Transfer Link between CERN and LNGS for Precise Neutrino Time of Flight Measurements. Proc...Esteban, M. Pallavicini, Va. Pettiti, C. Plantard, A. Razeto (2012) Measurement of CNGS Muon Neutrinos Speed with Borexino: INRIM and ROA Contribution
Computerized tomography calibrator
NASA Technical Reports Server (NTRS)
Engel, Herbert P. (Inventor)
1991-01-01
A set of interchangeable pieces comprising a computerized tomography calibrator, and a method of use thereof, permits focusing of a computerized tomographic (CT) system. The interchangeable pieces include a plurality of nestable, generally planar mother rings, adapted for the receipt of planar inserts of predetermined sizes, and of predetermined material densities. The inserts further define openings therein for receipt of plural sub-inserts. All pieces are of known sizes and densities, permitting the assembling of different configurations of materials of known sizes and combinations of densities, for calibration (i.e., focusing) of a computerized tomographic system through variation of operating variables thereof. Rather than serving as a phanton, which is intended to be representative of a particular workpiece to be tested, the set of interchangeable pieces permits simple and easy standardized calibration of a CT system. The calibrator and its related method of use further includes use of air or of particular fluids for filling various openings, as part of a selected configuration of the set of pieces.
Optical detector calibrator system
NASA Technical Reports Server (NTRS)
Strobel, James P. (Inventor); Moerk, John S. (Inventor); Youngquist, Robert C. (Inventor)
1996-01-01
An optical detector calibrator system simulates a source of optical radiation to which a detector to be calibrated is responsive. A light source selected to emit radiation in a range of wavelengths corresponding to the spectral signature of the source is disposed within a housing containing a microprocessor for controlling the light source and other system elements. An adjustable iris and a multiple aperture filter wheel are provided for controlling the intensity of radiation emitted from the housing by the light source to adjust the simulated distance between the light source and the detector to be calibrated. The geared iris has an aperture whose size is adjustable by means of a first stepper motor controlled by the microprocessor. The multiple aperture filter wheel contains neutral density filters of different attenuation levels which are selectively positioned in the path of the emitted radiation by a second stepper motor that is also controlled by the microprocessor. An operator can select a number of detector tests including range, maximum and minimum sensitivity, and basic functionality. During the range test, the geared iris and filter wheel are repeatedly adjusted by the microprocessor as necessary to simulate an incrementally increasing simulated source distance. A light source calibration subsystem is incorporated in the system which insures that the intensity of the light source is maintained at a constant level over time.
Improved Regression Calibration
ERIC Educational Resources Information Center
Skrondal, Anders; Kuha, Jouni
2012-01-01
The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form, which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration…
NASA Technical Reports Server (NTRS)
2008-01-01
Commodity-free calibration is a reaction rate calibration technique that does not require the addition of any commodities. This technique is a specific form of the reaction rate technique, where all of the necessary reactants, other than the sample being analyzed, are either inherent in the analyzing system or specifically added or provided to the system for a reason other than calibration. After introduction, the component of interest is exposed to other reactants or flow paths already present in the system. The instrument detector records one of the following to determine the rate of reaction: the increase in the response of the reaction product, a decrease in the signal of the analyte response, or a decrease in the signal from the inherent reactant. With this data, the initial concentration of the analyte is calculated. This type of system can analyze and calibrate simultaneously, reduce the risk of false positives and exposure to toxic vapors, and improve accuracy. Moreover, having an excess of the reactant already present in the system eliminates the need to add commodities, which further reduces cost, logistic problems, and potential contamination. Also, the calculations involved can be simplified by comparison to those of the reaction rate technique. We conducted tests with hypergols as an initial investigation into the feasiblility of the technique.
NVLAP calibration laboratory program
Cigler, J.L.
1993-12-31
This paper presents an overview of the progress up to April 1993 in the development of the Calibration Laboratories Accreditation Program within the framework of the National Voluntary Laboratory Accreditation Program (NVLAP) at the National Institute of Standards and Technology (NIST).
Multimodal spatial calibration for accurately registering EEG sensor positions.
Zhang, Jianhua; Chen, Jian; Chen, Shengyong; Xiao, Gang; Li, Xiaoli
2014-01-01
This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views' calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain.
Aircraft measurement of electric field - Self-calibration
NASA Technical Reports Server (NTRS)
Winn, W. P.
1993-01-01
Aircraft measurement of electric fields is difficult as the electrically conducting surface of the aircraft distorts the electric field. Calibration requires determining the relations between the undistorted electric field in the absence of the vehicle and the signals from electric field meters that sense the local distorted fields in their immediate vicinity. This paper describes a generalization of a calibration method which uses pitch and roll maneuvers. The technique determines both the calibration coefficients and the direction of the electric vector. The calibration of individual electric field meters and the elimination of the aircraft's self-charge are described. Linear combinations of field mill signals are examined and absolute calibration and error analysis are discussed. The calibration method was applied to data obtained during a flight near thunderstorms.
Calibrating ultrasonic test equipment for checking thin metal strip stock
NASA Technical Reports Server (NTRS)
Peterson, R. M.
1967-01-01
Calibration technique detects minute laminar-type discontinuities in thin metal strip stock. Patterns of plastic tape are preselected to include minutely calculated discontinuities and the tape is applied to the strip stock to intercept the incident sonic beam.
Simplified Vicarious Radiometric Calibration
NASA Technical Reports Server (NTRS)
Stanley, Thomas; Ryan, Robert; Holekamp, Kara; Pagnutti, Mary
2010-01-01
A measurement-based radiance estimation approach for vicarious radiometric calibration of spaceborne multispectral remote sensing systems has been developed. This simplified process eliminates the use of radiative transfer codes and reduces the number of atmospheric assumptions required to perform sensor calibrations. Like prior approaches, the simplified method involves the collection of ground truth data coincident with the overpass of the remote sensing system being calibrated, but this approach differs from the prior techniques in both the nature of the data collected and the manner in which the data are processed. In traditional vicarious radiometric calibration, ground truth data are gathered using ground-viewing spectroradiometers and one or more sun photometer( s), among other instruments, located at a ground target area. The measured data from the ground-based instruments are used in radiative transfer models to estimate the top-of-atmosphere (TOA) target radiances at the time of satellite overpass. These TOA radiances are compared with the satellite sensor readings to radiometrically calibrate the sensor. Traditional vicarious radiometric calibration methods require that an atmospheric model be defined such that the ground-based observations of solar transmission and diffuse-to-global ratios are in close agreement with the radiative transfer code estimation of these parameters. This process is labor-intensive and complex, and can be prone to errors. The errors can be compounded because of approximations in the model and inaccurate assumptions about the radiative coupling between the atmosphere and the terrain. The errors can increase the uncertainty of the TOA radiance estimates used to perform the radiometric calibration. In comparison, the simplified approach does not use atmospheric radiative transfer models and involves fewer assumptions concerning the radiative transfer properties of the atmosphere. This new technique uses two neighboring uniform
Fast Field Calibration of MIMU Based on the Powell Algorithm
Ma, Lin; Chen, Wanwan; Li, Bin; You, Zheng; Chen, Zhigang
2014-01-01
The calibration of micro inertial measurement units is important in ensuring the precision of navigation systems, which are equipped with microelectromechanical system sensors that suffer from various errors. However, traditional calibration methods cannot meet the demand for fast field calibration. This paper presents a fast field calibration method based on the Powell algorithm. As the key points of this calibration, the norm of the accelerometer measurement vector is equal to the gravity magnitude, and the norm of the gyro measurement vector is equal to the rotational velocity inputs. To resolve the error parameters by judging the convergence of the nonlinear equations, the Powell algorithm is applied by establishing a mathematical error model of the novel calibration. All parameters can then be obtained in this manner. A comparison of the proposed method with the traditional calibration method through navigation tests shows the classic performance of the proposed calibration method. The proposed calibration method also saves more time compared with the traditional calibration method. PMID:25177801
A Comparison of Two Balance Calibration Model Building Methods
NASA Technical Reports Server (NTRS)
DeLoach, Richard; Ulbrich, Norbert
2007-01-01
Simulated strain-gage balance calibration data is used to compare the accuracy of two balance calibration model building methods for different noise environments and calibration experiment designs. The first building method obtains a math model for the analysis of balance calibration data after applying a candidate math model search algorithm to the calibration data set. The second building method uses stepwise regression analysis in order to construct a model for the analysis. Four balance calibration data sets were simulated in order to compare the accuracy of the two math model building methods. The simulated data sets were prepared using the traditional One Factor At a Time (OFAT) technique and the Modern Design of Experiments (MDOE) approach. Random and systematic errors were introduced in the simulated calibration data sets in order to study their influence on the math model building methods. Residuals of the fitted calibration responses and other statistical metrics were compared in order to evaluate the calibration models developed with different combinations of noise environment, experiment design, and model building method. Overall, predicted math models and residuals of both math model building methods show very good agreement. Significant differences in model quality were attributable to noise environment, experiment design, and their interaction. Generally, the addition of systematic error significantly degraded the quality of calibration models developed from OFAT data by either method, but MDOE experiment designs were more robust with respect to the introduction of a systematic component of the unexplained variance.
Signal inference with unknown response: Calibration-uncertainty renormalized estimator
NASA Astrophysics Data System (ADS)
Dorn, Sebastian; Enßlin, Torsten A.; Greiner, Maksim; Selig, Marco; Boehm, Vanessa
2015-01-01
The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration-uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calibration, but its covariance structure. The idea of the CURE method, developed in the framework of information field theory, is to start with an assumed calibration to successively include more and more portions of calibration uncertainty into the signal inference equations and to absorb the resulting corrections into renormalized signal (and calibration) solutions. Thereby, the signal inference and calibration problem turns into a problem of solving a single system of ordinary differential equations and can be identified with common resummation techniques used in field theories. We verify the CURE method by applying it to a simplistic toy example and compare it against existent self-calibration schemes, Wiener filter solutions, and Markov chain Monte Carlo sampling. We conclude that the method is able to keep up in accuracy with the best self-calibration methods and serves as a noniterative alternative to them.
Signal inference with unknown response: calibration-uncertainty renormalized estimator.
Dorn, Sebastian; Enßlin, Torsten A; Greiner, Maksim; Selig, Marco; Boehm, Vanessa
2015-01-01
The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration-uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calibration, but its covariance structure. The idea of the CURE method, developed in the framework of information field theory, is to start with an assumed calibration to successively include more and more portions of calibration uncertainty into the signal inference equations and to absorb the resulting corrections into renormalized signal (and calibration) solutions. Thereby, the signal inference and calibration problem turns into a problem of solving a single system of ordinary differential equations and can be identified with common resummation techniques used in field theories. We verify the CURE method by applying it to a simplistic toy example and compare it against existent self-calibration schemes, Wiener filter solutions, and Markov chain Monte Carlo sampling. We conclude that the method is able to keep up in accuracy with the best self-calibration methods and serves as a noniterative alternative to them.
John F. Schabron; Joseph F. Rovani; Susan S. Sorini
2007-03-31
The Clean Air Mercury Rule (CAMR) which was published in the Federal Register on May 18, 2005, requires that calibration of mercury continuous emissions monitors (CEMs) be performed with NIST-traceable standards. Western Research Institute (WRI) is working closely with the Electric Power Research Institute (EPRI), the National Institute of Standards and Technology (NIST), and the Environmental Protection Agency (EPA) to facilitate the development of the experimental criteria for a NIST traceability protocol for dynamic elemental mercury vapor generators. The traceability protocol will be written by EPA. Traceability will be based on the actual analysis of the output of each calibration unit at several concentration levels ranging from about 2-40 ug/m{sup 3}, and this analysis will be directly traceable to analyses by NIST using isotope dilution inductively coupled plasma/mass spectrometry (ID ICP/MS) through a chain of analyses linking the calibration unit in the power plant to the NIST ID ICP/MS. Prior to this project, NIST did not provide a recommended mercury vapor pressure equation or list mercury vapor pressure in its vapor pressure database. The NIST Physical and Chemical Properties Division in Boulder, Colorado was subcontracted under this project to study the issue in detail and to recommend a mercury vapor pressure equation that the vendors of mercury vapor pressure calibration units can use to calculate the elemental mercury vapor concentration in an equilibrium chamber at a particular temperature. As part of this study, a preliminary evaluation of calibration units from five vendors was made. The work was performed by NIST in Gaithersburg, MD and Joe Rovani from WRI who traveled to NIST as a Visiting Scientist.
A pairwise interaction model for multivariate functional and longitudinal data.
Chiou, Jeng-Min; Müller, Hans-Georg
2016-06-01
Functional data vectors consisting of samples of multivariate data where each component is a random function are encountered increasingly often but have not yet been comprehensively investigated. We introduce a simple pairwise interaction model that leads to an interpretable and straightforward decomposition of multivariate functional data and of their variation into component-specific processes and pairwise interaction processes. The latter quantify the degree of pairwise interactions between the components of the functional data vectors, while the component-specific processes reflect the functional variation of a particular functional vector component that cannot be explained by the other components. Thus the proposed model provides an extension of the usual notion of a covariance or correlation matrix for multivariate vector data to functional data vectors and generates an interpretable functional interaction map. The decomposition provided by the model can also serve as a basis for subsequent analysis, such as study of the network structure of functional data vectors. The decomposition of the total variance into componentwise and interaction contributions can be quantified by an [Formula: see text]-like decomposition. We provide consistency results for the proposed methods and illustrate the model by applying it to sparsely sampled longitudinal data from the Baltimore Longitudinal Study of Aging, examining the relationships between body mass index and blood fats.
Application of multivariate statistical techniques in microbial ecology.
Paliy, O; Shankar, V
2016-03-01
Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure.
Multivariate Analysis and Prediction of Dioxin-Furan ...
Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE
Application of multivariate statistical techniques in microbial ecology
Paliy, O.; Shankar, V.
2016-01-01
Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large scale ecological datasets. Especially noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions, and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amounts of data, powerful statistical techniques of multivariate analysis are well suited to analyze and interpret these datasets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular dataset. In this review we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive, and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and dataset structure. PMID:26786791
Visualizing frequent patterns in large multivariate time series
NASA Astrophysics Data System (ADS)
Hao, M.; Marwah, M.; Janetzko, H.; Sharma, R.; Keim, D. A.; Dayal, U.; Patnaik, D.; Ramakrishnan, N.
2011-01-01
The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.
Multivariate meta-analysis using individual participant data
Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.
2016-01-01
When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484
Multivariate meta-analysis using individual participant data.
Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R
2015-06-01
When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.
Martins, Manoel L; Rizzetti, Tiele M; Kemmerich, Magali; Saibt, Nathália; Prestes, Osmar D; Adaime, Martha B; Zanella, Renato
2016-08-19
Among calibration approaches for organic compounds determination in complex matrices, external calibration, based in solutions of the analytes in solvent or in blank matrix extracts, is the most applied approach. Although matrix matched calibration (MMC) can compensates the matrix effects, it does not compensate low recovery results. In this way, standard addition (SA) and procedural standard calibration (PSC) are usual alternatives, despite they generate more sample and/or matrix blanks consumption need, extra sample preparations and higher time and costs. Thus, the goal of this work was to establish a fast and efficient calibration approach, the diluted standard addition calibration (DSAC), based on successive dilutions of a spiked blank sample. In order to evaluate the proposed approach, solvent calibration (SC), MMC, PSC and DSAC were applied to evaluate recovery results of grape blank samples spiked with 66 pesticides. Samples were extracted with the acetate QuEChERS method and the compounds determined by ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Results indicated that low recovery results for some pesticides were compensated by both PSC and DSAC approaches. Considering recoveries from 70 to 120% with RSD <20% as adequate, DSAC presented 83%, 98% and 100% of compounds meeting this criteria for the spiking levels 10, 50 and 100μgkg(-1), respectively. PSC presented same results (83%, 98% and 100%), better than those obtained by MMC (79%, 95% and 97%) and by SC (62%, 70% and 79%). The DSAC strategy showed to be suitable for calibration of multiresidue determination methods, producing adequate results in terms of trueness and is easier and faster to perform than other approaches.
Inertial Sensor Error Reduction through Calibration and Sensor Fusion
Lambrecht, Stefan; Nogueira, Samuel L.; Bortole, Magdo; Siqueira, Adriano A. G.; Terra, Marco H.; Rocon, Eduardo; Pons, José L.
2016-01-01
This paper presents the comparison between cooperative and local Kalman Filters (KF) for estimating the absolute segment angle, under two calibration conditions. A simplified calibration, that can be replicated in most laboratories; and a complex calibration, similar to that applied by commercial vendors. The cooperative filters use information from either all inertial sensors attached to the body, Matricial KF; or use information from the inertial sensors and the potentiometers of an exoskeleton, Markovian KF. A one minute walking trial of a subject walking with a 6-DoF exoskeleton was used to assess the absolute segment angle of the trunk, thigh, shank, and foot. The results indicate that regardless of the segment and filter applied, the more complex calibration always results in a significantly better performance compared to the simplified calibration. The interaction between filter and calibration suggests that when the quality of the calibration is unknown the Markovian KF is recommended. Applying the complex calibration, the Matricial and Markovian KF perform similarly, with average RMSE below 1.22 degrees. Cooperative KFs perform better or at least equally good as Local KF, we therefore recommend to use cooperative KFs instead of local KFs for control or analysis of walking. PMID:26901198
Inertial Sensor Error Reduction through Calibration and Sensor Fusion.
Lambrecht, Stefan; Nogueira, Samuel L; Bortole, Magdo; Siqueira, Adriano A G; Terra, Marco H; Rocon, Eduardo; Pons, José L
2016-02-17
This paper presents the comparison between cooperative and local Kalman Filters (KF) for estimating the absolute segment angle, under two calibration conditions. A simplified calibration, that can be replicated in most laboratories; and a complex calibration, similar to that applied by commercial vendors. The cooperative filters use information from either all inertial sensors attached to the body, Matricial KF; or use information from the inertial sensors and the potentiometers of an exoskeleton, Markovian KF. A one minute walking trial of a subject walking with a 6-DoF exoskeleton was used to assess the absolute segment angle of the trunk, thigh, shank, and foot. The results indicate that regardless of the segment and filter applied, the more complex calibration always results in a significantly better performance compared to the simplified calibration. The interaction between filter and calibration suggests that when the quality of the calibration is unknown the Markovian KF is recommended. Applying the complex calibration, the Matricial and Markovian KF perform similarly, with average RMSE below 1.22 degrees. Cooperative KFs perform better or at least equally good as Local KF, we therefore recommend to use cooperative KFs instead of local KFs for control or analysis of walking.
Calibration of a detector for nonlinear responses.
Asnin, Leonid; Guiochon, Georges
2005-09-30
A calibration curve is often needed to derive from the record of the detector signal the actual concentration profile of the eluate in many studies of the thermodynamic and kinetic of adsorption by chromatography. The calibration task is complicated in the frequent cases in which the detector response is nonlinear. The simplest approach consists in preparing a series of solutions of known concentrations, in flushing them successively through the detector cell, and in recording the height of the plateau response obtained. However, this method requires relatively large amounts of the pure solutes studied. These are not always available, may be most costly, and could be applied to better uses. An alternative procedure consists of deriving this calibration curve from a series of peaks recorded upon the injection of increasingly large pulses of the studied compound. We validated this new method in HPLC with a UV detector. Questions concerning the reproducibility and accuracy of the method are discussed.
Variable Acceleration Force Calibration System (VACS)
NASA Technical Reports Server (NTRS)
Rhew, Ray D.; Parker, Peter A.; Johnson, Thomas H.; Landman, Drew
2014-01-01
Conventionally, force balances have been calibrated manually, using a complex system of free hanging precision weights, bell cranks, and/or other mechanical components. Conventional methods may provide sufficient accuracy in some instances, but are often quite complex and labor-intensive, requiring three to four man-weeks to complete each full calibration. To ensure accuracy, gravity-based loading is typically utilized. However, this often causes difficulty when applying loads in three simultaneous, orthogonal axes. A complex system of levers, cranks, and cables must be used, introducing increased sources of systematic error, and significantly increasing the time and labor intensity required to complete the calibration. One aspect of the VACS is a method wherein the mass utilized for calibration is held constant, and the acceleration is changed to thereby generate relatively large forces with relatively small test masses. Multiple forces can be applied to a force balance without changing the test mass, and dynamic forces can be applied by rotation or oscillating acceleration. If rotational motion is utilized, a mass is rigidly attached to a force balance, and the mass is exposed to a rotational field. A large force can be applied by utilizing a large rotational velocity. A centrifuge or rotating table can be used to create the rotational field, and fixtures can be utilized to position the force balance. The acceleration may also be linear. For example, a table that moves linearly and accelerates in a sinusoidal manner may also be utilized. The test mass does not have to move in a path that is parallel to the ground, and no re-leveling is therefore required. Balance deflection corrections may be applied passively by monitoring the orientation of the force balance with a three-axis accelerometer package. Deflections are measured during each test run, and adjustments with respect to the true applied load can be made during the post-processing stage. This paper will
A direct-gradient multivariate index of biotic condition
Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.
2012-01-01
Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.
ERIC Educational Resources Information Center
de Oliveira, Rodrigo R.; das Neves, Luiz S.; de Lima, Kassio M. G.
2012-01-01
A chemometrics course is offered to students in their fifth semester of the chemistry undergraduate program that includes an in-depth project. Students carry out the project over five weeks (three 8-h sessions per week) and conduct it in parallel to other courses or other practical work. The students conduct a literature search, carry out…
Technology Transfer Automated Retrieval System (TEKTRAN)
A near-infrared spectroscopy (NIRS) method for automated non-destructive detection of insect infestation internal to small fruit is desirable because of the zero-to-zero tolerance of the fresh and processed fruit markets. Three NIRS instruments: the Ocean Optics SD2000, the Perten DA7000 and the Ori...
John Schabron; Eric Kalberer; Joseph Rovani; Mark Sanderson; Ryan Boysen; William Schuster
2009-03-11
U.S. Environmental Protection Agency (EPA) Performance Specification 12 in the Clean Air Mercury Rule (CAMR) states that a mercury CEM must be calibrated with National Institute for Standards and Technology (NIST)-traceable standards. In early 2009, a NIST traceable standard for elemental mercury CEM calibration still does not exist. Despite the vacature of CAMR by a Federal appeals court in early 2008, a NIST traceable standard is still needed for whatever regulation is implemented in the future. Thermo Fisher is a major vendor providing complete integrated mercury continuous emissions monitoring (CEM) systems to the industry. WRI is participating with EPA, EPRI, NIST, and Thermo Fisher towards the development of the criteria that will be used in the traceability protocols to be issued by EPA. An initial draft of an elemental mercury calibration traceability protocol was distributed for comment to the participating research groups and vendors on a limited basis in early May 2007. In August 2007, EPA issued an interim traceability protocol for elemental mercury calibrators. Various working drafts of the new interim traceability protocols were distributed in late 2008 and early 2009 to participants in the Mercury Standards Working Committee project. The protocols include sections on qualification and certification. The qualification section describes in general terms tests that must be conducted by the calibrator vendors to demonstrate that their calibration equipment meets the minimum requirements to be established by EPA for use in CAMR monitoring. Variables to be examined include linearity, ambient temperature, back pressure, ambient pressure, line voltage, and effects of shipping. None of the procedures were described in detail in the draft interim documents; however they describe what EPA would like to eventually develop. WRI is providing the data and results to EPA for use in developing revised experimental procedures and realistic acceptance criteria based on
Diagonal dominance using function minimization algorithms. [multivariable control system design
NASA Technical Reports Server (NTRS)
Leininger, G. G.
1977-01-01
A new approach to the design of multivariable control systems using the inverse Nyquist array method is proposed. The technique utilizes a conjugate direction function minimization algorithm to achieve dominance over a specified frequency range by minimizing the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal term of the inverse open loop transfer function matrix. The technique is easily implemented in either a batch or interactive computer mode and will yield diagonalization when previously suggested methods fail. The proposed method has been successfully applied to design a control system for a sixteenth order state model of the F-100 turbofan engine with three inputs.
alphaPDE: A new multivariate technique for parameter estimation
Knuteson, B.; Miettinen, H.; Holmstrom, L.
2002-06-01
We present alphaPDE, a new multivariate analysis technique for parameter estimation. The method is based on a direct construction of joint probability densities of known variables and the parameters to be estimated. We show how posterior densities and best-value estimates are then obtained for the parameters of interest by a straightforward manipulation of these densities. The method is essentially non-parametric and allows for an intuitive graphical interpretation. We illustrate the method by outlining how it can be used to estimate the mass of the top quark, and we explain how the method is applied to an ensemble of events containing background.
Calibration Matters: Advances in Strapdown Airborne Gravimetry
NASA Astrophysics Data System (ADS)
Becker, D.
2015-12-01
Using a commercial navigation-grade strapdown inertial measurement unit (IMU) for airborne gravimetry can be advantageous in terms of cost, handling, and space consumption compared to the classical stable-platform spring gravimeters. Up to now, however, large sensor errors made it impossible to reach the mGal-level using such type IMUs as they are not designed or optimized for this kind of application. Apart from a proper error-modeling in the filtering process, specific calibration methods that are tailored to the application of aerogravity may help to bridge this gap and to improve their performance. Based on simulations, a quantitative analysis is presented on how much IMU sensor errors, as biases, scale factors, cross couplings, and thermal drifts distort the determination of gravity and the deflection of the vertical (DOV). Several lab and in-field calibration methods are briefly discussed, and calibration results are shown for an iMAR RQH unit. In particular, a thermal lab calibration of its QA2000 accelerometers greatly improved the long-term drift behavior. Latest results from four recent airborne gravimetry campaigns confirm the effectiveness of the calibrations applied, with cross-over accuracies reaching 1.0 mGal (0.6 mGal after cross-over adjustment) and DOV accuracies reaching 1.1 arc seconds after cross-over adjustment.
NASA Astrophysics Data System (ADS)
Hu, Wei; Si, Bing Cheng
2016-08-01
The scale-specific and localized bivariate relationships in geosciences can be revealed using bivariate wavelet coherence. The objective of this study was to develop a multiple wavelet coherence method for examining scale-specific and localized multivariate relationships. Stationary and non-stationary artificial data sets, generated with the response variable as the summation of five predictor variables (cosine waves) with different scales, were used to test the new method. Comparisons were also conducted using existing multivariate methods, including multiple spectral coherence and multivariate empirical mode decomposition (MEMD). Results show that multiple spectral coherence is unable to identify localized multivariate relationships, and underestimates the scale-specific multivariate relationships for non-stationary processes. The MEMD method was able to separate all variables into components at the same set of scales, revealing scale-specific relationships when combined with multiple correlation coefficients, but has the same weakness as multiple spectral coherence. However, multiple wavelet coherences are able to identify scale-specific and localized multivariate relationships, as they are close to 1 at multiple scales and locations corresponding to those of predictor variables. Therefore, multiple wavelet coherence outperforms other common multivariate methods. Multiple wavelet coherence was applied to a real data set and revealed the optimal combination of factors for explaining temporal variation of free water evaporation at the Changwu site in China at multiple scale-location domains. Matlab codes for multiple wavelet coherence were developed and are provided in the Supplement.
Alamilla, Francisco; Calcerrada, Matías; García-Ruiz, Carmen; Torre, Mercedes
2013-05-10
The differentiation of blue ballpoint pen inks written on documents through an LA-ICP-MS methodology is proposed. Small common office paper portions containing ink strokes from 21 blue pens of known origin were cut and measured without any sample preparation. In a first step, Mg, Ca and Sr were proposed as internal standards (ISs) and used in order to normalize elemental intensities and subtract background signals from the paper. Then, specific criteria were designed and employed to identify target elements (Li, V, Mn, Co, Ni, Cu, Zn, Zr, Sn, W and Pb) which resulted independent of the IS chosen in a 98% of the cases and allowed a qualitative clustering of the samples. In a second step, an elemental-related ratio (ink ratio) based on the targets previously identified was used to obtain mass independent intensities and perform pairwise comparisons by means of multivariate statistical analyses (MANOVA, Tukey's HSD and T2 Hotelling). This treatment improved the discrimination power (DP) and provided objective results, achieving a complete differentiation among different brands and a partial differentiation within pen inks from the same brands. The designed data treatment, together with the use of multivariate statistical tools, represents an easy and useful tool for differentiating among blue ballpoint pen inks, with hardly sample destruction and without the need for methodological calibrations, being its use potentially advantageous from a forensic-practice standpoint. To test the procedure, it was applied to analyze real handwritten questioned contracts, previously studied by the Department of Forensic Document Exams of the Criminalistics Service of Civil Guard (Spain). The results showed that all questioned ink entries were clustered in the same group, being those different from the remaining ink on the document.
A multivariable control scheme for robot manipulators
NASA Technical Reports Server (NTRS)
Tarokh, M.; Seraji, H.
1991-01-01
The article puts forward a simple scheme for multivariable control of robot manipulators to achieve trajectory tracking. The scheme is composed of an inner loop stabilizing controller and an outer loop tracking controller. The inner loop utilizes a multivariable PD controller to stabilize the robot by placing the poles of the linearized robot model at some desired locations. The outer loop employs a multivariable PID controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated on-line to cope with variations in the robot model during gross motion and for payload change. Alternatively, the use of high gain controllers for gross motion and payload change is discussed. Computer simulation results are given for illustration.
Multivariate analysis: A statistical approach for computations
NASA Astrophysics Data System (ADS)
Michu, Sachin; Kaushik, Vandana
2014-10-01
Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.
Gallidabino, M; Romolo, F S; Weyermann, C
2017-03-01
Estimating the time since discharge of spent cartridges can be a valuable tool in the forensic investigation of firearm-related crimes. To reach this aim, it was previously proposed that the decrease of volatile organic compounds released during discharge is monitored over time using non-destructive headspace extraction techniques. While promising results were obtained for large-calibre cartridges (e.g., shotgun shells), handgun calibres yielded unsatisfying results. In addition to the natural complexity of the specimen itself, these can also be attributed to some selective choices in the methods development. Thus, the present series of papers aimed to systematically evaluate the potential of headspace analysis to estimate the time since discharge of cartridges through the use of more comprehensive analytical and interpretative techniques. Following the comprehensive optimisation and validation of an exhaustive headspace sorptive extraction (HSSE) method in the first part of this work, the present paper addresses the application of chemometric tools in order to systematically evaluate the potential of applying headspace analysis to estimate the time since discharge of 9mm Geco cartridges. Several multivariate regression and pre-treatment methods were tested and compared to univariate models based on non-linear regression. Random forests (RF) and partial least squares (PLS) proceeded by pairwise log-ratios normalisation (PLR) showed the best results, and allowed to estimate time since discharge up to 48h of ageing and to differentiate recently fired from older cartridges (e.g., less than 5h compared to more than 1-2 days). The proposed multivariate approaches showed significant improvement compared to univariate models. The effects of storage conditions were also tested and results demonstrated that temperature, humidity and cartridge position should be taken into account when estimating the time since discharge.
Redox State of Iron in Lunar Glasses using X-ray Absorption Spectroscopy and Multivariate Analysis
NASA Astrophysics Data System (ADS)
Dyar, M. D.; McCanta, M. C.; Lanzirotti, A.; Sutton, S. R.; Carey, C. J.; Mahadevan, S.; Rutherford, M. J.
2014-12-01
The oxidation state of igneous materials on a planet is a critically-important variable in understanding magma evolution on bodies in our solar system. However, direct and indirect methods for quantifying redox states are challenging, especially across the broad spectrum of silicate glass compositions found on airless bodies. On the Moon, early Mössbauer studies of bulk samples suggested the presence of significant Fe3+ (>10%) in lunar glasses (green, orange, brown); lunar analog glasses synthesized at fO2 <10-11 have similar Fe3+. All these Mössbauer spectra are challenging to interpret due to the presence of multiple coordination environments in the glasses. X-ray absorption spectroscopy (XAS) allows pico- and nano-scale interrogation of primitive planetary materials using the pre-edge, main edge, and EXAFS regions of absorption edge spectra. Current uses of XAS require availability of standards with compositions similar to those of unknowns and complex procedures for curve-fitting of pre-edge features that produce results with poorly constrained accuracy. A new approach to accurate and quantitative redox measurements with XAS is to couple use of spectra from synthetic glass standards covering a broad compositional range with multivariate analysis (MVA) techniques. Mössbauer and XAS spectra from a suite of 33 synthetic glass standards covering a wide range of compositions and fO2(Dyar et al., this meeting) were used to develop a MVA model that utilizes valuable predictive information not only in the major spectral peaks/features, but in all channels of the XAS region. Algorithms for multivariate analysis t were used to "learn" the characteristics of a data set as a function of varying spectral characteristics. These models were applied to the study of lunar glasses, which provide a challenging test case for these newly-developed techniques due to their very low fO2. Application of the new XAS calibration model to Apollo 15 green (15426, 15427 and 15425
A refined method for multivariate meta-analysis and meta-regression.
Jackson, Daniel; Riley, Richard D
2014-02-20
Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples.
NASA Technical Reports Server (NTRS)
Sigman, E. H.
1988-01-01
A phase calibration system was developed for the Deep Space Stations to generate reference microwave comb tones which are mixed in with signals received by the antenna. These reference tones are used to remove drifts of the station's receiving system from the detected data. This phase calibration system includes a cable stabilizer which transfers a 20 MHz reference signal from the control room to the antenna cone. The cable stabilizer compensates for delay changes in the long cable which connects its control room subassembly to its antenna cone subassembly in such a way that the 20 MHz is transferred to the cone with no significant degradation of the hydrogen maser atomic clock stability. The 20 MHz reference is used by the comb generator and is also available for use as a reference for receiver LO's in the cone.
NASA Astrophysics Data System (ADS)
Hodge, P. E.; Hulbert, S. J.; Lindler, D.; Busko, I.; Hsu, J.-C.; Baum, S.; McGrath, M.; Goudfrooij, P.; Shaw, R.; Katsanis, R.; Keener, S.; Bohlin, R.
The CALSTIS program for calibration of Space Telescope Imaging Spectrograph data in the OPUS pipeline differs in several significant ways from calibration for earlier HST instruments, such as the use of FITS format, computation of error estimates, and association of related exposures. Several steps are now done in the pipeline that previously had to be done off-line by the user, such as cosmic ray rejection and extraction of 1-D spectra. Although the program is linked with IRAF for image and table I/O, it is written in ANSI C rather than SPP, which should make the code more accessible. FITS extension I/O makes use of the new IRAF FITS kernel for images and the HEASARC FITSIO package for tables.
MIRO Calibration Switch Mechanism
NASA Technical Reports Server (NTRS)
Suchman, Jason; Salinas, Yuki; Kubo, Holly
2001-01-01
The Jet Propulsion Laboratory has designed, analyzed, built, and tested a calibration switch mechanism for the MIRO instrument on the ROSETTA spacecraft. MIRO is the Microwave Instrument for the Rosetta Orbiter; this instrument hopes to investigate the origin of the solar system by studying the origin of comets. Specifically, the instrument will be the first to use submillimeter and millimeter wave heterodyne receivers to remotely examine the P-54 Wirtanen comet. In order to calibrate the instrument, it needs to view a hot and cold target. The purpose of the mechanism is to divert the instrument's field of view from the hot target, to the cold target, and then back into space. This cycle is to be repeated every 30 minutes for the duration of the 1.5 year mission. The paper describes the development of the mechanism, as well as analysis and testing techniques.
Calibrated vapor generator source
Davies, J.P.; Larson, R.A.; Goodrich, L.D.; Hall, H.J.; Stoddard, B.D.; Davis, S.G.; Kaser, T.G.; Conrad, F.J.
1995-09-26
A portable vapor generator is disclosed that can provide a controlled source of chemical vapors, such as, narcotic or explosive vapors. This source can be used to test and calibrate various types of vapor detection systems by providing a known amount of vapors to the system. The vapor generator is calibrated using a reference ion mobility spectrometer. A method of providing this vapor is described, as follows: explosive or narcotic is deposited on quartz wool, placed in a chamber that can be heated or cooled (depending on the vapor pressure of the material) to control the concentration of vapors in the reservoir. A controlled flow of air is pulsed over the quartz wool releasing a preset quantity of vapors at the outlet. 10 figs.
Calibrated vapor generator source
Davies, John P.; Larson, Ronald A.; Goodrich, Lorenzo D.; Hall, Harold J.; Stoddard, Billy D.; Davis, Sean G.; Kaser, Timothy G.; Conrad, Frank J.
1995-01-01
A portable vapor generator is disclosed that can provide a controlled source of chemical vapors, such as, narcotic or explosive vapors. This source can be used to test and calibrate various types of vapor detection systems by providing a known amount of vapors to the system. The vapor generator is calibrated using a reference ion mobility spectrometer. A method of providing this vapor is described, as follows: explosive or narcotic is deposited on quartz wool, placed in a chamber that can be heated or cooled (depending on the vapor pressure of the material) to control the concentration of vapors in the reservoir. A controlled flow of air is pulsed over the quartz wool releasing a preset quantity of vapors at the outlet.
A force calibration standard for magnetic tweezers
NASA Astrophysics Data System (ADS)
Yu, Zhongbo; Dulin, David; Cnossen, Jelmer; Köber, Mariana; van Oene, Maarten M.; Ordu, Orkide; Berghuis, Bojk A.; Hensgens, Toivo; Lipfert, Jan; Dekker, Nynke H.
2014-12-01
To study the behavior of biological macromolecules and enzymatic reactions under force, advances in single-molecule force spectroscopy have proven instrumental. Magnetic tweezers form one of the most powerful of these techniques, due to their overall simplicity, non-invasive character, potential for high throughput measurements, and large force range. Drawbacks of magnetic tweezers, however, are that accurate determination of the applied forces can be challenging for short biomolecules at high forces and very time-consuming for long tethers at low forces below ˜1 piconewton. Here, we address these drawbacks by presenting a calibration standard for magnetic tweezers consisting of measured forces for four magnet configurations. Each such configuration is calibrated for two commonly employed commercially available magnetic microspheres. We calculate forces in both time and spectral domains by analyzing bead fluctuations. The resulting calibration curves, validated through the use of different algorithms that yield close agreement in their determination of the applied forces, span a range from 100 piconewtons down to tens of femtonewtons. These generalized force calibrations will serve as a convenient resource for magnetic tweezers users and diminish variations between different experimental configurations or laboratories.
Fast calibration of gas flowmeters
NASA Technical Reports Server (NTRS)
Lisle, R. V.; Wilson, T. L.
1981-01-01
Digital unit automates calibration sequence using calculator IC and programmable read-only memory to solve calibration equations. Infrared sensors start and stop calibration sequence. Instrument calibrates mass flowmeters or rotameters where flow measurement is based on mass or volume. This automatic control reduces operator time by 80 percent. Solid-state components are very reliable, and digital character allows system accuracy to be determined primarily by accuracy of transducers.
H. H. Liu
2003-02-14
This report has documented the methodologies and the data used for developing rock property sets for three infiltration maps. Model calibration is necessary to obtain parameter values appropriate for the scale of the process being modeled. Although some hydrogeologic property data (prior information) are available, these data cannot be directly used to predict flow and transport processes because they were measured on scales smaller than those characterizing property distributions in models used for the prediction. Since model calibrations were done directly on the scales of interest, the upscaling issue was automatically considered. On the other hand, joint use of data and the prior information in inversions can further increase the reliability of the developed parameters compared with those for the prior information. Rock parameter sets were developed for both the mountain and drift scales because of the scale-dependent behavior of fracture permeability. Note that these parameter sets, except those for faults, were determined using the 1-D simulations. Therefore, they cannot be directly used for modeling lateral flow because of perched water in the unsaturated zone (UZ) of Yucca Mountain. Further calibration may be needed for two- and three-dimensional modeling studies. As discussed above in Section 6.4, uncertainties for these calibrated properties are difficult to accurately determine, because of the inaccuracy of simplified methods for this complex problem or the extremely large computational expense of more rigorous methods. One estimate of uncertainty that may be useful to investigators using these properties is the uncertainty used for the prior information. In most cases, the inversions did not change the properties very much with respect to the prior information. The Output DTNs (including the input and output files for all runs) from this study are given in Section 9.4.
NASA Astrophysics Data System (ADS)
Lorefice, Salvatore; Malengo, Andrea
2006-10-01
After a brief description of the different methods employed in periodic calibration of hydrometers used in most cases to measure the density of liquids in the range between 500 kg m-3 and 2000 kg m-3, particular emphasis is given to the multipoint procedure based on hydrostatic weighing, known as well as Cuckow's method. The features of the calibration apparatus and the procedure used at the INRiM (formerly IMGC-CNR) density laboratory have been considered to assess all relevant contributions involved in the calibration of different kinds of hydrometers. The uncertainty is strongly dependent on the kind of hydrometer; in particular, the results highlight the importance of the density of the reference buoyant liquid, the temperature of calibration and the skill of operator in the reading of the scale in the whole assessment of the uncertainty. It is also interesting to realize that for high-resolution hydrometers (division of 0.1 kg m-3), the uncertainty contribution of the density of the reference liquid is the main source of the total uncertainty, but its importance falls under about 50% for hydrometers with a division of 0.5 kg m-3 and becomes somewhat negligible for hydrometers with a division of 1 kg m-3, for which the reading uncertainty is the predominant part of the total uncertainty. At present the best INRiM result is obtained with commercially available hydrometers having a scale division of 0.1 kg m-3, for which the relative uncertainty is about 12 × 10-6.
Mesoscale hybrid calibration artifact
Tran, Hy D.; Claudet, Andre A.; Oliver, Andrew D.
2010-09-07
A mesoscale calibration artifact, also called a hybrid artifact, suitable for hybrid dimensional measurement and the method for make the artifact. The hybrid artifact has structural characteristics that make it suitable for dimensional measurement in both vision-based systems and touch-probe-based systems. The hybrid artifact employs the intersection of bulk-micromachined planes to fabricate edges that are sharp to the nanometer level and intersecting planes with crystal-lattice-defined angles.
1992-01-30
penetrometers of different designs, (iii) the effect of rod friction, (iv) the effect of discontinuous operation, and (v) sensing an interface between two sand...layers. Other test results on two designs of 10 cm2 Fugro penetrometers, each with a different position of friction sleeve, assisted in the selection...at different stages in the penetration of a specimen. The calibration tests had the prime purpose of establishing correlations between the penetration
Snell, Kym I.E.; Hua, Harry; Debray, Thomas P.A.; Ensor, Joie; Look, Maxime P.; Moons, Karel G.M.; Riley, Richard D.
2016-01-01
Objectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of “good” performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. Results In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of “good” performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of “good” performance. Conclusion Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. PMID:26142114
T. Ghezzehej
2004-10-04
The purpose of this model report is to document the calibrated properties model that provides calibrated property sets for unsaturated zone (UZ) flow and transport process models (UZ models). The calibration of the property sets is performed through inverse modeling. This work followed, and was planned in, ''Technical Work Plan (TWP) for: Unsaturated Zone Flow Analysis and Model Report Integration'' (BSC 2004 [DIRS 169654], Sections 1.2.6 and 2.1.1.6). Direct inputs to this model report were derived from the following upstream analysis and model reports: ''Analysis of Hydrologic Properties Data'' (BSC 2004 [DIRS 170038]); ''Development of Numerical Grids for UZ Flow and Transport Modeling'' (BSC 2004 [DIRS 169855]); ''Simulation of Net Infiltration for Present-Day and Potential Future Climates'' (BSC 2004 [DIRS 170007]); ''Geologic Framework Model'' (GFM2000) (BSC 2004 [DIRS 170029]). Additionally, this model report incorporates errata of the previous version and closure of the Key Technical Issue agreement TSPAI 3.26 (Section 6.2.2 and Appendix B), and it is revised for improved transparency.
NASA Technical Reports Server (NTRS)
1997-01-01
Several prominent features of Mars Pathfinder and surrounding terrain are seen in this image, taken by the Imager for Mars Pathfinder on July 4 (Sol 1), the spacecraft's first day on the Red Planet. Portions of a lander petal are at the lower part of the image. At the left, the mechanism for the high-gain antenna can be seen. The dark area along the right side of the image represents a portion of the low-gain antenna. The radiation calibration target is at the right. The calibration target is made up of a number of materials with well-characterized colors. The known colors of the calibration targets allow scientists to determine the true colors of the rocks and soils of Mars. Three bull's-eye rings provide a wide range of brightness for the camera, similar to a photographer's grayscale chart. In the middle of the bull's-eye is a 5-inch tall post that casts a shadow, which is distorted in this image due to its location with respect to the lander camera.
A large rock is located at the near center of the image. Smaller rocks and areas of soil are strewn across the Martian terrain up to the horizon line.
Mars Pathfinder is the second in NASA's Discovery program of low-cost spacecraft with highly focused science goals. The Jet Propulsion Laboratory, Pasadena, CA, developed and manages the Mars Pathfinder mission for NASA's Office of Space Science, Washington, D.C.
Liu, Na; Li, Jun; Li, Bao-Guo
2014-11-01
The study of quality control of Chinese medicine has always been the hot and the difficulty spot of the development of traditional Chinese medicine (TCM), which is also one of the key problems restricting the modernization and internationalization of Chinese medicine. Multivariate statistical analysis is an analytical method which is suitable for the analysis of characteristics of TCM. It has been used widely in the study of quality control of TCM. Multivariate Statistical analysis was used for multivariate indicators and variables that appeared in the study of quality control and had certain correlation between each other, to find out the hidden law or the relationship between the data can be found,.which could apply to serve the decision-making and realize the effective quality evaluation of TCM. In this paper, the application of multivariate statistical analysis in the quality control of Chinese medicine was summarized, which could provided the basis for its further study.
Multivariate permutation entropy and its application for complexity analysis of chaotic systems
NASA Astrophysics Data System (ADS)
He, Shaobo; Sun, Kehui; Wang, Huihai
2016-11-01
To measure the complexity of multivariate systems, the multivariate permutation entropy (MvPE) algorithm is proposed. It is employed to measure complexity of multivariate system in the phase space. As an application, MvPE is applied to analyze the complexity of chaotic systems, including hyperchaotic Hénon map, fractional-order simplified Lorenz system and financial chaotic system. Results show that MvPE algorithm is effective for analyzing the complexity of the multivariate systems. It also shows that fractional-order system does not become more complex with derivative order varying. Compared with PE, MvPE has better robustness for noise and sampling interval, and the results are not affected by different normalization methods.
Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data.
Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian
2015-01-01
In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies.
Internet-Based Calibration of a Multifunction Calibrator
BUNTING BACA,LISA A.; DUDA JR.,LEONARD E.; WALKER,RUSSELL M.; OLDHAM,NILE; PARKER,MARK
2000-12-19
A new way of providing calibration services is evolving which employs the Internet to expand present capabilities and make the calibration process more interactive. Sandia National Laboratories and the National Institute of Standards and Technology are collaborating to set up and demonstrate a remote calibration of multijunction calibrators using this Internet-based technique that is becoming known as e-calibration. This paper describes the measurement philosophy and the Internet resources that can provide real-time audio/video/data exchange, consultation and training, as well as web-accessible test procedures, software and calibration reports. The communication system utilizes commercial hardware and software that should be easy to integrate into most calibration laboratories.
Internet-based calibration of a multifunction calibrator
BUNTING BACA,LISA A.; DUDA JR.,LEONARD E.; WALKER,RUSSELL M.; OLDHAM,NILE; PARKER,MARK
2000-04-17
A new way of providing calibration services is evolving which employs the Internet to expand present capabilities and make the calibration process more interactive. Sandia National Laboratories and the National Institute of Standards and Technology are collaborating to set up and demonstrate a remote calibration of multifunction calibrators using this Internet-based technique that is becoming known as e-calibration. This paper describes the measurement philosophy and the Internet resources that can provide real-time audio/video/data exchange, consultation and training, as well as web-accessible test procedures, software and calibration reports. The communication system utilizes commercial hardware and software that should be easy to integrate into most calibration laboratories.
NASA Technical Reports Server (NTRS)
Djorgovski, S. G.
1994-01-01
We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has
NASA Technical Reports Server (NTRS)
Djorgovski, S. George
1994-01-01
We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.
Baptista, Patrícia; Felizardo, Pedro; Menezes, José C; Neiva Correia, M Joana
2008-10-19
Biodiesel is one of the main alternatives to fossil diesel. It is a non-toxic renewable resource, which leads to lower emissions of polluting gases. In fact, European governments are targeting the incorporation of 20% of biofuels in the fossil fuels until 2020. Chemically, biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol, in the presence of a catalyst. The European Standard (EN 14214) establishes 25 parameters that have to be analysed to certify biodiesel quality and the analytical methods that should be used to determine those properties. This work reports the use of near infrared (NIR) spectroscopy to determine some important biodiesel properties: the iodine value, the cold filter plugging point, the kinematic viscosity at 40 degrees C and the density at 15 degrees C. Principal component analysis was used to perform a qualitative analysis of the spectra and partial least squares regression to develop the calibration models between analytical and spectral data. The results support that NIR spectroscopy, in combination with multivariate calibration, is a promising technique applied to biodiesel quality control, in both laboratory and industrial-scale samples.
Using Matlab in a Multivariable Calculus Course.
ERIC Educational Resources Information Center
Schlatter, Mark D.
The benefits of high-level mathematics packages such as Matlab include both a computer algebra system and the ability to provide students with concrete visual examples. This paper discusses how both capabilities of Matlab were used in a multivariate calculus class. Graphical user interfaces which display three-dimensional surfaces, contour plots,…
Multivariate Granger causality and generalized variance
NASA Astrophysics Data System (ADS)
Barrett, Adam B.; Barnett, Lionel; Seth, Anil K.
2010-04-01
Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or “ensembles” of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke’s seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define “partial” Granger causality in the multivariate context and we also motivate reformulations of “causal density” and “Granger autonomy.” Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.
DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)
Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...
Multivariate Granger causality and generalized variance.
Barrett, Adam B; Barnett, Lionel; Seth, Anil K
2010-04-01
Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables but may occur among groups or "ensembles" of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer additional justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy." Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.
Multivariable Control System Design for a Submarine,
1984-05-01
Open Loop Singular Values for the 5 and 1S Knot Linear Modelo *~~* b % % V’ , * % ~ .%~ C 9 ~ V. --.- V. V.-.--.--46..- S. 77’ Model S20R5 20- 10- -0...Control, Addison-Wesley, 1976, pp 65-86. 14. Kevin Boettcher, Analysis of Multivariable Control Systems with Structured Uncertainty, Area Examination
Multivariate analysis: greater insights into complex systems
Technology Transfer Automated Retrieval System (TEKTRAN)
Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...
Primary calibration in acoustics metrology
NASA Astrophysics Data System (ADS)
Bacelar Milhomem, T. A.; Defilippo Soares, Z. M.
2015-01-01
SI unit in acoustics is realized by the reciprocity calibrations of laboratory standard microphones in pressure field, free field and diffuse field. Calibrations in pressure field and in free field are already consolidated and the Inmetro already done them. Calibration in diffuse field is not yet consolidated, however, some national metrology institutes, including Inmetro, are conducting researches on this subject. This paper presents the reciprocity calibration, the results of Inmetro in recent key comparisons and the research that is being developed for the implementation of reciprocity calibration in diffuse field.
Calibration of triaxial fluxgate gradiometer
Vcelak, Jan
2006-04-15
The description of simple and fast calibration procedures used for double-probe triaxial fluxgate gradiometer is provided in this paper. The calibration procedure consists of three basic steps. In the first step both probes are calibrated independently in order to reach constant total field reading in every position. Both probes are numerically aligned in the second step in order that the gradient reading is zero in homogenous magnetic field. The third step consists of periodic drift calibration during measurement. The results and detailed description of each calibration step are presented and discussed in the paper. The gradiometer is finally verified during the detection of the metal object in the measuring grid.
Multivariate Granger causality analysis of fMRI data.
Deshpande, Gopikrishna; LaConte, Stephan; James, George Andrew; Peltier, Scott; Hu, Xiaoping
2009-04-01
This article describes the combination of multivariate Granger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network.
John Schabron; Joseph Rovani; Mark Sanderson
2008-02-29
Mercury continuous emissions monitoring systems (CEMS) are being implemented in over 800 coal-fired power plant stacks. The power industry desires to conduct at least a full year of monitoring before the formal monitoring and reporting requirement begins on January 1, 2009. It is important for the industry to have available reliable, turnkey equipment from CEM vendors. Western Research Institute (WRI) is working closely with the Electric Power Research Institute (EPRI), the National Institute of Standards and Technology (NIST), and the Environmental Protection Agency (EPA) to facilitate the development of the experimental criteria for a NIST traceability protocol for dynamic elemental mercury vapor generators. The generators are used to calibrate mercury CEMs at power plant sites. The Clean Air Mercury Rule (CAMR) which was published in the Federal Register on May 18, 2005 requires that calibration be performed with NIST-traceable standards (Federal Register 2007). Traceability procedures will be defined by EPA. An initial draft traceability protocol was issued by EPA in May 2007 for comment. In August 2007, EPA issued an interim traceability protocol for elemental mercury generators (EPA 2007). The protocol is based on the actual analysis of the output of each calibration unit at several concentration levels ranging initially from about 2-40 {micro}g/m{sup 3} elemental mercury, and in the future down to 0.2 {micro}g/m{sup 3}, and this analysis will be directly traceable to analyses by NIST. The document is divided into two separate sections. The first deals with the qualification of generators by the vendors for use in mercury CEM calibration. The second describes the procedure that the vendors must use to certify the generator models that meet the qualification specifications. The NIST traceable certification is performance based, traceable to analysis using isotope dilution inductively coupled plasma/mass spectrometry performed by NIST in Gaithersburg, MD. The
Development and calibration of a pedal with force and moment sensors.
Gurgel, Jonas; Porto, Flávia; Russomano, Thais; Cambraia, Rodrigo; de Azevedo, Dario F G; Glock, Flávio S; Beck, João Carlos Pinheiro; Helegda, Sergio
2006-01-01
An instrumented bicycle pedal was built and calibrated. The pedal has good linearity and sensibility, comparable to other instruments in the literature. This study aimed to perform accurate calibration of a tri-axial pedal, including forces applied, deformations, nonlinearities, hysteresis and standard error for each axis. Calibration was based on Hull and Davis method, which is based on the application of known loads on the pedal in order to create a calibration matrix.
Calibrating reaction rates for the CREST model
NASA Astrophysics Data System (ADS)
Handley, Caroline A.; Christie, Michael A.
2017-01-01
The CREST reactive-burn model uses entropy-dependent reaction rates that, until now, have been manually tuned to fit shock-initiation and detonation data in hydrocode simulations. This paper describes the initial development of an automatic method for calibrating CREST reaction-rate coefficients, using particle swarm optimisation. The automatic method is applied to EDC32, to help develop the first CREST model for this conventional high explosive.
Automatic force balance calibration system
NASA Technical Reports Server (NTRS)
Ferris, Alice T. (Inventor)
1996-01-01
A system for automatically calibrating force balances is provided. The invention uses a reference balance aligned with the balance being calibrated to provide superior accuracy while minimizing the time required to complete the calibration. The reference balance and the test balance are rigidly attached together with closely aligned moment centers. Loads placed on the system equally effect each balance, and the differences in the readings of the two balances can be used to generate the calibration matrix for the test balance. Since the accuracy of the test calibration is determined by the accuracy of the reference balance and current technology allows for reference balances to be calibrated to within .+-.0.05%, the entire system has an accuracy of a .+-.0.2%. The entire apparatus is relatively small and can be mounted on a movable base for easy transport between test locations. The system can also accept a wide variety of reference balances, thus allowing calibration under diverse load and size requirements.
Self-Calibrating Pressure Transducer
NASA Technical Reports Server (NTRS)
Lueck, Dale E. (Inventor)
2006-01-01
A self-calibrating pressure transducer is disclosed. The device uses an embedded zirconia membrane which pumps a determined quantity of oxygen into the device. The associated pressure can be determined, and thus, the transducer pressure readings can be calibrated. The zirconia membrane obtains oxygen .from the surrounding environment when possible. Otherwise, an oxygen reservoir or other source is utilized. In another embodiment, a reversible fuel cell assembly is used to pump oxygen and hydrogen into the system. Since a known amount of gas is pumped across the cell, the pressure produced can be determined, and thus, the device can be calibrated. An isolation valve system is used to allow the device to be calibrated in situ. Calibration is optionally automated so that calibration can be continuously monitored. The device is preferably a fully integrated MEMS device. Since the device can be calibrated without removing it from the process, reductions in costs and down time are realized.
Zhou, Fei; Peng, Jiyu; Zhao, Yajing; Huang, Weisu; Jiang, Yirong; Li, Maiquan; Wu, Xiaodan; Lu, Baiyi
2017-02-15
This study was aimed to classify the varieties and predict the antioxidant activity of Osmanthus fragrans flowers by UPLC-PDA/QTOF-MS and multivariable analysis. The PLS-DA model successfully classified the four varieties based on both the 21 identified compounds and the effective compounds. For the antioxidant activity prediction, PLS performed well to predict the antioxidant activity of O. fragrans flowers. Furthermore, acteoside, suspensaside A, ligustroside, forsythoside A, phillygenin and caffeic acid were selected as effective compounds by UVE-SPA for prediction. On the basis of effective compounds, PLS, MLR and PCR were applied to establish the calibration models. The UVE-SPA-MLR model was the optimal method to predict the antioxidant activity values with Rp of 0.9200, 0.9010 and 0.8905 for DPPH, ABTS and FRAP assays, respectively. The results revealed that the UPLC-PDA/QTOF-MS combined with chemometrics could be a new method to classify the varieties and predict the antioxidant activity of O. fragrans flowers.
NASA Astrophysics Data System (ADS)
Haas, Marcelo B.; Guse, Björn; Pfannerstill, Matthias; Fohrer, Nicola
2016-05-01
Hydrological models are useful tools to investigate hydrology and water quality in catchments. The calibration of these models is a crucial step to adapt the model to the catchment conditions, allowing effective simulations of environmental processes. In the model calibration, different performance measures need to be considered to represent different hydrology and water quality conditions in combination. This study presents a joined multi-metric calibration of discharge and nitrate loads simulated with the ecohydrological model SWAT. For this purpose, a calibration approach based on flow duration curves (FDC) is advanced by also considering nitrate duration curves (NDC). Five segments of FDCs and of NDCs are evaluated separately to consider the different phases of hydrograph and nitrograph. To consider both magnitude and dynamics in river discharge and nitrate loads, the Kling-Gupta Efficiency (KGE) is used additionally as a statistical performance metric to achieve a joined multi-variable calibration. The results show that a separate assessment of five different magnitudes improves the calibrated nitrate loads. Subsequently, adequate model runs with good performance for different hydrological conditions both for discharge and nitrate are detected in a joined approach based on FDC, NDC, and KGE. In that manner, plausible results were obtained for discharge and nitrate loads in the same model run. Using a multi-metric performance approach, the simultaneous multi-variable calibration led to a balanced model result for all magnitudes of discharge and nitrate loads.
Asar, Ozgür; Ilk, Ozlem
2014-07-01
Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.
[Laser-based radiometric calibration].
Li, Zhi-gang; Zheng, Yu-quan
2014-12-01
Increasingly higher demands are put forward to spectral radiometric calibration accuracy and the development of new tunable laser based spectral radiometric calibration technology is promoted, along with the development of studies of terrestrial remote sensing, aeronautical and astronautical remote sensing, plasma physics, quantitative spectroscopy, etc. Internationally a number of national metrology scientific research institutes have built tunable laser based spectral radiometric calibration facilities in succession, which are traceable to cryogenic radiometers and have low uncertainties for spectral responsivity calibration and characterization of detectors and remote sensing instruments in the UK, the USA, Germany, etc. Among them, the facility for spectral irradiance and radiance responsivity calibrations using uniform sources (SIRCCUS) at the National Institute of Standards and Technology (NIST) in the USA and the Tunable Lasers in Photometry (TULIP) facility at the Physikalisch-Technische Bundesanstalt (PTB) in Germany have more representatives. Compared with lamp-monochromator systems, laser based spectral radiometric calibrations have many advantages, such as narrow spectral bandwidth, high wavelength accuracy, low calibration uncertainty and so on for radiometric calibration applications. In this paper, the development of laser-based spectral radiometric calibration and structures and performances of laser-based radiometric calibration facilities represented by the National Physical Laboratory (NPL) in the UK, NIST and PTB are presented, technical advantages of laser-based spectral radiometric calibration are analyzed, and applications of this technology are further discussed. Laser-based spectral radiometric calibration facilities can be widely used in important system-level radiometric calibration measurements with high accuracy, including radiance temperature, radiance and irradiance calibrations for space remote sensing instruments, and promote the
Uncertainty and Dimensional Calibrations
Doiron, Ted; Stoup, John
1997-01-01
The calculation of uncertainty for a measurement is an effort to set reasonable bounds for the measurement result according to standardized rules. Since every measurement produces only an estimate of the answer, the primary requisite of an uncertainty statement is to inform the reader of how sure the writer is that the answer is in a certain range. This report explains how we have implemented these rules for dimensional calibrations of nine different types of gages: gage blocks, gage wires, ring gages, gage balls, roundness standards, optical flats indexing tables, angle blocks, and sieves. PMID:27805114
Transient Calorimeter Calibration System
1975-03-01
can withstand the high heat flux levels encountered during calibration. 3 d. The verification of absorptivity of a colloidal graphite coat- ing used...CC4-)Ŕ ýz- ix w- Vj m ~ CL 4) > r- 4-) r-)W *. M 0 0 a 4) u.) . CJ C4) Lfl 4%. C6 0) 0 ~ 4 -0 E 36 The colloidal graphite remains the most...before and after exposure. Such tests performed with the colloidal graphite coating yielded no apparent change in absorptivity up through 5 kW/cm2 . In
NASA Astrophysics Data System (ADS)
Colina, Luis
1994-01-01
As a result of last November calibration workshop, all parties agreed that the HST should be switched to the WD basis for absolute fluxes. This proposal implements that decision. A measurement of the absolute sensitivity of the FOS detectors will be performed using theoretical pure hydrogen model atmosphere calculations for three white dwarfs. The high resolution gratings will be used in the 1 arcsec aperture. A four stage peakup of the standard star provides centering in the aperture. Observations are requested for fall 94 with repeated observations about two months after.
Uncertainty and Dimensional Calibrations.
Doiron, Ted; Stoup, John
1997-01-01
The calculation of uncertainty for a measurement is an effort to set reasonable bounds for the measurement result according to standardized rules. Since every measurement produces only an estimate of the answer, the primary requisite of an uncertainty statement is to inform the reader of how sure the writer is that the answer is in a certain range. This report explains how we have implemented these rules for dimensional calibrations of nine different types of gages: gage blocks, gage wires, ring gages, gage balls, roundness standards, optical flats indexing tables, angle blocks, and sieves.
SAR antenna calibration techniques
NASA Technical Reports Server (NTRS)
Carver, K. R.; Newell, A. C.
1978-01-01
Calibration of SAR antennas requires a measurement of gain, elevation and azimuth pattern shape, boresight error, cross-polarization levels, and phase vs. angle and frequency. For spaceborne SAR antennas of SEASAT size operating at C-band or higher, some of these measurements can become extremely difficult using conventional far-field antenna test ranges. Near-field scanning techniques offer an alternative approach and for C-band or X-band SARs, give much improved accuracy and precision as compared to that obtainable with a far-field approach.
Dynamic Torque Calibration Unit
NASA Technical Reports Server (NTRS)
Agronin, Michael L.; Marchetto, Carl A.
1989-01-01
Proposed dynamic torque calibration unit (DTCU) measures torque in rotary actuator components such as motors, bearings, gear trains, and flex couplings. Unique because designed specifically for testing components under low rates. Measures torque in device under test during controlled steady rotation or oscillation. Rotor oriented vertically, supported by upper angular-contact bearing and lower radial-contact bearing that floats axially to prevent thermal expansion from loading bearings. High-load capacity air bearing available to replace ball bearings when higher load capacity or reduction in rate noise required.
Implementation of a multivariate regional index-flood model
NASA Astrophysics Data System (ADS)
Requena, Ana Isabel; Chebana, Fateh; Mediero, Luis; Garrote, Luis
2014-05-01
A multivariate flood frequency approach is required to obtain appropriate estimates of the design flood associated to a given return period, as the nature of floods is multivariate. A regional frequency analysis is usually conducted to procure estimates or reduce the corresponding uncertainty when no information is available at ungauged sites or a short record is observed at gauged sites. In the present study a multivariate regional methodology based on the index-flood model is presented, seeking to enrich and complete the existing methods by i) considering more general two-parameter copulas for simulating synthetic homogeneous regions to test homogeneity; ii) using the latest definitions of bivariate return periods for quantile estimation; and iii) applying recent procedures for the selection of a subset of bivariate design events from the wider quantile curves. A complete description of the selection processes of both marginal distributions and copula is also included. The proposed methodology provides an entire procedure focused on its practical application. The proposed methodology was applied to a case study located in the Ebro basin in the north of Spain. Series of annual maximum flow peaks (Q) and their associated hydrograph volumes (V ) were selected as flood variables. The initial region was divided into two homogeneous sub-regions by a cluster analysis and a multivariate homogeneity test. The Gumbel and Generalised Extreme Value distributions were selected as marginal distributions to fit the two flood variables. The BB1 copula was found to be the best regional copula for characterising the dependence relation between variables. The OR bivariate joint return period related to the (non-exceedance) probability of the event{Q ≤ qδ§ V ≤ v}was considered for quantile estimation. The index flood was based on the mean of the flood variables. Multiple linear regressions were used to estimate the index flood at ungauged sites. Basin concentration time
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
Consequences of Secondary Calibrations on Divergence Time Estimates
Schenk, John J.
2016-01-01
Secondary calibrations (calibrations based on the results of previous molecular dating studies) are commonly applied in divergence time analyses in groups that lack fossil data; however, the consequences of applying secondary calibrations in a relaxed-clock approach are not fully understood. I tested whether applying the posterior estimate from a primary study as a prior distribution in a secondary study results in consistent age and uncertainty estimates. I compared age estimates from simulations with 100 randomly replicated secondary trees. On average, the 95% credible intervals of node ages for secondary estimates were significantly younger and narrower than primary estimates. The primary and secondary age estimates were significantly different in 97% of the replicates after Bonferroni corrections. Greater error in magnitude was associated with deeper than shallower nodes, but the opposite was found when standardized by median node age, and a significant positive relationship was determined between the number of tips/age of secondary trees and the total amount of error. When two secondary calibrated nodes were analyzed, estimates remained significantly different, and although the minimum and median estimates were associated with less error, maximum age estimates and credible interval widths had greater error. The shape of the prior also influenced error, in which applying a normal, rather than uniform, prior distribution resulted in greater error. Secondary calibrations, in summary, lead to a false impression of precision and the distribution of age estimates shift away from those that would be inferred by the primary analysis. These results suggest that secondary calibrations should not be applied as the only source of calibration in divergence time analyses that test time-dependent hypotheses until the additional error associated with secondary calibrations is more properly modeled to take into account increased uncertainty in age estimates. PMID:26824760
Principal Component Noise Filtering for NAST-I Radiometric Calibration
NASA Technical Reports Server (NTRS)
Tian, Jialin; Smith, William L., Sr.
2011-01-01
The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed- Interferometer (NAST-I) instrument is a high-resolution scanning interferometer that measures emitted thermal radiation between 3.3 and 18 microns. The NAST-I radiometric calibration is achieved using internal blackbody calibration references at ambient and hot temperatures. In this paper, we introduce a refined calibration technique that utilizes a principal component (PC) noise filter to compensate for instrument distortions and artifacts, therefore, further improve the absolute radiometric calibration accuracy. To test the procedure and estimate the PC filter noise performance, we form dependent and independent test samples using odd and even sets of blackbody spectra. To determine the optimal number of eigenvectors, the PC filter algorithm is applied to both dependent and independent blackbody spectra with a varying number of eigenvectors. The optimal number of PCs is selected so that the total root-mean-square (RMS) error is minimized. To estimate the filter noise performance, we examine four different scenarios: apply PC filtering to both dependent and independent datasets, apply PC filtering to dependent calibration data only, apply PC filtering to independent data only, and no PC filters. The independent blackbody radiances are predicted for each case and comparisons are made. The results show significant reduction in noise in the final calibrated radiances with the implementation of the PC filtering algorithm.
Tistaert, C; Chataigné, G; Dejaegher, B; Rivière, C; Nguyen Hoai, N; Chau Van, M; Quetin-Leclercq, J; Vander Heyden, Y
2012-12-01
The Mallotus genus comprises numerous species used as traditional medicines in oriental countries and provides scientists a broad basis in the search for pharmacologically active constituents. In this paper, the cytotoxicity of 39 Mallotus extracts, different in species, part of the plant used, origin, and harvest season, is evaluated combining cytotoxicity assays with fingerprint technology and data handling tools. At first, the antiproliferative activity of the plant extracts is analyzed both on a non-cancerous cell line (WI-38--human lung fibroblast) and on a cancerous cell line (HeLa human cervix carcinoma). The results are linked to a data set of high-performance liquid chromatographic fingerprint profiles of the samples using multivariate calibration techniques. The regression coefficients of the multivariate model are then evaluated to indicate those peaks potentially responsible for the cytotoxic activity of the Mallotus extracts. In a final step, the cytotoxic extracts are analyzed by HPLC-MS and the indicated peaks identified.
The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.
A Simple Accelerometer Calibrator
NASA Astrophysics Data System (ADS)
Salam, R. A.; Islamy, M. R. F.; Munir, M. M.; Latief, H.; Irsyam, M.; Khairurrijal
2016-08-01
High possibility of earthquake could lead to the high number of victims caused by it. It also can cause other hazards such as tsunami, landslide, etc. In that case it requires a system that can examine the earthquake occurrence. Some possible system to detect earthquake is by creating a vibration sensor system using accelerometer. However, the output of the system is usually put in the form of acceleration data. Therefore, a calibrator system for accelerometer to sense the vibration is needed. In this study, a simple accelerometer calibrator has been developed using 12 V DC motor, optocoupler, Liquid Crystal Display (LCD) and AVR 328 microcontroller as controller system. The system uses the Pulse Wave Modulation (PWM) form microcontroller to control the motor rotational speed as response to vibration frequency. The frequency of vibration was read by optocoupler and then those data was used as feedback to the system. The results show that the systems could control the rotational speed and the vibration frequencies in accordance with the defined PWM.
Calibration and validation of rockfall models
NASA Astrophysics Data System (ADS)
Frattini, Paolo; Valagussa, Andrea; Zenoni, Stefania; Crosta, Giovanni B.
2013-04-01
actual blocks, (2) the percentage of trajectories passing through the buffer of the actual rockfall path, (3) the mean distance between the location of arrest of each simulated blocks and the location of the nearest actual blocks; (4) the mean distance between the location of detachment of each simulated block and the location of detachment of the actual block located closer to the arrest position. By applying the four measures to the case studies, we observed that all measures are able to represent the model performance for validation purposes. However, the third measure is more simple and reliable than the others, and seems to be optimal for model calibration, especially when using a parameter estimation and optimization modelling software for automated calibration.
Automated Calibration of Atmospheric Oxidized Mercury Measurements.
Lyman, Seth; Jones, Colleen; O'Neil, Trevor; Allen, Tanner; Miller, Matthieu; Gustin, Mae Sexauer; Pierce, Ashley M; Luke, Winston; Ren, Xinrong; Kelley, Paul
2016-12-06
The atmosphere is an important reservoir for mercury pollution, and understanding of oxidation processes is essential to elucidating the fate of atmospheric mercury. Several recent studies have shown that a low bias exists in a widely applied method for atmospheric oxidized mercury measurements. We developed an automated, permeation tube-based calibrator for elemental and oxidized mercury, and we integrated this calibrator with atmospheric mercury instrumentation (Tekran 2537/1130/1135 speciation systems) in Reno, Nevada and at Mauna Loa Observatory, Hawaii, U.S.A. While the calibrator has limitations, it was able to routinely inject stable amounts of HgCl2 and HgBr2 into atmospheric mercury measurement systems over periods of several months. In Reno, recovery of injected mercury compounds as gaseous oxidized mercury (as opposed to elemental mercury) decreased with increasing specific humidity, as has been shown in other studies, although this trend was not observed at Mauna Loa, likely due to differences in atmospheric chemistry at the two locations. Recovery of injected mercury compounds as oxidized mercury was greater in Mauna Loa than in Reno, and greater still for a cation-exchange membrane-based measurement system. These results show that routine calibration of atmospheric oxidized mercury measurements is both feasible and necessary.
Crop physiology calibration in the CLM
Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.
2015-04-15
Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less
Crop physiology calibration in the CLM
Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.
2015-04-15
Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.
Online Sensor Calibration Monitoring Uncertainty Estimation
Hines, J. Wesley; Rasmussen, Brandon
2005-09-15
Empirical modeling techniques have been applied to online process monitoring to detect equipment and instrumentation degradations. However, few applications provide prediction uncertainty estimates, which can provide a measure of confidence in decisions. This paper presents the development of analytical prediction interval estimation methods for three common nonlinear empirical modeling strategies: artificial neural networks, neural network partial least squares, and local polynomial regression. The techniques are applied to nuclear power plant operational data for sensor calibration monitoring, and the prediction intervals are verified via bootstrap simulation studies.
A generalized multivariate regression model for modelling ocean wave heights
NASA Astrophysics Data System (ADS)
Wang, X. L.; Feng, Y.; Swail, V. R.
2012-04-01
In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.
Calibration-measurement unit for the automation of vector network analyzer measurements
NASA Astrophysics Data System (ADS)
Rolfes, I.; Will, B.; Schiek, B.
2008-05-01
With the availability of multi-port vector network analyzers, the need for automated, calibrated measurement facilities increases. In this contribution, a calibration-measurement unit is presented which realizes a repeatable automated calibration of the measurement setup as well as a user-friendly measurement of the device under test (DUT). In difference to commercially available calibration units, which are connected to the ports of the vector network analyzer preceding a measurement and which are then removed so that the DUT can be connected, the presented calibration-measurement unit is permanently connected to the ports of the VNA for the calibration as well as for the measurement of the DUT. This helps to simplify the calibrated measurement of complex scattering parameters. Moreover, a full integration of the calibration unit into the analyzer setup becomes possible. The calibration-measurement unit is based on a multiport switch setup of e.g. electromechanical relays. Under the assumption of symmetry of a switch, on the one hand the unit realizes the connection of calibration standards like one-port reflection standards and two-port through connections between different ports and on the other hand it enables the connection of the DUT. The calibration-measurement unit is applicable for two-port VNAs as well as for multiport VNAs. For the calibration of the unit, methods with completely known calibration standards like SOLT (short, open, load, through) as well as self-calibration procedures like TMR or TLR can be applied.
Lin, Yu-Shi; Lin, Chung-Chih; Tsai, Yuh-Show; Ku, Tien-Chuan; Huang, Yi-Hung; Hsu, Chun-Nan
2010-01-01
Motivation: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise. Results: We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A. Availability: Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/. Contact: chunnan@iis.sinica.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20529919
Quadrature phase interferometer used to calibrate dial indicator calibrators
NASA Astrophysics Data System (ADS)
Huang, Shau-Chi; Liou, Huay-Chung; Peng, Gwo-Sheng; Lu, Ming-Feng
2001-10-01
To calibrate dial indicators, gage blocks or dial indicator calibrators are usually used. For better accuracy and resolution, interferometers are used to calibrate dial indicator calibrators. Systematic errors of laser interferometers can be classified into three categories of intrinsic errors, environment errors and installation errors. Intrinsic errors include laser wavelength error, electronic error and optics nonlinearity. In order to achieve nanometer accuracy, minimizing intrinsic error is crucial. In this paper, we will address the problems of minimizing the optics nonlinearity error and describe the discrete-time signal processing method to minimize the electronic error, nonlinearity error and drift by simply using quadrature phase interferometer for nanometer accuracy and linearity.
Application of two tests of multivariate discordancy to fisheries data sets
Stapanian, M.A.; Kocovsky, P.M.; Garner, F.C.
2008-01-01
The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k=5) had a marked effect on a principal components
Sequencing human ribs into anatomical order by quantitative multivariate methods.
Cirillo, John; Henneberg, Maciej
2012-06-01
Little research has focussed on methods to anatomically sequence ribs. Correct anatomical sequencing of ribs assists in determining the location and distribution of regional trauma, age estimation, number of puncture wounds, number of individuals, and personal identification. The aim of the current study is to develop a method for placing fragmented and incomplete rib sets into correct anatomical position. Ribs 2-10 were used from eleven cadavers of an Australian population. Seven variables were measured from anatomical locations on the rib. General descriptive statistics were calculated for each variable along with an analysis of variance (ANOVA) and ANOVA with Bonferroni statistics. Considerable overlap was observed between ribs for univariate methods. Bivariate and multivariate methods were then applied. Results of the ANOVA with post hoc Bonferroni statistics show that ratios of various dimensions of a single rib could be used to sequence it within adjacent ribs. Using multiple regression formulae, the most accurate estimation of the anatomical rib number occurs when the entire rib is found in isolation. This however, is not always possible. Even when only the head and neck of the rib are preserved, a modified multivariate regression formula assigned 91.95% of ribs into correct anatomical position or as an adjacent rib. Using multivariate methods it is possible to sequence a single human rib with a high level of accuracy and they are superior to univariate methods. Left and right ribs were found to be highly symmetrical. Some rib dimensions were greater in males than in females, but overall the level of sexual dimorphism was low.
Multi-application controls: Robust nonlinear multivariable aerospace controls applications
NASA Technical Reports Server (NTRS)
Enns, Dale F.; Bugajski, Daniel J.; Carter, John; Antoniewicz, Bob
1994-01-01
This viewgraph presentation describes the general methodology used to apply Honywell's Multi-Application Control (MACH) and the specific application to the F-18 High Angle-of-Attack Research Vehicle (HARV) including piloted simulation handling qualities evaluation. The general steps include insertion of modeling data for geometry and mass properties, aerodynamics, propulsion data and assumptions, requirements and specifications, e.g. definition of control variables, handling qualities, stability margins and statements for bandwidth, control power, priorities, position and rate limits. The specific steps include choice of independent variables for least squares fits to aerodynamic and propulsion data, modifications to the management of the controls with regard to integrator windup and actuation limiting and priorities, e.g. pitch priority over roll, and command limiting to prevent departures and/or undesirable inertial coupling or inability to recover to a stable trim condition. The HARV control problem is characterized by significant nonlinearities and multivariable interactions in the low speed, high angle-of-attack, high angular rate flight regime. Systematic approaches to the control of vehicle motions modeled with coupled nonlinear equations of motion have been developed. This paper will discuss the dynamic inversion approach which explicity accounts for nonlinearities in the control design. Multiple control effectors (including aerodynamic control surfaces and thrust vectoring control) and sensors are used to control the motions of the vehicles in several degrees-of-freedom. Several maneuvers will be used to illustrate performance of MACH in the high angle-of-attack flight regime. Analytical methods for assessing the robust performance of the multivariable control system in the presence of math modeling uncertainty, disturbances, and commands have reached a high level of maturity. The structured singular value (mu) frequency response methodology is presented
Design of multivariable controllers for robot manipulators
NASA Technical Reports Server (NTRS)
Seraji, H.
1986-01-01
The paper presents a simple method for the design of linear multivariable controllers for multi-link robot manipulators. The control scheme consists of multivariable feedforward and feedback controllers. The feedforward controller is the minimal inverse of the linearized model of robot dynamics and contains only proportional-double-derivative (PD2) terms. This controller ensures that the manipulator joint angles track any reference trajectories. The feedback controller is of proportional-integral-derivative (PID) type and achieves pole placement. This controller reduces any initial tracking error to zero as desired and also ensures that robust steady-state tracking of step-plus-exponential trajectories is achieved by the joint angles. The two controllers are independent of each other and are designed separately based on the linearized robot model and then integrated in the overall control scheme. The proposed scheme is simple and can be implemented for real-time control of robot manipulators.
Multivariate temporal dictionary learning for EEG.
Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I
2013-04-30
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.
Multivariate Approaches to Classification in Extragalactic Astronomy
NASA Astrophysics Data System (ADS)
Fraix-Burnet, Didier; Thuillard, Marc; Chattopadhyay, Asis Kumar
2015-08-01
Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.
The Evolution of Multivariate Maternal Effects
Kuijper, Bram; Johnstone, Rufus A.; Townley, Stuart
2014-01-01
There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations. PMID:24722346
Multivariable PID Controller For Robotic Manipulator
NASA Technical Reports Server (NTRS)
Seraji, Homayoun; Tarokh, Mahmoud
1990-01-01
Gains updated during operation to cope with changes in characteristics and loads. Conceptual multivariable controller for robotic manipulator includes proportional/derivative (PD) controller in inner feedback loop, and proportional/integral/derivative (PID) controller in outer feedback loop. PD controller places poles of transfer function (in Laplace-transform space) of control system for linearized mathematical model of dynamics of robot. PID controller tracks trajectory and decouples input and output.
Preliminary Multivariable Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored
Multivariable root loci on the real axis
NASA Technical Reports Server (NTRS)
Yagle, A. E.; Levy, B. C.
1982-01-01
Some methods for determining the number of branches of multivariable root loci which are located on the real axis at a given point are obtained by using frequency domain methods. An equation for the number of branches is given for the general case, and simpler results for the special cases when the transfer function G(s) has size 2 x 2, and when G(s) is symmetric, are also presented.
Multi-Variable Analysis and Design Techniques.
1981-09-01
by A.G.J.MacFarlane 2 MULTIVARIABLE DESIGN TECHNIQUES BASED ON SINGULAR VALUE GENERALIZATIONS OF CLASSICAL CONTROL by J.C. Doyle 3 LIMITATIONS ON...prototypes to complex mathematical representations. All of these assemblages of information or information generators can loosely be termed "models...non linearities (e.g., control saturation) I neglect of high frequency dynamics. T hese approximations are well understood and in general their impact
NASA Metrology and Calibration, 1980
NASA Technical Reports Server (NTRS)
1981-01-01
The proceedings of the fourth annual NASA Metrology and Calibration Workshop are presented. This workshop covered (1) review and assessment of NASA metrology and calibration activities by NASA Headquarters, (2) results of audits by the Office of Inspector General, (3) review of a proposed NASA Equipment Management System, (4) current and planned field center activities, (5) National Bureau of Standards (NBS) calibration services for NASA, (6) review of NBS's Precision Measurement and Test Equipment Project activities, (7) NASA instrument loan pool operations at two centers, (8) mobile cart calibration systems at two centers, (9) calibration intervals and decals, (10) NASA Calibration Capabilities Catalog, and (11) development of plans and objectives for FY 1981. Several papers in this proceedings are slide presentations only.
Systematic Calibration for a Backpacked Spherical Photogrammetry Imaging System
NASA Astrophysics Data System (ADS)
Rau, J. Y.; Su, B. W.; Hsiao, K. W.; Jhan, J. P.
2016-06-01
A spherical camera can observe the environment for almost 720 degrees' field of view in one shoot, which is useful for augmented reality, environment documentation, or mobile mapping applications. This paper aims to develop a spherical photogrammetry imaging system for the purpose of 3D measurement through a backpacked mobile mapping system (MMS). The used equipment contains a Ladybug-5 spherical camera, a tactical grade positioning and orientation system (POS), i.e. SPAN-CPT, and an odometer, etc. This research aims to directly apply photogrammetric space intersection technique for 3D mapping from a spherical image stereo-pair. For this purpose, several systematic calibration procedures are required, including lens distortion calibration, relative orientation calibration, boresight calibration for direct georeferencing, and spherical image calibration. The lens distortion is serious on the ladybug-5 camera's original 6 images. Meanwhile, for spherical image mosaicking from these original 6 images, we propose the use of their relative orientation and correct their lens distortion at the same time. However, the constructed spherical image still contains systematic error, which will reduce the 3D measurement accuracy. Later for direct georeferencing purpose, we need to establish a ground control field for boresight/lever-arm calibration. Then, we can apply the calibrated parameters to obtain the exterior orientation parameters (EOPs) of all spherical images. In the end, the 3D positioning accuracy after space intersection will be evaluated, including EOPs obtained by structure from motion method.
Transmission electron microscope calibration methods for critical dimension standards
NASA Astrophysics Data System (ADS)
Orji, Ndubuisi G.; Dixson, Ronald G.; Garcia-Gutierrez, Domingo I.; Bunday, Benjamin D.; Bishop, Michael; Cresswell, Michael W.; Allen, Richard A.; Allgair, John A.
2016-10-01
One of the key challenges in critical dimension (CD) metrology is finding suitable dimensional calibration standards. The transmission electron microscope (TEM), which produces lattice-resolved images having scale traceability to the SI (International System of Units) definition of length through an atomic lattice constant, has gained wide usage in different areas of CD calibration. One such area is critical dimension atomic force microscope (CD-AFM) tip width calibration. To properly calibrate CD-AFM tip widths, errors in the calibration process must be quantified. Although the use of TEM for CD-AFM tip width calibration has been around for about a decade, there is still confusion on what should be considered in the uncertainty analysis. We characterized CD-AFM tip-width samples using high-resolution TEM and high angle annular dark field scanning TEM and two CD-AFMs that are implemented as reference measurement systems. The results are used to outline how to develop a rigorous uncertainty estimate for TEM/CD-AFM calibration, and to compare how information from the two electron microscopy modes are applied to practical CD-AFM measurements. The results also represent a separate validation of previous TEM/CD-AFM calibration. Excellent agreement was observed.
Compressive tracking with incremental multivariate Gaussian distribution
NASA Astrophysics Data System (ADS)
Li, Dongdong; Wen, Gongjian; Zhu, Gao; Zeng, Qiaoling
2016-09-01
Various approaches have been proposed for robust visual tracking, among which compressive tracking (CT) yields promising performance. In CT, Haar-like features are efficiently extracted with a very sparse measurement matrix and modeled as an online updated naïve Bayes classifier to account for target appearance change. The naïve Bayes classifier ignores overlap between Haar-like features and assumes that Haar-like features are independently distributed, which leads to drift in complex scenario. To address this problem, we present an extended CT algorithm, which assumes that all Haar-like features are correlated with each other and have multivariate Gaussian distribution. The mean vector and covariance matrix of multivariate normal distribution are incrementally updated with constant computational complexity to adapt to target appearance change. Each frame is associated with a temporal weight to expend less modeling power on old observation. Based on temporal weight, an update scheme with changing but convergent learning rate is derived with strict mathematic proof. Compared with CT, our extended algorithm achieves a richer representation of target appearance. The incremental multivariate Gaussian distribution is integrated into the particle filter framework to achieve better tracking performance. Extensive experiments on the CVPR2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of-the-art trackers.
Control of wastewater using multivariate control chart
NASA Astrophysics Data System (ADS)
Nugraha, Jaka; Fatimah, Is; Prabowo, Rino Galang
2017-03-01
Wastewater treatment is a crucial process in industry cause untreated or improper treatment of wastewater may leads some problems affecting to the other parts of environmental aspects. For many kinds of wastewater treatments, the parameters of Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and the Total Suspend Solid (TSS) are usual parameters to be controlled as a standard. In this paper, the application of multivariate Hotteling T2 Individual was reported to control wastewater treatment. By using wastewater treatment data from PT. ICBP, east Java branch, while the fulfillment of quality standards are based on East Java Governor Regulation No. 72 Year 2013 on Standards of Quality of Waste Water Industry and / or Other Business Activities. The obtained results are COD and TSS has a correlation with BOD values with the correlation coefficient higher than 50%, and it is is also found that influence of the COD and TSS to BOD values are 82% and 1.9% respectively. Based on Multivariate control chart Individual T2 Hotteling, it is found that BOD-COD and BOD-TSS are each one subgroup that are outside the control limits. Thus, it can be said there is a process that is not multivariate controlled, but univariately the variables of BOD, COD and TSS are within specification (standard quality) that has been determined.
Calibration Designs for Non-Monolithic Wind Tunnel Force Balances
NASA Technical Reports Server (NTRS)
Johnson, Thomas H.; Parker, Peter A.; Landman, Drew
2010-01-01
This research paper investigates current experimental designs and regression models for calibrating internal wind tunnel force balances of non-monolithic design. Such calibration methods are necessary for this class of balance because it has an electrical response that is dependent upon the sign of the applied forces and moments. This dependency gives rise to discontinuities in the response surfaces that are not easily modeled using traditional response surface methodologies. An analysis of current recommended calibration models is shown to lead to correlated response model terms. Alternative modeling methods are explored which feature orthogonal or near-orthogonal terms.
Internal Water Vapor Photoacoustic Calibration
NASA Technical Reports Server (NTRS)
Pilgrim, Jeffrey S.
2009-01-01
Water vapor absorption is ubiquitous in the infrared wavelength range where photoacoustic trace gas detectors operate. This technique allows for discontinuous wavelength tuning by temperature-jumping a laser diode from one range to another within a time span suitable for photoacoustic calibration. The use of an internal calibration eliminates the need for external calibrated reference gases. Commercial applications include an improvement of photoacoustic spectrometers in all fields of use.
Neural networks for calibration tomography
NASA Technical Reports Server (NTRS)
Decker, Arthur
1993-01-01
Artificial neural networks are suitable for performing pattern-to-pattern calibrations. These calibrations are potentially useful for facilities operations in aeronautics, the control of optical alignment, and the like. Computed tomography is compared with neural net calibration tomography for estimating density from its x-ray transform. X-ray transforms are measured, for example, in diffuse-illumination, holographic interferometry of fluids. Computed tomography and neural net calibration tomography are shown to have comparable performance for a 10 degree viewing cone and 29 interferograms within that cone. The system of tomography discussed is proposed as a relevant test of neural networks and other parallel processors intended for using flow visualization data.
Radiance calibration of spherical integrators
NASA Technical Reports Server (NTRS)
Mclean, James T.; Guenther, Bruce W.
1989-01-01
Techniques for improving the knowledge of the radiance of large area spherical and hemispherical integrating energy sources have been investigated. Such sources are used to calibrate numerous aircraft and spacecraft remote sensing instruments. Comparisons are made between using a standard source based calibration method and a quantum efficient detector (QED) based calibration method. The uncertainty involved in transferring the calibrated values of the point source standard lamp to the extended source is estimated to be 5 to 10 percent. The use of the QED allows an improvement in the uncertainty to 1 to 2 percent for the measurement of absolute radiance from a spherical integrator source.
Atmospheric optical calibration system
Hulstrom, R.L.; Cannon, T.W.
1988-10-25
An atmospheric optical calibration system is provided to compare actual atmospheric optical conditions to standard atmospheric optical conditions on the basis of aerosol optical depth, relative air mass, and diffuse horizontal skylight to global horizontal photon flux ratio. An indicator can show the extent to which the actual conditions vary from standard conditions. Aerosol scattering and absorption properties, diffuse horizontal skylight to global horizontal photon flux ratio, and precipitable water vapor determined on a real-time basis for optical and pressure measurements are also used to generate a computer spectral model and for correcting actual performance response of a photovoltaic device to standard atmospheric optical condition response on a real-time basis as the device is being tested in actual outdoor conditions. 7 figs.
Atmospheric optical calibration system
Hulstrom, Roland L.; Cannon, Theodore W.
1988-01-01
An atmospheric optical calibration system is provided to compare actual atmospheric optical conditions to standard atmospheric optical conditions on the basis of aerosol optical depth, relative air mass, and diffuse horizontal skylight to global horizontal photon flux ratio. An indicator can show the extent to which the actual conditions vary from standard conditions. Aerosol scattering and absorption properties, diffuse horizontal skylight to global horizontal photon flux ratio, and precipitable water vapor determined on a real-time basis for optical and pressure measurements are also used to generate a computer spectral model and for correcting actual performance response of a photovoltaic device to standard atmospheric optical condition response on a real-time basis as the device is being tested in actual outdoor conditions.
Inspection system calibration methods
Deason, Vance A.; Telschow, Kenneth L.
2004-12-28
An inspection system calibration method includes producing two sideband signals of a first wavefront; interfering the two sideband signals in a photorefractive material, producing an output signal therefrom having a frequency and a magnitude; and producing a phase modulated operational signal having a frequency different from the output signal frequency, a magnitude, and a phase modulation amplitude. The method includes determining a ratio of the operational signal magnitude to the output signal magnitude, determining a ratio of a 1st order Bessel function of the operational signal phase modulation amplitude to a 0th order Bessel function of the operational signal phase modulation amplitude, and comparing the magnitude ratio to the Bessel function ratio.
Quality Management and Calibration
NASA Astrophysics Data System (ADS)
Merkus, Henk G.
Good specification of a product’s performance requires adequate characterization of relevant properties. Particulate products are usually characterized by some PSD, shape or porosity parameter(s). For proper characterization, adequate sampling, dispersion, and measurement procedures should be available or developed and skilful personnel should use appropriate, well-calibrated/qualified equipment. The characterization should be executed, in agreement with customers, in a wellorganized laboratory. All related aspects should be laid down in a quality handbook. The laboratory should provide proof for its capability to perform the characterization of stated products and/or reference materials within stated confidence limits. This can be done either by internal validation and audits or by external GLP accreditation.
NASA Astrophysics Data System (ADS)
Boué-Bigne, Fabienne
2016-05-01
Laser induced breakdown spectroscopy (LIBS) scanning measurements can generally be used to detect the presence of non-metallic inclusions in steel samples. However, the inexistence of appropriate standards to calibrate the LIBS instrument signal means that its application is limited to identifying simple diatomic inclusions and inclusions that are chemically fully distinct from one another. Oxide inclusions in steel products have varied and complex chemical content, with an approximate size of interest of 1 μm. Several oxide inclusions types have chemical elements in common, but it is the concentration of these elements that makes an inclusion type have little or, on the contrary, deleterious impact on the final steel product quality. During the LIBS measurement of such inclusions, the spectroscopic signal is influenced not only by the inclusions' chemical concentrations but also by their varying size and associated laser ablation matrix effects. To address the complexity of calibrating the LIBS instrument signal for identifying such inclusion species, a new approach was developed where a calibration dataset was created, combining the elemental concentrations of typical oxide inclusions with the associated LIBS signal, in order to define a multivariate discriminant function capable of identifying oxide inclusions from LIBS data obtained from the measurement of unknown samples. The new method was applied to a variety of steel product samples. Inclusions populations consisting of mixtures of several complex oxides, with overlapping chemical content and size ranging typically from 1 to 5 μm, were identified and correlated well with validation data. The ability to identify complex inclusion types from LIBS data could open the way to new applications as, for a given sample area, the LIBS measurement is performed in a fraction of the time required by scanning electron microscopy, which is the conventional technique used for inclusion characterisation in steel
Multivariate Multi-Objective Allocation in Stratified Random Sampling: A Game Theoretic Approach
Hussain, Ijaz; Shoukry, Alaa Mohamd
2016-01-01
We consider the problem of multivariate multi-objective allocation where no or limited information is available within the stratum variance. Results show that a game theoretic approach (based on weighted goal programming) can be applied to sample size allocation problems. We use simulation technique to determine payoff matrix and to solve a minimax game. PMID:27936039
MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)
Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...
Mixture of normal distributions in multivariate null intercept measurement error model.
Aoki, Reiko; Pinto Júnior, Dorival Leão; Achcar, Jorge Alberto; Bolfarine, Heleno
2006-01-01
In this paper we propose the use of a multivariate null intercept measurement error model, where the true unobserved value of the covariate follows a mixture of two normal distributions. The proposed model is applied to a dental clinical trial presented in Hadgu and Koch (1999). A Bayesian approach is considered and a Gibbs Sampler is used to perform the computations.
A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times
ERIC Educational Resources Information Center
Jackson, Dan; Rollins, Katie; Coughlin, Patrick
2014-01-01
Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…
Parameter Estimation under Constraints for Multivariate Normal Distributions with Incomplete Data.
ERIC Educational Resources Information Center
Zoppe, Alice; Buu, Yuh-Pey Anne; Flury, Bernard
2001-01-01
Presents an application of the EM-algorithm to two problems of estimation and testing in a multivariate normal distribution with missing data. The two models are tested applying the log-likelihood ratio test. Solves the problem of different and nonmonotone patterns of missing data by introducing suitable transformations and partitions of the data…
Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.
ERIC Educational Resources Information Center
Molenaar, Peter C. M.; Nesselroade, John R.
2001-01-01
Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…
NASA Astrophysics Data System (ADS)
Bijl, Piet; Reynolds, Joseph P.; Vos, Wouter K.; Hogervorst, Maarten A.; Fanning, Jonathan D.
2011-05-01
The TTP (Targeting Task Performance) metric, developed at NVESD, is the current standard US Army model to predict EO/IR Target Acquisition performance. This model however does not have a corresponding lab or field test to empirically assess the performance of a camera system. The TOD (Triangle Orientation Discrimination) method, developed at TNO in The Netherlands, provides such a measurement. In this study, we make a direct comparison between TOD performance for a range of sensors and the extensive historical US observer performance database built to develop and calibrate the TTP metric. The US perception data were collected doing an identification task by military personnel on a standard 12 target, 12 aspect tactical vehicle image set that was processed through simulated sensors for which the most fundamental sensor parameters such as blur, sampling, spatial and temporal noise were varied. In the present study, we measured TOD sensor performance using exactly the same sensors processing a set of TOD triangle test patterns. The study shows that good overall agreement is obtained when the ratio between target characteristic size and TOD test pattern size at threshold equals 6.3. Note that this number is purely based on empirical data without any intermediate modeling. The calibration of the TOD to the TTP is highly beneficial to the sensor modeling and testing community for a variety of reasons. These include: i) a connection between requirement specification and acceptance testing, and ii) a very efficient method to quickly validate or extend the TTP range prediction model to new systems and tasks.
Optimal model-free prediction from multivariate time series
NASA Astrophysics Data System (ADS)
Runge, Jakob; Donner, Reik V.; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
Diagonal dominance for the multivariable Nyquist array using function minimization
NASA Technical Reports Server (NTRS)
Leininger, G. G.
1977-01-01
A new technique for the design of multivariable control systems using the multivariable Nyquist array method was developed. A conjugate direction function minimization algorithm is utilized to achieve a diagonal dominant condition over the extended frequency range of the control system. The minimization is performed on the ratio of the moduli of the off-diagonal terms to the moduli of the diagonal terms of either the inverse or direct open loop transfer function matrix. Several new feedback design concepts were also developed, including: (1) dominance control parameters for each control loop; (2) compensator normalization to evaluate open loop conditions for alternative design configurations; and (3) an interaction index to determine the degree and type of system interaction when all feedback loops are closed simultaneously. This new design capability was implemented on an IBM 360/75 in a batch mode but can be easily adapted to an interactive computer facility. The method was applied to the Pratt and Whitney F100 turbofan engine.
Escaping the curse of dimensionality in estimating multivariate transfer entropy.
Runge, Jakob; Heitzig, Jobst; Petoukhov, Vladimir; Kurths, Jürgen
2012-06-22
Multivariate transfer entropy (TE) is a model-free approach to detect causalities in multivariate time series. It is able to distinguish direct from indirect causality and common drivers without assuming any underlying model. But despite these advantages it has mostly been applied in a bivariate setting as it is hard to estimate reliably in high dimensions since its definition involves infinite vectors. To overcome this limitation, we propose to embed TE into the framework of graphical models and present a formula that decomposes TE into a sum of finite-dimensional contributions that we call decomposed transfer entropy. Graphical models further provide a richer picture because they also yield the causal coupling delays. To estimate the graphical model we suggest an iterative algorithm, a modified version of the PC-algorithm with a very low estimation dimension. We present an appropriate significance test and demonstrate the method's performance using examples of nonlinear stochastic delay-differential equations and observational climate data (sea level pressure).
A Method for Comparing Multivariate Time Series with Different Dimensions
Tapinos, Avraam; Mendes, Pedro
2013-01-01
In many situations it is desirable to compare dynamical systems based on their behavior. Similarity of behavior often implies similarity of internal mechanisms or dependency on common extrinsic factors. While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. Yet, comparison of multivariate time series has been limited to cases where they share a common dimensionality. A semi-metric is a distance function that has the properties of non-negativity, symmetry and reflexivity, but not sub-additivity. Here we develop a semi-metric – SMETS – that can be used for comparing groups of time series that may have different dimensions. To demonstrate its utility, the method is applied to dynamic models of biochemical networks and to portfolios of shares. The former is an example of a case where the dependencies between system variables are known, while in the latter the system is treated (and behaves) as a black box. PMID:23393554
Optimal model-free prediction from multivariate time series.
Runge, Jakob; Donner, Reik V; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
Fraud detection in medicare claims: A multivariate outlier detection approach
Burr, T.; Hale, C.; Kantor, M.
1997-04-01
We apply traditional and customized multivariate outlier detection methods to detect fraud in medicare claims. We use two sets of 11 derived features, and one set of the 22 combined features. The features are defined so that fraudulent medicare providers should tend to have larger features values than non-fraudulent providers. Therefore we have an apriori direction ({open_quotes}large values{close_quotes}) in high dimensional feature space to search for the multivariate outliers. We focus on three issues: (1) outlier masking (Example: the presence of one outlier can make it difficult to detect a second outlier), (2) the impact of having an apriori direction to search for fraud, and (3) how to compare our detection methods. Traditional methods include Mahalanobis distances, (with and without dimension reduction), k-nearest neighbor, and density estimation methods. Some methods attempt to mitigate the outlier masking problem (for example: minimum volume ellipsoid covariance estimator). Customized methods include ranking methods (such as Spearman rank ordering) that exploit the {open_quotes}large is suspicious{close_quotes} notion. No two methods agree completely which providers are most suspicious so we present ways to compare our methods. One comparison method uses a list of known-fraudulent providers. All comparison methods restrict attention to the most suspicious providers.
Nonlinear independent component analysis and multivariate time series analysis
NASA Astrophysics Data System (ADS)
Storck, Jan; Deco, Gustavo
1997-02-01
We derive an information-theory-based unsupervised learning paradigm for nonlinear independent component analysis (NICA) with neural networks. We demonstrate that under the constraint of bounded and invertible output transfer functions the two main goals of unsupervised learning, redundancy reduction and maximization of the transmitted information between input and output (Infomax-principle), are equivalent. No assumptions are made concerning the kind of input and output distributions, i.e. the kind of nonlinearity of correlations. An adapted version of the general NICA network is used for the modeling of multivariate time series by unsupervised learning. Given time series of various observables of a dynamical system, our net learns their evolution in time by extracting statistical dependencies between past and present elements of the time series. Multivariate modeling is obtained by making present value of each time series statistically independent not only from their own past but also from the past of the other series. Therefore, in contrast to univariate methods, the information lying in the couplings between the observables is also used and a detection of higher-order cross correlations is possible. We apply our method to time series of the two-dimensional Hénon map and to experimental time series obtained from the measurements of axial velocities in different locations in weakly turbulent Taylor-Couette flow.
Multivariate analysis of heavy metals concentrations in river estuary.
Alkarkhi, Abbas F M; Ahmad, Anees; Ismail, Norli; Easa, Azhar Mat
2008-08-01
Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.
A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.
Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz
2016-01-01
Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.
Mark-specific hazard ratio model with missing multivariate marks.
Juraska, Michal; Gilbert, Peter B
2016-10-01
An objective of randomized placebo-controlled preventive HIV vaccine efficacy (VE) trials is to assess the relationship between vaccine effects to prevent HIV acquisition and continuous genetic distances of the exposing HIVs to multiple HIV strains represented in the vaccine. The set of genetic distances, only observed in failures, is collectively termed the 'mark.' The objective has motivated a recent study of a multivariate mark-specific hazard ratio model in the competing risks failure time analysis framework. Marks of interest, however, are commonly subject to substantial missingness, largely due to rapid post-acquisition viral evolution. In this article, we investigate the mark-specific hazard ratio model with missing multivariate marks and develop two inferential procedures based on (i) inverse probability weighting (IPW) of the complete cases, and (ii) augmentation of the IPW estimating functions by leveraging auxiliary data predictive of the mark. Asymptotic properties and finite-sample performance of the inferential procedures are presented. This research also provides general inferential methods for semiparametric density ratio/biased sampling models with missing data. We apply the developed procedures to data from the HVTN 502 'Step' HIV VE trial.
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
NASA Technical Reports Server (NTRS)
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
New NIR Calibration Models Speed Biomass Composition and Reactivity Characterization
2015-09-01
Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. This highlight describes NREL's work to use near-infrared (NIR) spectroscopy and partial least squares multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. This highlight is being developed for the September 2015 Alliance S&T Board meeting.
A new polarimetric active radar calibrator and calibration technique
NASA Astrophysics Data System (ADS)
Tang, Jianguo; Xu, Xiaojian
2015-10-01
Polarimetric active radar calibrator (PARC) is one of the most important calibrators with high radar cross section (RCS) for polarimetry measurement. In this paper, a new double-antenna polarimetric active radar calibrator (DPARC) is proposed, which consists of two rotatable antennas with wideband electromagnetic polarization filters (EMPF) to achieve lower cross-polarization for transmission and reception. With two antennas which are rotatable around the radar line of sight (LOS), the DPARC provides a variety of standard polarimetric scattering matrices (PSM) through the rotation combination of receiving and transmitting polarization, which are useful for polarimatric calibration in different applications. In addition, a technique based on Fourier analysis is proposed for calibration processing. Numerical simulation results are presented to demonstrate the superior performance of the proposed DPARC and processing technique.
Self Calibrated Wireless Distributed Environmental Sensory Networks
Fishbain, Barak; Moreno-Centeno, Erick
2016-01-01
Recent advances in sensory and communication technologies have made Wireless Distributed Environmental Sensory Networks (WDESN) technically and economically feasible. WDESNs present an unprecedented tool for studying many environmental processes in a new way. However, the WDESNs’ calibration process is a major obstacle in them becoming the common practice. Here, we present a new, robust and efficient method for aggregating measurements acquired by an uncalibrated WDESN, and producing accurate estimates of the observed environmental variable’s true levels rendering the network as self-calibrated. The suggested method presents novelty both in group-decision-making and in environmental sensing as it offers a most valuable tool for distributed environmental monitoring data aggregation. Applying the method on an extensive real-life air-pollution dataset showed markedly more accurate results than the common practice and the state-of-the-art. PMID:27098279
Self Calibrated Wireless Distributed Environmental Sensory Networks.
Fishbain, Barak; Moreno-Centeno, Erick
2016-04-21
Recent advances in sensory and communication technologies have made Wireless Distributed Environmental Sensory Networks (WDESN) technically and economically feasible. WDESNs present an unprecedented tool for studying many environmental processes in a new way. However, the WDESNs' calibration process is a major obstacle in them becoming the common practice. Here, we present a new, robust and efficient method for aggregating measurements acquired by an uncalibrated WDESN, and producing accurate estimates of the observed environmental variable's true levels rendering the network as self-calibrated. The suggested method presents novelty both in group-decision-making and in environmental sensing as it offers a most valuable tool for distributed environmental monitoring data aggregation. Applying the method on an extensive real-life air-pollution dataset showed markedly more accurate results than the common practice and the state-of-the-art.
Calibration aspects of the JEM-EUSO mission
NASA Astrophysics Data System (ADS)
Adams, J. H.; Ahmad, S.; Albert, J.-N.; Allard, D.; Anchordoqui, L.; Andreev, V.; Anzalone, A.; Arai, Y.; Asano, K.; Ave Pernas, M.; Baragatti, P.; Barrillon, P.; Batsch, T.; Bayer, J.; Bechini, R.; Belenguer, T.; Bellotti, R.; Belov, K.; Berlind, A. A.; Bertaina, M.; Biermann, P. L.; Biktemerova, S.; Blaksley, C.; Blanc, N.; Błȩcki, J.; Blin-Bondil, S.; Blümer, J.; Bobik, P.; Bogomilov, M.; Bonamente, M.; Briggs, M. S.; Briz, S.; Bruno, A.; Cafagna, F.; Campana, D.; Capdevielle, J.-N.; Caruso, R.; Casolino, M.; Cassardo, C.; Castellinic, G.; Catalano, C.; Catalano, G.; Cellino, A.; Chikawa, M.; Christl, M. J.; Cline, D.; Connaughton, V.; Conti, L.; Cordero, G.; Crawford, H. J.; Cremonini, R.; Csorna, S.; Dagoret-Campagne, S.; de Castro, A. J.; De Donato, C.; de la Taille, C.; De Santis, C.; del Peral, L.; Dell'Oro, A.; De Simone, N.; Di Martino, M.; Distratis, G.; Dulucq, F.; Dupieux, M.; Ebersoldt, A.; Ebisuzaki, T.; Engel, R.; Falk, S.; Fang, K.; Fenu, F.; Fernández-Gómez, I.; Ferrarese, S.; Finco, D.; Flamini, M.; Fornaro, C.; Franceschi, A.; Fujimoto, J.; Fukushima, M.; Galeotti, P.; Garipov, G.; Geary, J.; Gelmini, G.; Giraudo, G.; Gonchar, M.; González Alvarado, C.; Gorodetzky, P.; Guarino, F.; Guzmán, A.; Hachisu, Y.; Harlov, B.; Haungs, A.; Hernández Carretero, J.; Higashide, K.; Ikeda, D.; Ikeda, H.; Inoue, N.; Inoue, S.; Insolia, A.; Isgrò, F.; Itow, Y.; Joven, E.; Judd, E. G.; Jung, A.; Kajino, F.; Kajino, T.; Kaneko, I.; Karadzhov, Y.; Karczmarczyk, J.; Karus, M.; Katahira, K.; Kawai, K.; Kawasaki, Y.; Keilhauer, B.; Khrenov, B. A.; Kim, J.-S.; Kim, S.-W.; Kim, S.-W.; Kleifges, M.; Klimov, P. A.; Kolev, D.; Kreykenbohm, I.; Kudela, K.; Kurihara, Y.; Kusenko, A.; Kuznetsov, E.; Lacombe, M.; Lachaud, C.; Lee, J.; Licandro, J.; Lim, H.; López, F.; Maccarone, M. C.; Mannheim, K.; Maravilla, D.; Marcelli, L.; Marini, A.; Martinez, O.; Masciantonio, G.; Mase, K.; Matev, R.; Medina-Tanco, G.; Mernik, T.; Miyamoto, H.; Miyazaki, Y.; Mizumoto, Y.; Modestino, G.; Monaco, A.; Monnier-Ragaigne, D.; Morales de los Ríos, J. A.; Moretto, C.; Morozenko, V. S.; Mot, B.; Murakami, T.; Murakami, M. Nagano; Nagata, M.; Nagataki, S.; Nakamura, T.; Napolitano, T.; Naumov, D.; Nava, R.; Neronov, A.; Nomoto, K.; Nonaka, T.; Ogawa, T.; Ogio, S.; Ohmori, H.; Olinto, A. V.; Orleański, P.; Osteria, G.; Panasyuk, M. I.; Parizot, E.; Park, I. H.; Park, H. W.; Pastircak, B.; Patzak, T.; Paul, T.; Pennypacker, C.; Perez Cano, S.; Peter, T.; Picozza, P.; Pierog, T.; Piotrowski, L. W.; Piraino, S.; Plebaniak, Z.; Pollini, A.; Prat, P.; Prévôt, G.; Prieto, H.; Putis, M.; Reardon, P.; Reyes, M.; Ricci, M.; Rodríguez, I.; Rodríguez Frías, M. D.; Ronga, F.; Roth, M.; Rothkaehl, H.; Roudil, G.; Rusinov, I.; Rybczyński, M.; Sabau, M. D.; Sáez-Cano, G.; Sagawa, H.; Saito, A.; Sakaki, N.; Sakata, M.; Salazar, H.; Sánchez, S.; Santangelo, A.; Santiago Crúz, L.; Sanz Palomino, M.; Saprykin, O.; Sarazin, F.; Sato, H.; Sato, M.; Schanz, T.; Schieler, H.; Scotti, V.; Segreto, A.; Selmane, S.; Semikoz, D.; Serra, M.; Sharakin, S.; Shibata, T.; Shimizu, H. M.; Shinozaki, K.; Shirahama, T.; Siemieniec-Oziȩbło, G.; Silva López, H. H.; Sledd, J.; Słomińska, K.; Sobey, A.; Sugiyama, T.; Supanitsky, D.; Suzuki, M.; Szabelska, B.; Szabelski, J.; Tajima, F.; Tajima, N.; Tajima, T.; Takahashi, Y.; Takami, H.; Takeda, M.; Takizawa, Y.; Tenzer, C.; Tibolla, O.; Tkachev, L.; Tokuno, H.; Tomida, T.; Tone, N.; Toscano, S.; Trillaud, F.; Tsenov, R.; Tsunesada, Y.; Tsuno, K.; Tymieniecka, T.; Uchihori, Y.; Unger, M.; Vaduvescu, O.; Valdés-Galicia, J. F.; Vallania, P.; Valore, L.; Vankova, G.; Vigorito, C.; Villaseñor, L.; von Ballmoos, P.; Wada, S.; Watanabe, J.; Watanabe, S.; Watts, J.; Weber, M.; Weiler, T. J.; Wibig, T.; Wiencke, L.; Wille, M.; Wilms, J.; Włodarczyk, Z.; Yamamoto, T.; Yamamoto, Y.; Yang, J.; Yano, H.; Yashin, I. V.; Yonetoku, D.; Yoshida, K.; Yoshida, S.; Young, R.; Zotov, M. Yu.; Zuccaro Marchi, A.
2015-11-01
The JEM-EUSO telescope will be, after calibration, a very accurate instrument which yields the number of received photons from the number of measured photo-electrons. The project is in phase A (demonstration of the concept) including already operating prototype instruments, i.e. many parts of the instrument have been constructed and tested. Calibration is a crucial part of the instrument and its use. The focal surface (FS) of the JEM-EUSO telescope will consist of about 5000 photo-multiplier tubes (PMTs), which have to be well calibrated to reach the required accuracy in reconstructing the air-shower parameters. The optics system consists of 3 plastic Fresnel (double-sided) lenses of 2.5 m diameter. The aim of the calibration system is to measure the efficiencies (transmittances) of the optics and absolute efficiencies of the entire focal surface detector. The system consists of 3 main components: (i) Pre-flight calibration devices on ground, where the efficiency and gain of the PMTs will be measured absolutely and also the transmittance of the optics will be. (ii) On-board relative calibration system applying two methods: a) operating during the day when the JEM-EUSO lid will be closed with small light sources on board. b) operating during the night, together with data taking: the monitoring of the background rate over identical sites. (iii) Absolute in-flight calibration, again, applying two methods: a) measurement of the moon light, reflected on high altitude, high albedo clouds. b) measurements of calibrated flashes and tracks produced by the Global Light System (GLS). Some details of each calibration method will be described in this paper.
A multivariate analysis approach for the Imaging Atmospheric Cerenkov Telescopes System H.E.S.S
Dubois, F.; Lamanna, G.
2008-12-24
We present a multivariate classification approach applied to the analysis of data from the H.E.S.S. Very High Energy (VHE){gamma}-ray IACT stereoscopic system. This approach combines three complementary analysis methods already successfully applied in the H.E.S.S. data analysis. The proposed approach, with the combined effective estimator X{sub eff}, is conceived to improve the signal-to-background ratio and therefore particularly relevant to the morphological studies of faint extended sources.
NASA Technical Reports Server (NTRS)
Doty, Keith L
1992-01-01
The author has formulated a new, general model for specifying the kinematic properties of serial manipulators. The new model kinematic parameters do not suffer discontinuities when nominally parallel adjacent axes deviate from exact parallelism. From this new theory the author develops a first-order, lumped-parameter, calibration-model for the ARID manipulator. Next, the author develops a calibration methodology for the ARID based on visual and acoustic sensing. A sensor platform, consisting of a camera and four sonars attached to the ARID end frame, performs calibration measurements. A calibration measurement consists of processing one visual frame of an accurately placed calibration image and recording four acoustic range measurements. A minimum of two measurement protocols determine the kinematics calibration-model of the ARID for a particular region: assuming the joint displacements are accurately measured, the calibration surface is planar, and the kinematic parameters do not vary rapidly in the region. No theoretical or practical limitations appear to contra-indicate the feasibility of the calibration method developed here.
Side looking radar calibration study
NASA Technical Reports Server (NTRS)
Edwards, W. D.
1975-01-01
Calibration of an airborne sidelooking radar is accomplished by the use of a model that relates the radar parameters to the physical mapping situation. Topics discussed include: characteristics of the transmitters; the antennas; target absorption and reradiation; the receiver and map making or radar data processing; and the calibration process.
Robotti, Elisa; Marengo, Emilio
2016-01-01
2-D gel electrophoresis usually provides complex maps characterized by a low reproducibility: this hampers the use of spot volume data for the identification of reliable biomarkers. Under these circumstances, effective and robust methods for the comparison and classification of 2-D maps are fundamental for the identification of an exhaustive panel of candidate biomarkers. Multivariate methods are the most suitable since they take into consideration the relationships between the variables, i.e., effects of synergy and antagonism between the spots. Here the most common multivariate methods used in spot volume datasets analysis are presented. The methods are applied on a sample dataset to prove their effectiveness.
Kotula, Paul G; Keenan, Michael R
2006-12-01
Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.
A Gaussian Copula Model for Multivariate Survival Data
Othus, Megan; Li, Yi
2011-01-01
We consider a Gaussian copula model for multivariate survival times. Estimation of the copula association parameter is easily implemented with existing software using a two-stage estimation procedure. Using the Gaussian copula, we are able to test whether the association parameter is equal to zero. When the association term is positive, the model can be extended to incorporate cluster-level frailty terms. Asymptotic properties are derived under the two-stage estimation scheme. Simulation studies verify finite sample utility. We apply the method to a Children’s Oncology Group multi-center study of acute lymphoblastic leukemia. The analysis estimates marginal treatment effects and examines potential clustering within treatment institution. PMID:22162742