The Analysis of Design of Robust Nonlinear Estimators and Robust Signal Coding Schemes.

DTIC Science & Technology

1982-09-16

b - )’/ 12. between uniform and nonuniform quantizers. For the nonuni- Proof: If b - acca then form quantizer we can expect the mean-square error to...in the window greater than or equal to the value at We define f7 ’(s) as the n-times filtered signal p + 1; consequently, point p + 1 is the median and

Optimal focal-plane restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1989-01-01

Image restoration can be implemented efficiently by calculating the convolution of the digital image and a small kernel during image acquisition. Processing the image in the focal-plane in this way requires less computation than traditional Fourier-transform-based techniques such as the Wiener filter and constrained least-squares filter. Here, the values of the convolution kernel that yield the restoration with minimum expected mean-square error are determined using a frequency analysis of the end-to-end imaging system. This development accounts for constraints on the size and shape of the spatial kernel and all the components of the imaging system. Simulation results indicate the technique is effective and efficient.

Using Least Squares for Error Propagation

ERIC Educational Resources Information Center

Tellinghuisen, Joel

2015-01-01

The method of least-squares (LS) has a built-in procedure for estimating the standard errors (SEs) of the adjustable parameters in the fit model: They are the square roots of the diagonal elements of the covariance matrix. This means that one can use least-squares to obtain numerical values of propagated errors by defining the target quantities as…

Two-body potential model based on cosine series expansion for ionic materials

DOE PAGES

Oda, Takuji; Weber, William J.; Tanigawa, Hisashi

2015-09-23

There is a method to construct a two-body potential model for ionic materials with a Fourier series basis and we examine it. For this method, the coefficients of cosine basis functions are uniquely determined by solving simultaneous linear equations to minimize the sum of weighted mean square errors in energy, force and stress, where first-principles calculation results are used as the reference data. As a validation test of the method, potential models for magnesium oxide are constructed. The mean square errors appropriately converge with respect to the truncation of the cosine series. This result mathematically indicates that the constructed potentialmore » model is sufficiently close to the one that is achieved with the non-truncated Fourier series and demonstrates that this potential virtually provides minimum error from the reference data within the two-body representation. The constructed potential models work appropriately in both molecular statics and dynamics simulations, especially if a two-step correction to revise errors expected in the reference data is performed, and the models clearly outperform two existing Buckingham potential models that were tested. Moreover, the good agreement over a broad range of energies and forces with first-principles calculations should enable the prediction of materials behavior away from equilibrium conditions, such as a system under irradiation.« less

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

PubMed

Davidov, Ori; Rosen, Sophia

2011-04-01

In medical studies, endpoints are often measured for each patient longitudinally. The mixed-effects model has been a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, in hearing loss studies, we expect hearing to deteriorate with time. This means that hearing thresholds which reflect hearing acuity will, on average, increase over time. Therefore, the regression coefficients associated with the mean effect of time on hearing ability will be constrained. Such constraints should be accounted for in the analysis. We propose maximum likelihood estimation procedures, based on the expectation-conditional maximization either algorithm, to estimate the parameters of the model while accounting for the constraints on them. The proposed methods improve, in terms of mean square error, on the unconstrained estimators. In some settings, the improvement may be substantial. Hypotheses testing procedures that incorporate the constraints are developed. Specifically, likelihood ratio, Wald, and score tests are proposed and investigated. Their empirical significance levels and power are studied using simulations. It is shown that incorporating the constraints improves the mean squared error of the estimates and the power of the tests. These improvements may be substantial. The methodology is used to analyze a hearing loss study.

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM.

PubMed

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

2016-10-14

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research.

Robust Transceiver Design for Multiuser MIMO Downlink with Channel Uncertainties

NASA Astrophysics Data System (ADS)

Miao, Wei; Li, Yunzhou; Chen, Xiang; Zhou, Shidong; Wang, Jing

This letter addresses the problem of robust transceiver design for the multiuser multiple-input-multiple-output (MIMO) downlink where the channel state information at the base station (BS) is imperfect. A stochastic approach which minimizes the expectation of the total mean square error (MSE) of the downlink conditioned on the channel estimates under a total transmit power constraint is adopted. The iterative algorithm reported in [2] is improved to handle the proposed robust optimization problem. Simulation results show that our proposed robust scheme effectively reduces the performance loss due to channel uncertainties and outperforms existing methods, especially when the channel errors of the users are different.

Analysis of the quality of image data acquired by the LANDSAT-4 Thematic Mapper and Multispectral Scanners

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1984-01-01

The geometric quality of TM film and digital products is evaluated by making selective photomeasurements and by measuring the coordinates of known features on both the TM products and map products. These paired observations are related using a standard linear least squares regression approach. Using regression equations and coefficients developed from 225 (TM film product) and 20 (TM digital product) control points, map coordinates of test points are predicted. The residual error vectors and analysis of variance (ANOVA) were performed on the east and north residual using nine image segments (blocks) as treatments. Based on the root mean square error of the 223 (TM film product) and 22 (TM digital product) test points, users of TM data expect the planimetric accuracy of mapped points to be within 91 meters and within 117 meters for the film products, and to be within 12 meters and within 14 meters for the digital products.

Demand Forecasting: An Evaluation of DODs Accuracy Metric and Navys Procedures

DTIC Science & Technology

2016-06-01

inventory management improvement plan, mean of absolute scaled error, lead time adjusted squared error, forecast accuracy, benchmarking, naïve method...Manager JASA Journal of the American Statistical Association LASE Lead-time Adjusted Squared Error LCI Life Cycle Indicator MA Moving Average MAE...Mean Squared Error xvi NAVSUP Naval Supply Systems Command NDAA National Defense Authorization Act NIIN National Individual Identification Number

Measurement System Characterization in the Presence of Measurement Errors

NASA Technical Reports Server (NTRS)

Commo, Sean A.

2012-01-01

In the calibration of a measurement system, data are collected in order to estimate a mathematical model between one or more factors of interest and a response. Ordinary least squares is a method employed to estimate the regression coefficients in the model. The method assumes that the factors are known without error; yet, it is implicitly known that the factors contain some uncertainty. In the literature, this uncertainty is known as measurement error. The measurement error affects both the estimates of the model coefficients and the prediction, or residual, errors. There are some methods, such as orthogonal least squares, that are employed in situations where measurement errors exist, but these methods do not directly incorporate the magnitude of the measurement errors. This research proposes a new method, known as modified least squares, that combines the principles of least squares with knowledge about the measurement errors. This knowledge is expressed in terms of the variance ratio - the ratio of response error variance to measurement error variance.

Two Enhancements of the Logarithmic Least-Squares Method for Analyzing Subjective Comparisons

DTIC Science & Technology

1989-03-25

error term. 1 For this model, the total sum of squares ( SSTO ), defined as n 2 SSTO = E (yi y) i=1 can be partitioned into error and regression sums...of the regression line around the mean value. Mathematically, for the model given by equation A.4, SSTO = SSE + SSR (A.6) A-4 where SSTO is the total...sum of squares (i.e., the variance of the yi’s), SSE is error sum of squares, and SSR is the regression sum of squares. SSTO , SSE, and SSR are given

Optimal estimation of the optomechanical coupling strength

NASA Astrophysics Data System (ADS)

Bernád, József Zsolt; Sanavio, Claudio; Xuereb, André

2018-06-01

We apply the formalism of quantum estimation theory to obtain information about the value of the nonlinear optomechanical coupling strength. In particular, we discuss the minimum mean-square error estimator and a quantum Cramér-Rao-type inequality for the estimation of the coupling strength. Our estimation strategy reveals some cases where quantum statistical inference is inconclusive and merely results in the reinforcement of prior expectations. We show that these situations also involve the highest expected information losses. We demonstrate that interaction times on the order of one time period of mechanical oscillations are the most suitable for our estimation scenario, and compare situations involving different photon and phonon excitations.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

[Can the scattering of differences from the target refraction be avoided?].

PubMed

Janknecht, P

2008-10-01

We wanted to check how the stochastic error is affected by two lens formulae. The power of the intraocular lens was calculated using the SRK-II formula and the Haigis formula after eye length measurement with ultrasound and the IOL Master. Both lens formulae were partially derived and Gauss error analysis was used for examination of the propagated error. 61 patients with a mean age of 73.8 years were analysed. The postoperative refraction differed from the calculated refraction after ultrasound biometry using the SRK-II formula by 0.05 D (-1.56 to + 1.31, S. D.: 0.59 D; 92 % within +/- 1.0 D), after IOL Master biometry using the SRK-II formula by -0.15 D (-1.18 to + 1.25, S. D.: 0.52 D; 97 % within +/- 1.0 D), and after IOL Master biometry using the Haigis formula by -0.11 D (-1.14 to + 1.14, S. D.: 0.48 D; 95 % within +/- 1.0 D). The results did not differ from one another. The propagated error of the Haigis formula can be calculated according to DeltaP = square root (deltaL x (-4.206))(2) + (deltaVK x 0.9496)(2) + (DeltaDC x (-1.4950))(2). (DeltaL: error measuring axial length, DeltaVK error measuring anterior chamber depth, DeltaDC error measuring corneal power), the propagated error of the SRK-II formula according to DeltaP = square root (DeltaL x (-2.5))(2) + (DeltaDC x (-0.9))(2). The propagated error of the Haigis formula is always larger than the propagated error of the SRK-II formula. Scattering of the postoperative difference from the expected refraction cannot be avoided completely. It is possible to limit the systematic error by developing complicated formulae like the Haigis formula. However, increasing the number of parameters which need to be measured increases the dispersion of the calculated postoperative refraction. A compromise has to be found, and therefore the SRK-II formula is not outdated.

AKLSQF - LEAST SQUARES CURVE FITTING

NASA Technical Reports Server (NTRS)

Kantak, A. V.

1994-01-01

The Least Squares Curve Fitting program, AKLSQF, computes the polynomial which will least square fit uniformly spaced data easily and efficiently. The program allows the user to specify the tolerable least squares error in the fitting or allows the user to specify the polynomial degree. In both cases AKLSQF returns the polynomial and the actual least squares fit error incurred in the operation. The data may be supplied to the routine either by direct keyboard entry or via a file. AKLSQF produces the least squares polynomial in two steps. First, the data points are least squares fitted using the orthogonal factorial polynomials. The result is then reduced to a regular polynomial using Sterling numbers of the first kind. If an error tolerance is specified, the program starts with a polynomial of degree 1 and computes the least squares fit error. The degree of the polynomial used for fitting is then increased successively until the error criterion specified by the user is met. At every step the polynomial as well as the least squares fitting error is printed to the screen. In general, the program can produce a curve fitting up to a 100 degree polynomial. All computations in the program are carried out under Double Precision format for real numbers and under long integer format for integers to provide the maximum accuracy possible. AKLSQF was written for an IBM PC X/AT or compatible using Microsoft's Quick Basic compiler. It has been implemented under DOS 3.2.1 using 23K of RAM. AKLSQF was developed in 1989.

An Empirical State Error Covariance Matrix for Batch State Estimation

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2011-01-01

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty. Also, in its most straight forward form, the technique only requires supplemental calculations to be added to existing batch algorithms. The generation of this direct, empirical form of the state error covariance matrix is independent of the dimensionality of the observations. Mixed degrees of freedom for an observation set are allowed. As is the case with any simple, empirical sample variance problems, the presented approach offers an opportunity (at least in the case of weighted least squares) to investigate confidence interval estimates for the error covariance matrix elements. The diagonal or variance terms of the error covariance matrix have a particularly simple form to associate with either a multiple degree of freedom chi-square distribution (more approximate) or with a gamma distribution (less approximate). The off diagonal or covariance terms of the matrix are less clear in their statistical behavior. However, the off diagonal covariance matrix elements still lend themselves to standard confidence interval error analysis. The distributional forms associated with the off diagonal terms are more varied and, perhaps, more approximate than those associated with the diagonal terms. Using a simple weighted least squares sample problem, results obtained through use of the proposed technique are presented. The example consists of a simple, two observer, triangulation problem with range only measurements. Variations of this problem reflect an ideal case (perfect knowledge of the range errors) and a mismodeled case (incorrect knowledge of the range errors).

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

Ordinary least squares regression is indicated for studies of allometry.

PubMed

Kilmer, J T; Rodríguez, R L

2017-01-01

When it comes to fitting simple allometric slopes through measurement data, evolutionary biologists have been torn between regression methods. On the one hand, there is the ordinary least squares (OLS) regression, which is commonly used across many disciplines of biology to fit lines through data, but which has a reputation for underestimating slopes when measurement error is present. On the other hand, there is the reduced major axis (RMA) regression, which is often recommended as a substitute for OLS regression in studies of allometry, but which has several weaknesses of its own. Here, we review statistical theory as it applies to evolutionary biology and studies of allometry. We point out that the concerns that arise from measurement error for OLS regression are small and straightforward to deal with, whereas RMA has several key properties that make it unfit for use in the field of allometry. The recommended approach for researchers interested in allometry is to use OLS regression on measurements taken with low (but realistically achievable) measurement error. If measurement error is unavoidable and relatively large, it is preferable to correct for slope attenuation rather than to turn to RMA regression, or to take the expected amount of attenuation into account when interpreting the data. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

The Relationship Between Technical Errors and Decision Making Skills in the Junior Resident

PubMed Central

Nathwani, J. N.; Fiers, R.M.; Ray, R.D.; Witt, A.K.; Law, K. E.; DiMarco, S.M.; Pugh, C.M.

2017-01-01

Objective The purpose of this study is to co-evaluate resident technical errors and decision-making capabilities during placement of a subclavian central venous catheter (CVC). We hypothesize that there will be significant correlations between scenario based decision making skills, and technical proficiency in central line insertion. We also predict residents will have problems in anticipating common difficulties and generating solutions associated with line placement. Design Participants were asked to insert a subclavian central line on a simulator. After completion, residents were presented with a real life patient photograph depicting CVC placement and asked to anticipate difficulties and generate solutions. Error rates were analyzed using chi-square tests and a 5% expected error rate. Correlations were sought by comparing technical errors and scenario based decision making. Setting This study was carried out at seven tertiary care centers. Participants Study participants (N=46) consisted of largely first year research residents that could be followed longitudinally. Second year research and clinical residents were not excluded. Results Six checklist errors were committed more often than anticipated. Residents performed an average of 1.9 errors, significantly more than the 1 error, at most, per person expected (t(44)=3.82, p<.001). The most common error was performance of the procedure steps in the wrong order (28.5%, P<.001). Some of the residents (24%) had no errors, 30% committed one error, and 46 % committed more than one error. The number of technical errors committed negatively correlated with the total number of commonly identified difficulties and generated solutions (r(33)= −.429, p=.021, r(33)= −.383, p=.044 respectively). Conclusions Almost half of the surgical residents committed multiple errors while performing subclavian CVC placement. The correlation between technical errors and decision making skills suggests a critical need to train residents in both technique and error management. ACGME Competencies Medical Knowledge, Practice Based Learning and Improvement, Systems Based Practice PMID:27671618

Best estimate of luminal cross-sectional area of coronary arteries from angiograms

NASA Technical Reports Server (NTRS)

Lee, P. L.; Selzer, R. H.

1988-01-01

We have reexamined the problem of estimating the luminal area of an elliptically-shaped coronary artery cross section from two or more radiographic diameter measurements. The expected error is found to be much smaller than the maximum potential error. In the cae of two orthogonal views, closed form expressions have been derived for calculating the area and the uncertainty. Assuming that the underlying ellipse has limited ellipticity (major/minor axis ratio less than five), it is shown that the average uncertainty in the area is less than 14 percent. When more than two views are available, we suggest using a least-squares fit method to extract all available information from the data.

Chromosomal locus tracking with proper accounting of static and dynamic errors

PubMed Central

Backlund, Mikael P.; Joyner, Ryan; Moerner, W. E.

2015-01-01

The mean-squared displacement (MSD) and velocity autocorrelation (VAC) of tracked single particles or molecules are ubiquitous metrics for extracting parameters that describe the object’s motion, but they are both corrupted by experimental errors that hinder the quantitative extraction of underlying parameters. For the simple case of pure Brownian motion, the effects of localization error due to photon statistics (“static error”) and motion blur due to finite exposure time (“dynamic error”) on the MSD and VAC are already routinely treated. However, particles moving through complex environments such as cells, nuclei, or polymers often exhibit anomalous diffusion, for which the effects of these errors are less often sufficiently treated. We present data from tracked chromosomal loci in yeast that demonstrate the necessity of properly accounting for both static and dynamic error in the context of an anomalous diffusion that is consistent with a fractional Brownian motion (FBM). We compare these data to analytical forms of the expected values of the MSD and VAC for a general FBM in the presence of these errors. PMID:26172745

An analysis of the least-squares problem for the DSN systematic pointing error model

NASA Technical Reports Server (NTRS)

Alvarez, L. S.

1991-01-01

A systematic pointing error model is used to calibrate antennas in the Deep Space Network. The least squares problem is described and analyzed along with the solution methods used to determine the model's parameters. Specifically studied are the rank degeneracy problems resulting from beam pointing error measurement sets that incorporate inadequate sky coverage. A least squares parameter subset selection method is described and its applicability to the systematic error modeling process is demonstrated on Voyager 2 measurement distribution.

Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 2. Applications

USGS Publications Warehouse

Cooley, Richard L.

1983-01-01

This paper investigates factors influencing the degree of improvement in estimates of parameters of a nonlinear regression groundwater flow model by incorporating prior information of unknown reliability. Consideration of expected behavior of the regression solutions and results of a hypothetical modeling problem lead to several general conclusions. First, if the parameters are properly scaled, linearized expressions for the mean square error (MSE) in parameter estimates of a nonlinear model will often behave very nearly as if the model were linear. Second, by using prior information, the MSE in properly scaled parameters can be reduced greatly over the MSE of ordinary least squares estimates of parameters. Third, plots of estimated MSE and the estimated standard deviation of MSE versus an auxiliary parameter (the ridge parameter) specifying the degree of influence of the prior information on regression results can help determine the potential for improvement of parameter estimates. Fourth, proposed criteria can be used to make appropriate choices for the ridge parameter and another parameter expressing degree of overall bias in the prior information. Results of a case study of Truckee Meadows, Reno-Sparks area, Washoe County, Nevada, conform closely to the results of the hypothetical problem. In the Truckee Meadows case, incorporation of prior information did not greatly change the parameter estimates from those obtained by ordinary least squares. However, the analysis showed that both sets of estimates are more reliable than suggested by the standard errors from ordinary least squares.

SU-E-T-261: Plan Quality Assurance of VMAT Using Fluence Images Reconstituted From Log-Files

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katsuta, Y; Shimizu, E; Matsunaga, K

2014-06-01

Purpose: A successful VMAT plan delivery includes precise modulations of dose rate, gantry rotational and multi-leaf collimator (MLC) shapes. One of the main problem in the plan quality assurance is dosimetric errors associated with leaf-positional errors are difficult to analyze because they vary with MU delivered and leaf number. In this study, we calculated integrated fluence error image (IFEI) from log-files and evaluated plan quality in the area of all and individual MLC leaves scanned. Methods: The log-file reported the expected and actual position for inner 20 MLC leaves and the dose fraction every 0.25 seconds during prostate VMAT onmore » Elekta Synergy. These data were imported to in-house software that developed to calculate expected and actual fluence images from the difference of opposing leaf trajectories and dose fraction at each time. The IFEI was obtained by adding all of the absolute value of the difference between expected and actual fluence images corresponding. Results: In the area all MLC leaves scanned in the IFEI, the average and root mean square (rms) were 2.5 and 3.6 MU, the area of errors below 10, 5 and 3 MU were 98.5, 86.7 and 68.1 %, the 95 % of area was covered with less than error of 7.1 MU. In the area individual MLC leaves scanned in the IFEI, the average and rms value were 2.1 – 3.0 and 3.1 – 4.0 MU, the area of errors below 10, 5 and 3 MU were 97.6 – 99.5, 81.7 – 89.5 and 51.2 – 72.8 %, the 95 % of area was covered with less than error of 6.6 – 8.2 MU. Conclusion: The analysis of the IFEI reconstituted from log-file was provided detailed information about the delivery in the area of all and individual MLC leaves scanned.« less

Response Surface Analysis of Experiments with Random Blocks

DTIC Science & Technology

1988-09-01

partitioned into a lack of fit sum of squares, SSLOF, and a pure error sum of squares, SSPE . The latter is obtained by pooling the pure error sums of squares...from the blocks. Tests concerning the polynomial effects can then proceed using SSPE as the error term in the denominators of the F test statistics. 3.2...the center point in each of the three blocks is equal to SSPE = 2.0127 with 5 degrees of freedom. Hence, the lack of fit sum of squares is SSLoF

Least-Squares Curve-Fitting Program

NASA Technical Reports Server (NTRS)

Kantak, Anil V.

1990-01-01

Least Squares Curve Fitting program, AKLSQF, easily and efficiently computes polynomial providing least-squares best fit to uniformly spaced data. Enables user to specify tolerable least-squares error in fit or degree of polynomial. AKLSQF returns polynomial and actual least-squares-fit error incurred in operation. Data supplied to routine either by direct keyboard entry or via file. Written for an IBM PC X/AT or compatible using Microsoft's Quick Basic compiler.

Outcome expectations for exercise scale: utility and psychometrics.

PubMed

Resnick, B; Zimmerman, S I; Orwig, D; Furstenberg, A L; Magaziner, J

2000-11-01

The purpose of this study was to develop a measure of outcome expectations for exercise specifically for the older adult (The Outcome Expectations for Exercise [OEE] Scale), and to test the reliability and validity of this measure in a sample of older individuals. This scale was developed based on Bandura's theory of self-efficacy and the work of prior researchers in the development of measures of outcome expectations. The OEE scale, which was completed during a face-to-face interview, was tested in a sample of 175 residents in a continuing care retirement community. There was support for the internal consistency of the OEE scale (alpha coefficient of .89), and some support for reliability based on a structural equation modeling approach that used R2 estimates, although less than half of these were greater than 0.5. There was evidence of validity of the measure based on: (a) a confirmatory factor analysis in which the model fit the data (normed fit index [NFI] = .99, root mean square error of approximation [RMSEA] - .07, chi2/df = 2.8); (b) support for the hypothesis that those who exercised regularly had higher OEE scores than those who did not (F = 31.3, p < .05, eta squared = .15); and (c) a statistically significant relationship between outcome expectations and self-efficacy expectations (r = .66). This study provides some initial support for the reliability and validity of the OEE scale. Outcome expectations for exercise were related to exercise behavior in the older adult, and the OEE scale can help identify older adults with low outcome expectations for exercise. Interventions can then be implemented to help these individuals strengthen their outcome expectations, which may subsequently improve exercise behavior.

Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains

NASA Technical Reports Server (NTRS)

Draper, Clara S.; Reichle, Rolf; de Jeu, Richard; Naeimi, Vahid; Parinussa, Robert; Wagner, Wolfgang

2013-01-01

Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions.

Error propagation of partial least squares for parameters optimization in NIR modeling.

PubMed

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

Error propagation of partial least squares for parameters optimization in NIR modeling

NASA Astrophysics Data System (ADS)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

Aberrations in square pore micro-channel optics used for x-ray lobster eye telescopes

NASA Astrophysics Data System (ADS)

Willingale, R.; Pearson, J. F.; Martindale, A.; Feldman, C. H.; Fairbend, R.; Schyns, E.; Petit, S.; Osborne, J. P.; O'Brien, P. T.

2016-07-01

We identify all the significant aberrations that limit the performance of square pore micro-channel plate optics (MPOs) used as an X-ray lobster eye. These include aberrations intrinsic to the geometry, intrinsic errors associated with the slumping process used to introduce a spherical form to the plates and imperfections associated with the plate manufacturing process. The aberrations are incorporated into a comprehensive software model of the X-ray response of the optics and the predicted imaging response is compared with the measured X-ray performance obtained from a breadboard lobster eye. The results reveal the manufacturing tolerances which limit the current performance of MPOs and enable us to identify particular intrinsic aberrations which will limit the ultimate performance we can expect from MPO-lobster eye telescopes.

Robust radio interferometric calibration using the t-distribution

NASA Astrophysics Data System (ADS)

Kazemi, S.; Yatawatta, S.

2013-10-01

A major stage of radio interferometric data processing is calibration or the estimation of systematic errors in the data and the correction for such errors. A stochastic error (noise) model is assumed, and in most cases, this underlying model is assumed to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources, could be attributed to the deviations of the underlying noise model. In this paper, we propose to improve the robustness of calibration by using a noise model based on Student's t-distribution. Student's t-noise is a special case of Gaussian noise when the variance is unknown. Unlike Gaussian-noise-model-based calibration, traditional least-squares minimization would not directly extend to a case when we have a Student's t-noise model. Therefore, we use a variant of the expectation-maximization algorithm, called the expectation-conditional maximization either algorithm, when we have a Student's t-noise model and use the Levenberg-Marquardt algorithm in the maximization step. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian-noise-model-based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

Parameter Estimation for Thurstone Choice Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vojnovic, Milan; Yun, Seyoung

We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one ormore » more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.« less

Efficient estimation of Pareto model: Some modified percentile estimators.

PubMed

Bhatti, Sajjad Haider; Hussain, Shahzad; Ahmad, Tanvir; Aslam, Muhammad; Aftab, Muhammad; Raza, Muhammad Ali

2018-01-01

The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.

Chemical library subset selection algorithms: a unified derivation using spatial statistics.

PubMed

Hamprecht, Fred A; Thiel, Walter; van Gunsteren, Wilfred F

2002-01-01

If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.

SU-E-T-484: In Vivo Dosimetry Tolerances in External Beam Fast Neutron Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, L; Gopan, O

Purpose: Optical stimulated luminescence (OSL) dosimetry with Landauer Al2O3:C nanodots was developed at our institution as a passive in vivo dosimetry (IVD) system for patients treated with fast neutron therapy. The purpose of this study was to establish clinically relevant tolerance limits for detecting treatment errors requiring further investigation. Methods: Tolerance levels were estimated by conducting a series of IVD expected dose calculations for square field sizes ranging between 2.8 and 28.8 cm. For each field size evaluated, doses were calculated for open and internal wedged fields with angles of 30°, 45°, or 60°. Theoretical errors were computed for variationsmore » of incorrect beam configurations. Dose errors, defined as the percent difference from the expected dose calculation, were measured with groups of three nanodots placed in a 30 x 30 cm solid water phantom, at beam isocenter (150 cm SAD, 1.7 cm Dmax). The tolerances were applied to IVD patient measurements. Results: The overall accuracy of the nanodot measurements is 2–3% for open fields. Measurement errors agreed with calculated errors to within 3%. Theoretical estimates of dosimetric errors showed that IVD measurements with OSL nanodots will detect the absence of an internal wedge or a wrong wedge angle. Incorrect nanodot placement on a wedged field is more likely to be caught if the offset is in the direction of the “toe” of the wedge where the dose difference in percentage is about 12%. Errors caused by an incorrect flattening filter size produced a 2% measurement error that is not detectable by IVD measurement alone. Conclusion: IVD with nanodots will detect treatment errors associated with the incorrect implementation of the internal wedge. The results of this study will streamline the physicists’ investigations in determining the root cause of an IVD reading that is out of normally accepted tolerances.« less

The DES Science Verification Weak Lensing Shear Catalogs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jarvis, M.

We present weak lensing shear catalogs for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey (DES). We describe our object selection, point spread function estimation and shear measurement procedures using two independent shear pipelines, IM3SHAPE and NGMIX, which produce catalogs of 2.12 million and 3.44 million galaxies respectively. We also detail a set of null tests for the shear measurements and find that they pass the requirements for systematic errors at the level necessary for weak lensing science applications using the SVmore » data. Furthermore, we discuss some of the planned algorithmic improvements that will be necessary to produce sufficiently accurate shear catalogs for the full 5-year DES, which is expected to cover 5000 square degrees.« less

The DES Science Verification Weak Lensing Shear Catalogs

DOE PAGES

Jarvis, M.

2016-05-01

We present weak lensing shear catalogs for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey (DES). We describe our object selection, point spread function estimation and shear measurement procedures using two independent shear pipelines, IM3SHAPE and NGMIX, which produce catalogs of 2.12 million and 3.44 million galaxies respectively. We also detail a set of null tests for the shear measurements and find that they pass the requirements for systematic errors at the level necessary for weak lensing science applications using the SVmore » data. Furthermore, we discuss some of the planned algorithmic improvements that will be necessary to produce sufficiently accurate shear catalogs for the full 5-year DES, which is expected to cover 5000 square degrees.« less

Selective Weighted Least Squares Method for Fourier Transform Infrared Quantitative Analysis.

PubMed

Wang, Xin; Li, Yan; Wei, Haoyun; Chen, Xia

2017-06-01

Classical least squares (CLS) regression is a popular multivariate statistical method used frequently for quantitative analysis using Fourier transform infrared (FT-IR) spectrometry. Classical least squares provides the best unbiased estimator for uncorrelated residual errors with zero mean and equal variance. However, the noise in FT-IR spectra, which accounts for a large portion of the residual errors, is heteroscedastic. Thus, if this noise with zero mean dominates in the residual errors, the weighted least squares (WLS) regression method described in this paper is a better estimator than CLS. However, if bias errors, such as the residual baseline error, are significant, WLS may perform worse than CLS. In this paper, we compare the effect of noise and bias error in using CLS and WLS in quantitative analysis. Results indicated that for wavenumbers with low absorbance, the bias error significantly affected the error, such that the performance of CLS is better than that of WLS. However, for wavenumbers with high absorbance, the noise significantly affected the error, and WLS proves to be better than CLS. Thus, we propose a selective weighted least squares (SWLS) regression that processes data with different wavenumbers using either CLS or WLS based on a selection criterion, i.e., lower or higher than an absorbance threshold. The effects of various factors on the optimal threshold value (OTV) for SWLS have been studied through numerical simulations. These studies reported that: (1) the concentration and the analyte type had minimal effect on OTV; and (2) the major factor that influences OTV is the ratio between the bias error and the standard deviation of the noise. The last part of this paper is dedicated to quantitative analysis of methane gas spectra, and methane/toluene mixtures gas spectra as measured using FT-IR spectrometry and CLS, WLS, and SWLS. The standard error of prediction (SEP), bias of prediction (bias), and the residual sum of squares of the errors (RSS) from the three quantitative analyses were compared. In methane gas analysis, SWLS yielded the lowest SEP and RSS among the three methods. In methane/toluene mixture gas analysis, a modification of the SWLS has been presented to tackle the bias error from other components. The SWLS without modification presents the lowest SEP in all cases but not bias and RSS. The modification of SWLS reduced the bias, which showed a lower RSS than CLS, especially for small components.

Microwave Photonic Architecture for Direction Finding of LPI Emitters: Post-Processing for Angle of Arrival Estimation

DTIC Science & Technology

2016-09-01

mean- square (RMS) error of 0.29° at ə° resolution. For a P4 coded signal, the RMS error in estimating the AOA is 0.32° at 1° resolution. 14...FMCW signal, it was demonstrated that the system is capable of estimating the AOA with a root-mean- square (RMS) error of 0.29° at ə° resolution. For a...Modulator PCB printed circuit board PD photodetector RF radio frequency RMS root-mean- square xvi THIS PAGE INTENTIONALLY LEFT BLANK xvii

Past observable dynamics of a continuously monitored qubit

NASA Astrophysics Data System (ADS)

García-Pintos, Luis Pedro; Dressel, Justin

2017-12-01

Monitoring a quantum observable continuously in time produces a stochastic measurement record that noisily tracks the observable. For a classical process, such noise may be reduced to recover an average signal by minimizing the mean squared error between the noisy record and a smooth dynamical estimate. We show that for a monitored qubit, this usual procedure returns unusual results. While the record seems centered on the expectation value of the observable during causal generation, examining the collected past record reveals that it better approximates a moving-mean Gaussian stochastic process centered at a distinct (smoothed) observable estimate. We show that this shifted mean converges to the real part of a generalized weak value in the time-continuous limit without additional postselection. We verify that this smoothed estimate minimizes the mean squared error even for individual measurement realizations. We go on to show that if a second observable is weakly monitored concurrently, then that second record is consistent with the smoothed estimate of the second observable based solely on the information contained in the first observable record. Moreover, we show that such a smoothed estimate made from incomplete information can still outperform estimates made using full knowledge of the causal quantum state.

Comparison of estimators of standard deviation for hydrologic time series

USGS Publications Warehouse

Tasker, Gary D.; Gilroy, Edward J.

1982-01-01

Unbiasing factors as a function of serial correlation, ρ, and sample size, n for the sample standard deviation of a lag one autoregressive model were generated by random number simulation. Monte Carlo experiments were used to compare the performance of several alternative methods for estimating the standard deviation σ of a lag one autoregressive model in terms of bias, root mean square error, probability of underestimation, and expected opportunity design loss. Three methods provided estimates of σ which were much less biased but had greater mean square errors than the usual estimate of σ: s = (1/(n - 1) ∑ (xi −x¯)2)½. The three methods may be briefly characterized as (1) a method using a maximum likelihood estimate of the unbiasing factor, (2) a method using an empirical Bayes estimate of the unbiasing factor, and (3) a robust nonparametric estimate of σ suggested by Quenouille. Because s tends to underestimate σ, its use as an estimate of a model parameter results in a tendency to underdesign. If underdesign losses are considered more serious than overdesign losses, then the choice of one of the less biased methods may be wise.

Predictors of FIFA 11+ Implementation Intention in Female Adolescent Soccer: An Application of the Health Action Process Approach (HAPA) Model

PubMed Central

McKay, Carly D.; Merrett, Charlotte K.; Emery, Carolyn A.

2016-01-01

The Fédération Internationale de Football (FIFA) 11+ warm-up program is efficacious at preventing lower limb injury in youth soccer; however, there has been poor adoption of the program in the community. The purpose of this study was to determine the utility of the Health Action Process Approach (HAPA) behavior change model in predicting intention to use the FIFA 11+ in a sample of 12 youth soccer teams (coaches n = 10; 12–16 year old female players n = 200). A bespoke cross-sectional questionnaire measured pre-season risk perceptions, outcome expectancies, task self-efficacy, facilitators, barriers, and FIFA 11+ implementation intention. Most coaches (90.0%) and players (80.0%) expected the program to reduce injury risk but reported limited intention to use it. Player data demonstrated an acceptable fit to the hypothesized model (standardized root mean square residual (SRMR) = 0.08; root mean square of error of approximation (RMSEA) = 0.06 (0.047–0.080); comparative fit index (CFI) = 0.93; Tucker Lewis index (TLI) = 0.91) Task self-efficacy (β = 0.53, p ≤ 0.01) and outcome expectancies (β = 0.13 p ≤ 0.05) were positively associated with intention, but risk perceptions were not (β = −0.02). The findings suggest that the HAPA model is appropriate for use in this context, and highlight the need to target task self-efficacy and outcome expectancies in FIFA 11+ implementation strategies. PMID:27399746

Optimal full motion video registration with rigorous error propagation

NASA Astrophysics Data System (ADS)

Dolloff, John; Hottel, Bryant; Doucette, Peter; Theiss, Henry; Jocher, Glenn

2014-06-01

Optimal full motion video (FMV) registration is a crucial need for the Geospatial community. It is required for subsequent and optimal geopositioning with simultaneous and reliable accuracy prediction. An overall approach being developed for such registration is presented that models relevant error sources in terms of the expected magnitude and correlation of sensor errors. The corresponding estimator is selected based on the level of accuracy of the a priori information of the sensor's trajectory and attitude (pointing) information, in order to best deal with non-linearity effects. Estimator choices include near real-time Kalman Filters and batch Weighted Least Squares. Registration solves for corrections to the sensor a priori information for each frame. It also computes and makes available a posteriori accuracy information, i.e., the expected magnitude and correlation of sensor registration errors. Both the registered sensor data and its a posteriori accuracy information are then made available to "down-stream" Multi-Image Geopositioning (MIG) processes. An object of interest is then measured on the registered frames and a multi-image optimal solution, including reliable predicted solution accuracy, is then performed for the object's 3D coordinates. This paper also describes a robust approach to registration when a priori information of sensor attitude is unavailable. It makes use of structure-from-motion principles, but does not use standard Computer Vision techniques, such as estimation of the Essential Matrix which can be very sensitive to noise. The approach used instead is a novel, robust, direct search-based technique.

Determination of suitable drying curve model for bread moisture loss during baking

NASA Astrophysics Data System (ADS)

Soleimani Pour-Damanab, A. R.; Jafary, A.; Rafiee, S.

2013-03-01

This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.

Correcting Four Similar Correlational Measures for Attenuation Due to Errors of Measurement in the Dependent Variable: Eta, Epsilon, Omega, and Intraclass r.

ERIC Educational Resources Information Center

Stanley, Julian C.; Livingston, Samuel A.

Besides the ubiquitous Pearson product-moment r, there are a number of other measures of relationship that are attenuated by errors of measurement and for which the relationship between true measures can be estimated. Among these are the correlation ratio (eta squared), Kelley's unbiased correlation ratio (epsilon squared), Hays' omega squared,…

Study on the Rationality and Validity of Probit Models of Domino Effect to Chemical Process Equipment caused by Overpressure

NASA Astrophysics Data System (ADS)

Sun, Dongliang; Huang, Guangtuan; Jiang, Juncheng; Zhang, Mingguang; Wang, Zhirong

2013-04-01

Overpressure is one important cause of domino effect in accidents of chemical process equipments. Some models considering propagation probability and threshold values of the domino effect caused by overpressure have been proposed in previous study. In order to prove the rationality and validity of the models reported in the reference, two boundary values of three damage degrees reported were considered as random variables respectively in the interval [0, 100%]. Based on the overpressure data for damage to the equipment and the damage state, and the calculation method reported in the references, the mean square errors of the four categories of damage probability models of overpressure were calculated with random boundary values, and then a relationship of mean square error vs. the two boundary value was obtained, the minimum of mean square error was obtained, compared with the result of the present work, mean square error decreases by about 3%. Therefore, the error was in the acceptable range of engineering applications, the models reported can be considered reasonable and valid.

Multilevel sequential Monte Carlo: Mean square error bounds under verifiable conditions

DOE PAGES

Del Moral, Pierre; Jasra, Ajay; Law, Kody J. H.

2017-01-09

We consider the multilevel sequential Monte Carlo (MLSMC) method of Beskos et al. (Stoch. Proc. Appl. [to appear]). This technique is designed to approximate expectations w.r.t. probability laws associated to a discretization. For instance, in the context of inverse problems, where one discretizes the solution of a partial differential equation. The MLSMC approach is especially useful when independent, coupled sampling is not possible. Beskos et al. show that for MLSMC the computational effort to achieve a given error, can be less than independent sampling. In this article we significantly weaken the assumptions of Beskos et al., extending the proofs tomore » non-compact state-spaces. The assumptions are based upon multiplicative drift conditions as in Kontoyiannis and Meyn (Electron. J. Probab. 10 [2005]: 61–123). The assumptions are verified for an example.« less

Multilevel sequential Monte Carlo: Mean square error bounds under verifiable conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Del Moral, Pierre; Jasra, Ajay; Law, Kody J. H.

We consider the multilevel sequential Monte Carlo (MLSMC) method of Beskos et al. (Stoch. Proc. Appl. [to appear]). This technique is designed to approximate expectations w.r.t. probability laws associated to a discretization. For instance, in the context of inverse problems, where one discretizes the solution of a partial differential equation. The MLSMC approach is especially useful when independent, coupled sampling is not possible. Beskos et al. show that for MLSMC the computational effort to achieve a given error, can be less than independent sampling. In this article we significantly weaken the assumptions of Beskos et al., extending the proofs tomore » non-compact state-spaces. The assumptions are based upon multiplicative drift conditions as in Kontoyiannis and Meyn (Electron. J. Probab. 10 [2005]: 61–123). The assumptions are verified for an example.« less

An Empirical State Error Covariance Matrix for the Weighted Least Squares Estimation Method

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2011-01-01

State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the un-certainty in the estimated states. By a reinterpretation of the equations involved in the weighted least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. This proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. Results based on the proposed technique will be presented for a simple, two observer, measurement error only problem.

Small convolution kernels for high-fidelity image restoration

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Park, Stephen K.

1991-01-01

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

A Novel Low-Cost, Large Curvature Bend Sensor Based on a Bowden-Cable

PubMed Central

Jeong, Useok; Cho, Kyu-Jin

2016-01-01

Bend sensors have been developed based on conductive ink, optical fiber, and electronic textiles. Each type has advantages and disadvantages in terms of performance, ease of use, and cost. This study proposes a new and low-cost bend sensor that can measure a wide range of accumulated bend angles with large curvatures. This bend sensor utilizes a Bowden-cable, which consists of a coil sheath and an inner wire. Displacement changes of the Bowden-cable’s inner wire, when the shape of the sheath changes, have been considered to be a position error in previous studies. However, this study takes advantage of this position error to detect the bend angle of the sheath. The bend angle of the sensor can be calculated from the displacement measurement of the sensing wire using a Hall-effect sensor or a potentiometer. Simulations and experiments have shown that the accumulated bend angle of the sensor is linearly related to the sensor signal, with an R-square value up to 0.9969 and a root mean square error of 2% of the full sensing range. The proposed sensor is not affected by a bend curvature of up to 80.0 m−1, unlike previous bend sensors. The proposed sensor is expected to be useful for various applications, including motion capture devices, wearable robots, surgical devices, or generally any device that requires an affordable and low-cost bend sensor. PMID:27347959

Mathematical Model and Calibration Experiment of a Large Measurement Range Flexible Joints 6-UPUR Six-Axis Force Sensor

PubMed Central

Zhao, Yanzhi; Zhang, Caifeng; Zhang, Dan; Shi, Zhongpan; Zhao, Tieshi

2016-01-01

Nowadays improving the accuracy and enlarging the measuring range of six-axis force sensors for wider applications in aircraft landing, rocket thrust, and spacecraft docking testing experiments has become an urgent objective. However, it is still difficult to achieve high accuracy and large measuring range with traditional parallel six-axis force sensors due to the influence of the gap and friction of the joints. Therefore, to overcome the mentioned limitations, this paper proposed a 6-Universal-Prismatic-Universal-Revolute (UPUR) joints parallel mechanism with flexible joints to develop a large measurement range six-axis force sensor. The structural characteristics of the sensor are analyzed in comparison with traditional parallel sensor based on the Stewart platform. The force transfer relation of the sensor is deduced, and the force Jacobian matrix is obtained using screw theory in two cases of the ideal state and the state of flexibility of each flexible joint is considered. The prototype and loading calibration system are designed and developed. The K value method and least squares method are used to process experimental data, and in errors of kind Ι and kind II linearity are obtained. The experimental results show that the calibration error of the K value method is more than 13.4%, and the calibration error of the least squares method is 2.67%. The experimental results prove the feasibility of the sensor and the correctness of the theoretical analysis which are expected to be adopted in practical applications. PMID:27529244

Analysis of tractable distortion metrics for EEG compression applications.

PubMed

Bazán-Prieto, Carlos; Blanco-Velasco, Manuel; Cárdenas-Barrera, Julián; Cruz-Roldán, Fernando

2012-07-01

Coding distortion in lossy electroencephalographic (EEG) signal compression methods is evaluated through tractable objective criteria. The percentage root-mean-square difference, which is a global and relative indicator of the quality held by reconstructed waveforms, is the most widely used criterion. However, this parameter does not ensure compliance with clinical standard guidelines that specify limits to allowable noise in EEG recordings. As a result, expert clinicians may have difficulties interpreting the resulting distortion of the EEG for a given value of this parameter. Conversely, the root-mean-square error is an alternative criterion that quantifies distortion in understandable units. In this paper, we demonstrate that the root-mean-square error is better suited to control and to assess the distortion introduced by compression methods. The experiments conducted in this paper show that the use of the root-mean-square error as target parameter in EEG compression allows both clinicians and scientists to infer whether coding error is clinically acceptable or not at no cost for the compression ratio.

Long-term forecasting of meteorological time series using Nonlinear Canonical Correlation Analysis (NLCCA)

NASA Astrophysics Data System (ADS)

Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.

2016-12-01

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction

Localization of Southern Resident Killer Whales Using Two Star Arrays to Support Marine Renewable Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Huiying; Deng, Zhiqun; Carlson, Thomas J.

2012-10-19

Tidal power has been identified as one of the most potential commercial-scale renewable energy sources. Puget Sound, Washington, is a potential site to deploy tidal power generating devices. The risk of injury for killer whales needs to be managed before the deployment of these types of devices can be approved by regulating authorities. A passive acoustic system consisting of two star arrays, each with four hydrophones, was designed and implemented for the detection and localization of Southern Resident killer whales. Deployment of the passive acoustic system was conducted at Sequim Bay, Washington. A total of nine test locations were chosen,more » within a radius of 250 m around the star arrays, to test our localization approach. For the localization algorithm, a least square solver was applied to obtain a bearing location from each star array. The final source location was determined by the intersection of the bearings given by each of the two star arrays. Bearing and distance errors were obtained to conduct comparison between the calculated and true (from Global Positioning System) locations. The results indicated that bearing errors were within 1.04º for eight of the test locations; one location had bearing errors slightly larger than expected due to the strong background noise at that position. For the distance errors, six of the test locations were within the range of 1.91 to 32.36 m. The other two test locations were near the intersection line between the centers of the two star arrays, which were expected to have large errors from the theoretical sensitivity analysis performed.« less

A Survey of Terrain Modeling Technologies and Techniques

DTIC Science & Technology

2007-09-01

Washington , DC 20314-1000 ERDC/TEC TR-08-2 ii Abstract: Test planning, rehearsal, and distributed test events for Future Combat Systems (FCS) require...distance) for all five lines of control points. Blue circles are errors of DSM (original data), red squares are DTM (bare Earth, processed by Intermap...circles are DSM, red squares are DTM ........... 8 5 Distribution of errors for line No. 729. Blue circles are DSM, red squares are DTM

Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

NASA Astrophysics Data System (ADS)

Hu, Chia-Chang; Lin, Hsuan-Yu; Chen, Yu-Fan; Wen, Jyh-Horng

2006-12-01

An adaptive minimum mean-square error (MMSE) array receiver based on the fuzzy-logic recursive least-squares (RLS) algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ([InlineEquation not available: see fulltext.],[InlineEquation not available: see fulltext.]), into a forgetting factor[InlineEquation not available: see fulltext.]. For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS) algorithm using the fuzzy-inference-controlled step-size[InlineEquation not available: see fulltext.]. This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS) and variable forgetting factor RLS (VFF-RLS) algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER) for multipath fading channels.

Obstacle Detection in Indoor Environment for Visually Impaired Using Mobile Camera

NASA Astrophysics Data System (ADS)

Rahman, Samiur; Ullah, Sana; Ullah, Sehat

2018-01-01

Obstacle detection can improve the mobility as well as the safety of visually impaired people. In this paper, we present a system using mobile camera for visually impaired people. The proposed algorithm works in indoor environment and it uses a very simple technique of using few pre-stored floor images. In indoor environment all unique floor types are considered and a single image is stored for each unique floor type. These floor images are considered as reference images. The algorithm acquires an input image frame and then a region of interest is selected and is scanned for obstacle using pre-stored floor images. The algorithm compares the present frame and the next frame and compute mean square error of the two frames. If mean square error is less than a threshold value α then it means that there is no obstacle in the next frame. If mean square error is greater than α then there are two possibilities; either there is an obstacle or the floor type is changed. In order to check if the floor is changed, the algorithm computes mean square error of next frame and all stored floor types. If minimum of mean square error is less than a threshold value α then flour is changed otherwise there exist an obstacle. The proposed algorithm works in real-time and 96% accuracy has been achieved.

Using weighted power mean for equivalent square estimation.

PubMed

Zhou, Sumin; Wu, Qiuwen; Li, Xiaobo; Ma, Rongtao; Zheng, Dandan; Wang, Shuo; Zhang, Mutian; Li, Sicong; Lei, Yu; Fan, Qiyong; Hyun, Megan; Diener, Tyler; Enke, Charles

2017-11-01

Equivalent Square (ES) enables the calculation of many radiation quantities for rectangular treatment fields, based only on measurements from square fields. While it is widely applied in radiotherapy, its accuracy, especially for extremely elongated fields, still leaves room for improvement. In this study, we introduce a novel explicit ES formula based on Weighted Power Mean (WPM) function and compare its performance with the Sterling formula and Vadash/Bjärngard's formula. The proposed WPM formula is ESWPMa,b=waα+1-wbα1/α for a rectangular photon field with sides a and b. The formula performance was evaluated by three methods: standard deviation of model fitting residual error, maximum relative model prediction error, and model's Akaike Information Criterion (AIC). Testing datasets included the ES table from British Journal of Radiology (BJR), photon output factors (S cp ) from the Varian TrueBeam Representative Beam Data (Med Phys. 2012;39:6981-7018), and published S cp data for Varian TrueBeam Edge (J Appl Clin Med Phys. 2015;16:125-148). For the BJR dataset, the best-fit parameter value α = -1.25 achieved a 20% reduction in standard deviation in ES estimation residual error compared with the two established formulae. For the two Varian datasets, employing WPM reduced the maximum relative error from 3.5% (Sterling) or 2% (Vadash/Bjärngard) to 0.7% for open field sizes ranging from 3 cm to 40 cm, and the reduction was even more prominent for 1 cm field sizes on Edge (J Appl Clin Med Phys. 2015;16:125-148). The AIC value of the WPM formula was consistently lower than its counterparts from the traditional formulae on photon output factors, most prominent on very elongated small fields. The WPM formula outperformed the traditional formulae on three testing datasets. With increasing utilization of very elongated, small rectangular fields in modern radiotherapy, improved photon output factor estimation is expected by adopting the WPM formula in treatment planning and secondary MU check. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error

NASA Astrophysics Data System (ADS)

Khair, Ummul; Fahmi, Hasanul; Hakim, Sarudin Al; Rahim, Robbi

2017-12-01

Prediction using a forecasting method is one of the most important things for an organization, the selection of appropriate forecasting methods is also important but the percentage error of a method is more important in order for decision makers to adopt the right culture, the use of the Mean Absolute Deviation and Mean Absolute Percentage Error to calculate the percentage of mistakes in the least square method resulted in a percentage of 9.77% and it was decided that the least square method be worked for time series and trend data.

Updating expected action outcome in the medial frontal cortex involves an evaluation of error type.

PubMed

Maier, Martin E; Steinhauser, Marco

2013-10-02

Forming expectations about the outcome of an action is an important prerequisite for action control and reinforcement learning in the human brain. The medial frontal cortex (MFC) has been shown to play an important role in the representation of outcome expectations, particularly when an update of expected outcome becomes necessary because an error is detected. However, error detection alone is not always sufficient to compute expected outcome because errors can occur in various ways and different types of errors may be associated with different outcomes. In the present study, we therefore investigate whether updating expected outcome in the human MFC is based on an evaluation of error type. Our approach was to consider an electrophysiological correlate of MFC activity on errors, the error-related negativity (Ne/ERN), in a task in which two types of errors could occur. Because the two error types were associated with different amounts of monetary loss, updating expected outcomes on error trials required an evaluation of error type. Our data revealed a pattern of Ne/ERN amplitudes that closely mirrored the amount of monetary loss associated with each error type, suggesting that outcome expectations are updated based on an evaluation of error type. We propose that this is achieved by a proactive evaluation process that anticipates error types by continuously monitoring error sources or by dynamically representing possible response-outcome relations.

Optical PAyload for Lasercomm Science (OPALS) link validation

NASA Technical Reports Server (NTRS)

Biswas, Abhijit; Oaida, Bogdan V.; Andrews, Kenneth S.; Kovalik, Joseph M.; Abrahamson, Matthew J.; Wright, Malcolm W.

2015-01-01

Recently several day and nighttime links under diverse atmospheric conditions were completed using the Optical Payload for Lasercomm Science (OPALS) flight system on-board the International Space Station (ISS). In this paper we compare measured optical power and its variance at either end of the link with predictions that include atmospheric propagation models. For the 976 nm laser beacon mean power transmitted from the ground to the ISS the predicted mean irradiance of 10's of microwatts per square meter close to zenith and its decrease with range and increased air mass shows good agreement with predictions. The irradiance fluctuations sampled at 100 Hz also follow the expected increase in scintillation with air mass representative of atmospheric coherence lengths at zenith at 500 nm in the 3-8 cm range. The downlink predicted power of 100's of nanowatts was also reconciled within the uncertainty of the atmospheric losses. Expected link performance with uncoded bit-error rates less than 1E-4 required for the Reed-Solomon code to correct errors for video, text and file transmission was verified. The results of predicted and measured powers and fluctuations suggest the need for further study and refinement.

Constrained independent component analysis approach to nonobtrusive pulse rate measurements

NASA Astrophysics Data System (ADS)

Tsouri, Gill R.; Kyal, Survi; Dianat, Sohail; Mestha, Lalit K.

2012-07-01

Nonobtrusive pulse rate measurement using a webcam is considered. We demonstrate how state-of-the-art algorithms based on independent component analysis suffer from a sorting problem which hinders their performance, and propose a novel algorithm based on constrained independent component analysis to improve performance. We present how the proposed algorithm extracts a photoplethysmography signal and resolves the sorting problem. In addition, we perform a comparative study between the proposed algorithm and state-of-the-art algorithms over 45 video streams using a finger probe oxymeter for reference measurements. The proposed algorithm provides improved accuracy: the root mean square error is decreased from 20.6 and 9.5 beats per minute (bpm) for existing algorithms to 3.5 bpm for the proposed algorithm. An error of 3.5 bpm is within the inaccuracy expected from the reference measurements. This implies that the proposed algorithm provided performance of equal accuracy to the finger probe oximeter.

Constrained independent component analysis approach to nonobtrusive pulse rate measurements.

PubMed

Tsouri, Gill R; Kyal, Survi; Dianat, Sohail; Mestha, Lalit K

2012-07-01

Nonobtrusive pulse rate measurement using a webcam is considered. We demonstrate how state-of-the-art algorithms based on independent component analysis suffer from a sorting problem which hinders their performance, and propose a novel algorithm based on constrained independent component analysis to improve performance. We present how the proposed algorithm extracts a photoplethysmography signal and resolves the sorting problem. In addition, we perform a comparative study between the proposed algorithm and state-of-the-art algorithms over 45 video streams using a finger probe oxymeter for reference measurements. The proposed algorithm provides improved accuracy: the root mean square error is decreased from 20.6 and 9.5 beats per minute (bpm) for existing algorithms to 3.5 bpm for the proposed algorithm. An error of 3.5 bpm is within the inaccuracy expected from the reference measurements. This implies that the proposed algorithm provided performance of equal accuracy to the finger probe oximeter.

Superresolution Interferometric Imaging with Sparse Modeling Using Total Squared Variation: Application to Imaging the Black Hole Shadow

NASA Astrophysics Data System (ADS)

Kuramochi, Kazuki; Akiyama, Kazunori; Ikeda, Shiro; Tazaki, Fumie; Fish, Vincent L.; Pu, Hung-Yi; Asada, Keiichi; Honma, Mareki

2018-05-01

We propose a new imaging technique for interferometry using sparse modeling, utilizing two regularization terms: the ℓ 1-norm and a new function named total squared variation (TSV) of the brightness distribution. First, we demonstrate that our technique may achieve a superresolution of ∼30% compared with the traditional CLEAN beam size using synthetic observations of two point sources. Second, we present simulated observations of three physically motivated static models of Sgr A* with the Event Horizon Telescope (EHT) to show the performance of proposed techniques in greater detail. Remarkably, in both the image and gradient domains, the optimal beam size minimizing root-mean-squared errors is ≲10% of the traditional CLEAN beam size for ℓ 1+TSV regularization, and non-convolved reconstructed images have smaller errors than beam-convolved reconstructed images. This indicates that TSV is well matched to the expected physical properties of the astronomical images and the traditional post-processing technique of Gaussian convolution in interferometric imaging may not be required. We also propose a feature-extraction method to detect circular features from the image of a black hole shadow and use it to evaluate the performance of the image reconstruction. With this method and reconstructed images, the EHT can constrain the radius of the black hole shadow with an accuracy of ∼10%–20% in present simulations for Sgr A*, suggesting that the EHT would be able to provide useful independent measurements of the mass of the supermassive black holes in Sgr A* and also another primary target, M87.

47 CFR 87.145 - Acceptability of transmitters for licensing.

Code of Federal Regulations, 2014 CFR

2014-10-01

... square error which assumes zero error for the received ground earth station signal and includes the AES transmit/receive frequency reference error and the AES automatic frequency control residual errors.) The...

47 CFR 87.145 - Acceptability of transmitters for licensing.

Code of Federal Regulations, 2013 CFR

2013-10-01

... square error which assumes zero error for the received ground earth station signal and includes the AES transmit/receive frequency reference error and the AES automatic frequency control residual errors.) The...

47 CFR 87.145 - Acceptability of transmitters for licensing.

Code of Federal Regulations, 2012 CFR

2012-10-01

... square error which assumes zero error for the received ground earth station signal and includes the AES transmit/receive frequency reference error and the AES automatic frequency control residual errors.) The...

47 CFR 87.145 - Acceptability of transmitters for licensing.

Code of Federal Regulations, 2011 CFR

2011-10-01

... square error which assumes zero error for the received ground earth station signal and includes the AES transmit/receive frequency reference error and the AES automatic frequency control residual errors.) The...

Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malinowski, Kathleen T.; Fischell Department of Bioengineering, University of Maryland, College Park, MD; McAvoy, Thomas J.

2012-04-01

Purpose: To investigate the effect of tumor site, measurement precision, tumor-surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position. Methods and Materials: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor-surrogate correlation and the precisionmore » in both the tumor and the external surrogate position measurements were explored by adding noise to the data. Results: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor-surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3-3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares. Conclusions: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.« less

Expected accuracy of proximal and distal temperature estimated by wireless sensors, in relation to their number and position on the skin.

PubMed

Longato, Enrico; Garrido, Maria; Saccardo, Desy; Montesinos Guevara, Camila; Mani, Ali R; Bolognesi, Massimo; Amodio, Piero; Facchinetti, Andrea; Sparacino, Giovanni; Montagnese, Sara

2017-01-01

A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints.

Expected accuracy of proximal and distal temperature estimated by wireless sensors, in relation to their number and position on the skin

PubMed Central

Longato, Enrico; Garrido, Maria; Saccardo, Desy; Montesinos Guevara, Camila; Mani, Ali R.; Bolognesi, Massimo; Amodio, Piero; Facchinetti, Andrea; Sparacino, Giovanni

2017-01-01

A popular method to estimate proximal/distal temperature (TPROX and TDIST) consists in calculating a weighted average of nine wireless sensors placed on pre-defined skin locations. Specifically, TPROX is derived from five sensors placed on the infra-clavicular and mid-thigh area (left and right) and abdomen, and TDIST from four sensors located on the hands and feet. In clinical practice, the loss/removal of one or more sensors is a common occurrence, but limited information is available on how this affects the accuracy of temperature estimates. The aim of this study was to determine the accuracy of temperature estimates in relation to number/position of sensors removed. Thirteen healthy subjects wore all nine sensors for 24 hours and reference TPROX and TDIST time-courses were calculated using all sensors. Then, all possible combinations of reduced subsets of sensors were simulated and suitable weights for each sensor calculated. The accuracy of TPROX and TDIST estimates resulting from the reduced subsets of sensors, compared to reference values, was assessed by the mean squared error, the mean absolute error (MAE), the cross-validation error and the 25th and 75th percentiles of the reconstruction error. Tables of the accuracy and sensor weights for all possible combinations of sensors are provided. For instance, in relation to TPROX, a subset of three sensors placed in any combination of three non-homologous areas (abdominal, right or left infra-clavicular, right or left mid-thigh) produced an error of 0.13°C MAE, while the loss/removal of the abdominal sensor resulted in an error of 0.25°C MAE, with the greater impact on the quality of the reconstruction. This information may help researchers/clinicians: i) evaluate the expected goodness of their TPROX and TDIST estimates based on the number of available sensors; ii) select the most appropriate subset of sensors, depending on goals and operational constraints. PMID:28666029

Three filters for visualization of phase objects with large variations of phase gradients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sagan, Arkadiusz; Antosiewicz, Tomasz J.; Szoplik, Tomasz

2009-02-20

We propose three amplitude filters for visualization of phase objects. They interact with the spectra of pure-phase objects in the frequency plane and are based on tangent and error functions as well as antisymmetric combination of square roots. The error function is a normalized form of the Gaussian function. The antisymmetric square-root filter is composed of two square-root filters to widen its spatial frequency spectral range. Their advantage over other known amplitude frequency-domain filters, such as linear or square-root graded ones, is that they allow high-contrast visualization of objects with large variations of phase gradients.

Spiral tracing on a touchscreen is influenced by age, hand, implement, and friction.

PubMed

Heintz, Brittany D; Keenan, Kevin G

2018-01-01

Dexterity impairments are well documented in older adults, though it is unclear how these influence touchscreen manipulation. This study examined age-related differences while tracing on high- and low-friction touchscreens using the finger or stylus. 26 young and 24 older adults completed an Archimedes spiral tracing task on a touchscreen mounted on a force sensor. Root mean square error was calculated to quantify performance. Root mean square error increased by 29.9% for older vs. young adults using the fingertip, but was similar to young adults when using the stylus. Although other variables (e.g., touchscreen usage, sensation, and reaction time) differed between age groups, these variables were not related to increased error in older adults while using their fingertip. Root mean square error also increased on the low-friction surface for all subjects. These findings suggest that utilizing a stylus and increasing surface friction may improve touchscreen use in older adults.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

The microchannel x-ray telescope status

NASA Astrophysics Data System (ADS)

Götz, D.; Meuris, A.; Pinsard, F.; Doumayrou, E.; Tourrette, T.; Osborne, J. P.; Willingale, R.; Sykes, J. M.; Pearson, J. F.; Le Duigou, J. M.; Mercier, K.

2016-07-01

We present design status of the Microchannel X-ray Telescope, the focussing X-ray telescope on board the Sino- French SVOM mission dedicated to Gamma-Ray Bursts. Its optical design is based on square micro-pore optics (MPOs) in a Lobster-Eye configuration. The optics will be coupled to a low-noise pnCCD sensitive in the 0.2{10 keV energy range. With an expected point spread function of 4.5 arcmin (FWHM) and an estimated sensitivity adequate to detect all the afterglows of the SVOM GRBs, MXT will be able to provide error boxes smaller than 60 (90% c.l.) arc sec after five minutes of observation.

Hypothesis Testing Using Factor Score Regression

PubMed Central

Devlieger, Ines; Mayer, Axel; Rosseel, Yves

2015-01-01

In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate. PMID:29795886

Some Results on Mean Square Error for Factor Score Prediction

ERIC Educational Resources Information Center

Krijnen, Wim P.

2006-01-01

For the confirmatory factor model a series of inequalities is given with respect to the mean square error (MSE) of three main factor score predictors. The eigenvalues of these MSE matrices are a monotonic function of the eigenvalues of the matrix gamma[subscript rho] = theta[superscript 1/2] lambda[subscript rho] 'psi[subscript rho] [superscript…

Weighted linear regression using D2H and D2 as the independent variables

Treesearch

Hans T. Schreuder; Michael S. Williams

1998-01-01

Several error structures for weighted regression equations used for predicting volume were examined for 2 large data sets of felled and standing loblolly pine trees (Pinus taeda L.). The generally accepted model with variance of error proportional to the value of the covariate squared ( D2H = diameter squared times height or D...

The Relationship between Root Mean Square Error of Approximation and Model Misspecification in Confirmatory Factor Analysis Models

ERIC Educational Resources Information Center

Savalei, Victoria

2012-01-01

The fit index root mean square error of approximation (RMSEA) is extremely popular in structural equation modeling. However, its behavior under different scenarios remains poorly understood. The present study generates continuous curves where possible to capture the full relationship between RMSEA and various "incidental parameters," such as…

A method of bias correction for maximal reliability with dichotomous measures.

PubMed

Penev, Spiridon; Raykov, Tenko

2010-02-01

This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.

Synthetic Aperture Sonar Processing with MMSE Estimation of Image Sample Values

DTIC Science & Technology

2016-12-01

UNCLASSIFIED/UNLIMITED 13. SUPPLEMENTARY NOTES 14. ABSTRACT MMSE (minimum mean- square error) target sample estimation using non-orthogonal basis...orthogonal, they can still be used in a minimum mean‐ square  error (MMSE)  estimator that models the object echo as a weighted sum of the multi‐aspect basis...problem.                     3    Introduction      Minimum mean‐ square  error (MMSE) estimation is applied to target imaging with synthetic aperture

Development and evaluation of social cognitive measures related to adolescent physical activity.

PubMed

Dewar, Deborah L; Lubans, David Revalds; Morgan, Philip James; Plotnikoff, Ronald C

2013-05-01

This study aimed to develop and evaluate the construct validity and reliability of modernized social cognitive measures relating to physical activity behaviors in adolescents. An instrument was developed based on constructs from Bandura's Social Cognitive Theory and included the following scales: self-efficacy, situation (perceived physical environment), social support, behavioral strategies, and outcome expectations and expectancies. The questionnaire was administered in a sample of 171 adolescents (age = 13.6 ± 1.2 years, females = 61%). Confirmatory factor analysis was employed to examine model-fit for each scale using multiple indices, including chi-square index, comparative-fit index (CFI), goodness-of-fit index (GFI), and the root mean square error of approximation (RMSEA). Reliability properties were also examined (ICC and Cronbach's alpha). Each scale represented a statistically sound measure: fit indices indicated each model to be an adequate-to-exact fit to the data; internal consistency was acceptable to good (α = 0.63-0.79); rank order repeatability was strong (ICC = 0.82-0.91). Results support the validity and reliability of social cognitive scales relating to physical activity among adolescents. As such, the developed scales have utility for the identification of potential social cognitive correlates of youth physical activity, mediators of physical activity behavior changes and the testing of theoretical models based on Social Cognitive Theory.

On the appropriateness of applying chi-square distribution based confidence intervals to spectral estimates of helicopter flyover data

NASA Technical Reports Server (NTRS)

Rutledge, Charles K.

1988-01-01

The validity of applying chi-square based confidence intervals to far-field acoustic flyover spectral estimates was investigated. Simulated data, using a Kendall series and experimental acoustic data from the NASA/McDonnell Douglas 500E acoustics test, were analyzed. Statistical significance tests to determine the equality of distributions of the simulated and experimental data relative to theoretical chi-square distributions were performed. Bias and uncertainty errors associated with the spectral estimates were easily identified from the data sets. A model relating the uncertainty and bias errors to the estimates resulted, which aided in determining the appropriateness of the chi-square distribution based confidence intervals. Such confidence intervals were appropriate for nontonally associated frequencies of the experimental data but were inappropriate for tonally associated estimate distributions. The appropriateness at the tonally associated frequencies was indicated by the presence of bias error and noncomformity of the distributions to the theoretical chi-square distribution. A technique for determining appropriate confidence intervals at the tonally associated frequencies was suggested.

Eta Squared, Partial Eta Squared, and Misreporting of Effect Size in Communication Research.

ERIC Educational Resources Information Center

Levine, Timothy R.; Hullett, Craig R.

2002-01-01

Alerts communication researchers to potential errors stemming from the use of SPSS (Statistical Package for the Social Sciences) to obtain estimates of eta squared in analysis of variance (ANOVA). Strives to clarify issues concerning the development and appropriate use of eta squared and partial eta squared in ANOVA. Discusses the reporting of…

POOLMS: A computer program for fitting and model selection for two level factorial replication-free experiments

NASA Technical Reports Server (NTRS)

Amling, G. E.; Holms, A. G.

1973-01-01

A computer program is described that performs a statistical multiple-decision procedure called chain pooling. It uses a number of mean squares assigned to error variance that is conditioned on the relative magnitudes of the mean squares. The model selection is done according to user-specified levels of type 1 or type 2 error probabilities.

Validating Clusters with the Lower Bound for Sum-of-Squares Error

ERIC Educational Resources Information Center

Steinley, Douglas

2007-01-01

Given that a minor condition holds (e.g., the number of variables is greater than the number of clusters), a nontrivial lower bound for the sum-of-squares error criterion in K-means clustering is derived. By calculating the lower bound for several different situations, a method is developed to determine the adequacy of cluster solution based on…

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.

PubMed

Bannan, Caitlin C; Burley, Kalistyn H; Chiu, Michael; Shirts, Michael R; Gilson, Michael K; Mobley, David L

2016-11-01

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge

PubMed Central

Bannan, Caitlin C.; Burley, Kalistyn H.; Chiu, Michael; Shirts, Michael R.; Gilson, Michael K.; Mobley, David L.

2016-01-01

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty – how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided. PMID:27677750

A suggestion for computing objective function in model calibration

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang

2014-01-01

A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).

The association of perceived organizational justice and organizational expectations with nurses' efforts.

PubMed

Motlagh, Farhad Shafiepour; Yarmohammadian, Mohammad Hossein; Yaghoubi, Maryam

2012-03-01

One important factor in growth, progress, and increase in work efficiency of employees of any enterprise is to make considerable effort. Supreme leader of the Islamic Republic of Iran also addressed the issue of need for more efforts. The goal of this study was to determine the association of perceived organizational justice and organizational expectations with efforts of nurses to provide a suitable model. The current study was a descriptive study. The study group consists of all nurses who worked in hospitals of Isfahan. Due to some limitations all nurses of the special unit, surgery wards and operating room were questioned. The data collection tools were the Organizational Justice Questionnaire, organizational expectations questionnaire, and double effort questionnaire. Content validity of the mentioned questionnaires was confirmed after considering the experts' comments. The reliability of these questionnaires, using the Cronbach's alpha, were 0.79, 0.83 and 0.92, respectively. The Pearson correlation and the structural equation model were used for the analysis of data. There was a significant correlation between the perceived organizational justice and the double effort of nurses during the surgery of patients. Correlation of the expectation from job, usefulness of job, and its attractiveness with double effort of nurses before the surgery was also statistically significant. Moreover, it was shown that the root of the mean square error of estimation (RMSEA) was 0.087, the fitted goodness index (GFI) was 0.953, the value of chi-square was 268.5, and the model was statistically significant (p < 0.001). Today Justice is an essential need for human life and its importance in organizations and social life of individuals is evident.

Estimating magnitude and frequency of peak discharges for rural, unregulated, streams in West Virginia

USGS Publications Warehouse

Wiley, J.B.; Atkins, John T.; Tasker, Gary D.

2000-01-01

Multiple and simple least-squares regression models for the log10-transformed 100-year discharge with independent variables describing the basin characteristics (log10-transformed and untransformed) for 267 streamflow-gaging stations were evaluated, and the regression residuals were plotted as areal distributions that defined three regions of the State, designated East, North, and South. Exploratory data analysis procedures identified 31 gaging stations at which discharges are different than would be expected for West Virginia. Regional equations for the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year peak discharges were determined by generalized least-squares regression using data from 236 gaging stations. Log10-transformed drainage area was the most significant independent variable for all regions.Equations developed in this study are applicable only to rural, unregulated, streams within the boundaries of West Virginia. The accuracy of estimating equations is quantified by measuring the average prediction error (from 27.7 to 44.7 percent) and equivalent years of record (from 1.6 to 20.0 years).

[Formula: see text]-regularized recursive total least squares based sparse system identification for the error-in-variables.

PubMed

Lim, Jun-Seok; Pang, Hee-Suk

2016-01-01

In this paper an [Formula: see text]-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed [Formula: see text]-RTLS algorithm is an RLS like iteration using the [Formula: see text] regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed [Formula: see text]-regularized RTLS for the sparse system identification setting.

An algorithm for propagating the square-root covariance matrix in triangular form

NASA Technical Reports Server (NTRS)

Tapley, B. D.; Choe, C. Y.

1976-01-01

A method for propagating the square root of the state error covariance matrix in lower triangular form is described. The algorithm can be combined with any triangular square-root measurement update algorithm to obtain a triangular square-root sequential estimation algorithm. The triangular square-root algorithm compares favorably with the conventional sequential estimation algorithm with regard to computation time.

Fitting a function to time-dependent ensemble averaged data.

PubMed

Fogelmark, Karl; Lomholt, Michael A; Irbäck, Anders; Ambjörnsson, Tobias

2018-05-03

Time-dependent ensemble averages, i.e., trajectory-based averages of some observable, are of importance in many fields of science. A crucial objective when interpreting such data is to fit these averages (for instance, squared displacements) with a function and extract parameters (such as diffusion constants). A commonly overlooked challenge in such function fitting procedures is that fluctuations around mean values, by construction, exhibit temporal correlations. We show that the only available general purpose function fitting methods, correlated chi-square method and the weighted least squares method (which neglects correlation), fail at either robust parameter estimation or accurate error estimation. We remedy this by deriving a new closed-form error estimation formula for weighted least square fitting. The new formula uses the full covariance matrix, i.e., rigorously includes temporal correlations, but is free of the robustness issues, inherent to the correlated chi-square method. We demonstrate its accuracy in four examples of importance in many fields: Brownian motion, damped harmonic oscillation, fractional Brownian motion and continuous time random walks. We also successfully apply our method, weighted least squares including correlation in error estimation (WLS-ICE), to particle tracking data. The WLS-ICE method is applicable to arbitrary fit functions, and we provide a publically available WLS-ICE software.

Quality assessment of gasoline using comprehensive two-dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration.

PubMed

Parastar, Hadi; Mostafapour, Sara; Azimi, Gholamhasan

2016-01-01

Comprehensive two-dimensional gas chromatography and flame ionization detection combined with unfolded-partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two-dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root-mean square error of leave-one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996-0.998, root-mean square error of prediction of 0.005-0.010 and relative error of prediction of 1.54-3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70-85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Derivation of formulas for root-mean-square errors in location, orientation, and shape in triangulation solution of an elongated object in space

NASA Technical Reports Server (NTRS)

Long, S. A. T.

1974-01-01

Formulas are derived for the root-mean-square (rms) displacement, slope, and curvature errors in an azimuth-elevation image trace of an elongated object in space, as functions of the number and spacing of the input data points and the rms elevation error in the individual input data points from a single observation station. Also, formulas are derived for the total rms displacement, slope, and curvature error vectors in the triangulation solution of an elongated object in space due to the rms displacement, slope, and curvature errors, respectively, in the azimuth-elevation image traces from different observation stations. The total rms displacement, slope, and curvature error vectors provide useful measure numbers for determining the relative merits of two or more different triangulation procedures applicable to elongated objects in space.

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

PubMed Central

Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

2012-01-01

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling. PMID:23483015

Error properties of Argos satellite telemetry locations using least squares and Kalman filtering.

PubMed

Boyd, Janice D; Brightsmith, Donald J

2013-01-01

Study of animal movements is key for understanding their ecology and facilitating their conservation. The Argos satellite system is a valuable tool for tracking species which move long distances, inhabit remote areas, and are otherwise difficult to track with traditional VHF telemetry and are not suitable for GPS systems. Previous research has raised doubts about the magnitude of position errors quoted by the satellite service provider CLS. In addition, no peer-reviewed publications have evaluated the usefulness of the CLS supplied error ellipses nor the accuracy of the new Kalman filtering (KF) processing method. Using transmitters hung from towers and trees in southeastern Peru, we show the Argos error ellipses generally contain <25% of the true locations and therefore do not adequately describe the true location errors. We also find that KF processing does not significantly increase location accuracy. The errors for both LS and KF processing methods were found to be lognormally distributed, which has important repercussions for error calculation, statistical analysis, and data interpretation. In brief, "good" positions (location codes 3, 2, 1, A) are accurate to about 2 km, while 0 and B locations are accurate to about 5-10 km. However, due to the lognormal distribution of the errors, larger outliers are to be expected in all location codes and need to be accounted for in the user's data processing. We evaluate five different empirical error estimates and find that 68% lognormal error ellipses provided the most useful error estimates. Longitude errors are larger than latitude errors by a factor of 2 to 3, supporting the use of elliptical error ellipses. Numerous studies over the past 15 years have also found fault with the CLS-claimed error estimates yet CLS has failed to correct their misleading information. We hope this will be reversed in the near future.

Least-squares model-based halftoning

NASA Astrophysics Data System (ADS)

Pappas, Thrasyvoulos N.; Neuhoff, David L.

1992-08-01

A least-squares model-based approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It attempts to produce an 'optimal' halftoned reproduction, by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. Conventional methods, such as clustered ordered dither, use the properties of the eye only implicitly, and resist printer distortions at the expense of spatial and gray-scale resolution. In previous work we showed that our printer model can be used to modify error diffusion to account for printer distortions. The modified error diffusion algorithm has better spatial and gray-scale resolution than conventional techniques, but produces some well known artifacts and asymmetries because it does not make use of an explicit eye model. Least-squares model-based halftoning uses explicit eye models and relies on printer models that predict distortions and exploit them to increase, rather than decrease, both spatial and gray-scale resolution. We have shown that the one-dimensional least-squares problem, in which each row or column of the image is halftoned independently, can be implemented with the Viterbi's algorithm. Unfortunately, no closed form solution can be found in two dimensions. The two-dimensional least squares solution is obtained by iterative techniques. Experiments show that least-squares model-based halftoning produces more gray levels and better spatial resolution than conventional techniques. We also show that the least- squares approach eliminates the problems associated with error diffusion. Model-based halftoning can be especially useful in transmission of high quality documents using high fidelity gray-scale image encoders. As we have shown, in such cases halftoning can be performed at the receiver, just before printing. Apart from coding efficiency, this approach permits the halftoner to be tuned to the individual printer, whose characteristics may vary considerably from those of other printers, for example, write-black vs. write-white laser printers.

Does the sensorimotor system minimize prediction error or select the most likely prediction during object lifting?

PubMed Central

McGregor, Heather R.; Pun, Henry C. H.; Buckingham, Gavin; Gribble, Paul L.

2016-01-01

The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy. NEW & NOTEWORTHY Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems. PMID:27760821

Variable forgetting factor mechanisms for diffusion recursive least squares algorithm in sensor networks

NASA Astrophysics Data System (ADS)

Zhang, Ling; Cai, Yunlong; Li, Chunguang; de Lamare, Rodrigo C.

2017-12-01

In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed low-complexity VFF-DRLS algorithms achieve superior performance to the existing DRLS algorithm with fixed forgetting factor when applied to scenarios of distributed parameter and spectrum estimation. Besides, the simulation results also demonstrate a good match for our proposed analytical expressions.

Fission matrix-based Monte Carlo criticality analysis of fuel storage pools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farlotti, M.; Ecole Polytechnique, Palaiseau, F 91128; Larsen, E. W.

2013-07-01

Standard Monte Carlo transport procedures experience difficulties in solving criticality problems in fuel storage pools. Because of the strong neutron absorption between fuel assemblies, source convergence can be very slow, leading to incorrect estimates of the eigenvalue and the eigenfunction. This study examines an alternative fission matrix-based Monte Carlo transport method that takes advantage of the geometry of a storage pool to overcome this difficulty. The method uses Monte Carlo transport to build (essentially) a fission matrix, which is then used to calculate the criticality and the critical flux. This method was tested using a test code on a simplemore » problem containing 8 assemblies in a square pool. The standard Monte Carlo method gave the expected eigenfunction in 5 cases out of 10, while the fission matrix method gave the expected eigenfunction in all 10 cases. In addition, the fission matrix method provides an estimate of the error in the eigenvalue and the eigenfunction, and it allows the user to control this error by running an adequate number of cycles. Because of these advantages, the fission matrix method yields a higher confidence in the results than standard Monte Carlo. We also discuss potential improvements of the method, including the potential for variance reduction techniques. (authors)« less

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

PubMed

Guelpa, Anina; Bevilacqua, Marta; Marini, Federico; O'Kennedy, Kim; Geladi, Paul; Manley, Marena

2015-04-15

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Relationship between Mean Square Differences and Standard Error of Measurement: Comment on Barchard (2012)

ERIC Educational Resources Information Center

Pan, Tianshu; Yin, Yue

2012-01-01

In the discussion of mean square difference (MSD) and standard error of measurement (SEM), Barchard (2012) concluded that the MSD between 2 sets of test scores is greater than 2(SEM)[superscript 2] and SEM underestimates the score difference between 2 tests when the 2 tests are not parallel. This conclusion has limitations for 2 reasons. First,…

Quantified Choice of Root-Mean-Square Errors of Approximation for Evaluation and Power Analysis of Small Differences between Structural Equation Models

ERIC Educational Resources Information Center

Li, Libo; Bentler, Peter M.

2011-01-01

MacCallum, Browne, and Cai (2006) proposed a new framework for evaluation and power analysis of small differences between nested structural equation models (SEMs). In their framework, the null and alternative hypotheses for testing a small difference in fit and its related power analyses were defined by some chosen root-mean-square error of…

Improved error estimates of a discharge algorithm for remotely sensed river measurements: Test cases on Sacramento and Garonne Rivers

NASA Astrophysics Data System (ADS)

Yoon, Yeosang; Garambois, Pierre-André; Paiva, Rodrigo C. D.; Durand, Michael; Roux, Hélène; Beighley, Edward

2016-01-01

We present an improvement to a previously presented algorithm that used a Bayesian Markov Chain Monte Carlo method for estimating river discharge from remotely sensed observations of river height, width, and slope. We also present an error budget for discharge calculations from the algorithm. The algorithm may be utilized by the upcoming Surface Water and Ocean Topography (SWOT) mission. We present a detailed evaluation of the method using synthetic SWOT-like observations (i.e., SWOT and AirSWOT, an airborne version of SWOT). The algorithm is evaluated using simulated AirSWOT observations over the Sacramento and Garonne Rivers that have differing hydraulic characteristics. The algorithm is also explored using SWOT observations over the Sacramento River. SWOT and AirSWOT height, width, and slope observations are simulated by corrupting the "true" hydraulic modeling results with instrument error. Algorithm discharge root mean square error (RMSE) was 9% for the Sacramento River and 15% for the Garonne River for the AirSWOT case using expected observation error. The discharge uncertainty calculated from Manning's equation was 16.2% and 17.1%, respectively. For the SWOT scenario, the RMSE and uncertainty of the discharge estimate for the Sacramento River were 15% and 16.2%, respectively. A method based on the Kalman filter to correct errors of discharge estimates was shown to improve algorithm performance. From the error budget, the primary source of uncertainty was the a priori uncertainty of bathymetry and roughness parameters. Sensitivity to measurement errors was found to be a function of river characteristics. For example, Steeper Garonne River is less sensitive to slope errors than the flatter Sacramento River.

Accuracy assessment of high-rate GPS measurements for seismology

NASA Astrophysics Data System (ADS)

Elosegui, P.; Davis, J. L.; Ekström, G.

2007-12-01

Analysis of GPS measurements with a controlled laboratory system, built to simulate the ground motions caused by tectonic earthquakes and other transient geophysical signals such as glacial earthquakes, enables us to assess the technique of high-rate GPS. The root-mean-square (rms) position error of this system when undergoing realistic simulated seismic motions is 0.05~mm, with maximum position errors of 0.1~mm, thus providing "ground truth" GPS displacements. We have acquired an extensive set of high-rate GPS measurements while inducing seismic motions on a GPS antenna mounted on this system with a temporal spectrum similar to real seismic events. We found that, for a particular 15-min-long test event, the rms error of the 1-Hz GPS position estimates was 2.5~mm, with maximum position errors of 10~mm, and the error spectrum of the GPS estimates was approximately flicker noise. These results may however represent a best-case scenario since they were obtained over a short (~10~m) baseline, thereby greatly mitigating baseline-dependent errors, and when the number and distribution of satellites on the sky was good. For example, we have determined that the rms error can increase by a factor of 2--3 as the GPS constellation changes throughout the day, with an average value of 3.5~mm for eight identical, hourly-spaced, consecutive test events. The rms error also increases with increasing baseline, as one would expect, with an average rms error for a ~1400~km baseline of 9~mm. We will present an assessment of the accuracy of high-rate GPS based on these measurements, discuss the implications of this study for seismology, and describe new applications in glaciology.

[Locally weighted least squares estimation of DPOAE evoked by continuously sweeping primaries].

PubMed

Han, Xiaoli; Fu, Xinxing; Cui, Jie; Xiao, Ling

2013-12-01

Distortion product otoacoustic emission (DPOAE) signal can be used for diagnosis of hearing loss so that it has an important clinical value. Continuously using sweeping primaries to measure DPOAE provides an efficient tool to record DPOAE data rapidly when DPOAE is measured in a large frequency range. In this paper, locally weighted least squares estimation (LWLSE) of 2f1-f2 DPOAE is presented based on least-squares-fit (LSF) algorithm, in which DPOAE is evoked by continuously sweeping tones. In our study, we used a weighted error function as the loss function and the weighting matrixes in the local sense to obtain a smaller estimated variance. Firstly, ordinary least squares estimation of the DPOAE parameters was obtained. Then the error vectors were grouped and the different local weighting matrixes were calculated in each group. And finally, the parameters of the DPOAE signal were estimated based on least squares estimation principle using the local weighting matrixes. The simulation results showed that the estimate variance and fluctuation errors were reduced, so the method estimates DPOAE and stimuli more accurately and stably, which facilitates extraction of clearer DPOAE fine structure.

Analysis of surface-water data network in Kansas for effectiveness in providing regional streamflow information; with a section on theory and application of generalized least squares

USGS Publications Warehouse

Medina, K.D.; Tasker, Gary D.

1987-01-01

This report documents the results of an analysis of the surface-water data network in Kansas for its effectiveness in providing regional streamflow information. The network was analyzed using generalized least squares regression. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-, low-, and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow-gaging-station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations, and (or) adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and for discontinued stations for which unregulated flow characteristics, as well as physical and climatic characteristics, were available. The State was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for the three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean-square error for each cost level could be obtained by adding new stations and discontinuing some current network stations. Large reductions in sampling mean-square error for low-flow information could be achieved in all three network areas, the reduction in western Kansas being the most dramatic. The addition of new stations would be most beneficial for mean-flow information in western Kansas. The reduction of sampling mean-square error for high-flow information would benefit most from the addition of new stations in western Kansas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas and establishing more new stations in western Kansas.

An Application of Interactive Computer Graphics to the Study of Inferential Statistics and the General Linear Model

DTIC Science & Technology

1991-09-01

matrix, the Regression Sum of Squares (SSR) and Error Sum of Squares (SSE) are also displayed as a percentage of the Total Sum of Squares ( SSTO ...vector when the student compares the SSR to the SSE. In addition to the plot, the actual values of SSR, SSE, and SSTO are also provided. Figure 3 gives the...Es ainSpace = E 3 Error- Eor Space =n t! L . Pro~cio q Yonto Pro~rct on of Y onto the simaton, pac ror Space SSR SSEL0.20 IV = 14,1 +IErrorI 2 SSTO

Validation of the ASTER Global Digital Elevation Model Version 2 over the conterminous United States

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Zhang, Zheng; Meyer, David J.; Danielson, Jeffrey J.

2012-01-01

The ASTER Global Digital Elevation Model Version 2 (GDEM v2) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009. The absolute vertical accuracy of GDEM v2 was calculated by comparison with more than 18,000 independent reference geodetic ground control points from the National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v2 is 8.68 meters. This compares with the RMSE of 9.34 meters for GDEM v1. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v2 mean error of -0.20 meters is a significant improvement over the GDEM v1 mean error of -3.69 meters. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover to examine the effects of cover types on measured errors. The GDEM v2 mean errors by land cover class verify that the presence of aboveground features (tree canopies and built structures) cause a positive elevation bias, as would be expected for an imaging system like ASTER. In open ground classes (little or no vegetation with significant aboveground height), GDEM v2 exhibits a negative bias on the order of 1 meter. GDEM v2 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v2 has elevations that are higher in the canopy than SRTM.

Gravity and isostatic anomaly maps of Greece produced

NASA Astrophysics Data System (ADS)

Lagios, E.; Chailas, S.; Hipkin, R. G.

A gravity anomaly map of Greece was first compiled in the early 1970s [Makris and Stavrou, 1984] from all available gravity data collected by different Hellenic institutions. However, to compose this map the data had to be smoothed to the point that many of the smaller-wavelength gravity anomalies were lost. New work begun in 1987 has resulted in the publication of an updated map [Lagios et al., 1994] and an isostatic anomaly map derived from it.The gravity data cover the area between east longitudes 19° and 27° and north latitudes 32° and 42°, organized in files of 100-km squares and grouped in 10-km squares using UTM zone 34 coordinates. Most of the data on land come from the gravity observations of Makris and Stavrou [1984] with additional data from the Institute of Geology and Mining Exploration, the Public Oil Corporation of Greece, and Athens University. These data were checked using techniques similar to those used in compiling the gravity anomaly map of the United States, but the horizontal gradient was used as a check rather than the gravity difference. Marine data were digitized from the maps of Morelli et al. [1975a, 1975b]. All gravity anomaly values are referred to the IGSN-71 system, reduced with the standard Bouger density of 2.67 Mg/m3. We estimate the errors of the anomalies in the continental part of Greece to be ±0.9 mGal; this is expected to be smaller over fairly flat regions. For stations whose height has been determined by leveling, the error is only ±0.3 mGal. For the marine areas, the errors are about ±5 mGal [Morelli, 1990].

Atomic charge transfer-counter polarization effects determine infrared CH intensities of hydrocarbons: a quantum theory of atoms in molecules model.

PubMed

Silva, Arnaldo F; Richter, Wagner E; Meneses, Helen G C; Bruns, Roy E

2014-11-14

Atomic charge transfer-counter polarization effects determine most of the infrared fundamental CH intensities of simple hydrocarbons, methane, ethylene, ethane, propyne, cyclopropane and allene. The quantum theory of atoms in molecules/charge-charge flux-dipole flux model predicted the values of 30 CH intensities ranging from 0 to 123 km mol(-1) with a root mean square (rms) error of only 4.2 km mol(-1) without including a specific equilibrium atomic charge term. Sums of the contributions from terms involving charge flux and/or dipole flux averaged 20.3 km mol(-1), about ten times larger than the average charge contribution of 2.0 km mol(-1). The only notable exceptions are the CH stretching and bending intensities of acetylene and two of the propyne vibrations for hydrogens bound to sp hybridized carbon atoms. Calculations were carried out at four quantum levels, MP2/6-311++G(3d,3p), MP2/cc-pVTZ, QCISD/6-311++G(3d,3p) and QCISD/cc-pVTZ. The results calculated at the QCISD level are the most accurate among the four with root mean square errors of 4.7 and 5.0 km mol(-1) for the 6-311++G(3d,3p) and cc-pVTZ basis sets. These values are close to the estimated aggregate experimental error of the hydrocarbon intensities, 4.0 km mol(-1). The atomic charge transfer-counter polarization effect is much larger than the charge effect for the results of all four quantum levels. Charge transfer-counter polarization effects are expected to also be important in vibrations of more polar molecules for which equilibrium charge contributions can be large.

Effect of Numerical Error on Gravity Field Estimation for GRACE and Future Gravity Missions

NASA Astrophysics Data System (ADS)

McCullough, Christopher; Bettadpur, Srinivas

2015-04-01

In recent decades, gravity field determination from low Earth orbiting satellites, such as the Gravity Recovery and Climate Experiment (GRACE), has become increasingly more effective due to the incorporation of high accuracy measurement devices. Since instrumentation quality will only increase in the near future and the gravity field determination process is computationally and numerically intensive, numerical error from the use of double precision arithmetic will eventually become a prominent error source. While using double-extended or quadruple precision arithmetic will reduce these errors, the numerical limitations of current orbit determination algorithms and processes must be accurately identified and quantified in order to adequately inform the science data processing techniques of future gravity missions. The most obvious numerical limitation in the orbit determination process is evident in the comparison of measured observables with computed values, derived from mathematical models relating the satellites' numerically integrated state to the observable. Significant error in the computed trajectory will corrupt this comparison and induce error in the least squares solution of the gravitational field. In addition, errors in the numerically computed trajectory propagate into the evaluation of the mathematical measurement model's partial derivatives. These errors amalgamate in turn with numerical error from the computation of the state transition matrix, computed using the variational equations of motion, in the least squares mapping matrix. Finally, the solution of the linearized least squares system, computed using a QR factorization, is also susceptible to numerical error. Certain interesting combinations of each of these numerical errors are examined in the framework of GRACE gravity field determination to analyze and quantify their effects on gravity field recovery.

Identification and compensation of the temperature influences in a miniature three-axial accelerometer based on the least squares method

NASA Astrophysics Data System (ADS)

Grigorie, Teodor Lucian; Corcau, Ileana Jenica; Tudosie, Alexandru Nicolae

2017-06-01

The paper presents a way to obtain an intelligent miniaturized three-axial accelerometric sensor, based on the on-line estimation and compensation of the sensor errors generated by the environmental temperature variation. Taking into account that this error's value is a strongly nonlinear complex function of the values of environmental temperature and of the acceleration exciting the sensor, its correction may not be done off-line and it requires the presence of an additional temperature sensor. The proposed identification methodology for the error model is based on the least square method which process off-line the numerical values obtained from the accelerometer experimental testing for different values of acceleration applied to its axes of sensitivity and for different values of operating temperature. A final analysis of the error level after the compensation highlights the best variant for the matrix in the error model. In the sections of the paper are shown the results of the experimental testing of the accelerometer on all the three sensitivity axes, the identification of the error models on each axis by using the least square method, and the validation of the obtained models with experimental values. For all of the three detection channels was obtained a reduction by almost two orders of magnitude of the acceleration absolute maximum error due to environmental temperature variation.

Accurate phase extraction algorithm based on Gram–Schmidt orthonormalization and least square ellipse fitting method

NASA Astrophysics Data System (ADS)

Lei, Hebing; Yao, Yong; Liu, Haopeng; Tian, Yiting; Yang, Yanfu; Gu, Yinglong

2018-06-01

An accurate algorithm by combing Gram-Schmidt orthonormalization and least square ellipse fitting technology is proposed, which could be used for phase extraction from two or three interferograms. The DC term of background intensity is suppressed by subtraction operation on three interferograms or by high-pass filter on two interferograms. Performing Gram-Schmidt orthonormalization on pre-processing interferograms, the phase shift error is corrected and a general ellipse form is derived. Then the background intensity error and the corrected error could be compensated by least square ellipse fitting method. Finally, the phase could be extracted rapidly. The algorithm could cope with the two or three interferograms with environmental disturbance, low fringe number or small phase shifts. The accuracy and effectiveness of the proposed algorithm are verified by both of the numerical simulations and experiments.

Screening actuator locations for static shape control

NASA Technical Reports Server (NTRS)

Haftka, Raphael T.

1990-01-01

Correction of shape distortion due to zero-mean normally distributed errors in structural sizes which are random variables is examined. A bound on the maximum improvement in the expected value of the root-mean-square shape error is obtained. The shape correction associated with the optimal actuators is also characterized. An actuator effectiveness index is developed and shown to be helpful in screening actuator locations in the structure. The results are specialized to a simple form for truss structures composed of nominally identical members. The bound and effectiveness index are tested on a 55-m radiometer antenna truss structure. It is found that previously obtained results for optimum actuators had a performance close to the bound obtained here. Furthermore, the actuators associated with the optimum design are shown to have high effectiveness indices. Since only a small fraction of truss elements tend to have high effectiveness indices, the proposed screening procedure can greatly reduce the number of truss members that need to be considered as actuator sites.

Multi-Dielectric Brownian Dynamics and Design-Space-Exploration Studies of Permeation in Ion Channels.

PubMed

Siksik, May; Krishnamurthy, Vikram

2017-09-01

This paper proposes a multi-dielectric Brownian dynamics simulation framework for design-space-exploration (DSE) studies of ion-channel permeation. The goal of such DSE studies is to estimate the channel modeling-parameters that minimize the mean-squared error between the simulated and expected "permeation characteristics." To address this computational challenge, we use a methodology based on statistical inference that utilizes the knowledge of channel structure to prune the design space. We demonstrate the proposed framework and DSE methodology using a case study based on the KcsA ion channel, in which the design space is successfully reduced from a 6-D space to a 2-D space. Our results show that the channel dielectric map computed using the framework matches with that computed directly using molecular dynamics with an error of 7%. Finally, the scalability and resolution of the model used are explored, and it is shown that the memory requirements needed for DSE remain constant as the number of parameters (degree of heterogeneity) increases.

Intrinsic Raman spectroscopy for quantitative biological spectroscopy Part II

PubMed Central

Bechtel, Kate L.; Shih, Wei-Chuan; Feld, Michael S.

2009-01-01

We demonstrate the effectiveness of intrinsic Raman spectroscopy (IRS) at reducing errors caused by absorption and scattering. Physical tissue models, solutions of varying absorption and scattering coefficients with known concentrations of Raman scatterers, are studied. We show significant improvement in prediction error by implementing IRS to predict concentrations of Raman scatterers using both ordinary least squares regression (OLS) and partial least squares regression (PLS). In particular, we show that IRS provides a robust calibration model that does not increase in error when applied to samples with optical properties outside the range of calibration. PMID:18711512

Novel edge treatment method for improving the transmission reconstruction quality in Tomographic Gamma Scanning.

PubMed

Han, Miaomiao; Guo, Zhirong; Liu, Haifeng; Li, Qinghua

2018-05-01

Tomographic Gamma Scanning (TGS) is a method used for the nondestructive assay of radioactive wastes. In TGS, the actual irregular edge voxels are regarded as regular cubic voxels in the traditional treatment method. In this study, in order to improve the performance of TGS, a novel edge treatment method is proposed that considers the actual shapes of these voxels. The two different edge voxel treatment methods were compared by computing the pixel-level relative errors and normalized mean square errors (NMSEs) between the reconstructed transmission images and the ideal images. Both methods were coupled with two different interative algorithms comprising Algebraic Reconstruction Technique (ART) with a non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM). The results demonstrated that the traditional method for edge voxel treatment can introduce significant error and that the real irregular edge voxel treatment method can improve the performance of TGS by obtaining better transmission reconstruction images. With the real irregular edge voxel treatment method, MLEM algorithm and ART algorithm can be comparable when assaying homogenous matrices, but MLEM algorithm is superior to ART algorithm when assaying heterogeneous matrices. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparing least-squares and quantile regression approaches to analyzing median hospital charges.

PubMed

Olsen, Cody S; Clark, Amy E; Thomas, Andrea M; Cook, Lawrence J

2012-07-01

Emergency department (ED) and hospital charges obtained from administrative data sets are useful descriptors of injury severity and the burden to EDs and the health care system. However, charges are typically positively skewed due to costly procedures, long hospital stays, and complicated or prolonged treatment for few patients. The median is not affected by extreme observations and is useful in describing and comparing distributions of hospital charges. A least-squares analysis employing a log transformation is one approach for estimating median hospital charges, corresponding confidence intervals (CIs), and differences between groups; however, this method requires certain distributional properties. An alternate method is quantile regression, which allows estimation and inference related to the median without making distributional assumptions. The objective was to compare the log-transformation least-squares method to the quantile regression approach for estimating median hospital charges, differences in median charges between groups, and associated CIs. The authors performed simulations using repeated sampling of observed statewide ED and hospital charges and charges randomly generated from a hypothetical lognormal distribution. The median and 95% CI and the multiplicative difference between the median charges of two groups were estimated using both least-squares and quantile regression methods. Performance of the two methods was evaluated. In contrast to least squares, quantile regression produced estimates that were unbiased and had smaller mean square errors in simulations of observed ED and hospital charges. Both methods performed well in simulations of hypothetical charges that met least-squares method assumptions. When the data did not follow the assumed distribution, least-squares estimates were often biased, and the associated CIs had lower than expected coverage as sample size increased. Quantile regression analyses of hospital charges provide unbiased estimates even when lognormal and equal variance assumptions are violated. These methods may be particularly useful in describing and analyzing hospital charges from administrative data sets. © 2012 by the Society for Academic Emergency Medicine.

Error Analyses of the North Alabama Lightning Mapping Array (LMA)

NASA Technical Reports Server (NTRS)

Koshak, W. J.; Solokiewicz, R. J.; Blakeslee, R. J.; Goodman, S. J.; Christian, H. J.; Hall, J. M.; Bailey, J. C.; Krider, E. P.; Bateman, M. G.; Boccippio, D. J.

2003-01-01

Two approaches are used to characterize how accurately the North Alabama Lightning Mapping Array (LMA) is able to locate lightning VHF sources in space and in time. The first method uses a Monte Carlo computer simulation to estimate source retrieval errors. The simulation applies a VHF source retrieval algorithm that was recently developed at the NASA-MSFC and that is similar, but not identical to, the standard New Mexico Tech retrieval algorithm. The second method uses a purely theoretical technique (i.e., chi-squared Curvature Matrix theory) to estimate retrieval errors. Both methods assume that the LMA system has an overall rms timing error of 50ns, but all other possible errors (e.g., multiple sources per retrieval attempt) are neglected. The detailed spatial distributions of retrieval errors are provided. Given that the two methods are completely independent of one another, it is shown that they provide remarkably similar results, except that the chi-squared theory produces larger altitude error estimates than the (more realistic) Monte Carlo simulation.

Synthesis and optimization of four bar mechanism with six design parameters

NASA Astrophysics Data System (ADS)

Jaiswal, Ankur; Jawale, H. P.

2018-04-01

Function generation is synthesis of mechanism for specific task, involves complexity for specially synthesis above five precision of coupler points. Thus pertains to large structural error. The methodology for arriving to better precision solution is to use the optimization technique. Work presented herein considers methods of optimization of structural error in closed kinematic chain with single degree of freedom, for generating functions like log(x), ex, tan(x), sin(x) with five precision points. The equation in Freudenstein-Chebyshev method is used to develop five point synthesis of mechanism. The extended formulation is proposed and results are obtained to verify existing results in literature. Optimization of structural error is carried out using least square approach. Comparative structural error analysis is presented on optimized error through least square method and extended Freudenstein-Chebyshev method.

Theoretical and experimental studies of error in square-law detector circuits

NASA Technical Reports Server (NTRS)

Stanley, W. D.; Hearn, C. P.; Williams, J. B.

1984-01-01

Square law detector circuits to determine errors from the ideal input/output characteristic function were investigated. The nonlinear circuit response is analyzed by a power series expansion containing terms through the fourth degree, from which the significant deviation from square law can be predicted. Both fixed bias current and flexible bias current configurations are considered. The latter case corresponds with the situation where the mean current can change with the application of a signal. Experimental investigations of the circuit arrangements are described. Agreement between the analytical models and the experimental results are established. Factors which contribute to differences under certain conditions are outlined.

Calibration and compensation method of three-axis geomagnetic sensor based on pre-processing total least square iteration

NASA Astrophysics Data System (ADS)

Zhou, Y.; Zhang, X.; Xiao, W.

2018-04-01

As the geomagnetic sensor is susceptible to interference, a pre-processing total least square iteration method is proposed for calibration compensation. Firstly, the error model of the geomagnetic sensor is analyzed and the correction model is proposed, then the characteristics of the model are analyzed and converted into nine parameters. The geomagnetic data is processed by Hilbert transform (HHT) to improve the signal-to-noise ratio, and the nine parameters are calculated by using the combination of Newton iteration method and the least squares estimation method. The sifter algorithm is used to filter the initial value of the iteration to ensure that the initial error is as small as possible. The experimental results show that this method does not need additional equipment and devices, can continuously update the calibration parameters, and better than the two-step estimation method, it can compensate geomagnetic sensor error well.

Peelle's pertinent puzzle using the Monte Carlo technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kawano, Toshihiko; Talou, Patrick; Burr, Thomas

2009-01-01

We try to understand the long-standing problem of the Peelle's Pertinent Puzzle (PPP) using the Monte Carlo technique. We allow the probability density functions to be any kind of form to assume the impact of distribution, and obtain the least-squares solution directly from numerical simulations. We found that the standard least squares method gives the correct answer if a weighting function is properly provided. Results from numerical simulations show that the correct answer of PPP is 1.1 {+-} 0.25 if the common error is multiplicative. The thought-provoking answer of 0.88 is also correct, if the common error is additive, andmore » if the error is proportional to the measured values. The least squares method correctly gives us the most probable case, where the additive component has a negative value. Finally, the standard method fails for PPP due to a distorted (non Gaussian) joint distribution.« less

An Empirical State Error Covariance Matrix Orbit Determination Example

NASA Technical Reports Server (NTRS)

Frisbee, Joseph H., Jr.

2015-01-01

State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. First, consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. Then it follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix of the estimate will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully include all of the errors in the state estimate. The empirical error covariance matrix is determined from a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm. It is a formally correct, empirical state error covariance matrix obtained through use of the average form of the weighted measurement residual variance performance index rather than the usual total weighted residual form. Based on its formulation, this matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty and whether the source is anticipated or not. It is expected that the empirical error covariance matrix will give a better, statistical representation of the state error in poorly modeled systems or when sensor performance is suspect. In its most straight forward form, the technique only requires supplemental calculations to be added to existing batch estimation algorithms. In the current problem being studied a truth model making use of gravity with spherical, J2 and J4 terms plus a standard exponential type atmosphere with simple diurnal and random walk components is used. The ability of the empirical state error covariance matrix to account for errors is investigated under four scenarios during orbit estimation. These scenarios are: exact modeling under known measurement errors, exact modeling under corrupted measurement errors, inexact modeling under known measurement errors, and inexact modeling under corrupted measurement errors. For this problem a simple analog of a distributed space surveillance network is used. The sensors in this network make only range measurements and with simple normally distributed measurement errors. The sensors are assumed to have full horizon to horizon viewing at any azimuth. For definiteness, an orbit at the approximate altitude and inclination of the International Space Station is used for the study. The comparison analyses of the data involve only total vectors. No investigation of specific orbital elements is undertaken. The total vector analyses will look at the chisquare values of the error in the difference between the estimated state and the true modeled state using both the empirical and theoretical error covariance matrices for each of scenario.

Aerosol Climate Effects: Local Radiative Forcing and Column Closure Experiments

NASA Technical Reports Server (NTRS)

Russell, P. B.; Bergstrom, Robert W.; Kinne, S. A.

2000-01-01

In an effort to reduce uncertainties in climate change predictions, experiments are being planned and conducted to measure anthropogenic aerosol properties and effects, including effects on radiative fields. The global average, direct anthropogenic aerosol effect on upwelling shortwave fluxes is estimated to be about +1/2 W/sq m, whereas errors in flux changes measured with airborne and spaceborne radiometers are 2 to 8 W/sq m or larger. This poses the question of whether flux changes expected in field experiments will be large enough to measure accurately. This paper obtains a new expression for the aerosol-induced change in upwelling flux, compares it to two-stream and adding-doubling (AD) results, and uses all three methods to estimate expected flux changes. The new expression accounts for the solar zenith angle dependences of aerosol transmission and reflection, as well as of surface albedo, all of which can have a strong effect in determining flux changes measured in field experiments. Despite its relative simplicity, the new expression gives results similar to previous two-stream results. Relative to AD results, it agrees within a few watts per square meter for the intermediate solar elevation angles where the flux changes peak (roughly 10 to 30 degrees), but it has negative errors for higher Sun and positive errors for lower Sun. All three techniques yield aerosol-induced changes in upwelling flux of +8 to +50 W/sq m for aerosol midvisible optical depths of 0.1 to 0.5. Because such aerosol optical depths occur frequently off the U.S. and European Atlantic coasts in summer, the flux changes they induce should be measurable by airborne, and possibly by spaceborne, radiometers, provided sufficient care is taken in experiment design (including measurements to separate aerosol radiative effects from those of absorbing gases). The expected flux changes are about 15 to 100 times larger than the global average flux change expected for the global average anthropogenic sulfate optical depth of 0.04. Reasons for the larger flux changes include the larger optical depths considered here (factor 2.5 to 12), plus restricting the measurements to cloud-free, daytime conditions over the ocean (factor 5 to 9).

Analysis of Point Based Image Registration Errors With Applications in Single Molecule Microscopy

PubMed Central

Cohen, E. A. K.; Ober, R. J.

2014-01-01

We present an asymptotic treatment of errors involved in point-based image registration where control point (CP) localization is subject to heteroscedastic noise; a suitable model for image registration in fluorescence microscopy. Assuming an affine transform, CPs are used to solve a multivariate regression problem. With measurement errors existing for both sets of CPs this is an errors-in-variable problem and linear least squares is inappropriate; the correct method being generalized least squares. To allow for point dependent errors the equivalence of a generalized maximum likelihood and heteroscedastic generalized least squares model is achieved allowing previously published asymptotic results to be extended to image registration. For a particularly useful model of heteroscedastic noise where covariance matrices are scalar multiples of a known matrix (including the case where covariance matrices are multiples of the identity) we provide closed form solutions to estimators and derive their distribution. We consider the target registration error (TRE) and define a new measure called the localization registration error (LRE) believed to be useful, especially in microscopy registration experiments. Assuming Gaussianity of the CP localization errors, it is shown that the asymptotic distribution for the TRE and LRE are themselves Gaussian and the parameterized distributions are derived. Results are successfully applied to registration in single molecule microscopy to derive the key dependence of the TRE and LRE variance on the number of CPs and their associated photon counts. Simulations show asymptotic results are robust for low CP numbers and non-Gaussianity. The method presented here is shown to outperform GLS on real imaging data. PMID:24634573

Improved model for correcting the ionospheric impact on bending angle in radio occultation measurements

NASA Astrophysics Data System (ADS)

Angling, Matthew J.; Elvidge, Sean; Healy, Sean B.

2018-04-01

The standard approach to remove the effects of the ionosphere from neutral atmosphere GPS radio occultation measurements is to estimate a corrected bending angle from a combination of the L1 and L2 bending angles. This approach is known to result in systematic errors and an extension has been proposed to the standard ionospheric correction that is dependent on the squared L1 / L2 bending angle difference and a scaling term (κ). The variation of κ with height, time, season, location and solar activity (i.e. the F10.7 flux) has been investigated by applying a 1-D bending angle operator to electron density profiles provided by a monthly median ionospheric climatology model. As expected, the residual bending angle is well correlated (negatively) with the vertical total electron content (TEC). κ is more strongly dependent on the solar zenith angle, indicating that the TEC-dependent component of the residual error is effectively modelled by the squared L1 / L2 bending angle difference term in the correction. The residual error from the ionospheric correction is likely to be a major contributor to the overall error budget of neutral atmosphere retrievals between 40 and 80 km. Over this height range κ is approximately linear with height. A simple κ model has also been developed. It is independent of ionospheric measurements, but incorporates geophysical dependencies (i.e. solar zenith angle, solar flux, altitude). The global mean error (i.e. bias) and the standard deviation of the residual errors are reduced from -1.3×10-8 and 2.2×10-8 for the uncorrected case to -2.2×10-10 rad and 2.0×10-9 rad, respectively, for the corrections using the κ model. Although a fixed scalar κ also reduces bias for the global average, the selected value of κ (14 rad-1) is only appropriate for a small band of locations around the solar terminator. In the daytime, the scalar κ is consistently too high and this results in an overcorrection of the bending angles and a positive bending angle bias. Similarly, in the nighttime, the scalar κ is too low. However, in this case, the bending angles are already small and the impact of the choice of κ is less pronounced.

Wetland Assessment Using Unmanned Aerial Vehicle (uav) Photogrammetry

NASA Astrophysics Data System (ADS)

Boon, M. A.; Greenfield, R.; Tesfamichael, S.

2016-06-01

The use of Unmanned Arial Vehicle (UAV) photogrammetry is a valuable tool to enhance our understanding of wetlands. Accurate planning derived from this technological advancement allows for more effective management and conservation of wetland areas. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool to enhance the assessment of wetland ecosystems. The UAV images were collected during a single flight within 2½ hours over a 100 ha area at the Kameelzynkraal farm, Gauteng Province, South Africa. An AKS Y-6 MKII multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. Twenty ground control points (GCPs) were surveyed using a Trimble GPS to achieve geometrical precision and georeferencing accuracy. Structure-from-Motion (SfM) computer vision techniques were used to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The geometric accuracy of the data based on the 20 GCP's were 0.018 m for the overall, 0.0025 m for the vertical root mean squared error (RMSE) and an over all root mean square reprojection error of 0.18 pixel. The UAV products were then edited and subsequently analysed, interpreted and key attributes extracted using a selection of tools/ software applications to enhance the wetland assessment. The results exceeded our expectations and provided a valuable and accurate enhancement to the wetland delineation, classification and health assessment which even with detailed field studies would have been difficult to achieve.

A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong

2001-01-01

This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

Optimum nonparametric estimation of population density based on ordered distances

USGS Publications Warehouse

Patil, S.A.; Kovner, J.L.; Burnham, Kenneth P.

1982-01-01

The asymptotic mean and error mean square are determined for the nonparametric estimator of plant density by distance sampling proposed by Patil, Burnham and Kovner (1979, Biometrics 35, 597-604. On the basis of these formulae, a bias-reduced version of this estimator is given, and its specific form is determined which gives minimum mean square error under varying assumptions about the true probability density function of the sampled data. Extension is given to line-transect sampling.

Ambiguity resolution for satellite Doppler positioning systems

NASA Technical Reports Server (NTRS)

Argentiero, P. D.; Marini, J. W.

1977-01-01

A test for ambiguity resolution was derived which was the most powerful in the sense that it maximized the probability of a correct decision. When systematic error sources were properly included in the least squares reduction process to yield an optimal solution, the test reduced to choosing the solution which provided the smaller valuation of the least squares loss function. When systematic error sources were ignored in the least squares reduction, the most powerful test was a quadratic form comparison with the weighting matrix of the quadratic form obtained by computing the pseudo-inverse of a reduced rank square matrix. A formula is presented for computing the power of the most powerful test. A numerical example is included in which the power of the test is computed for a situation which may occur during an actual satellite aided search and rescue mission.

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

NASA Astrophysics Data System (ADS)

Yu, Lean; Wang, Shouyang; Lai, K. K.

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

Error analysis on squareness of multi-sensor integrated CMM for the multistep registration method

NASA Astrophysics Data System (ADS)

Zhao, Yan; Wang, Yiwen; Ye, Xiuling; Wang, Zhong; Fu, Luhua

2018-01-01

The multistep registration(MSR) method in [1] is to register two different classes of sensors deployed on z-arm of CMM(coordinate measuring machine): a video camera and a tactile probe sensor. In general, it is difficult to obtain a very precise registration result with a single common standard, instead, this method is achieved by measuring two different standards with a constant distance between them two which are fixed on a steel plate. Although many factors have been considered such as the measuring ability of sensors, the uncertainty of the machine and the number of data pairs, there is no exact analysis on the squareness between the x-axis and the y-axis on the xy plane. For this sake, error analysis on the squareness of multi-sensor integrated CMM for the multistep registration method will be made to examine the validation of the MSR method. Synthetic experiments on the squareness on the xy plane for the simplified MSR with an inclination rotation are simulated, which will lead to a regular result. Experiments have been carried out with the multi-standard device designed also in [1], meanwhile, inspections with the help of a laser interferometer on the xy plane have been carried out. The final results are conformed to the simulations, and the squareness errors of the MSR method are also similar to the results of interferometer. In other word, the MSR can also adopted/utilized to verify the squareness of a CMM.

Random errors in interferometry with the least-squares method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Qi

2011-01-20

This investigation analyzes random errors in interferometric surface profilers using the least-squares method when random noises are present. Two types of random noise are considered here: intensity noise and position noise. Two formulas have been derived for estimating the standard deviations of the surface height measurements: one is for estimating the standard deviation when only intensity noise is present, and the other is for estimating the standard deviation when only position noise is present. Measurements on simulated noisy interferometric data have been performed, and standard deviations of the simulated measurements have been compared with those theoretically derived. The relationships havemore » also been discussed between random error and the wavelength of the light source and between random error and the amplitude of the interference fringe.« less

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

Least Squares Metric, Unidimensional Scaling of Multivariate Linear Models.

ERIC Educational Resources Information Center

Poole, Keith T.

1990-01-01

A general approach to least-squares unidimensional scaling is presented. Ordering information contained in the parameters is used to transform the standard squared error loss function into a discrete rather than continuous form. Monte Carlo tests with 38,094 ratings of 261 senators, and 1,258 representatives demonstrate the procedure's…

Attenuation of the Squared Canonical Correlation Coefficient under Varying Estimates of Score Reliability

ERIC Educational Resources Information Center

Wilson, Celia M.

2010-01-01

Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability.…

Quadcopter Path Following Control Design Using Output Feedback with Command Generator Tracker LOS Based At Square Path

NASA Astrophysics Data System (ADS)

Nugraha, A. T.; Agustinah, T.

2018-01-01

Quadcopter an unstable system, underactuated and nonlinear in quadcopter control research developments become an important focus of attention. In this study, following the path control method for position on the X and Y axis, used structure-Generator Tracker Command (CGT) is tested. Attitude control and position feedback quadcopter is compared using the optimal output. The addition of the H∞ performance optimal output feedback control is used to maintain the stability and robustness of quadcopter. Iterative numerical techniques Linear Matrix Inequality (LMI) is used to find the gain controller. The following path control problems is solved using the method of LQ regulators with output feedback. Simulations show that the control system can follow the paths that have been defined in the form of a reference signal square shape. The result of the simulation suggest that the method which used can bring the yaw angle at the expected value algorithm. Quadcopter can do automatically following path with cross track error mean X=0.5 m and Y=0.2 m.

The stress intensity factors for a periodic array of interacting coplanar penny-shaped cracks

PubMed Central

Lekesiz, Huseyin; Katsube, Noriko; Rokhlin, Stanislav I.; Seghi, Robert R.

2013-01-01

The effect of crack interactions on stress intensity factors is examined for a periodic array of coplanar penny-shaped cracks. Kachanov’s approximate method for crack interactions (Int. J. Solid. Struct. 1987; 23(1):23–43) is employed to analyze both hexagonal and square crack configurations. In approximating crack interactions, the solution converges when the total truncation number of the cracks is 109. As expected, due to high density packing crack interaction in the hexagonal configuration is stronger than that in the square configuration. Based on the numerical results, convenient fitting equations for quick evaluation of the mode I stress intensity factors are obtained as a function of crack density and angle around the crack edge for both crack configurations. Numerical results for the mode II and III stress intensity factors are presented in the form of contour lines for the case of Poisson’s ratio ν =0.3. Possible errors for these problems due to Kachanov’s approximate method are estimated. Good agreement is observed with the limited number of results available in the literature and obtained by different methods. PMID:27175035

An error covariance model for sea surface topography and velocity derived from TOPEX/POSEIDON altimetry

NASA Technical Reports Server (NTRS)

Tsaoussi, Lucia S.; Koblinsky, Chester J.

1994-01-01

In order to facilitate the use of satellite-derived sea surface topography and velocity oceanographic models, methodology is presented for deriving the total error covariance and its geographic distribution from TOPEX/POSEIDON measurements. The model is formulated using a parametric model fit to the altimeter range observations. The topography and velocity modeled with spherical harmonic expansions whose coefficients are found through optimal adjustment to the altimeter range residuals using Bayesian statistics. All other parameters, including the orbit, geoid, surface models, and range corrections are provided as unadjusted parameters. The maximum likelihood estimates and errors are derived from the probability density function of the altimeter range residuals conditioned with a priori information. Estimates of model errors for the unadjusted parameters are obtained from the TOPEX/POSEIDON postlaunch verification results and the error covariances for the orbit and the geoid, except for the ocean tides. The error in the ocean tides is modeled, first, as the difference between two global tide models and, second, as the correction to the present tide model, the correction derived from the TOPEX/POSEIDON data. A formal error covariance propagation scheme is used to derive the total error. Our global total error estimate for the TOPEX/POSEIDON topography relative to the geoid for one 10-day period is found tio be 11 cm RMS. When the error in the geoid is removed, thereby providing an estimate of the time dependent error, the uncertainty in the topography is 3.5 cm root mean square (RMS). This level of accuracy is consistent with direct comparisons of TOPEX/POSEIDON altimeter heights with tide gauge measurements at 28 stations. In addition, the error correlation length scales are derived globally in both east-west and north-south directions, which should prove useful for data assimilation. The largest error correlation length scales are found in the tropics. Errors in the velocity field are smallest in midlatitude regions. For both variables the largest errors caused by uncertainty in the geoid. More accurate representations of the geoid await a dedicated geopotential satellite mission. Substantial improvements in the accuracy of ocean tide models are expected in the very near future from research with TOPEX/POSEIDON data.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

Analysis of surface-water data network in Kansas for effectiveness in providing regional streamflow information

USGS Publications Warehouse

Medina, K.D.; Tasker, Gary D.

1985-01-01

The surface water data network in Kansas was analyzed using generalized least squares regression for its effectiveness in providing regional streamflow information. The correlation and time-sampling error of the streamflow characteristic are considered in the generalized least squares method. Unregulated medium-flow, low-flow and high-flow characteristics were selected to be representative of the regional information that can be obtained from streamflow gaging station records for use in evaluating the effectiveness of continuing the present network stations, discontinuing some stations; and/or adding new stations. The analysis used streamflow records for all currently operated stations that were not affected by regulation and discontinued stations for which unregulated flow characteristics , as well as physical and climatic characteristics, were available. The state was divided into three network areas, western, northeastern, and southeastern Kansas, and analysis was made for three streamflow characteristics in each area, using three planning horizons. The analysis showed that the maximum reduction of sampling mean square error for each cost level could be obtained by adding new stations and discontinuing some of the present network stations. Large reductions in sampling mean square error for low-flow information could be accomplished in all three network areas, with western Kansas having the most dramatic reduction. The addition of new stations would be most beneficial for man- flow information in western Kansas, and to lesser degrees in the other two areas. The reduction of sampling mean square error for high-flow information would benefit most from the addition of new stations in western Kansas, and the effect diminishes to lesser degrees in the other two areas. Southeastern Kansas showed the smallest error reduction in high-flow information. A comparison among all three network areas indicated that funding resources could be most effectively used by discontinuing more stations in northeastern and southeastern Kansas and establishing more new stations in western Kansas. (Author 's abstract)

Modelling of the batch biosorption system: study on exchange of protons with cell wall-bound mineral ions.

PubMed

Mishra, Vishal

2015-01-01

The interchange of the protons with the cell wall-bound calcium and magnesium ions at the interface of solution/bacterial cell surface in the biosorption system at various concentrations of protons has been studied in the present work. A mathematical model for establishing the correlation between concentration of protons and active sites was developed and optimized. The sporadic limited residence time reactor was used to titrate the calcium and magnesium ions at the individual data point. The accuracy of the proposed mathematical model was estimated using error functions such as nonlinear regression, adjusted nonlinear regression coefficient, the chi-square test, P-test and F-test. The values of the chi-square test (0.042-0.017), P-test (<0.001-0.04), sum of square errors (0.061-0.016), root mean square error (0.01-0.04) and F-test (2.22-19.92) reported in the present research indicated the suitability of the model over a wide range of proton concentrations. The zeta potential of the bacterium surface at various concentrations of protons was observed to validate the denaturation of active sites.

Simple Forest Canopy Thermal Exitance Model

NASA Technical Reports Server (NTRS)

Smith J. A.; Goltz, S. M.

1999-01-01

We describe a model to calculate brightness temperature and surface energy balance for a forest canopy system. The model is an extension of an earlier vegetation only model by inclusion of a simple soil layer. The root mean square error in brightness temperature for a dense forest canopy was 2.5 C. Surface energy balance predictions were also in good agreement. The corresponding root mean square errors for net radiation, latent, and sensible heat were 38.9, 30.7, and 41.4 W/sq m respectively.

Did I Do That? Expectancy Effects of Brain Stimulation on Error-related Negativity and Sense of Agency.

PubMed

Hoogeveen, Suzanne; Schjoedt, Uffe; van Elk, Michiel

2018-06-19

This study examines the effects of expected transcranial stimulation on the error(-related) negativity (Ne or ERN) and the sense of agency in participants who perform a cognitive control task. Placebo transcranial direct current stimulation was used to elicit expectations of transcranially induced cognitive improvement or impairment. The improvement/impairment manipulation affected both the Ne/ERN and the sense of agency (i.e., whether participants attributed errors to oneself or the brain stimulation device): Expected improvement increased the ERN in response to errors compared with both impairment and control conditions. Expected impairment made participants falsely attribute errors to the transcranial stimulation. This decrease in sense of agency was correlated with a reduced ERN amplitude. These results show that expectations about transcranial stimulation impact users' neural response to self-generated errors and the attribution of responsibility-especially when actions lead to negative outcomes. We discuss our findings in relation to predictive processing theory according to which the effect of prior expectations on the ERN reflects the brain's attempt to generate predictive models of incoming information. By demonstrating that induced expectations about transcranial stimulation can have effects at a neural level, that is, beyond mere demand characteristics, our findings highlight the potential for placebo brain stimulation as a promising tool for research.

Measuring Dispersion Effects of Factors in Factorial Experiments.

DTIC Science & Technology

1988-01-01

error is MSE =i=l j=1 i n r (SSE/(N-p)), the sum of squares of pure error is SSPE = Z E Y i=1 j=1 and the mean square of pure error is MSPE - ( SSPE /n...the level of the factor in the ith run is 0. 3.1. First Measure We have n r n r SSPE = 1 Is it -yi) 2 + E r (1-8 )(yjj li-l j=l (iYjj +i= j=l l - i...The first component in SSPE corresponds to level I of the factor and has n degrees of freedom ( E 6i)(r-I). The second component corresponds to i=l n

Smooth empirical Bayes estimation of observation error variances in linear systems

NASA Technical Reports Server (NTRS)

Martz, H. F., Jr.; Lian, M. W.

1972-01-01

A smooth empirical Bayes estimator was developed for estimating the unknown random scale component of each of a set of observation error variances. It is shown that the estimator possesses a smaller average squared error loss than other estimators for a discrete time linear system.

Validation and Expected Error Estimation of Suomi-NNP VIIRS Aerosol Optical Thickness and Angstrom Exponent with AERONET

NASA Technical Reports Server (NTRS)

Huang, Jingfeng; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Zhang, Hai; Superczynski, Stephen; Ciren, Pubu; Holben, Brent N.; Petrenko, Maksym

2016-01-01

The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.

Modeling Multiplicative Error Variance: An Example Predicting Tree Diameter from Stump Dimensions in Baldcypress

Treesearch

Bernard R. Parresol

1993-01-01

In the context of forest modeling, it is often reasonable to assume a multiplicative heteroscedastic error structure to the data. Under such circumstances ordinary least squares no longer provides minimum variance estimates of the model parameters. Through study of the error structure, a suitable error variance model can be specified and its parameters estimated. This...

Application of Least Mean Square Algorithms to Spacecraft Vibration Compensation

NASA Technical Reports Server (NTRS)

Woodard , Stanley E.; Nagchaudhuri, Abhijit

1998-01-01

This paper describes the application of the Least Mean Square (LMS) algorithm in tandem with the Filtered-X Least Mean Square algorithm for controlling a science instrument's line-of-sight pointing. Pointing error is caused by a periodic disturbance and spacecraft vibration. A least mean square algorithm is used on-orbit to produce the transfer function between the instrument's servo-mechanism and error sensor. The result is a set of adaptive transversal filter weights tuned to the transfer function. The Filtered-X LMS algorithm, which is an extension of the LMS, tunes a set of transversal filter weights to the transfer function between the disturbance source and the servo-mechanism's actuation signal. The servo-mechanism's resulting actuation counters the disturbance response and thus maintains accurate science instrumental pointing. A simulation model of the Upper Atmosphere Research Satellite is used to demonstrate the algorithms.

A partial least squares based spectrum normalization method for uncertainty reduction for laser-induced breakdown spectroscopy measurements

NASA Astrophysics Data System (ADS)

Li, Xiongwei; Wang, Zhe; Lui, Siu-Lung; Fu, Yangting; Li, Zheng; Liu, Jianming; Ni, Weidou

2013-10-01

A bottleneck of the wide commercial application of laser-induced breakdown spectroscopy (LIBS) technology is its relatively high measurement uncertainty. A partial least squares (PLS) based normalization method was proposed to improve pulse-to-pulse measurement precision for LIBS based on our previous spectrum standardization method. The proposed model utilized multi-line spectral information of the measured element and characterized the signal fluctuations due to the variation of plasma characteristic parameters (plasma temperature, electron number density, and total number density) for signal uncertainty reduction. The model was validated by the application of copper concentration prediction in 29 brass alloy samples. The results demonstrated an improvement on both measurement precision and accuracy over the generally applied normalization as well as our previously proposed simplified spectrum standardization method. The average relative standard deviation (RSD), average of the standard error (error bar), the coefficient of determination (R2), the root-mean-square error of prediction (RMSEP), and average value of the maximum relative error (MRE) were 1.80%, 0.23%, 0.992, 1.30%, and 5.23%, respectively, while those for the generally applied spectral area normalization were 3.72%, 0.71%, 0.973, 1.98%, and 14.92%, respectively.

Super-linear Precision in Simple Neural Population Codes

NASA Astrophysics Data System (ADS)

Schwab, David; Fiete, Ila

2015-03-01

A widely used tool for quantifying the precision with which a population of noisy sensory neurons encodes the value of an external stimulus is the Fisher Information (FI). Maximizing the FI is also a commonly used objective for constructing optimal neural codes. The primary utility and importance of the FI arises because it gives, through the Cramer-Rao bound, the smallest mean-squared error achievable by any unbiased stimulus estimator. However, it is well-known that when neural firing is sparse, optimizing the FI can result in codes that perform very poorly when considering the resulting mean-squared error, a measure with direct biological relevance. Here we construct optimal population codes by minimizing mean-squared error directly and study the scaling properties of the resulting network, focusing on the optimal tuning curve width. We then extend our results to continuous attractor networks that maintain short-term memory of external stimuli in their dynamics. Here we find similar scaling properties in the structure of the interactions that minimize diffusive information loss.

Geospatial distribution modeling and determining suitability of groundwater quality for irrigation purpose using geospatial methods and water quality index (WQI) in Northern Ethiopia

NASA Astrophysics Data System (ADS)

Gidey, Amanuel

2018-06-01

Determining suitability and vulnerability of groundwater quality for irrigation use is a key alarm and first aid for careful management of groundwater resources to diminish the impacts on irrigation. This study was conducted to determine the overall suitability of groundwater quality for irrigation use and to generate their spatial distribution maps in Elala catchment, Northern Ethiopia. Thirty-nine groundwater samples were collected to analyze and map the water quality variables. Atomic absorption spectrophotometer, ultraviolet spectrophotometer, titration and calculation methods were used for laboratory groundwater quality analysis. Arc GIS, geospatial analysis tools, semivariogram model types and interpolation methods were used to generate geospatial distribution maps. Twelve and eight water quality variables were used to produce weighted overlay and irrigation water quality index models, respectively. Root-mean-square error, mean square error, absolute square error, mean error, root-mean-square standardized error, measured values versus predicted values were used for cross-validation. The overall weighted overlay model result showed that 146 km2 areas are highly suitable, 135 km2 moderately suitable and 60 km2 area unsuitable for irrigation use. The result of irrigation water quality index confirms 10.26% with no restriction, 23.08% with low restriction, 20.51% with moderate restriction, 15.38% with high restriction and 30.76% with the severe restriction for irrigation use. GIS and irrigation water quality index are better methods for irrigation water resources management to achieve a full yield irrigation production to improve food security and to sustain it for a long period, to avoid the possibility of increasing environmental problems for the future generation.

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

USGS Publications Warehouse

Smith, S. Jerrod; Lewis, Jason M.; Graves, Grant M.

2015-09-28

Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Ventilator-associated pneumonia: the influence of bacterial resistance, prescription errors, and de-escalation of antimicrobial therapy on mortality rates.

PubMed

Souza-Oliveira, Ana Carolina; Cunha, Thúlio Marquez; Passos, Liliane Barbosa da Silva; Lopes, Gustavo Camargo; Gomes, Fabiola Alves; Röder, Denise Von Dolinger de Brito

2016-01-01

Ventilator-associated pneumonia is the most prevalent nosocomial infection in intensive care units and is associated with high mortality rates (14-70%). This study evaluated factors influencing mortality of patients with Ventilator-associated pneumonia (VAP), including bacterial resistance, prescription errors, and de-escalation of antibiotic therapy. This retrospective study included 120 cases of Ventilator-associated pneumonia admitted to the adult adult intensive care unit of the Federal University of Uberlândia. The chi-square test was used to compare qualitative variables. Student's t-test was used for quantitative variables and multiple logistic regression analysis to identify independent predictors of mortality. De-escalation of antibiotic therapy and resistant bacteria did not influence mortality. Mortality was 4 times and 3 times higher, respectively, in patients who received an inappropriate antibiotic loading dose and in patients whose antibiotic dose was not adjusted for renal function. Multiple logistic regression analysis revealed the incorrect adjustment for renal function was the only independent factor associated with increased mortality. Prescription errors influenced mortality of patients with Ventilator-associated pneumonia, underscoring the challenge of proper Ventilator-associated pneumonia treatment, which requires continuous reevaluation to ensure that clinical response to therapy meets expectations. Copyright © 2016. Published by Elsevier Editora Ltda.

Empirical tools for simulating salinity in the estuaries in Everglades National Park, Florida

NASA Astrophysics Data System (ADS)

Marshall, F. E.; Smith, D. T.; Nickerson, D. M.

2011-12-01

Salinity in a shallow estuary is affected by upland freshwater inputs (surface runoff, stream/canal flows, groundwater), atmospheric processes (precipitation, evaporation), marine connectivity, and wind patterns. In Everglades National Park (ENP) in South Florida, the unique Everglades ecosystem exists as an interconnected system of fresh, brackish, and salt water marshes, mangroves, and open water. For this effort a coastal aquifer conceptual model of the Everglades hydrologic system was used with traditional correlation and regression hydrologic techniques to create a series of multiple linear regression (MLR) salinity models from observed hydrologic, marine, and weather data. The 37 ENP MLR salinity models cover most of the estuarine areas of ENP and produce daily salinity simulations that are capable of estimating 65-80% of the daily variability in salinity depending upon the model. The Root Mean Squared Error is typically about 2-4 salinity units, and there is little bias in the predictions. However, the absolute error of a model prediction in the nearshore embayments and the mangrove zone of Florida Bay may be relatively large for a particular daily simulation during the seasonal transitions. Comparisons show that the models group regionally by similar independent variables and salinity regimes. The MLR salinity models have approximately the same expected range of simulation accuracy and error as higher spatial resolution salinity models.

[NIR Assignment of Magnolol by 2D-COS Technology and Model Application Huoxiangzhengqi Oral Liduid].

PubMed

Pei, Yan-ling; Wu, Zhi-sheng; Shi, Xin-yuan; Pan, Xiao-ning; Peng, Yan-fang; Qiao, Yan-jiang

2015-08-01

Near infrared (NIR) spectroscopy assignment of Magnolol was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. According to the synchronous spectra of deuterated chloroform solvent and Magnolol, 1365~1455, 1600~1720, 2000~2181 and 2275~2465 nm were the characteristic absorption of Magnolol. Connected with the structure of Magnolol, 1440 nm was the stretching vibration of phenolic group O-H, 1679 nm was the stretching vibration of aryl and methyl which connected with aryl, 2117, 2304, 2339 and 2370 nm were the combination of the stretching vibration, bending vibration and deformation vibration for aryl C-H, 2445 nm were the bending vibration of methyl which linked with aryl group, these bands attribut to the characteristics of Magnolol. Huoxiangzhengqi Oral Liduid was adopted to study the Magnolol, the characteristic band by spectral assignment and the band by interval Partial Least Squares (iPLS) and Synergy interval Partial Least Squares (SiPLS) were used to establish Partial Least Squares (PLS) quantitative model, the coefficient of determination Rcal(2) and Rpre(2) were greater than 0.99, the Root Mean of Square Error of Calibration (RM-SEC), Root Mean of Square Error of Cross Validation (RMSECV) and Root Mean of Square Error of Prediction (RMSEP) were very small. It indicated that the characteristic band by spectral assignment has the same results with the Chemometrics in PLS model. It provided a reference for NIR spectral assignment of chemical compositions in Chinese Materia Medica, and the band filters of NIR were interpreted.

SU-G-BRC-17: Using Generalized Mean for Equivalent Square Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, S; Fan, Q; Lei, Y

Purpose: Equivalent Square (ES) is a widely used concept in radiotherapy. It enables us to determine many important quantities for a rectangular treatment field, without measurement, based on the corresponding values from its ES field. In this study, we propose a Generalized Mean (GM) type ES formula and compare it with other established formulae using benchmark datasets. Methods: Our GM approach is expressed as ES=(w•fx^α+(1-w)•fy^α)^(1/α), where fx, fy, α, and w represent field sizes, power index, and a weighting factor, respectively. When α=−1 it reduces to well-known Sterling type ES formulae. In our study, α and w are determined throughmore » least-square-fitting. Akaike Information Criterion (AIC) was used to benchmark the performance of each formula. BJR (Supplement 17) ES field table for X-ray PDDs and open field output factor tables in Varian TrueBeam representative dataset were used for validation. Results: Switching from α=−1 to α=−1.25, a 20% reduction in standard deviation of residual error in ES estimation was achieved for the BJR dataset. The maximum relative residual error was reduced from ∼3% (in Sterling formula) or ∼2% (in Vadash/Bjarngard formula) down to ∼1% in GM formula for open fields of all energies and at rectangular field sizes from 3cm to 40cm in the Varian dataset. The improvement of the GM over the Sterling type ES formulae is particularly noticeable for very elongated rectangular fields with short width. AIC analysis confirmed the superior performance of the GM formula after taking into account the expanded parameter space. Conclusion: The GM significantly outperforms Sterling type formulae at slightly increased computational cost. The GM calculation may nullify the requirement of data measurement for many rectangular fields and hence shorten the Linac commissioning process. Improved dose calculation accuracy is also expected by adopting the GM formula into treatment planning and secondary MU check systems.« less

Results and Error Estimates from GRACE Forward Modeling over Greenland, Canada, and Alaska

NASA Astrophysics Data System (ADS)

Bonin, J. A.; Chambers, D. P.

2012-12-01

Forward modeling using a weighted least squares technique allows GRACE information to be projected onto a pre-determined collection of local basins. This decreases the impact of spatial leakage, allowing estimates of mass change to be better localized. The technique is especially valuable where models of current-day mass change are poor, such as over Greenland and Antarctica. However, the accuracy of the forward model technique has not been determined, nor is it known how the distribution of the local basins affects the results. We use a "truth" model composed of hydrology and ice-melt slopes as an example case, to estimate the uncertainties of this forward modeling method and expose those design parameters which may result in an incorrect high-resolution mass distribution. We then apply these optimal parameters in a forward model estimate created from RL05 GRACE data. We compare the resulting mass slopes with the expected systematic errors from the simulation, as well as GIA and basic trend-fitting uncertainties. We also consider whether specific regions (such as Ellesmere Island and Baffin Island) can be estimated reliably using our optimal basin layout.

The Effect of Ionospheric Models on Electromagnetic Pulse Locations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fenimore, Edward E.; Triplett, Laurie A.

2014-07-01

Locations of electromagnetic pulses (EMPs) determined by time-of-arrival (TOA) often have outliers with significantly larger errors than expected. In the past, these errors were thought to arise from high order terms in the Appleton-Hartree equation. We simulated 1000 events randomly spread around the Earth into a constellation of 22 GPS satellites. We used four different ionospheres: “simple” where the time delay goes as the inverse of the frequency-squared, “full Appleton-Hartree”, the “BobRD integrals” and a full raytracing code. The simple and full Appleton-Hartree ionospheres do not show outliers whereas the BobRD and raytracing do. This strongly suggests that the causemore » of the outliers is not additional terms in the Appleton-Hartree equation, but rather is due to the additional path length due to refraction. A method to fix the outliers is suggested based on fitting a time to the delays calculated at the 5 GPS frequencies with BobRD and simple ionospheres. The difference in time is used as a correction to the TOAs.« less

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis

PubMed Central

van de Schoot, Rens; Hox, Joop

2014-01-01

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation coefficient (ICC) and estimation method; maximum likelihood estimation with robust chi-squares and standard errors and Bayesian estimation, on the convergence rate are investigated. The other variables of interest were rate of inadmissible solutions and the relative parameter and standard error bias on the between level. The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood. In the within level multicollinearity condition, all of the solutions were admissible but the bias values were higher compared with the between level collinearity condition. Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation. Finally, as expected, high ICC produced less biased results compared to medium ICC conditions. PMID:29795827

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations.

PubMed

Can, Seda; van de Schoot, Rens; Hox, Joop

2015-06-01

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation coefficient (ICC) and estimation method; maximum likelihood estimation with robust chi-squares and standard errors and Bayesian estimation, on the convergence rate are investigated. The other variables of interest were rate of inadmissible solutions and the relative parameter and standard error bias on the between level. The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood. In the within level multicollinearity condition, all of the solutions were admissible but the bias values were higher compared with the between level collinearity condition. Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation. Finally, as expected, high ICC produced less biased results compared to medium ICC conditions.

A technique for the determination of center of gravity and rolling resistance for tilt-seat wheelchairs.

PubMed

Lemaire, E D; Lamontagne, M; Barclay, H W; John, T; Martel, G

1991-01-01

A balance platform setup was defined for use in the determination of the center of gravity in the sagittal plane for a wheelchair and patient. Using the center of gravity information, measurements from the wheelchair and patient (weight, tire coefficients of friction), and various assumptions (constant speed, level-concrete surface, patient-wheelchair system is a rigid body), a method for estimating the rolling resistance for a wheelchair was outlined. The center of gravity and rolling resistance techniques were validated against criterion values (center of gravity error = 1 percent, rolling resistance root mean square error = 0.33 N, rolling resistance Pearson correlation coefficient = 0.995). Consistent results were also obtained from a test dummy and five subjects. Once the center of gravity is known, it is possible to evaluate the stability of a wheelchair (in terms of tipping over) and the interaction between the level of stability and rolling resistance. These quantitative measures are expected to be of use in the setup of wheelchairs with a variable seat angle and variable wheelbase length or when making comparisons between different wheelchairs.

A Lagrangian stochastic model for aerial spray transport above an oak forest

USGS Publications Warehouse

Wang, Yansen; Miller, David R.; Anderson, Dean E.; McManus, Michael L.

1995-01-01

An aerial spray droplets' transport model has been developed by applying recent advances in Lagrangian stochastic simulation of heavy particles. A two-dimensional Lagrangian stochastic model was adopted to simulate the spray droplet dispersion in atmospheric turbulence by adjusting the Lagrangian integral time scale along the drop trajectory. The other major physical processes affecting the transport of spray droplets above a forest canopy, the aircraft wingtip vortices and the droplet evaporation, were also included in each time step of the droplets' transport.The model was evaluated using data from an aerial spray field experiment. In generally neutral stability conditions, the accuracy of the model predictions varied from run-to-run as expected. The average root-mean-square error was 24.61 IU cm−2, and the average relative error was 15%. The model prediction was adequate in two-dimensional steady wind conditions, but was less accurate in variable wind condition. The results indicated that the model can simulate successfully the ensemble; average transport of aerial spray droplets under neutral, steady atmospheric wind conditions.

Risk prediction and aversion by anterior cingulate cortex.

PubMed

Brown, Joshua W; Braver, Todd S

2007-12-01

The recently proposed error-likelihood hypothesis suggests that anterior cingulate cortex (ACC) and surrounding areas will become active in proportion to the perceived likelihood of an error. The hypothesis was originally derived from a computational model prediction. The same computational model now makes a further prediction that ACC will be sensitive not only to predicted error likelihood, but also to the predicted magnitude of the consequences, should an error occur. The product of error likelihood and predicted error consequence magnitude collectively defines the general "expected risk" of a given behavior in a manner analogous but orthogonal to subjective expected utility theory. New fMRI results from an incentivechange signal task now replicate the error-likelihood effect, validate the further predictions of the computational model, and suggest why some segments of the population may fail to show an error-likelihood effect. In particular, error-likelihood effects and expected risk effects in general indicate greater sensitivity to earlier predictors of errors and are seen in risk-averse but not risk-tolerant individuals. Taken together, the results are consistent with an expected risk model of ACC and suggest that ACC may generally contribute to cognitive control by recruiting brain activity to avoid risk.

A Preliminary ZEUS Lightning Location Error Analysis Using a Modified Retrieval Theory

NASA Technical Reports Server (NTRS)

Elander, Valjean; Koshak, William; Phanord, Dieudonne

2004-01-01

The ZEUS long-range VLF arrival time difference lightning detection network now covers both Europe and Africa, and there are plans for further expansion into the western hemisphere. In order to fully optimize and assess ZEUS lightning location retrieval errors and to determine the best placement of future receivers expected to be added to the network, a software package is being developed jointly between the NASA Marshall Space Flight Center (MSFC) and the University of Nevada Las Vegas (UNLV). The software package, called the ZEUS Error Analysis for Lightning (ZEAL), will be used to obtain global scale lightning location retrieval error maps using both a Monte Carlo approach and chi-squared curvature matrix theory. At the core of ZEAL will be an implementation of an Iterative Oblate (IO) lightning location retrieval method recently developed at MSFC. The IO method will be appropriately modified to account for variable wave propagation speed, and the new retrieval results will be compared with the current ZEUS retrieval algorithm to assess potential improvements. In this preliminary ZEAL work effort, we defined 5000 source locations evenly distributed across the Earth. We then used the existing (as well as potential future ZEUS sites) to simulate arrival time data between source and ZEUS site. A total of 100 sources were considered at each of the 5000 locations, and timing errors were selected from a normal distribution having a mean of 0 seconds and a standard deviation of 20 microseconds. This simulated "noisy" dataset was analyzed using the IO algorithm to estimate source locations. The exact locations were compared with the retrieved locations, and the results are summarized via several color-coded "error maps."

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

Effective Algorithm for Detection and Correction of the Wave Reconstruction Errors Caused by the Tilt of Reference Wave in Phase-shifting Interferometry

NASA Astrophysics Data System (ADS)

Xu, Xianfeng; Cai, Luzhong; Li, Dailin; Mao, Jieying

2010-04-01

In phase-shifting interferometry (PSI) the reference wave is usually supposed to be an on-axis plane wave. But in practice a slight tilt of reference wave often occurs, and this tilt will introduce unexpected errors of the reconstructed object wave-front. Usually the least-square method with iterations, which is time consuming, is employed to analyze the phase errors caused by the tilt of reference wave. Here a simple effective algorithm is suggested to detect and then correct this kind of errors. In this method, only some simple mathematic operation is used, avoiding using least-square equations as needed in most methods reported before. It can be used for generalized phase-shifting interferometry with two or more frames for both smooth and diffusing objects, and the excellent performance has been verified by computer simulations. The numerical simulations show that the wave reconstruction errors can be reduced by 2 orders of magnitude.

Validation of Core Temperature Estimation Algorithm

DTIC Science & Technology

2016-01-29

plot of observed versus estimated core temperature with the line of identity (dashed) and the least squares regression line (solid) and line equation...estimated PSI with the line of identity (dashed) and the least squares regression line (solid) and line equation in the top left corner. (b) Bland...for comparison. The root mean squared error (RMSE) was also computed, as given by Equation 2.

Laser-Induced Breakdown Spectroscopy (LIBS) Measurement of Uranium in Molten Salt.

PubMed

Williams, Ammon; Phongikaroon, Supathorn

2018-01-01

In this current study, the molten salt aerosol-laser-induced breakdown spectroscopy (LIBS) system was used to measure the uranium (U) content in a ternary UCl 3 -LiCl-KCl salt to investigate and assess a near real-time analytical approach for material safeguards and accountability. Experiments were conducted using five different U concentrations to determine the analytical figures of merit for the system with respect to U. In the analysis, three U lines were used to develop univariate calibration curves at the 367.01 nm, 385.96 nm, and 387.10 nm lines. The 367.01 nm line had the lowest limit of detection (LOD) of 0.065 wt% U. The 385.96 nm line had the best root mean square error of cross-validation (RMSECV) of 0.20 wt% U. In addition to the univariate calibration approach, a multivariate partial least squares (PLS) model was developed to further analyze the data. Using partial least squares (PLS) modeling, an RMSECV of 0.085 wt% U was determined. The RMSECV from the multivariate approach was significantly better than the univariate case and the PLS model is recommended for future LIBS analysis. Overall, the aerosol-LIBS system performed well in monitoring the U concentration and it is expected that the system could be used to quantitatively determine the U compositions within the normal operational concentrations of U in pyroprocessing molten salts.

Systematic Error Modeling and Bias Estimation

PubMed Central

Zhang, Feihu; Knoll, Alois

2016-01-01

This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. PMID:27213386

Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex.

PubMed

Takahashi, Yuji K; Roesch, Matthew R; Wilson, Robert C; Toreson, Kathy; O'Donnell, Patricio; Niv, Yael; Schoenbaum, Geoffrey

2011-10-30

The orbitofrontal cortex has been hypothesized to carry information regarding the value of expected rewards. Such information is essential for associative learning, which relies on comparisons between expected and obtained reward for generating instructive error signals. These error signals are thought to be conveyed by dopamine neurons. To test whether orbitofrontal cortex contributes to these error signals, we recorded from dopamine neurons in orbitofrontal-lesioned rats performing a reward learning task. Lesions caused marked changes in dopaminergic error signaling. However, the effect of lesions was not consistent with a simple loss of information regarding expected value. Instead, without orbitofrontal input, dopaminergic error signals failed to reflect internal information about the impending response that distinguished externally similar states leading to differently valued future rewards. These results are consistent with current conceptualizations of orbitofrontal cortex as supporting model-based behavior and suggest an unexpected role for this information in dopaminergic error signaling.

Photogrammetric Method and Software for Stream Planform Identification

NASA Astrophysics Data System (ADS)

Stonedahl, S. H.; Stonedahl, F.; Lohberg, M. M.; Lusk, K.; Miller, D.

2013-12-01

Accurately characterizing the planform of a stream is important for many purposes, including recording measurement and sampling locations, monitoring change due to erosion or volumetric discharge, and spatial modeling of stream processes. While expensive surveying equipment or high resolution aerial photography can be used to obtain planform data, our research focused on developing a close-range photogrammetric method (and accompanying free/open-source software) to serve as a cost-effective alternative. This method involves securing and floating a wooden square frame on the stream surface at several locations, taking photographs from numerous angles at each location, and then post-processing and merging data from these photos using the corners of the square for reference points, unit scale, and perspective correction. For our test field site we chose a ~35m reach along Black Hawk Creek in Sunderbruch Park (Davenport, IA), a small, slow-moving stream with overhanging trees. To quantify error we measured 88 distances between 30 marked control points along the reach. We calculated error by comparing these 'ground truth' distances to the corresponding distances extracted from our photogrammetric method. We placed the square at three locations along our reach and photographed it from multiple angles. The square corners, visible control points, and visible stream outline were hand-marked in these photos using the GIMP (open-source image editor). We wrote an open-source GUI in Java (hosted on GitHub), which allows the user to load marked-up photos, designate square corners and label control points. The GUI also extracts the marked pixel coordinates from the images. We also wrote several scripts (currently in MATLAB) that correct the pixel coordinates for radial distortion using Brown's lens distortion model, correct for perspective by forcing the four square corner pixels to form a parallelogram in 3-space, and rotate the points in order to correctly orient all photos of the same square location. Planform data from multiple photos (and multiple square locations) are combined using weighting functions that mitigate the error stemming from the markup-process, imperfect camera calibration, etc. We have used our (beta) software to mark and process over 100 photos, yielding an average error of only 1.5% relative to our 88 measured lengths. Next we plan to translate the MATLAB scripts into Python and release their source code, at which point only free software, consumer-grade digital cameras, and inexpensive building materials will be needed for others to replicate this method at new field sites. Three sample photographs of the square with the created planform and control points

Cursor Control Device Test Battery

NASA Technical Reports Server (NTRS)

Holden, Kritina; Sandor, Aniko; Pace, John; Thompson, Shelby

2013-01-01

The test battery was developed to provide a standard procedure for cursor control device evaluation. The software was built in Visual Basic and consists of nine tasks and a main menu that integrates the set-up of the tasks. The tasks can be used individually, or in a series defined in the main menu. Task 1, the Unidirectional Pointing Task, tests the speed and accuracy of clicking on targets. Two rectangles with an adjustable width and adjustable center- to-center distance are presented. The task is to click back and forth between the two rectangles. Clicks outside of the rectangles are recorded as errors. Task 2, Multidirectional Pointing Task, measures speed and accuracy of clicking on targets approached from different angles. Twenty-five numbered squares of adjustable width are arranged around an adjustable diameter circle. The task is to point and click on the numbered squares (placed on opposite sides of the circle) in consecutive order. Clicks outside of the squares are recorded as errors. Task 3, Unidirectional (horizontal) Dragging Task, is similar to dragging a file into a folder on a computer desktop. Task 3 requires dragging a square of adjustable width from one rectangle and dropping it into another. The width of each rectangle is adjustable, as well as the distance between the two rectangles. Dropping the square outside of the rectangles is recorded as an error. Task 4, Unidirectional Path Following, is similar to Task 3. The task is to drag a square through a tunnel consisting of two lines. The size of the square and the width of the tunnel are adjustable. If the square touches any of the lines, it is counted as an error and the task is restarted. Task 5, Text Selection, involves clicking on a Start button, and then moving directly to the underlined portion of the displayed text and highlighting it. The pointing distance to the text is adjustable, as well as the to-be-selected font size and the underlined character length. If the selection does not include all of the underlined characters, or includes non-underlined characters, it is recorded as an error. Task 6, Multi-size and Multi-distance Pointing, presents the participant with 24 consecutively numbered buttons of different sizes (63 to 163 pixels), and at different distances (60 to 80 pixels) from the Start button. The task is to click on the Start button, and then move directly to, and click on, each numbered target button in consecutive order. Clicks outside of the target area are errors. Task 7, Standard Interface Elements Task, involves interacting with standard interface elements as instructed in written procedures, including: drop-down menus, sliders, text boxes, radio buttons, and check boxes. Task completion time is recorded. In Task 8, a circular track is presented with a disc in it at the top. Track width and disc size are adjustable. The task is to move the disc with circular motion within the path without touching the boundaries of the track. Time and errors are recorded. Task 9 is a discrete task that allows evaluation of discrete cursor control devices that tab from target to target, such as a castle switch. The task is to follow a predefined path and to click on the yellow targets along the path.

Insights into the Earth System mass variability from CSR-RL05 GRACE gravity fields

NASA Astrophysics Data System (ADS)

Bettadpur, S.

2012-04-01

The next-generation Release-05 GRACE gravity field data products are the result of extensive effort applied to the improvements to the GRACE Level-1 (tracking) data products, and to improvements in the background gravity models and processing methodology. As a result, the squared-error upper-bound in RL05 fields is half or less than the squared-error upper-bound in RL04 fields. The CSR-RL05 field release consists of unconstrained gravity fields as well as a regularized gravity field time-series that can be used for several applications without any post-processing error reduction. This paper will describe the background and the nature of these improvements in the data products, and provide an error characterization. We will describe the insights these new series offer in measuring the mass flux due to diverse Hydrologic, Oceanographic and Cryospheric processes.

Retrieval of the aerosol optical thickness from UV global irradiance measurements

NASA Astrophysics Data System (ADS)

Costa, M. J.; Salgueiro, V.; Bortoli, D.; Obregón, M. A.; Antón, M.; Silva, A. M.

2015-12-01

The UV irradiance is measured at Évora since several years, where a CIMEL sunphotometer integrated in AERONET is also installed. In the present work, measurements of UVA (315 - 400 nm) irradiances taken with Kipp&Zonen radiometers, as well as satellite data of ozone total column values, are used in combination with radiative transfer calculations, to estimate the aerosol optical thickness (AOT) in the UV. The retrieved UV AOT in Évora is compared with AERONET AOT (at 340 and 380 nm) and a fairly good agreement is found with a root mean square error of 0.05 (normalized root mean square error of 8.3%) and a mean absolute error of 0.04 (mean percentage error of 2.9%). The methodology is then used to estimate the UV AOT in Sines, an industrialized site on the Atlantic western coast, where the UV irradiance is monitored since 2013 but no aerosol information is available.

Uncertainty based pressure reconstruction from velocity measurement with generalized least squares

NASA Astrophysics Data System (ADS)

Zhang, Jiacheng; Scalo, Carlo; Vlachos, Pavlos

2017-11-01

A method using generalized least squares reconstruction of instantaneous pressure field from velocity measurement and velocity uncertainty is introduced and applied to both planar and volumetric flow data. Pressure gradients are computed on a staggered grid from flow acceleration. The variance-covariance matrix of the pressure gradients is evaluated from the velocity uncertainty by approximating the pressure gradient error to a linear combination of velocity errors. An overdetermined system of linear equations which relates the pressure and the computed pressure gradients is formulated and then solved using generalized least squares with the variance-covariance matrix of the pressure gradients. By comparing the reconstructed pressure field against other methods such as solving the pressure Poisson equation, the omni-directional integration, and the ordinary least squares reconstruction, generalized least squares method is found to be more robust to the noise in velocity measurement. The improvement on pressure result becomes more remarkable when the velocity measurement becomes less accurate and more heteroscedastic. The uncertainty of the reconstructed pressure field is also quantified and compared across the different methods.

A low-cost polarimeter for an undergraduate laboratory to study the polarization pattern of skylight

NASA Astrophysics Data System (ADS)

Abayaratne, Chula P.; Bandara, Vibodha

2017-03-01

A simple, low-cost, fully automated polarimeter, which demonstrates fundamental properties of skylight scattering and polarization for undergraduate physics students, is described. The polarimeter includes a microprocessor-based control unit, a Sun tracker, an elevation-azimuth mount with two degrees of freedom, and a polarization sensor unit equipped with a light-dependent resistor for measuring light intensity. Results obtained in the principal plane of the Sun using the polarimeter on a relatively clear day, together with the theoretically expected results for a molecular atmosphere, are presented. A root-mean-square error comparison indicates fairly good agreement between theory and experiment. Construction and experimentation with the polarimeter will provide students with insight into important physical concepts involved in skylight scattering and polarization as well as improve their instrumentation capabilities.

Development Roadmap for an Adjustable X-Ray Optics Observatory

NASA Technical Reports Server (NTRS)

Schwartz, Dan; Brissenden, R.; Bookbinder, J.; Davis, W.; Forman, W.; Freeman, M.; O'Dell, S.; Ramsey, B.; Reid, P.; Romaine, S.;

The analytical design of spectral measurements for multispectral remote sensor systems

NASA Technical Reports Server (NTRS)

Wiersma, D. J.; Landgrebe, D. A. (Principal Investigator)

1979-01-01

The author has identified the following significant results. In order to choose a design which will be optimal for the largest class of remote sensing problems, a method was developed which attempted to represent the spectral response function from a scene as accurately as possible. The performance of the overall recognition system was studied relative to the accuracy of the spectral representation. The spectral representation was only one of a set of five interrelated parameter categories which also included the spatial representation parameter, the signal to noise ratio, ancillary data, and information classes. The spectral response functions observed from a stratum were modeled as a stochastic process with a Gaussian probability measure. The criterion for spectral representation was defined by the minimum expected mean-square error.

Parameterization of cloud lidar backscattering profiles by means of asymmetrical Gaussians

NASA Astrophysics Data System (ADS)

del Guasta, Massimo; Morandi, Marco; Stefanutti, Leopoldo

1995-06-01

A fitting procedure for cloud lidar data processing is shown that is based on the computation of the first three moments of the vertical-backscattering (or -extinction) profile. Single-peak clouds or single cloud layers are approximated to asymmetrical Gaussians. The algorithm is particularly stable with respect to noise and processing errors, and it is much faster than the equivalent least-squares approach. Multilayer clouds can easily be treated as a sum of single asymmetrical Gaussian peaks. The method is suitable for cloud-shape parametrization in noisy lidar signatures (like those expected from satellite lidars). It also permits an improvement of cloud radiative-property computations that are based on huge lidar data sets for which storage and careful examination of single lidar profiles can't be carried out.

Precise estimation of tropospheric path delays with GPS techniques

NASA Technical Reports Server (NTRS)

Lichten, S. M.

1990-01-01

Tropospheric path delays are a major source of error in deep space tracking. However, the tropospheric-induced delay at tracking sites can be calibrated using measurements of Global Positioning System (GPS) satellites. A series of experiments has demonstrated the high sensitivity of GPS to tropospheric delays. A variety of tests and comparisons indicates that current accuracy of the GPS zenith tropospheric delay estimates is better than 1-cm root-mean-square over many hours, sampled continuously at intervals of six minutes. These results are consistent with expectations from covariance analyses. The covariance analyses also indicate that by the mid-1990s, when the GPS constellation is complete and the Deep Space Network is equipped with advanced GPS receivers, zenith tropospheric delay accuracy with GPS will improve further to 0.5 cm or better.

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

NASA Astrophysics Data System (ADS)

Endelt, B.

2017-09-01

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet’08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.

Online measurement of urea concentration in spent dialysate during hemodialysis.

PubMed

Olesberg, Jonathon T; Arnold, Mark A; Flanigan, Michael J

2004-01-01

We describe online optical measurements of urea in the effluent dialysate line during regular hemodialysis treatment of several patients. Monitoring urea removal can provide valuable information about dialysis efficiency. Spectral measurements were performed with a Fourier-transform infrared spectrometer equipped with a flow-through cell. Spectra were recorded across the 5000-4000 cm(-1) (2.0-2.5 microm) wavelength range at 1-min intervals. Savitzky-Golay filtering was used to remove baseline variations attributable to the temperature dependence of the water absorption spectrum. Urea concentrations were extracted from the filtered spectra by use of partial least-squares regression and the net analyte signal of urea. Urea concentrations predicted by partial least-squares regression matched concentrations obtained from standard chemical assays with a root mean square error of 0.30 mmol/L (0.84 mg/dL urea nitrogen) over an observed concentration range of 0-11 mmol/L. The root mean square error obtained with the net analyte signal of urea was 0.43 mmol/L with a calibration based only on a set of pure-component spectra. The error decreased to 0.23 mmol/L when a slope and offset correction were used. Urea concentrations can be continuously monitored during hemodialysis by near-infrared spectroscopy. Calibrations based on the net analyte signal of urea are particularly appealing because they do not require a training step, as do statistical multivariate calibration procedures such as partial least-squares regression.

Mixed effects versus fixed effects modelling of binary data with inter-subject variability.

PubMed

Murphy, Valda; Dunne, Adrian

2005-04-01

The question of whether or not a mixed effects model is required when modelling binary data with inter-subject variability and within subject correlation was reported in this journal by Yano et al. (J. Pharmacokin. Pharmacodyn. 28:389-412 [2001]). That report used simulation experiments to demonstrate that, under certain circumstances, the use of a fixed effects model produced more accurate estimates of the fixed effect parameters than those produced by a mixed effects model. The Laplace approximation to the likelihood was used when fitting the mixed effects model. This paper repeats one of those simulation experiments, with two binary observations recorded for every subject, and uses both the Laplace and the adaptive Gaussian quadrature approximations to the likelihood when fitting the mixed effects model. The results show that the estimates produced using the Laplace approximation include a small number of extreme outliers. This was not the case when using the adaptive Gaussian quadrature approximation. Further examination of these outliers shows that they arise in situations in which the Laplace approximation seriously overestimates the likelihood in an extreme region of the parameter space. It is also demonstrated that when the number of observations per subject is increased from two to three, the estimates based on the Laplace approximation no longer include any extreme outliers. The root mean squared error is a combination of the bias and the variability of the estimates. Increasing the sample size is known to reduce the variability of an estimator with a consequent reduction in its root mean squared error. The estimates based on the fixed effects model are inherently biased and this bias acts as a lower bound for the root mean squared error of these estimates. Consequently, it might be expected that for data sets with a greater number of subjects the estimates based on the mixed effects model would be more accurate than those based on the fixed effects model. This is borne out by the results of a further simulation experiment with an increased number of subjects in each set of data. The difference in the interpretation of the parameters of the fixed and mixed effects models is discussed. It is demonstrated that the mixed effects model and parameter estimates can be used to estimate the parameters of the fixed effects model but not vice versa.

Daily Rainfall Simulation Using Climate Variables and Nonhomogeneous Hidden Markov Model

NASA Astrophysics Data System (ADS)

Jung, J.; Kim, H. S.; Joo, H. J.; Han, D.

2017-12-01

Markov chain is an easy method to handle when we compare it with other ones for the rainfall simulation. However, it also has limitations in reflecting seasonal variability of rainfall or change on rainfall patterns caused by climate change. This study applied a Nonhomogeneous Hidden Markov Model(NHMM) to consider these problems. The NHMM compared with a Hidden Markov Model(HMM) for the evaluation of a goodness of the model. First, we chose Gum river basin in Korea to apply the models and collected daily rainfall data from the stations. Also, the climate variables of geopotential height, temperature, zonal wind, and meridional wind date were collected from NCEP/NCAR reanalysis data to consider external factors affecting the rainfall event. We conducted a correlation analysis between rainfall and climate variables then developed a linear regression equation using the climate variables which have high correlation with rainfall. The monthly rainfall was obtained by the regression equation and it became input data of NHMM. Finally, the daily rainfall by NHMM was simulated and we evaluated the goodness of fit and prediction capability of NHMM by comparing with those of HMM. As a result of simulation by HMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.2076 and 10.8243/131.1304mm each. In case of NHMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.6652 and 10.5112/100.9865mm each. We could verify that the error of daily and monthly rainfall simulated by NHMM was improved by 2.89% and 22.99% compared with HMM. Therefore, it is expected that the results of the study could provide more accurate data for hydrologic analysis. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)

Accuracy of a Basketball Indoor Tracking System Based on Standard Bluetooth Low Energy Channels (NBN23®).

PubMed

Figueira, Bruno; Gonçalves, Bruno; Folgado, Hugo; Masiulis, Nerijus; Calleja-González, Julio; Sampaio, Jaime

2018-06-14

The present study aims to identify the accuracy of the NBN23 ® system, an indoor tracking system based on radio-frequency and standard Bluetooth Low Energy channels. Twelve capture tags were attached to a custom cart with fixed distances of 0.5, 1.0, 1.5, and 1.8 m. The cart was pushed along a predetermined course following the lines of a standard dimensions Basketball court. The course was performed at low speed (<10.0 km/h), medium speed (>10.0 km/h and <20.0 km/h) and high speed (>20.0 km/h). Root mean square error (RMSE) and percentage of variance accounted for (%VAF) were used as accuracy measures. The obtained data showed acceptable accuracy results for both RMSE and %VAF, despite the expected degree of error in position measurement at higher speeds. The RMSE for all the distances and velocities presented an average absolute error of 0.30 ± 0.13 cm with 90.61 ± 8.34 of %VAF, in line with most available systems, and considered acceptable for indoor sports. The processing of data with filter correction seemed to reduce the noise and promote a lower relative error, increasing the %VAF for each measured distance. Research using positional-derived variables in Basketball is still very scarce; thus, this independent test of the NBN23 ® tracking system provides accuracy details and opens up opportunities to develop new performance indicators that help to optimize training adaptations and performance.

Criterion Predictability: Identifying Differences Between [r-squares

ERIC Educational Resources Information Center

Malgady, Robert G.

1976-01-01

An analysis of variance procedure for testing differences in r-squared, the coefficient of determination, across independent samples is proposed and briefly discussed. The principal advantage of the procedure is to minimize Type I error for follow-up tests of pairwise differences. (Author/JKS)

Performance of the Generalized S-X[squared] Item Fit Index for the Graded Response Model

ERIC Educational Resources Information Center

Kang, Taehoon; Chen, Troy T.

2011-01-01

The utility of Orlando and Thissen's ("2000", "2003") S-X[squared] fit index was extended to the model-fit analysis of the graded response model (GRM). The performance of a modified S-X[squared] in assessing item-fit of the GRM was investigated in light of empirical Type I error rates and power with a simulation study having…

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Mohamed, Heba M.

2016-01-01

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference.

An Interactive Computer Package for Use with Simulation Models Which Performs Multidimensional Sensitivity Analysis by Employing the Techniques of Response Surface Methodology.

DTIC Science & Technology

1984-12-01

total sum of squares at the center points minus the correction factor for the mean at the center points ( SSpe =Y’Y-nlY), where n1 is the number of...SSlac=SSres- SSpe ). The sum of squares due to pure error estimates 0" and the sum of squares due to lack-of-fit estimates 0’" plus a bias term if...Response Surface Methodology Source d.f. SS MS Regression n b’X1 Y b’XVY/n Residual rn-n Y’Y-b’X’ *Y (Y’Y-b’X’Y)/(n-n) Pure Error ni-i Y’Y-nl1Y SSpe / (ni

RM2: rms error comparisons

NASA Technical Reports Server (NTRS)

Rice, R. F.

1976-01-01

The root-mean-square error performance measure is used to compare the relative performance of several widely known source coding algorithms with the RM2 image data compression system. The results demonstrate that RM2 has a uniformly significant performance advantage.

Cervicocephalic kinesthetic sensibility in young and middle-aged adults with or without a history of mild neck pain.

PubMed

Teng, C-C; Chai, H; Lai, D-M; Wang, S-F

2007-02-01

Previous research has shown that there is no significant relationship between the degree of structural degeneration of the cervical spine and neck pain. We therefore sought to investigate the potential role of sensory dysfunction in chronic neck pain. Cervicocephalic kinesthetic sensibility, expressed by how accurately an individual can reposition the head, was studied in three groups of individuals, a control group of 20 asymptomatic young adults and two groups of middle-aged adults (20 subjects in each group) with or without a history of mild neck pain. An ultrasound-based three-dimensional coordinate measuring system was used to measure the position of the head and to test the accuracy of repositioning. Constant error (indicating that the subject overshot or undershot the intended position) and root mean square errors (representing total errors of accuracy and variability) were measured during repositioning of the head to the neutral head position (Head-to-NHP) and repositioning of the head to the target (Head-to-Target) in three cardinal planes (sagittal, transverse, and frontal). Analysis of covariance (ANCOVA) was used to test the group effect, with age used as a covariate. The constant errors during repositioning from a flexed position and from an extended position to the NHP were significantly greater in the middle-aged subjects than in the control group (beta=0.30 and beta=0.60, respectively; P<0.05 for both). In addition, the root mean square errors during repositioning from a flexed or extended position to the NHP were greater in the middle-aged subjects than in the control group (beta=0.27 and beta=0.49, respectively; P<0.05 for both). The root mean square errors also increased during Head-to-Target in left rotation (beta=0.24;P<0.05), but there was no difference in the constant errors or root mean square errors during Head-to-NHP repositioning from other target positions (P>0.05). The results indicate that, after controlling for age as a covariate, there was no group effect. Thus, age appears to have a profound effect on an individual's ability to accurately reposition the head toward the neutral position in the sagittal plane and repositioning the head toward left rotation. A history of mild chronic neck pain alone had no significant effect on cervicocephalic kinesthetic sensibility.

What Are Error Rates for Classifying Teacher and School Performance Using Value-Added Models?

ERIC Educational Resources Information Center

Schochet, Peter Z.; Chiang, Hanley S.

2013-01-01

This article addresses likely error rates for measuring teacher and school performance in the upper elementary grades using value-added models applied to student test score gain data. Using a realistic performance measurement system scheme based on hypothesis testing, the authors develop error rate formulas based on ordinary least squares and…

On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms

NASA Astrophysics Data System (ADS)

Schaffrin, Burkhard; Felus, Yaron A.

2008-06-01

The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model ( Y- E Y = ( X- E X ) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E Y and E X . Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem by using the nonlinear Euler-Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition. For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335-342). The differences between the standard least-squares and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion matrix of the estimated parameters.

Evaluating the influence of perceived organizational learning capability on user acceptance of information technology among operating room nurse staff.

PubMed

Lee, Chien-Ching; Lin, Shih-Pin; Yang, Shu-Ling; Tsou, Mei-Yung; Chang, Kuang-Yi

2013-03-01

Medical institutions are eager to introduce new information technology to improve patient safety and clinical efficiency. However, the acceptance of new information technology by medical personnel plays a key role in its adoption and application. This study aims to investigate whether perceived organizational learning capability (OLC) is associated with user acceptance of information technology among operating room nurse staff. Nurse anesthetists and operating room nurses were recruited in this questionnaire survey. A pilot study was performed to ensure the reliability and validity of the translated questionnaire, which consisted of 14 items from the four dimensions of OLC, and 16 items from the four constructs of user acceptance of information technology, including performance expectancy, effort expectancy, social influence, and behavioral intention. Confirmatory factor analysis was applied in the main survey to evaluate the construct validity of the questionnaire. Structural equation modeling was used to test the hypothetical relationships between the four dimensions of user acceptance of information technology and the second-ordered OLC. Goodness of fit of the hypothetic model was also assessed. Performance expectancy, effort expectancy, and social influence positively influenced behavioral intention of users of the clinical information system (all p < 0.001) and accounted for 75% of its variation. The second-ordered OLC was positively associated with performance expectancy, effort expectancy, and social influence (all p < 0.001). However, the hypothetic relationship between perceived OLC and behavioral intention was not significant (p = 0.87). The fit statistical analysis indicated reasonable model fit to data (root mean square error of approximation = 0.07 and comparative fit index = 0.91). Perceived OLC indirectly affects user behavioral intention through the mediation of performance expectancy, effort expectancy, and social influence in the operating room setting. Copyright © 2013. Published by Elsevier B.V.

Selection of neural network structure for system error correction of electro-optical tracker system with horizontal gimbal

NASA Astrophysics Data System (ADS)

Liu, Xing-fa; Cen, Ming

2007-12-01

Neural Network system error correction method is more precise than lest square system error correction method and spheric harmonics function system error correction method. The accuracy of neural network system error correction method is mainly related to the frame of Neural Network. Analysis and simulation prove that both BP neural network system error correction method and RBF neural network system error correction method have high correction accuracy; it is better to use RBF Network system error correction method than BP Network system error correction method for little studying stylebook considering training rate and neural network scale.

Testing a path-analytic mediation model of how motivational enhancement physiotherapy improves physical functioning in pain patients.

PubMed

Cheing, Gladys; Vong, Sinfia; Chan, Fong; Ditchman, Nicole; Brooks, Jessica; Chan, Chetwyn

2014-12-01

Pain is a complex phenomenon not easily discerned from psychological, social, and environmental characteristics and is an oft cited barrier to return to work for people experiencing low back pain (LBP). The purpose of this study was to evaluate a path-analytic mediation model to examine how motivational enhancement physiotherapy, which incorporates tenets of motivational interviewing, improves physical functioning of patients with chronic LBP. Seventy-six patients with chronic LBP were recruited from the outpatient physiotherapy department of a government hospital in Hong Kong. The re-specified path-analytic model fit the data very well, χ (2)(3, N = 76) = 3.86, p = .57; comparative fit index = 1.00; and the root mean square error of approximation = 0.00. Specifically, results indicated that (a) using motivational interviewing techniques in physiotherapy was associated with increased working alliance with patients, (b) working alliance increased patients' outcome expectancy and (c) greater outcome expectancy resulted in a reduction of subjective pain intensity and improvement in physical functioning. Change in pain intensity also directly influenced improvement in physical functioning. The effect of motivational enhancement therapy on physical functioning can be explained by social-cognitive factors such as motivation, outcome expectancy, and working alliance. The use of motivational interviewing techniques to increase outcome expectancy of patients and improve working alliance could further strengthen the impact of physiotherapy on rehabilitation outcomes of patients with chronic LBP.

Determining particle size and water content by near-infrared spectroscopy in the granulation of naproxen sodium.

PubMed

Bär, David; Debus, Heiko; Brzenczek, Sina; Fischer, Wolfgang; Imming, Peter

2018-03-20

Near-infrared spectroscopy is frequently used by the pharmaceutical industry to monitor and optimize several production processes. In combination with chemometrics, a mathematical-statistical technique, the following advantages of near-infrared spectroscopy can be applied: It is a fast, non-destructive, non-invasive, and economical analytical method. One of the most advanced and popular chemometric technique is the partial least square algorithm with its best applicability in routine and its results. The required reference analytic enables the analysis of various parameters of interest, for example, moisture content, particle size, and many others. Parameters like the correlation coefficient, root mean square error of prediction, root mean square error of calibration, and root mean square error of validation have been used for evaluating the applicability and robustness of these analytical methods developed. This study deals with investigating a Naproxen Sodium granulation process using near-infrared spectroscopy and the development of water content and particle-size methods. For the water content method, one should consider a maximum water content of about 21% in the granulation process, which must be confirmed by the loss on drying. Further influences to be considered are the constantly changing product temperature, rising to about 54 °C, the creation of hydrated states of Naproxen Sodium when using a maximum of about 21% water content, and the large quantity of about 87% Naproxen Sodium in the formulation. It was considered to use a combination of these influences in developing the near-infrared spectroscopy method for the water content of Naproxen Sodium granules. The "Root Mean Square Error" was 0.25% for calibration dataset and 0.30% for the validation dataset, which was obtained after different stages of optimization by multiplicative scatter correction and the first derivative. Using laser diffraction, the granules have been analyzed for particle sizes and obtaining the summary sieve sizes of >63 μm and >100 μm. The following influences should be considered for application in routine production: constant changes in water content up to 21% and a product temperature up to 54 °C. The different stages of optimization result in a "Root Mean Square Error" of 2.54% for the calibration data set and 3.53% for the validation set by using the Kubelka-Munk conversion and first derivative for the near-infrared spectroscopy method for a particle size >63 μm. For the near-infrared spectroscopy method using a particle size >100 μm, the "Root Mean Square Error" was 3.47% for the calibration data set and 4.51% for the validation set, while using the same pre-treatments. - The robustness and suitability of this methodology has already been demonstrated by its recent successful implementation in a routine granulate production process. Copyright © 2018 Elsevier B.V. All rights reserved.

The Influence of Dimensionality on Estimation in the Partial Credit Model.

ERIC Educational Resources Information Center

De Ayala, R. J.

1995-01-01

The effect of multidimensionality on partial credit model parameter estimation was studied with noncompensatory and compensatory data. Analysis results, consisting of root mean square error bias, Pearson product-moment corrections, standardized root mean squared differences, standardized differences between means, and descriptive statistics…

Modeling Water Temperature in the Yakima River, Washington, from Roza Diversion Dam to Prosser Dam, 2005-06

USGS Publications Warehouse

Voss, Frank D.; Curran, Christopher A.; Mastin, Mark C.

2008-01-01

A mechanistic water-temperature model was constructed by the U.S. Geological Survey for use by the Bureau of Reclamation for studying the effect of potential water management decisions on water temperature in the Yakima River between Roza and Prosser, Washington. Flow and water temperature data for model input were obtained from the Bureau of Reclamation Hydromet database and from measurements collected by the U.S. Geological Survey during field trips in autumn 2005. Shading data for the model were collected by the U.S. Geological Survey in autumn 2006. The model was calibrated with data collected from April 1 through October 31, 2005, and tested with data collected from April 1 through October 31, 2006. Sensitivity analysis results showed that for the parameters tested, daily maximum water temperature was most sensitive to changes in air temperature and solar radiation. Root mean squared error for the five sites used for model calibration ranged from 1.3 to 1.9 degrees Celsius (?C) and mean error ranged from ?1.3 to 1.6?C. The root mean squared error for the five sites used for testing simulation ranged from 1.6 to 2.2?C and mean error ranged from 0.1 to 1.3?C. The accuracy of the stream temperatures estimated by the model is limited by four errors (model error, data error, parameter error, and user error).

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

PubMed

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Noninvasive hemoglobin measurement using dynamic spectrum

NASA Astrophysics Data System (ADS)

Yi, Xiaoqing; Li, Gang; Lin, Ling

2017-08-01

Spectroscopy methods for noninvasive hemoglobin (Hgb) measurement are interfered by individual difference and particular weak signal. In order to address these problems, we have put forward a series of improvement methods based on dynamic spectrum (DS), including instrument design, spectrum extraction algorithm, and modeling approach. The instrument adopts light sources composed of eight laser diodes with the wavelength range from 600 nm to 1100 nm and records photoplethysmography signals at eight wavelengths synchronously. In order to simplify the optical design, we modulate the light sources with orthogonal square waves and design the corresponding demodulation algorithm, instead of adopting a beam-splitting system. A newly designed algorithm named difference accumulation has been proved to be effective in improving the accuracy of dynamic spectrum extraction. 220 subjects are involved in the clinical experiment. An extreme learning machine calibration model between the DS data and the Hgb levels is established. Correlation coefficient and root-mean-square error of prediction sets are 0.8645 and 8.48 g/l, respectively. The results indicate that the Hgb level can be derived by this approach noninvasively with acceptable precision and accuracy. It is expected to achieve a clinic application in the future.

Italian version of the Task and Ego Orientation in physical education questionnaire.

PubMed

Bortoli, Laura; Robazza, Claudio

2005-12-01

The 1992 Task and Ego Orientation in Sport Questionnaire developed by Duda and Nicholls was modified by Walling and Duda in 1995 to assess task and ego orientation in physical education. The modified version was translated into Italian and administered to 1,547 students, 786 girls and 761 boys ages 14 to 19 years, to examine the factor structure. To evaluate the goodness of fit of the expected two-factor solution as in the original questionnaire, confirmatory factor analysis was conducted on four samples of boys and girls of two classes of age (14-16 and 17-19 years). Across sex and age, chi-squared/df ratios were less than 5.0, fit indices (GFI, NNFI, and CFI) not less than .90, and root mean square error of approximation (RMSEA) below .10. Thus, the two-factor solution of the questionnaire was supported. In the total sample, the two scales showed good internal consistency, with Cronbach alpha values of .92 for the Ego factor and .83 for the Task factor. The Ego factor accounted for 34.1% of variance and the Task factor accounted for 21.0% of variance.

Sustained prediction ability of net analyte preprocessing methods using reduced calibration sets. Theoretical and experimental study involving the spectrophotometric analysis of multicomponent mixtures.

PubMed

Goicoechea, H C; Olivieri, A C

2001-07-01

A newly developed multivariate method involving net analyte preprocessing (NAP) was tested using central composite calibration designs of progressively decreasing size regarding the multivariate simultaneous spectrophotometric determination of three active components (phenylephrine, diphenhydramine and naphazoline) and one excipient (methylparaben) in nasal solutions. Its performance was evaluated and compared with that of partial least-squares (PLS-1). Minimisation of the calibration predicted error sum of squares (PRESS) as a function of a moving spectral window helped to select appropriate working spectral ranges for both methods. The comparison of NAP and PLS results was carried out using two tests: (1) the elliptical joint confidence region for the slope and intercept of a predicted versus actual concentrations plot for a large validation set of samples and (2) the D-optimality criterion concerning the information content of the calibration data matrix. Extensive simulations and experimental validation showed that, unlike PLS, the NAP method is able to furnish highly satisfactory results when the calibration set is reduced from a full four-component central composite to a fractional central composite, as expected from the modelling requirements of net analyte based methods.

Rapid Detection of Volatile Oil in Mentha haplocalyx by Near-Infrared Spectroscopy and Chemometrics.

PubMed

Yan, Hui; Guo, Cheng; Shao, Yang; Ouyang, Zhen

2017-01-01

Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient ( R ) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had of 0.8805, of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm-1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The and were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx . The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx . Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx . For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx . Abbreviations used: 1 st der: First-order derivative; 2 nd der: Second-order derivative; LOO: Leave-one-out; LVs: Latent variables; MC: Mean centering, NIR: Near-infrared; NIRS: Near infrared spectroscopy; PCR: Principal component regression, PLSR: Partial least squares regression; RBF: Radial basis function; RMSEC: Root mean square error of cross validation, RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; SNV: Standard normal variate transformation; SVM: Support vector machine; VIP: Variable Importance in projection.

Navy Fuel Composition and Screening Tool (FCAST) v2.8

DTIC Science & Technology

2016-05-10

allowed us to develop partial least squares (PLS) models based on gas chromatography–mass spectrometry (GC-MS) data that predict fuel properties. The...Chemometric property modeling Partial least squares PLS Compositional profiler Naval Air Systems Command Air-4.4.5 Patuxent River Naval Air Station Patuxent...Cumulative predicted residual error sum of squares DiEGME Diethylene glycol monomethyl ether FCAST Fuel Composition and Screening Tool FFP Fit for

Robust Mean and Covariance Structure Analysis through Iteratively Reweighted Least Squares.

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2000-01-01

Adapts robust schemes to mean and covariance structures, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is weighted according to its distance, based on first and second order moments. Test statistics and standard error estimators are given. (SLD)

Latin-square three-dimensional gage master

DOEpatents

Jones, L.

1981-05-12

A gage master for coordinate measuring machines has an nxn array of objects distributed in the Z coordinate utilizing the concept of a Latin square experimental design. Using analysis of variance techniques, the invention may be used to identify sources of error in machine geometry and quantify machine accuracy.

Latin square three dimensional gage master

DOEpatents

Jones, Lynn L.

1982-01-01

A gage master for coordinate measuring machines has an nxn array of objects distributed in the Z coordinate utilizing the concept of a Latin square experimental design. Using analysis of variance techniques, the invention may be used to identify sources of error in machine geometry and quantify machine accuracy.

Effect of nonideal square-law detection on static calibration in noise-injection radiometers

NASA Technical Reports Server (NTRS)

Hearn, C. P.

1984-01-01

The effect of nonideal square-law detection on the static calibration for a class of Dicke radiometers is examined. It is shown that fourth-order curvature in the detection characteristic adds a nonlinear term to the linear calibration relationship normally ascribed to noise-injection, balanced Dicke radiometers. The minimum error, based on an optimum straight-line fit to the calibration curve, is derived in terms of the power series coefficients describing the input-output characteristics of the detector. These coefficients can be determined by simple measurements, and detection nonlinearity is, therefore, quantitatively related to radiometric measurement error.

VizieR Online Data Catalog: delta Cep VEGA/CHARA observing log (Nardetto+, 2016)

NASA Astrophysics Data System (ADS)

Nardetto, N.; Merand, A.; Mourard, D.; Storm, J.; Gieren, W.; Fouque, P.; Gallenne, A.; Graczyk, D.; Kervella, P.; Neilson, H.; Pietrzynski, G.; Pilecki, B.; Breitfelder, J.; Berio, P.; Challouf, M.; Clausse, J.-M.; Ligi, R.; Mathias, P.; Meilland, A.; Perraut, K.; Poretti, E.; Rainer, M.; Spang, A.; Stee, P.; Tallon-Bosc, I.; Ten Brummelaar, T.

2016-07-01

The columns give, respectively, the date, the RJD, the hour angle (HA), the minimum and maximum wavelengths over which the squared visibility is calculated, the projected baseline length Bp and its orientation PA, the signal-to-noise ratio on the fringe peak; the last column provides the calibrated squared visibility V2 together with the statistic error on V2, and the systematic error on V2 (see text for details). The data are available on the Jean-Marie Mariotti Center OiDB service (Available at http://oidb.jmmc.fr). (1 data file).

A network application for modeling a centrifugal compressor performance map

NASA Astrophysics Data System (ADS)

Nikiforov, A.; Popova, D.; Soldatova, K.

2017-08-01

The approximation of aerodynamic performance of a centrifugal compressor stage and vaneless diffuser by neural networks is presented. Advantages, difficulties and specific features of the method are described. An example of a neural network and its structure is shown. The performances in terms of efficiency, pressure ratio and work coefficient of 39 model stages within the range of flow coefficient from 0.01 to 0.08 were modeled with mean squared error 1.5 %. In addition, the loss and friction coefficients of vaneless diffusers of relative widths 0.014-0.10 are modeled with mean squared error 2.45 %.

Tissue resistivity estimation in the presence of positional and geometrical uncertainties.

PubMed

Baysal, U; Eyüboğlu, B M

2000-08-01

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

A nonlinear model of gold production in Malaysia

NASA Astrophysics Data System (ADS)

Ramli, Norashikin; Muda, Nora; Umor, Mohd Rozi

2014-06-01

Malaysia is a country which is rich in natural resources and one of it is a gold. Gold has already become an important national commodity. This study is conducted to determine a model that can be well fitted with the gold production in Malaysia from the year 1995-2010. Five nonlinear models are presented in this study which are Logistic model, Gompertz, Richard, Weibull and Chapman-Richard model. These model are used to fit the cumulative gold production in Malaysia. The best model is then selected based on the model performance. The performance of the fitted model is measured by sum squares error, root mean squares error, coefficient of determination, mean relative error, mean absolute error and mean absolute percentage error. This study has found that a Weibull model is shown to have significantly outperform compare to the other models. To confirm that Weibull is the best model, the latest data are fitted to the model. Once again, Weibull model gives the lowest readings at all types of measurement error. We can concluded that the future gold production in Malaysia can be predicted according to the Weibull model and this could be important findings for Malaysia to plan their economic activities.

Comparison of structural and least-squares lines for estimating geologic relations

USGS Publications Warehouse

Williams, G.P.; Troutman, B.M.

1990-01-01

Two different goals in fitting straight lines to data are to estimate a "true" linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating "true" straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal-predicting the dependent variable-OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples. ?? 1990 International Association for Mathematical Geology.

A Case of Error Disclosure: A Communication Privacy Management Analysis

PubMed Central

Petronio, Sandra; Helft, Paul R.; Child, Jeffrey T.

2013-01-01

To better understand the process of disclosing medical errors to patients, this research offers a case analysis using Petronios’s theoretical frame of Communication Privacy Management (CPM). Given the resistance clinicians often feel about error disclosure, insights into the way choices are made by the clinicians in telling patients about the mistake has the potential to address reasons for resistance. Applying the evidenced-based CPM theory, developed over the last 35 years and dedicated to studying disclosure phenomenon, to disclosing medical mistakes potentially has the ability to reshape thinking about the error disclosure process. Using a composite case representing a surgical mistake, analysis based on CPM theory is offered to gain insights into conversational routines and disclosure management choices of revealing a medical error. The results of this analysis show that an underlying assumption of health information ownership by the patient and family can be at odds with the way the clinician tends to control disclosure about the error. In addition, the case analysis illustrates that there are embedded patterns of disclosure that emerge out of conversations the clinician has with the patient and the patient’s family members. These patterns unfold privacy management decisions on the part of the clinician that impact how the patient is told about the error and the way that patients interpret the meaning of the disclosure. These findings suggest the need for a better understanding of how patients manage their private health information in relationship to their expectations for the way they see the clinician caring for or controlling their health information about errors. Significance for public health Much of the mission central to public health sits squarely on the ability to communicate effectively. This case analysis offers an in-depth assessment of how error disclosure is complicated by misunderstandings, assuming ownership and control over information, unwittingly following conversational scripts that convey misleading messages, and the difficulty in regulating privacy boundaries in the stressful circumstances that occur with error disclosures. As a consequence, the potential contribution to public health is the ability to more clearly see the significance of the disclosure process that has implications for many public health issues. PMID:25170501

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Estimating errors in least-squares fitting

NASA Technical Reports Server (NTRS)

Richter, P. H.

1995-01-01

While least-squares fitting procedures are commonly used in data analysis and are extensively discussed in the literature devoted to this subject, the proper assessment of errors resulting from such fits has received relatively little attention. The present work considers statistical errors in the fitted parameters, as well as in the values of the fitted function itself, resulting from random errors in the data. Expressions are derived for the standard error of the fit, as a function of the independent variable, for the general nonlinear and linear fitting problems. Additionally, closed-form expressions are derived for some examples commonly encountered in the scientific and engineering fields, namely ordinary polynomial and Gaussian fitting functions. These results have direct application to the assessment of the antenna gain and system temperature characteristics, in addition to a broad range of problems in data analysis. The effects of the nature of the data and the choice of fitting function on the ability to accurately model the system under study are discussed, and some general rules are deduced to assist workers intent on maximizing the amount of information obtained form a given set of measurements.

Methods of automatic nucleotide-sequence analysis. Multicomponent spectrophotometric analysis of mixtures of nucleic acid components by a least-squares procedure

PubMed Central

Lee, Sheila; McMullen, D.; Brown, G. L.; Stokes, A. R.

1965-01-01

1. A theoretical analysis of the errors in multicomponent spectrophotometric analysis of nucleoside mixtures, by a least-squares procedure, has been made to obtain an expression for the error coefficient, relating the error in calculated concentration to the error in extinction measurements. 2. The error coefficients, which depend only on the `library' of spectra used to fit the experimental curves, have been computed for a number of `libraries' containing the following nucleosides found in s-RNA: adenosine, guanosine, cytidine, uridine, 5-ribosyluracil, 7-methylguanosine, 6-dimethylaminopurine riboside, 6-methylaminopurine riboside and thymine riboside. 3. The error coefficients have been used to determine the best conditions for maximum accuracy in the determination of the compositions of nucleoside mixtures. 4. Experimental determinations of the compositions of nucleoside mixtures have been made and the errors found to be consistent with those predicted by the theoretical analysis. 5. It has been demonstrated that, with certain precautions, the multicomponent spectrophotometric method described is suitable as a basis for automatic nucleotide-composition analysis of oligonucleotides containing nine nucleotides. Used in conjunction with continuous chromatography and flow chemical techniques, this method can be applied to the study of the sequence of s-RNA. PMID:14346087

From least squares to multilevel modeling: A graphical introduction to Bayesian inference

NASA Astrophysics Data System (ADS)

Loredo, Thomas J.

2016-01-01

This tutorial presentation will introduce some of the key ideas and techniques involved in applying Bayesian methods to problems in astrostatistics. The focus will be on the big picture: understanding the foundations (interpreting probability, Bayes's theorem, the law of total probability and marginalization), making connections to traditional methods (propagation of errors, least squares, chi-squared, maximum likelihood, Monte Carlo simulation), and highlighting problems where a Bayesian approach can be particularly powerful (Poisson processes, density estimation and curve fitting with measurement error). The "graphical" component of the title reflects an emphasis on pictorial representations of some of the math, but also on the use of graphical models (multilevel or hierarchical models) for analyzing complex data. Code for some examples from the talk will be available to participants, in Python and in the Stan probabilistic programming language.

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol.

PubMed

Yehia, Ali M; Mohamed, Heba M

2016-01-05

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference. Copyright © 2015 Elsevier B.V. All rights reserved.

CARS Spectral Fitting with Multiple Resonant Species using Sparse Libraries

NASA Technical Reports Server (NTRS)

Cutler, Andrew D.; Magnotti, Gaetano

2010-01-01

The dual pump CARS technique is often used in the study of turbulent flames. Fast and accurate algorithms are needed for fitting dual-pump CARS spectra for temperature and multiple chemical species. This paper describes the development of such an algorithm. The algorithm employs sparse libraries, whose size grows much more slowly with number of species than a conventional library. The method was demonstrated by fitting synthetic "experimental" spectra containing 4 resonant species (N2, O2, H2 and CO2), both with noise and without it, and by fitting experimental spectra from a H2-air flame produced by a Hencken burner. In both studies, weighted least squares fitting of signal, as opposed to least squares fitting signal or square-root signal, was shown to produce the least random error and minimize bias error in the fitted parameters.

Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Tao, Yang

2005-09-01

This research evaluated the feasibility of using Fourier-transform near-infrared (FT-NIR) spectroscopy to quantify the soluble-solids content (SSC) and the available acidity (VA) in intact apples. Partial least-squares calibration models, obtained from several preprocessing techniques (smoothing, derivative, etc.) in several wave-number ranges were compared. The best models were obtained with the high coefficient determination (r) 0.940 for the SSC and a moderate r of 0.801 for the VA, root-mean-square errors of prediction of 0.272% and 0.053%, and root-mean-square errors of calibration of 0.261% and 0.046%, respectively. The results indicate that the FT-NIR spectroscopy yields good predictions of the SSC and also showed the feasibility of using it to predict the VA of apples.

VizieR Online Data Catalog: PMA Catalogue (Akhmetov+, 2017)

NASA Astrophysics Data System (ADS)

Akhmetov, V. S.; Fedorov, P. N.; Velichko, A. B.; Shulga, V. M.

2017-06-01

The idea for creating the catalogue is very simple. The PMA catalogue has been derived from a combination of two catalogues, namely 2MASS and Gaia DR1. The difference of epochs of observations for these catalogues is approximately 15 yr. The positions of objects in the Gaia DR1 catalogue are referred to the reference frame, which is consistent with ICRF to better than 0.1 mas for the J2015.0 epoch. The positions of objects in 2MASS are referred to HCRF, which, as was shown in Kovalevsky et al. (1997A&A...323..620K), is aligned with the ICRF to within ±0.6 mas at the epoch 1991.25 and is non-rotating with respect to distant extragalactic objects to within ±0.25mas/yr. By comparing the positions of the common objects contained in the catalogues, it is possible to determine their proper motions within their common range of stellar magnitudes by dividing differences of positions over the time interval between their observations. Formally, proper motions derived in such a way are given in the ICRF system, because the positions of both Gaia DR1 stars and those of 2MASS objects (through Hipparcos/Tycho-2 stars) are given in the ICRF and cover the whole sphere without gaps. We designate them further in this paper as relative, with the aim of discriminating them from absolute ones, which refer to the reference frame defined by the positions of about 1.6 million galaxies from Gaia DR1. There is no possibility of obtaining estimates of individual errors of proper motions of stars for the PMA Catalogue from the intrinsic convergence, because the direct errors for positions are not indicated in 2MASS. Therefore we use some indirect methods to obtain the estimates of uncertainties for proper motions. After elimination of the systematic errors, the root-mean-squared deviation of the coordinate differences of extended sources is about 200mas, and the mean number of galaxies inside each pixel is about 1300, so we expect the error of the absolute calibration to be 0.35mas/yr. We compared the proper motions of common objects from PMA and from the TGAS and UCAC4 catalogues. Knowing the mean-square errors of (PMA-TGAS) and (PMA-UCAC4) proper motion differences in each pixel, the appropriate errors in PMA vary from 2 to 10mas/yr, depending on magnitude, which are consistent with the errors calculated above. In case of any problems or questions, please contact by e-mail V.S. Akhmetov (akhmetovvs(at)gmail.com or akhmetov(at)astron.kharkov.ua). (1 data file).

Rovibrational spectra of ammonia. I. Unprecedented accuracy of a potential energy surface used with nonadiabatic corrections.

PubMed

Huang, Xinchuan; Schwenke, David W; Lee, Timothy J

2011-01-28

In this work, we build upon our previous work on the theoretical spectroscopy of ammonia, NH(3). Compared to our 2008 study, we include more physics in our rovibrational calculations and more experimental data in the refinement procedure, and these enable us to produce a potential energy surface (PES) of unprecedented accuracy. We call this the HSL-2 PES. The additional physics we include is a second-order correction for the breakdown of the Born-Oppenheimer approximation, and we find it to be critical for improved results. By including experimental data for higher rotational levels in the refinement procedure, we were able to greatly reduce our systematic errors for the rotational dependence of our predictions. These additions together lead to a significantly improved total angular momentum (J) dependence in our computed rovibrational energies. The root-mean-square error between our predictions using the HSL-2 PES and the reliable energy levels from the HITRAN database for J = 0-6 and J = 7∕8 for (14)NH(3) is only 0.015 cm(-1) and 0.020∕0.023 cm(-1), respectively. The root-mean-square errors for the characteristic inversion splittings are approximately 1∕3 smaller than those for energy levels. The root-mean-square error for the 6002 J = 0-8 transition energies is 0.020 cm(-1). Overall, for J = 0-8, the spectroscopic data computed with HSL-2 is roughly an order of magnitude more accurate relative to our previous best ammonia PES (denoted HSL-1). These impressive numbers are eclipsed only by the root-mean-square error between our predictions for purely rotational transition energies of (15)NH(3) and the highly accurate Cologne database (CDMS): 0.00034 cm(-1) (10 MHz), in other words, 2 orders of magnitude smaller. In addition, we identify a deficiency in the (15)NH(3) energy levels determined from a model of the experimental data.

Simultaneous estimation of cross-validation errors in least squares collocation applied for statistical testing and evaluation of the noise variance components

NASA Astrophysics Data System (ADS)

Behnabian, Behzad; Mashhadi Hossainali, Masoud; Malekzadeh, Ahad

2018-02-01

The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the reduction in estimated noise levels for those groups with the fewer number of noisy data points.

Ambiguity resolution for satellite Doppler positioning systems

NASA Technical Reports Server (NTRS)

Argentiero, P.; Marini, J.

1979-01-01

The implementation of satellite-based Doppler positioning systems frequently requires the recovery of transmitter position from a single pass of Doppler data. The least-squares approach to the problem yields conjugate solutions on either side of the satellite subtrack. It is important to develop a procedure for choosing the proper solution which is correct in a high percentage of cases. A test for ambiguity resolution which is the most powerful in the sense that it maximizes the probability of a correct decision is derived. When systematic error sources are properly included in the least-squares reduction process to yield an optimal solution the test reduces to choosing the solution which provides the smaller valuation of the least-squares loss function. When systematic error sources are ignored in the least-squares reduction, the most powerful test is a quadratic form comparison with the weighting matrix of the quadratic form obtained by computing the pseudoinverse of a reduced-rank square matrix. A formula for computing the power of the most powerful test is provided. Numerical examples are included in which the power of the test is computed for situations that are relevant to the design of a satellite-aided search and rescue system.

Red Wine Age Estimation by the Alteration of Its Color Parameters: Fourier Transform Infrared Spectroscopy as a Tool to Monitor Wine Maturation Time

PubMed Central

Basalekou, M.; Pappas, C.; Kotseridis, Y.; Tarantilis, P. A.; Kontaxakis, E.

2017-01-01

Color, phenolic content, and chemical age values of red wines made from Cretan grape varieties (Kotsifali, Mandilari) were evaluated over nine months of maturation in different containers for two vintages. The wines differed greatly on their anthocyanin profiles. Mid-IR spectra were also recorded with the use of a Fourier Transform Infrared Spectrophotometer in ZnSe disk mode. Analysis of Variance was used to explore the parameter's dependency on time. Determination models were developed for the chemical age indexes using Partial Least Squares (PLS) (TQ Analyst software) considering the spectral region 1830–1500 cm−1. The correlation coefficients (r) for chemical age index i were 0.86 for Kotsifali (Root Mean Square Error of Calibration (RMSEC) = 0.067, Root Mean Square Error of Prediction (RMSEP) = 0,115, and Root Mean Square Error of Validation (RMSECV) = 0.164) and 0.90 for Mandilari (RMSEC = 0.050, RMSEP = 0.040, and RMSECV = 0.089). For chemical age index ii the correlation coefficients (r) were 0.86 and 0.97 for Kotsifali (RMSEC 0.044, RMSEP = 0.087, and RMSECV = 0.214) and Mandilari (RMSEC = 0.024, RMSEP = 0.033, and RMSECV = 0.078), respectively. The proposed method is simpler, less time consuming, and more economical and does not require chemical reagents. PMID:29225994

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

Quick method (FT-NIR) for the determination of oil and major fatty acids content in whole achenes of milk thistle (Silybum marianum (L.) Gaertn.).

PubMed

Koláčková, Pavla; Růžičková, Gabriela; Gregor, Tomáš; Šišperová, Eliška

2015-08-30

Calibration models for the Fourier transform-near infrared (FT-NIR) instrument were developed for quick and non-destructive determination of oil and fatty acids in whole achenes of milk thistle. Samples with a range of oil and fatty acid levels were collected and their transmittance spectra were obtained by the FT-NIR instrument. Based on these spectra and data gained by the means of the reference method - Soxhlet extraction and gas chromatography (GC) - calibration models were created by means of partial least square (PLS) regression analysis. Precision and accuracy of the calibration models was verified via the cross-validation of validation samples whose spectra were not part of the calibration model and also according to the root mean square error of prediction (RMSEP), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV) and the validation coefficient of determination (R(2) ). R(2) for whole seeds were 0.96, 0.96, 0.83 and 0.67 and the RMSEP values were 0.76, 1.68, 1.24, 0.54 for oil, linoleic (C18:2), oleic (C18:1) and palmitic (C16:0) acids, respectively. The calibration models are appropriate for the non-destructive determination of oil and fatty acids levels in whole seeds of milk thistle. © 2014 Society of Chemical Industry.

Green method by diffuse reflectance infrared spectroscopy and spectral region selection for the quantification of sulphamethoxazole and trimethoprim in pharmaceutical formulations.

PubMed

da Silva, Fabiana E B; Flores, Érico M M; Parisotto, Graciele; Müller, Edson I; Ferrão, Marco F

2016-03-01

An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.

New method for propagating the square root covariance matrix in triangular form. [using Kalman-Bucy filter

NASA Technical Reports Server (NTRS)

Choe, C. Y.; Tapley, B. D.

1975-01-01

A method proposed by Potter of applying the Kalman-Bucy filter to the problem of estimating the state of a dynamic system is described, in which the square root of the state error covariance matrix is used to process the observations. A new technique which propagates the covariance square root matrix in lower triangular form is given for the discrete observation case. The technique is faster than previously proposed algorithms and is well-adapted for use with the Carlson square root measurement algorithm.

A Comparison of Normal and Elliptical Estimation Methods in Structural Equation Models.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Cheevatanarak, Suchittra

Monte Carlo simulation compared chi-square statistics, parameter estimates, and root mean square error of approximation values using normal and elliptical estimation methods. Three research conditions were imposed on the simulated data: sample size, population contamination percent, and kurtosis. A Bentler-Weeks structural model established the…

Phase modulation for reduced vibration sensitivity in laser-cooled clocks in space

NASA Technical Reports Server (NTRS)

Klipstein, W.; Dick, G.; Jefferts, S.; Walls, F.

2001-01-01

The standard interrogation technique in atomic beam clocks is square-wave frequency modulation (SWFM), which suffers a first order sensitivity to vibrations as changes in the transit time of the atoms translates to perceived frequency errors. Square-wave phase modulation (SWPM) interrogation eliminates sensitivity to this noise.

An Examination of Statistical Power in Multigroup Dynamic Structural Equation Models

ERIC Educational Resources Information Center

Prindle, John J.; McArdle, John J.

2012-01-01

This study used statistical simulation to calculate differential statistical power in dynamic structural equation models with groups (as in McArdle & Prindle, 2008). Patterns of between-group differences were simulated to provide insight into how model parameters influence power approximations. Chi-square and root mean square error of…

Parameter estimation of Monod model by the Least-Squares method for microalgae Botryococcus Braunii sp

NASA Astrophysics Data System (ADS)

See, J. J.; Jamaian, S. S.; Salleh, R. M.; Nor, M. E.; Aman, F.

2018-04-01

This research aims to estimate the parameters of Monod model of microalgae Botryococcus Braunii sp growth by the Least-Squares method. Monod equation is a non-linear equation which can be transformed into a linear equation form and it is solved by implementing the Least-Squares linear regression method. Meanwhile, Gauss-Newton method is an alternative method to solve the non-linear Least-Squares problem with the aim to obtain the parameters value of Monod model by minimizing the sum of square error ( SSE). As the result, the parameters of the Monod model for microalgae Botryococcus Braunii sp can be estimated by the Least-Squares method. However, the estimated parameters value obtained by the non-linear Least-Squares method are more accurate compared to the linear Least-Squares method since the SSE of the non-linear Least-Squares method is less than the linear Least-Squares method.

Comparison of Asymmetric and Ice-cream Cone Models for Halo Coronal Mass Ejections

NASA Astrophysics Data System (ADS)

Na, H.; Moon, Y.

2011-12-01

Halo coronal mass ejections (HCMEs) are major cause of the geomagnetic storms. To minimize the projection effect by coronagraph observation, several cone models have been suggested: an ice-cream cone model, an asymmetric cone model etc. These models allow us to determine the three dimensional parameters of HCMEs such as radial speed, angular width, and the angle between sky plane and central axis of the cone. In this study, we compare these parameters obtained from different models using 48 well-observed HCMEs from 2001 to 2002. And we obtain the root mean square error (RMS error) between measured projection speeds and calculated projection speeds for both cone models. As a result, we find that the radial speeds obtained from the models are well correlated with each other (R = 0.86), and the correlation coefficient of angular width is 0.6. The correlation coefficient of the angle between sky plane and central axis of the cone is 0.31, which is much smaller than expected. The reason may be due to the fact that the source locations of the asymmetric cone model are distributed near the center, while those of the ice-cream cone model are located in a wide range. The average RMS error of the asymmetric cone model (85.6km/s) is slightly smaller than that of the ice-cream cone model (87.8km/s).

Image inversion analysis of the HST OTA (Hubble Space Telescope Optical Telescope Assembly), phase A

NASA Technical Reports Server (NTRS)

Litvak, M. M.

1991-01-01

Technical work during September-December 1990 consisted of: (1) analyzing HST point source images obtained from JPL; (2) retrieving phase information from the images by a direct (noniterative) technique; and (3) characterizing the wavefront aberration due to the errors in the Hubble Space Telescope (HST) mirrors, in a preliminary manner. This work was in support of JPL design of compensating optics for the next generation wide-field planetary camera on HST. This digital technique for phase retrieval from pairs of defocused images, is based on the energy transport equation between these image planes. In addition, an end-to-end wave optics routine, based on the JPL Code 5 prescription of the unaberrated HST and WFPC, was derived for output of the reference phase front when mirror error is absent. Also, the Roddier routine unwrapped the retrieved phase by inserting the required jumps of +/- 2(pi) radians for the sake of smoothness. A least-squares fitting routine, insensitive to phase unwrapping, but nonlinear, was used to obtain estimates of the Zernike polynomial coefficients that describe the aberration. The phase results were close to, but higher than, the expected error in conic constant of the primary mirror suggested by the fossil evidence. The analysis of aberration contributed by the camera itself could be responsible for the small discrepancy, but was not verified by analysis.

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

PubMed Central

Wang, Xuan; Liu, Jinghong; Zhou, Qianfei

2016-01-01

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions. PMID:28029145

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles.

PubMed

Wang, Xuan; Liu, Jinghong; Zhou, Qianfei

2016-12-25

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions.

Understanding Scaling Relations in Fracture and Mechanical Deformation of Single Crystal and Polycrystalline Silicon by Performing Atomistic Simulations at Mesoscale

DTIC Science & Technology

2009-07-16

0.25 0.26 -0.85 1 SSR SSE R SSTO SSTO = = − 2 2 ˆ( ) : Regression sum of square, ˆwhere : mean value, : value from the fitted line ˆ...Error sum of square : Total sum of square i i i i SSR Y Y Y Y SSE Y Y SSTO SSE SSR = − = − = + ∑ ∑ Statistical analysis: Coefficient of correlation

Synthesis of hover autopilots for rotary-wing VTOL aircraft

NASA Technical Reports Server (NTRS)

Hall, W. E.; Bryson, A. E., Jr.

1972-01-01

The practical situation is considered where imperfect information on only a few rotor and fuselage state variables is available. Filters are designed to estimate all the state variables from noisy measurements of fuselage pitch/roll angles and from noisy measurements of both fuselage and rotor pitch/roll angles. The mean square response of the vehicle to a very gusty, random wind is computed using various filter/controllers and is found to be quite satisfactory although, of course, not so good as when one has perfect information (idealized case). The second part of the report considers precision hover over a point on the ground. A vehicle model without rotor dynamics is used and feedback signals in position and integral of position error are added. The mean square response of the vehicle to a very gusty, random wind is computed, assuming perfect information feedback, and is found to be excellent. The integral error feedback gives zero position error for a steady wind, and smaller position error for a random wind.

Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation

NASA Astrophysics Data System (ADS)

Reis, D. S.; Stedinger, J. R.; Martins, E. S.

2005-10-01

This paper develops a Bayesian approach to analysis of a generalized least squares (GLS) regression model for regional analyses of hydrologic data. The new approach allows computation of the posterior distributions of the parameters and the model error variance using a quasi-analytic approach. Two regional skew estimation studies illustrate the value of the Bayesian GLS approach for regional statistical analysis of a shape parameter and demonstrate that regional skew models can be relatively precise with effective record lengths in excess of 60 years. With Bayesian GLS the marginal posterior distribution of the model error variance and the corresponding mean and variance of the parameters can be computed directly, thereby providing a simple but important extension of the regional GLS regression procedures popularized by Tasker and Stedinger (1989), which is sensitive to the likely values of the model error variance when it is small relative to the sampling error in the at-site estimator.

A Comprehensive Study of Gridding Methods for GPS Horizontal Velocity Fields

NASA Astrophysics Data System (ADS)

Wu, Yanqiang; Jiang, Zaisen; Liu, Xiaoxia; Wei, Wenxin; Zhu, Shuang; Zhang, Long; Zou, Zhenyu; Xiong, Xiaohui; Wang, Qixin; Du, Jiliang

2017-03-01

Four gridding methods for GPS velocities are compared in terms of their precision, applicability and robustness by analyzing simulated data with uncertainties from 0.0 to ±3.0 mm/a. When the input data are 1° × 1° grid sampled and the uncertainty of the additional error is greater than ±1.0 mm/a, the gridding results show that the least-squares collocation method is highly robust while the robustness of the Kriging method is low. In contrast, the spherical harmonics and the multi-surface function are moderately robust, and the regional singular values for the multi-surface function method and the edge effects for the spherical harmonics method become more significant with increasing uncertainty of the input data. When the input data (with additional errors of ±2.0 mm/a) are decimated by 50% from the 1° × 1° grid data and then erased in three 6° × 12° regions, the gridding results in these three regions indicate that the least-squares collocation and the spherical harmonics methods have good performances, while the multi-surface function and the Kriging methods may lead to singular values. The gridding techniques are also applied to GPS horizontal velocities with an average error of ±0.8 mm/a over the Chinese mainland and the surrounding areas, and the results show that the least-squares collocation method has the best performance, followed by the Kriging and multi-surface function methods. Furthermore, the edge effects of the spherical harmonics method are significantly affected by the sparseness and geometric distribution of the input data. In general, the least-squares collocation method is superior in terms of its robustness, edge effect, error distribution and stability, while the other methods have several positive features.

Analysis of S-box in Image Encryption Using Root Mean Square Error Method

NASA Astrophysics Data System (ADS)

Hussain, Iqtadar; Shah, Tariq; Gondal, Muhammad Asif; Mahmood, Hasan

2012-07-01

The use of substitution boxes (S-boxes) in encryption applications has proven to be an effective nonlinear component in creating confusion and randomness. The S-box is evolving and many variants appear in literature, which include advanced encryption standard (AES) S-box, affine power affine (APA) S-box, Skipjack S-box, Gray S-box, Lui J S-box, residue prime number S-box, Xyi S-box, and S8 S-box. These S-boxes have algebraic and statistical properties which distinguish them from each other in terms of encryption strength. In some circumstances, the parameters from algebraic and statistical analysis yield results which do not provide clear evidence in distinguishing an S-box for an application to a particular set of data. In image encryption applications, the use of S-boxes needs special care because the visual analysis and perception of a viewer can sometimes identify artifacts embedded in the image. In addition to existing algebraic and statistical analysis already used for image encryption applications, we propose an application of root mean square error technique, which further elaborates the results and enables the analyst to vividly distinguish between the performances of various S-boxes. While the use of the root mean square error analysis in statistics has proven to be effective in determining the difference in original data and the processed data, its use in image encryption has shown promising results in estimating the strength of the encryption method. In this paper, we show the application of the root mean square error analysis to S-box image encryption. The parameters from this analysis are used in determining the strength of S-boxes

The mean field theory in EM procedures for blind Markov random field image restoration.

PubMed

Zhang, J

1993-01-01

A Markov random field (MRF) model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described. The MRF is a coupled one which provides continuity (inside regions of smooth gray tones) and discontinuity (at region boundaries) constraints for the restoration problem which is, in general, ill posed. The computational difficulty associated with the EM procedure for MRFs is resolved by using the mean field theory from statistical mechanics. An orthonormal blur decomposition is used to reduce the chances of undesirable locally optimal estimates. Experimental results on synthetic and real-world images show that this approach provides good blur estimates and restored images. The restored images are comparable to those obtained by a Wiener filter in mean-square error, but are most visually pleasing.

An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yunlong; Wang, Aiping; Guo, Lei

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.

Error-Based Design Space Windowing

NASA Technical Reports Server (NTRS)

Papila, Melih; Papila, Nilay U.; Shyy, Wei; Haftka, Raphael T.; Fitz-Coy, Norman

2002-01-01

Windowing of design space is considered in order to reduce the bias errors due to low-order polynomial response surfaces (RS). Standard design space windowing (DSW) uses a region of interest by setting a requirement on response level and checks it by a global RS predictions over the design space. This approach, however, is vulnerable since RS modeling errors may lead to the wrong region to zoom on. The approach is modified by introducing an eigenvalue error measure based on point-to-point mean squared error criterion. Two examples are presented to demonstrate the benefit of the error-based DSW.

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First,more » the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE PAGES

Zhou, Ping; Wang, Chenyu; Li, Mingjie; ...

2018-01-31

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Quadratic correlation filters for optical correlators

NASA Astrophysics Data System (ADS)

Mahalanobis, Abhijit; Muise, Robert R.; Vijaya Kumar, Bhagavatula V. K.

2003-08-01

Linear correlation filters have been implemented in optical correlators and successfully used for a variety of applications. The output of an optical correlator is usually sensed using a square law device (such as a CCD array) which forces the output to be the squared magnitude of the desired correlation. It is however not a traditional practice to factor the effect of the square-law detector in the design of the linear correlation filters. In fact, the input-output relationship of an optical correlator is more accurately modeled as a quadratic operation than a linear operation. Quadratic correlation filters (QCFs) operate directly on the image data without the need for feature extraction or segmentation. In this sense, the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects. Not only is more processing required to detect peaks in the outputs of multiple linear filters, but choosing a winner among them is an error prone task. In contrast, all channels in a QCF work together to optimize the same performance metric and produce a combined output that leads to considerable simplification of the post-processing. In this paper, we propose a novel approach to the design of quadratic correlation based on the Fukunaga Koontz transform. Although quadratic filters are known to be optimum when the data is Gaussian, it is expected that they will perform as well as or better than linear filters in general. Preliminary performance results are provided that show that quadratic correlation filters perform better than their linear counterparts.

Error in telemetry studies: Effects of animal movement on triangulation

USGS Publications Warehouse

Schmutz, Joel A.; White, Gary C.

1990-01-01

We used Monte Carlo simulations to investigate the effects of animal movement on error of estimated animal locations derived from radio-telemetry triangulation of sequentially obtained bearings. Simulated movements of 0-534 m resulted in up to 10-fold increases in average location error but <10% decreases in location precision when observer-to-animal distances were <1,000 m. Location error and precision were minimally affected by censorship of poor locations with Chi-square goodness-of-fit tests. Location error caused by animal movement can only be eliminated by taking simultaneous bearings.

The theory precision analyse of RFM localization of satellite remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhang, Jianqing; Xv, Biao

2009-11-01

The tradition method of detecting precision of Rational Function Model(RFM) is to make use of a great deal check points, and it calculates mean square error through comparing calculational coordinate with known coordinate. This method is from theory of probability, through a large number of samples to statistic estimate value of mean square error, we can think its estimate value approaches in its true when samples are well enough. This paper is from angle of survey adjustment, take law of propagation of error as the theory basis, and it calculates theory precision of RFM localization. Then take the SPOT5 three array imagery as experiment data, and the result of traditional method and narrated method in the paper are compared, while has confirmed tradition method feasible, and answered its theory precision question from the angle of survey adjustment.

Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data. Ph.D. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Whitlock, C. H., III

1977-01-01

Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

Diffuse-flow conceptualization and simulation of the Edwards aquifer, San Antonio region, Texas

USGS Publications Warehouse

Lindgren, R.J.

2006-01-01

A numerical ground-water-flow model (hereinafter, the conduit-flow Edwards aquifer model) of the karstic Edwards aquifer in south-central Texas was developed for a previous study on the basis of a conceptualization emphasizing conduit development and conduit flow, and included simulating conduits as one-cell-wide, continuously connected features. Uncertainties regarding the degree to which conduits pervade the Edwards aquifer and influence ground-water flow, as well as other uncertainties inherent in simulating conduits, raised the question of whether a model based on the conduit-flow conceptualization was the optimum model for the Edwards aquifer. Accordingly, a model with an alternative hydraulic conductivity distribution without conduits was developed in a study conducted during 2004-05 by the U.S. Geological Survey, in cooperation with the San Antonio Water System. The hydraulic conductivity distribution for the modified Edwards aquifer model (hereinafter, the diffuse-flow Edwards aquifer model), based primarily on a conceptualization in which flow in the aquifer predominantly is through a network of numerous small fractures and openings, includes 38 zones, with hydraulic conductivities ranging from 3 to 50,000 feet per day. Revision of model input data for the diffuse-flow Edwards aquifer model was limited to changes in the simulated hydraulic conductivity distribution. The root-mean-square error for 144 target wells for the calibrated steady-state simulation for the diffuse-flow Edwards aquifer model is 20.9 feet. This error represents about 3 percent of the total head difference across the model area. The simulated springflows for Comal and San Marcos Springs for the calibrated steady-state simulation were within 2.4 and 15 percent of the median springflows for the two springs, respectively. The transient calibration period for the diffuse-flow Edwards aquifer model was 1947-2000, with 648 monthly stress periods, the same as for the conduit-flow Edwards aquifer model. The root-mean-square error for a period of drought (May-November 1956) for the calibrated transient simulation for 171 target wells is 33.4 feet, which represents about 5 percent of the total head difference across the model area. The root-mean-square error for a period of above-normal rainfall (November 1974-July 1975) for the calibrated transient simulation for 169 target wells is 25.8 feet, which represents about 4 percent of the total head difference across the model area. The root-mean-square error ranged from 6.3 to 30.4 feet in 12 target wells with long-term water-level measurements for varying periods during 1947-2000 for the calibrated transient simulation for the diffuse-flow Edwards aquifer model, and these errors represent 5.0 to 31.3 percent of the range in water-level fluctuations of each of those wells. The root-mean-square errors for the five major springs in the San Antonio segment of the aquifer for the calibrated transient simulation, as a percentage of the range of discharge fluctuations measured at the springs, varied from 7.2 percent for San Marcos Springs and 8.1 percent for Comal Springs to 28.8 percent for Leona Springs. The root-mean-square errors for hydraulic heads for the conduit-flow Edwards aquifer model are 27, 76, and 30 percent greater than those for the diffuse-flow Edwards aquifer model for the steady-state, drought, and above-normal rainfall synoptic time periods, respectively. The goodness-of-fit between measured and simulated springflows is similar for Comal, San Marcos, and Leona Springs for the diffuse-flow Edwards aquifer model and the conduit-flow Edwards aquifer model. The root-mean-square errors for Comal and Leona Springs were 15.6 and 21.3 percent less, respectively, whereas the root-mean-square error for San Marcos Springs was 3.3 percent greater for the diffuse-flow Edwards aquifer model compared to the conduit-flow Edwards aquifer model. The root-mean-square errors for San Antonio and San Pedro Springs were appreciably greater, 80.2 and 51.0 percent, respectively, for the diffuse-flow Edwards aquifer model. The simulated water budgets for the diffuse-flow Edwards aquifer model are similar to those for the conduit-flow Edwards aquifer model. Differences in percentage of total sources or discharges for a budget component are 2.0 percent or less for all budget components for the steady-state and transient simulations. The largest difference in terms of the magnitude of water budget components for the transient simulation for 1956 was a decrease of about 10,730 acre-feet per year (about 2 per-cent) in springflow for the diffuse-flow Edwards aquifer model compared to the conduit-flow Edwards aquifer model. This decrease in springflow (a water budget discharge) was largely offset by the decreased net loss of water from storage (a water budget source) of about 10,500 acre-feet per year.

Bayesian inversions of a dynamic vegetation model in four European grassland sites

NASA Astrophysics Data System (ADS)

Minet, J.; Laloy, E.; Tychon, B.; François, L.

2015-01-01

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB dynamic vegetation model (DVM) with ten unknown parameters, using the DREAM(ZS) Markov chain Monte Carlo (MCMC) sampler. We compare model inversions considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a~priori or jointly inferred with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root-mean-square error (RMSE) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19 g C m-2 day-1, 1.04 to 1.56 g C m-2 day-1, and 0.50 to 1.28 mm day-1, respectively. In validation, mismatches between measured and simulated data are larger, but still with Nash-Sutcliffe efficiency scores above 0.5 for three out of the four sites. Although measurement errors associated with eddy covariance data are known to be heteroscedastic, we showed that assuming a classical linear heteroscedastic model of the residual errors in the inversion do not fully remove heteroscedasticity. Since the employed heteroscedastic error model allows for larger deviations between simulated and measured data as the magnitude of the measured data increases, this error model expectedly lead to poorer data fitting compared to inversions considering a constant variance of the residual errors. Furthermore, sampling the residual error variances along with model parameters results in overall similar model parameter posterior distributions as those obtained by fixing these variances beforehand, while slightly improving model performance. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides model behaviour, difference between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics. Lastly, the possibility of finding a common set of parameters among the four experimental sites is discussed.

Optimization and Calibration of Slat Position for a SPECT With Slit-Slat Collimator and Pixelated Detector Crystals

NASA Astrophysics Data System (ADS)

Deng, Xiao; Ma, Tianyu; Lecomte, Roger; Yao, Rutao

2011-10-01

To expand the availability of SPECT for biomedical research, we developed a SPECT imaging system on an existing animal PET detector by adding a slit-slat collimator. As the detector crystals are pixelated, the relative slat-to-crystal position (SCP) in the axial direction affects the photon flux distribution onto the crystals. The accurate knowledge of SCP is important to the axial resolution and sensitivity of the system. This work presents a method for optimizing SCP in system design and for determining SCP in system geometrical calibration. The optimization was achieved by finding the SCP that provides higher spatial resolution in terms of average-root-mean-square (R̅M̅S̅) width of the axial point spread function (PSF) without loss of sensitivity. The calibration was based on the least-square-error method that minimizes the difference between the measured and modeled axial point spread projections. The uniqueness and accuracy of the calibration results were validated through a singular value decomposition (SVD) based approach. Both the optimization and calibration techniques were evaluated with Monte Carlo (MC) simulated data. We showed that the [R̅M̅S̅] was improved about 15% with the optimal SCP as compared to the least-optimal SCP, and system sensitivity was not affected by SCP. The SCP error achieved by the proposed calibration method was less than 0.04 mm. The calibrated SCP value was used in MC simulation to generate the system matrix which was used for image reconstruction. The images of simulated phantoms showed the expected resolution performance and were artifact free. We conclude that the proposed optimization and calibration method is effective for the slit-slat collimator based SPECT systems.

The effect of regularization in motion compensated PET image reconstruction: a realistic numerical 4D simulation study.

PubMed

Tsoumpas, C; Polycarpou, I; Thielemans, K; Buerger, C; King, A P; Schaeffter, T; Marsden, P K

2013-03-21

Following continuous improvement in PET spatial resolution, respiratory motion correction has become an important task. Two of the most common approaches that utilize all detected PET events to motion-correct PET data are the reconstruct-transform-average method (RTA) and motion-compensated image reconstruction (MCIR). In RTA, separate images are reconstructed for each respiratory frame, subsequently transformed to one reference frame and finally averaged to produce a motion-corrected image. In MCIR, the projection data from all frames are reconstructed by including motion information in the system matrix so that a motion-corrected image is reconstructed directly. Previous theoretical analyses have explained why MCIR is expected to outperform RTA. It has been suggested that MCIR creates less noise than RTA because the images for each separate respiratory frame will be severely affected by noise. However, recent investigations have shown that in the unregularized case RTA images can have fewer noise artefacts, while MCIR images are more quantitatively accurate but have the common salt-and-pepper noise. In this paper, we perform a realistic numerical 4D simulation study to compare the advantages gained by including regularization within reconstruction for RTA and MCIR, in particular using the median-root-prior incorporated in the ordered subsets maximum a posteriori one-step-late algorithm. In this investigation we have demonstrated that MCIR with proper regularization parameters reconstructs lesions with less bias and root mean square error and similar CNR and standard deviation to regularized RTA. This finding is reproducible for a variety of noise levels (25, 50, 100 million counts), lesion sizes (8 mm, 14 mm diameter) and iterations. Nevertheless, regularized RTA can also be a practical solution for motion compensation as a proper level of regularization reduces both bias and mean square error.

Medusa spectroscopy of A400, A576, A1767, and A2124

NASA Technical Reports Server (NTRS)

Hintzen, P.; Hill, J. M.; Lindley, D.; Scott, J. S.; Angel, J. R. P.

1982-01-01

Galaxy velocity data taken with the Steward Observatory multiple aperture fiber optic spectrograph are presented for four Abell clusters. The root-mean-square external errors in these velocities are about 100 km/s; accuracy which compares favorably with that obtained from single-object observations. It is expected that the recent adoption of a CCD detector should decrease external errors to about 50 km/s. All four of the clusters observed are known X-ray sources and the present data agree well with empirically derived velocity dispersion-X-ray luminosity relations for clusters of galaxies. Abell 400 is interesting in this regard, since both its X-ray luminosity and its velocity dispersion are quite small. Such objects are particularly important in determining the slope of the velocity dispersion-X-ray luminosity relation. The large microwave decrement observed in A576 was initially interpreted as due to Compton scattering of the microwave background by the X-ray-emitting intracluster gas. White and Silk have presented Einstein X-ray data which indicate that A576 contains too little gas to produce the observed microwave decrement by Compton scattering. The velocity dispersion obtained here for 47 members of this cluster strengthens their conclusion.

Competition between learned reward and error outcome predictions in anterior cingulate cortex.

PubMed

Alexander, William H; Brown, Joshua W

2010-02-15

The anterior cingulate cortex (ACC) is implicated in performance monitoring and cognitive control. Non-human primate studies of ACC show prominent reward signals, but these are elusive in human studies, which instead show mainly conflict and error effects. Here we demonstrate distinct appetitive and aversive activity in human ACC. The error likelihood hypothesis suggests that ACC activity increases in proportion to the likelihood of an error, and ACC is also sensitive to the consequence magnitude of the predicted error. Previous work further showed that error likelihood effects reach a ceiling as the potential consequences of an error increase, possibly due to reductions in the average reward. We explored this issue by independently manipulating reward magnitude of task responses and error likelihood while controlling for potential error consequences in an Incentive Change Signal Task. The fMRI results ruled out a modulatory effect of expected reward on error likelihood effects in favor of a competition effect between expected reward and error likelihood. Dynamic causal modeling showed that error likelihood and expected reward signals are intrinsic to the ACC rather than received from elsewhere. These findings agree with interpretations of ACC activity as signaling both perceptions of risk and predicted reward. Copyright 2009 Elsevier Inc. All rights reserved.

Investigation of adaptive filtering and MDL mitigation based on space-time block-coding for spatial division multiplexed coherent receivers

NASA Astrophysics Data System (ADS)

Weng, Yi; He, Xuan; Yao, Wang; Pacheco, Michelle C.; Wang, Junyi; Pan, Zhongqi

2017-07-01

In this paper, we explored the performance of space-time block-coding (STBC) assisted multiple-input multiple-output (MIMO) scheme for modal dispersion and mode-dependent loss (MDL) mitigation in spatial-division multiplexed optical communication systems, whereas the weight matrices of frequency-domain equalization (FDE) were updated heuristically using decision-directed recursive least squares (RLS) algorithm for convergence and channel estimation. The proposed STBC-RLS algorithm can achieve 43.6% enhancement on convergence rate over conventional least mean squares (LMS) for quadrature phase-shift keying (QPSK) signals with merely 16.2% increase in hardware complexity. The overall optical signal to noise ratio (OSNR) tolerance can be improved via STBC by approximately 3.1, 4.9, 7.8 dB for QPSK, 16-quadrature amplitude modulation (QAM) and 64-QAM with respective bit-error-rates (BER) and minimum-mean-square-error (MMSE).

Quantitative determination of additive Chlorantraniliprole in Abamectin preparation: Investigation of bootstrapping soft shrinkage approach by mid-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Yan, Hong; Song, Xiangzhong; Tian, Kuangda; Chen, Yilin; Xiong, Yanmei; Min, Shungeng

2018-02-01

A novel method, mid-infrared (MIR) spectroscopy, which enables the determination of Chlorantraniliprole in Abamectin within minutes, is proposed. We further evaluate the prediction ability of four wavelength selection methods, including bootstrapping soft shrinkage approach (BOSS), Monte Carlo uninformative variable elimination (MCUVE), genetic algorithm partial least squares (GA-PLS) and competitive adaptive reweighted sampling (CARS) respectively. The results showed that BOSS method obtained the lowest root mean squared error of cross validation (RMSECV) (0.0245) and root mean squared error of prediction (RMSEP) (0.0271), as well as the highest coefficient of determination of cross-validation (Qcv2) (0.9998) and the coefficient of determination of test set (Q2test) (0.9989), which demonstrated that the mid infrared spectroscopy can be used to detect Chlorantraniliprole in Abamectin conveniently. Meanwhile, a suitable wavelength selection method (BOSS) is essential to conducting a component spectral analysis.

RLS Channel Estimation with Adaptive Forgetting Factor for DS-CDMA Frequency-Domain Equalization

NASA Astrophysics Data System (ADS)

Kojima, Yohei; Tomeba, Hiromichi; Takeda, Kazuaki; Adachi, Fumiyuki

Frequency-domain equalization (FDE) based on the minimum mean square error (MMSE) criterion can increase the downlink bit error rate (BER) performance of DS-CDMA beyond that possible with conventional rake combining in a frequency-selective fading channel. FDE requires accurate channel estimation. Recently, we proposed a pilot-assisted channel estimation (CE) based on the MMSE criterion. Using MMSE-CE, the channel estimation accuracy is almost insensitive to the pilot chip sequence, and a good BER performance is achieved. In this paper, we propose a channel estimation scheme using one-tap recursive least square (RLS) algorithm, where the forgetting factor is adapted to the changing channel condition by the least mean square (LMS)algorithm, for DS-CDMA with FDE. We evaluate the BER performance using RLS-CE with adaptive forgetting factor in a frequency-selective fast Rayleigh fading channel by computer simulation.

Accuracy of least-squares methods for the Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Bochev, Pavel B.; Gunzburger, Max D.

1993-01-01

Recently there has been substantial interest in least-squares finite element methods for velocity-vorticity-pressure formulations of the incompressible Navier-Stokes equations. The main cause for this interest is the fact that algorithms for the resulting discrete equations can be devised which require the solution of only symmetric, positive definite systems of algebraic equations. On the other hand, it is well-documented that methods using the vorticity as a primary variable often yield very poor approximations. Thus, here we study the accuracy of these methods through a series of computational experiments, and also comment on theoretical error estimates. It is found, despite the failure of standard methods for deriving error estimates, that computational evidence suggests that these methods are, at the least, nearly optimally accurate. Thus, in addition to the desirable matrix properties yielded by least-squares methods, one also obtains accurate approximations.

Nondestructive quantification of the soluble-solids content and the available acidity of apples by Fourier-transform near-infrared spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ying Yibin; Liu Yande; Tao Yang

2005-09-01

This research evaluated the feasibility of using Fourier-transform near-infrared (FT-NIR) spectroscopy to quantify the soluble-solids content (SSC) and the available acidity (VA) in intact apples. Partial least-squares calibration models, obtained from several preprocessing techniques (smoothing, derivative, etc.) in several wave-number ranges were compared. The best models were obtained with the high coefficient determination (r{sup 2}) 0.940 for the SSC and a moderate r{sup 2} of 0.801 for the VA, root-mean-square errors of prediction of 0.272% and 0.053%, and root-mean-square errors of calibration of 0.261% and 0.046%, respectively. The results indicate that the FT-NIR spectroscopy yields good predictions of the SSCmore » and also showed the feasibility of using it to predict the VA of apples.« less

Aerodynamic coefficient identification package dynamic data accuracy determinations: Lessons learned

NASA Technical Reports Server (NTRS)

Heck, M. L.; Findlay, J. T.; Compton, H. R.

1983-01-01

The errors in the dynamic data output from the Aerodynamic Coefficient Identification Packages (ACIP) flown on Shuttle flights 1, 3, 4, and 5 were determined using the output from the Inertial Measurement Units (IMU). A weighted least-squares batch algorithm was empolyed. Using an averaging technique, signal detection was enhanced; this allowed improved calibration solutions. Global errors as large as 0.04 deg/sec for the ACIP gyros, 30 mg for linear accelerometers, and 0.5 deg/sec squared in the angular accelerometer channels were detected and removed with a combination is bias, scale factor, misalignment, and g-sensitive calibration constants. No attempt was made to minimize local ACIP dynamic data deviations representing sensed high-frequency vibration or instrument noise. Resulting 1sigma calibrated ACIP global accuracies were within 0.003 eg/sec, 1.0 mg, and 0.05 deg/sec squared for the gyros, linear accelerometers, and angular accelerometers, respectively.

Combating speckle in SAR images - Vector filtering and sequential classification based on a multiplicative noise model

NASA Technical Reports Server (NTRS)

Lin, Qian; Allebach, Jan P.

1990-01-01

An adaptive vector linear minimum mean-squared error (LMMSE) filter for multichannel images with multiplicative noise is presented. It is shown theoretically that the mean-squared error in the filter output is reduced by making use of the correlation between image bands. The vector and conventional scalar LMMSE filters are applied to a three-band SIR-B SAR, and their performance is compared. Based on a mutliplicative noise model, the per-pel maximum likelihood classifier was derived. The authors extend this to the design of sequential and robust classifiers. These classifiers are also applied to the three-band SIR-B SAR image.

Reliable and accurate extraction of Hamaker constants from surface force measurements.

PubMed

Miklavcic, S J

2018-08-15

A simple and accurate closed-form expression for the Hamaker constant that best represents experimental surface force data is presented. Numerical comparisons are made with the current standard least squares approach, which falsely assumes error-free separation measurements, and a nonlinear version assuming independent measurements of force and separation are subject to error. The comparisons demonstrate that not only is the proposed formula easily implemented it is also considerably more accurate. This option is appropriate for any value of Hamaker constant, high or low, and certainly for any interacting system exhibiting an inverse square distance dependent van der Waals force. Copyright © 2018 Elsevier Inc. All rights reserved.

Stochastic Least-Squares Petrov--Galerkin Method for Parameterized Linear Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Kookjin; Carlberg, Kevin; Elman, Howard C.

Here, we consider the numerical solution of parameterized linear systems where the system matrix, the solution, and the right-hand side are parameterized by a set of uncertain input parameters. We explore spectral methods in which the solutions are approximated in a chosen finite-dimensional subspace. It has been shown that the stochastic Galerkin projection technique fails to minimize any measure of the solution error. As a remedy for this, we propose a novel stochatic least-squares Petrov--Galerkin (LSPG) method. The proposed method is optimal in the sense that it produces the solution that minimizes a weightedmore » $$\\ell^2$$-norm of the residual over all solutions in a given finite-dimensional subspace. Moreover, the method can be adapted to minimize the solution error in different weighted $$\\ell^2$$-norms by simply applying a weighting function within the least-squares formulation. In addition, a goal-oriented seminorm induced by an output quantity of interest can be minimized by defining a weighting function as a linear functional of the solution. We establish optimality and error bounds for the proposed method, and extensive numerical experiments show that the weighted LSPG method outperforms other spectral methods in minimizing corresponding target weighted norms.« less

[Gaussian process regression and its application in near-infrared spectroscopy analysis].

PubMed

Feng, Ai-Ming; Fang, Li-Min; Lin, Min

2011-06-01

Gaussian process (GP) is applied in the present paper as a chemometric method to explore the complicated relationship between the near infrared (NIR) spectra and ingredients. After the outliers were detected by Monte Carlo cross validation (MCCV) method and removed from dataset, different preprocessing methods, such as multiplicative scatter correction (MSC), smoothing and derivate, were tried for the best performance of the models. Furthermore, uninformative variable elimination (UVE) was introduced as a variable selection technique and the characteristic wavelengths obtained were further employed as input for modeling. A public dataset with 80 NIR spectra of corn was introduced as an example for evaluating the new algorithm. The optimal models for oil, starch and protein were obtained by the GP regression method. The performance of the final models were evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (r). The models give good calibration ability with r values above 0.99 and the prediction ability is also satisfactory with r values higher than 0.96. The overall results demonstrate that GP algorithm is an effective chemometric method and is promising for the NIR analysis.

Optimal least-squares finite element method for elliptic problems

NASA Technical Reports Server (NTRS)

Jiang, Bo-Nan; Povinelli, Louis A.

1991-01-01

An optimal least squares finite element method is proposed for two dimensional and three dimensional elliptic problems and its advantages are discussed over the mixed Galerkin method and the usual least squares finite element method. In the usual least squares finite element method, the second order equation (-Delta x (Delta u) + u = f) is recast as a first order system (-Delta x p + u = f, Delta u - p = 0). The error analysis and numerical experiment show that, in this usual least squares finite element method, the rate of convergence for flux p is one order lower than optimal. In order to get an optimal least squares method, the irrotationality Delta x p = 0 should be included in the first order system.

Performance of the S - [chi][squared] Statistic for Full-Information Bifactor Models

ERIC Educational Resources Information Center

Li, Ying; Rupp, Andre A.

2011-01-01

This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…

F-Test Alternatives to Fisher's Exact Test and to the Chi-Square Test of Homogeneity in 2x2 Tables.

ERIC Educational Resources Information Center

Overall, John E.; Starbuck, Robert R.

1983-01-01

An alternative to Fisher's exact test and the chi-square test for homogeneity in two-by-two tables is developed. The method provides for Type I error rates which are closer to the stated alpha level than either of the alternatives. (JKS)

Multilevel Modeling and Ordinary Least Squares Regression: How Comparable Are They?

ERIC Educational Resources Information Center

Huang, Francis L.

2018-01-01

Studies analyzing clustered data sets using both multilevel models (MLMs) and ordinary least squares (OLS) regression have generally concluded that resulting point estimates, but not the standard errors, are comparable with each other. However, the accuracy of the estimates of OLS models is important to consider, as several alternative techniques…

Discrete Tchebycheff orthonormal polynomials and applications

NASA Technical Reports Server (NTRS)

Lear, W. M.

1980-01-01

Discrete Tchebycheff orthonormal polynomials offer a convenient way to make least squares polynomial fits of uniformly spaced discrete data. Computer programs to do so are simple and fast, and appear to be less affected by computer roundoff error, for the higher order fits, than conventional least squares programs. They are useful for any application of polynomial least squares fits: approximation of mathematical functions, noise analysis of radar data, and real time smoothing of noisy data, to name a few.

Spatial durbin error model for human development index in Province of Central Java.

NASA Astrophysics Data System (ADS)

Septiawan, A. R.; Handajani, S. S.; Martini, T. S.

2018-05-01

The Human Development Index (HDI) is an indicator used to measure success in building the quality of human life, explaining how people access development outcomes when earning income, health and education. Every year HDI in Central Java has improved to a better direction. In 2016, HDI in Central Java was 69.98 %, an increase of 0.49 % over the previous year. The objective of this study was to apply the spatial Durbin error model using angle weights queen contiguity to measure HDI in Central Java Province. Spatial Durbin error model is used because the model overcomes the spatial effect of errors and the effects of spatial depedency on the independent variable. Factors there use is life expectancy, mean years of schooling, expected years of schooling, and purchasing power parity. Based on the result of research, we get spatial Durbin error model for HDI in Central Java with influencing factors are life expectancy, mean years of schooling, expected years of schooling, and purchasing power parity.

Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Wang, Xiu; Peng, Yankun

This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.

The Short Form Vaping Consequences Questionnaire: Psychometric Properties of a Measure of Vaping Expectancies for Use With Adult E-cigarette Users.

PubMed

Morean, Meghan E; L'Insalata, Alexa

2017-02-01

E-cigarettes are popular in the United States, but psychometrically sound measures of vaping beliefs and behaviors are lacking. We evaluated the psychometrics of the Short Form Vaping Consequences Questionnaire (S-VCQ), a modified version of the Short Form Smoking Consequences Questionnaire that assesses expectancies for negative consequences, positive reinforcement, negative reinforcement, and appetite/weight control associated with vaping. Adult, past-month e-cigarette users completed an anonymous survey in Fall 2015 (N = 522, 50.4% female; 71.5% white; 34.10 [SD = 9.66] years). Psychometric analyses included confirmatory factor analysis, internal consistency, measurement invariance, t tests, correlations, and test-criterion relationships with vaping outcomes. The S-VCQ evidenced a four-factor latent structure (Bentler's Comparative Fit Index = .95, Root Mean Square Error of Approximation = .05, Standardized Root Mean Square Residual = .06), and subscales evidenced internal consistency (mean α = 0.89). S-VCQ scores were scalar invariant for sex and smoking status; women reported stronger appetite/weight control than men and dual cigarette/e-cigarette users (n = 309) reported stronger negative vaping consequences and negative reinforcement than nonsmokers. Among dual users, vaping and smoking expectancies also were scalar invariant; dual users reported stronger positive reinforcement associated with vaping than smoking but stronger negative consequences, negative reinforcement, and appetite/weight control associated with smoking than vaping. Correlations indicated that vaping and smoking expectancies were related, yet distinct constructs. Univariate general linear models indicated that vaping frequency and dependence were associated with positive reinforcement (ηp2 = .02/.02), negative reinforcement (ηp2 = .02/.08), and appetite/weight control (ηp2 = .02/.02) from vaping. The S-VCQ evidences solid psychometrics as a measure of adult e-cigarette users' vaping expectancies. The current study provides evidence for the reliability and validity of the S-VCQ, the first measure of vaping expectancies that has been validated for use with adult e-cigarette users. Results indicated that the S-VCQ comprises four subscales that evidence internal consistency, scalar measurement invariance for important groups of interest, and test-criterion relationships with vaping outcomes. Researchers are encouraged to consider using this measure for assessing vaping expectancies in adult e-cigarette users. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Disambiguating ventral striatum fMRI-related bold signal during reward prediction in schizophrenia

PubMed Central

Morris, R W; Vercammen, A; Lenroot, R; Moore, L; Langton, J M; Short, B; Kulkarni, J; Curtis, J; O'Donnell, M; Weickert, C S; Weickert, T W

2012-01-01

Reward detection, surprise detection and prediction-error signaling have all been proposed as roles for the ventral striatum (vStr). Previous neuroimaging studies of striatal function in schizophrenia have found attenuated neural responses to reward-related prediction errors; however, as prediction errors represent a discrepancy in mesolimbic neural activity between expected and actual events, it is critical to examine responses to both expected and unexpected rewards (URs) in conjunction with expected and UR omissions in order to clarify the nature of ventral striatal dysfunction in schizophrenia. In the present study, healthy adults and people with schizophrenia were tested with a reward-related prediction-error task during functional magnetic resonance imaging to determine whether schizophrenia is associated with altered neural responses in the vStr to rewards, surprise prediction errors or all three factors. In healthy adults, we found neural responses in the vStr were correlated more specifically with prediction errors than to surprising events or reward stimuli alone. People with schizophrenia did not display the normal differential activation between expected and URs, which was partially due to exaggerated ventral striatal responses to expected rewards (right vStr) but also included blunted responses to unexpected outcomes (left vStr). This finding shows that neural responses, which typically are elicited by surprise, can also occur to well-predicted events in schizophrenia and identifies aberrant activity in the vStr as a key node of dysfunction in the neural circuitry used to differentiate expected and unexpected feedback in schizophrenia. PMID:21709684

Development of regional skews for selected flood durations for the Central Valley Region, California, based on data through water year 2008

USGS Publications Warehouse

Lamontagne, Jonathan R.; Stedinger, Jery R.; Berenbrock, Charles; Veilleux, Andrea G.; Ferris, Justin C.; Knifong, Donna L.

2012-01-01

Flood-frequency information is important in the Central Valley region of California because of the high risk of catastrophic flooding. Most traditional flood-frequency studies focus on peak flows, but for the assessment of the adequacy of reservoirs, levees, other flood control structures, sustained flood flow (flood duration) frequency data are needed. This study focuses on rainfall or rain-on-snow floods, rather than the annual maximum, because rain events produce the largest floods in the region. A key to estimating flood-duration frequency is determining the regional skew for such data. Of the 50 sites used in this study to determine regional skew, 28 sites were considered to have little to no significant regulated flows, and for the 22 sites considered significantly regulated, unregulated daily flow data were synthesized by using reservoir storage changes and diversion records. The unregulated, annual maximum rainfall flood flows for selected durations (1-day, 3-day, 7-day, 15-day, and 30-day) for all 50 sites were furnished by the U.S. Army Corps of Engineers. Station skew was determined by using the expected moments algorithm program for fitting the Pearson Type 3 flood-frequency distribution to the logarithms of annual flood-duration data. Bayesian generalized least squares regression procedures used in earlier studies were modified to address problems caused by large cross correlations among concurrent rainfall floods in California and to address the extensive censoring of low outliers at some sites, by using the new expected moments algorithm for fitting the LP3 distribution to rainfall flood-duration data. To properly account for these problems and to develop suitable regional-skew regression models and regression diagnostics, a combination of ordinary least squares, weighted least squares, and Bayesian generalized least squares regressions were adopted. This new methodology determined that a nonlinear model relating regional skew to mean basin elevation was the best model for each flood duration. The regional-skew values ranged from -0.74 for a flood duration of 1-day and a mean basin elevation less than 2,500 feet to values near 0 for a flood duration of 7-days and a mean basin elevation greater than 4,500 feet. This relation between skew and elevation reflects the interaction of snow and rain, which increases with increased elevation. The regional skews are more accurate, and the mean squared errors are less than in the Interagency Advisory Committee on Water Data's National skew map of Bulletin 17B.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Determining the Uncertainty of X-Ray Absorption Measurements

PubMed Central

Wojcik, Gary S.

2004-01-01

X-ray absorption (or more properly, x-ray attenuation) techniques have been applied to study the moisture movement in and moisture content of materials like cement paste, mortar, and wood. An increase in the number of x-ray counts with time at a location in a specimen may indicate a decrease in moisture content. The uncertainty of measurements from an x-ray absorption system, which must be known to properly interpret the data, is often assumed to be the square root of the number of counts, as in a Poisson process. No detailed studies have heretofore been conducted to determine the uncertainty of x-ray absorption measurements or the effect of averaging data on the uncertainty. In this study, the Poisson estimate was found to adequately approximate normalized root mean square errors (a measure of uncertainty) of counts for point measurements and profile measurements of water specimens. The Poisson estimate, however, was not reliable in approximating the magnitude of the uncertainty when averaging data from paste and mortar specimens. Changes in uncertainty from differing averaging procedures were well-approximated by a Poisson process. The normalized root mean square errors decreased when the x-ray source intensity, integration time, collimator size, and number of scanning repetitions increased. Uncertainties in mean paste and mortar count profiles were kept below 2 % by averaging vertical profiles at horizontal spacings of 1 mm or larger with counts per point above 4000. Maximum normalized root mean square errors did not exceed 10 % in any of the tests conducted. PMID:27366627

Direct and simultaneous quantification of tannin mean degree of polymerization and percentage of galloylation in grape seeds using diffuse reflectance fourier transform-infrared spectroscopy.

PubMed

Pappas, Christos; Kyraleou, Maria; Voskidi, Eleni; Kotseridis, Yorgos; Taranilis, Petros A; Kallithraka, Stamatina

2015-02-01

The direct and simultaneous quantitative determination of the mean degree of polymerization (mDP) and the degree of galloylation (%G) in grape seeds were quantified using diffuse reflectance infrared Fourier transform spectroscopy and partial least squares (PLS). The results were compared with those obtained using the conventional analysis employing phloroglucinolysis as pretreatment followed by high performance liquid chromatography-UV and mass spectrometry detection. Infrared spectra were recorded in solid state samples after freeze drying. The 2nd derivative of the 1832 to 1416 and 918 to 739 cm(-1) spectral regions for the quantification of mDP, the 2nd derivative of the 1813 to 607 cm(-1) spectral region for the degree of %G determination and PLS regression were used. The determination coefficients (R(2) ) of mDP and %G were 0.99 and 0.98, respectively. The corresponding values of the root-mean-square error of calibration were found 0.506 and 0.692, the root-mean-square error of cross validation 0.811 and 0.921, and the root-mean-square error of prediction 0.612 and 0.801. The proposed method in comparison with the conventional method is simpler, less time consuming, more economical, and requires reduced quantities of chemical reagents and fewer sample pretreatment steps. It could be a starting point for the design of more specific models according to the requirements of the wineries. © 2015 Institute of Food Technologists®

Rapid discrimination between buffalo and cow milk and detection of adulteration of buffalo milk with cow milk using synchronous fluorescence spectroscopy in combination with multivariate methods.

PubMed

Durakli Velioglu, Serap; Ercioglu, Elif; Boyaci, Ismail Hakki

2017-05-01

This research paper describes the potential of synchronous fluorescence (SF) spectroscopy for authentication of buffalo milk, a favourable raw material in the production of some premium dairy products. Buffalo milk is subjected to fraudulent activities like many other high priced foodstuffs. The current methods widely used for the detection of adulteration of buffalo milk have various disadvantages making them unattractive for routine analysis. Thus, the aim of the present study was to assess the potential of SF spectroscopy in combination with multivariate methods for rapid discrimination between buffalo and cow milk and detection of the adulteration of buffalo milk with cow milk. SF spectra of cow and buffalo milk samples were recorded between 400-550 nm excitation range with Δλ of 10-100 nm, in steps of 10 nm. The data obtained for ∆λ = 10 nm were utilised to classify the samples using principal component analysis (PCA), and detect the adulteration level of buffalo milk with cow milk using partial least square (PLS) methods. Successful discrimination of samples and detection of adulteration of buffalo milk with limit of detection value (LOD) of 6% are achieved with the models having root mean square error of calibration (RMSEC) and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) values of 2, 7, and 4%, respectively. The results reveal the potential of SF spectroscopy for rapid authentication of buffalo milk.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

A study of GPS measurement errors due to noise and multipath interference for CGADS

NASA Technical Reports Server (NTRS)

Axelrad, Penina; MacDoran, Peter F.; Comp, Christopher J.

1996-01-01

This report describes a study performed by the Colorado Center for Astrodynamics Research (CCAR) on GPS measurement errors in the Codeless GPS Attitude Determination System (CGADS) due to noise and multipath interference. Preliminary simulation models fo the CGADS receiver and orbital multipath are described. The standard FFT algorithms for processing the codeless data is described and two alternative algorithms - an auto-regressive/least squares (AR-LS) method, and a combined adaptive notch filter/least squares (ANF-ALS) method, are also presented. Effects of system noise, quantization, baseband frequency selection, and Doppler rates on the accuracy of phase estimates with each of the processing methods are shown. Typical electrical phase errors for the AR-LS method are 0.2 degrees, compared to 0.3 and 0.5 degrees for the FFT and ANF-ALS algorithms, respectively. Doppler rate was found to have the largest effect on the performance.

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Spectrophotometric determination of ternary mixtures of thiamin, riboflavin and pyridoxal in pharmaceutical and human plasma by least-squares support vector machines.

PubMed

Niazi, Ali; Zolgharnein, Javad; Afiuni-Zadeh, Somaie

2007-11-01

Ternary mixtures of thiamin, riboflavin and pyridoxal have been simultaneously determined in synthetic and real samples by applications of spectrophotometric and least-squares support vector machines. The calibration graphs were linear in the ranges of 1.0 - 20.0, 1.0 - 10.0 and 1.0 - 20.0 microg ml(-1) with detection limits of 0.6, 0.5 and 0.7 microg ml(-1) for thiamin, riboflavin and pyridoxal, respectively. The experimental calibration matrix was designed with 21 mixtures of these chemicals. The concentrations were varied between calibration graph concentrations of vitamins. The simultaneous determination of these vitamin mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares (PLS) modeling and least-squares support vector machines were used for the multivariate calibration of the spectrophotometric data. An excellent model was built using LS-SVM, with low prediction errors and superior performance in relation to PLS. The root mean square errors of prediction (RMSEP) for thiamin, riboflavin and pyridoxal with PLS and LS-SVM were 0.6926, 0.3755, 0.4322 and 0.0421, 0.0318, 0.0457, respectively. The proposed method was satisfactorily applied to the rapid simultaneous determination of thiamin, riboflavin and pyridoxal in commercial pharmaceutical preparations and human plasma samples.

Multiparameter linear least-squares fitting to Poisson data one count at a time

NASA Technical Reports Server (NTRS)

Wheaton, Wm. A.; Dunklee, Alfred L.; Jacobsen, Allan S.; Ling, James C.; Mahoney, William A.; Radocinski, Robert G.

1995-01-01

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multicomponent linear model, with underlying physical count rates or fluxes which are to be estimated from the data. Despite its conceptual simplicity, the linear least-squares (LLSQ) method for solving this problem has generally been limited to situations in which the number n(sub i) of counts in each bin i is not too small, conventionally more than 5-30. It seems to be widely believed that the failure of the LLSQ method for small counts is due to the failure of the Poisson distribution to be even approximately normal for small numbers. The cause is more accurately the strong anticorrelation between the data and the wieghts w(sub i) in the weighted LLSQ method when square root of n(sub i) instead of square root of bar-n(sub i) is used to approximate the uncertainties, sigma(sub i), in the data, where bar-n(sub i) = E(n(sub i)), the expected value of N(sub i). We show in an appendix that, avoiding this approximation, the correct equations for the Poisson LLSQ (PLLSQ) problems are actually identical to those for the maximum likelihood estimate using the exact Poisson distribution. We apply the method to solve a problem in high-resolution gamma-ray spectroscopy for the JPL High-Resolution Gamma-Ray Spectrometer flown on HEAO 3. Systematic error in subtracting the strong, highly variable background encountered in the low-energy gamma-ray region can be significantly reduced by closely pairing source and background data in short segments. Significant results can be built up by weighted averaging of the net fluxes obtained from the subtraction of many individual source/background pairs. Extension of the approach to complex situations, with multiple cosmic sources and realistic background parameterizations, requires a means of efficiently fitting to data from single scans in the narrow (approximately = 1.2 keV, HEAO 3) energy channels of a Ge spectrometer, where the expected number of counts obtained per scan may be very low. Such an analysis system is discussed and compared to the method previously used.

Modeling error analysis of stationary linear discrete-time filters

NASA Technical Reports Server (NTRS)

Patel, R.; Toda, M.

1977-01-01

The performance of Kalman-type, linear, discrete-time filters in the presence of modeling errors is considered. The discussion is limited to stationary performance, and bounds are obtained for the performance index, the mean-squared error of estimates for suboptimal and optimal (Kalman) filters. The computation of these bounds requires information on only the model matrices and the range of errors for these matrices. Consequently, a design can easily compare the performance of a suboptimal filter with that of the optimal filter, when only the range of errors in the elements of the model matrices is available.

An error analysis of least-squares finite element method of velocity-pressure-vorticity formulation for Stokes problem

NASA Technical Reports Server (NTRS)

Chang, Ching L.; Jiang, Bo-Nan

1990-01-01

A theoretical proof of the optimal rate of convergence for the least-squares method is developed for the Stokes problem based on the velocity-pressure-vorticity formula. The 2D Stokes problem is analyzed to define the product space and its inner product, and the a priori estimates are derived to give the finite-element approximation. The least-squares method is found to converge at the optimal rate for equal-order interpolation.

Mapping intended spinal site of care from the upright to prone position: an interexaminer reliability study.

PubMed

Cooperstein, Robert; Young, Morgan

2014-01-01

Upright examination procedures like radiology, thermography, manual muscle testing, and spinal motion palpation may lead to spinal interventions with the patient prone. The reliability and accuracy of mapping upright examination findings to the prone position is unknown. This study had 2 primary goals: (1) investigate how erroneous spine-scapular landmark associations may lead to errors in treating and charting spine levels; and (2) study the interexaminer reliability of a novel method for mapping upright spinal sites to the prone position. Experiment 1 was a thought experiment exploring the consequences of depending on the erroneous landmark association of the inferior scapular tip with the T7 spinous process upright and T6 spinous process prone (relatively recent studies suggest these levels are T8 and T9, respectively). This allowed deduction of targeting and charting errors. In experiment 2, 10 examiners (2 experienced, 8 novice) used an index finger to maintain contact with a mid-thoracic spinous process as each of 2 participants slowly moved from the upright to the prone position. Interexaminer reliability was assessed by computing Intraclass Correlation Coefficient, standard error of the mean, root mean squared error, and the absolute value of the mean difference for each examiner from the 10 examiner mean for each of the 2 participants. The thought experiment suggesting that using the (inaccurate) scapular tip landmark rule would result in a 3 level targeting and charting error when radiological findings are mapped to the prone position. Physical upright exam procedures like motion palpation would result in a 2 level targeting error for intervention, and a 3 level error for charting. The reliability experiment showed examiners accurately maintained contact with the same thoracic spinous process as the participant went from upright to prone, ICC (2,1) = 0.83. As manual therapists, the authors have emphasized how targeting errors may impact upon manual care of the spine. Practitioners in other fields that need to accurately locate spinal levels, such as acupuncture and anesthesiology, would also be expected to draw important conclusions from these findings.

Mapping intended spinal site of care from the upright to prone position: an interexaminer reliability study

PubMed Central

2014-01-01

Background Upright examination procedures like radiology, thermography, manual muscle testing, and spinal motion palpation may lead to spinal interventions with the patient prone. The reliability and accuracy of mapping upright examination findings to the prone position is unknown. This study had 2 primary goals: (1) investigate how erroneous spine-scapular landmark associations may lead to errors in treating and charting spine levels; and (2) study the interexaminer reliability of a novel method for mapping upright spinal sites to the prone position. Methods Experiment 1 was a thought experiment exploring the consequences of depending on the erroneous landmark association of the inferior scapular tip with the T7 spinous process upright and T6 spinous process prone (relatively recent studies suggest these levels are T8 and T9, respectively). This allowed deduction of targeting and charting errors. In experiment 2, 10 examiners (2 experienced, 8 novice) used an index finger to maintain contact with a mid-thoracic spinous process as each of 2 participants slowly moved from the upright to the prone position. Interexaminer reliability was assessed by computing Intraclass Correlation Coefficient, standard error of the mean, root mean squared error, and the absolute value of the mean difference for each examiner from the 10 examiner mean for each of the 2 participants. Results The thought experiment suggesting that using the (inaccurate) scapular tip landmark rule would result in a 3 level targeting and charting error when radiological findings are mapped to the prone position. Physical upright exam procedures like motion palpation would result in a 2 level targeting error for intervention, and a 3 level error for charting. The reliability experiment showed examiners accurately maintained contact with the same thoracic spinous process as the participant went from upright to prone, ICC (2,1) = 0.83. Conclusions As manual therapists, the authors have emphasized how targeting errors may impact upon manual care of the spine. Practitioners in other fields that need to accurately locate spinal levels, such as acupuncture and anesthesiology, would also be expected to draw important conclusions from these findings. PMID:24904747

An adaptive modeling and simulation environment for combined-cycle data reconciliation and degradation estimation

NASA Astrophysics Data System (ADS)

Lin, Tsungpo

Performance engineers face the major challenge in modeling and simulation for the after-market power system due to system degradation and measurement errors. Currently, the majority in power generation industries utilizes the deterministic data matching method to calibrate the model and cascade system degradation, which causes significant calibration uncertainty and also the risk of providing performance guarantees. In this research work, a maximum-likelihood based simultaneous data reconciliation and model calibration (SDRMC) is used for power system modeling and simulation. By replacing the current deterministic data matching with SDRMC one can reduce the calibration uncertainty and mitigate the error propagation to the performance simulation. A modeling and simulation environment for a complex power system with certain degradation has been developed. In this environment multiple data sets are imported when carrying out simultaneous data reconciliation and model calibration. Calibration uncertainties are estimated through error analyses and populated to performance simulation by using principle of error propagation. System degradation is then quantified by performance comparison between the calibrated model and its expected new & clean status. To mitigate smearing effects caused by gross errors, gross error detection (GED) is carried out in two stages. The first stage is a screening stage, in which serious gross errors are eliminated in advance. The GED techniques used in the screening stage are based on multivariate data analysis (MDA), including multivariate data visualization and principal component analysis (PCA). Subtle gross errors are treated at the second stage, in which the serial bias compensation or robust M-estimator is engaged. To achieve a better efficiency in the combined scheme of the least squares based data reconciliation and the GED technique based on hypotheses testing, the Levenberg-Marquardt (LM) algorithm is utilized as the optimizer. To reduce the computation time and stabilize the problem solving for a complex power system such as a combined cycle power plant, meta-modeling using the response surface equation (RSE) and system/process decomposition are incorporated with the simultaneous scheme of SDRMC. The goal of this research work is to reduce the calibration uncertainties and, thus, the risks of providing performance guarantees arisen from uncertainties in performance simulation.

The Vanishing Site of Mina Shaughnessy's "Error and Expectations."

ERIC Educational Resources Information Center

Laurence, Patricia

1993-01-01

Claims that recent reassessments of Mina Shaughnessy's "Errors and Expectations" and the field of composition in the 1970s overlook the institutional forces that helped shape the rhetoric and methodology of researchers at that time. (HB)

Coupling finite element and spectral methods: First results

NASA Technical Reports Server (NTRS)

Bernardi, Christine; Debit, Naima; Maday, Yvon

1987-01-01

A Poisson equation on a rectangular domain is solved by coupling two methods: the domain is divided in two squares, a finite element approximation is used on the first square and a spectral discretization is used on the second one. Two kinds of matching conditions on the interface are presented and compared. In both cases, error estimates are proved.

Building on crossvalidation for increasing the quality of geostatistical modeling

USGS Publications Warehouse

Olea, R.A.

2012-01-01

The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria. ?? 2011 US Government.

Analysis of Students' Error in Learning of Quadratic Equations

ERIC Educational Resources Information Center

Zakaria, Effandi; Ibrahim; Maat, Siti Mistima

2010-01-01

The purpose of the study was to determine the students' error in learning quadratic equation. The samples were 30 form three students from a secondary school in Jambi, Indonesia. Diagnostic test was used as the instrument of this study that included three components: factorization, completing the square and quadratic formula. Diagnostic interview…

Interval Predictor Models for Data with Measurement Uncertainty

NASA Technical Reports Server (NTRS)

Lacerda, Marcio J.; Crespo, Luis G.

2017-01-01

An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.

Asymptotic properties of Pearson's rank-variate correlation coefficient under contaminated Gaussian model.

PubMed

Ma, Rubao; Xu, Weichao; Zhang, Yun; Ye, Zhongfu

2014-01-01

This paper investigates the robustness properties of Pearson's rank-variate correlation coefficient (PRVCC) in scenarios where one channel is corrupted by impulsive noise and the other is impulsive noise-free. As shown in our previous work, these scenarios that frequently encountered in radar and/or sonar, can be well emulated by a particular bivariate contaminated Gaussian model (CGM). Under this CGM, we establish the asymptotic closed forms of the expectation and variance of PRVCC by means of the well known Delta method. To gain a deeper understanding, we also compare PRVCC with two other classical correlation coefficients, i.e., Spearman's rho (SR) and Kendall's tau (KT), in terms of the root mean squared error (RMSE). Monte Carlo simulations not only verify our theoretical findings, but also reveal the advantage of PRVCC by an example of estimating the time delay in the particular impulsive noise environment.

Modified Regression Correlation Coefficient for Poisson Regression Model

NASA Astrophysics Data System (ADS)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

NASA Astrophysics Data System (ADS)

Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

2017-08-01

Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

An ab initio potential energy surface for the formic acid dimer: zero-point energy, selected anharmonic fundamental energies, and ground-state tunneling splitting calculated in relaxed 1-4-mode subspaces.

PubMed

Qu, Chen; Bowman, Joel M

2016-09-14

We report a full-dimensional, permutationally invariant potential energy surface (PES) for the cyclic formic acid dimer. This PES is a least-squares fit to 13475 CCSD(T)-F12a/haTZ (VTZ for H and aVTZ for C and O) energies. The energy-weighted, root-mean-square fitting error is 11 cm -1 and the barrier for the double-proton transfer on the PES is 2848 cm -1 , in good agreement with the directly-calculated ab initio value of 2853 cm -1 . The zero-point vibrational energy of 15 337 ± 7 cm -1 is obtained from diffusion Monte Carlo calculations. Energies of fundamentals of fifteen modes are calculated using the vibrational self-consistent field and virtual-state configuration interaction method. The ground-state tunneling splitting is computed using a reduced-dimensional Hamiltonian with relaxed potentials. The highest-level, four-mode coupled calculation gives a tunneling splitting of 0.037 cm -1 , which is roughly twice the experimental value. The tunneling splittings of (DCOOH) 2 and (DCOOD) 2 from one to three mode calculations are, as expected, smaller than that for (HCOOH) 2 and consistent with experiment.

An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas.

PubMed

Mesas-Carrascosa, Francisco-Javier; Notario García, María Dolores; Meroño de Larriva, Jose Emilio; García-Ferrer, Alfonso

2016-11-01

This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red-green-blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%-50% and 70%-40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE).

An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas

PubMed Central

Mesas-Carrascosa, Francisco-Javier; Notario García, María Dolores; Meroño de Larriva, Jose Emilio; García-Ferrer, Alfonso

2016-01-01

This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%–50% and 70%–40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE). PMID:27809293

Nonparametric probability density estimation by optimization theoretic techniques

NASA Technical Reports Server (NTRS)

Scott, D. W.

1976-01-01

Two nonparametric probability density estimators are considered. The first is the kernel estimator. The problem of choosing the kernel scaling factor based solely on a random sample is addressed. An interactive mode is discussed and an algorithm proposed to choose the scaling factor automatically. The second nonparametric probability estimate uses penalty function techniques with the maximum likelihood criterion. A discrete maximum penalized likelihood estimator is proposed and is shown to be consistent in the mean square error. A numerical implementation technique for the discrete solution is discussed and examples displayed. An extensive simulation study compares the integrated mean square error of the discrete and kernel estimators. The robustness of the discrete estimator is demonstrated graphically.

Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

PubMed

Wang, Rong

2015-01-01

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Cortical dipole imaging using truncated total least squares considering transfer matrix error.

PubMed

Hori, Junichi; Takeuchi, Kosuke

2013-01-01

Cortical dipole imaging has been proposed as a method to visualize electroencephalogram in high spatial resolution. We investigated the inverse technique of cortical dipole imaging using a truncated total least squares (TTLS). The TTLS is a regularization technique to reduce the influence from both the measurement noise and the transfer matrix error caused by the head model distortion. The estimation of the regularization parameter was also investigated based on L-curve. The computer simulation suggested that the estimation accuracy was improved by the TTLS compared with Tikhonov regularization. The proposed method was applied to human experimental data of visual evoked potentials. We confirmed the TTLS provided the high spatial resolution of cortical dipole imaging.

A new algorithm to build bridges between two patient-reported health outcome instruments: the MOS SF-36® and the VR-12 Health Survey.

PubMed

Selim, Alfredo; Rogers, William; Qian, Shirley; Rothendler, James A; Kent, Erin E; Kazis, Lewis E

2018-04-19

To develop bridging algorithms to score the Veterans Rand-12 (VR-12) scales for comparability to those of the SF-36® for facilitating multi-cohort studies using data from the National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) linked to Medicare Health Outcomes Survey (MHOS), and to provide a model for minimizing non-statistical error in pooled analyses stemming from changes to survey instruments over time. Observational study of MHOS cohorts 1-12 (1998-2011). We modeled 2-year follow-up SF-36 scale scores from cohorts 1-6 based on baseline SF-36 scores, age, and gender, yielding 100 clusters using Classification and Regression Trees. Within each cluster, we averaged follow-up SF-36 scores. Using the same cluster specifications, expected follow-up SF-36 scores, based on cohorts 1-6, were computed for cohorts 7-8 (where the VR-12 was the follow-up survey). We created a new criterion validity measure, termed "extensibility," calculated from the square root of the mean square difference between expected SF-36 scale averages and observed VR-12 item score from cohorts 7-8, weighted by cluster size. VR-12 items were rescored to minimize this quantity. Extensibility of rescored VR-12 items and scales was considerably improved from the "simple" scoring method for comparability to the SF-36 scales. The algorithms are appropriate across a wide range of potential subsamples within the MHOS and provide robust application for future studies that span the SF-36 and VR-12 eras. It is possible that these surveys in a different setting outside the MHOS, especially in younger age groups, could produce somewhat different results.

Spectral combination of spherical gravitational curvature boundary-value problems

NASA Astrophysics Data System (ADS)

PitoÅák, Martin; Eshagh, Mehdi; Šprlák, Michal; Tenzer, Robert; Novák, Pavel

2018-04-01

Four solutions of the spherical gravitational curvature boundary-value problems can be exploited for the determination of the Earth's gravitational potential. In this article we discuss the combination of simulated satellite gravitational curvatures, i.e., components of the third-order gravitational tensor, by merging these solutions using the spectral combination method. For this purpose, integral estimators of biased- and unbiased-types are derived. In numerical studies, we investigate the performance of the developed mathematical models for the gravitational field modelling in the area of Central Europe based on simulated satellite measurements. Firstly, we verify the correctness of the integral estimators for the spectral downward continuation by a closed-loop test. Estimated errors of the combined solution are about eight orders smaller than those from the individual solutions. Secondly, we perform a numerical experiment by considering the Gaussian noise with the standard deviation of 6.5× 10-17 m-1s-2 in the input data at the satellite altitude of 250 km above the mean Earth sphere. This value of standard deviation is equivalent to a signal-to-noise ratio of 10. Superior results with respect to the global geopotential model TIM-r5 are obtained by the spectral downward continuation of the vertical-vertical-vertical component with the standard deviation of 2.104 m2s-2, but the root mean square error is the largest and reaches 9.734 m2s-2. Using the spectral combination of all gravitational curvatures the root mean square error is more than 400 times smaller but the standard deviation reaches 17.234 m2s-2. The combination of more components decreases the root mean square error of the corresponding solutions while the standard deviations of the combined solutions do not improve as compared to the solution from the vertical-vertical-vertical component. The presented method represents a weight mean in the spectral domain that minimizes the root mean square error of the combined solutions and improves standard deviation of the solution based only on the least accurate components.

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception

PubMed Central

Davis, Matthew H.

2016-01-01

Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conversely, Predictive Coding suggests that expected features are suppressed so that unexpected features of the speech input (Prediction Errors) are processed further. The present work is aimed at distinguishing between these two accounts of how prior knowledge influences speech perception. By combining behavioural, univariate, and multivariate fMRI measures of how sensory detail and prior expectations influence speech perception with computational modelling, we provide evidence in favour of Prediction Error computations. Increased sensory detail and informative expectations have additive behavioural and univariate neural effects because they both improve the accuracy of word report and reduce the BOLD signal in lateral temporal lobe regions. However, sensory detail and informative expectations have interacting effects on speech representations shown by multivariate fMRI in the posterior superior temporal sulcus. When prior knowledge was absent, increased sensory detail enhanced the amount of speech information measured in superior temporal multivoxel patterns, but with informative expectations, increased sensory detail reduced the amount of measured information. Computational simulations of Sharpened Signals and Prediction Errors during speech perception could both explain these behavioural and univariate fMRI observations. However, the multivariate fMRI observations were uniquely simulated by a Prediction Error and not a Sharpened Signal model. The interaction between prior expectation and sensory detail provides evidence for a Predictive Coding account of speech perception. Our work establishes methods that can be used to distinguish representations of Prediction Error and Sharpened Signals in other perceptual domains. PMID:27846209

Suppression of Striatal Prediction Errors by the Prefrontal Cortex in Placebo Hypoalgesia.

PubMed

Schenk, Lieven A; Sprenger, Christian; Onat, Selim; Colloca, Luana; Büchel, Christian

2017-10-04

Classical learning theories predict extinction after the discontinuation of reinforcement through prediction errors. However, placebo hypoalgesia, although mediated by associative learning, has been shown to be resistant to extinction. We tested the hypothesis that this is mediated by the suppression of prediction error processing through the prefrontal cortex (PFC). We compared pain modulation through treatment cues (placebo hypoalgesia, treatment context) with pain modulation through stimulus intensity cues (stimulus context) during functional magnetic resonance imaging in 48 male and female healthy volunteers. During acquisition, our data show that expectations are correctly learned and that this is associated with prediction error signals in the ventral striatum (VS) in both contexts. However, in the nonreinforced test phase, pain modulation and expectations of pain relief persisted to a larger degree in the treatment context, indicating that the expectations were not correctly updated in the treatment context. Consistently, we observed significantly stronger neural prediction error signals in the VS in the stimulus context compared with the treatment context. A connectivity analysis revealed negative coupling between the anterior PFC and the VS in the treatment context, suggesting that the PFC can suppress the expression of prediction errors in the VS. Consistent with this, a participant's conceptual views and beliefs about treatments influenced the pain modulation only in the treatment context. Our results indicate that in placebo hypoalgesia contextual treatment information engages prefrontal conceptual processes, which can suppress prediction error processing in the VS and lead to reduced updating of treatment expectancies, resulting in less extinction of placebo hypoalgesia. SIGNIFICANCE STATEMENT In aversive and appetitive reinforcement learning, learned effects show extinction when reinforcement is discontinued. This is thought to be mediated by prediction errors (i.e., the difference between expectations and outcome). Although reinforcement learning has been central in explaining placebo hypoalgesia, placebo hypoalgesic effects show little extinction and persist after the discontinuation of reinforcement. Our results support the idea that conceptual treatment beliefs bias the neural processing of expectations in a treatment context compared with a more stimulus-driven processing of expectations with stimulus intensity cues. We provide evidence that this is associated with the suppression of prediction error processing in the ventral striatum by the prefrontal cortex. This provides a neural basis for persisting effects in reinforcement learning and placebo hypoalgesia. Copyright © 2017 the authors 0270-6474/17/379715-09$15.00/0.

Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

NASA Astrophysics Data System (ADS)

Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

2017-03-01

Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dayman, Kenneth J.; Coble, Jamie B.; Orton, Christopher R.

2014-01-01

The Multi-Isotope Process (MIP) Monitor combines gamma spectroscopy and multivariate analysis to detect anomalies in various process streams in a nuclear fuel reprocessing system. Measured spectra are compared to models of nominal behavior at each measurement location to detect unexpected changes in system behavior. In order to improve the accuracy and specificity of process monitoring, fuel characterization may be used to more accurately train subsequent models in a full analysis scheme. This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict fuel burnup. Nuclide activities for prototypic usedmore » fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial 235U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in this work are artificially low, because the models were trained, optimized, and tested on simulated, noise-free data. However, these results indicate that the developed models may generalize well to new data and that the proposed approach constitutes a viable first step in developing a fuel characterization algorithm based on gamma spectra.« less

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, Hong Yi; Milne, Alice; Webster, Richard

2016-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σK2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR = MSE/σK2 ≈ 1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (ECa) of the topsoil was measured at 525 points in a field of 2.3 ha. The marginal distribution of the observations was strongly positively skewed, and so the observed ECas were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, HongYi; Milne, Alice; Webster, Richard

2015-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σ_K^2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR=MSE/ σ_K2 ≈1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (EC_a) of the topsoil was measured at 525 points in a field of 2.3~ha. The marginal distribution of the observations was strongly positively skewed, and so the observed EC_as were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Evaluation of LiDAR-Acquired Bathymetric and Topographic Data Accuracy in Various Hydrogeomorphic Settings in the Lower Boise River, Southwestern Idaho, 2007

USGS Publications Warehouse

Skinner, Kenneth D.

2009-01-01

Elevation data in riverine environments can be used in various applications for which different levels of accuracy are required. The Experimental Advanced Airborne Research LiDAR (Light Detection and Ranging) - or EAARL - system was used to obtain topographic and bathymetric data along the lower Boise River, southwestern Idaho, for use in hydraulic and habitat modeling. The EAARL data were post-processed into bare earth and bathymetric raster and point datasets. Concurrently with the EAARL data collection, real-time kinetic global positioning system and total station ground-survey data were collected in three areas within the lower Boise River basin to assess the accuracy of the EAARL elevation data in different hydrogeomorphic settings. The accuracies of the EAARL-derived elevation data, determined in open, flat terrain, to provide an optimal vertical comparison surface, had root mean square errors ranging from 0.082 to 0.138 m. Accuracies for bank, floodplain, and in-stream bathymetric data had root mean square errors ranging from 0.090 to 0.583 m. The greater root mean square errors for the latter data are the result of high levels of turbidity in the downstream ground-survey area, dense tree canopy, and horizontal location discrepancies between the EAARL and ground-survey data in steeply sloping areas such as riverbanks. The EAARL point to ground-survey comparisons produced results similar to those for the EAARL raster to ground-survey comparisons, indicating that the interpolation of the EAARL points to rasters did not introduce significant additional error. The mean percent error for the wetted cross-sectional areas of the two upstream ground-survey areas was 1 percent. The mean percent error increases to -18 percent if the downstream ground-survey area is included, reflecting the influence of turbidity in that area.

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods

USGS Publications Warehouse

Linhart, S. Mike; Nania, Jon F.; Sanders, Curtis L.; Archfield, Stacey A.

2012-01-01

The U.S. Geological Survey (USGS) maintains approximately 148 real-time streamgages in Iowa for which daily mean streamflow information is available, but daily mean streamflow data commonly are needed at locations where no streamgages are present. Therefore, the USGS conducted a study as part of a larger project in cooperation with the Iowa Department of Natural Resources to develop methods to estimate daily mean streamflow at locations in ungaged watersheds in Iowa by using two regression-based statistical methods. The regression equations for the statistical methods were developed from historical daily mean streamflow and basin characteristics from streamgages within the study area, which includes the entire State of Iowa and adjacent areas within a 50-mile buffer of Iowa in neighboring states. Results of this study can be used with other techniques to determine the best method for application in Iowa and can be used to produce a Web-based geographic information system tool to compute streamflow estimates automatically. The Flow Anywhere statistical method is a variation of the drainage-area-ratio method, which transfers same-day streamflow information from a reference streamgage to another location by using the daily mean streamflow at the reference streamgage and the drainage-area ratio of the two locations. The Flow Anywhere method modifies the drainage-area-ratio method in order to regionalize the equations for Iowa and determine the best reference streamgage from which to transfer same-day streamflow information to an ungaged location. Data used for the Flow Anywhere method were retrieved for 123 continuous-record streamgages located in Iowa and within a 50-mile buffer of Iowa. The final regression equations were computed by using either left-censored regression techniques with a low limit threshold set at 0.1 cubic feet per second (ft3/s) and the daily mean streamflow for the 15th day of every other month, or by using an ordinary-least-squares multiple linear regression method and the daily mean streamflow for the 15th day of every other month. The Flow Duration Curve Transfer method was used to estimate unregulated daily mean streamflow from the physical and climatic characteristics of gaged basins. For the Flow Duration Curve Transfer method, daily mean streamflow quantiles at the ungaged site were estimated with the parameter-based regression model, which results in a continuous daily flow-duration curve (the relation between exceedance probability and streamflow for each day of observed streamflow) at the ungaged site. By the use of a reference streamgage, the Flow Duration Curve Transfer is converted to a time series. Data used in the Flow Duration Curve Transfer method were retrieved for 113 continuous-record streamgages in Iowa and within a 50-mile buffer of Iowa. The final statewide regression equations for Iowa were computed by using a weighted-least-squares multiple linear regression method and were computed for the 0.01-, 0.05-, 0.10-, 0.15-, 0.20-, 0.30-, 0.40-, 0.50-, 0.60-, 0.70-, 0.80-, 0.85-, 0.90-, and 0.95-exceedance probability statistics determined from the daily mean streamflow with a reporting limit set at 0.1 ft3/s. The final statewide regression equation for Iowa computed by using left-censored regression techniques was computed for the 0.99-exceedance probability statistic determined from the daily mean streamflow with a low limit threshold and a reporting limit set at 0.1 ft3/s. For the Flow Anywhere method, results of the validation study conducted by using six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 1,016 to 138 ft3/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 1,690 to 237 ft3/s. Values of the percent root-mean-square error ranged from 115 percent to 26.2 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 13.0 to 5.3 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.80 to 0.40. Percent-bias values ranged from 25.4 to 4.0 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.35. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.86 to 0.56. For the streamgage with the best agreement between observed and estimated streamflow, higher streamflows appear to be underestimated. For the streamgage with the worst agreement between observed and estimated streamflow, low flows appear to be overestimated whereas higher flows seem to be underestimated. Estimated cumulative streamflows for the period October 1, 2004, to September 30, 2009, are underestimated by -25.8 and -7.4 percent for the closest and poorest comparisons, respectively. For the Flow Duration Curve Transfer method, results of the validation study conducted by using the same six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 437 to 93.9 ft3/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 906 to 169 ft3/s. Values of the percent root-mean-square-error ranged from 67.0 to 25.6 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 12.5 to 4.4 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.79 to 0.40. Percent-bias values ranged from 22.7 to 0.94 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.38. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.89 to 0.48. For the streamgage with the closest agreement between observed and estimated streamflow, there is relatively good agreement between observed and estimated streamflows. For the streamgage with the poorest agreement between observed and estimated streamflow, streamflows appear to be substantially underestimated for much of the time period. Estimated cumulative streamflow for the period October 1, 2004, to September 30, 2009, are underestimated by -9.3 and -22.7 percent for the closest and poorest comparisons, respectively.

Improving Barotropic Tides by Two-way Nesting High and Low Resolution Domains

NASA Astrophysics Data System (ADS)

Jeon, C. H.; Buijsman, M. C.; Wallcraft, A. J.; Shriver, J. F.; Hogan, P. J.; Arbic, B. K.; Richman, J. G.

2017-12-01

In a realistically forced global ocean model, relatively large sea-surface-height root-mean-square (RMS) errors are observed in the North Atlantic near the Hudson Strait. These may be associated with large tidal resonances interacting with coastal bathymetry that are not correctly represented with a low resolution grid. This issue can be overcome by using high resolution grids, but at a high computational cost. In this paper we apply two-way nesting as an alternative solution. This approach applies high resolution to the area with large RMS errors and a lower resolution to the rest. It is expected to improve the tidal solution as well as reduce the computational cost. To minimize modification of the original source codes of the ocean circulation model (HYCOM), we apply the coupler OASIS3-MCT. This coupler is used to exchange barotropic pressures and velocity fields through its APIs (Application Programming Interface) between the parent and the child components. The developed two-way nesting framework has been validated with an idealized test case where the parent and the child domains have identical grid resolutions. The result of the idealized case shows very small RMS errors between the child and parent solutions. We plan to show results for a case with realistic tidal forcing in which the resolution of the child grid is three times that of the parent grid. The numerical results of this realistic case are compared to TPXO data.

Validation of geometric accuracy of Global Land Survey (GLS) 2000 data

USGS Publications Warehouse

Rengarajan, Rajagopalan; Sampath, Aparajithan; Storey, James C.; Choate, Michael J.

2015-01-01

The Global Land Survey (GLS) 2000 data were generated from Geocover™ 2000 data with the aim of producing a global data set of accuracy better than 25 m Root Mean Square Error (RMSE). An assessment and validation of accuracy of GLS 2000 data set, and its co-registration with Geocover™ 2000 data set is presented here. Since the availability of global data sets that have higher nominal accuracy than the GLS 2000 is a concern, the data sets were assessed in three tiers. In the first tier, the data were compared with the Geocover™ 2000 data. This comparison provided a means of localizing regions of higher differences. In the second tier, the GLS 2000 data were compared with systematically corrected Landsat-7 scenes that were obtained in a time period when the spacecraft pointing information was extremely accurate. These comparisons localize regions where the data are consistently off, which may indicate regions of higher errors. The third tier consisted of comparing the GLS 2000 data against higher accuracy reference data. The reference data were the Digital Ortho Quads over the United States, orthorectified SPOT data over Australia, and high accuracy check points obtained using triangulation bundle adjustment of Landsat-7 images over selected sites around the world. The study reveals that the geometric errors in Geocover™ 2000 data have been rectified in GLS 2000 data, and that the accuracy of GLS 2000 data can be expected to be better than 25 m RMSE for most of its constituent scenes.

Empirical State Error Covariance Matrix for Batch Estimation

NASA Technical Reports Server (NTRS)

Frisbee, Joe

2015-01-01

State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted batch least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. The proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. This empirical error covariance matrix may be calculated as a side computation for each unique batch solution. Results based on the proposed technique will be presented for a simple, two observer and measurement error only problem.

Optimal estimation of large structure model errors. [in Space Shuttle controller design

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1979-01-01

In-flight estimation of large structure model errors is usually required as a means of detecting inevitable deficiencies in large structure controller/estimator models. The present paper deals with a least-squares formulation which seeks to minimize a quadratic functional of the model errors. The properties of these error estimates are analyzed. It is shown that an arbitrary model error can be decomposed as the sum of two components that are orthogonal in a suitably defined function space. Relations between true and estimated errors are defined. The estimates are found to be approximations that retain many of the significant dynamics of the true model errors. Current efforts are directed toward application of the analytical results to a reference large structure model.

The Mentoring Competency Assessment: validation of a new instrument to evaluate skills of research mentors.

PubMed

Fleming, Michael; House, Stephanie; Hanson, Vansa Shewakramani; Yu, Lan; Garbutt, Jane; McGee, Richard; Kroenke, Kurt; Abedin, Zainab; Rubio, Doris M

2013-07-01

To determine the psychometric properties of the Mentoring Competency Assessment (MCA), a 26-item skills inventory that enables research mentors and mentees to evaluate six competencies of mentors: maintaining effective communication, aligning expectations, assessing understanding, addressing diversity, fostering independence, and promoting professional development. In 2010, investigators administered the MCA to 283 mentor-mentee pairs from 16 universities participating in a trial of a mentoring curriculum for clinical and translational research mentors. The authors analyzed baseline MCA data to describe the instrument's psychometric properties. Coefficient alpha scores for the MCA showed reliability (internal consistency). The hypothesized model with its six latent constructs (competencies) resulted in an acceptable fit to the data. For the instrument completed by mentors, chi-square = 663.20; df = 284; P < .001; root mean square error of approximation (RMSEA) = 0.069 (90% CI, 0.062-0.076); comparative fit index (CFI) = 0.85; and Tucker-Lewis index (TLI) = 0.83. For the instrument completed by mentees, chi-square = 840.62; df = 284; P < .001; RMSEA = 0.080 (90% CI, 0.063-0.077); CFI = 0.87; and TLI = 0.85. The correlations among the six competencies were high: 0.49-0.87 for mentors, 0.58-0.92 for mentees. All parameter estimates for the individual items were significant; standardized factor loadings ranged from 0.32 to 0.81 for mentors and 0.56 to 0.86 for mentees. The findings demonstrate that the MCA has reliability and validity. In addition, this study provides preliminary norms derived from a national sample of mentors and mentees.

Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen

2016-02-01

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

[Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS].

PubMed

Li, Wen-bing; Yao, Lin-tao; Liu, Mu-hua; Huang, Lin; Yao, Ming-yin; Chen, Tian-bing; He, Xiu-wen; Yang, Ping; Hu, Hui-qin; Nie, Jiang-hui

2015-05-01

Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.

Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems

PubMed Central

Li, Zhining; Zhang, Yingtang; Yin, Gang

2018-01-01

The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor’s system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg–Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the “overcalibration” problem. The accuracy of the error parameters’ estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust. PMID:29373544

Bias correction by use of errors-in-variables regression models in studies with K-X-ray fluorescence bone lead measurements.

PubMed

Lamadrid-Figueroa, Héctor; Téllez-Rojo, Martha M; Angeles, Gustavo; Hernández-Ávila, Mauricio; Hu, Howard

2011-01-01

In-vivo measurement of bone lead by means of K-X-ray fluorescence (KXRF) is the preferred biological marker of chronic exposure to lead. Unfortunately, considerable measurement error associated with KXRF estimations can introduce bias in estimates of the effect of bone lead when this variable is included as the exposure in a regression model. Estimates of uncertainty reported by the KXRF instrument reflect the variance of the measurement error and, although they can be used to correct the measurement error bias, they are seldom used in epidemiological statistical analyzes. Errors-in-variables regression (EIV) allows for correction of bias caused by measurement error in predictor variables, based on the knowledge of the reliability of such variables. The authors propose a way to obtain reliability coefficients for bone lead measurements from uncertainty data reported by the KXRF instrument and compare, by the use of Monte Carlo simulations, results obtained using EIV regression models vs. those obtained by the standard procedures. Results of the simulations show that Ordinary Least Square (OLS) regression models provide severely biased estimates of effect, and that EIV provides nearly unbiased estimates. Although EIV effect estimates are more imprecise, their mean squared error is much smaller than that of OLS estimates. In conclusion, EIV is a better alternative than OLS to estimate the effect of bone lead when measured by KXRF. Copyright © 2010 Elsevier Inc. All rights reserved.

Identifying model error in metabolic flux analysis - a generalized least squares approach.

PubMed

Sokolenko, Stanislav; Quattrociocchi, Marco; Aucoin, Marc G

2016-09-13

The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range). The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

Tests of Independence in Contingency Tables with Small Samples: A Comparison of Statistical Power.

ERIC Educational Resources Information Center

Parshall, Cynthia G.; Kromrey, Jeffrey D.

1996-01-01

Power and Type I error rates were estimated for contingency tables with small sample sizes for the following four types of tests: (1) Pearson's chi-square; (2) chi-square with Yates's continuity correction; (3) the likelihood ratio test; and (4) Fisher's Exact Test. Various marginal distributions, sample sizes, and effect sizes were examined. (SLD)

A Generalized Least Squares Regression Approach for Computing Effect Sizes in Single-Case Research: Application Examples

ERIC Educational Resources Information Center

Maggin, Daniel M.; Swaminathan, Hariharan; Rogers, Helen J.; O'Keeffe, Breda V.; Sugai, George; Horner, Robert H.

2011-01-01

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of…

Difference-based ridge-type estimator of parameters in restricted partial linear model with correlated errors.

PubMed

Wu, Jibo

2016-01-01

In this article, a generalized difference-based ridge estimator is proposed for the vector parameter in a partial linear model when the errors are dependent. It is supposed that some additional linear constraints may hold to the whole parameter space. Its mean-squared error matrix is compared with the generalized restricted difference-based estimator. Finally, the performance of the new estimator is explained by a simulation study and a numerical example.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Least Median of Squares Filtering of Locally Optimal Point Matches for Compressible Flow Image Registration

PubMed Central

Castillo, Edward; Castillo, Richard; White, Benjamin; Rojo, Javier; Guerrero, Thomas

2012-01-01

Compressible flow based image registration operates under the assumption that the mass of the imaged material is conserved from one image to the next. Depending on how the mass conservation assumption is modeled, the performance of existing compressible flow methods is limited by factors such as image quality, noise, large magnitude voxel displacements, and computational requirements. The Least Median of Squares Filtered Compressible Flow (LFC) method introduced here is based on a localized, nonlinear least squares, compressible flow model that describes the displacement of a single voxel that lends itself to a simple grid search (block matching) optimization strategy. Spatially inaccurate grid search point matches, corresponding to erroneous local minimizers of the nonlinear compressible flow model, are removed by a novel filtering approach based on least median of squares fitting and the forward search outlier detection method. The spatial accuracy of the method is measured using ten thoracic CT image sets and large samples of expert determined landmarks (available at www.dir-lab.com). The LFC method produces an average error within the intra-observer error on eight of the ten cases, indicating that the method is capable of achieving a high spatial accuracy for thoracic CT registration. PMID:22797602

High-Accuracy Decoupling Estimation of the Systematic Coordinate Errors of an INS and Intensified High Dynamic Star Tracker Based on the Constrained Least Squares Method

PubMed Central

Jiang, Jie; Yu, Wenbo; Zhang, Guangjun

2017-01-01

Navigation accuracy is one of the key performance indicators of an inertial navigation system (INS). Requirements for an accuracy assessment of an INS in a real work environment are exceedingly urgent because of enormous differences between real work and laboratory test environments. An attitude accuracy assessment of an INS based on the intensified high dynamic star tracker (IHDST) is particularly suitable for a real complex dynamic environment. However, the coupled systematic coordinate errors of an INS and the IHDST severely decrease the attitude assessment accuracy of an INS. Given that, a high-accuracy decoupling estimation method of the above systematic coordinate errors based on the constrained least squares (CLS) method is proposed in this paper. The reference frame of the IHDST is firstly converted to be consistent with that of the INS because their reference frames are completely different. Thereafter, the decoupling estimation model of the systematic coordinate errors is established and the CLS-based optimization method is utilized to estimate errors accurately. After compensating for error, the attitude accuracy of an INS can be assessed based on IHDST accurately. Both simulated experiments and real flight experiments of aircraft are conducted, and the experimental results demonstrate that the proposed method is effective and shows excellent performance for the attitude accuracy assessment of an INS in a real work environment. PMID:28991179

The GEOS Ozone Data Assimilation System: Specification of Error Statistics

NASA Technical Reports Server (NTRS)

Stajner, Ivanka; Riishojgaard, Lars Peter; Rood, Richard B.

2000-01-01

A global three-dimensional ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center. The Total Ozone Mapping Spectrometer (TOMS) total ozone and the Solar Backscatter Ultraviolet (SBUV) or (SBUV/2) partial ozone profile observations are assimilated. The assimilation, into an off-line ozone transport model, is done using the global Physical-space Statistical Analysis Scheme (PSAS). This system became operational in December 1999. A detailed description of the statistical analysis scheme, and in particular, the forecast and observation error covariance models is given. A new global anisotropic horizontal forecast error correlation model accounts for a varying distribution of observations with latitude. Correlations are largest in the zonal direction in the tropics where data is sparse. Forecast error variance model is proportional to the ozone field. The forecast error covariance parameters were determined by maximum likelihood estimation. The error covariance models are validated using x squared statistics. The analyzed ozone fields in the winter 1992 are validated against independent observations from ozone sondes and HALOE. There is better than 10% agreement between mean Halogen Occultation Experiment (HALOE) and analysis fields between 70 and 0.2 hPa. The global root-mean-square (RMS) difference between TOMS observed and forecast values is less than 4%. The global RMS difference between SBUV observed and analyzed ozone between 50 and 3 hPa is less than 15%.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error.

PubMed

Lu, Xinjiang; Liu, Wenbo; Zhou, Chuang; Huang, Minghui

2017-06-13

The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers. In this paper, a robust LS-SVM method is proposed and is shown to have more reliable performance when modeling a nonlinear system under conditions where Gaussian or non-Gaussian noise is present. The construction of a new objective function allows for a reduction of the mean of the modeling error as well as the minimization of its variance, and it does not constrain the mean of the modeling error to zero. This differs from the traditional LS-SVM, which uses a worst-case scenario approach in order to minimize the modeling error and constrains the mean of the modeling error to zero. In doing so, the proposed method takes the modeling error distribution information into consideration and is thus less conservative and more robust in regards to random noise. A solving method is then developed in order to determine the optimal parameters for the proposed robust LS-SVM. An additional analysis indicates that the proposed LS-SVM gives a smaller weight to a large-error training sample and a larger weight to a small-error training sample, and is thus more robust than the traditional LS-SVM. The effectiveness of the proposed robust LS-SVM is demonstrated using both artificial and real life cases.

Topographic Map of Pathfinder Landing Site

NASA Technical Reports Server (NTRS)

1997-01-01

Topographic map of the landing site, to a distance of 60 meters from the lander in the LSC coordinate system. The lander is shown schematically in the center; 2.5 meter radius circle (black) centered on the camera was not mapped. Gentle relief [root mean square (rms) elevation variation 0.5 m; rms a directional slope 4O] and organization of topography into northwest and northeast-trending ridges about 20 meters apart are apparent. Roughly 30% of the illustrated area is hidden from the camera behind these ridges. Contours (0.2 m interval) and color coding of elevations were generated from a digital terrain model, which was interpolated by kriging from approximately 700 measured points. Angular and parallax point coordinates were measured manually on a large (5 m length) anaglyphic uncontrolled mosaic and used to calculate Cartesian (LSC) coordinates. Errors in azimuth on the order of 10 are therefore likely; elevation errors were minimized by referencing elevations to the local horizon. The uncertainty in range measurements increases quadratically with range. Given a measurement error of 1/2 pixel, the expected precision in range is 0.3 meter at 10 meter range, and 10 meters at 60 meter range. Repeated measurements were made, compared, and edited for consistency to improve the range precision. Systematic errors undoubtedly remain and will be corrected in future maps compiled digitally from geometrically controlled images. Cartographic processing by U.S. Geological Survey.

NOTE: original caption as published in Science Magazine

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. JPL is a division of the California Institute of Technology (Caltech).

Synchronous front-face fluorescence spectroscopy for authentication of the adulteration of edible vegetable oil with refined used frying oil.

PubMed

Tan, Jin; Li, Rong; Jiang, Zi-Tao; Tang, Shu-Hua; Wang, Ying; Shi, Meng; Xiao, Yi-Qian; Jia, Bin; Lu, Tian-Xiang; Wang, Hao

2017-02-15

Synchronous front-face fluorescence spectroscopy has been developed for the discrimination of used frying oil (UFO) from edible vegetable oil (EVO), the estimation of the using time of UFO, and the determination of the adulteration of EVO with UFO. Both the heating time of laboratory prepared UFO and the adulteration of EVO with UFO could be determined by partial least squares regression (PLSR). To simulate the EVO adulteration with UFO, for each kind of oil, fifty adulterated samples at the adulterant amounts range of 1-50% were prepared. PLSR was then adopted to build the model and both full (leave-one-out) cross-validation and external validation were performed to evaluate the predictive ability. Under the optimum condition, the plots of observed versus predicted values exhibited high linearity (R(2)>0.96). The root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were both lower than 3%. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effects of window shape and reticle presence on performance in a vertical alignment task

NASA Technical Reports Server (NTRS)

Rosenberg, Erika L.; Haines, Richard F.; Jordan, Kevin

1989-01-01

This study was conducted to evaluate the effect of selected interior work-station orientational cuing upon the ability to align a target image with local vertical in the frontal plane. Angular error from gravitational vertical in an alignment task was measured for 20 observers viewing through two window shapes (square, round), two initial orientations of a computer-generated space shuttle image, and the presence or absence of a stabilized optical alignment reticle. In terms of overall accuracy, it was found that observer error was significantly smaller for the square window and reticle-present conditions than for the round window and reticle-absent conditions. Response bias data reflected an overall tendency to undershoot and greater variability of response in the round window/no reticle condition. These results suggest that environmental cuing information, such as that provided by square window frames and alignment reticles, may aid in subjective orientation and increase accuracy of response in a Space Station proximity operations alignment task.

Feasibility of using near infrared spectroscopy to detect and quantify an adulterant in high quality sandalwood oil.

PubMed

Kuriakose, Saji; Joe, I Hubert

2013-11-01

Determination of the authenticity of essential oils has become more significant, in recent years, following some illegal adulteration and contamination scandals. The present investigative study focuses on the application of near infrared spectroscopy to detect sample authenticity and quantify economic adulteration of sandalwood oils. Several data pre-treatments are investigated for calibration and prediction using partial least square regression (PLSR). The quantitative data analysis is done using a new spectral approach - full spectrum or sequential spectrum. The optimum number of PLS components is obtained according to the lowest root mean square error of calibration (RMSEC=0.00009% v/v). The lowest root mean square error of prediction (RMSEP=0.00016% v/v) in the test set and the highest coefficient of determination (R(2)=0.99989) are used as the evaluation tools for the best model. A nonlinear method, locally weighted regression (LWR), is added to extract nonlinear information and to compare with the linear PLSR model. Copyright © 2013 Elsevier B.V. All rights reserved.

The effect of constraints on the analytical figures of merit achieved by extended multivariate curve resolution-alternating least-squares.

PubMed

Pellegrino Vidal, Rocío B; Allegrini, Franco; Olivieri, Alejandro C

2018-03-20

Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to chemically interpretable solutions, the use of constraints becomes essential. In this work, both simulated and experimental data have been analyzed by MCR-ALS, applying chemically reasonable constraints, and investigating the relationship between selectivity, analytical sensitivity (γ) and root mean square error of prediction (RMSEP). As the selectivity in the instrumental modes decreases, the estimated values for γ did not fully represent the predictive model capabilities, judged from the obtained RMSEP values. Since the available sensitivity expressions have been developed by error propagation theory in unconstrained systems, there is a need of developing new expressions or analytical indicators. They should not only consider the specific profiles retrieved by MCR-ALS, but also the constraints under which the latter ones have been obtained. Copyright © 2017 Elsevier B.V. All rights reserved.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Feasibility of using near infrared spectroscopy to detect and quantify an adulterant in high quality sandalwood oil

NASA Astrophysics Data System (ADS)

Kuriakose, Saji; Joe, I. Hubert

2013-11-01

Determination of the authenticity of essential oils has become more significant, in recent years, following some illegal adulteration and contamination scandals. The present investigative study focuses on the application of near infrared spectroscopy to detect sample authenticity and quantify economic adulteration of sandalwood oils. Several data pre-treatments are investigated for calibration and prediction using partial least square regression (PLSR). The quantitative data analysis is done using a new spectral approach - full spectrum or sequential spectrum. The optimum number of PLS components is obtained according to the lowest root mean square error of calibration (RMSEC = 0.00009% v/v). The lowest root mean square error of prediction (RMSEP = 0.00016% v/v) in the test set and the highest coefficient of determination (R2 = 0.99989) are used as the evaluation tools for the best model. A nonlinear method, locally weighted regression (LWR), is added to extract nonlinear information and to compare with the linear PLSR model.

A New Test of Linear Hypotheses in OLS Regression under Heteroscedasticity of Unknown Form

ERIC Educational Resources Information Center

Cai, Li; Hayes, Andrew F.

2008-01-01

When the errors in an ordinary least squares (OLS) regression model are heteroscedastic, hypothesis tests involving the regression coefficients can have Type I error rates that are far from the nominal significance level. Asymptotically, this problem can be rectified with the use of a heteroscedasticity-consistent covariance matrix (HCCM)…

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: applications to differential gene expression.

PubMed

Montazeri, Zahra; Yanofsky, Corey M; Bickel, David R

2010-01-01

Research on analyzing microarray data has focused on the problem of identifying differentially expressed genes to the neglect of the problem of how to integrate evidence that a gene is differentially expressed with information on the extent of its differential expression. Consequently, researchers currently prioritize genes for further study either on the basis of volcano plots or, more commonly, according to simple estimates of the fold change after filtering the genes with an arbitrary statistical significance threshold. While the subjective and informal nature of the former practice precludes quantification of its reliability, the latter practice is equivalent to using a hard-threshold estimator of the expression ratio that is not known to perform well in terms of mean-squared error, the sum of estimator variance and squared estimator bias. On the basis of two distinct simulation studies and data from different microarray studies, we systematically compared the performance of several estimators representing both current practice and shrinkage. We find that the threshold-based estimators usually perform worse than the maximum-likelihood estimator (MLE) and they often perform far worse as quantified by estimated mean-squared risk. By contrast, the shrinkage estimators tend to perform as well as or better than the MLE and never much worse than the MLE, as expected from what is known about shrinkage. However, a Bayesian measure of performance based on the prior information that few genes are differentially expressed indicates that hard-threshold estimators perform about as well as the local false discovery rate (FDR), the best of the shrinkage estimators studied. Based on the ability of the latter to leverage information across genes, we conclude that the use of the local-FDR estimator of the fold change instead of informal or threshold-based combinations of statistical tests and non-shrinkage estimators can be expected to substantially improve the reliability of gene prioritization at very little risk of doing so less reliably. Since the proposed replacement of post-selection estimates with shrunken estimates applies as well to other types of high-dimensional data, it could also improve the analysis of SNP data from genome-wide association studies.

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, J; Hu, W; Xing, Y

Purpose: Different particle scanning beam delivery systems have different delivery accuracies. This study was performed to determine, for our particle treatment system, an appropriate composition (n=FWHM/GS) of spot size(FWHM) and grid size (GS), which can provide homogenous delivered dose distributions for both proton and heavy ion scanning beam radiotherapy. Methods: We analyzed the delivery errors of our beam delivery system using log files from the treatment of 28 patients. We used a homemade program to simulate square fields for different n values with and without considering the delivery errors and analyzed the homogeneity. All spots were located on a rectilinearmore » grid with equal spacing in the × and y directions. After that, we selected 7 energy levels for both proton and carbon ions. For each energy level, we made 6 square field plans with different n values (1, 1.5, 2, 2.5, 3, 3.5). Then we delivered those plans and used films to measure the homogeneity of each field. Results: For program simulation without delivery errors, when n≥1.1 the homogeneity can be within ±3%. For both proton and carbon program simulations with delivery errors and film measurements, the homogeneity can be within ±3% when n≥2.5. Conclusion: For our facility with system errors, the n≥2.5 is appropriate for maintaining homogeneity within ±3%.« less

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution.

PubMed

Koch, Cosima; Posch, Andreas E; Goicoechea, Héctor C; Herwig, Christoph; Lendl, Bernhard

2014-01-07

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution - alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L(-1) for Penicillin V and 0.32 g L(-1) for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L(-1) for Penicillin V and 0.15 g L(-1) for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Demonstration of a quantum error detection code using a square lattice of four superconducting qubits

PubMed Central

Córcoles, A.D.; Magesan, Easwar; Srinivasan, Srikanth J.; Cross, Andrew W.; Steffen, M.; Gambetta, Jay M.; Chow, Jerry M.

2015-01-01

The ability to detect and deal with errors when manipulating quantum systems is a fundamental requirement for fault-tolerant quantum computing. Unlike classical bits that are subject to only digital bit-flip errors, quantum bits are susceptible to a much larger spectrum of errors, for which any complete quantum error-correcting code must account. Whilst classical bit-flip detection can be realized via a linear array of qubits, a general fault-tolerant quantum error-correcting code requires extending into a higher-dimensional lattice. Here we present a quantum error detection protocol on a two-by-two planar lattice of superconducting qubits. The protocol detects an arbitrary quantum error on an encoded two-qubit entangled state via quantum non-demolition parity measurements on another pair of error syndrome qubits. This result represents a building block towards larger lattices amenable to fault-tolerant quantum error correction architectures such as the surface code. PMID:25923200

Demonstration of a quantum error detection code using a square lattice of four superconducting qubits.

PubMed

Córcoles, A D; Magesan, Easwar; Srinivasan, Srikanth J; Cross, Andrew W; Steffen, M; Gambetta, Jay M; Chow, Jerry M

2015-04-29

The ability to detect and deal with errors when manipulating quantum systems is a fundamental requirement for fault-tolerant quantum computing. Unlike classical bits that are subject to only digital bit-flip errors, quantum bits are susceptible to a much larger spectrum of errors, for which any complete quantum error-correcting code must account. Whilst classical bit-flip detection can be realized via a linear array of qubits, a general fault-tolerant quantum error-correcting code requires extending into a higher-dimensional lattice. Here we present a quantum error detection protocol on a two-by-two planar lattice of superconducting qubits. The protocol detects an arbitrary quantum error on an encoded two-qubit entangled state via quantum non-demolition parity measurements on another pair of error syndrome qubits. This result represents a building block towards larger lattices amenable to fault-tolerant quantum error correction architectures such as the surface code.

Expected Science Performance of the Square Kilometre Array Phase 1 (SKA1)

NASA Astrophysics Data System (ADS)

Bourke, Tyler; Braun, Robert; Bonaldi, Anna; Garcia-Miro, Cristina; Keane, Evan; Wagg, Jeff; SKAO Science Team

2018-01-01

The Square Kilometre Array (SKA) will be the world’s largest radio telescope when Phase 1 (SKA1) is completed in the next decade. The past few years have seen great progress toward this goal, through extensive design activities, with construction to start before the end of this decade, and early operations anticipated to begin around 2026. This poster describes the SKA and presents the expected performance and capabilities of SKA1 based on the modelling and proto-typing to date.

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

NASA Astrophysics Data System (ADS)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

Propagation of error from parameter constraints in quantitative MRI: Example application of multiple spin echo T2 mapping.

PubMed

Lankford, Christopher L; Does, Mark D

2018-02-01

Quantitative MRI may require correcting for nuisance parameters which can or must be constrained to independently measured or assumed values. The noise and/or bias in these constraints propagate to fitted parameters. For example, the case of refocusing pulse flip angle constraint in multiple spin echo T 2 mapping is explored. An analytical expression for the mean-squared error of a parameter of interest was derived as a function of the accuracy and precision of an independent estimate of a nuisance parameter. The expression was validated by simulations and then used to evaluate the effects of flip angle (θ) constraint on the accuracy and precision of T⁁2 for a variety of multi-echo T 2 mapping protocols. Constraining θ improved T⁁2 precision when the θ-map signal-to-noise ratio was greater than approximately one-half that of the first spin echo image. For many practical scenarios, constrained fitting was calculated to reduce not just the variance but the full mean-squared error of T⁁2, for bias in θ⁁≲6%. The analytical expression derived in this work can be applied to inform experimental design in quantitative MRI. The example application to T 2 mapping provided specific cases, depending on θ⁁ accuracy and precision, in which θ⁁ measurement and constraint would be beneficial to T⁁2 variance or mean-squared error. Magn Reson Med 79:673-682, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Medium-range Performance of the Global NWP Model

NASA Astrophysics Data System (ADS)

Kim, J.; Jang, T.; Kim, J.; Kim, Y.

2017-12-01

The medium-range performance of the global numerical weather prediction (NWP) model in the Korea Meteorological Administration (KMA) is investigated. The performance is based on the prediction of the extratropical circulation. The mean square error is expressed by sum of spatial variance of discrepancy between forecasts and observations and the square of the mean error (ME). Thus, it is important to investigate the ME effect in order to understand the model performance. The ME is expressed by the subtraction of an anomaly from forecast difference against the real climatology. It is found that the global model suffers from a severe systematic ME in medium-range forecasts. The systematic ME is dominant in the entire troposphere in all months. Such ME can explain at most 25% of root mean square error. We also compare the extratropical ME distribution with that from other NWP centers. NWP models exhibit similar spatial ME structure each other. It is found that the spatial ME pattern is highly correlated to that of an anomaly, implying that the ME varies with seasons. For example, the correlation coefficient between ME and anomaly ranges from -0.51 to -0.85 by months. The pattern of the extratropical circulation also has a high correlation to an anomaly. The global model has trouble in faithfully simulating extratropical cyclones and blockings in the medium-range forecast. In particular, the model has a hard to simulate an anomalous event in medium-range forecasts. If we choose an anomalous period for a test-bed experiment, we will suffer from a large error due to an anomaly.

Force and Directional Force Modulation Effects on Accuracy and Variability in Low-Level Pinch Force Tracking.

PubMed

Park, Sangsoo; Spirduso, Waneen; Eakin, Tim; Abraham, Lawrence

2018-01-01

The authors investigated how varying the required low-level forces and the direction of force change affect accuracy and variability of force production in a cyclic isometric pinch force tracking task. Eighteen healthy right-handed adult volunteers performed the tracking task over 3 different force ranges. Root mean square error and coefficient of variation were higher at lower force levels and during minimum reversals compared with maximum reversals. Overall, the thumb showed greater root mean square error and coefficient of variation scores than did the index finger during maximum reversals, but not during minimum reversals. The observed impaired performance during minimum reversals might originate from history-dependent mechanisms of force production and highly coupled 2-digit performance.

JASMINE -- Japan Astrometry Satellite Mission for INfrared Exploration: Data Analysis and Accuracy Assessment with a Kalman Filter

NASA Astrophysics Data System (ADS)

Yamada, Y.; Shimokawa, T.; Shinomoto, S. Yano, T.; Gouda, N.

2009-09-01

For the purpose of determining the celestial coordinates of stellar positions, consecutive observational images are laid overlapping each other with clues of stars belonging to multiple plates. In the analysis, one has to estimate not only the coordinates of individual plates, but also the possible expansion and distortion of the frame. This problem reduces to a least-squares fit that can in principle be solved by a huge matrix inversion, which is, however, impracticable. Here, we propose using Kalman filtering to perform the least-squares fit and implement a practical iterative algorithm. We also estimate errors associated with this iterative method and suggest a design of overlapping plates to minimize the error.

Adaptive control strategies for flexible robotic arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity if not unstable closed-loop behavior. Therefore a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

A study of image quality for radar image processing. [synthetic aperture radar imagery

NASA Technical Reports Server (NTRS)

King, R. W.; Kaupp, V. H.; Waite, W. P.; Macdonald, H. C.

1982-01-01

Methods developed for image quality metrics are reviewed with focus on basic interpretation or recognition elements including: tone or color; shape; pattern; size; shadow; texture; site; association or context; and resolution. Seven metrics are believed to show promise as a way of characterizing the quality of an image: (1) the dynamic range of intensities in the displayed image; (2) the system signal-to-noise ratio; (3) the system spatial bandwidth or bandpass; (4) the system resolution or acutance; (5) the normalized-mean-square-error as a measure of geometric fidelity; (6) the perceptual mean square error; and (7) the radar threshold quality factor. Selective levels of degradation are being applied to simulated synthetic radar images to test the validity of these metrics.

Increasing point-count duration increases standard error

USGS Publications Warehouse

Smith, W.P.; Twedt, D.J.; Hamel, P.B.; Ford, R.P.; Wiedenfeld, D.A.; Cooper, R.J.

1998-01-01

We examined data from point counts of varying duration in bottomland forests of west Tennessee and the Mississippi Alluvial Valley to determine if counting interval influenced sampling efficiency. Estimates of standard error increased as point count duration increased both for cumulative number of individuals and species in both locations. Although point counts appear to yield data with standard errors proportional to means, a square root transformation of the data may stabilize the variance. Using long (>10 min) point counts may reduce sample size and increase sampling error, both of which diminish statistical power and thereby the ability to detect meaningful changes in avian populations.

The moving-window Bayesian maximum entropy framework: estimation of PM(2.5) yearly average concentration across the contiguous United States.

PubMed

Akita, Yasuyuki; Chen, Jiu-Chiuan; Serre, Marc L

2012-09-01

Geostatistical methods are widely used in estimating long-term exposures for epidemiological studies on air pollution, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and the uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian maximum entropy (BME) method and applied this framework to estimate fine particulate matter (PM(2.5)) yearly average concentrations over the contiguous US. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingness in the air-monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM(2.5) data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM(2.5). Moreover, the MWBME method further reduces the MSE by 8.4-43.7%, with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM(2.5) across large geographical domains with expected spatial non-stationarity.

A more accurate scheme for calculating Earth's skin temperature

NASA Astrophysics Data System (ADS)

Tsuang, Ben-Jei; Tu, Chia-Ying; Tsai, Jeng-Lin; Dracup, John A.; Arpe, Klaus; Meyers, Tilden

2009-02-01

The theoretical framework of the vertical discretization of a ground column for calculating Earth’s skin temperature is presented. The suggested discretization is derived from the evenly heat-content discretization with the optimal effective thickness for layer-temperature simulation. For the same level number, the suggested discretization is more accurate in skin temperature as well as surface ground heat flux simulations than those used in some state-of-the-art models. A proposed scheme (“op(3,2,0)”) can reduce the normalized root-mean-square error (or RMSE/STD ratio) of the calculated surface ground heat flux of a cropland site significantly to 2% (or 0.9 W m-2), from 11% (or 5 W m-2) by a 5-layer scheme used in ECMWF, from 19% (or 8 W m-2) by a 5-layer scheme used in ECHAM, and from 74% (or 32 W m-2) by a single-layer scheme used in the UCLA GCM. Better accuracy can be achieved by including more layers to the vertical discretization. Similar improvements are expected for other locations with different land types since the numerical error is inherited into the models for all the land types. The proposed scheme can be easily implemented into state-of-the-art climate models for the temperature simulation of snow, ice and soil.

Time-Series Approaches for Forecasting the Number of Hospital Daily Discharged Inpatients.

PubMed

Ting Zhu; Li Luo; Xinli Zhang; Yingkang Shi; Wenwu Shen

2017-03-01

For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.

Human operator response to error-likely situations in complex engineering systems

NASA Technical Reports Server (NTRS)

Morris, Nancy M.; Rouse, William B.

1988-01-01

The causes of human error in complex systems are examined. First, a conceptual framework is provided in which two broad categories of error are discussed: errors of action, or slips, and errors of intention, or mistakes. Conditions in which slips and mistakes might be expected to occur are identified, based on existing theories of human error. Regarding the role of workload, it is hypothesized that workload may act as a catalyst for error. Two experiments are presented in which humans' response to error-likely situations were examined. Subjects controlled PLANT under a variety of conditions and periodically provided subjective ratings of mental effort. A complex pattern of results was obtained, which was not consistent with predictions. Generally, the results of this research indicate that: (1) humans respond to conditions in which errors might be expected by attempting to reduce the possibility of error, and (2) adaptation to conditions is a potent influence on human behavior in discretionary situations. Subjects' explanations for changes in effort ratings are also explored.

Maritime Adaptive Optics Beam Control

DTIC Science & Technology

2010-09-01

Liquid Crystal LMS Least Mean Square MIMO Multiple- Input Multiple-Output MMDM Micromachined Membrane Deformable Mirror MSE Mean Square Error...determine how the beam is distorted, a control computer to calculate the correction to be applied, and a corrective element, usually a deformable mirror ...during this research, an overview of the system modification is provided here. Using additional mirrors and reflecting the beam to and from an

The microcomputer scientific software series 3: general linear model--analysis of variance.

Treesearch

Harold M. Rauscher

1985-01-01

A BASIC language set of programs, designed for use on microcomputers, is presented. This set of programs will perform the analysis of variance for any statistical model describing either balanced or unbalanced designs. The program computes and displays the degrees of freedom, Type I sum of squares, and the mean square for the overall model, the error, and each factor...

Evaluation of estimation methods and power of tests of discrete covariates in repeated time-to-event parametric models: application to Gaucher patients treated by imiglucerase.

PubMed

Vigan, Marie; Stirnemann, Jérôme; Mentré, France

2014-05-01

Analysis of repeated time-to-event data is increasingly performed in pharmacometrics using parametric frailty models. The aims of this simulation study were (1) to assess estimation performance of Stochastic Approximation Expectation Maximization (SAEM) algorithm in MONOLIX, Adaptive Gaussian Quadrature (AGQ), and Laplace algorithm in PROC NLMIXED of SAS and (2) to evaluate properties of test of a dichotomous covariate on occurrence of events. The simulation setting is inspired from an analysis of occurrence of bone events after the initiation of treatment by imiglucerase in patients with Gaucher Disease (GD). We simulated repeated events with an exponential model and various dropout rates: no, low, or high. Several values of baseline hazard model, variability, number of subject, and effect of covariate were studied. For each scenario, 100 datasets were simulated for estimation performance and 500 for test performance. We evaluated estimation performance through relative bias and relative root mean square error (RRMSE). We studied properties of Wald and likelihood ratio test (LRT). We used these methods to analyze occurrence of bone events in patients with GD after starting an enzyme replacement therapy. SAEM with three chains and AGQ algorithms provided good estimates of parameters much better than SAEM with one chain and Laplace which often provided poor estimates. Despite a small number of repeated events, SAEM with three chains and AGQ gave small biases and RRMSE. Type I errors were closed to 5%, and power varied as expected for SAEM with three chains and AGQ. Probability of having at least one event under treatment was 19.1%.

A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes.

PubMed

Hernández Alava, Mónica; Wailoo, Allan; Wolfe, Fred; Michaud, Kaleb

2014-10-01

Analysts frequently estimate health state utility values from other outcomes. Utility values like EQ-5D have characteristics that make standard statistical methods inappropriate. We have developed a bespoke, mixture model approach to directly estimate EQ-5D. An indirect method, "response mapping," first estimates the level on each of the 5 dimensions of the EQ-5D and then calculates the expected tariff score. These methods have never previously been compared. We use a large observational database from patients with rheumatoid arthritis (N = 100,398). Direct estimation of UK EQ-5D scores as a function of the Health Assessment Questionnaire (HAQ), pain, and age was performed with a limited dependent variable mixture model. Indirect modeling was undertaken with a set of generalized ordered probit models with expected tariff scores calculated mathematically. Linear regression was reported for comparison purposes. Impact on cost-effectiveness was demonstrated with an existing model. The linear model fits poorly, particularly at the extremes of the distribution. The bespoke mixture model and the indirect approaches improve fit over the entire range of EQ-5D. Mean average error is 10% and 5% lower compared with the linear model, respectively. Root mean squared error is 3% and 2% lower. The mixture model demonstrates superior performance to the indirect method across almost the entire range of pain and HAQ. These lead to differences in cost-effectiveness of up to 20%. There are limited data from patients in the most severe HAQ health states. Modeling of EQ-5D from clinical measures is best performed directly using the bespoke mixture model. This substantially outperforms the indirect method in this example. Linear models are inappropriate, suffer from systematic bias, and generate values outside the feasible range. © The Author(s) 2013.

Comparing Parameter Estimation Techniques for an Electrical Power Transformer Oil Temperature Prediction Model

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1999-01-01

This paper examines various sources of error in MIT's improved top oil temperature rise over ambient temperature model and estimation process. The sources of error are the current parameter estimation technique, quantization noise, and post-processing of the transformer data. Results from this paper will show that an output error parameter estimation technique should be selected to replace the current least squares estimation technique. The output error technique obtained accurate predictions of transformer behavior, revealed the best error covariance, obtained consistent parameter estimates, and provided for valid and sensible parameters. This paper will also show that the output error technique should be used to minimize errors attributed to post-processing (decimation) of the transformer data. Models used in this paper are validated using data from a large transformer in service.

Computation of aerodynamic interference effects on oscillating airfoils with controls in ventilated subsonic wind tunnels

NASA Technical Reports Server (NTRS)

Fromme, J. A.; Golberg, M. A.

1979-01-01

Lift interference effects are discussed based on Bland's (1968) integral equation. A mathematical existence theory is utilized for which convergence of the numerical method has been proved for general (square-integrable) downwashes. Airloads are computed using orthogonal airfoil polynomial pairs in conjunction with a collocation method which is numerically equivalent to Galerkin's method and complex least squares. Convergence exhibits exponentially decreasing error with the number n of collocation points for smooth downwashes, whereas errors are proportional to 1/n for discontinuous downwashes. The latter can be reduced to 1/n to the m+1 power with mth-order Richardson extrapolation (by using m = 2, hundredfold error reductions were obtained with only a 13% increase of computer time). Numerical results are presented showing acoustic resonance, as well as the effect of Mach number, ventilation, height-to-chord ratio, and mode shape on wind-tunnel interference. Excellent agreement with experiment is obtained in steady flow, and good agreement is obtained for unsteady flow.

Precise calibration of spatial phase response nonuniformity arising in liquid crystal on silicon.

PubMed

Xu, Jingquan; Qin, SiYi; Liu, Chen; Fu, Songnian; Liu, Deming

2018-06-15

In order to calibrate the spatial phase response nonuniformity of liquid crystal on silicon (LCoS), we propose to use a Twyman-Green interferometer to characterize the wavefront distortion, due to the inherent curvature of the device. During the characterization, both the residual carrier frequency introduced by the Fourier transform evaluation method and the lens aberration are error sources. For the tilted phase error introduced by residual carrier frequency, the least mean square fitting method is used to obtain the tilted phase error. Meanwhile, we use Zernike polynomials fitting based on plane mirror calibration to mitigate the lens aberration. For a typical LCoS with 1×12,288 pixels after calibration, the peak-to-valley value of the inherent wavefront distortion is approximately 0.25λ at 1550 nm, leading to a half-suppression of wavefront distortion. All efforts can suppress the root mean squares value of the inherent wavefront distortion to approximately λ/34.

Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection

NASA Astrophysics Data System (ADS)

Sun, Li-wei; Ye, Xin; Fang, Wei; He, Zhen-lei; Yi, Xiao-long; Wang, Yu-peng

2017-11-01

Hyper-spectral imaging spectrometer has high spatial and spectral resolution. Its radiometric calibration needs the knowledge of the sources used with high spectral resolution. In order to satisfy the requirement of source, an on-orbit radiometric calibration method is designed in this paper. This chain is based on the spectral inversion accuracy of the calibration light source. We compile the genetic algorithm progress which is used to optimize the channel design of the transfer radiometer and consider the degradation of the halogen lamp, thus realizing the high accuracy inversion of spectral curve in the whole working time. The experimental results show the average root mean squared error is 0.396%, the maximum root mean squared error is 0.448%, and the relative errors at all wavelengths are within 1% in the spectral range from 500 nm to 900 nm during 100 h operating time. The design lays a foundation for the high accuracy calibration of imaging spectrometer.

Research on the infiltration processes of lawn soils of the Babao River in the Qilian Mountain.

PubMed

Li, GuangWen; Feng, Qi; Zhang, FuPing; Cheng, AiFang

2014-01-01

Using a Guelph Permeameter, the soil water infiltration processes were analyzed in the Babao River of the Qilian Mountain in China. The results showed that the average soil initial infiltration and the steady infiltration rates in the upstream reaches of the Babao River are 1.93 and 0.99 cm/min, whereas those of the middle area are 0.48 cm/min and 0.21 cm/min, respectively. The infiltration processes can be divided into three stages: the rapidly changing stage (0-10 min), the slowly changing stage (10-30 min) and the stabilization stage (after 30 min). We used field data collected from lawn soils and evaluated the performances of the infiltration models of Philip, Kostiakov and Horton with the sum of squared error, the root mean square error, the coefficient of determination, the mean error, the model efficiency and Willmott's index of agreement. The results indicated that the Kostiakov model was most suitable for studying the infiltration process in the alpine lawn soils.

Uncertainties in extracted parameters of a Gaussian emission line profile with continuum background.

PubMed

Minin, Serge; Kamalabadi, Farzad

2009-12-20

We derive analytical equations for uncertainties in parameters extracted by nonlinear least-squares fitting of a Gaussian emission function with an unknown continuum background component in the presence of additive white Gaussian noise. The derivation is based on the inversion of the full curvature matrix (equivalent to Fisher information matrix) of the least-squares error, chi(2), in a four-variable fitting parameter space. The derived uncertainty formulas (equivalent to Cramer-Rao error bounds) are found to be in good agreement with the numerically computed uncertainties from a large ensemble of simulated measurements. The derived formulas can be used for estimating minimum achievable errors for a given signal-to-noise ratio and for investigating some aspects of measurement setup trade-offs and optimization. While the intended application is Fabry-Perot spectroscopy for wind and temperature measurements in the upper atmosphere, the derivation is generic and applicable to other spectroscopy problems with a Gaussian line shape.

Combining forecast weights: Why and how?

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

2012-09-01

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2014-01-01

The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.

Performance metrics for the assessment of satellite data products: an ocean color case study

PubMed Central

Seegers, Bridget N.; Stumpf, Richard P.; Schaeffer, Blake A.; Loftin, Keith A.; Werdell, P. Jeremy

2018-01-01

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities. PMID:29609296

Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel.

PubMed

Zhao, Haiquan; Zhang, Jiashu

2009-04-01

This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.

Performance Metrics, Error Modeling, and Uncertainty Quantification

NASA Technical Reports Server (NTRS)

Tian, Yudong; Nearing, Grey S.; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Tang, Ling

2016-01-01

A common set of statistical metrics has been used to summarize the performance of models or measurements-­ the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying un­certainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model should be an alternative to the metrics-based approach. The error-modeling meth­odology is applicable to both linear and nonlinear errors, while the metrics are only meaningful for linear errors. In addition, the error model expresses the error structure more naturally, and directly quantifies uncertainty. This argument is further explained by highlighting the intrinsic connections between the performance metrics, the error model, and the joint distribution between the data and the reference.

Moderation of the Relationship Between Reward Expectancy and Prediction Error-Related Ventral Striatal Reactivity by Anhedonia in Unmedicated Major Depressive Disorder: Findings From the EMBARC Study

PubMed Central

Greenberg, Tsafrir; Chase, Henry W.; Almeida, Jorge R.; Stiffler, Richelle; Zevallos, Carlos R.; Aslam, Haris A.; Deckersbach, Thilo; Weyandt, Sarah; Cooper, Crystal; Toups, Marisa; Carmody, Thomas; Kurian, Benji; Peltier, Scott; Adams, Phillip; McInnis, Melvin G.; Oquendo, Maria A.; McGrath, Patrick J.; Fava, Maurizio; Weissman, Myrna; Parsey, Ramin; Trivedi, Madhukar H.; Phillips, Mary L.

2016-01-01

Objective Anhedonia, disrupted reward processing, is a core symptom of major depressive disorder. Recent findings demonstrate altered reward-related ventral striatal reactivity in depressed individuals, but the extent to which this is specific to anhedonia remains poorly understood. The authors examined the effect of anhedonia on reward expectancy (expected outcome value) and prediction error-(discrepancy between expected and actual outcome) related ventral striatal reactivity, as well as the relationship between these measures. Method A total of 148 unmedicated individuals with major depressive disorder and 31 healthy comparison individuals recruited for the multisite EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study underwent functional MRI during a well-validated reward task. Region of interest and whole-brain data were examined in the first- (N=78) and second- (N=70) recruited cohorts, as well as the total sample, of depressed individuals, and in healthy individuals. Results Healthy, but not depressed, individuals showed a significant inverse relationship between reward expectancy and prediction error-related right ventral striatal reactivity. Across all participants, and in depressed individuals only, greater anhedonia severity was associated with a reduced reward expectancy-prediction error inverse relationship, even after controlling for other symptoms. Conclusions The normal reward expectancy and prediction error-related ventral striatal reactivity inverse relationship concords with conditioning models, predicting a shift in ventral striatal responding from reward outcomes to reward cues. This study shows, for the first time, an absence of this relationship in two cohorts of unmedicated depressed individuals and a moderation of this relationship by anhedonia, suggesting reduced reward-contingency learning with greater anhedonia. These findings help elucidate neural mechanisms of anhedonia, as a step toward identifying potential biosignatures of treatment response. PMID:26183698

Moderation of the Relationship Between Reward Expectancy and Prediction Error-Related Ventral Striatal Reactivity by Anhedonia in Unmedicated Major Depressive Disorder: Findings From the EMBARC Study.

PubMed

Greenberg, Tsafrir; Chase, Henry W; Almeida, Jorge R; Stiffler, Richelle; Zevallos, Carlos R; Aslam, Haris A; Deckersbach, Thilo; Weyandt, Sarah; Cooper, Crystal; Toups, Marisa; Carmody, Thomas; Kurian, Benji; Peltier, Scott; Adams, Phillip; McInnis, Melvin G; Oquendo, Maria A; McGrath, Patrick J; Fava, Maurizio; Weissman, Myrna; Parsey, Ramin; Trivedi, Madhukar H; Phillips, Mary L

2015-09-01

Anhedonia, disrupted reward processing, is a core symptom of major depressive disorder. Recent findings demonstrate altered reward-related ventral striatal reactivity in depressed individuals, but the extent to which this is specific to anhedonia remains poorly understood. The authors examined the effect of anhedonia on reward expectancy (expected outcome value) and prediction error- (discrepancy between expected and actual outcome) related ventral striatal reactivity, as well as the relationship between these measures. A total of 148 unmedicated individuals with major depressive disorder and 31 healthy comparison individuals recruited for the multisite EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study underwent functional MRI during a well-validated reward task. Region of interest and whole-brain data were examined in the first- (N=78) and second- (N=70) recruited cohorts, as well as the total sample, of depressed individuals, and in healthy individuals. Healthy, but not depressed, individuals showed a significant inverse relationship between reward expectancy and prediction error-related right ventral striatal reactivity. Across all participants, and in depressed individuals only, greater anhedonia severity was associated with a reduced reward expectancy-prediction error inverse relationship, even after controlling for other symptoms. The normal reward expectancy and prediction error-related ventral striatal reactivity inverse relationship concords with conditioning models, predicting a shift in ventral striatal responding from reward outcomes to reward cues. This study shows, for the first time, an absence of this relationship in two cohorts of unmedicated depressed individuals and a moderation of this relationship by anhedonia, suggesting reduced reward-contingency learning with greater anhedonia. These findings help elucidate neural mechanisms of anhedonia, as a step toward identifying potential biosignatures of treatment response.

A Geomagnetic Estimate of Mean Paleointensity

NASA Technical Reports Server (NTRS)

Voorhies, Coerte V.

2004-01-01

To test a statistical hypothesis about Earth's magnetic field against paleomagnetism, the present field is used to estimate time averaged paleointensity. The estimate used the modern magnetic multipole spectrum R(n), which gives the mean square induction represented by spherical harmonics of degree n averaged over the sphere of radius a = 6371.2 km. The hypothesis asserts that low degree multi-pole powers of the coresource field are distributed as chi-squared with 2n+1 degrees of freedom and expectation values, where c is the 3480 km radius of the Earth's core. (This is compatible with a usually mainly geocentric axial dipolar field). Amplitude K is estimated by fitting theoretical to observational spectra through degree 12. The resulting calibrated expectation spectrum is summed through degree 12 to estimate expected square intensity F(exp 2). The sum also estimates F(exp 2) averaged over geologic time, in so far as the present magnetic spectrum is a fair sample of that generated in the past by core geodynamic processes. Additional information is included in the original extended abstract.

A weighted least squares estimation of the polynomial regression model on paddy production in the area of Kedah and Perlis

NASA Astrophysics Data System (ADS)

Musa, Rosliza; Ali, Zalila; Baharum, Adam; Nor, Norlida Mohd

2017-08-01

The linear regression model assumes that all random error components are identically and independently distributed with constant variance. Hence, each data point provides equally precise information about the deterministic part of the total variation. In other words, the standard deviations of the error terms are constant over all values of the predictor variables. When the assumption of constant variance is violated, the ordinary least squares estimator of regression coefficient lost its property of minimum variance in the class of linear and unbiased estimators. Weighted least squares estimation are often used to maximize the efficiency of parameter estimation. A procedure that treats all of the data equally would give less precisely measured points more influence than they should have and would give highly precise points too little influence. Optimizing the weighted fitting criterion to find the parameter estimates allows the weights to determine the contribution of each observation to the final parameter estimates. This study used polynomial model with weighted least squares estimation to investigate paddy production of different paddy lots based on paddy cultivation characteristics and environmental characteristics in the area of Kedah and Perlis. The results indicated that factors affecting paddy production are mixture fertilizer application cycle, average temperature, the squared effect of average rainfall, the squared effect of pest and disease, the interaction between acreage with amount of mixture fertilizer, the interaction between paddy variety and NPK fertilizer application cycle and the interaction between pest and disease and NPK fertilizer application cycle.

Application of the Polynomial-Based Least Squares and Total Least Squares Models for the Attenuated Total Reflection Fourier Transform Infrared Spectra of Binary Mixtures of Hydroxyl Compounds.

PubMed

Shan, Peng; Peng, Silong; Zhao, Yuhui; Tang, Liang

2016-03-01

An analysis of binary mixtures of hydroxyl compound by Attenuated Total Reflection Fourier transform infrared spectroscopy (ATR FT-IR) and classical least squares (CLS) yield large model error due to the presence of unmodeled components such as H-bonded components. To accommodate these spectral variations, polynomial-based least squares (LSP) and polynomial-based total least squares (TLSP) are proposed to capture the nonlinear absorbance-concentration relationship. LSP is based on assuming that only absorbance noise exists; while TLSP takes both absorbance noise and concentration noise into consideration. In addition, based on different solving strategy, two optimization algorithms (limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm and Levenberg-Marquardt (LM) algorithm) are combined with TLSP and then two different TLSP versions (termed as TLSP-LBFGS and TLSP-LM) are formed. The optimum order of each nonlinear model is determined by cross-validation. Comparison and analyses of the four models are made from two aspects: absorbance prediction and concentration prediction. The results for water-ethanol solution and ethanol-ethyl lactate solution show that LSP, TLSP-LBFGS, and TLSP-LM can, for both absorbance prediction and concentration prediction, obtain smaller root mean square error of prediction than CLS. Additionally, they can also greatly enhance the accuracy of estimated pure component spectra. However, from the view of concentration prediction, the Wilcoxon signed rank test shows that there is no statistically significant difference between each nonlinear model and CLS. © The Author(s) 2016.

First-Order System Least-Squares for Second-Order Elliptic Problems with Discontinuous Coefficients

NASA Technical Reports Server (NTRS)

Manteuffel, Thomas A.; McCormick, Stephen F.; Starke, Gerhard

1996-01-01

The first-order system least-squares methodology represents an alternative to standard mixed finite element methods. Among its advantages is the fact that the finite element spaces approximating the pressure and flux variables are not restricted by the inf-sup condition and that the least-squares functional itself serves as an appropriate error measure. This paper studies the first-order system least-squares approach for scalar second-order elliptic boundary value problems with discontinuous coefficients. Ellipticity of an appropriately scaled least-squares bilinear form of the size of the jumps in the coefficients leading to adequate finite element approximation results. The occurrence of singularities at interface corners and cross-points is discussed. and a weighted least-squares functional is introduced to handle such cases. Numerical experiments are presented for two test problems to illustrate the performance of this approach.

Author Correction: Re-designing Interleukin-12 to enhance its safety and potential as an anti-tumor immunotherapeutic agent.

PubMed

Wang, Pengju; Li, Xiaozhu; Wang, Jiwei; Gao, Dongling; Li, Yuenan; Li, Haoze; Chu, Yongchao; Zhang, Zhongxian; Liu, Hongtao; Jiang, Guozhong; Cheng, Zhenguo; Wang, Shengdian; Dong, Jianzeng; Feng, Baisui; Chard, Louisa S; Lemoine, Nicholas R; Wang, Yaohe

2018-01-10

The originally published version of this Article contained errors in Figure 4. In panel b, the square and diamond labels associated with the uppermost survival curve were incorrectly displayed as 'n' and 'u', respectively. These errors have now been corrected in both the PDF and HTML versions of the Article.

{lambda} elements for singular problems in CFD: Viscoelastic fluids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, K.K.; Surana, K.S.

1996-10-01

This paper presents two dimensional {lambda} element formulation for viscoelastic fluid flow containing point singularities in the flow field. The flow of viscoelastic fluid even without singularities are a difficult class of problems for increasing Deborah number or Weissenburg number due to increased dominance of convective terms and thus increased hyperbolicity. In the present work the equations of fluid motion and the constitutive laws are recast in the form of a first order system of coupled equations with the use of auxiliary variables. The velocity, pressure and stresses are interpolated using equal order C{sup 0} {lambda} element approximations. The Leastmore » Squares Finite Element Method (LSFEM) is used to construct the integral form (error functional I) corresponding to these equations. The error functional is constructed by taking the integrated sum of the squares of the errors or residuals (over the whole discretization) resulting when the element approximation is substituted into these equations. The conditions resulting from the minimization of the error functional are satisfied by using Newton`s method with line search. LSFEM has much superior performance when dealing with non-linear and convection dominated problems.« less

Controlled sound field with a dual layer loudspeaker array

NASA Astrophysics Data System (ADS)

Shin, Mincheol; Fazi, Filippo M.; Nelson, Philip A.; Hirono, Fabio C.

2014-08-01

Controlled sound interference has been extensively investigated using a prototype dual layer loudspeaker array comprised of 16 loudspeakers. Results are presented for measures of array performance such as input signal power, directivity of sound radiation and accuracy of sound reproduction resulting from the application of conventional control methods such as minimization of error in mean squared pressure, maximization of energy difference and minimization of weighted pressure error and energy. Procedures for selecting the tuning parameters have also been introduced. With these conventional concepts aimed at the production of acoustically bright and dark zones, all the control methods used require a trade-off between radiation directivity and reproduction accuracy in the bright zone. An alternative solution is proposed which can achieve better performance based on the measures presented simultaneously by inserting a low priority zone named as the “gray” zone. This involves the weighted minimization of mean-squared errors in both bright and dark zones together with the gray zone in which the minimization error is given less importance. This results in the production of directional bright zone in which the accuracy of sound reproduction is maintained with less required input power. The results of simulations and experiments are shown to be in excellent agreement.

Swing arm profilometer: analytical solutions of misalignment errors for testing axisymmetric optics

NASA Astrophysics Data System (ADS)

Xiong, Ling; Luo, Xiao; Liu, Zhenyu; Wang, Xiaokun; Hu, Haixiang; Zhang, Feng; Zheng, Ligong; Zhang, Xuejun

2016-07-01

The swing arm profilometer (SAP) has been playing a very important role in testing large aspheric optics. As one of most significant error sources that affects the test accuracy, misalignment error leads to low-order errors such as aspherical aberrations and coma apart from power. In order to analyze the effect of misalignment errors, the relation between alignment parameters and test results of axisymmetric optics is presented. Analytical solutions of SAP system errors from tested mirror misalignment, arm length L deviation, tilt-angle θ deviation, air-table spin error, and air-table misalignment are derived, respectively; and misalignment tolerance is given to guide surface measurement. In addition, experiments on a 2-m diameter parabolic mirror are demonstrated to verify the model; according to the error budget, we achieve the SAP test for low-order errors except power with accuracy of 0.1 μm root-mean-square.

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing

NASA Astrophysics Data System (ADS)

Gao, Jing; Burt, James E.

2017-12-01

This study investigates the usefulness of a per-pixel bias-variance error decomposition (BVD) for understanding and improving spatially-explicit data-driven models of continuous variables in environmental remote sensing (ERS). BVD is a model evaluation method originated from machine learning and have not been examined for ERS applications. Demonstrated with a showcase regression tree model mapping land imperviousness (0-100%) using Landsat images, our results showed that BVD can reveal sources of estimation errors, map how these sources vary across space, reveal the effects of various model characteristics on estimation accuracy, and enable in-depth comparison of different error metrics. Specifically, BVD bias maps can help analysts identify and delineate model spatial non-stationarity; BVD variance maps can indicate potential effects of ensemble methods (e.g. bagging), and inform efficient training sample allocation - training samples should capture the full complexity of the modeled process, and more samples should be allocated to regions with more complex underlying processes rather than regions covering larger areas. Through examining the relationships between model characteristics and their effects on estimation accuracy revealed by BVD for both absolute and squared errors (i.e. error is the absolute or the squared value of the difference between observation and estimate), we found that the two error metrics embody different diagnostic emphases, can lead to different conclusions about the same model, and may suggest different solutions for performance improvement. We emphasize BVD's strength in revealing the connection between model characteristics and estimation accuracy, as understanding this relationship empowers analysts to effectively steer performance through model adjustments.

Analysis of basic clustering algorithms for numerical estimation of statistical averages in biomolecules.

PubMed

Anandakrishnan, Ramu; Onufriev, Alexey

2008-03-01

In statistical mechanics, the equilibrium properties of a physical system of particles can be calculated as the statistical average over accessible microstates of the system. In general, these calculations are computationally intractable since they involve summations over an exponentially large number of microstates. Clustering algorithms are one of the methods used to numerically approximate these sums. The most basic clustering algorithms first sub-divide the system into a set of smaller subsets (clusters). Then, interactions between particles within each cluster are treated exactly, while all interactions between different clusters are ignored. These smaller clusters have far fewer microstates, making the summation over these microstates, tractable. These algorithms have been previously used for biomolecular computations, but remain relatively unexplored in this context. Presented here, is a theoretical analysis of the error and computational complexity for the two most basic clustering algorithms that were previously applied in the context of biomolecular electrostatics. We derive a tight, computationally inexpensive, error bound for the equilibrium state of a particle computed via these clustering algorithms. For some practical applications, it is the root mean square error, which can be significantly lower than the error bound, that may be more important. We how that there is a strong empirical relationship between error bound and root mean square error, suggesting that the error bound could be used as a computationally inexpensive metric for predicting the accuracy of clustering algorithms for practical applications. An example of error analysis for such an application-computation of average charge of ionizable amino-acids in proteins-is given, demonstrating that the clustering algorithm can be accurate enough for practical purposes.

Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini

2014-12-01

The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies-both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the choice of the wavelets in multiscale model evaluation. It was found that the proposed wavelet-based performance measures, namely the MNSC (Multiscale Nash-Sutcliffe Criteria) and MNRMSE (Multiscale Normalized Root Mean Square Error), are a more reliable measure than traditional performance measures such as the Nash-Sutcliffe Criteria (NSC), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Further, the proposed methodology can be used to: i) compare different hydrological models (both physical and statistical models), and ii) help in model calibration.

Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration.

PubMed

Wood, Clive; Alwati, Abdolati; Halsey, Sheelagh; Gough, Tim; Brown, Elaine; Kelly, Adrian; Paradkar, Anant

2016-09-10

The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen-nicotinamide (IBU-NIC) and 1:1 carbamazepine-nicotinamide (CBZ-NIC) has been evaluated. A partial least squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Quantitative transmission Raman spectroscopy of pharmaceutical tablets and capsules.

PubMed

Johansson, Jonas; Sparén, Anders; Svensson, Olof; Folestad, Staffan; Claybourn, Mike

2007-11-01

Quantitative analysis of pharmaceutical formulations using the new approach of transmission Raman spectroscopy has been investigated. For comparison, measurements were also made in conventional backscatter mode. The experimental setup consisted of a Raman probe-based spectrometer with 785 nm excitation for measurements in backscatter mode. In transmission mode the same system was used to detect the Raman scattered light, while an external diode laser of the same type was used as excitation source. Quantitative partial least squares models were developed for both measurement modes. The results for tablets show that the prediction error for an independent test set was lower for the transmission measurements with a relative root mean square error of about 2.2% as compared with 2.9% for the backscatter mode. Furthermore, the models were simpler in the transmission case, for which only a single partial least squares (PLS) component was required to explain the variation. The main reason for the improvement using the transmission mode is a more representative sampling of the tablets compared with the backscatter mode. Capsules containing mixtures of pharmaceutical powders were also assessed by transmission only. The quantitative results for the capsules' contents were good, with a prediction error of 3.6% w/w for an independent test set. The advantage of transmission Raman over backscatter Raman spectroscopy has been demonstrated for quantitative analysis of pharmaceutical formulations, and the prospects for reliable, lean calibrations for pharmaceutical analysis is discussed.

[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method].

PubMed

Wang, L; Qin, X C; Lin, H C; Deng, K F; Luo, Y W; Sun, Q R; Du, Q X; Wang, Z Y; Tuo, Y; Sun, J H

2018-02-01

To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method. Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method. The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （ R ²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R ² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h. The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Analysis of Learning Curve Fitting Techniques.

DTIC Science & Technology

1987-09-01

1986. 15. Neter, John and others. Applied Linear Regression Models. Homewood IL: Irwin, 19-33. 16. SAS User’s Guide: Basics, Version 5 Edition. SAS... Linear Regression Techniques (15:23-52). Random errors are assumed to be normally distributed when using -# ordinary least-squares, according to Johnston...lot estimated by the improvement curve formula. For a more detailed explanation of the ordinary least-squares technique, see Neter, et. al., Applied

First-Order System Least-Squares for the Navier-Stokes Equations

NASA Technical Reports Server (NTRS)

Bochev, P.; Cai, Z.; Manteuffel, T. A.; McCormick, S. F.

1996-01-01

This paper develops a least-squares approach to the solution of the incompressible Navier-Stokes equations in primitive variables. As with our earlier work on Stokes equations, we recast the Navier-Stokes equations as a first-order system by introducing a velocity flux variable and associated curl and trace equations. We show that the resulting system is well-posed, and that an associated least-squares principle yields optimal discretization error estimates in the H(sup 1) norm in each variable (including the velocity flux) and optimal multigrid convergence estimates for the resulting algebraic system.

Source Attribution of Cyanides Using Anionic Impurity Profiling, Stable Isotope Ratios, Trace Elemental Analysis and Chemometrics.

PubMed

Mirjankar, Nikhil S; Fraga, Carlos G; Carman, April J; Moran, James J

2016-02-02

Chemical attribution signatures (CAS) for chemical threat agents (CTAs), such as cyanides, are being investigated to provide an evidentiary link between CTAs and specific sources to support criminal investigations and prosecutions. Herein, stocks of KCN and NaCN were analyzed for trace anions by high performance ion chromatography (HPIC), carbon stable isotope ratio (δ(13)C) by isotope ratio mass spectrometry (IRMS), and trace elements by inductively coupled plasma optical emission spectroscopy (ICP-OES). The collected analytical data were evaluated using hierarchical cluster analysis (HCA), Fisher-ratio (F-ratio), interval partial least-squares (iPLS), genetic algorithm-based partial least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN), and support vector machines discriminant analysis (SVMDA). HCA of anion impurity profiles from multiple cyanide stocks from six reported countries of origin resulted in cyanide samples clustering into three groups, independent of the associated alkali metal (K or Na). The three groups were independently corroborated by HCA of cyanide elemental profiles and corresponded to countries each having one known solid cyanide factory: Czech Republic, Germany, and United States. Carbon stable isotope measurements resulted in two clusters: Germany and United States (the single Czech stock grouped with United States stocks). Classification errors for two validation studies using anion impurity profiles collected over five years on different instruments were as low as zero for KNN and SVMDA, demonstrating the excellent reliability associated with using anion impurities for matching a cyanide sample to its factory using our current cyanide stocks. Variable selection methods reduced errors for those classification methods having errors greater than zero; iPLS-forward selection and F-ratio typically provided the lowest errors. Finally, using anion profiles to classify cyanides to a specific stock or stock group for a subset of United States stocks resulted in cross-validation errors ranging from 0 to 5.3%.

Application of least median of squared orthogonal distance (LMD) and LMD-based reweighted least squares (RLS) methods on the stock-recruitment relationship

NASA Astrophysics Data System (ADS)

Wang, Yan-Jun; Liu, Qun

1999-03-01

Analysis of stock-recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD-based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.

Network Adjustment of Orbit Errors in SAR Interferometry

NASA Astrophysics Data System (ADS)

Bahr, Hermann; Hanssen, Ramon

2010-03-01

Orbit errors can induce significant long wavelength error signals in synthetic aperture radar (SAR) interferograms and thus bias estimates of wide-scale deformation phenomena. The presented approach aims for correcting orbit errors in a preprocessing step to deformation analysis by modifying state vectors. Whereas absolute errors in the orbital trajectory are negligible, the influence of relative errors (baseline errors) is parametrised by their parallel and perpendicular component as a linear function of time. As the sensitivity of the interferometric phase is only significant with respect to the perpendicular base-line and the rate of change of the parallel baseline, the algorithm focuses on estimating updates to these two parameters. This is achieved by a least squares approach, where the unwrapped residual interferometric phase is observed and atmospheric contributions are considered to be stochastic with constant mean. To enhance reliability, baseline errors are adjusted in an overdetermined network of interferograms, yielding individual orbit corrections per acquisition.

Maximum Likelihood Time-of-Arrival Estimation of Optical Pulses via Photon-Counting Photodetectors

NASA Technical Reports Server (NTRS)

Erkmen, Baris I.; Moision, Bruce E.

2010-01-01

Many optical imaging, ranging, and communications systems rely on the estimation of the arrival time of an optical pulse. Recently, such systems have been increasingly employing photon-counting photodetector technology, which changes the statistics of the observed photocurrent. This requires time-of-arrival estimators to be developed and their performances characterized. The statistics of the output of an ideal photodetector, which are well modeled as a Poisson point process, were considered. An analytical model was developed for the mean-square error of the maximum likelihood (ML) estimator, demonstrating two phenomena that cause deviations from the minimum achievable error at low signal power. An approximation was derived to the threshold at which the ML estimator essentially fails to provide better than a random guess of the pulse arrival time. Comparing the analytic model performance predictions to those obtained via simulations, it was verified that the model accurately predicts the ML performance over all regimes considered. There is little prior art that attempts to understand the fundamental limitations to time-of-arrival estimation from Poisson statistics. This work establishes both a simple mathematical description of the error behavior, and the associated physical processes that yield this behavior. Previous work on mean-square error characterization for ML estimators has predominantly focused on additive Gaussian noise. This work demonstrates that the discrete nature of the Poisson noise process leads to a distinctly different error behavior.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

The perils of the imperfect expectation of the perfect baby.

PubMed

Chervenak, Frank A; McCullough, Laurence B; Brent, Robert L

2010-08-01

Advances in modern medicine invite the assumption that medicine can control human biology. There is a perilous logic that leads from expectations of medicine's control over reproductive biology to the expectation of having a perfect baby. This article proposes that obstetricians should take a preventive ethics approach to the care of pregnant women with expectations for a perfect baby. We use Nathaniel Hawthorne's classic short story, "The Birthmark," to illustrate the perils of the logic of control and perfection through science and then identify possible contemporary sources of the expectation of the perfect baby. We propose that the informed consent process should be used as a preventive ethics tool throughout the course of pregnancy to educate pregnant women about the inherent errors of human reproduction, the highly variable clinical outcomes of these errors, the limited capacity of medicine to detect these errors, and the even more limited capacity to correct them. Copyright (c) 2010 Mosby, Inc. All rights reserved.

Neural self-tuning adaptive control of non-minimum phase system

NASA Technical Reports Server (NTRS)

Ho, Long T.; Bialasiewicz, Jan T.; Ho, Hai T.

1993-01-01

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

A channel estimation scheme for MIMO-OFDM systems

NASA Astrophysics Data System (ADS)

He, Chunlong; Tian, Chu; Li, Xingquan; Zhang, Ce; Zhang, Shiqi; Liu, Chaowen

2017-08-01

In view of the contradiction of the time-domain least squares (LS) channel estimation performance and the practical realization complexity, a reduced complexity channel estimation method for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) based on pilot is obtained. This approach can transform the complexity of MIMO-OFDM channel estimation problem into a simple single input single output-orthogonal frequency division multiplexing (SISO-OFDM) channel estimation problem and therefore there is no need for large matrix pseudo-inverse, which greatly reduces the complexity of algorithms. Simulation results show that the bit error rate (BER) performance of the obtained method with time orthogonal training sequences and linear minimum mean square error (LMMSE) criteria is better than that of time-domain LS estimator and nearly optimal performance.

On the robustness of a Bayes estimate. [in reliability theory

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1974-01-01

This paper examines the robustness of a Bayes estimator with respect to the assigned prior distribution. A Bayesian analysis for a stochastic scale parameter of a Weibull failure model is summarized in which the natural conjugate is assigned as the prior distribution of the random parameter. The sensitivity analysis is carried out by the Monte Carlo method in which, although an inverted gamma is the assigned prior, realizations are generated using distribution functions of varying shape. For several distributional forms and even for some fixed values of the parameter, simulated mean squared errors of Bayes and minimum variance unbiased estimators are determined and compared. Results indicate that the Bayes estimator remains squared-error superior and appears to be largely robust to the form of the assigned prior distribution.

Observation and analysis of high-speed human motion with frequent occlusion in a large area

NASA Astrophysics Data System (ADS)

Wang, Yuru; Liu, Jiafeng; Liu, Guojun; Tang, Xianglong; Liu, Peng

2009-12-01

The use of computer vision technology in collecting and analyzing statistics during sports matches or training sessions is expected to provide valuable information for tactics improvement. However, the measurements published in the literature so far are either unreliably documented to be used in training planning due to their limitations or unsuitable for studying high-speed motion in large area with frequent occlusions. A sports annotation system is introduced in this paper for tracking high-speed non-rigid human motion over a large playing area with the aid of motion camera, taking short track speed skating competitions as an example. The proposed system is composed of two sub-systems: precise camera motion compensation and accurate motion acquisition. In the video registration step, a distinctive invariant point feature detector (probability density grads detector) and a global parallax based matching points filter are used, to provide reliable and robust matching across a large range of affine distortion and illumination change. In the motion acquisition step, a two regions' relationship constrained joint color model and Markov chain Monte Carlo based joint particle filter are emphasized, by dividing the human body into two relative key regions. Several field tests are performed to assess measurement errors, including comparison to popular algorithms. With the help of the system presented, the system obtains position data on a 30 m × 60 m large rink with root-mean-square error better than 0.3975 m, velocity and acceleration data with absolute error better than 1.2579 m s-1 and 0.1494 m s-2, respectively.

Trends in statistical methods in articles published in Archives of Plastic Surgery between 2012 and 2017.

PubMed

Han, Kyunghwa; Jung, Inkyung

2018-05-01

This review article presents an assessment of trends in statistical methods and an evaluation of their appropriateness in articles published in the Archives of Plastic Surgery (APS) from 2012 to 2017. We reviewed 388 original articles published in APS between 2012 and 2017. We categorized the articles that used statistical methods according to the type of statistical method, the number of statistical methods, and the type of statistical software used. We checked whether there were errors in the description of statistical methods and results. A total of 230 articles (59.3%) published in APS between 2012 and 2017 used one or more statistical method. Within these articles, there were 261 applications of statistical methods with continuous or ordinal outcomes, and 139 applications of statistical methods with categorical outcome. The Pearson chi-square test (17.4%) and the Mann-Whitney U test (14.4%) were the most frequently used methods. Errors in describing statistical methods and results were found in 133 of the 230 articles (57.8%). Inadequate description of P-values was the most common error (39.1%). Among the 230 articles that used statistical methods, 71.7% provided details about the statistical software programs used for the analyses. SPSS was predominantly used in the articles that presented statistical analyses. We found that the use of statistical methods in APS has increased over the last 6 years. It seems that researchers have been paying more attention to the proper use of statistics in recent years. It is expected that these positive trends will continue in APS.

Comparison of three-dimensional parameters of Halo CMEs using three cone models

NASA Astrophysics Data System (ADS)

Na, H.; Moon, Y.; Jang, S.; Lee, K.

2012-12-01

Halo coronal mass ejections (HCMEs) are a major cause of geomagnetic storms and their three dimensional structures are important for space weather. In this study, we compare three cone models: an elliptical cone model, an ice-cream cone model, and an asymmetric cone model. These models allow us to determine the three dimensional parameters of HCMEs such as radial speed, angular width, and the angle (γ) between sky plane and cone axis. We compare these parameters obtained from three models using 62 well-observed HCMEs observed by SOHO/LASCO from 2001 to 2002. Then we obtain the root mean square error (RMS error) between maximum measured projection speeds and their calculated projection speeds from the cone models. As a result, we find that the radial speeds obtained from the models are well correlated with one another (R > 0.84). The correlation coefficients between angular widths are ranges from 0.04 to 0.53 and those between γ values are from -0.15 to 0.47, which are much smaller than expected. The reason may be due to different assumptions and methods. The RMS errors between the maximum measured projection speeds and the maximum estimated projection speeds of the elliptical cone model, the ice-cream cone model, and the asymmetric cone model are 213 km/s, 254 km/s, and 267 km/s, respectively. And we obtain the correlation coefficients between the location from the models and the flare location (R > 0.75). Finally, we discuss strengths and weaknesses of these models in terms of space weather application.

The first US Naval Observatory CCD Astrograph Catalog

NASA Astrophysics Data System (ADS)

Germain, M. E.; Zacharias, N.; Urban, S. E.; Rafferty, T. J.; Holdenried, E. R.; Zacharias, M. I.; Hall, D. M.; Wycoff, G. L.; Monet, D. G.

2000-05-01

The USNO CCD Astrograph Catalog (UCAC) project is a high precision, astrometric survey of stars having R magnitudes between 7th and 16th. The positional accuracy is 20 mas for stars between 9th and 14th, and 70 mas for fainter stars. This gives a density (stars per square degree) higher than that of the Guide Star Catalog (GSC), with an improvement in positional accuracy of about a factor of ten. Observations began in January 1998 at Cerro Tololo Inter--American Observatory (CTIO) using a five-element 0.2 meter astrograph equipped with a 4k by 4k CCD. The instrument will be moved north in early 2001, and full sky coverage is expected by early 2003. A preliminary catalog (UCAC1) of positions and proper motions of 27 million stars has been constructed which is available on CD-ROM from USNO. Observations between 13 Feb 1998 and 07 Nov 1999 are included with a total of over 79,000 CCD frames covering 80% of the Southern Hemisphere. The catalog is on the International Celestial Reference System (ICRS), which is consistent with J2000. Proper motions of bright stars (V <= 12.5) were derived using a combination of ground-based astrometric catalogs, Hipparcos, and Tycho-2 positions, giving a typical error of 3 mas/yr. For the fainter stars the USNO A2.0 (Monet, 1998) was used as first epoch, with typical proper motion errors of 10 to 15 mas/yr. External comparisons with Tycho-2 and the Yale Southern Proper Motion (SPM) 2.0 data reveal systematic errors to be only on the 10 mas level.

Surface Downward Longwave Radiation Retrieval Algorithm for GEO-KOMPSAT-2A/AMI

NASA Astrophysics Data System (ADS)

Ahn, Seo-Hee; Lee, Kyu-Tae; Rim, Se-Hun; Zo, Il-Sung; Kim, Bu-Yo

2018-05-01

This study contributes to the development of an algorithm to retrieve the Earth's surface downward longwave radiation (DLR) for 2nd Geostationary Earth Orbit KOrea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A)/Advanced Meteorological Imager (AMI). Regarding simulation data for algorithm development, we referred to Clouds and the Earth's Radiant Energy System (CERES), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim reanalysis data. The clear sky DLR calculations were in good agreement with the Gangneung-Wonju National University (GWNU) Line-By-Line (LBL) model. Compared with CERES data, the Root Mean Square Error (RMSE) was 10.14Wm-2. In the case of cloudy sky DLR, we estimated the cloud base temperature empirically by utilizing cloud liquid water content (LWC) according to the cloud type. As a result, the correlation coefficients with CERES all sky DLRs were greater than 0.99. However, the RMSE between calculated DLR and CERES data was about 16.67Wm-2, due to ice clouds and problems of mismatched spatial and temporal resolutions for input data. This error may be reduced when GK-2A is launched and its products can be used as input data. Accordingly, further study is needed to improve the accuracy of DLR calculation by using high-resolution input data. In addition, when compared with BSRN surface-based observational data and retrieved DLR for all sky, the correlation coefficient was 0.86 and the RMSE was 31.55 Wm-2, which indicates relatively high accuracy. It is expected that increasing the number of experimental Cases will reduce the error.

Effects of expected-value information and display format on recognition of aircraft subsystem abnormalities

NASA Technical Reports Server (NTRS)

Palmer, Michael T.; Abbott, Kathy H.

1994-01-01

This study identifies improved methods to present system parameter information for detecting abnormal conditions and to identify system status. Two workstation experiments were conducted. The first experiment determined if including expected-value-range information in traditional parameter display formats affected subject performance. The second experiment determined if using a nontraditional parameter display format, which presented relative deviation from expected value, was better than traditional formats with expected-value ranges included. The inclusion of expected-value-range information onto traditional parameter formats was found to have essentially no effect. However, subjective results indicated support for including this information. The nontraditional column deviation parameter display format resulted in significantly fewer errors compared with traditional formats with expected-value-ranges included. In addition, error rates for the column deviation parameter display format remained stable as the scenario complexity increased, whereas error rates for the traditional parameter display formats with expected-value ranges increased. Subjective results also indicated that the subjects preferred this new format and thought that their performance was better with it. The column deviation parameter display format is recommended for display applications that require rapid recognition of out-of-tolerance conditions, especially for a large number of parameters.

Effects of Correlated Errors on the Analysis of Space Geodetic Data

NASA Technical Reports Server (NTRS)

Romero-Wolf, Andres; Jacobs, C. S.

2011-01-01

As thermal errors are reduced instrumental and troposphere correlated errors will increasingly become more important. Work in progress shows that troposphere covariance error models improve data analysis results. We expect to see stronger effects with higher data rates. Temperature modeling of delay errors may further reduce temporal correlations in the data.

The Mentoring Competency Assessment: Validation of a New Instrument to Evaluate Skills of Research Mentors

PubMed Central

Fleming, Michael; House, Stephanie; Shewakramani, Vansa; Yu, Lan; Garbutt, Jane; McGee, Richard; Kroenke, Kurt; Abedin, Zainab; Rubio, Doris M.

2013-01-01

Purpose To determine the psychometric properties of the Mentoring Competency Assessment (MCA), a 26-item skills inventory that enables research mentors and mentees to evaluate six competencies of mentors: maintaining effective communication, aligning expectations, assessing understanding, addressing diversity, and fostering independence promoting professional development. Method In 2010, investigators administered the MCA to 283 mentor–mentee pairs from 16 universities participating in a trial of a mentoring curriculum for clinical and translational research mentors. The authors analyzed baseline MCA data to describe the instrument’s psychometric properties. Results Coefficient alpha scores for the MCA showed reliability (internal consistency). The hypothesized model with its six latent constructs (competencies) resulted in an acceptable fit to the data. For the instrument completed by mentors, chi-square = 663.20; df = 284; P < .001; root mean square error of approximation (RMSEA) = 0.069 (90% CI, 0.062–0.076); confirmatory fit index (CFI) = 0.85; and Tucker-Lewis index (TLI) = 0.83. For the instrument completed by mentees, chi-square = 840.62; df = 284; P < .001; RMSEA = 0.080 (90% CI, 0.063–0.077); CFI = 0.87; and TLI = 0.85. The correlations among the six competencies were high: 0.49–0.87 for mentors, 0.58–0.92 for mentees. All parameter estimates for the individual items were significant; standardized factor loadings ranged from 0.32–0.81 for mentors and 0.56–0.86 for mentees. Conclusions The findings demonstrate that the MCA has reliability and validity. In addition, this study provides preliminary norms derived from a national sample of mentors and mentees. PMID:23702534

Psychometric Properties and Correlates of the Beck Hopelessness Scale in Family Caregivers of Nigerian Patients with Psychiatric Disorders in Southwestern Nigeria

PubMed Central

Aloba, Olutayo; Ajao, Olayinka; Alimi, Taiwo; Esan, Olufemi

2016-01-01

Objectives: To examine the construct and correlates of hopelessness among family caregivers of Nigerian psychiatric patients. Materials and Methods: This is a cross-sectional, descriptive study involving 264 family caregiver-patients’ dyads recruited from two university teaching hospitals psychiatric clinics in Southwestern Nigeria. Results: Exploratory factor analysis revealed a two-factor 9-item model of the Beck Hopelessness Scale (BHS) among the family caregivers. Confirmatory factor analysis of the model revealed satisfactory indices of fitness (goodness of fit index = 0.97, comparative fit index = 0.96, Chi-square/degree of freedom (CMIN/DF) = 1.60, root mean square error of approximation = 0.048, expected cross-validation index = 0.307, and standardized root mean residual = 0.005). Reliability of the scale was modestly satisfactory (Cronbach's alpha 0.72). Construct validity of scale was supported by significant correlations with the family caregivers’ scores on the Zarit Burden Interview, mini international neuropsychiatric interview suicidality module, General Health Questionnaire-12 (GHQ-12), and Patient Health Questionnaire-9. The greatest variance in the family caregivers’ scores on the BHS was contributed by their scores on the psychological distress scale (GHQ-12). Conclusions: The BHS has adequate psychometric properties among Nigerian psychiatric patients’ family caregivers. There is the need to pay attention to the psychological well-being of the family caregivers of Nigerian psychiatric patients. PMID:28163498

Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

PubMed

Catana, Cornel; Gao, Hua; Orrenius, Christian; Stouten, Pieter F W

2005-01-01

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

An Unbiased Estimator of Gene Diversity with Improved Variance for Samples Containing Related and Inbred Individuals of any Ploidy

PubMed Central

Harris, Alexandre M.; DeGiorgio, Michael

2016-01-01

Gene diversity, or expected heterozygosity (H), is a common statistic for assessing genetic variation within populations. Estimation of this statistic decreases in accuracy and precision when individuals are related or inbred, due to increased dependence among allele copies in the sample. The original unbiased estimator of expected heterozygosity underestimates true population diversity in samples containing relatives, as it only accounts for sample size. More recently, a general unbiased estimator of expected heterozygosity was developed that explicitly accounts for related and inbred individuals in samples. Though unbiased, this estimator’s variance is greater than that of the original estimator. To address this issue, we introduce a general unbiased estimator of gene diversity for samples containing related or inbred individuals, which employs the best linear unbiased estimator of allele frequencies, rather than the commonly used sample proportion. We examine the properties of this estimator, H∼BLUE, relative to alternative estimators using simulations and theoretical predictions, and show that it predominantly has the smallest mean squared error relative to others. Further, we empirically assess the performance of H∼BLUE on a global human microsatellite dataset of 5795 individuals, from 267 populations, genotyped at 645 loci. Additionally, we show that the improved variance of H∼BLUE leads to improved estimates of the population differentiation statistic, FST, which employs measures of gene diversity within its calculation. Finally, we provide an R script, BestHet, to compute this estimator from genomic and pedigree data. PMID:28040781

Pearson-type goodness-of-fit test with bootstrap maximum likelihood estimation.

PubMed

Yin, Guosheng; Ma, Yanyuan

2013-01-01

The Pearson test statistic is constructed by partitioning the data into bins and computing the difference between the observed and expected counts in these bins. If the maximum likelihood estimator (MLE) of the original data is used, the statistic generally does not follow a chi-squared distribution or any explicit distribution. We propose a bootstrap-based modification of the Pearson test statistic to recover the chi-squared distribution. We compute the observed and expected counts in the partitioned bins by using the MLE obtained from a bootstrap sample. This bootstrap-sample MLE adjusts exactly the right amount of randomness to the test statistic, and recovers the chi-squared distribution. The bootstrap chi-squared test is easy to implement, as it only requires fitting exactly the same model to the bootstrap data to obtain the corresponding MLE, and then constructs the bin counts based on the original data. We examine the test size and power of the new model diagnostic procedure using simulation studies and illustrate it with a real data set.

Four Types of Pulse Oximeters Accurately Detect Hypoxia during Low Perfusion and Motion.

PubMed

Louie, Aaron; Feiner, John R; Bickler, Philip E; Rhodes, Laura; Bernstein, Michael; Lucero, Jennifer

2018-03-01

Pulse oximeter performance is degraded by motion artifacts and low perfusion. Manufacturers developed algorithms to improve instrument performance during these challenges. There have been no independent comparisons of these devices. We evaluated the performance of four pulse oximeters (Masimo Radical-7, USA; Nihon Kohden OxyPal Neo, Japan; Nellcor N-600, USA; and Philips Intellivue MP5, USA) in 10 healthy adult volunteers. Three motions were evaluated: tapping, pseudorandom, and volunteer-generated rubbing, adjusted to produce photoplethsmogram disturbance similar to arterial pulsation amplitude. During motion, inspired gases were adjusted to achieve stable target plateaus of arterial oxygen saturation (SaO2) at 75%, 88%, and 100%. Pulse oximeter readings were compared with simultaneous arterial blood samples to calculate bias (oxygen saturation measured by pulse oximetry [SpO2] - SaO2), mean, SD, 95% limits of agreement, and root mean square error. Receiver operating characteristic curves were determined to detect mild (SaO2 < 90%) and severe (SaO2 < 80%) hypoxemia. Pulse oximeter readings corresponding to 190 blood samples were analyzed. All oximeters detected hypoxia but motion and low perfusion degraded performance. Three of four oximeters (Masimo, Nellcor, and Philips) had root mean square error greater than 3% for SaO2 70 to 100% during any motion, compared to a root mean square error of 1.8% for the stationary control. A low perfusion index increased error. All oximeters detected hypoxemia during motion and low-perfusion conditions, but motion impaired performance at all ranges, with less accuracy at lower SaO2. Lower perfusion degraded performance in all but the Nihon Kohden instrument. We conclude that different types of pulse oximeters can be similarly effective in preserving sensitivity to clinically relevant hypoxia.

Estimation of water level and steam temperature using ensemble Kalman filter square root (EnKF-SR)

NASA Astrophysics Data System (ADS)

Herlambang, T.; Mufarrikoh, Z.; Karya, D. F.; Rahmalia, D.

2018-04-01

The equipment unit which has the most vital role in the steam-powered electric power plant is boiler. Steam drum boiler is a tank functioning to separate fluida into has phase and liquid phase. The existence in boiler system has a vital role. The controlled variables in the steam drum boiler are water level and the steam temperature. If the water level is higher than the determined level, then the gas phase resulted will contain steam endangering the following process and making the resulted steam going to turbine get less, and the by causing damages to pipes in the boiler. On the contrary, if less than the height of determined water level, the resulted height will result in dry steam likely to endanger steam drum. Thus an error was observed between the determined. This paper studied the implementation of the Ensemble Kalman Filter Square Root (EnKF-SR) method in nonlinear model of the steam drum boiler equation. The computation to estimate the height of water level and the temperature of steam was by simulation using Matlab software. Thus an error was observed between the determined water level and the steam temperature, and that of estimated water level and steam temperature. The result of simulation by Ensemble Kalman Filter Square Root (EnKF-SR) on the nonlinear model of steam drum boiler showed that the error was less than 2%. The implementation of EnKF-SR on the steam drum boiler r model comprises of three simulations, each of which generates 200, 300 and 400 ensembles. The best simulation exhibited the error between the real condition and the estimated result, by generating 400 ensemble. The simulation in water level in order of 0.00002145 m, whereas in the steam temperature was some 0.00002121 kelvin.

Frontal Theta Links Prediction Errors to Behavioral Adaptation in Reinforcement Learning

PubMed Central

Cavanagh, James F.; Frank, Michael J.; Klein, Theresa J.; Allen, John J.B.

2009-01-01

Investigations into action monitoring have consistently detailed a fronto-central voltage deflection in the Event-Related Potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the Feedback Related Negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Medio-frontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations: with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice. PMID:19969093

Demand forecasting of electricity in Indonesia with limited historical data

NASA Astrophysics Data System (ADS)

Dwi Kartikasari, Mujiati; Rohmad Prayogi, Arif

2018-03-01

Demand forecasting of electricity is an important activity for electrical agents to know the description of electricity demand in future. Prediction of demand electricity can be done using time series models. In this paper, double moving average model, Holt’s exponential smoothing model, and grey model GM(1,1) are used to predict electricity demand in Indonesia under the condition of limited historical data. The result shows that grey model GM(1,1) has the smallest value of MAE (mean absolute error), MSE (mean squared error), and MAPE (mean absolute percentage error).

Low complexity adaptive equalizers for underwater acoustic communications

NASA Astrophysics Data System (ADS)

Soflaei, Masoumeh; Azmi, Paeiz

2014-08-01

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor APA(SR-APA), family of selective partial update (SPU) algorithms, family of set-membership (SM) algorithms and selective partial update selective regressor APA (SPU-SR-APA) are compared with conventional algorithms such as the least mean square (LMS) in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA, SPU-APA, SPU-normalized least mean square (SPU-NLMS), SPU-SR-APA, SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

Multiple point least squares equalization in a room

NASA Technical Reports Server (NTRS)

Elliott, S. J.; Nelson, P. A.

1988-01-01

Equalization filters designed to minimize the mean square error between a delayed version of the original electrical signal and the equalized response at a point in a room have previously been investigated. In general, such a strategy degrades the response at positions in a room away from the equalization point. A method is presented for designing an equalization filter by adjusting the filter coefficients to minimize the sum of the squares of the errors between the equalized responses at multiple points in the room and delayed versions of the original, electrical signal. Such an equalization filter can give a more uniform frequency response over a greater volume of the enclosure than can the single point equalizer above. Computer simulation results are presented of equalizing the frequency responses from a loudspeaker to various typical ear positions, in a room with dimensions and acoustic damping typical of a car interior, using the two approaches outlined above. Adaptive filter algorithms, which can automatically adjust the coefficients of a digital equalization filter to achieve this minimization, will also be discussed.

A deep proper motion catalog within the Sloan digital sky survey footprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Munn, Jeffrey A.; Harris, Hugh C.; Tilleman, Trudy M.

2014-12-01

A new proper motion catalog is presented, combining the Sloan Digital Sky Survey (SDSS) with second epoch observations in the r band within a portion of the SDSS imaging footprint. The new observations were obtained with the 90prime camera on the Steward Observatory Bok 90 inch telescope, and the Array Camera on the U.S. Naval Observatory, Flagstaff Station, 1.3 m telescope. The catalog covers 1098 square degrees to r = 22.0, an additional 1521 square degrees to r = 20.9, plus a further 488 square degrees of lesser quality data. Statistical errors in the proper motions range from 5 masmore » year{sup −1} at the bright end to 15 mas year{sup −1} at the faint end, for a typical epoch difference of six years. Systematic errors are estimated to be roughly 1 mas year{sup −1} for the Array Camera data, and as much as 2–4 mas year{sup −1} for the 90prime data (though typically less). The catalog also includes a second epoch of r band photometry.« less

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression.

PubMed

Hao, Z Q; Li, C M; Shen, M; Yang, X Y; Li, K H; Guo, L B; Li, X Y; Lu, Y F; Zeng, X Y

2015-03-23

Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.

[Study on predicting sugar content and valid acidity of apples by near infrared diffuse reflectance technique].

PubMed

Liu, Yan-de; Ying, Yi-bin; Fu, Xia-ping

2005-11-01

The nondestructive method for quantifying sugar content (SC) and available acid (VA) of intact apples using diffuse near infrared reflectance and optical fiber sensing techniques were explored in the present research. The standard sample sets and prediction models were established by partial least squares analysis (PLS). A total of 120 Shandong Fuji apples were tested in the wave number of 12,500 - 4000 cm(-1) using Fourier transform near infrared spectroscopy. The results of the research indicated that the nondestructive quantification of SC and VA, gave a high correlation coefficient 0.970 and 0.906, a low root mean square error of prediction (RMSEP) 0.272 and 0.056 2, a low root mean square error of calibration (RMSEC) 0.261 and 0.0677, and a small difference between RMSEP and RMSEC 0.011 a nd 0.0115. It was suggested that the diffuse nearinfrared reflectance technique be feasible for nondestructive determination of apple sugar content in the wave number range of 10,341 - 5461 cm(-1) and for available acid in the wave number range of 10,341 - 3818 cm(-1).

Analysis of Lard in Lipstick Formulation Using FTIR Spectroscopy and Multivariate Calibration: A Comparison of Three Extraction Methods.

PubMed

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.

Detection of Butter Adulteration with Lard by Employing (1)H-NMR Spectroscopy and Multivariate Data Analysis.

PubMed

Fadzillah, Nurrulhidayah Ahmad; Man, Yaakob bin Che; Rohman, Abdul; Rosman, Arieff Salleh; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2015-01-01

The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.

Single-Event Effect Performance of a Conductive-Bridge Memory EEPROM

NASA Technical Reports Server (NTRS)

Chen, Dakai; Wilcox, Edward; Berg, Melanie; Kim, Hak; Phan, Anthony; Figueiredo, Marco; Seidleck, Christina; LaBel, Kenneth

2015-01-01

We investigated the heavy ion single-event effect (SEE) susceptibility of the industry’s first stand-alone memory based on conductive-bridge memory (CBRAM) technology. The device is available as an electrically erasable programmable read-only memory (EEPROM). We found that single-event functional interrupt (SEFI) is the dominant SEE type for each operational mode (standby, dynamic read, and dynamic write/read). SEFIs occurred even while the device is statically biased in standby mode. Worst case SEFIs resulted in errors that filled the entire memory space. Power cycle did not always clear the errors. Thus the corrupted cells had to be reprogrammed in some cases. The device is also vulnerable to bit upsets during dynamic write/read tests, although the frequency of the upsets are relatively low. The linear energy transfer threshold for cell upset is between 10 and 20 megaelectron volts per square centimeter per milligram, with an upper limit cross section of 1.6 times 10(sup -11) square centimeters per bit (95 percent confidence level) at 10 megaelectronvolts per square centimeter per milligram. In standby mode, the CBRAM array appears invulnerable to bit upsets.

Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample.

PubMed

Le, Quang A; Doctor, Jason N

2011-05-01

As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain quality-adjusted life years has become a solution when health utilities are not directly available. However, current mapping methods are limited due to their predictive validity, reliability, and/or other methodological issues. We employ probability theory together with a graphical model, called a Bayesian network, to convert health-profile measures into preference-based measures and to compare the results to those estimated with current mapping methods. A sample of 19,678 adults who completed both the 12-item Short Form Health Survey (SF-12v2) and EuroQoL 5D (EQ-5D) questionnaires from the 2003 Medical Expenditure Panel Survey was split into training and validation sets. Bayesian networks were constructed to explore the probabilistic relationships between each EQ-5D domain and 12 items of the SF-12v2. The EQ-5D utility scores were estimated on the basis of the predicted probability of each response level of the 5 EQ-5D domains obtained from the Bayesian inference process using the following methods: Monte Carlo simulation, expected utility, and most-likely probability. Results were then compared with current mapping methods including multinomial logistic regression, ordinary least squares, and censored least absolute deviations. The Bayesian networks consistently outperformed other mapping models in the overall sample (mean absolute error=0.077, mean square error=0.013, and R overall=0.802), in different age groups, number of chronic conditions, and ranges of the EQ-5D index. Bayesian networks provide a new robust and natural approach to map health status responses into health utility measures for health economic evaluations.

Estimation of flood discharges at selected annual exceedance probabilities for unregulated, rural streams in Vermont, with a section on Vermont regional skew regression

USGS Publications Warehouse

Olson, Scott A.; with a section by Veilleux, Andrea G.

2014-01-01

This report provides estimates of flood discharges at selected annual exceedance probabilities (AEPs) for streamgages in and adjacent to Vermont and equations for estimating flood discharges at AEPs of 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent (recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-years, respectively) for ungaged, unregulated, rural streams in Vermont. The equations were developed using generalized least-squares regression. Flood-frequency and drainage-basin characteristics from 145 streamgages were used in developing the equations. The drainage-basin characteristics used as explanatory variables in the regression equations include drainage area, percentage of wetland area, and the basin-wide mean of the average annual precipitation. The average standard errors of prediction for estimating the flood discharges at the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEP with these equations are 34.9, 36.0, 38.7, 42.4, 44.9, 47.3, 50.7, and 55.1 percent, respectively. Flood discharges at selected AEPs for streamgages were computed by using the Expected Moments Algorithm. To improve estimates of the flood discharges for given exceedance probabilities at streamgages in Vermont, a new generalized skew coefficient was developed. The new generalized skew for the region is a constant, 0.44. The mean square error of the generalized skew coefficient is 0.078. This report describes a technique for using results from the regression equations to adjust an AEP discharge computed from a streamgage record. This report also describes a technique for using a drainage-area adjustment to estimate flood discharge at a selected AEP for an ungaged site upstream or downstream from a streamgage. The final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is a World Wide Web application providing automated regression-equation solutions for user-selected sites on streams.

Stable estimate of primary OC/EC ratios in the EC tracer method

NASA Astrophysics Data System (ADS)

Chu, Shao-Hang

In fine particulate matter studies, the primary OC/EC ratio plays an important role in estimating the secondary organic aerosol contribution to PM2.5 concentrations using the EC tracer method. In this study, numerical experiments are carried out to test and compare various statistical techniques in the estimation of primary OC/EC ratios. The influence of random measurement errors in both primary OC and EC measurements on the estimation of the expected primary OC/EC ratios is examined. It is found that random measurement errors in EC generally create an underestimation of the slope and an overestimation of the intercept of the ordinary least-squares regression line. The Deming regression analysis performs much better than the ordinary regression, but it tends to overcorrect the problem by slightly overestimating the slope and underestimating the intercept. Averaging the ratios directly is usually undesirable because the average is strongly influenced by unrealistically high values of OC/EC ratios resulting from random measurement errors at low EC concentrations. The errors generally result in a skewed distribution of the OC/EC ratios even if the parent distributions of OC and EC are close to normal. When measured OC contains a significant amount of non-combustion OC Deming regression is a much better tool and should be used to estimate both the primary OC/EC ratio and the non-combustion OC. However, if the non-combustion OC is negligibly small the best and most robust estimator of the OC/EC ratio turns out to be the simple ratio of the OC and EC averages. It not only reduces random errors by averaging individual variables separately but also acts as a weighted average of ratios to minimize the influence of unrealistically high OC/EC ratios created by measurement errors at low EC concentrations. The median of OC/EC ratios ranks a close second, and the geometric mean of ratios ranks third. This is because their estimations are insensitive to questionable extreme values. A real world example is given using the ambient data collected from an Atlanta STN site during the winter of 2001-2002.

Near-infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Parching Process of Traditional Chinese Medicine Based on Two Kinds of Chemical Indicators.

PubMed

Li, Kaiyue; Wang, Weiying; Liu, Yanping; Jiang, Su; Huang, Guo; Ye, Liming

2017-01-01

The active ingredients and thus pharmacological efficacy of traditional Chinese medicine (TCM) at different degrees of parching process vary greatly. Near-infrared spectroscopy (NIR) was used to develop a new method for rapid online analysis of TCM parching process, using two kinds of chemical indicators (5-(hydroxymethyl) furfural [5-HMF] content and 420 nm absorbance) as reference values which were obviously observed and changed in most TCM parching process. Three representative TCMs, Areca ( Areca catechu L.), Malt ( Hordeum Vulgare L.), and Hawthorn ( Crataegus pinnatifida Bge.), were used in this study. With partial least squares regression, calibration models of NIR were generated based on two kinds of reference values, i.e. 5-HMF contents measured by high-performance liquid chromatography (HPLC) and 420 nm absorbance measured by ultraviolet-visible spectroscopy (UV/Vis), respectively. In the optimized models for 5-HMF, the root mean square errors of prediction (RMSEP) for Areca, Malt, and Hawthorn was 0.0192, 0.0301, and 0.2600 and correlation coefficients ( R cal ) were 99.86%, 99.88%, and 99.88%, respectively. Moreover, in the optimized models using 420 nm absorbance as reference values, the RMSEP for Areca, Malt, and Hawthorn was 0.0229, 0.0096, and 0.0409 and R cal were 99.69%, 99.81%, and 99.62%, respectively. NIR models with 5-HMF content and 420 nm absorbance as reference values can rapidly and effectively identify three kinds of TCM in different parching processes. This method has great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online analysis and quality control in TCM industrial manufacturing process. Near-infrared spectroscopy.(NIR) was used to develop a new method for online analysis of traditional Chinese medicine.(TCM) parching processCalibration and validation models of Areca, Malt, and Hawthorn were generated by partial least squares regression using 5.(hydroxymethyl) furfural contents and 420.nm absorbance as reference values, respectively, which were main indicator components during parching process of most TCMThe established NIR models of three TCMs had low root mean square errors of prediction and high correlation coefficientsThe NIR method has great promise for use in TCM industrial manufacturing processes for rapid online analysis and quality control. Abbreviations used: NIR: Near-infrared Spectroscopy; TCM: Traditional Chinese medicine; Areca: Areca catechu L.; Hawthorn: Crataegus pinnatifida Bge.; Malt: Hordeum vulgare L.; 5-HMF: 5-(hydroxymethyl) furfural; PLS: Partial least squares; D: Dimension faction; SLS: Straight line subtraction, MSC: Multiplicative scatter correction; VN: Vector normalization; RMSECV: Root mean square errors of cross-validation; RMSEP: Root mean square errors of validation; R cal : Correlation coefficients; RPD: Residual predictive deviation; PAT: Process analytical technology; FDA: Food and Drug Administration; ICH: International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use.

Analytical probabilistic proton dose calculation and range uncertainties

NASA Astrophysics Data System (ADS)

Bangert, M.; Hennig, P.; Oelfke, U.

2014-03-01

We introduce the concept of analytical probabilistic modeling (APM) to calculate the mean and the standard deviation of intensity-modulated proton dose distributions under the influence of range uncertainties in closed form. For APM, range uncertainties are modeled with a multivariate Normal distribution p(z) over the radiological depths z. A pencil beam algorithm that parameterizes the proton depth dose d(z) with a weighted superposition of ten Gaussians is used. Hence, the integrals ∫ dz p(z) d(z) and ∫ dz p(z) d(z)2 required for the calculation of the expected value and standard deviation of the dose remain analytically tractable and can be efficiently evaluated. The means μk, widths δk, and weights ωk of the Gaussian components parameterizing the depth dose curves are found with least squares fits for all available proton ranges. We observe less than 0.3% average deviation of the Gaussian parameterizations from the original proton depth dose curves. Consequently, APM yields high accuracy estimates for the expected value and standard deviation of intensity-modulated proton dose distributions for two dimensional test cases. APM can accommodate arbitrary correlation models and account for the different nature of random and systematic errors in fractionated radiation therapy. Beneficial applications of APM in robust planning are feasible.

Reinforcement Learning Models and Their Neural Correlates: An Activation Likelihood Estimation Meta-Analysis

PubMed Central

Kumar, Poornima; Eickhoff, Simon B.; Dombrovski, Alexandre Y.

2015-01-01

Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments – prediction error – is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies suggest that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that employed algorithmic reinforcement learning models, across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, while instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually-estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies. PMID:25665667

A second-order all-digital phase-locked loop

NASA Technical Reports Server (NTRS)

Holmes, J. K.; Tegnelia, C. R.

1974-01-01

A simple second-order digital phase-locked loop has been designed to synchronize itself to a square-wave subcarrier. Analysis and experimental performance are given for both acquisition behavior and steady-state phase error performance. In addition, the damping factor and the noise bandwidth are derived analytically. Although all the data are given for the square-wave subcarrier case, the results are applicable to arbitrary subcarriers that are odd symmetric about their transition region.

A weak Galerkin least-squares finite element method for div-curl systems

NASA Astrophysics Data System (ADS)

Li, Jichun; Ye, Xiu; Zhang, Shangyou

2018-06-01

In this paper, we introduce a weak Galerkin least-squares method for solving div-curl problem. This finite element method leads to a symmetric positive definite system and has the flexibility to work with general meshes such as hybrid mesh, polytopal mesh and mesh with hanging nodes. Error estimates of the finite element solution are derived. The numerical examples demonstrate the robustness and flexibility of the proposed method.

Program documentation: Surface heating rate of thin skin models (THNSKN)

NASA Technical Reports Server (NTRS)

Mcbryde, J. D.

1975-01-01

Program THNSKN computes the mean heating rate at a maximum of 100 locations on the surface of thin skin transient heating rate models. Output is printed in tabular form and consists of time history tabulation of temperatures, average temperatures, heat loss without conduction correction, mean heating rate, least squares heating rate, and the percent standard error of the least squares heating rates. The input tape used is produced by the program EHTS03.

Certification in Structural Health Monitoring Systems

DTIC Science & Technology

2011-09-01

validation [3,8]. This may be accomplished by computing the sum of squares of pure error ( SSPE ) and its associated squared correlation [3,8]. To compute...these values, a cross- validation sample must be established. In general, if the SSPE is high, the model does not predict well on independent data...plethora of cross- validation methods, some of which are more useful for certain models than others [3,8]. When possible, a disclosure of the SSPE

Circulation patterns in the deep Subtropical Northeast Atlantic with ARGO data

NASA Astrophysics Data System (ADS)

Calheiros, Tomas; Bashmachnikov, Igor

2014-05-01

In this work we study the dominant circulation patterns in the Subtropical Northeast Atlantic using ARGO data [25-45o N, 5-35o W]. The data were obtained from the Coriolis operational data center (ftp://ftp.ifremer.fr) for the years 1999-2013. During this period of time in the study there were available area 376 floats with 15062 float-months of total time. The floats were launched in the depths range between 300 and 2000 m, but most of the floats were concentrated at 1000 m (2000 float-months) and 1500 m (3400 float-months). In the upper 400-m layer there were also about 1000 float-months, but their number and distribution did not allow analysis of the mean currents over the study region. For each float position Lagrangian current velocity was computed as the difference between the position when the buoy started sinking to the reference depth and the consequent position of surfacing of the float, divided by the respective time interval. This allowed reducing the noise related with sea-surface drift of the buoys during the data-transmission periods. Mean Eulerian velocity and its error were computed in each of the 2ox2o square. Whenever in a 2ox2o square more than 150 observations of the Lagrangian velocity were available, the square was split into 4 smaller 1ox1o squares, in each of which the mean Eulerian velocities and their errors were estimated. Eulerian currents at 1000 m, as well as at 1500 m depth formed an overall anticyclonic circulation pattern in the study region. The modal velocity of all buoys at 1000 m level was 4 cm/s with an error of the mean of 1.8 cm/s. The modal velocity of all buoys at 1500m was 3 cm/s with an error of the mean of 1.4 cm/s. The southwestward flows near the Madeira Island and further westwards flow along the zonal band of 25-30o N at 1500 m depth well corresponded to the extension of the deep fraction of the Mediterranean Water salt tong.

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

A Geomagnetic Estimate of Mean Paleointensity

NASA Technical Reports Server (NTRS)

Voorhies, Coerte

2004-01-01

To test a statistical hypothesis about Earth's magnetic field against paleomagnetism, the present field is used to estimate time averaged paleointensity. The estimate uses the modem magnetic multipole spectrum R(n), which gives the mean square induction represented by spherical harmonics of degree n averaged over the sphere of radius a = 6371.2 km. The hypothesis asserts that the low degree multipole powers of the core-source field are distributed as chi-squared with 2n+l degrees of freedom and expectation values {R(n)} = K[(n+l/2)/n(n+l](c/a)(sup 2n+4), where c is the 3480 km radius of Earth's core. (This is compatible with a usually mainly geocentric axial dipolar field). Amplitude K is estimated by fitting theoretical to observational spectra through degree 12. The resulting calibrated expectation spectrum is summed through degree 12 to estimate expected square intensity {F(sup 2)}. The sum also estimates {F(sup 2)} averaged over geologic time, in so far as the present magnetic spectrum is a fair sample of that generated in the past by core geodynamic processes.

Expected distributions of root-mean-square positional deviations in proteins.

PubMed

Pitera, Jed W

2014-06-19

The atom positional root-mean-square deviation (RMSD) is a standard tool for comparing the similarity of two molecular structures. It is used to characterize the quality of biomolecular simulations, to cluster conformations, and as a reaction coordinate for conformational changes. This work presents an approximate analytic form for the expected distribution of RMSD values for a protein or polymer fluctuating about a stable native structure. The mean and maximum of the expected distribution are independent of chain length for long chains and linearly proportional to the average atom positional root-mean-square fluctuations (RMSF). To approximate the RMSD distribution for random-coil or unfolded ensembles, numerical distributions of RMSD were generated for ensembles of self-avoiding and non-self-avoiding random walks. In both cases, for all reference structures tested for chains more than three monomers long, the distributions have a maximum distant from the origin with a power-law dependence on chain length. The purely entropic nature of this result implies that care must be taken when interpreting stable high-RMSD regions of the free-energy landscape as "intermediates" or well-defined stable states.

Aircraft system modeling error and control error

NASA Technical Reports Server (NTRS)

Kulkarni, Nilesh V. (Inventor); Kaneshige, John T. (Inventor); Krishnakumar, Kalmanje S. (Inventor); Burken, John J. (Inventor)

2012-01-01

A method for modeling error-driven adaptive control of an aircraft. Normal aircraft plant dynamics is modeled, using an original plant description in which a controller responds to a tracking error e(k) to drive the component to a normal reference value according to an asymptote curve. Where the system senses that (1) at least one aircraft plant component is experiencing an excursion and (2) the return of this component value toward its reference value is not proceeding according to the expected controller characteristics, neural network (NN) modeling of aircraft plant operation may be changed. However, if (1) is satisfied but the error component is returning toward its reference value according to expected controller characteristics, the NN will continue to model operation of the aircraft plant according to an original description.

On-board error correction improves IR earth sensor accuracy

NASA Astrophysics Data System (ADS)

Alex, T. K.; Kasturirangan, K.; Shrivastava, S. K.

1989-10-01

Infra-red earth sensors are used in satellites for attitude sensing. Their accuracy is limited by systematic and random errors. The sources of errors in a scanning infra-red earth sensor are analyzed in this paper. The systematic errors arising from seasonal variation of infra-red radiation, oblate shape of the earth, ambient temperature of sensor, changes in scan/spin rates have been analyzed. Simple relations are derived using least square curve fitting for on-board correction of these errors. Random errors arising out of noise from detector and amplifiers, instability of alignment and localized radiance anomalies are analyzed and possible correction methods are suggested. Sun and Moon interference on earth sensor performance has seriously affected a number of missions. The on-board processor detects Sun/Moon interference and corrects the errors on-board. It is possible to obtain eight times improvement in sensing accuracy, which will be comparable with ground based post facto attitude refinement.

Perceptual Color Characterization of Cameras

PubMed Central

Vazquez-Corral, Javier; Connah, David; Bertalmío, Marcelo

2014-01-01

Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as XY Z, is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a 3 × 3 matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson et al., to perform a perceptual color characterization. In particular, we search for the 3 × 3 matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE ΔE error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3% for the ΔE error, 7% for the S-CIELAB error and 13% for the CID error measures. PMID:25490586

Visual Prediction Error Spreads Across Object Features in Human Visual Cortex

PubMed Central

Summerfield, Christopher; Egner, Tobias

2016-01-01

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might “spread” from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. SIGNIFICANCE STATEMENT We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of (and attention to) multiple object features with computational modeling and fMRI, we demonstrate that behavior and fMRI activity patterns in visual cortex are best accounted for by a model in which prediction error in one object feature spreads to other object features. These results demonstrate how predictive vision forms object-level expectations out of multiple independent features. PMID:27810936

Anxiety and Error Monitoring: Increased Error Sensitivity or Altered Expectations?

ERIC Educational Resources Information Center

Compton, Rebecca J.; Carp, Joshua; Chaddock, Laura; Fineman, Stephanie L.; Quandt, Lorna C.; Ratliff, Jeffrey B.

2007-01-01

This study tested the prediction that the error-related negativity (ERN), a physiological measure of error monitoring, would be enhanced in anxious individuals, particularly in conditions with threatening cues. Participants made gender judgments about faces whose expressions were either happy, angry, or neutral. Replicating prior studies, midline…

The ground-truth problem for satellite estimates of rain rate

NASA Technical Reports Server (NTRS)

North, Gerald R.; Valdes, Juan B.; Eunho, HA; Shen, Samuel S. P.

1994-01-01

In this paper a scheme is proposed to use a point raingage to compare contemporaneous measurements of rain rate from a single-field-of-view (FOV) estimate based on a satellite remote sensor such as a microwave radiometer. Even in the ideal case the measurements are different because one is at a point and the other is an area average over the field of view. Also the point gage will be located randomly inside the field of view on different overpasses. A space-time spectral formalism is combined with a simple stochastic rain field to find the mean-square deviations between the two systems. It is found that by combining about 60 visits of the satellite to the ground-truth site, the expected error can be reduced to about 10% of the standard deviation of the fluctuations of the systems alone. This seems to be a useful level of tolerance in terms of isolating and evaluating typical biases that might be contaminating retrieval algorithms.

Short-term Time Step Convergence in a Climate Model

DOE PAGES

Wan, Hui; Rasch, Philip J.; Taylor, Mark; ...

2015-02-11

A testing procedure is designed to assess the convergence property of a global climate model with respect to time step size, based on evaluation of the root-mean-square temperature difference at the end of very short (1 h) simulations with time step sizes ranging from 1 s to 1800 s. A set of validation tests conducted without sub-grid scale parameterizations confirmed that the method was able to correctly assess the convergence rate of the dynamical core under various configurations. The testing procedure was then applied to the full model, and revealed a slow convergence of order 0.4 in contrast to themore » expected first-order convergence. Sensitivity experiments showed without ambiguity that the time stepping errors in the model were dominated by those from the stratiform cloud parameterizations, in particular the cloud microphysics. This provides a clear guidance for future work on the design of more accurate numerical methods for time stepping and process coupling in the model.« less

Lee-Carter state space modeling: Application to the Malaysia mortality data

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-06-01

This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.

Spitzer Instrument Pointing Frame (IPF) Kalman Filter Algorithm

NASA Technical Reports Server (NTRS)

Bayard, David S.; Kang, Bryan H.

2004-01-01

This paper discusses the Spitzer Instrument Pointing Frame (IPF) Kalman Filter algorithm. The IPF Kalman filter is a high-order square-root iterated linearized Kalman filter, which is parametrized for calibrating the Spitzer Space Telescope focal plane and aligning the science instrument arrays with respect to the telescope boresight. The most stringent calibration requirement specifies knowledge of certain instrument pointing frames to an accuracy of 0.1 arcseconds, per-axis, 1-sigma relative to the Telescope Pointing Frame. In order to achieve this level of accuracy, the filter carries 37 states to estimate desired parameters while also correcting for expected systematic errors due to: (1) optical distortions, (2) scanning mirror scale-factor and misalignment, (3) frame alignment variations due to thermomechanical distortion, and (4) gyro bias and bias-drift in all axes. The resulting estimated pointing frames and calibration parameters are essential for supporting on-board precision pointing capability, in addition to end-to-end 'pixels on the sky' ground pointing reconstruction efforts.

An impact analysis of forecasting methods and forecasting parameters on bullwhip effect

NASA Astrophysics Data System (ADS)

Silitonga, R. Y. H.; Jelly, N.

2018-04-01

Bullwhip effect is an increase of variance of demand fluctuation from downstream to upstream of supply chain. Forecasting methods and forecasting parameters were recognized as some factors that affect bullwhip phenomena. To study these factors, we can develop simulations. There are several ways to simulate bullwhip effect in previous studies, such as mathematical equation modelling, information control modelling, computer program, and many more. In this study a spreadsheet program named Bullwhip Explorer was used to simulate bullwhip effect. Several scenarios were developed to show the change in bullwhip effect ratio because of the difference in forecasting methods and forecasting parameters. Forecasting methods used were mean demand, moving average, exponential smoothing, demand signalling, and minimum expected mean squared error. Forecasting parameters were moving average period, smoothing parameter, signalling factor, and safety stock factor. It showed that decreasing moving average period, increasing smoothing parameter, increasing signalling factor can create bigger bullwhip effect ratio. Meanwhile, safety stock factor had no impact to bullwhip effect.

Real estate value prediction using multivariate regression models

NASA Astrophysics Data System (ADS)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Eliciting Naturalistic Cortical Responses with a Sensory Prosthesis via Optimized Microstimulation

DTIC Science & Technology

2016-08-12

error and correlation as metrics amenable to highly efficient convex optimization. This study concentrates on characterizing the neural responses to both...spiking signal. For LFP, distance measures such as the traditional mean-squared error and cross- correlation can be used, whereas distances between spike...with parameters that describe their associated temporal dynamics and relations to the observed output. A description of the model follows, but we

Theoretical Bounds of Direct Binary Search Halftoning.

PubMed

Liao, Jan-Ray

2015-11-01

Direct binary search (DBS) produces the images of the best quality among half-toning algorithms. The reason is that it minimizes the total squared perceived error instead of using heuristic approaches. The search for the optimal solution involves two operations: (1) toggle and (2) swap. Both operations try to find the binary states for each pixel to minimize the total squared perceived error. This error energy minimization leads to a conjecture that the absolute value of the filtered error after DBS converges is bounded by half of the peak value of the autocorrelation filter. However, a proof of the bound's existence has not yet been found. In this paper, we present a proof that shows the bound existed as conjectured under the condition that at least one swap occurs after toggle converges. The theoretical analysis also indicates that a swap with a pixel further away from the center of the autocorrelation filter results in a tighter bound. Therefore, we propose a new DBS algorithm which considers toggle and swap separately, and the swap operations are considered in the order from the edge to the center of the filter. Experimental results show that the new algorithm is more efficient than the previous algorithm and can produce half-toned images of the same quality as the previous algorithm.

LOGISTIC FUNCTION PROFILE FIT: A least-squares program for fitting interface profiles to an extended logistic function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirchhoff, William H.

2012-09-15

The extended logistic function provides a physically reasonable description of interfaces such as depth profiles or line scans of surface topological or compositional features. It describes these interfaces with the minimum number of parameters, namely, position, width, and asymmetry. Logistic Function Profile Fit (LFPF) is a robust, least-squares fitting program in which the nonlinear extended logistic function is linearized by a Taylor series expansion (equivalent to a Newton-Raphson approach) with no apparent introduction of bias in the analysis. The program provides reliable confidence limits for the parameters when systematic errors are minimal and provides a display of the residuals frommore » the fit for the detection of systematic errors. The program will aid researchers in applying ASTM E1636-10, 'Standard practice for analytically describing sputter-depth-profile and linescan-profile data by an extended logistic function,' and may also prove useful in applying ISO 18516: 2006, 'Surface chemical analysis-Auger electron spectroscopy and x-ray photoelectron spectroscopy-determination of lateral resolution.' Examples are given of LFPF fits to a secondary ion mass spectrometry depth profile, an Auger surface line scan, and synthetic data generated to exhibit known systematic errors for examining the significance of such errors to the extrapolation of partial profiles.« less

Post-processing through linear regression

NASA Astrophysics Data System (ADS)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Modified fast frequency acquisition via adaptive least squares algorithm

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor)

1992-01-01

A method and the associated apparatus for estimating the amplitude, frequency, and phase of a signal of interest are presented. The method comprises the following steps: (1) inputting the signal of interest; (2) generating a reference signal with adjustable amplitude, frequency and phase at an output thereof; (3) mixing the signal of interest with the reference signal and a signal 90 deg out of phase with the reference signal to provide a pair of quadrature sample signals comprising respectively a difference between the signal of interest and the reference signal and a difference between the signal of interest and the signal 90 deg out of phase with the reference signal; (4) using the pair of quadrature sample signals to compute estimates of the amplitude, frequency, and phase of an error signal comprising the difference between the signal of interest and the reference signal employing a least squares estimation; (5) adjusting the amplitude, frequency, and phase of the reference signal from the numerically controlled oscillator in a manner which drives the error signal towards zero; and (6) outputting the estimates of the amplitude, frequency, and phase of the error signal in combination with the reference signal to produce a best estimate of the amplitude, frequency, and phase of the signal of interest. The preferred method includes the step of providing the error signal as a real time confidence measure as to the accuracy of the estimates wherein the closer the error signal is to zero, the higher the probability that the estimates are accurate. A matrix in the estimation algorithm provides an estimate of the variance of the estimation error.

Multistage estimation of received carrier signal parameters under very high dynamic conditions of the receiver

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor)

1991-01-01

A multistage estimator is provided for the parameters of a received carrier signal possibly phase-modulated by unknown data and experiencing very high Doppler, Doppler rate, etc., as may arise, for example, in the case of Global Positioning Systems (GPS) where the signal parameters are directly related to the position, velocity and jerk of the GPS ground-based receiver. In a two-stage embodiment of the more general multistage scheme, the first stage, selected to be a modified least squares algorithm referred to as differential least squares (DLS), operates as a coarse estimator resulting in higher rms estimation errors but with a relatively small probability of the frequency estimation error exceeding one-half of the sampling frequency, provides relatively coarse estimates of the frequency and its derivatives. The second stage of the estimator, an extended Kalman filter (EKF), operates on the error signal available from the first stage refining the overall estimates of the phase along with a more refined estimate of frequency as well and in the process also reduces the number of cycle slips.

A new analytical method for quantification of olive and palm oil in blends with other vegetable edible oils based on the chromatographic fingerprints from the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the quantification of olive oil and palm oil in blends with other vegetable edible oils (canola, safflower, corn, peanut, seeds, grapeseed, linseed, sesame and soybean) using normal phase liquid chromatography, and applying chemometric tools was developed. The procedure for obtaining of chromatographic fingerprint from the methyl-transesterified fraction from each blend is described. The multivariate quantification methods used were Partial Least Square-Regression (PLS-R) and Support Vector Regression (SVR). The quantification results were evaluated by several parameters as the Root Mean Square Error of Validation (RMSEV), Mean Absolute Error of Validation (MAEV) and Median Absolute Error of Validation (MdAEV). It has to be highlighted that the new proposed analytical method, the chromatographic analysis takes only eight minutes and the results obtained showed the potential of this method and allowed quantification of mixtures of olive oil and palm oil with other vegetable oils. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of least-squares fitting of ellipse and hyperbola for two dimensional data

NASA Astrophysics Data System (ADS)

Lawiyuniarti, M. P.; Rahmadiantri, E.; Alamsyah, I. M.; Rachmaputri, G.

2018-01-01

Application of the least-square method of ellipse and hyperbola for two-dimensional data has been applied to analyze the spatial continuity of coal deposits in the mining field, by using the fitting method introduced by Fitzgibbon, Pilu, and Fisher in 1996. This method uses 4{a_0}{a_2} - a_12 = 1 as a constrain function. Meanwhile, in 1994, Gander, Golub and Strebel have introduced ellipse and hyperbola fitting methods using the singular value decomposition approach. This SVD approach can be generalized into a three-dimensional fitting. In this research we, will discuss about those two fitting methods and apply it to four data content of coal that is in the form of ash, calorific value, sulfur and thickness of seam so as to produce form of ellipse or hyperbola. In addition, we compute the error difference resulting from each method and from that calculation, we conclude that although the errors are not much different, the error of the method introduced by Fitzgibbon et al is smaller than the fitting method that introduced by Golub et al.

Utilization of electrical impedance imaging for estimation of in-vivo tissue resistivities

NASA Astrophysics Data System (ADS)

Eyuboglu, B. Murat; Pilkington, Theo C.

1993-08-01

In order to determine in vivo resistivity of tissues in the thorax, the possibility of combining electrical impedance imaging (EII) techniques with (1) anatomical data extracted from high resolution images, (2) a prior knowledge of tissue resistivities, and (3) a priori noise information was assessed in this study. A Least Square Error Estimator (LSEE) and a statistically constrained Minimum Mean Square Error Estimator (MiMSEE) were implemented to estimate regional electrical resistivities from potential measurements made on the body surface. A two dimensional boundary element model of the human thorax, which consists of four different conductivity regions (the skeletal muscle, the heart, the right lung, and the left lung) was adopted to simulate the measured EII torso potentials. The calculated potentials were then perturbed by simulated instrumentation noise. The signal information used to form the statistical constraint for the MiMSEE was obtained from a prior knowledge of the physiological range of tissue resistivities. The noise constraint was determined from a priori knowledge of errors due to linearization of the forward problem and to the instrumentation noise.