On the mean squared error of the ridge estimator of the covariance and precision matrix

NARCIS (Netherlands)

van Wieringen, Wessel N.

2017-01-01

For a suitably chosen ridge penalty parameter, the ridge regression estimator uniformly dominates the maximum likelihood regression estimator in terms of the mean squared error. Analogous results for the ridge maximum likelihood estimators of covariance and precision matrix are presented.

Square-Wave Voltage Injection Algorithm for PMSM Position Sensorless Control With High Robustness to Voltage Errors

DEFF Research Database (Denmark)

Ni, Ronggang; Xu, Dianguo; Blaabjerg, Frede

2017-01-01

relationship with the magnetic field distortion. Position estimation errors caused by higher order harmonic inductances and voltage harmonics generated by the SVPWM are also discussed. Both simulations and experiments are carried out based on a commercial PMSM to verify the superiority of the proposed method......Rotor position estimated with high-frequency (HF) voltage injection methods can be distorted by voltage errors due to inverter nonlinearities, motor resistance, and rotational voltage drops, etc. This paper proposes an improved HF square-wave voltage injection algorithm, which is robust to voltage...... errors without any compensations meanwhile has less fluctuation in the position estimation error. The average position estimation error is investigated based on the analysis of phase harmonic inductances, and deduced in the form of the phase shift of the second-order harmonic inductances to derive its...

Tensor hypercontraction. II. Least-squares renormalization

Science.gov (United States)

Parrish, Robert M.; Hohenstein, Edward G.; Martínez, Todd J.; Sherrill, C. David

2012-12-01

The least-squares tensor hypercontraction (LS-THC) representation for the electron repulsion integral (ERI) tensor is presented. Recently, we developed the generic tensor hypercontraction (THC) ansatz, which represents the fourth-order ERI tensor as a product of five second-order tensors [E. G. Hohenstein, R. M. Parrish, and T. J. Martínez, J. Chem. Phys. 137, 044103 (2012)], 10.1063/1.4732310. Our initial algorithm for the generation of the THC factors involved a two-sided invocation of overlap-metric density fitting, followed by a PARAFAC decomposition, and is denoted PARAFAC tensor hypercontraction (PF-THC). LS-THC supersedes PF-THC by producing the THC factors through a least-squares renormalization of a spatial quadrature over the otherwise singular 1/r12 operator. Remarkably, an analytical and simple formula for the LS-THC factors exists. Using this formula, the factors may be generated with O(N^5) effort if exact integrals are decomposed, or O(N^4) effort if the decomposition is applied to density-fitted integrals, using any choice of density fitting metric. The accuracy of LS-THC is explored for a range of systems using both conventional and density-fitted integrals in the context of MP2. The grid fitting error is found to be negligible even for extremely sparse spatial quadrature grids. For the case of density-fitted integrals, the additional error incurred by the grid fitting step is generally markedly smaller than the underlying Coulomb-metric density fitting error. The present results, coupled with our previously published factorizations of MP2 and MP3, provide an efficient, robust O(N^4) approach to both methods. Moreover, LS-THC is generally applicable to many other methods in quantum chemistry.

Direct integral linear least square regression method for kinetic evaluation of hepatobiliary scintigraphy

International Nuclear Information System (INIS)

Shuke, Noriyuki

1991-01-01

In hepatobiliary scintigraphy, kinetic model analysis, which provides kinetic parameters like hepatic extraction or excretion rate, have been done for quantitative evaluation of liver function. In this analysis, unknown model parameters are usually determined using nonlinear least square regression method (NLS method) where iterative calculation and initial estimate for unknown parameters are required. As a simple alternative to NLS method, direct integral linear least square regression method (DILS method), which can determine model parameters by a simple calculation without initial estimate, is proposed, and tested the applicability to analysis of hepatobiliary scintigraphy. In order to see whether DILS method could determine model parameters as good as NLS method, or to determine appropriate weight for DILS method, simulated theoretical data based on prefixed parameters were fitted to 1 compartment model using both DILS method with various weightings and NLS method. The parameter values obtained were then compared with prefixed values which were used for data generation. The effect of various weights on the error of parameter estimate was examined, and inverse of time was found to be the best weight to make the error minimum. When using this weight, DILS method could give parameter values close to those obtained by NLS method and both parameter values were very close to prefixed values. With appropriate weighting, the DILS method could provide reliable parameter estimate which is relatively insensitive to the data noise. In conclusion, the DILS method could be used as a simple alternative to NLS method, providing reliable parameter estimate. (author)

ERF/ERFC, Calculation of Error Function, Complementary Error Function, Probability Integrals

International Nuclear Information System (INIS)

Vogel, J.E.

1983-01-01

1 - Description of problem or function: ERF and ERFC are used to compute values of the error function and complementary error function for any real number. They may be used to compute other related functions such as the normal probability integrals. 4. Method of solution: The error function and complementary error function are approximated by rational functions. Three such rational approximations are used depending on whether - x .GE.4.0. In the first region the error function is computed directly and the complementary error function is computed via the identity erfc(x)=1.0-erf(x). In the other two regions the complementary error function is computed directly and the error function is computed from the identity erf(x)=1.0-erfc(x). The error function and complementary error function are real-valued functions of any real argument. The range of the error function is (-1,1). The range of the complementary error function is (0,2). 5. Restrictions on the complexity of the problem: The user is cautioned against using ERF to compute the complementary error function by using the identity erfc(x)=1.0-erf(x). This subtraction may cause partial or total loss of significance for certain values of x

Construction of a Mean Square Error Adaptive Euler–Maruyama Method With Applications in Multilevel Monte Carlo

KAUST Repository

Hoel, Hakon

2016-06-13

A formal mean square error expansion (MSE) is derived for Euler-Maruyama numerical solutions of stochastic differential equations (SDE). The error expansion is used to construct a pathwise, a posteriori, adaptive time-stepping Euler-Maruyama algorithm for numerical solutions of SDE, and the resulting algorithm is incorporated into a multilevel Monte Carlo (MLMC) algorithm for weak approximations of SDE. This gives an efficient MSE adaptive MLMC algorithm for handling a number of low-regularity approximation problems. In low-regularity numerical example problems, the developed adaptive MLMC algorithm is shown to outperform the uniform time-stepping MLMC algorithm by orders of magnitude, producing output whose error with high probability is bounded by TOL > 0 at the near-optimal MLMC cost rate б(TOL log(TOL)) that is achieved when the cost of sample generation is б(1).

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

Science.gov (United States)

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.

Over-Sampling Codebook-Based Hybrid Minimum Sum-Mean-Square-Error Precoding for Millimeter-Wave 3D-MIMO

KAUST Repository

Mao, Jiening

2018-05-23

Abstract: Hybrid precoding design is challenging for millimeter-wave (mmWave) massive MIMO. Most prior hybrid precoding schemes are designed to maximize the sum spectral efficiency (SSE), while seldom investigate the bit-error-rate (BER). Therefore, this letter designs an over-sampling codebook (OSC)-based hybrid minimum sum-mean-square-error (min-SMSE) precoding to optimize the BER. Specifically, given the effective baseband channel consisting of the real channel and analog precoding, we first design the digital precoder/combiner based on min-SMSE criterion to optimize the BER. To further reduce the SMSE between the transmit and receive signals, we propose an OSC-based joint analog precoder/combiner (JAPC) design. Simulation results show that the proposed scheme can achieve the better performance than its conventional counterparts.

Over-Sampling Codebook-Based Hybrid Minimum Sum-Mean-Square-Error Precoding for Millimeter-Wave 3D-MIMO

KAUST Repository

Mao, Jiening; Gao, Zhen; Wu, Yongpeng; Alouini, Mohamed-Slim

2018-01-01

Hybrid precoding design is challenging for millimeter-wave (mmWave) massive MIMO. Most prior hybrid precoding schemes are designed to maximize the sum spectral efficiency (SSE), while seldom investigate the bit-error-rate (BER). Therefore, this letter designs an over-sampling codebook (OSC)-based hybrid minimum sum-mean-square-error (min-SMSE) precoding to optimize the BER. Specifically, given the effective baseband channel consisting of the real channel and analog precoding, we first design the digital precoder/combiner based on min-SMSE criterion to optimize the BER. To further reduce the SMSE between the transmit and receive signals, we propose an OSC-based joint analog precoder/combiner (JAPC) design. Simulation results show that the proposed scheme can achieve the better performance than its conventional counterparts.

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

Science.gov (United States)

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

Least Squares Inference on Integrated Volatility and the Relationship between Efficient Prices and Noise

DEFF Research Database (Denmark)

Nolte, Ingmar; Voev, Valeri

The expected value of sums of squared intraday returns (realized variance) gives rise to a least squares regression which adapts itself to the assumptions of the noise process and allows for a joint inference on integrated volatility (IV), noise moments and price-noise relations. In the iid noise...

Least-squares model-based halftoning

Science.gov (United States)

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

Comprehensive Anti-error Study on Power Grid Dispatching Based on Regional Regulation and Integration

Science.gov (United States)

Zhang, Yunju; Chen, Zhongyi; Guo, Ming; Lin, Shunsheng; Yan, Yinyang

2018-01-01

With the large capacity of the power system, the development trend of the large unit and the high voltage, the scheduling operation is becoming more frequent and complicated, and the probability of operation error increases. This paper aims at the problem of the lack of anti-error function, single scheduling function and low working efficiency for technical support system in regional regulation and integration, the integrated construction of the error prevention of the integrated architecture of the system of dispatching anti - error of dispatching anti - error of power network based on cloud computing has been proposed. Integrated system of error prevention of Energy Management System, EMS, and Operation Management System, OMS have been constructed either. The system architecture has good scalability and adaptability, which can improve the computational efficiency, reduce the cost of system operation and maintenance, enhance the ability of regional regulation and anti-error checking with broad development prospects.

Stability and square integrability of solutions of nonlinear fourth order differential equations

Directory of Open Access Journals (Sweden)

Moussadek Remili

2016-05-01

Full Text Available The aim of the present paper is to establish a new result, which guarantees the asymptotic stability of zero solution and square integrability of solutions and their derivatives to nonlinear differential equations of fourth order.

Square-integrable wave packets from the Volkov solutions

International Nuclear Information System (INIS)

Zakowicz, Stephan

2005-01-01

Rigorous mathematical proofs of some properties of the Volkov solutions are presented, which describe the motion of a relativistic charged Dirac particle in a classical, plane electromagnetic wave. The Volkov solutions are first rewritten in a convenient form, which clearly reveals some of the symmetries of the underlying Dirac equation. Assuming continuity and boundedness of the electromagnetic vector potential, it is shown how one may construct square-integrable wave packets from momentum distributions in the space C 0 ∞ (R 3 ) 4 . If, in addition, the vector potential is C 1 and the derivative is bounded, these wave packets decay in space faster than any polynomial and fulfill the Dirac equation. The mapping which takes momentum distributions into wave packets is shown to be isometric with respect to the L 2 (R 3 ) 4 norm and may therefore be continuously extended to a mapping from L 2 (R 3 ) 4 . For a momentum function in L 1 (R 3 ) 4 intersection L 2 (R 3 ) 4 , an integral representation of this extension is presented

A comparison of least squares linear regression and measurement error modeling of warm/cold multipole correlation in SSC prototype dipole magnets

International Nuclear Information System (INIS)

Pollock, D.; Kim, K.; Gunst, R.; Schucany, W.

1993-05-01

Linear estimation of cold magnetic field quality based on warm multipole measurements is being considered as a quality control method for SSC production magnet acceptance. To investigate prediction uncertainties associated with such an approach, axial-scan (Z-scan) magnetic measurements from SSC Prototype Collider Dipole Magnets (CDM's) have been studied. This paper presents a preliminary evaluation of the explanatory ability of warm measurement multipole variation on the prediction of cold magnet multipoles. Two linear estimation methods are presented: least-squares regression, which uses the assumption of fixed independent variable (xi) observations, and the measurement error model, which includes measurement error in the xi's. The influence of warm multipole measurement errors on predicted cold magnet multipole averages is considered. MSD QA is studying warm/cold correlation to answer several magnet quality control questions. How well do warm measurements predict cold (2kA) multipoles? Does sampling error significantly influence estimates of the linear coefficients (slope, intercept and residual standard error)? Is estimation error for the predicted cold magnet average small compared to typical variation along the Z-Axis? What fraction of the multipole RMS tolerance is accounted for by individual magnet prediction uncertainty?

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

Science.gov (United States)

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

Least Squares Inference on Integrated Volatility and the Relationship between Efficient Prices and Noise

OpenAIRE

Nolte, Ingmar; Voev, Valeri

2009-01-01

The expected value of sums of squared intraday returns (realized variance)gives rise to a least squares regression which adapts itself to the assumptions ofthe noise process and allows for a joint inference on integrated volatility (IV),noise moments and price-noise relations. In the iid noise case we derive theasymptotic variance of the regression parameter estimating the IV, show thatit is consistent and compare its asymptotic efficiency against alternative consistentIV measures. In case of...

Therapeutic self-disclosure in integrative psychotherapy: When is this a clinical error?

Science.gov (United States)

Ziv-Beiman, Sharon; Shahar, Golan

2016-09-01

Ascending to prominence in virtually all forms of psychotherapy, therapist self-disclosure (TSD) has recently been identified as a primarily integrative intervention (Ziv-Beiman, 2013). In the present article, we discuss various instances in which using TSD in integrative psychotherapy might constitute a clinical error. First, we briefly review extant theory and empirical research on TSD, followed by our preferred version of integrative psychotherapy (i.e., a version of Wachtel's Cyclical Psychodynamics [Wachtel, 1977, 1997, 2014]), which we title cognitive existential psychodynamics. Next, we provide and discuss three examples in which implementing TSD constitutes a clinical error. In essence, we submit that using TSD constitutes an error when patients, constrained by their representational structures (object relations), experience the subjectivity of the other as impinging, and thus propels them to "react" instead of "emerge." (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Pressurized water reactor monitoring. Study of detection, diagnostic and estimation methods (least error squares and filtering)

International Nuclear Information System (INIS)

Gillet, M.

1986-07-01

This thesis presents a study for the surveillance of the ''primary coolant circuit inventory monitoring'' of a pressurized water reactor. A reference model is developed in view of an automatic system ensuring detection and diagnostic in real time. The methods used for the present application are statistical tests and a method related to pattern recognition. The estimation of failures detected, difficult owing to the non-linearity of the problem, is treated by the least error squares method of the predictor or corrector type, and by filtering. It is in this frame that a new optimized method with superlinear convergence is developed, and that a segmented linearization of the model is introduced, in view of a multiple filtering [fr

Improved linear least squares estimation using bounded data uncertainty

KAUST Repository

Ballal, Tarig

2015-04-01

This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.

Improved linear least squares estimation using bounded data uncertainty

KAUST Repository

Ballal, Tarig; Al-Naffouri, Tareq Y.

2015-01-01

This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.

THERP and HEART integrated methodology for human error assessment

Science.gov (United States)

Castiglia, Francesco; Giardina, Mariarosa; Tomarchio, Elio

2015-11-01

THERP and HEART integrated methodology is proposed to investigate accident scenarios that involve operator errors during high-dose-rate (HDR) treatments. The new approach has been modified on the basis of fuzzy set concept with the aim of prioritizing an exhaustive list of erroneous tasks that can lead to patient radiological overexposures. The results allow for the identification of human errors that are necessary to achieve a better understanding of health hazards in the radiotherapy treatment process, so that it can be properly monitored and appropriately managed.

On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment

Science.gov (United States)

Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai

2017-10-01

With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.

Weighted conditional least-squares estimation

International Nuclear Information System (INIS)

Booth, J.G.

1987-01-01

A two-stage estimation procedure is proposed that generalizes the concept of conditional least squares. The method is instead based upon the minimization of a weighted sum of squares, where the weights are inverses of estimated conditional variance terms. Some general conditions are given under which the estimators are consistent and jointly asymptotically normal. More specific details are given for ergodic Markov processes with stationary transition probabilities. A comparison is made with the ordinary conditional least-squares estimators for two simple branching processes with immigration. The relationship between weighted conditional least squares and other, more well-known, estimators is also investigated. In particular, it is shown that in many cases estimated generalized least-squares estimators can be obtained using the weighted conditional least-squares approach. Applications to stochastic compartmental models, and linear models with nested error structures are considered

Proportionate-type normalized last mean square algorithms

CERN Document Server

Wagner, Kevin

2013-01-01

The topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms ar

Pre-University Students' Errors in Integration of Rational Functions and Implications for Classroom Teaching

Science.gov (United States)

Yee, Ng Kin; Lam, Toh Tin

2008-01-01

This paper reports on students' errors in performing integration of rational functions, a topic of calculus in the pre-university mathematics classrooms. Generally the errors could be classified as those due to the students' weak algebraic concepts and their lack of understanding of the concept of integration. With the students' inability to link…

A Theoretically Consistent Method for Minimum Mean-Square Error Estimation of Mel-Frequency Cepstral Features

DEFF Research Database (Denmark)

Jensen, Jesper; Tan, Zheng-Hua

2014-01-01

We propose a method for minimum mean-square error (MMSE) estimation of mel-frequency cepstral features for noise robust automatic speech recognition (ASR). The method is based on a minimum number of well-established statistical assumptions; no assumptions are made which are inconsistent with others....... The strength of the proposed method is that it allows MMSE estimation of mel-frequency cepstral coefficients (MFCC's), cepstral mean-subtracted MFCC's (CMS-MFCC's), velocity, and acceleration coefficients. Furthermore, the method is easily modified to take into account other compressive non-linearities than...... the logarithmic which is usually used for MFCC computation. The proposed method shows estimation performance which is identical to or better than state-of-the-art methods. It further shows comparable ASR performance, where the advantage of being able to use mel-frequency speech features based on a power non...

Study on Network Error Analysis and Locating based on Integrated Information Decision System

Science.gov (United States)

Yang, F.; Dong, Z. H.

2017-10-01

Integrated information decision system (IIDS) integrates multiple sub-system developed by many facilities, including almost hundred kinds of software, which provides with various services, such as email, short messages, drawing and sharing. Because the under-layer protocols are different, user standards are not unified, many errors are occurred during the stages of setup, configuration, and operation, which seriously affect the usage. Because the errors are various, which may be happened in different operation phases, stages, TCP/IP communication protocol layers, sub-system software, it is necessary to design a network error analysis and locating tool for IIDS to solve the above problems. This paper studies on network error analysis and locating based on IIDS, which provides strong theory and technology supports for the running and communicating of IIDS.

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

Science.gov (United States)

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.

Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2017-08-22

The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large. In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first local minimum is retained. In this paper, a modification of UVE-PLS is proposed and investigated, in which UVE is repeated until no further reduction in variables is possible, followed by a search for the global RMSECV minimum. The method is called Global-Minimum Error Uninformative-Variable Elimination for PLS, denoted as GME-UVE-PLS or simply GME-UVE. After each iteration, the predictive ability of the PLS model, built with the remaining variable set, is assessed by RMSECV. The variable set with the global RMSECV minimum is then finally selected. The goal is to obtain smaller sets of variables with similar or improved predictability than those from the classical UVE-PLS method. The performance of the GME-UVE-PLS method is investigated using four data sets, i.e. a simulated set, NIR and NMR spectra, and a theoretical molecular descriptors set, resulting in twelve profile-response (X-y) calibrations. The selective and predictive performances of the models resulting from GME-UVE-PLS are statistically compared to those from UVE-PLS and 1-step UVE, one-sided paired t-tests. The results demonstrate that variable reduction with the proposed GME-UVE-PLS method, usually eliminates significantly more variables than the classical UVE-PLS, while the predictive abilities of the resulting models are better. With GME-UVE-PLS, a lower number of uninformative variables, without a chemical meaning for the response, may be retained than with UVE-PLS. The selectivity of the classical UVE method

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

Science.gov (United States)

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.

Dependence of the compensation error on the error of a sensor and corrector in an adaptive optics phase-conjugating system

International Nuclear Information System (INIS)

Kiyko, V V; Kislov, V I; Ofitserov, E N

2015-01-01

In the framework of a statistical model of an adaptive optics system (AOS) of phase conjugation, three algorithms based on an integrated mathematical approach are considered, each of them intended for minimisation of one of the following characteristics: the sensor error (in the case of an ideal corrector), the corrector error (in the case of ideal measurements) and the compensation error (with regard to discreteness and measurement noises and to incompleteness of a system of response functions of the corrector actuators). Functional and statistical relationships between the algorithms are studied and a relation is derived to ensure calculation of the mean-square compensation error as a function of the errors of the sensor and corrector with an accuracy better than 10%. Because in adjusting the AOS parameters, it is reasonable to proceed from the equality of the sensor and corrector errors, in the case the Hartmann sensor is used as a wavefront sensor, the required number of actuators in the absence of the noise component in the sensor error turns out 1.5 – 2.5 times less than the number of counts, and that difference grows with increasing measurement noise. (adaptive optics)

Dependence of the compensation error on the error of a sensor and corrector in an adaptive optics phase-conjugating system

Energy Technology Data Exchange (ETDEWEB)

Kiyko, V V; Kislov, V I; Ofitserov, E N [A M Prokhorov General Physics Institute, Russian Academy of Sciences, Moscow (Russian Federation)

2015-08-31

In the framework of a statistical model of an adaptive optics system (AOS) of phase conjugation, three algorithms based on an integrated mathematical approach are considered, each of them intended for minimisation of one of the following characteristics: the sensor error (in the case of an ideal corrector), the corrector error (in the case of ideal measurements) and the compensation error (with regard to discreteness and measurement noises and to incompleteness of a system of response functions of the corrector actuators). Functional and statistical relationships between the algorithms are studied and a relation is derived to ensure calculation of the mean-square compensation error as a function of the errors of the sensor and corrector with an accuracy better than 10%. Because in adjusting the AOS parameters, it is reasonable to proceed from the equality of the sensor and corrector errors, in the case the Hartmann sensor is used as a wavefront sensor, the required number of actuators in the absence of the noise component in the sensor error turns out 1.5 – 2.5 times less than the number of counts, and that difference grows with increasing measurement noise. (adaptive optics)

Solution of Large Systems of Linear Equations in the Presence of Errors. A Constructive Criticism of the Least Squares Method

Energy Technology Data Exchange (ETDEWEB)

Nygaard, K

1968-09-15

From the point of view that no mathematical method can ever minimise or alter errors already made in a physical measurement, the classical least squares method has severe limitations which makes it unsuitable for the statistical analysis of many physical measurements. Based on the assumptions that the experimental errors are characteristic for each single experiment and that the errors must be properly estimated rather than minimised, a new method for solving large systems of linear equations is developed. The new method exposes the entire range of possible solutions before the decision is taken which of the possible solutions should be chosen as a representative one. The choice is based on physical considerations which (in two examples, curve fitting and unfolding of a spectrum) are presented in such a form that a computer is able to make the decision, A description of the computation is given. The method described is a tool for removing uncertainties due to conventional mathematical formulations (zero determinant, linear dependence) and which are not inherent in the physical problem as such. The method is therefore especially well fitted for unfolding of spectra.

Solution of Large Systems of Linear Equations in the Presence of Errors. A Constructive Criticism of the Least Squares Method

International Nuclear Information System (INIS)

Nygaard, K.

1968-09-01

From the point of view that no mathematical method can ever minimise or alter errors already made in a physical measurement, the classical least squares method has severe limitations which makes it unsuitable for the statistical analysis of many physical measurements. Based on the assumptions that the experimental errors are characteristic for each single experiment and that the errors must be properly estimated rather than minimised, a new method for solving large systems of linear equations is developed. The new method exposes the entire range of possible solutions before the decision is taken which of the possible solutions should be chosen as a representative one. The choice is based on physical considerations which (in two examples, curve fitting and unfolding of a spectrum) are presented in such a form that a computer is able to make the decision, A description of the computation is given. The method described is a tool for removing uncertainties due to conventional mathematical formulations (zero determinant, linear dependence) and which are not inherent in the physical problem as such. The method is therefore especially well fitted for unfolding of spectra

Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

Science.gov (United States)

Calvo, Roque; D’Amato, Roberto; Gómez, Emilio; Domingo, Rosario

2016-01-01

The development of an error compensation model for coordinate measuring machines (CMMs) and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included. PMID:27690052

Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

Directory of Open Access Journals (Sweden)

Roque Calvo

2016-09-01

Full Text Available The development of an error compensation model for coordinate measuring machines (CMMs and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.

Weighted least-squares criteria for electrical impedance tomography

International Nuclear Information System (INIS)

Kallman, J.S.; Berryman, J.G.

1992-01-01

Methods are developed for design of electrical impedance tomographic reconstruction algorithms with specified properties. Assuming a starting model with constant conductivity or some other specified background distribution, an algorithm with the following properties is found: (1) the optimum constant for the starting model is determined automatically; (2) the weighted least-squares error between the predicted and measured power dissipation data is as small as possible; (3) the variance of the reconstructed conductivity from the starting model is minimized; (4) potential distributions with the largest volume integral of gradient squared have the least influence on the reconstructed conductivity, and therefore distributions most likely to be corrupted by contact impedance effects are deemphasized; (5) cells that dissipate the most power during the current injection tests tend to deviate least from the background value. The resulting algorithm maps the reconstruction problem into a vector space where the contribution to the inversion from the background conductivity remains invariant, while the optimum contributions in orthogonal directions are found. For a starting model with nonconstant conductivity, the reconstruction algorithm has analogous properties

An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle's Motion Sensors.

Science.gov (United States)

Karamat, Tashfeen B; Atia, Mohamed M; Noureldin, Aboelmagd

2015-09-22

Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers' measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer's errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors. The new error model and the augmented EKF design are developed in a tightly-coupled RISS/GPS integrated navigation system. The proposed system was tested on real trajectories' data under degraded GPS environments, and the results were compared to earlier works on RISS/GPS systems. The findings demonstrated that the proposed enhanced system introduced significant improvements in navigational performance.

Error analysis of stochastic gradient descent ranking.

Science.gov (United States)

Chen, Hong; Tang, Yi; Li, Luoqing; Yuan, Yuan; Li, Xuelong; Tang, Yuanyan

2013-06-01

Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.

Exact Symbol Error Probability of Square M-QAM Signaling over Generalized Fading Channels subject to Additive Generalized Gaussian Noise

KAUST Repository

Soury, Hamza

2013-07-01

This paper considers the average symbol error probability of square Quadrature Amplitude Modulation (QAM) coherent signaling over flat fading channels subject to additive generalized Gaussian noise. More specifically, a generic closedform expression in terms of the Fox H function and the bivariate Fox H function is offered for the extended generalized-K fading case. Simplifications for some special fading distributions such as generalized-K fading, Nakagami-m fading, and Rayleigh fading and special additive noise distributions such as Gaussian and Laplacian noise are then presented. Finally, the mathematical formalism is illustrated by some numerical examples verified by computer based simulations for a variety of fading and additive noise parameters.

Fractional Order Differentiation by Integration and Error Analysis in Noisy Environment

KAUST Repository

Liu, Dayan; Gibaru, Olivier; Perruquetti, Wilfrid; Laleg-Kirati, Taous-Meriem

2015-01-01

respectively. Exact and simple formulae for these differentiators are given by integral expressions. Hence, they can be used for both continuous-time and discrete-time models in on-line or off-line applications. Secondly, some error bounds are provided

Penalized linear regression for discrete ill-posed problems: A hybrid least-squares and mean-squared error approach

KAUST Repository

Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Kammoun, Abla; Al-Naffouri, Tareq Y.

2016-01-01

This paper proposes a new approach to find the regularization parameter for linear least-squares discrete ill-posed problems. In the proposed approach, an artificial perturbation matrix with a bounded norm is forced into the discrete ill-posed model

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

Science.gov (United States)

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Square-Integrable Wave Packets from the Volkov Solutions

CERN Document Server

Zakowicz, S

2004-01-01

Rigorous mathematical proofs of some properties of the Volkov solutions are presented, which describe the motion of a relativistic charged Dirac particle in a classical, plane electromagnetic wave. The Volkov solutions are first rewritten in a convenient form, which clearly reveals some of the symmetries of the underlying Dirac equation. Assuming continuity and boundedness of the electromagnetic vector potential, it is shown how one may construct square-integrable wave packets from momentum distributions in the space $\\mathcal{C}^{\\infty}_0(\\mathbb{R}^3)^4$. If, in addition, the vector potential is $\\mathcal{C}^1$ and the derivative is bounded, these wave packets decay in space faster than any polynomial and fulfill the Dirac equation. The mapping which takes momentum distributions into wave packets is shown to be isometric with respect to the $L^2(\\mathbb{R}^3)^4$ norm and may therefore be continuously extended to a mapping from $L^2(\\mathbb{R}^3)^4$. For a momen! tum function in $L^1(\\mathbb{R}^3)^4 \\cap L^...

Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

Science.gov (United States)

Bai, Mingsian R; Hsieh, Ping-Ju; Hur, Kur-Nan

2009-02-01

The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.

An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle’s Motion Sensors

Science.gov (United States)

Karamat, Tashfeen B.; Atia, Mohamed M.; Noureldin, Aboelmagd

2015-01-01

Reduced inertial sensor systems (RISS) have been introduced by many researchers as a low-cost, low-complexity sensor assembly that can be integrated with GPS to provide a robust integrated navigation system for land vehicles. In earlier works, the developed error models were simplified based on the assumption that the vehicle is mostly moving on a flat horizontal plane. Another limitation is the simplified estimation of the horizontal tilt angles, which is based on simple averaging of the accelerometers’ measurements without modelling their errors or tilt angle errors. In this paper, a new error model is developed for RISS that accounts for the effect of tilt angle errors and the accelerometer’s errors. Additionally, it also includes important terms in the system dynamic error model, which were ignored during the linearization process in earlier works. An augmented extended Kalman filter (EKF) is designed to incorporate tilt angle errors and transversal accelerometer errors. The new error model and the augmented EKF design are developed in a tightly-coupled RISS/GPS integrated navigation system. The proposed system was tested on real trajectories’ data under degraded GPS environments, and the results were compared to earlier works on RISS/GPS systems. The findings demonstrated that the proposed enhanced system introduced significant improvements in navigational performance. PMID:26402680

Quality controls in integrative approaches to detect errors and inconsistencies in biological databases

Directory of Open Access Journals (Sweden)

Ghisalberti Giorgio

2010-12-01

Full Text Available Numerous biomolecular data are available, but they are scattered in many databases and only some of them are curated by experts. Most available data are computationally derived and include errors and inconsistencies. Effective use of available data in order to derive new knowledge hence requires data integration and quality improvement. Many approaches for data integration have been proposed. Data warehousing seams to be the most adequate when comprehensive analysis of integrated data is required. This makes it the most suitable also to implement comprehensive quality controls on integrated data. We previously developed GFINDer (http://www.bioinformatics.polimi.it/GFINDer/, a web system that supports scientists in effectively using available information. It allows comprehensive statistical analysis and mining of functional and phenotypic annotations of gene lists, such as those identified by high-throughput biomolecular experiments. GFINDer backend is composed of a multi-organism genomic and proteomic data warehouse (GPDW. Within the GPDW, several controlled terminologies and ontologies, which describe gene and gene product related biomolecular processes, functions and phenotypes, are imported and integrated, together with their associations with genes and proteins of several organisms. In order to ease maintaining updated the GPDW and to ensure the best possible quality of data integrated in subsequent updating of the data warehouse, we developed several automatic procedures. Within them, we implemented numerous data quality control techniques to test the integrated data for a variety of possible errors and inconsistencies. Among other features, the implemented controls check data structure and completeness, ontological data consistency, ID format and evolution, unexpected data quantification values, and consistency of data from single and multiple sources. We use the implemented controls to analyze the quality of data available from several

Nonlinear Least Square Based on Control Direction by Dual Method and Its Application

Directory of Open Access Journals (Sweden)

Zhengqing Fu

2016-01-01

Full Text Available A direction controlled nonlinear least square (NLS estimation algorithm using the primal-dual method is proposed. The least square model is transformed into the primal-dual model; then direction of iteration can be controlled by duality. The iterative algorithm is designed. The Hilbert morbid matrix is processed by the new model and the least square estimate and ridge estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The root mean square error (RMSE is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum root mean square error. The new estimate model has effectiveness and high precision. The genuine data of Jining area in unwrapping experiments are used and the comparison with other classical unwrapping algorithms is made, so better results in precision aspects can be achieved through the proposed algorithm.

Cuba: Multidimensional numerical integration library

Science.gov (United States)

Hahn, Thomas

2016-08-01

The Cuba library offers four independent routines for multidimensional numerical integration: Vegas, Suave, Divonne, and Cuhre. The four algorithms work by very different methods, and can integrate vector integrands and have very similar Fortran, C/C++, and Mathematica interfaces. Their invocation is very similar, making it easy to cross-check by substituting one method by another. For further safeguarding, the output is supplemented by a chi-square probability which quantifies the reliability of the error estimate.

Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error.

Science.gov (United States)

Kiani, M A; Sim, K S; Nia, M E; Tso, C P

2015-05-01

A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Stability and square integrability of derivatives of solutions of nonlinear fourth order differential equations with delay.

Science.gov (United States)

Korkmaz, Erdal

2017-01-01

In this paper, we give sufficient conditions for the boundedness, uniform asymptotic stability and square integrability of the solutions to a certain fourth order non-autonomous differential equations with delay by using Lyapunov's second method. The results obtained essentially improve, include and complement the results in the literature.

Errors prevention in manufacturing process through integration of Poka Yoke and TRIZ

Science.gov (United States)

Helmi, Syed Ahmad; Nordin, Nur Nashwa; Hisjam, Muhammad

2017-11-01

Integration of Poka Yoke and TRIZ is a method of solving problems by using a different approach. Poka Yoke is a trial and error method while TRIZ is using a systematic approach. The main purpose of this technique is to get rid of product defects by preventing or correcting errors as soon as possible. Blame the workers for their mistakes is not the best way, but the work process should be reviewed so that every workers behavior or movement may not cause errors. This study is to demonstrate the importance of using both of these methods in which everyone in the industry needs to improve quality, increase productivity and at the same time reducing production cost.

Observability analysis of a MEMS INS/GPS integration system with gyroscope G-sensitivity errors.

Science.gov (United States)

Fan, Chen; Hu, Xiaoping; He, Xiaofeng; Tang, Kanghua; Luo, Bing

2014-08-28

Gyroscopes based on micro-electromechanical system (MEMS) technology suffer in high-dynamic applications due to obvious g-sensitivity errors. These errors can induce large biases in the gyroscope, which can directly affect the accuracy of attitude estimation in the integration of the inertial navigation system (INS) and the Global Positioning System (GPS). The observability determines the existence of solutions for compensating them. In this paper, we investigate the observability of the INS/GPS system with consideration of the g-sensitivity errors. In terms of two types of g-sensitivity coefficients matrix, we add them as estimated states to the Kalman filter and analyze the observability of three or nine elements of the coefficient matrix respectively. A global observable condition of the system is presented and validated. Experimental results indicate that all the estimated states, which include position, velocity, attitude, gyro and accelerometer bias, and g-sensitivity coefficients, could be made observable by maneuvering based on the conditions. Compared with the integration system without compensation for the g-sensitivity errors, the attitude accuracy is raised obviously.

Observability Analysis of a MEMS INS/GPS Integration System with Gyroscope G-Sensitivity Errors

Directory of Open Access Journals (Sweden)

Chen Fan

2014-08-01

Full Text Available Gyroscopes based on micro-electromechanical system (MEMS technology suffer in high-dynamic applications due to obvious g-sensitivity errors. These errors can induce large biases in the gyroscope, which can directly affect the accuracy of attitude estimation in the integration of the inertial navigation system (INS and the Global Positioning System (GPS. The observability determines the existence of solutions for compensating them. In this paper, we investigate the observability of the INS/GPS system with consideration of the g-sensitivity errors. In terms of two types of g-sensitivity coefficients matrix, we add them as estimated states to the Kalman filter and analyze the observability of three or nine elements of the coefficient matrix respectively. A global observable condition of the system is presented and validated. Experimental results indicate that all the estimated states, which include position, velocity, attitude, gyro and accelerometer bias, and g-sensitivity coefficients, could be made observable by maneuvering based on the conditions. Compared with the integration system without compensation for the g-sensitivity errors, the attitude accuracy is raised obviously.

Integration of error tolerance into the design of control rooms of nuclear power plants

International Nuclear Information System (INIS)

Sepanloo, Kamran

1998-08-01

Many complex technological systems' failures have been attributed to human errors. Today, based on extensive research on the role of human element in technological systems it is known that human error can not totally be eliminated in modern, flexible, or changing work environments by conventional style design strategies(e.g. defence in depth), or better instructions nor should they be. Instead, the operators' ability to explore degrees of freedom should be supported and means for recovering from the effects of errors should be included. This calls for innovative error tolerant design of technological systems. Integration of error tolerant concept into the design, construction, startup, and operation of nuclear power plants provides an effective means of reducing human error occurrence during all stages of life of it and therefore leads to considerable enhancement of plant's safety

Regularized plane-wave least-squares Kirchhoff migration

KAUST Repository

Wang, Xin; Dai, Wei; Schuster, Gerard T.

2013-01-01

A Kirchhoff least-squares migration (LSM) is developed in the prestack plane-wave domain to increase the quality of migration images. A regularization term is included that accounts for mispositioning of reflectors due to errors in the velocity

Stability and square integrability of derivatives of solutions of nonlinear fourth order differential equations with delay

Directory of Open Access Journals (Sweden)

Erdal Korkmaz

2017-06-01

Full Text Available Abstract In this paper, we give sufficient conditions for the boundedness, uniform asymptotic stability and square integrability of the solutions to a certain fourth order non-autonomous differential equations with delay by using Lyapunov’s second method. The results obtained essentially improve, include and complement the results in the literature.

Performance comparison of weighted sum-minimum mean square error and virtual signal-to-interference plus noise ratio algorithms in simulated and measured channels

DEFF Research Database (Denmark)

Rahimi, Maryam; Nielsen, Jesper Ødum; Pedersen, Troels

2014-01-01

A comparison in data achievement between two well-known algorithms with simulated and real measured data is presented. The algorithms maximise the data rate in cooperative base stations (BS) multiple-input-single-output scenario. Weighted sum-minimum mean square error algorithm could be used...... in multiple-input-multiple-output scenarios, but it has lower performance than virtual signal-to-interference plus noise ratio algorithm in theory and practice. A real measurement environment consisting of two BS and two users have been studied to evaluate the simulation results....

Latin square three dimensional gage master

Science.gov (United States)

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.

Latin-square three-dimensional gage master

Science.gov (United States)

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.

DO DYNAMIC NEURAL NETWORKS STAND A BETTER CHANCE IN FRACTIONALLY INTEGRATED PROCESS FORECASTING?

Directory of Open Access Journals (Sweden)

Majid Delavari

2013-04-01

Full Text Available The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model and a regressive (Auto Regressive Fractionally Integrated Moving Average model which is based on Fractional Integration Approach in forecasting daily data related to the return index of Tehran Stock Exchange (TSE. In order to compare these models under similar conditions, Mean Square Error (MSE and also Root Mean Square Error (RMSE were selected as criteria for the models’ simulated out-of-sample forecasting performance. Besides, fractal markets hypothesis was examined and according to the findings, fractal structure was confirmed to exist in the time series under investigation. Another finding of the study was that dynamic artificial neural network model had the best performance in out-of-sample forecasting based on the criteria introduced for calculating forecasting error in comparison with the ARFIMA model.

Zero-Forcing and Minimum Mean-Square Error Multiuser Detection in Generalized Multicarrier DS-CDMA Systems for Cognitive Radio

Directory of Open Access Journals (Sweden)

Lie-Liang Yang

2008-01-01

Full Text Available In wireless communications, multicarrier direct-sequence code-division multiple access (MC DS-CDMA constitutes one of the highly flexible multiple access schemes. MC DS-CDMA employs a high number of degrees-of-freedom, which are beneficial to design and reconfiguration for communications in dynamic communications environments, such as in the cognitive radios. In this contribution, we consider the multiuser detection (MUD in MC DS-CDMA, which motivates lowcomplexity, high flexibility, and robustness so that the MUD schemes are suitable for deployment in dynamic communications environments. Specifically, a range of low-complexity MUDs are derived based on the zero-forcing (ZF, minimum mean-square error (MMSE, and interference cancellation (IC principles. The bit-error rate (BER performance of the MC DS-CDMA aided by the proposed MUDs is investigated by simulation approaches. Our study shows that, in addition to the advantages provided by a general ZF, MMSE, or IC-assisted MUD, the proposed MUD schemes can be implemented using modular structures, where most modules are independent of each other. Due to the independent modular structure, in the proposed MUDs one module may be reconfigured without yielding impact on the others. Therefore, the MC DS-CDMA, in conjunction with the proposed MUDs, constitutes one of the promising multiple access schemes for communications in the dynamic communications environments such as in the cognitive radios.

Zero-Forcing and Minimum Mean-Square Error Multiuser Detection in Generalized Multicarrier DS-CDMA Systems for Cognitive Radio

Directory of Open Access Journals (Sweden)

Wang Li-Chun

2008-01-01

Full Text Available Abstract In wireless communications, multicarrier direct-sequence code-division multiple access (MC DS-CDMA constitutes one of the highly flexible multiple access schemes. MC DS-CDMA employs a high number of degrees-of-freedom, which are beneficial to design and reconfiguration for communications in dynamic communications environments, such as in the cognitive radios. In this contribution, we consider the multiuser detection (MUD in MC DS-CDMA, which motivates lowcomplexity, high flexibility, and robustness so that the MUD schemes are suitable for deployment in dynamic communications environments. Specifically, a range of low-complexity MUDs are derived based on the zero-forcing (ZF, minimum mean-square error (MMSE, and interference cancellation (IC principles. The bit-error rate (BER performance of the MC DS-CDMA aided by the proposed MUDs is investigated by simulation approaches. Our study shows that, in addition to the advantages provided by a general ZF, MMSE, or IC-assisted MUD, the proposed MUD schemes can be implemented using modular structures, where most modules are independent of each other. Due to the independent modular structure, in the proposed MUDs one module may be reconfigured without yielding impact on the others. Therefore, the MC DS-CDMA, in conjunction with the proposed MUDs, constitutes one of the promising multiple access schemes for communications in the dynamic communications environments such as in the cognitive radios.

Quantized kernel least mean square algorithm.

Science.gov (United States)

Chen, Badong; Zhao, Songlin; Zhu, Pingping; Príncipe, José C

2012-01-01

In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering. The basic idea behind this method is to quantize and hence compress the input (or feature) space. Different from sparsification, the new approach uses the "redundant" data to update the coefficient of the closest center. In particular, a quantized kernel least mean square (QKLMS) algorithm is developed, which is based on a simple online vector quantization method. The analytical study of the mean square convergence has been carried out. The energy conservation relation for QKLMS is established, and on this basis we arrive at a sufficient condition for mean square convergence, and a lower and upper bound on the theoretical value of the steady-state excess mean square error. Static function estimation and short-term chaotic time-series prediction examples are presented to demonstrate the excellent performance.

Comparison of Prediction-Error-Modelling Criteria

DEFF Research Database (Denmark)

Jørgensen, John Bagterp; Jørgensen, Sten Bay

2007-01-01

Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

Fractional Order Differentiation by Integration and Error Analysis in Noisy Environment

KAUST Repository

Liu, Dayan

2015-03-31

The integer order differentiation by integration method based on the Jacobi orthogonal polynomials for noisy signals was originally introduced by Mboup, Join and Fliess. We propose to extend this method from the integer order to the fractional order to estimate the fractional order derivatives of noisy signals. Firstly, two fractional order differentiators are deduced from the Jacobi orthogonal polynomial filter, using the Riemann-Liouville and the Caputo fractional order derivative definitions respectively. Exact and simple formulae for these differentiators are given by integral expressions. Hence, they can be used for both continuous-time and discrete-time models in on-line or off-line applications. Secondly, some error bounds are provided for the corresponding estimation errors. These bounds allow to study the design parameters\\' influence. The noise error contribution due to a large class of stochastic processes is studied in discrete case. The latter shows that the differentiator based on the Caputo fractional order derivative can cope with a class of noises, whose mean value and variance functions are polynomial time-varying. Thanks to the design parameters analysis, the proposed fractional order differentiators are significantly improved by admitting a time-delay. Thirdly, in order to reduce the calculation time for on-line applications, a recursive algorithm is proposed. Finally, the proposed differentiator based on the Riemann-Liouville fractional order derivative is used to estimate the state of a fractional order system and numerical simulations illustrate the accuracy and the robustness with respect to corrupting noises.

Quantifying and handling errors in instrumental measurements using the measurement error theory

DEFF Research Database (Denmark)

Andersen, Charlotte Møller; Bro, R.; Brockhoff, P.B.

2003-01-01

. This is a new way of using the measurement error theory. Reliability ratios illustrate that the models for the two fish species are influenced differently by the error. However, the error seems to influence the predictions of the two reference measures in the same way. The effect of using replicated x...... measurements. A new general formula is given for how to correct the least squares regression coefficient when a different number of replicated x-measurements is used for prediction than for calibration. It is shown that the correction should be applied when the number of replicates in prediction is less than...

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

Science.gov (United States)

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.

Newton-Gauss Algorithm of Robust Weighted Total Least Squares Model

Directory of Open Access Journals (Sweden)

WANG Bin

2015-06-01

Full Text Available Based on the Newton-Gauss iterative algorithm of weighted total least squares (WTLS, a robust WTLS (RWTLS model is presented. The model utilizes the standardized residuals to construct the weight factor function and the square root of the variance component estimator with robustness is obtained by introducing the median method. Therefore, the robustness in both the observation and structure spaces can be simultaneously achieved. To obtain standardized residuals, the linearly approximate cofactor propagation law is employed to derive the expression of the cofactor matrix of WTLS residuals. The iterative calculation steps for RWTLS are also described. The experiment indicates that the model proposed in this paper exhibits satisfactory robustness for gross errors handling problem of WTLS, the obtained parameters have no significant difference with the results of WTLS without gross errors. Therefore, it is superior to the robust weighted total least squares model directly constructed with residuals.

Error in the determination of the deformed shape of prismatic beams using the double integration of curvature

Science.gov (United States)

Sigurdardottir, Dorotea H.; Stearns, Jett; Glisic, Branko

2017-07-01

The deformed shape is a consequence of loading the structure and it is defined by the shape of the centroid line of the beam after deformation. The deformed shape is a universal parameter of beam-like structures. It is correlated with the curvature of the cross-section; therefore, any unusual behavior that affects the curvature is reflected through the deformed shape. Excessive deformations cause user discomfort, damage to adjacent structural members, and may ultimately lead to issues in structural safety. However, direct long-term monitoring of the deformed shape in real-life settings is challenging, and an alternative is indirect determination of the deformed shape based on curvature monitoring. The challenge of the latter is an accurate evaluation of error in the deformed shape determination, which is directly correlated with the number of sensors needed to achieve the desired accuracy. The aim of this paper is to study the deformed shape evaluated by numerical double integration of the monitored curvature distribution along the beam, and create a method to predict the associated errors and suggest the number of sensors needed to achieve the desired accuracy. The error due to the accuracy in the curvature measurement is evaluated within the scope of this work. Additionally, the error due to the numerical integration is evaluated. This error depends on the load case (i.e., the shape of the curvature diagram), the magnitude of curvature, and the density of the sensor network. The method is tested on a laboratory specimen and a real structure. In a laboratory setting, the double integration is in excellent agreement with the beam theory solution which was within the predicted error limits of the numerical integration. Consistent results are also achieved on a real structure—Streicker Bridge on Princeton University campus.

A comparison between different error modeling of MEMS applied to GPS/INS integrated systems.

Science.gov (United States)

Quinchia, Alex G; Falco, Gianluca; Falletti, Emanuela; Dovis, Fabio; Ferrer, Carles

2013-07-24

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

Regularized plane-wave least-squares Kirchhoff migration

KAUST Repository

Wang, Xin

2013-09-22

A Kirchhoff least-squares migration (LSM) is developed in the prestack plane-wave domain to increase the quality of migration images. A regularization term is included that accounts for mispositioning of reflectors due to errors in the velocity model. Both synthetic and field results show that: 1) LSM with a reflectivity model common for all the plane-wave gathers provides the best image when the migration velocity model is accurate, but it is more sensitive to the velocity errors, 2) the regularized plane-wave LSM is more robust in the presence of velocity errors, and 3) LSM achieves both computational and IO saving by plane-wave encoding compared to shot-domain LSM for the models tested.

Total elimination of sampling errors in polarization imagery obtained with integrated microgrid polarimeters.

Science.gov (United States)

Tyo, J Scott; LaCasse, Charles F; Ratliff, Bradley M

2009-10-15

Microgrid polarimeters operate by integrating a focal plane array with an array of micropolarizers. The Stokes parameters are estimated by comparing polarization measurements from pixels in a neighborhood around the point of interest. The main drawback is that the measurements used to estimate the Stokes vector are made at different locations, leading to a false polarization signature owing to instantaneous field-of-view (IFOV) errors. We demonstrate for the first time, to our knowledge, that spatially band limited polarization images can be ideally reconstructed with no IFOV error by using a linear system framework.

Iterative methods for weighted least-squares

Energy Technology Data Exchange (ETDEWEB)

Bobrovnikova, E.Y.; Vavasis, S.A. [Cornell Univ., Ithaca, NY (United States)

1996-12-31

A weighted least-squares problem with a very ill-conditioned weight matrix arises in many applications. Because of round-off errors, the standard conjugate gradient method for solving this system does not give the correct answer even after n iterations. In this paper we propose an iterative algorithm based on a new type of reorthogonalization that converges to the solution.

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Directory of Open Access Journals (Sweden)

Fabio Dovis

2013-07-01

Full Text Available Advances in the development of micro-electromechanical systems (MEMS have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS and the inertial navigation system (INS integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs, stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV and the power spectral density (PSD techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade presents error sources with short-term (high-frequency and long-term (low-frequency components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling

Science.gov (United States)

Gupta, Hoshin V.; Kling, Harald; Yilmaz, Koray K.; Martinez-Baquero, Guillermo F.

2009-01-01

The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification.

Least Squares Estimate of the Initial Phases in STFT based Speech Enhancement

DEFF Research Database (Denmark)

Nørholm, Sidsel Marie; Krawczyk-Becker, Martin; Gerkmann, Timo

2015-01-01

In this paper, we consider single-channel speech enhancement in the short time Fourier transform (STFT) domain. We suggest to improve an STFT phase estimate by estimating the initial phases. The method is based on the harmonic model and a model for the phase evolution over time. The initial phases...... are estimated by setting up a least squares problem between the noisy phase and the model for phase evolution. Simulations on synthetic and speech signals show a decreased error on the phase when an estimate of the initial phase is included compared to using the noisy phase as an initialisation. The error...... on the phase is decreased at input SNRs from -10 to 10 dB. Reconstructing the signal using the clean amplitude, the mean squared error is decreased and the PESQ score is increased....

A hybrid least squares support vector machines and GMDH approach for river flow forecasting

Science.gov (United States)

Samsudin, R.; Saad, P.; Shabri, A.

2010-06-01

This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

Radiation Field of a Square, Helical Beam Antenna

DEFF Research Database (Denmark)

Knudsen, Hans Lottrup

1952-01-01

square helices are used. Further, in connection with corresponding rigorous formulas for the field from a circular, helical antenna with a uniformly progressing current wave of constant amplitude the present formulas may be used for an investigation of the magnitude of the error introduced in Kraus......' approximate calculation of the field from a circular, helical antenna by replacing this antenna with an ``equivalent'' square helix. This investigation is carried out by means of a numerical example. The investigation shows that Kraus' approximate method of calculation yields results in fair agreement...

The maximally achievable accuracy of linear optimal regulators and linear optimal filters

NARCIS (Netherlands)

Kwakernaak, H.; Sivan, Raphael

1972-01-01

A linear system with a quadratic cost function, which is a weighted sum of the integral square regulation error and the integral square input, is considered. What happens to the integral square regulation error as the relative weight of the integral square input reduces to zero is investigated. In

Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

Directory of Open Access Journals (Sweden)

Zongze Wu

2015-08-01

Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.

Effect of patient setup errors on simultaneously integrated boost head and neck IMRT treatment plans

International Nuclear Information System (INIS)

Siebers, Jeffrey V.; Keall, Paul J.; Wu Qiuwen; Williamson, Jeffrey F.; Schmidt-Ullrich, Rupert K.

2005-01-01

Purpose: The purpose of this study is to determine dose delivery errors that could result from random and systematic setup errors for head-and-neck patients treated using the simultaneous integrated boost (SIB)-intensity-modulated radiation therapy (IMRT) technique. Methods and Materials: Twenty-four patients who participated in an intramural Phase I/II parotid-sparing IMRT dose-escalation protocol using the SIB treatment technique had their dose distributions reevaluated to assess the impact of random and systematic setup errors. The dosimetric effect of random setup error was simulated by convolving the two-dimensional fluence distribution of each beam with the random setup error probability density distribution. Random setup errors of σ = 1, 3, and 5 mm were simulated. Systematic setup errors were simulated by randomly shifting the patient isocenter along each of the three Cartesian axes, with each shift selected from a normal distribution. Systematic setup error distributions with Σ = 1.5 and 3.0 mm along each axis were simulated. Combined systematic and random setup errors were simulated for σ = Σ = 1.5 and 3.0 mm along each axis. For each dose calculation, the gross tumor volume (GTV) received by 98% of the volume (D 98 ), clinical target volume (CTV) D 90 , nodes D 90 , cord D 2 , and parotid D 50 and parotid mean dose were evaluated with respect to the plan used for treatment for the structure dose and for an effective planning target volume (PTV) with a 3-mm margin. Results: Simultaneous integrated boost-IMRT head-and-neck treatment plans were found to be less sensitive to random setup errors than to systematic setup errors. For random-only errors, errors exceeded 3% only when the random setup error σ exceeded 3 mm. Simulated systematic setup errors with Σ = 1.5 mm resulted in approximately 10% of plan having more than a 3% dose error, whereas a Σ = 3.0 mm resulted in half of the plans having more than a 3% dose error and 28% with a 5% dose error

Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS

Directory of Open Access Journals (Sweden)

Ade Widyaningsih

2015-04-01

Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.

Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS

Directory of Open Access Journals (Sweden)

Ade Widyaningsih

2014-06-01

Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.

An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction

Directory of Open Access Journals (Sweden)

Yong Zhu

2015-01-01

Full Text Available After summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empirical mode decomposition in dealing with nonlinear and nonstationary signals. This research merged the superiorities of kurtosis, mean square error, energy, and singular value decomposition on signal feature extraction. The values of the four indexes aforementioned were combined into a feature vector. Then, the connotative characteristic components in vibration signal were accurately extracted by Euclidean distance search, and the desired integral signals were precisely reconstructed. With this method, the interference problem of invalid signal such as trend item and noise which plague traditional methods is commendably solved. The great cumulative error from the traditional time-domain integral is effectively overcome. Moreover, the large low-frequency error from the traditional frequency-domain integral is successfully avoided. Comparing with the traditional integral methods, this method is outstanding at removing noise and retaining useful feature information and shows higher accuracy and superiority.

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

Science.gov (United States)

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.

An emerging network storage management standard: Media error monitoring and reporting information (MEMRI) - to determine optical tape data integrity

Science.gov (United States)

Podio, Fernando; Vollrath, William; Williams, Joel; Kobler, Ben; Crouse, Don

1998-01-01

Sophisticated network storage management applications are rapidly evolving to satisfy a market demand for highly reliable data storage systems with large data storage capacities and performance requirements. To preserve a high degree of data integrity, these applications must rely on intelligent data storage devices that can provide reliable indicators of data degradation. Error correction activity generally occurs within storage devices without notification to the host. Early indicators of degradation and media error monitoring 333 and reporting (MEMR) techniques implemented in data storage devices allow network storage management applications to notify system administrators of these events and to take appropriate corrective actions before catastrophic errors occur. Although MEMR techniques have been implemented in data storage devices for many years, until 1996 no MEMR standards existed. In 1996 the American National Standards Institute (ANSI) approved the only known (world-wide) industry standard specifying MEMR techniques to verify stored data on optical disks. This industry standard was developed under the auspices of the Association for Information and Image Management (AIIM). A recently formed AIIM Optical Tape Subcommittee initiated the development of another data integrity standard specifying a set of media error monitoring tools and media error monitoring information (MEMRI) to verify stored data on optical tape media. This paper discusses the need for intelligent storage devices that can provide data integrity metadata, the content of the existing data integrity standard for optical disks, and the content of the MEMRI standard being developed by the AIIM Optical Tape Subcommittee.

Noninteracting Fermi gas in a square-well potential.

Science.gov (United States)

Nash, C. E.

1971-01-01

The problem of a noninteracting Fermi gas in a finite square-well potential is solved analytically in the limit that the well becomes infinitely wide. The errors of previous authors using this model as a first approximation to the problem of a simple metal with surfaces are pointed out.

On root mean square approximation by exponential functions

OpenAIRE

Sharipov, Ruslan

2014-01-01

The problem of root mean square approximation of a square integrable function by finite linear combinations of exponential functions is considered. It is subdivided into linear and nonlinear parts. The linear approximation problem is solved. Then the nonlinear problem is studied in some particular example.

Analysis of total least squares in estimating the parameters of a mortar trajectory

Energy Technology Data Exchange (ETDEWEB)

Lau, D.L.; Ng, L.C.

1994-12-01

Least Squares (LS) is a method of curve fitting used with the assumption that error exists in the observation vector. The method of Total Least Squares (TLS) is more useful in cases where there is error in the data matrix as well as the observation vector. This paper describes work done in comparing the LS and TLS results for parameter estimation of a mortar trajectory based on a time series of angular observations. To improve the results, we investigated several derivations of the LS and TLS methods, and early findings show TLS provided slightly, 10%, improved results over the LS method.

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

Science.gov (United States)

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.…

Modeling the cosmic-ray-induced soft-error rate in integrated circuits: An overview

International Nuclear Information System (INIS)

Srinivasan, G.R.

1996-01-01

This paper is an overview of the concepts and methodologies used to predict soft-error rates (SER) due to cosmic and high-energy particle radiation in integrated circuit chips. The paper emphasizes the need for the SER simulation using the actual chip circuit model which includes device, process, and technology parameters as opposed to using either the discrete device simulation or generic circuit simulation that is commonly employed in SER modeling. Concepts such as funneling, event-by-event simulation, nuclear history files, critical charge, and charge sharing are examined. Also discussed are the relative importance of elastic and inelastic nuclear collisions, rare event statistics, and device vs. circuit simulations. The semi-empirical methodologies used in the aerospace community to arrive at SERs [also referred to as single-event upset (SEU) rates] in integrated circuit chips are reviewed. This paper is one of four in this special issue relating to SER modeling. Together, they provide a comprehensive account of this modeling effort, which has resulted in a unique modeling tool called the Soft-Error Monte Carlo Model, or SEMM

BIOMECHANICS. Why the seahorse tail is square.

Science.gov (United States)

Porter, Michael M; Adriaens, Dominique; Hatton, Ross L; Meyers, Marc A; McKittrick, Joanna

2015-07-03

Whereas the predominant shapes of most animal tails are cylindrical, seahorse tails are square prisms. Seahorses use their tails as flexible grasping appendages, in spite of a rigid bony armor that fully encases their bodies. We explore the mechanics of two three-dimensional-printed models that mimic either the natural (square prism) or hypothetical (cylindrical) architecture of a seahorse tail to uncover whether or not the square geometry provides any functional advantages. Our results show that the square prism is more resilient when crushed and provides a mechanism for preserving articulatory organization upon extensive bending and twisting, as compared with its cylindrical counterpart. Thus, the square architecture is better than the circular one in the context of two integrated functions: grasping ability and crushing resistance. Copyright © 2015, American Association for the Advancement of Science.

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

International Nuclear Information System (INIS)

Malinowski, Kathleen T.; McAvoy, Thomas J.; George, Rohini; Dieterich, Sonja; D'Souza, Warren D.

2012-01-01

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 precision 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.

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

Energy Technology Data Exchange (ETDEWEB)

Malinowski, Kathleen T. [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD (United States); Fischell Department of Bioengineering, University of Maryland, College Park, MD (United States); McAvoy, Thomas J. [Fischell Department of Bioengineering, University of Maryland, College Park, MD (United States); Department of Chemical and Biomolecular Engineering and Institute of Systems Research, University of Maryland, College Park, MD (United States); George, Rohini [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD (United States); Dieterich, Sonja [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA (United States); D' Souza, Warren D., E-mail: wdsou001@umaryland.edu [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD (United States); Fischell Department of Bioengineering, University of Maryland, College Park, MD (United States)

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 precision 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.

Some Modified Integrated Squared Error Procedures for Multivariate Normal Data.

Science.gov (United States)

1982-06-01

p-dimensional Gaussian. There are a number of measures of qualitative robustness but the most important is the influence function . Most of the other...measures are derived from the influence function . The influence function is simply proportional to the score function (Huber, 1981, p. 45 ). The... influence function at the p-variate Gaussian distribution Np (UV) is as -1P IC(x; ,N) = IE&) ;-") sD=XV = (I+c) (p+2)(x-p) exp(- ! (x-p) TV-.1-)) (3.6

Global minimum profile error (GMPE) - a least-squares-based approach for extracting macroscopic rate coefficients for complex gas-phase chemical reactions.

Science.gov (United States)

Duong, Minh V; Nguyen, Hieu T; Mai, Tam V-T; Huynh, Lam K

2018-01-03

Master equation/Rice-Ramsperger-Kassel-Marcus (ME/RRKM) has shown to be a powerful framework for modeling kinetic and dynamic behaviors of a complex gas-phase chemical system on a complicated multiple-species and multiple-channel potential energy surface (PES) for a wide range of temperatures and pressures. Derived from the ME time-resolved species profiles, the macroscopic or phenomenological rate coefficients are essential for many reaction engineering applications including those in combustion and atmospheric chemistry. Therefore, in this study, a least-squares-based approach named Global Minimum Profile Error (GMPE) was proposed and implemented in the MultiSpecies-MultiChannel (MSMC) code (Int. J. Chem. Kinet., 2015, 47, 564) to extract macroscopic rate coefficients for such a complicated system. The capability and limitations of the new approach were discussed in several well-defined test cases.

Constrained least squares regularization in PET

International Nuclear Information System (INIS)

Choudhury, K.R.; O'Sullivan, F.O.

1996-01-01

Standard reconstruction methods used in tomography produce images with undesirable negative artifacts in background and in areas of high local contrast. While sophisticated statistical reconstruction methods can be devised to correct for these artifacts, their computational implementation is excessive for routine operational use. This work describes a technique for rapid computation of approximate constrained least squares regularization estimates. The unique feature of the approach is that it involves no iterative projection or backprojection steps. This contrasts with the familiar computationally intensive algorithms based on algebraic reconstruction (ART) or expectation-maximization (EM) methods. Experimentation with the new approach for deconvolution and mixture analysis shows that the root mean square error quality of estimators based on the proposed algorithm matches and usually dominates that of more elaborate maximum likelihood, at a fraction of the computational effort

Estimating the kinetic parameters of activated sludge storage using weighted non-linear least-squares and accelerating genetic algorithm.

Science.gov (United States)

Fang, Fang; Ni, Bing-Jie; Yu, Han-Qing

2009-06-01

In this study, weighted non-linear least-squares analysis and accelerating genetic algorithm are integrated to estimate the kinetic parameters of substrate consumption and storage product formation of activated sludge. A storage product formation equation is developed and used to construct the objective function for the determination of its production kinetics. The weighted least-squares analysis is employed to calculate the differences in the storage product concentration between the model predictions and the experimental data as the sum of squared weighted errors. The kinetic parameters for the substrate consumption and the storage product formation are estimated to be the maximum heterotrophic growth rate of 0.121/h, the yield coefficient of 0.44 mg CODX/mg CODS (COD, chemical oxygen demand) and the substrate half saturation constant of 16.9 mg/L, respectively, by minimizing the objective function using a real-coding-based accelerating genetic algorithm. Also, the fraction of substrate electrons diverted to the storage product formation is estimated to be 0.43 mg CODSTO/mg CODS. The validity of our approach is confirmed by the results of independent tests and the kinetic parameter values reported in literature, suggesting that this approach could be useful to evaluate the product formation kinetics of mixed cultures like activated sludge. More importantly, as this integrated approach could estimate the kinetic parameters rapidly and accurately, it could be applied to other biological processes.

Mitigation of defocusing by statics and near-surface velocity errors by interferometric least-squares migration with a reference datum

KAUST Repository

Sinha, Mrinal

2016-05-23

Imaging seismic data with an erroneous migration velocity can lead to defocused migration images. To mitigate this problem, we first choose a reference reflector whose topography is well-known from the well logs, for example. Reflections from this reference layer are correlated with the traces associated with reflections from deeper interfaces to get crosscorrelograms. Interferometric least-squares migration (ILSM) is then used to get the migration image that maximizes the crosscorrelation between the observed and the predicted crosscorrelograms. Deeper reference reflectors are used to image deeper parts of the subsurface with a greater accuracy. Results on synthetic and field data show that defocusing caused by velocity errors is largely suppressed by ILSM. We have also determined that ILSM can be used for 4D surveys in which environmental conditions and acquisition parameters are significantly different from one survey to the next. The limitations of ILSM are that it requires prior knowledge of a reference reflector in the subsurface and the velocity model below the reference reflector should be accurate.

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

Science.gov (United States)

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.

SU-G-BRB-03: Assessing the Sensitivity and False Positive Rate of the Integrated Quality Monitor (IQM) Large Area Ion Chamber to MLC Positioning Errors

Energy Technology Data Exchange (ETDEWEB)

Boehnke, E McKenzie; DeMarco, J; Steers, J; Fraass, B [Cedars-Sinai Medical Center, Los Angeles, CA (United States)

2016-06-15

Purpose: To examine both the IQM’s sensitivity and false positive rate to varying MLC errors. By balancing these two characteristics, an optimal tolerance value can be derived. Methods: An un-modified SBRT Liver IMRT plan containing 7 fields was randomly selected as a representative clinical case. The active MLC positions for all fields were perturbed randomly from a square distribution of varying width (±1mm to ±5mm). These unmodified and modified plans were measured multiple times each by the IQM (a large area ion chamber mounted to a TrueBeam linac head). Measurements were analyzed relative to the initial, unmodified measurement. IQM readings are analyzed as a function of control points. In order to examine sensitivity to errors along a field’s delivery, each measured field was divided into 5 groups of control points, and the maximum error in each group was recorded. Since the plans have known errors, we compared how well the IQM is able to differentiate between unmodified and error plans. ROC curves and logistic regression were used to analyze this, independent of thresholds. Results: A likelihood-ratio Chi-square test showed that the IQM could significantly predict whether a plan had MLC errors, with the exception of the beginning and ending control points. Upon further examination, we determined there was ramp-up occurring at the beginning of delivery. Once the linac AFC was tuned, the subsequent measurements (relative to a new baseline) showed significant (p <0.005) abilities to predict MLC errors. Using the area under the curve, we show the IQM’s ability to detect errors increases with increasing MLC error (Spearman’s Rho=0.8056, p<0.0001). The optimal IQM count thresholds from the ROC curves are ±3%, ±2%, and ±7% for the beginning, middle 3, and end segments, respectively. Conclusion: The IQM has proven to be able to detect not only MLC errors, but also differences in beam tuning (ramp-up). Partially supported by the Susan Scott Foundation.

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

Energy Technology Data Exchange (ETDEWEB)

Lee, Kookjin [Univ. of Maryland, College Park, MD (United States). Dept. of Computer Science; Carlberg, Kevin [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Elman, Howard C. [Univ. of Maryland, College Park, MD (United States). Dept. of Computer Science and Inst. for Advanced Computer Studies

2018-03-29

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 weighted $\\ell^2$-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.

Optimally weighted least-squares steganalysis

Science.gov (United States)

Ker, Andrew D.

2007-02-01

Quantitative steganalysis aims to estimate the amount of payload in a stego object, and such estimators seem to arise naturally in steganalysis of Least Significant Bit (LSB) replacement in digital images. However, as with all steganalysis, the estimators are subject to errors, and their magnitude seems heavily dependent on properties of the cover. In very recent work we have given the first derivation of estimation error, for a certain method of steganalysis (the Least-Squares variant of Sample Pairs Analysis) of LSB replacement steganography in digital images. In this paper we make use of our theoretical results to find an improved estimator and detector. We also extend the theoretical analysis to another (more accurate) steganalysis estimator (Triples Analysis) and hence derive an improved version of that estimator too. Experimental results show that the new steganalyzers have improved accuracy, particularly in the difficult case of never-compressed covers.

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

KAUST Repository

Migliorati, Giovanni

2015-08-28

We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. The convergence estimates are given in mean-square sense with respect to the sampling measure. The noise may be correlated with the location of the evaluation and may have nonzero mean (offset). We consider both cases of bounded or square-integrable noise / offset. We prove conditions between the number of sampling points and the dimension of the underlying approximation space that ensure a stable and accurate approximation. Particular focus is on deriving estimates in probability within a given confidence level. We analyze how the best approximation error and the noise terms affect the convergence rate and the overall confidence level achieved by the convergence estimate. The proofs of our convergence estimates in probability use arguments from the theory of large deviations to bound the noise term. Finally we address the particular case of multivariate polynomial approximation spaces with any density in the beta family, including uniform and Chebyshev.

Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction

International Nuclear Information System (INIS)

Carlberg, Kevin Thomas; Barone, Matthew F.; Antil, Harbir

2016-01-01

Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible flow problems where standard Galerkin techniques have failed. Furthermore, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform optimal projection associated with residual minimization at the time-continuous level, while LSPG techniques do so at the time-discrete level. This work provides a detailed theoretical and computational comparison of the two techniques for two common classes of time integrators: linear multistep schemes and Runge–Kutta schemes. We present a number of new findings, including conditions under which the LSPG ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and computationally that decreasing the time step does not necessarily decrease the error for the LSPG ROM; instead, the time step should be ‘matched’ to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible-flow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the LSPG reduced-order model by an order of magnitude.

Application of the tuning algorithm with the least squares approximation to the suboptimal control algorithm for integrating objects

Science.gov (United States)

Kuzishchin, V. F.; Merzlikina, E. I.; Van Va, Hoang

2017-11-01

The problem of PID and PI-algorithms tuning by means of the approximation by the least square method of the frequency response of a linear algorithm to the sub-optimal algorithm is considered. The advantage of the method is that the parameter values are obtained through one cycle of calculation. Recommendations how to choose the parameters of the least square method taking into consideration the plant dynamics are given. The parameters mentioned are the time constant of the filter, the approximation frequency range and the correction coefficient for the time delay parameter. The problem is considered for integrating plants for some practical cases (the level control system in a boiler drum). The transfer function of the suboptimal algorithm is determined relating to the disturbance that acts in the point of the control impact input, it is typical for thermal plants. In the recommendations it is taken into consideration that the overregulation for the transient process when the setpoint is changed is also limited. In order to compare the results the systems under consideration are also calculated by the classical method with the limited frequency oscillation index. The results given in the paper can be used by specialists dealing with tuning systems with the integrating plants.

Plane-wave least-squares reverse-time migration

KAUST Repository

Dai, Wei

2013-06-03

A plane-wave least-squares reverse-time migration (LSRTM) is formulated with a new parameterization, where the migration image of each shot gather is updated separately and an ensemble of prestack images is produced along with common image gathers. The merits of plane-wave prestack LSRTM are the following: (1) plane-wave prestack LSRTM can sometimes offer stable convergence even when the migration velocity has bulk errors of up to 5%; (2) to significantly reduce computation cost, linear phase-shift encoding is applied to hundreds of shot gathers to produce dozens of plane waves. Unlike phase-shift encoding with random time shifts applied to each shot gather, plane-wave encoding can be effectively applied to data with a marine streamer geometry. (3) Plane-wave prestack LSRTM can provide higher-quality images than standard reverse-time migration. Numerical tests on the Marmousi2 model and a marine field data set are performed to illustrate the benefits of plane-wave LSRTM. Empirical results show that LSRTM in the plane-wave domain, compared to standard reversetime migration, produces images efficiently with fewer artifacts and better spatial resolution. Moreover, the prestack image ensemble accommodates more unknowns to makes it more robust than conventional least-squares migration in the presence of migration velocity errors. © 2013 Society of Exploration Geophysicists.

IMPACT OF TRADE OPENNESS ON OUTPUT GROWTH: CO INTEGRATION AND ERROR CORRECTION MODEL APPROACH

Directory of Open Access Journals (Sweden)

Asma Arif

2012-01-01

Full Text Available This study analyzed the long run relationship between trade openness and output growth for Pakistan using annual time series data for 1972-2010. This study follows the Engle and Granger co integration analysis and error correction approach to analyze the long run relationship between the two variables. The Error Correction Term (ECT for output growth and trade openness is significant at 5% level of significance and indicates a positive long run relation between the variables. This study has also analyzed the causality between trade openness and output growth by using granger causality test. The results of granger causality show that there is a bi-directional significant relationship between trade openness and economic growth.

Mean-Square Convergence of Drift-Implicit One-Step Methods for Neutral Stochastic Delay Differential Equations with Jump Diffusion

Directory of Open Access Journals (Sweden)

Lin Hu

2011-01-01

Full Text Available A class of drift-implicit one-step schemes are proposed for the neutral stochastic delay differential equations (NSDDEs driven by Poisson processes. A general framework for mean-square convergence of the methods is provided. It is shown that under certain conditions global error estimates for a method can be inferred from estimates on its local error. The applicability of the mean-square convergence theory is illustrated by the stochastic θ-methods and the balanced implicit methods. It is derived from Theorem 3.1 that the order of the mean-square convergence of both of them for NSDDEs with jumps is 1/2. Numerical experiments illustrate the theoretical results. It is worth noting that the results of mean-square convergence of the stochastic θ-methods and the balanced implicit methods are also new.

An Integrated Signaling-Encryption Mechanism to Reduce Error Propagation in Wireless Communications: Performance Analyses

Energy Technology Data Exchange (ETDEWEB)

Olama, Mohammed M [ORNL; Matalgah, Mustafa M [ORNL; Bobrek, Miljko [ORNL

2015-01-01

Traditional encryption techniques require packet overhead, produce processing time delay, and suffer from severe quality of service deterioration due to fades and interference in wireless channels. These issues reduce the effective transmission data rate (throughput) considerably in wireless communications, where data rate with limited bandwidth is the main constraint. In this paper, performance evaluation analyses are conducted for an integrated signaling-encryption mechanism that is secure and enables improved throughput and probability of bit-error in wireless channels. This mechanism eliminates the drawbacks stated herein by encrypting only a small portion of an entire transmitted frame, while the rest is not subject to traditional encryption but goes through a signaling process (designed transformation) with the plaintext of the portion selected for encryption. We also propose to incorporate error correction coding solely on the small encrypted portion of the data to drastically improve the overall bit-error rate performance while not noticeably increasing the required bit-rate. We focus on validating the signaling-encryption mechanism utilizing Hamming and convolutional error correction coding by conducting an end-to-end system-level simulation-based study. The average probability of bit-error and throughput of the encryption mechanism are evaluated over standard Gaussian and Rayleigh fading-type channels and compared to the ones of the conventional advanced encryption standard (AES).

Integrated parallel reception, excitation, and shimming (iPRES).

Science.gov (United States)

Han, Hui; Song, Allen W; Truong, Trong-Kha

2013-07-01

To develop a new concept for a hardware platform that enables integrated parallel reception, excitation, and shimming. This concept uses a single coil array rather than separate arrays for parallel excitation/reception and B0 shimming. It relies on a novel design that allows a radiofrequency current (for excitation/reception) and a direct current (for B0 shimming) to coexist independently in the same coil. Proof-of-concept B0 shimming experiments were performed with a two-coil array in a phantom, whereas B0 shimming simulations were performed with a 48-coil array in the human brain. Our experiments show that individually optimized direct currents applied in each coil can reduce the B0 root-mean-square error by 62-81% and minimize distortions in echo-planar images. The simulations show that dynamic shimming with the 48-coil integrated parallel reception, excitation, and shimming array can reduce the B0 root-mean-square error in the prefrontal and temporal regions by 66-79% as compared with static second-order spherical harmonic shimming and by 12-23% as compared with dynamic shimming with a 48-coil conventional shim array. Our results demonstrate the feasibility of the integrated parallel reception, excitation, and shimming concept to perform parallel excitation/reception and B0 shimming with a unified coil system as well as its promise for in vivo applications. Copyright © 2013 Wiley Periodicals, Inc.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

A FORTRAN program for a least-square fitting

International Nuclear Information System (INIS)

Yamazaki, Tetsuo

1978-01-01

A practical FORTRAN program for a least-squares fitting is presented. Although the method is quite usual, the program calculates not only the most satisfactory set of values of unknowns but also the plausible errors associated with them. As an example, a measured lateral absorbed-dose distribution in water for a narrow 25-MeV electron beam is fitted to a Gaussian distribution. (auth.)

Sums of squares of integers

CERN Document Server

Moreno, Carlos J

2005-01-01

Introduction Prerequisites Outline of Chapters 2 - 8 Elementary Methods Introduction Some Lemmas Two Fundamental Identities Euler's Recurrence for Sigma(n)More Identities Sums of Two Squares Sums of Four Squares Still More Identities Sums of Three Squares An Alternate Method Sums of Polygonal Numbers Exercises Bernoulli Numbers Overview Definition of the Bernoulli Numbers The Euler-MacLaurin Sum Formula The Riemann Zeta Function Signs of Bernoulli Numbers Alternate The von Staudt-Clausen Theorem Congruences of Voronoi and Kummer Irregular Primes Fractional Parts of Bernoulli Numbers Exercises Examples of Modular Forms Introduction An Example of Jacobi and Smith An Example of Ramanujan and Mordell An Example of Wilton: t (n) Modulo 23 An Example of Hamburger Exercises Hecke's Theory of Modular FormsIntroduction Modular Group ? and its Subgroup ? 0 (N) Fundamental Domains For ? and ? 0 (N) Integral Modular Forms Modular Forms of Type Mk(? 0(N);chi) and Euler-Poincare series Hecke Operators Dirichlet Series and ...

Mean-square performance of a convex combination of two adaptive filters

DEFF Research Database (Denmark)

Garcia, Jeronimo; Figueiras-Vidal, A.R.; Sayed, A.H.

2006-01-01

Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination of two transversal filters. The individual filters are independently adapted using their own error signals, while the combination i...

An Empirical State Error Covariance Matrix for Batch State Estimation

Science.gov (United States)

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

Regularization Techniques for Linear Least-Squares Problems

KAUST Repository

Suliman, Mohamed

2016-04-01

Linear estimation is a fundamental branch of signal processing that deals with estimating the values of parameters from a corrupted measured data. Throughout the years, several optimization criteria have been used to achieve this task. The most astonishing attempt among theses is the linear least-squares. Although this criterion enjoyed a wide popularity in many areas due to its attractive properties, it appeared to suffer from some shortcomings. Alternative optimization criteria, as a result, have been proposed. These new criteria allowed, in one way or another, the incorporation of further prior information to the desired problem. Among theses alternative criteria is the regularized least-squares (RLS). In this thesis, we propose two new algorithms to find the regularization parameter for linear least-squares problems. In the constrained perturbation regularization algorithm (COPRA) for random matrices and COPRA for linear discrete ill-posed problems, an artificial perturbation matrix with a bounded norm is forced into the model matrix. This perturbation is introduced to enhance the singular value structure of the matrix. As a result, the new modified model is expected to provide a better stabilize substantial solution when used to estimate the original signal through minimizing the worst-case residual error function. Unlike many other regularization algorithms that go in search of minimizing the estimated data error, the two new proposed algorithms are developed mainly to select the artifcial perturbation bound and the regularization parameter in a way that approximately minimizes the mean-squared error (MSE) between the original signal and its estimate under various conditions. The first proposed COPRA method is developed mainly to estimate the regularization parameter when the measurement matrix is complex Gaussian, with centered unit variance (standard), and independent and identically distributed (i.i.d.) entries. Furthermore, the second proposed COPRA

The derivation of vector magnetic fields from Stokes profiles - Integral versus least squares fitting techniques

Science.gov (United States)

Ronan, R. S.; Mickey, D. L.; Orrall, F. Q.

1987-01-01

The results of two methods for deriving photospheric vector magnetic fields from the Zeeman effect, as observed in the Fe I line at 6302.5 A at high spectral resolution (45 mA), are compared. The first method does not take magnetooptical effects into account, but determines the vector magnetic field from the integral properties of the Stokes profiles. The second method is an iterative least-squares fitting technique which fits the observed Stokes profiles to the profiles predicted by the Unno-Rachkovsky solution to the radiative transfer equation. For sunspot fields above about 1500 gauss, the two methods are found to agree in derived azimuthal and inclination angles to within about + or - 20 deg.

A new finite element formulation for CFD:VIII. The Galerkin/least-squares method for advective-diffusive equations

International Nuclear Information System (INIS)

Hughes, T.J.R.; Hulbert, G.M.; Franca, L.P.

1988-10-01

Galerkin/least-squares finite element methods are presented for advective-diffusive equations. Galerkin/least-squares represents a conceptual simplification of SUPG, and is in fact applicable to a wide variety of other problem types. A convergence analysis and error estimates are presented. (author) [pt

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

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

Science.gov (United States)

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.

On the Spatial and Temporal Sampling Errors of Remotely Sensed Precipitation Products

Directory of Open Access Journals (Sweden)

Ali Behrangi

2017-11-01

Full Text Available Observation with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and duration of precipitation events. In this study, the errors resulting from temporal and spatial sampling of precipitation events were quantified and examined using the latest version (V4 of the Global Precipitation Measurement (GPM mission integrated multi-satellite retrievals for GPM (IMERG, which is available since spring of 2014. Relative mean square error was calculated at 0.1° × 0.1° every 0.5 h between the degraded (temporally and spatially and original IMERG products. The temporal and spatial degradation was performed by producing three-hour (T3, six-hour (T6, 0.5° × 0.5° (S5, and 1.0° × 1.0° (S10 maps. The results show generally larger errors over land than ocean, especially over mountainous regions. The relative error of T6 is almost 20% larger than T3 over tropical land, but is smaller in higher latitudes. Over land relative error of T6 is larger than S5 across all latitudes, while T6 has larger relative error than S10 poleward of 20°S–20°N. Similarly, the relative error of T3 exceeds S5 poleward of 20°S–20°N, but does not exceed S10, except in very high latitudes. Similar results are also seen over ocean, but the error ratios are generally less sensitive to seasonal changes. The results also show that the spatial and temporal relative errors are not highly correlated. Overall, lower correlations between the spatial and temporal relative errors are observed over ocean than over land. Quantification of such spatiotemporal effects provides additional insights into evaluation studies, especially when different products are cross-compared at a range of spatiotemporal scales.

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.

Science.gov (United States)

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.

Error threshold ghosts in a simple hypercycle with error prone self-replication

International Nuclear Information System (INIS)

Sardanyes, Josep

2008-01-01

A delayed transition because of mutation processes is shown to happen in a simple hypercycle composed by two indistinguishable molecular species with error prone self-replication. The appearance of a ghost near the hypercycle error threshold causes a delay in the extinction and thus in the loss of information of the mutually catalytic replicators, in a kind of information memory. The extinction time, τ, scales near bifurcation threshold according to the universal square-root scaling law i.e. τ ∼ (Q hc - Q) -1/2 , typical of dynamical systems close to a saddle-node bifurcation. Here, Q hc represents the bifurcation point named hypercycle error threshold, involved in the change among the asymptotic stability phase and the so-called Random Replication State (RRS) of the hypercycle; and the parameter Q is the replication quality factor. The ghost involves a longer transient towards extinction once the saddle-node bifurcation has occurred, being extremely long near the bifurcation threshold. The role of this dynamical effect is expected to be relevant in fluctuating environments. Such a phenomenon should also be found in larger hypercycles when considering the hypercycle species in competition with their error tail. The implications of the ghost in the survival and evolution of error prone self-replicating molecules with hypercyclic organization are discussed

A method based on moving least squares for XRII image distortion correction

International Nuclear Information System (INIS)

Yan Shiju; Wang Chengtao; Ye Ming

2007-01-01

This paper presents a novel integrated method to correct geometric distortions of XRII (x-ray image intensifier) images. The method has been compared, in terms of mean-squared residual error measured at control and intermediate points, with two traditional local methods and a traditional global methods. The proposed method is based on the methods of moving least squares (MLS) and polynomial fitting. Extensive experiments were performed on simulated and real XRII images. In simulation, the effect of pincushion distortion, sigmoidal distortion, local distortion, noise, and the number of control points was tested. The traditional local methods were sensitive to pincushion and sigmoidal distortion. The traditional global method was only sensitive to sigmoidal distortion. The proposed method was found neither sensitive to pincushion distortion nor sensitive to sigmoidal distortion. The sensitivity of the proposed method to local distortion was lower than or comparable with that of the traditional global method. The sensitivity of the proposed method to noise was higher than that of all three traditional methods. Nevertheless, provided the standard deviation of noise was not greater than 0.1 pixels, accuracy of the proposed method is still higher than the traditional methods. The sensitivity of the proposed method to the number of control points was greatly lower than that of the traditional methods. Provided that a proper cutoff radius is chosen, accuracy of the proposed method is higher than that of the traditional methods. Experiments on real images, carried out by using a 9 in. XRII, showed that residual error of the proposed method (0.2544±0.2479 pixels) is lower than that of the traditional global method (0.4223±0.3879 pixels) and local methods (0.4555±0.3518 pixels and 0.3696±0.4019 pixels, respectively)

Application of the error propagation theory in estimates of static formation temperatures in geothermal and petroleum boreholes

International Nuclear Information System (INIS)

Verma, Surendra P.; Andaverde, Jorge; Santoyo, E.

2006-01-01

We used the error propagation theory to calculate uncertainties in static formation temperature estimates in geothermal and petroleum wells from three widely used methods (line-source or Horner method; spherical and radial heat flow method; and cylindrical heat source method). Although these methods commonly use an ordinary least-squares linear regression model considered in this study, we also evaluated two variants of a weighted least-squares linear regression model for the actual relationship between the bottom-hole temperature and the corresponding time functions. Equations based on the error propagation theory were derived for estimating uncertainties in the time function of each analytical method. These uncertainties in conjunction with those on bottom-hole temperatures were used to estimate individual weighting factors required for applying the two variants of the weighted least-squares regression model. Standard deviations and 95% confidence limits of intercept were calculated for both types of linear regressions. Applications showed that static formation temperatures computed with the spherical and radial heat flow method were generally greater (at the 95% confidence level) than those from the other two methods under study. When typical measurement errors of 0.25 h in time and 5 deg. C in bottom-hole temperature were assumed for the weighted least-squares model, the uncertainties in the estimated static formation temperatures were greater than those for the ordinary least-squares model. However, if these errors were smaller (about 1% in time and 0.5% in temperature measurements), the weighted least-squares linear regression model would generally provide smaller uncertainties for the estimated temperatures than the ordinary least-squares linear regression model. Therefore, the weighted model would be statistically correct and more appropriate for such applications. We also suggest that at least 30 precise and accurate BHT and time measurements along with

Conjugate descent formulation of backpropagation error in ...

African Journals Online (AJOL)

The feedforward neural network architecture uses backpropagation learning to determine optimal weights between dierent interconnected layers. This learning procedure uses a gradient descent technique applied to a sum-of-squares error function for the given input-output pattern. It employs an iterative procedure to ...

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

Science.gov (United States)

Cohen, E A K; Ober, R J

2013-12-15

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.

Integrating bathymetric and topographic data

Science.gov (United States)

Teh, Su Yean; Koh, Hock Lye; Lim, Yong Hui; Tan, Wai Kiat

2017-11-01

The quality of bathymetric and topographic resolution significantly affect the accuracy of tsunami run-up and inundation simulation. However, high resolution gridded bathymetric and topographic data sets for Malaysia are not freely available online. It is desirable to have seamless integration of high resolution bathymetric and topographic data. The bathymetric data available from the National Hydrographic Centre (NHC) of the Royal Malaysian Navy are in scattered form; while the topographic data from the Department of Survey and Mapping Malaysia (JUPEM) are given in regularly spaced grid systems. Hence, interpolation is required to integrate the bathymetric and topographic data into regularly-spaced grid systems for tsunami simulation. The objective of this research is to analyze the most suitable interpolation methods for integrating bathymetric and topographic data with minimal errors. We analyze four commonly used interpolation methods for generating gridded topographic and bathymetric surfaces, namely (i) Kriging, (ii) Multiquadric (MQ), (iii) Thin Plate Spline (TPS) and (iv) Inverse Distance to Power (IDP). Based upon the bathymetric and topographic data for the southern part of Penang Island, our study concluded, via qualitative visual comparison and Root Mean Square Error (RMSE) assessment, that the Kriging interpolation method produces an interpolated bathymetric and topographic surface that best approximate the admiralty nautical chart of south Penang Island.

A negative-norm least-squares method for time-harmonic Maxwell equations

KAUST Repository

Copeland, Dylan M.

2012-04-01

This paper presents and analyzes a negative-norm least-squares finite element discretization method for the dimension-reduced time-harmonic Maxwell equations in the case of axial symmetry. The reduced equations are expressed in cylindrical coordinates, and the analysis consequently involves weighted Sobolev spaces based on the degenerate radial weighting. The main theoretical results established in this work include existence and uniqueness of the continuous and discrete formulations and error estimates for simple finite element functions. Numerical experiments confirm the error estimates and efficiency of the method for piecewise constant coefficients. © 2011 Elsevier Inc.

Learning mechanisms to limit medication administration errors.

Science.gov (United States)

Drach-Zahavy, Anat; Pud, Dorit

2010-04-01

This paper is a report of a study conducted to identify and test the effectiveness of learning mechanisms applied by the nursing staff of hospital wards as a means of limiting medication administration errors. Since the influential report ;To Err Is Human', research has emphasized the role of team learning in reducing medication administration errors. Nevertheless, little is known about the mechanisms underlying team learning. Thirty-two hospital wards were randomly recruited. Data were collected during 2006 in Israel by a multi-method (observations, interviews and administrative data), multi-source (head nurses, bedside nurses) approach. Medication administration error was defined as any deviation from procedures, policies and/or best practices for medication administration, and was identified using semi-structured observations of nurses administering medication. Organizational learning was measured using semi-structured interviews with head nurses, and the previous year's reported medication administration errors were assessed using administrative data. The interview data revealed four learning mechanism patterns employed in an attempt to learn from medication administration errors: integrated, non-integrated, supervisory and patchy learning. Regression analysis results demonstrated that whereas the integrated pattern of learning mechanisms was associated with decreased errors, the non-integrated pattern was associated with increased errors. Supervisory and patchy learning mechanisms were not associated with errors. Superior learning mechanisms are those that represent the whole cycle of team learning, are enacted by nurses who administer medications to patients, and emphasize a system approach to data analysis instead of analysis of individual cases.

Efficient orbit integration by manifold correction methods.

Science.gov (United States)

Fukushima, Toshio

2005-12-01

Triggered by a desire to investigate, numerically, the planetary precession through a long-term numerical integration of the solar system, we developed a new formulation of numerical integration of orbital motion named manifold correct on methods. The main trick is to rigorously retain the consistency of physical relations, such as the orbital energy, the orbital angular momentum, or the Laplace integral, of a binary subsystem. This maintenance is done by applying a correction to the integrated variables at each integration step. Typical methods of correction are certain geometric transformations, such as spatial scaling and spatial rotation, which are commonly used in the comparison of reference frames, or mathematically reasonable operations, such as modularization of angle variables into the standard domain [-pi, pi). The form of the manifold correction methods finally evolved are the orbital longitude methods, which enable us to conduct an extremely precise integration of orbital motions. In unperturbed orbits, the integration errors are suppressed at the machine epsilon level for an indefinitely long period. In perturbed cases, on the other hand, the errors initially grow in proportion to the square root of time and then increase more rapidly, the onset of which depends on the type and magnitude of the perturbations. This feature is also realized for highly eccentric orbits by applying the same idea as used in KS-regularization. In particular, the introduction of time elements greatly enhances the performance of numerical integration of KS-regularized orbits, whether the scaling is applied or not.

Modified Firefly Algorithm based controller design for integrating and unstable delay processes

Directory of Open Access Journals (Sweden)

A. Gupta

2016-03-01

Full Text Available In this paper, Modified Firefly Algorithm has been used for optimizing the controller parameters of Smith predictor structure. The proposed algorithm modifies the position formula of the standard Firefly Algorithm in order to achieve faster convergence rate. Performance criteria Integral Square Error (ISE is optimized using this optimization technique. Simulation results show high performance for Modified Firefly Algorithm as compared to conventional Firefly Algorithm in terms of convergence rate. Integrating and unstable delay processes are taken as examples to indicate the performance of the proposed method.

Creating Magic Squares.

Science.gov (United States)

Lyon, Betty Clayton

1990-01-01

One method of making magic squares using a prolongated square is illustrated. Discussed are third-order magic squares, fractional magic squares, fifth-order magic squares, decimal magic squares, and even magic squares. (CW)

Dancoff Correction in Square and Hexagonal Lattices

Energy Technology Data Exchange (ETDEWEB)

Carlvik, I

1966-11-15

This report presents the results of a series of calculations of Dancoff corrections for square and hexagonal rod lattices. The tables cover a wide range of volume ratios and moderator cross sections. The results were utilized for checking the approximative formula of Sauer and also the modification of Bonalumi to Sauer's formula. The modified formula calculates the Dancoff correction with an accuracy of 0.01 - 0.02 in cases of practical interest. Calculations have also been performed on square lattices with an empty gap surrounding the rods. The results demonstrate the error involved in treating this kind of geometry by means of homogenizing the gap and the moderator. The calculations were made on the Ferranti Mercury computer of AB Atomenergi before it was closed down. Since then FORTRAN routines for Dancoff corrections have been written, and a subroutine DASQHE is included in the report.

Improvement of least-squares collocation error estimates using local GOCE Tzz signal standard deviations

DEFF Research Database (Denmark)

Tscherning, Carl Christian

2015-01-01

outside the data area. On the other hand, a comparison of predicted quantities with observed values show that the error also varies depending on the local data standard deviation. This quantity may be (and has been) estimated using the GOCE second order vertical derivative, Tzz, in the area covered...... by the satellite. The ratio between the nearly constant standard deviations of a predicted quantity (e.g. in a 25° × 25° area) and the standard deviations of Tzz in smaller cells (e.g., 1° × 1°) have been used as a scale factor in order to obtain more realistic error estimates. This procedure has been applied...

Using Squares to Sum Squares

Science.gov (United States)

DeTemple, Duane

2010-01-01

Purely combinatorial proofs are given for the sum of squares formula, 1[superscript 2] + 2[superscript 2] + ... + n[superscript 2] = n(n + 1) (2n + 1) / 6, and the sum of sums of squares formula, 1[superscript 2] + (1[superscript 2] + 2[superscript 2]) + ... + (1[superscript 2] + 2[superscript 2] + ... + n[superscript 2]) = n(n + 1)[superscript 2]…

Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives

DEFF Research Database (Denmark)

Lascu, Cristian; Jafarzadeh, Saeed; Fadali, M.Sami

2013-01-01

This paper investigates the application, design, and implementation of the square root unscented Kalman filter (UKF) (SRUKF) for induction motor (IM) sensorless drives. The UKF uses nonlinear unscented transforms (UTs) in the prediction step in order to preserve the stochastic characteristics...... of a nonlinear system. The advantage of using the UT is its ability to capture the nonlinear behavior of the system, unlike the extended Kalman filter (EKF) that uses linearized models. The SRUKF implements the UKF using square root filtering to reduce computational errors. We discuss the theoretical aspects...

An integrated supply chain model for deteriorating items considering delay in payments, imperfect quality and inspection errors under inflationary conditions

DEFF Research Database (Denmark)

Mahmoudinezhad, Mahvash; Mirzazadeh, Abolfazl; Ghoreishi, Maryam

2017-01-01

of the manufacturer is not perfect and makes inspection errors of Type 1 and Type 2. The second-stage inspection of the manufacturer is at the end of production period without inspection errors. Also, the demand is linear function of time. Once the retailer receives the lot, a 100% screening process of the lot......In this article, an integrated production–distribution model is presented for a manufacturer and retailer supply chain under inflationary conditions, permissible delay in payments, deterioration, imperfect production process and inspection errors. We assume that the first-stage inspection...

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR Soil Moisture Retrieval Errors

Directory of Open Access Journals (Sweden)

Ju Hyoung Lee

2016-04-01

Full Text Available Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE, while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

Least-squares finite element discretizations of neutron transport equations in 3 dimensions

Energy Technology Data Exchange (ETDEWEB)

Manteuffel, T.A [Univ. of Colorado, Boulder, CO (United States); Ressel, K.J. [Interdisciplinary Project Center for Supercomputing, Zurich (Switzerland); Starkes, G. [Universtaet Karlsruhe (Germany)

1996-12-31

The least-squares finite element framework to the neutron transport equation introduced in is based on the minimization of a least-squares functional applied to the properly scaled neutron transport equation. Here we report on some practical aspects of this approach for neutron transport calculations in three space dimensions. The systems of partial differential equations resulting from a P{sub 1} and P{sub 2} approximation of the angular dependence are derived. In the diffusive limit, the system is essentially a Poisson equation for zeroth moment and has a divergence structure for the set of moments of order 1. One of the key features of the least-squares approach is that it produces a posteriori error bounds. We report on the numerical results obtained for the minimum of the least-squares functional augmented by an additional boundary term using trilinear finite elements on a uniform tesselation into cubes.

Least square regularized regression in sum space.

Science.gov (United States)

Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu

2013-04-01

This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.

Mean value estimates of the error terms of Lehmer problem

Indian Academy of Sciences (India)

Mean value estimates of the error terms of Lehmer problem. DONGMEI REN1 and YAMING ... For further properties of N(a,p) in [6], he studied the mean square value of the error term. E(a, p) = N(a,p) − 1. 2 (p − 1) ..... [1] Apostol Tom M, Introduction to Analytic Number Theory (New York: Springer-Verlag). (1976). [2] Guy R K ...

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

Science.gov (United States)

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…

On the inversion of geodetic integrals defined over the sphere using 1-D FFT

Science.gov (United States)

García, R. V.; Alejo, C. A.

2005-08-01

An iterative method is presented which performs inversion of integrals defined over the sphere. The method is based on one-dimensional fast Fourier transform (1-D FFT) inversion and is implemented with the projected Landweber technique, which is used to solve constrained least-squares problems reducing the associated 1-D cyclic-convolution error. The results obtained are as precise as the direct matrix inversion approach, but with better computational efficiency. A case study uses the inversion of Hotine’s integral to obtain gravity disturbances from geoid undulations. Numerical convergence is also analyzed and comparisons with respect to the direct matrix inversion method using conjugate gradient (CG) iteration are presented. Like the CG method, the number of iterations needed to get the optimum (i.e., small) error decreases as the measurement noise increases. Nevertheless, for discrete data given over a whole parallel band, the method can be applied directly without implementing the projected Landweber method, since no cyclic convolution error exists.

FEL small signal gain reduction due to phase error of undulator

International Nuclear Information System (INIS)

Jia Qika

2002-01-01

The effects of undulator phase errors on the Free Electron Laser small signal gain is analyzed and discussed. The gain reduction factor due to the phase error is given analytically for low-gain regimes, it shows that degradation of the gain is similar to that of the spontaneous radiation, has a simple exponential relation with square of the rms phase error, and the linear variation part of phase error induces the position shift of maximum gain. The result also shows that the Madey's theorem still hold in the presence of phase error. The gain reduction factor due to the phase error for high-gain regimes also can be given in a simple way

Decision-Directed Recursive Least Squares MIMO Channels Tracking

Directory of Open Access Journals (Sweden)

Karami Ebrahim

2006-01-01

Full Text Available A new approach for joint data estimation and channel tracking for multiple-input multiple-output (MIMO channels is proposed based on the decision-directed recursive least squares (DD-RLS algorithm. RLS algorithm is commonly used for equalization and its application in channel estimation is a novel idea. In this paper, after defining the weighted least squares cost function it is minimized and eventually the RLS MIMO channel estimation algorithm is derived. The proposed algorithm combined with the decision-directed algorithm (DDA is then extended for the blind mode operation. From the computational complexity point of view being versus the number of transmitter and receiver antennas, the proposed algorithm is very efficient. Through various simulations, the mean square error (MSE of the tracking of the proposed algorithm for different joint detection algorithms is compared with Kalman filtering approach which is one of the most well-known channel tracking algorithms. It is shown that the performance of the proposed algorithm is very close to Kalman estimator and that in the blind mode operation it presents a better performance with much lower complexity irrespective of the need to know the channel model.

Bounded Perturbation Regularization for Linear Least Squares Estimation

KAUST Repository

Ballal, Tarig

2017-10-18

This paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded norm is allowed into the linear transformation matrix to improve the singular-value structure. Following this, the problem is formulated as a min-max optimization problem. Next, the min-max problem is converted to an equivalent minimization problem to estimate the unknown vector quantity. The solution of the minimization problem is shown to converge to that of the ℓ2 -regularized least squares problem, with the unknown regularizer related to the norm bound of the introduced perturbation through a nonlinear constraint. A procedure is proposed that combines the constraint equation with the mean squared error (MSE) criterion to develop an approximately optimal regularization parameter selection algorithm. Both direct and indirect applications of the proposed method are considered. Comparisons with different Tikhonov regularization parameter selection methods, as well as with other relevant methods, are carried out. Numerical results demonstrate that the proposed method provides significant improvement over state-of-the-art methods.

Error Modeling and Experimental Study of a Flexible Joint 6-UPUR Parallel Six-Axis Force Sensor.

Science.gov (United States)

Zhao, Yanzhi; Cao, Yachao; Zhang, Caifeng; Zhang, Dan; Zhang, Jie

2017-09-29

By combining a parallel mechanism with integrated flexible joints, a large measurement range and high accuracy sensor is realized. However, the main errors of the sensor involve not only assembly errors, but also deformation errors of its flexible leg. Based on a flexible joint 6-UPUR (a kind of mechanism configuration where U-universal joint, P-prismatic joint, R-revolute joint) parallel six-axis force sensor developed during the prephase, assembly and deformation error modeling and analysis of the resulting sensors with a large measurement range and high accuracy are made in this paper. First, an assembly error model is established based on the imaginary kinematic joint method and the Denavit-Hartenberg (D-H) method. Next, a stiffness model is built to solve the stiffness matrix. The deformation error model of the sensor is obtained. Then, the first order kinematic influence coefficient matrix when the synthetic error is taken into account is solved. Finally, measurement and calibration experiments of the sensor composed of the hardware and software system are performed. Forced deformation of the force-measuring platform is detected by using laser interferometry and analyzed to verify the correctness of the synthetic error model. In addition, the first order kinematic influence coefficient matrix in actual circumstances is calculated. By comparing the condition numbers and square norms of the coefficient matrices, the conclusion is drawn theoretically that it is very important to take into account the synthetic error for design stage of the sensor and helpful to improve performance of the sensor in order to meet needs of actual working environments.

Error Modeling and Experimental Study of a Flexible Joint 6-UPUR Parallel Six-Axis Force Sensor

Directory of Open Access Journals (Sweden)

Yanzhi Zhao

2017-09-01

Full Text Available By combining a parallel mechanism with integrated flexible joints, a large measurement range and high accuracy sensor is realized. However, the main errors of the sensor involve not only assembly errors, but also deformation errors of its flexible leg. Based on a flexible joint 6-UPUR (a kind of mechanism configuration where U-universal joint, P-prismatic joint, R-revolute joint parallel six-axis force sensor developed during the prephase, assembly and deformation error modeling and analysis of the resulting sensors with a large measurement range and high accuracy are made in this paper. First, an assembly error model is established based on the imaginary kinematic joint method and the Denavit-Hartenberg (D-H method. Next, a stiffness model is built to solve the stiffness matrix. The deformation error model of the sensor is obtained. Then, the first order kinematic influence coefficient matrix when the synthetic error is taken into account is solved. Finally, measurement and calibration experiments of the sensor composed of the hardware and software system are performed. Forced deformation of the force-measuring platform is detected by using laser interferometry and analyzed to verify the correctness of the synthetic error model. In addition, the first order kinematic influence coefficient matrix in actual circumstances is calculated. By comparing the condition numbers and square norms of the coefficient matrices, the conclusion is drawn theoretically that it is very important to take into account the synthetic error for design stage of the sensor and helpful to improve performance of the sensor in order to meet needs of actual working environments.

$L^{p}$-square function estimates on spaces of homogeneous type and on uniformly rectifiable sets

CERN Document Server

Hofmann, Steve; Mitrea, Marius; Morris, Andrew J

2017-01-01

The authors establish square function estimates for integral operators on uniformly rectifiable sets by proving a local T(b) theorem and applying it to show that such estimates are stable under the so-called big pieces functor. More generally, they consider integral operators associated with Ahlfors-David regular sets of arbitrary codimension in ambient quasi-metric spaces. The local T(b) theorem is then used to establish an inductive scheme in which square function estimates on so-called big pieces of an Ahlfors-David regular set are proved to be sufficient for square function estimates to hold on the entire set. Extrapolation results for L^p and Hardy space versions of these estimates are also established. Moreover, the authors prove square function estimates for integral operators associated with variable coefficient kernels, including the Schwartz kernels of pseudodifferential operators acting between vector bundles on subdomains with uniformly rectifiable boundaries on manifolds.

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

Science.gov (United States)

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.

Performance analysis of different tuning rules for an isothermal CSTR using integrated EPC and SPC

Science.gov (United States)

Roslan, A. H.; Karim, S. F. Abd; Hamzah, N.

2018-03-01

This paper demonstrates the integration of Engineering Process Control (EPC) and Statistical Process Control (SPC) for the control of product concentration of an isothermal CSTR. The objectives of this study are to evaluate the performance of Ziegler-Nichols (Z-N), Direct Synthesis, (DS) and Internal Model Control (IMC) tuning methods and determine the most effective method for this process. The simulation model was obtained from past literature and re-constructed using SIMULINK MATLAB to evaluate the process response. Additionally, the process stability, capability and normality were analyzed using Process Capability Sixpack reports in Minitab. Based on the results, DS displays the best response for having the smallest rise time, settling time, overshoot, undershoot, Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). Also, based on statistical analysis, DS yields as the best tuning method as it exhibits the highest process stability and capability.

Boundary integral method to calculate the sensitivity temperature error of microstructured fibre plasmonic sensors

International Nuclear Information System (INIS)

Esmaeilzadeh, Hamid; Arzi, Ezatollah; Légaré, François; Hassani, Alireza

2013-01-01

In this paper, using the boundary integral method (BIM), we simulate the effect of temperature fluctuation on the sensitivity of microstructured optical fibre (MOF) surface plasmon resonance (SPR) sensors. The final results indicate that, as the temperature increases, the refractometry sensitivity of our sensor decreases from 1300 nm/RIU at 0 °C to 1200 nm/RIU at 50 °C, leading to ∼7.7% sensitivity reduction and the sensitivity temperature error of 0.15% °C −1 for this case. These results can be used for biosensing temperature-error adjustment in MOF SPR sensors, since biomaterials detection usually happens in this temperature range. Moreover, the signal-to-noise ratio (SNR) of our sensor decreases from 0.265 at 0 °C to 0.154 at 100 °C with the average reduction rate of ∼0.42% °C −1 . The results suggest that at lower temperatures the sensor has a higher SNR. (paper)

Measurement Error in Income and Schooling and the Bias of Linear Estimators

DEFF Research Database (Denmark)

Bingley, Paul; Martinello, Alessandro

2017-01-01

and Retirement in Europe data with Danish administrative registers. Contrary to most validation studies, we find that measurement error in income is classical once we account for imperfect validation data. We find nonclassical measurement error in schooling, causing a 38% amplification bias in IV estimators......We propose a general framework for determining the extent of measurement error bias in ordinary least squares and instrumental variable (IV) estimators of linear models while allowing for measurement error in the validation source. We apply this method by validating Survey of Health, Ageing...

Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems.

Science.gov (United States)

Bhadra, Anindya; Carroll, Raymond J

2016-07-01

In truncated polynomial spline or B-spline models where the covariates are measured with error, a fully Bayesian approach to model fitting requires the covariates and model parameters to be sampled at every Markov chain Monte Carlo iteration. Sampling the unobserved covariates poses a major computational problem and usually Gibbs sampling is not possible. This forces the practitioner to use a Metropolis-Hastings step which might suffer from unacceptable performance due to poor mixing and might require careful tuning. In this article we show for the cases of truncated polynomial spline or B-spline models of degree equal to one, the complete conditional distribution of the covariates measured with error is available explicitly as a mixture of double-truncated normals, thereby enabling a Gibbs sampling scheme. We demonstrate via a simulation study that our technique performs favorably in terms of computational efficiency and statistical performance. Our results indicate up to 62 and 54 % increase in mean integrated squared error efficiency when compared to existing alternatives while using truncated polynomial splines and B-splines respectively. Furthermore, there is evidence that the gain in efficiency increases with the measurement error variance, indicating the proposed method is a particularly valuable tool for challenging applications that present high measurement error. We conclude with a demonstration on a nutritional epidemiology data set from the NIH-AARP study and by pointing out some possible extensions of the current work.

Commutative discrete filtering on unstructured grids based on least-squares techniques

International Nuclear Information System (INIS)

Haselbacher, Andreas; Vasilyev, Oleg V.

2003-01-01

The present work is concerned with the development of commutative discrete filters for unstructured grids and contains two main contributions. First, building on the work of Marsden et al. [J. Comp. Phys. 175 (2002) 584], a new commutative discrete filter based on least-squares techniques is constructed. Second, a new analysis of the discrete commutation error is carried out. The analysis indicates that the discrete commutation error is not only dependent on the number of vanishing moments of the filter weights, but also on the order of accuracy of the discrete gradient operator. The results of the analysis are confirmed by grid-refinement studies

The possibilities of least-squares migration of internally scattered seismic energy

KAUST Repository

Aldawood, Ali

2015-05-26

Approximate images of the earth’s subsurface structures are usually obtained by migrating surface seismic data. Least-squares migration, under the single-scattering assumption, is used as an iterative linearized inversion scheme to suppress migration artifacts, deconvolve the source signature, mitigate the acquisition fingerprint, and enhance the spatial resolution of migrated images. The problem with least-squares migration of primaries, however, is that it may not be able to enhance events that are mainly illuminated by internal multiples, such as vertical and nearly vertical faults or salt flanks. To alleviate this problem, we adopted a linearized inversion framework to migrate internally scattered energy. We apply the least-squares migration of first-order internal multiples to image subsurface vertical fault planes. Tests on synthetic data demonstrated the ability of the proposed method to resolve vertical fault planes, which are poorly illuminated by the least-squares migration of primaries only. The proposed scheme is robust in the presence of white Gaussian observational noise and in the case of imaging the fault planes using inaccurate migration velocities. Our results suggested that the proposed least-squares imaging, under the double-scattering assumption, still retrieved the vertical fault planes when imaging the scattered data despite a slight defocusing of these events due to the presence of noise or velocity errors.

The possibilities of least-squares migration of internally scattered seismic energy

KAUST Repository

Aldawood, Ali; Hoteit, Ibrahim; Zuberi, Mohammad; Turkiyyah, George; Alkhalifah, Tariq Ali

2015-01-01

Approximate images of the earth’s subsurface structures are usually obtained by migrating surface seismic data. Least-squares migration, under the single-scattering assumption, is used as an iterative linearized inversion scheme to suppress migration artifacts, deconvolve the source signature, mitigate the acquisition fingerprint, and enhance the spatial resolution of migrated images. The problem with least-squares migration of primaries, however, is that it may not be able to enhance events that are mainly illuminated by internal multiples, such as vertical and nearly vertical faults or salt flanks. To alleviate this problem, we adopted a linearized inversion framework to migrate internally scattered energy. We apply the least-squares migration of first-order internal multiples to image subsurface vertical fault planes. Tests on synthetic data demonstrated the ability of the proposed method to resolve vertical fault planes, which are poorly illuminated by the least-squares migration of primaries only. The proposed scheme is robust in the presence of white Gaussian observational noise and in the case of imaging the fault planes using inaccurate migration velocities. Our results suggested that the proposed least-squares imaging, under the double-scattering assumption, still retrieved the vertical fault planes when imaging the scattered data despite a slight defocusing of these events due to the presence of noise or velocity errors.

Medication errors with the use of allopurinol and colchicine: a retrospective study of a national, anonymous Internet-accessible error reporting system.

Science.gov (United States)

Mikuls, Ted R; Curtis, Jeffrey R; Allison, Jeroan J; Hicks, Rodney W; Saag, Kenneth G

2006-03-01

To more closely assess medication errors in gout care, we examined data from a national, Internet-accessible error reporting program over a 5-year reporting period. We examined data from the MEDMARX database, covering the period from January 1, 1999 through December 31, 2003. For allopurinol and colchicine, we examined error severity, source, type, contributing factors, and healthcare personnel involved in errors, and we detailed errors resulting in patient harm. Causes of error and the frequency of other error characteristics were compared for gout medications versus other musculoskeletal treatments using the chi-square statistic. Gout medication errors occurred in 39% (n = 273) of facilities participating in the MEDMARX program. Reported errors were predominantly from the inpatient hospital setting and related to the use of allopurinol (n = 524), followed by colchicine (n = 315), probenecid (n = 50), and sulfinpyrazone (n = 2). Compared to errors involving other musculoskeletal treatments, allopurinol and colchicine errors were more often ascribed to problems with physician prescribing (7% for other therapies versus 23-39% for allopurinol and colchicine, p < 0.0001) and less often due to problems with drug administration or nursing error (50% vs 23-27%, p < 0.0001). Our results suggest that inappropriate prescribing practices are characteristic of errors occurring with the use of allopurinol and colchicine. Physician prescribing practices are a potential target for quality improvement interventions in gout care.

Multi-source least-squares reverse time migration

KAUST Repository

Dai, Wei

2012-06-15

Least-squares migration has been shown to improve image quality compared to the conventional migration method, but its computational cost is often too high to be practical. In this paper, we develop two numerical schemes to implement least-squares migration with the reverse time migration method and the blended source processing technique to increase computation efficiency. By iterative migration of supergathers, which consist in a sum of many phase-encoded shots, the image quality is enhanced and the crosstalk noise associated with the encoded shots is reduced. Numerical tests on 2D HESS VTI data show that the multisource least-squares reverse time migration (LSRTM) algorithm suppresses migration artefacts, balances the amplitudes, improves image resolution and reduces crosstalk noise associated with the blended shot gathers. For this example, the multisource LSRTM is about three times faster than the conventional RTM method. For the 3D example of the SEG/EAGE salt model, with a comparable computational cost, multisource LSRTM produces images with more accurate amplitudes, better spatial resolution and fewer migration artefacts compared to conventional RTM. The empirical results suggest that multisource LSRTM can produce more accurate reflectivity images than conventional RTM does with a similar or less computational cost. The caveat is that the LSRTM image is sensitive to large errors in the migration velocity model. © 2012 European Association of Geoscientists & Engineers.

Multi-source least-squares reverse time migration

KAUST Repository

Dai, Wei; Fowler, Paul J.; Schuster, Gerard T.

2012-01-01

Least-squares migration has been shown to improve image quality compared to the conventional migration method, but its computational cost is often too high to be practical. In this paper, we develop two numerical schemes to implement least-squares migration with the reverse time migration method and the blended source processing technique to increase computation efficiency. By iterative migration of supergathers, which consist in a sum of many phase-encoded shots, the image quality is enhanced and the crosstalk noise associated with the encoded shots is reduced. Numerical tests on 2D HESS VTI data show that the multisource least-squares reverse time migration (LSRTM) algorithm suppresses migration artefacts, balances the amplitudes, improves image resolution and reduces crosstalk noise associated with the blended shot gathers. For this example, the multisource LSRTM is about three times faster than the conventional RTM method. For the 3D example of the SEG/EAGE salt model, with a comparable computational cost, multisource LSRTM produces images with more accurate amplitudes, better spatial resolution and fewer migration artefacts compared to conventional RTM. The empirical results suggest that multisource LSRTM can produce more accurate reflectivity images than conventional RTM does with a similar or less computational cost. The caveat is that the LSRTM image is sensitive to large errors in the migration velocity model. © 2012 European Association of Geoscientists & Engineers.

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

Science.gov (United States)

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

Error Control for Network-on-Chip Links

CERN Document Server

Fu, Bo

2012-01-01

As technology scales into nanoscale regime, it is impossible to guarantee the perfect hardware design. Moreover, if the requirement of 100% correctness in hardware can be relaxed, the cost of manufacturing, verification, and testing will be significantly reduced. Many approaches have been proposed to address the reliability problem of on-chip communications. This book focuses on the use of error control codes (ECCs) to improve on-chip interconnect reliability. Coverage includes detailed description of key issues in NOC error control faced by circuit and system designers, as well as practical error control techniques to minimize the impact of these errors on system performance. Provides a detailed background on the state of error control methods for on-chip interconnects; Describes the use of more complex concatenated codes such as Hamming Product Codes with Type-II HARQ, while emphasizing integration techniques for on-chip interconnect links; Examines energy-efficient techniques for integrating multiple error...

Observing others stay or switch - How social prediction errors are integrated into reward reversal learning.

Science.gov (United States)

Ihssen, Niklas; Mussweiler, Thomas; Linden, David E J

2016-08-01

Reward properties of stimuli can undergo sudden changes, and the detection of these 'reversals' is often made difficult by the probabilistic nature of rewards/punishments. Here we tested whether and how humans use social information (someone else's choices) to overcome uncertainty during reversal learning. We show a substantial social influence during reversal learning, which was modulated by the type of observed behavior. Participants frequently followed observed conservative choices (no switches after punishment) made by the (fictitious) other player but ignored impulsive choices (switches), even though the experiment was set up so that both types of response behavior would be similarly beneficial/detrimental (Study 1). Computational modeling showed that participants integrated the observed choices as a 'social prediction error' instead of ignoring or blindly following the other player. Modeling also confirmed higher learning rates for 'conservative' versus 'impulsive' social prediction errors. Importantly, this 'conservative bias' was boosted by interpersonal similarity, which in conjunction with the lack of effects observed in a non-social control experiment (Study 2) confirmed its social nature. A third study suggested that relative weighting of observed impulsive responses increased with increased volatility (frequency of reversals). Finally, simulations showed that in the present paradigm integrating social and reward information was not necessarily more adaptive to maximize earnings than learning from reward alone. Moreover, integrating social information increased accuracy only when conservative and impulsive choices were weighted similarly during learning. These findings suggest that to guide decisions in choice contexts that involve reward reversals humans utilize social cues conforming with their preconceptions more strongly than cues conflicting with them, especially when the other is similar. Copyright © 2016 The Authors. Published by Elsevier B

Child- and elder-friendly urban public places in Fatahillah Square Historical District

Science.gov (United States)

Srinaga, F.; LKatoppo, M.; Hidayat, J.

2018-03-01

Fatahillah square as an important historical urban square in Jakarta has problems in eye level area integrative processing. Visitors cannot enjoy their time while in the square regarding their visuals, feelings, space, and bodies comfort. These also lead to other problems in which the square is lack of friendly and convenient places for children, the elderly and also the disabled, especially people with limited moving space. The research will attempt in proposing design inception for the Fatahillah Square that is using inclusive user-centered design approach, while in the same time incorporate theoretical studies of children and elderly-design considerations. The first stage of this research was building inclusive design parameter; begin with a context-led research which assesses the quality of Fatahillah square through three basic components of urban space: hardware, software and orgware. The second stage of this research is to propose inclusive design inception for the Fatahillah square.

Trajectory errors of different numerical integration schemes diagnosed with the MPTRAC advection module driven by ECMWF operational analyses

Science.gov (United States)

Rößler, Thomas; Stein, Olaf; Heng, Yi; Baumeister, Paul; Hoffmann, Lars

2018-02-01

The accuracy of trajectory calculations performed by Lagrangian particle dispersion models (LPDMs) depends on various factors. The optimization of numerical integration schemes used to solve the trajectory equation helps to maximize the computational efficiency of large-scale LPDM simulations. We analyzed global truncation errors of six explicit integration schemes of the Runge-Kutta family, which we implemented in the Massive-Parallel Trajectory Calculations (MPTRAC) advection module. The simulations were driven by wind fields from operational analysis and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) at T1279L137 spatial resolution and 3 h temporal sampling. We defined separate test cases for 15 distinct regions of the atmosphere, covering the polar regions, the midlatitudes, and the tropics in the free troposphere, in the upper troposphere and lower stratosphere (UT/LS) region, and in the middle stratosphere. In total, more than 5000 different transport simulations were performed, covering the months of January, April, July, and October for the years 2014 and 2015. We quantified the accuracy of the trajectories by calculating transport deviations with respect to reference simulations using a fourth-order Runge-Kutta integration scheme with a sufficiently fine time step. Transport deviations were assessed with respect to error limits based on turbulent diffusion. Independent of the numerical scheme, the global truncation errors vary significantly between the different regions. Horizontal transport deviations in the stratosphere are typically an order of magnitude smaller compared with the free troposphere. We found that the truncation errors of the six numerical schemes fall into three distinct groups, which mostly depend on the numerical order of the scheme. Schemes of the same order differ little in accuracy, but some methods need less computational time, which gives them an advantage in efficiency. The selection of the integration

The refractive index in electron microscopy and the errors of its approximations

Energy Technology Data Exchange (ETDEWEB)

Lentzen, M.

2017-05-15

In numerical calculations for electron diffraction often a simplified form of the electron-optical refractive index, linear in the electric potential, is used. In recent years improved calculation schemes have been proposed, aiming at higher accuracy by including higher-order terms of the electric potential. These schemes start from the relativistically corrected Schrödinger equation, and use a second simplified form, now for the refractive index squared, being linear in the electric potential. The second and higher-order corrections thus determined have, however, a large error, compared to those derived from the relativistically correct refractive index. The impact of the two simplifications on electron diffraction calculations is assessed through numerical comparison of the refractive index at high-angle Coulomb scattering and of cross-sections for a wide range of scattering angles, kinetic energies, and atomic numbers. - Highlights: • The standard model for the refractive index in electron microscopy is investigated. • The error of the standard model is proportional to the electric potential squared. • Relativistically correct error terms are derived from the energy-momentum relation. • The errors are assessed for Coulomb scattering varying energy and atomic number. • Errors of scattering cross-sections are pronounced at large angles and attain 10%.

The refractive index in electron microscopy and the errors of its approximations

International Nuclear Information System (INIS)

Lentzen, M.

2017-01-01

In numerical calculations for electron diffraction often a simplified form of the electron-optical refractive index, linear in the electric potential, is used. In recent years improved calculation schemes have been proposed, aiming at higher accuracy by including higher-order terms of the electric potential. These schemes start from the relativistically corrected Schrödinger equation, and use a second simplified form, now for the refractive index squared, being linear in the electric potential. The second and higher-order corrections thus determined have, however, a large error, compared to those derived from the relativistically correct refractive index. The impact of the two simplifications on electron diffraction calculations is assessed through numerical comparison of the refractive index at high-angle Coulomb scattering and of cross-sections for a wide range of scattering angles, kinetic energies, and atomic numbers. - Highlights: • The standard model for the refractive index in electron microscopy is investigated. • The error of the standard model is proportional to the electric potential squared. • Relativistically correct error terms are derived from the energy-momentum relation. • The errors are assessed for Coulomb scattering varying energy and atomic number. • Errors of scattering cross-sections are pronounced at large angles and attain 10%.

Space-time least-squares Petrov-Galerkin projection in nonlinear model reduction.

Energy Technology Data Exchange (ETDEWEB)

Choi, Youngsoo [Sandia National Laboratories (SNL-CA), Livermore, CA (United States). Extreme-scale Data Science and Analytics Dept.; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Carlberg, Kevin Thomas [Sandia National Laboratories (SNL-CA), Livermore, CA (United States). Extreme-scale Data Science and Analytics Dept.

2017-09-01

Our work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model-reduction methods that first apply Petrov-Galerkin projection in the spatial dimension and subsequently apply time integration to numerically resolve the resulting low-dimensional dynamical system, the proposed method applies projection in space and time simultaneously. To accomplish this, the method first introduces a low-dimensional space-time trial subspace, which can be obtained by computing tensor decompositions of state-snapshot data. The method then computes discrete-optimal approximations in this space-time trial subspace by minimizing the residual arising after time discretization over all space and time in a weighted ℓ²-norm. This norm can be de ned to enable complexity reduction (i.e., hyper-reduction) in time, which leads to space-time collocation and space-time GNAT variants of the ST-LSPG method. Advantages of the approach relative to typical spatial-projection-based nonlinear model reduction methods such as Galerkin projection and least-squares Petrov-Galerkin projection include: (1) a reduction of both the spatial and temporal dimensions of the dynamical system, (2) the removal of spurious temporal modes (e.g., unstable growth) from the state space, and (3) error bounds that exhibit slower growth in time. Numerical examples performed on model problems in fluid dynamics demonstrate the ability of the method to generate orders-of-magnitude computational savings relative to spatial-projection-based reduced-order models without sacrificing accuracy.

Resimulation of noise: a precision estimator for least square error curve-fitting tested for axial strain time constant imaging

Science.gov (United States)

Nair, S. P.; Righetti, R.

2015-05-01

Recent elastography techniques focus on imaging information on properties of materials which can be modeled as viscoelastic or poroelastic. These techniques often require the fitting of temporal strain data, acquired from either a creep or stress-relaxation experiment to a mathematical model using least square error (LSE) parameter estimation. It is known that the strain versus time relationships for tissues undergoing creep compression have a non-linear relationship. In non-linear cases, devising a measure of estimate reliability can be challenging. In this article, we have developed and tested a method to provide non linear LSE parameter estimate reliability: which we called Resimulation of Noise (RoN). RoN provides a measure of reliability by estimating the spread of parameter estimates from a single experiment realization. We have tested RoN specifically for the case of axial strain time constant parameter estimation in poroelastic media. Our tests show that the RoN estimated precision has a linear relationship to the actual precision of the LSE estimator. We have also compared results from the RoN derived measure of reliability against a commonly used reliability measure: the correlation coefficient (CorrCoeff). Our results show that CorrCoeff is a poor measure of estimate reliability for non-linear LSE parameter estimation. While the RoN is specifically tested only for axial strain time constant imaging, a general algorithm is provided for use in all LSE parameter estimation.

Feasibility study on the least square method for fitting non-Gaussian noise data

Science.gov (United States)

Xu, Wei; Chen, Wen; Liang, Yingjie

2018-02-01

This study is to investigate the feasibility of least square method in fitting non-Gaussian noise data. We add different levels of the two typical non-Gaussian noises, Lévy and stretched Gaussian noises, to exact value of the selected functions including linear equations, polynomial and exponential equations, and the maximum absolute and the mean square errors are calculated for the different cases. Lévy and stretched Gaussian distributions have many applications in fractional and fractal calculus. It is observed that the non-Gaussian noises are less accurately fitted than the Gaussian noise, but the stretched Gaussian cases appear to perform better than the Lévy noise cases. It is stressed that the least-squares method is inapplicable to the non-Gaussian noise cases when the noise level is larger than 5%.

Least median of squares filtering of locally optimal point matches for compressible flow image registration

International Nuclear Information System (INIS)

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

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. (paper)

Integral transform solution of natural convection in a square cavity with volumetric heat generation

Directory of Open Access Journals (Sweden)

C. An

2013-12-01

Full Text Available The generalized integral transform technique (GITT is employed to obtain a hybrid numerical-analytical solution of natural convection in a cavity with volumetric heat generation. The hybrid nature of this approach allows for the establishment of benchmark results in the solution of non-linear partial differential equation systems, including the coupled set of heat and fluid flow equations that govern the steady natural convection problem under consideration. Through performing the GITT, the resulting transformed ODE system is then numerically solved by making use of the subroutine DBVPFD from the IMSL Library. Therefore, numerical results under user prescribed accuracy are obtained for different values of Rayleigh numbers, and the convergence behavior of the proposed eigenfunction expansions is illustrated. Critical comparisons against solutions produced by ANSYS CFX 12.0 are then conducted, which demonstrate excellent agreement. Several sets of reference results for natural convection with volumetric heat generation in a bi-dimensional square cavity are also provided for future verification of numerical results obtained by other researchers.

Least-squares dual characterization for ROI assessment in emission tomography

International Nuclear Information System (INIS)

Ben Bouallègue, F; Mariano-Goulart, D; Crouzet, J F; Dubois, A; Buvat, I

2013-01-01

Our aim is to describe an original method for estimating the statistical properties of regions of interest (ROIs) in emission tomography. Drawn upon the works of Louis on the approximate inverse, we propose a dual formulation of the ROI estimation problem to derive the ROI activity and variance directly from the measured data without any image reconstruction. The method requires the definition of an ROI characteristic function that can be extracted from a co-registered morphological image. This characteristic function can be smoothed to optimize the resolution-variance tradeoff. An iterative procedure is detailed for the solution of the dual problem in the least-squares sense (least-squares dual (LSD) characterization), and a linear extrapolation scheme is described to compensate for sampling partial volume effect and reduce the estimation bias (LSD-ex). LSD and LSD-ex are compared with classical ROI estimation using pixel summation after image reconstruction and with Huesman's method. For this comparison, we used Monte Carlo simulations (GATE simulation tool) of 2D PET data of a Hoffman brain phantom containing three small uniform high-contrast ROIs and a large non-uniform low-contrast ROI. Our results show that the performances of LSD characterization are at least as good as those of the classical methods in terms of root mean square (RMS) error. For the three small tumor regions, LSD-ex allows a reduction in the estimation bias by up to 14%, resulting in a reduction in the RMS error of up to 8.5%, compared with the optimal classical estimation. For the large non-specific region, LSD using appropriate smoothing could intuitively and efficiently handle the resolution-variance tradeoff. (paper)

Least-squares dual characterization for ROI assessment in emission tomography

Science.gov (United States)

Ben Bouallègue, F.; Crouzet, J. F.; Dubois, A.; Buvat, I.; Mariano-Goulart, D.

2013-06-01

Our aim is to describe an original method for estimating the statistical properties of regions of interest (ROIs) in emission tomography. Drawn upon the works of Louis on the approximate inverse, we propose a dual formulation of the ROI estimation problem to derive the ROI activity and variance directly from the measured data without any image reconstruction. The method requires the definition of an ROI characteristic function that can be extracted from a co-registered morphological image. This characteristic function can be smoothed to optimize the resolution-variance tradeoff. An iterative procedure is detailed for the solution of the dual problem in the least-squares sense (least-squares dual (LSD) characterization), and a linear extrapolation scheme is described to compensate for sampling partial volume effect and reduce the estimation bias (LSD-ex). LSD and LSD-ex are compared with classical ROI estimation using pixel summation after image reconstruction and with Huesman's method. For this comparison, we used Monte Carlo simulations (GATE simulation tool) of 2D PET data of a Hoffman brain phantom containing three small uniform high-contrast ROIs and a large non-uniform low-contrast ROI. Our results show that the performances of LSD characterization are at least as good as those of the classical methods in terms of root mean square (RMS) error. For the three small tumor regions, LSD-ex allows a reduction in the estimation bias by up to 14%, resulting in a reduction in the RMS error of up to 8.5%, compared with the optimal classical estimation. For the large non-specific region, LSD using appropriate smoothing could intuitively and efficiently handle the resolution-variance tradeoff.

A Conditionally Integrable Bi-confluent Heun Potential Involving Inverse Square Root and Centrifugal Barrier Terms

Science.gov (United States)

Ishkhanyan, Tigran A.; Krainov, Vladimir P.; Ishkhanyan, Artur M.

2018-05-01

We present a conditionally integrable potential, belonging to the bi-confluent Heun class, for which the Schrödinger equation is solved in terms of the confluent hypergeometric functions. The potential involves an attractive inverse square root term x-1/2 with arbitrary strength and a repulsive centrifugal barrier core x-2 with the strength fixed to a constant. This is a potential well defined on the half-axis. Each of the fundamental solutions composing the general solution of the Schrödinger equation is written as an irreducible linear combination, with non-constant coefficients, of two confluent hypergeometric functions. We present the explicit solution in terms of the non-integer order Hermite functions of scaled and shifted argument and discuss the bound states supported by the potential. We derive the exact equation for the energy spectrum and approximate that by a highly accurate transcendental equation involving trigonometric functions. Finally, we construct an accurate approximation for the bound-state energy levels.

Testing and tuning symplectic integrators for the hybrid Monte Carlo algorithm in lattice QCD

International Nuclear Information System (INIS)

Takaishi, Tetsuya; Forcrand, Philippe de

2006-01-01

We examine a new second-order integrator recently found by Omelyan et al. The integration error of the new integrator measured in the root mean square of the energy difference, 2 > 1/2 , is about 10 times smaller than that of the standard second-order leapfrog (2LF) integrator. As a result, the step size of the new integrator can be made about three times larger. Taking into account a factor 2 increase in cost, the new integrator is about 50% more efficient than the 2LF integrator. Integrating over positions first, then momenta, is slightly more advantageous than the reverse. Further parameter tuning is possible. We find that the optimal parameter for the new integrator is slightly different from the value obtained by Omelyan et al., and depends on the simulation parameters. This integrator could also be advantageous for the Trotter-Suzuki decomposition in quantum Monte Carlo

Multilevel weighted least squares polynomial approximation

KAUST Repository

Haji-Ali, Abdul-Lateef

2017-06-30

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi-optimal approximation in a given polynomial subspace scales, up to a logarithmic factor, linearly in the dimension of this space. However, in many applications, the computation of samples includes a numerical discretization error. Thus, obtaining polynomial approximations with a single level method can become prohibitively expensive, as it requires a sufficiently large number of samples, each computed with a sufficiently small discretization error. As a solution to this problem, we propose a multilevel method that utilizes samples computed with different accuracies and is able to match the accuracy of single-level approximations with reduced computational cost. We derive complexity bounds under certain assumptions about polynomial approximability and sample work. Furthermore, we propose an adaptive algorithm for situations where such assumptions cannot be verified a priori. Finally, we provide an efficient algorithm for the sampling from optimal distributions and an analysis of computationally favorable alternative distributions. Numerical experiments underscore the practical applicability of our method.

Monthly streamflow forecasting with auto-regressive integrated moving average

Science.gov (United States)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

Demonstration Integrated Knowledge-Based System for Estimating Human Error Probabilities

Energy Technology Data Exchange (ETDEWEB)

Auflick, Jack L.

1999-04-21

Human Reliability Analysis (HRA) is currently comprised of at least 40 different methods that are used to analyze, predict, and evaluate human performance in probabilistic terms. Systematic HRAs allow analysts to examine human-machine relationships, identify error-likely situations, and provide estimates of relative frequencies for human errors on critical tasks, highlighting the most beneficial areas for system improvements. Unfortunately, each of HRA's methods has a different philosophical approach, thereby producing estimates of human error probabilities (HEPs) that area better or worse match to the error likely situation of interest. Poor selection of methodology, or the improper application of techniques can produce invalid HEP estimates, where that erroneous estimation of potential human failure could have potentially severe consequences in terms of the estimated occurrence of injury, death, and/or property damage.

Statistical error of spin transfer to hyperon at RHIC energy

International Nuclear Information System (INIS)

Han Ran; Mao Yajun

2009-01-01

From the RHIC/PHENIX experiment data, it is found that the statistical error of spin transfer is few times larger than the statistical error of the single spin asymmetry. In order to verify the difference between σDLL and σAL, the linear least squares method was used to check it first, and then a simple Monte-Carlo simulation to test this factor again. The simulation is consistent with the calculation result which indicates that the few times difference is reasonable. (authors)

Applying Intelligent Algorithms to Automate the Identification of Error Factors.

Science.gov (United States)

Jin, Haizhe; Qu, Qingxing; Munechika, Masahiko; Sano, Masataka; Kajihara, Chisato; Duffy, Vincent G; Chen, Han

2018-05-03

Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)-back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. The combination of "error-related items, their different levels, and the GA-BPNN model" was proposed as an error-factor identification technology, which could automatically identify medical error factors.

Square well model and the functional form for the resonance integral

International Nuclear Information System (INIS)

Raina, V.; Srivastava, P.K.; Sane, K.V.

1975-01-01

It is shown that the relation β = eta(Z 1 Z 2 )/sup 1/2/ exp/broken bracket/ -X(Z 1 + Z 2 )R/broken bracket//R can be derived by assuming that the effective potential in π-electron systems can be approximated by a three-dimensional square well form. (U.S.)

Filter Tuning Using the Chi-Squared Statistic

Science.gov (United States)

Lilly-Salkowski, Tyler

2017-01-01

The Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF) performs orbit determination (OD) for the Aqua and Aura satellites. Both satellites are located in low Earth orbit (LEO), and are part of what is considered the A-Train satellite constellation. Both spacecraft are currently in the science phase of their respective missions. The FDF has recently been tasked with delivering definitive covariance for each satellite.The main source of orbit determination used for these missions is the Orbit Determination Toolkit developed by Analytical Graphics Inc. (AGI). This software uses an Extended Kalman Filter (EKF) to estimate the states of both spacecraft. The filter incorporates force modelling, ground station and space network measurements to determine spacecraft states. It also generates a covariance at each measurement. This covariance can be useful for evaluating the overall performance of the tracking data measurements and the filter itself. An accurate covariance is also useful for covariance propagation which is utilized in collision avoidance operations. It is also valuable when attempting to determine if the current orbital solution will meet mission requirements in the future.This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The Chi-square statistic is calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance.For the EKF to correctly calculate the covariance, error models associated with tracking data measurements must be accurately tuned. Over estimating or under estimating these error values can have detrimental effects on the overall filter performance. The filter incorporates ground station measurements, which can be tuned based on the accuracy of the individual ground stations. It also includes

A general approach to error propagation

International Nuclear Information System (INIS)

Sanborn, J.B.

1987-01-01

A computational approach to error propagation is explained. It is shown that the application of the first-order Taylor theory to a fairly general expression representing an inventory or inventory-difference quantity leads naturally to a data structure that is useful for structuring error-propagation calculations. This data structure incorporates six types of data entities: (1) the objects in the material balance, (2) numerical parameters that describe these objects, (3) groups or sets of objects, (4) the terms which make up the material-balance equation, (5) the errors or sources of variance and (6) the functions or subroutines that represent Taylor partial derivatives. A simple algorithm based on this data structure can be defined using formulas that are sums of squares of sums. The data structures and algorithms described above have been implemented as computer software in FORTRAN for IBM PC-type machines. A free-form data-entry format allows users to separate data as they wish into separate files and enter data using a text editor. The program has been applied to the computation of limits of error for inventory differences (LEIDs) within the DOE complex. 1 ref., 3 figs

PENERAPAN METODE LEAST MEDIAN SQUARE-MINIMUM COVARIANCE DETERMINANT (LMS-MCD DALAM REGRESI KOMPONEN UTAMA

Directory of Open Access Journals (Sweden)

I PUTU EKA IRAWAN

2013-11-01

Full Text Available Principal Component Regression is a method to overcome multicollinearity techniques by combining principal component analysis with regression analysis. The calculation of classical principal component analysis is based on the regular covariance matrix. The covariance matrix is optimal if the data originated from a multivariate normal distribution, but is very sensitive to the presence of outliers. Alternatives are used to overcome this problem the method of Least Median Square-Minimum Covariance Determinant (LMS-MCD. The purpose of this research is to conduct a comparison between Principal Component Regression (RKU and Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD in dealing with outliers. In this study, Method of Least Median Square - Minimum Covariance Determinant (LMS-MCD has a bias and mean square error (MSE is smaller than the parameter RKU. Based on the difference of parameter estimators, still have a test that has a difference of parameter estimators method LMS-MCD greater than RKU method.

Wavefront-Error Performance Characterization for the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM) Science Instruments

Science.gov (United States)

Aronstein, David L.; Smith, J. Scott; Zielinski, Thomas P.; Telfer, Randal; Tournois, Severine C.; Moore, Dustin B.; Fienup, James R.

2016-01-01

The science instruments (SIs) comprising the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM) were tested in three cryogenic-vacuum test campaigns in the NASA Goddard Space Flight Center (GSFC)'s Space Environment Simulator (SES). In this paper, we describe the results of optical wavefront-error performance characterization of the SIs. The wavefront error is determined using image-based wavefront sensing (also known as phase retrieval), and the primary data used by this process are focus sweeps, a series of images recorded by the instrument under test in its as-used configuration, in which the focal plane is systematically changed from one image to the next. High-precision determination of the wavefront error also requires several sources of secondary data, including 1) spectrum, apodization, and wavefront-error characterization of the optical ground-support equipment (OGSE) illumination module, called the OTE Simulator (OSIM), 2) plate scale measurements made using a Pseudo-Nonredundant Mask (PNRM), and 3) pupil geometry predictions as a function of SI and field point, which are complicated because of a tricontagon-shaped outer perimeter and small holes that appear in the exit pupil due to the way that different light sources are injected into the optical path by the OGSE. One set of wavefront-error tests, for the coronagraphic channel of the Near-Infrared Camera (NIRCam) Longwave instruments, was performed using data from transverse translation diversity sweeps instead of focus sweeps, in which a sub-aperture is translated andor rotated across the exit pupil of the system.Several optical-performance requirements that were verified during this ISIM-level testing are levied on the uncertainties of various wavefront-error-related quantities rather than on the wavefront errors themselves. This paper also describes the methodology, based on Monte Carlo simulations of the wavefront-sensing analysis of focus-sweep data, used to establish the

Study of the convergence behavior of the complex kernel least mean square algorithm.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2013-09-01

The complex kernel least mean square (CKLMS) algorithm is recently derived and allows for online kernel adaptive learning for complex data. Kernel adaptive methods can be used in finding solutions for neural network and machine learning applications. The derivation of CKLMS involved the development of a modified Wirtinger calculus for Hilbert spaces to obtain the cost function gradient. We analyze the convergence of the CKLMS with different kernel forms for complex data. The expressions obtained enable us to generate theory-predicted mean-square error curves considering the circularity of the complex input signals and their effect on nonlinear learning. Simulations are used for verifying the analysis results.

A task specific uncertainty analysis method for least-squares-based form characterization of ultra-precision freeform surfaces

International Nuclear Information System (INIS)

Ren, M J; Cheung, C F; Kong, L B

2012-01-01

In the measurement of ultra-precision freeform surfaces, least-squares-based form characterization methods are widely used to evaluate the form error of the measured surfaces. Although many methodologies have been proposed in recent years to improve the efficiency of the characterization process, relatively little research has been conducted on the analysis of associated uncertainty in the characterization results which may result from those characterization methods being used. As a result, this paper presents a task specific uncertainty analysis method with application in the least-squares-based form characterization of ultra-precision freeform surfaces. That is, the associated uncertainty in the form characterization results is estimated when the measured data are extracted from a specific surface with specific sampling strategy. Three factors are considered in this study which include measurement error, surface form error and sample size. The task specific uncertainty analysis method has been evaluated through a series of experiments. The results show that the task specific uncertainty analysis method can effectively estimate the uncertainty of the form characterization results for a specific freeform surface measurement

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

Energy Technology Data Exchange (ETDEWEB)

Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels (Belgium); Shabbir, A. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching (Germany); Hornung, G. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium)

2016-11-15

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.

Updating QR factorization procedure for solution of linear least squares problem with equality constraints.

Science.gov (United States)

Zeb, Salman; Yousaf, Muhammad

2017-01-01

In this article, we present a QR updating procedure as a solution approach for linear least squares problem with equality constraints. We reduce the constrained problem to unconstrained linear least squares and partition it into a small subproblem. The QR factorization of the subproblem is calculated and then we apply updating techniques to its upper triangular factor R to obtain its solution. We carry out the error analysis of the proposed algorithm to show that it is backward stable. We also illustrate the implementation and accuracy of the proposed algorithm by providing some numerical experiments with particular emphasis on dense problems.

Testing and tuning new symplectic integrators for Hybrid Monte Carlo algorithm in lattice QCD

CERN Document Server

Takaishi, T; Takaishi, Tetsuya; Forcrand, Philippe de

2006-01-01

We examine a new 2nd order integrator recently found by Omelyan et al. The integration error of the new integrator measured in the root mean square of the energy difference, $\\bra\\Delta H^2\\ket^{1/2}$, is about 10 times smaller than that of the standard 2nd order leapfrog (2LF) integrator. As a result, the step size of the new integrator can be made about three times larger. Taking into account a factor 2 increase in cost, the new integrator is about 50% more efficient than the 2LF integrator. Integrating over positions first, then momenta, is slightly more advantageous than the reverse. Further parameter tuning is possible. We find that the optimal parameter for the new integrator is slightly different from the value obtained by Omelyan et al., and depends on the simulation parameters. This integrator, together with a new 4th order integrator, could also be advantageous for the Trotter-Suzuki decomposition in Quantum Monte Carlo.

Error or "act of God"? A study of patients' and operating room team members' perceptions of error definition, reporting, and disclosure.

Science.gov (United States)

Espin, Sherry; Levinson, Wendy; Regehr, Glenn; Baker, G Ross; Lingard, Lorelei

2006-01-01

Calls abound for a culture change in health care to improve patient safety. However, effective change cannot proceed without a clear understanding of perceptions and beliefs about error. In this study, we describe and compare operative team members' and patients' perceptions of error, reporting of error, and disclosure of error. Thirty-nine interviews of team members (9 surgeons, 9 nurses, 10 anesthesiologists) and patients (11) were conducted at 2 teaching hospitals using 4 scenarios as prompts. Transcribed responses to open questions were analyzed by 2 researchers for recurrent themes using the grounded-theory method. Yes/no answers were compared across groups using chi-square analyses. Team members and patients agreed on what constitutes an error. Deviation from standards and negative outcome were emphasized as definitive features. Patients and nurse professionals differed significantly in their perception of whether errors should be reported. Nurses were willing to report only events within their disciplinary scope of practice. Although most patients strongly advocated full disclosure of errors (what happened and how), team members preferred to disclose only what happened. When patients did support partial disclosure, their rationales varied from that of team members. Both operative teams and patients define error in terms of breaking the rules and the concept of "no harm no foul." These concepts pose challenges for treating errors as system failures. A strong culture of individualism pervades nurses' perception of error reporting, suggesting that interventions are needed to foster collective responsibility and a constructive approach to error identification.

State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

Directory of Open Access Journals (Sweden)

Bo Xu

2016-06-01

Full Text Available This paper focuses on an improved square root unscented Kalman filter (SRUKF and its application for rotor speed and position estimation of permanent magnet synchronous motor (PMSM. The approach, which combines the SRUKF and strong tracking filter, uses the minimal skew simplex transformation to reduce the number of the sigma points, and utilizes the square root filtering to reduce computational errors. The time-varying fading factor and softening factor are introduced to self-adjust the gain matrices and the state forecast covariance square root matrix, which can realize the residuals orthogonality and force the SRUKF to track the real state rapidly. The theoretical analysis of the improved SRUKF and implementation details for PMSM state estimation are examined. The simulation results show that the improved SRUKF has higher nonlinear approximation accuracy, stronger numerical stability and computational efficiency, and it is an effective and powerful tool for PMSM state estimation under the conditions of step response or load disturbance.

Analysis of Students' Error in Learning of Quadratic Equations

Science.gov (United States)

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…

Computational logic with square rings of nanomagnets

Science.gov (United States)

Arava, Hanu; Derlet, Peter M.; Vijayakumar, Jaianth; Cui, Jizhai; Bingham, Nicholas S.; Kleibert, Armin; Heyderman, Laura J.

2018-06-01

Nanomagnets are a promising low-power alternative to traditional computing. However, the successful implementation of nanomagnets in logic gates has been hindered so far by a lack of reliability. Here, we present a novel design with dipolar-coupled nanomagnets arranged on a square lattice to (i) support transfer of information and (ii) perform logic operations. We introduce a thermal protocol, using thermally active nanomagnets as a means to perform computation. Within this scheme, the nanomagnets are initialized by a global magnetic field and thermally relax on raising the temperature with a resistive heater. We demonstrate error-free transfer of information in chains of up to 19 square rings and we show a high level of reliability with successful gate operations of ∼94% across more than 2000 logic gates. Finally, we present a functionally complete prototype NAND/NOR logic gate that could be implemented for advanced logic operations. Here we support our experiments with simulations of the thermally averaged output and determine the optimal gate parameters. Our approach provides a new pathway to a long standing problem concerning reliability in the use of nanomagnets for computation.

Least-squares methods for identifying biochemical regulatory networks from noisy measurements

Directory of Open Access Journals (Sweden)

Heslop-Harrison Pat

2007-01-01

Full Text Available Abstract Background We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS. The Total Least Squares (TLS technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks. Results The superior performance of the CTLS method in identifying network interactions is demonstrated on three examples: a genetic network containing four genes, a network describing p53 activity and mdm2 messenger RNA interactions, and a recently proposed kinetic model for interleukin (IL-6 and (IL-12b messenger RNA expression as a function of ATF3 and NF-κB promoter binding. For the first example, the CTLS significantly reduces the errors in the estimation of the Jacobian for the gene network. For the second, the CTLS reduces the errors from the measurements that are corrupted by white noise and the effect of neglected kinetics. For the third, it allows the correct identification, from noisy data, of the negative regulation of (IL-6 and (IL-12b by ATF3. Conclusion The significant improvements in performance demonstrated by the CTLS method under the wide range of conditions tested here, including different levels and types of measurement noise and different numbers of data points, suggests that its application will enable

Making the most out of the least (squares migration)

KAUST Repository

Dutta, Gaurav

2014-08-05

Standard migration images can suffer from migration artifacts due to 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution due to limited source-receiver aperture, and 6) ringiness caused by a ringy source wavelet. To partly remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), proposes to linearly invert seismic data for the reflectivity distribution. If the migration velocity model is sufficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image, sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for velocity errors of 5% or more. We now show how to get the most from least-squares migration by reducing the cost and velocity sensitivity of LSM.

Prevalence of refractive error and visual impairment among rural school-age children of Goro District, Gurage Zone, Ethiopia.

Science.gov (United States)

Kedir, Jafer; Girma, Abonesh

2014-10-01

Refractive error is one of the major causes of blindness and visual impairment in children; but community based studies are scarce especially in rural parts of Ethiopia. So, this study aims to assess the prevalence of refractive error and its magnitude as a cause of visual impairment among school-age children of rural community. This community-based cross-sectional descriptive study was conducted from March 1 to April 30, 2009 in rural villages of Goro district of Gurage Zone, found south west of Addis Ababa, the capital of Ethiopia. A multistage cluster sampling method was used with simple random selection of representative villages in the district. Chi-Square and t-tests were used in the data analysis. A total of 570 school-age children (age 7-15) were evaluated, 54% boys and 46% girls. The prevalence of refractive error was 3.5% (myopia 2.6% and hyperopia 0.9%). Refractive error was the major cause of visual impairment accounting for 54% of all causes in the study group. No child was found wearing corrective spectacles during the study period. Refractive error was the commonest cause of visual impairment in children of the district, but no measures were taken to reduce the burden in the community. So, large scale community level screening for refractive error should be conducted and integrated with regular school eye screening programs. Effective strategies need to be devised to provide low cost corrective spectacles in the rural community.

Stable Galerkin versus equal-order Galerkin least-squares elements for the stokes flow problem

International Nuclear Information System (INIS)

Franca, L.P.; Frey, S.L.; Sampaio, R.

1989-11-01

Numerical experiments are performed for the stokes flow problem employing a stable Galerkin method and a Galerkin/Least-squares method with equal-order elements. Error estimates for the methods tested herein are reviewed. The numerical results presented attest the good stability properties of all methods examined herein. (A.C.A.S.) [pt

Estimating the Acquisition Price of Enshi Yulu Young Tea Shoots Using Near-Infrared Spectroscopy by the Back Propagation Artificial Neural Network Model in Conjunction with Backward Interval Partial Least Squares Algorithm

Science.gov (United States)

Wang, Sh.-P.; Gong, Z.-M.; Su, X.-Zh.; Liao, J.-Zh.

2017-09-01

Near infrared spectroscopy and the back propagation artificial neural network model in conjunction with backward interval partial least squares algorithm were used to estimate the purchasing price of Enshi yulu young tea shoots. The near-infrared spectra regions most relevant to the tea shoots price model (5700.5-5935.8, 7613.6-7848.9, 8091.8-8327.1, 8331-8566.2, 9287.5-9522.5, and 9526.6-9761.9 cm-1) were selected using backward interval partial least squares algorithm. The first five principal components that explained 99.96% of the variability in those selected spectral data were then used to calibrate the back propagation artificial neural tea shoots purchasing price model. The performance of this model (coefficient of determination for prediction 0.9724; root-mean-square error of prediction 4.727) was superior to those of the back propagation artificial neural model (coefficient of determination for prediction 0.8653, root-mean-square error of prediction 5.125) and the backward interval partial least squares model (coefficient of determination for prediction 0.5932, root-mean-square error of prediction 25.125). The acquisition price model with the combined backward interval partial least squares-back propagation artificial neural network algorithms can evaluate the price of Enshi yulu tea shoots accurately, quickly and objectively.

Making the most out of least-squares migration

KAUST Repository

Huang, Yunsong

2014-09-01

Standard migration images can suffer from (1) migration artifacts caused by an undersampled acquisition geometry, (2) poor resolution resulting from a limited recording aperture, (3) ringing artifacts caused by ripples in the source wavelet, and (4) weak amplitudes resulting from geometric spreading, attenuation, and defocusing. These problems can be remedied in part by least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), which aims to linearly invert seismic data for the reflectivity distribution. Given a sufficiently accurate migration velocity model, LSM can mitigate many of the above problems and can produce more resolved migration images, sometimes with more than twice the spatial resolution of standard migration. However, LSM faces two challenges: The computational cost can be an order of magnitude higher than that of standard migration, and the resulting image quality can fail to improve for migration velocity errors of about 5% or more. It is possible to obtain the most from least-squares migration by reducing the cost and velocity sensitivity of LSM.

Selective and divided attention modulates auditory-vocal integration in the processing of pitch feedback errors.

Science.gov (United States)

Liu, Ying; Hu, Huijing; Jones, Jeffery A; Guo, Zhiqiang; Li, Weifeng; Chen, Xi; Liu, Peng; Liu, Hanjun

2015-08-01

Speakers rapidly adjust their ongoing vocal productions to compensate for errors they hear in their auditory feedback. It is currently unclear what role attention plays in these vocal compensations. This event-related potential (ERP) study examined the influence of selective and divided attention on the vocal and cortical responses to pitch errors heard in auditory feedback regarding ongoing vocalisations. During the production of a sustained vowel, participants briefly heard their vocal pitch shifted up two semitones while they actively attended to auditory or visual events (selective attention), or both auditory and visual events (divided attention), or were not told to attend to either modality (control condition). The behavioral results showed that attending to the pitch perturbations elicited larger vocal compensations than attending to the visual stimuli. Moreover, ERPs were likewise sensitive to the attentional manipulations: P2 responses to pitch perturbations were larger when participants attended to the auditory stimuli compared to when they attended to the visual stimuli, and compared to when they were not explicitly told to attend to either the visual or auditory stimuli. By contrast, dividing attention between the auditory and visual modalities caused suppressed P2 responses relative to all the other conditions and caused enhanced N1 responses relative to the control condition. These findings provide strong evidence for the influence of attention on the mechanisms underlying the auditory-vocal integration in the processing of pitch feedback errors. In addition, selective attention and divided attention appear to modulate the neurobehavioral processing of pitch feedback errors in different ways. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

The Effects of Discrete-Trial Training Commission Errors on Learner Outcomes: An Extension

Science.gov (United States)

Jenkins, Sarah R.; Hirst, Jason M.; DiGennaro Reed, Florence D.

2015-01-01

We conducted a parametric analysis of treatment integrity errors during discrete-trial training and investigated the effects of three integrity conditions (0, 50, or 100 % errors of commission) on performance in the presence and absence of programmed errors. The presence of commission errors impaired acquisition for three of four participants.…

Mackie's Error Theory and Reasons | Farland | South African Journal ...

African Journals Online (AJOL)

The error theory has, for some time, served as a last resort for those who would like to take moral realism seriously but who cannot countenance the thought that moral properties might be non-natural. As soon as their attempts to 'square' moral properties with natural properties appear to be in trouble, such philosophers ...

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

Energy Technology Data Exchange (ETDEWEB)

Liu, Yunlong; Wang, Aiping; Guo, Lei; Wang, Hong

2017-07-09

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.

DEM4-26, Least Square Fit for IBM PC by Deming Method

International Nuclear Information System (INIS)

Rinard, P.M.; Bosler, G.E.

1989-01-01

1 - Description of program or function: DEM4-26 is a generalized least square fitting program based on Deming's method. Functions built into the program for fitting include linear, quadratic, cubic, power, Howard's, exponential, and Gaussian; others can easily be added. The program has the following capabilities: (1) entry, editing, and saving of data; (2) fitting of any of the built-in functions or of a user-supplied function; (3) plotting the data and fitted function on the display screen, with error limits if requested, and with the option of copying the plot to the printer; (4) interpolation of x or y values from the fitted curve with error estimates based on error limits selected by the user; and (5) plotting the residuals between the y data values and the fitted curve, with the option copying the plot to the printer. 2 - Method of solution: Deming's method

Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines

International Nuclear Information System (INIS)

Niazi, Ali; Jameh-Bozorghi, Saeed; Nori-Shargh, Davood

2008-01-01

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of toxicity (IGC 50 ) of nitrobenzenes. Ab initio theory was used to calculate some quantum chemical descriptors including electrostatic potentials and local charges at each atom, HOMO and LUMO energies, etc. Modeling of the IGC 50 of nitrobenzenes as a function of molecular structures was established by means of the least squares support vector machines (LS-SVM). This model was applied for the prediction of the toxicity (IGC 50 ) of nitrobenzenes, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.0049 for LS-SVM. Results have shown that the introduction of LS-SVM for quantum chemical descriptors drastically enhances the ability of prediction in QSAR studies superior to multiple linear regression and partial least squares

An improved partial least-squares regression method for Raman spectroscopy

Science.gov (United States)

Momenpour Tehran Monfared, Ali; Anis, Hanan

2017-10-01

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

A weak Galerkin least-squares finite element method for div-curl systems

Science.gov (United States)

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.

Positive Scattering Cross Sections using Constrained Least Squares

International Nuclear Information System (INIS)

Dahl, J.A.; Ganapol, B.D.; Morel, J.E.

1999-01-01

A method which creates a positive Legendre expansion from truncated Legendre cross section libraries is presented. The cross section moments of order two and greater are modified by a constrained least squares algorithm, subject to the constraints that the zeroth and first moments remain constant, and that the standard discrete ordinate scattering matrix is positive. A method using the maximum entropy representation of the cross section which reduces the error of these modified moments is also presented. These methods are implemented in PARTISN, and numerical results from a transport calculation using highly anisotropic scattering cross sections with the exponential discontinuous spatial scheme is presented

Merging bottom-up and top-down precipitation products using a stochastic error model

Science.gov (United States)

Maggioni, Viviana; Massari, Christian; Brocca, Luca; Ciabatta, Luca

2017-04-01

operational hydrology. The study, conducted in Italy for a 5-yr period (2010-2014) using a dense network of raingauges (about 3000) as a benchmark, demonstrates that the integration is able to enhance the correlation and the root mean squared error of SM2RAIN+3B42RT with respect to the parent products. This suggests a potential benefit of merging SM2RAIN derived rainfall with state-of-the-art satellite precipitation estimates for creating a product characterized by higher accuracy and better performance when used in the contest of operational hydrology. REFERENCES 1. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141. 2. Hossain, F.; Anagnostou, E. N. A two-dimensional satellite rainfall error model. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1511-1522.

Analysis of interactive fixed effects dynamic linear panel regression with measurement error

OpenAIRE

Nayoung Lee; Hyungsik Roger Moon; Martin Weidner

2011-01-01

This paper studies a simple dynamic panel linear regression model with interactive fixed effects in which the variable of interest is measured with error. To estimate the dynamic coefficient, we consider the least-squares minimum distance (LS-MD) estimation method.

PERFORMANCE OF THE ZERO FORCING PRECODING MIMO BROADCAST SYSTEMS WITH CHANNEL ESTIMATION ERRORS

Institute of Scientific and Technical Information of China (English)

Wang Jing; Liu Zhanli; Wang Yan; You Xiaohu

2007-01-01

In this paper, the effect of channel estimation errors upon the Zero Forcing (ZF) precoding Multiple Input Multiple Output Broadcast (MIMO BC) systems was studied. Based on the two kinds of Gaussian estimation error models, the performance analysis is conducted under different power allocation strategies. Analysis and simulation show that if the covariance of channel estimation errors is independent of the received Signal to Noise Ratio (SNR), imperfect channel knowledge deteriorates the sum capacity and the Bit Error Rate (BER) performance severely. However, under the situation of orthogonal training and the Minimum Mean Square Error (MMSE) channel estimation, the sum capacity and BER performance are consistent with those of the perfect Channel State Information (CSI)with only a performance degradation.

A channel-by-channel method of reducing the errors associated with peak area integration

International Nuclear Information System (INIS)

Luedeke, T.P.; Tripard, G.E.

1996-01-01

A new method of reducing the errors associated with peak area integration has been developed. This method utilizes the signal content of each channel as an estimate of the overall peak area. These individual estimates can then be weighted according to the precision with which each estimate is known, producing an overall area estimate. Experimental measurements were performed on a small peak sitting on a large background, and the results compared to those obtained from a commercial software program. Results showed a marked decrease in the spread of results around the true value (obtained by counting for a long period of time), and a reduction in the statistical uncertainty associated with the peak area. (orig.)

Operator quantum error-correcting subsystems for self-correcting quantum memories

International Nuclear Information System (INIS)

Bacon, Dave

2006-01-01

The most general method for encoding quantum information is not to encode the information into a subspace of a Hilbert space, but to encode information into a subsystem of a Hilbert space. Recently this notion has led to a more general notion of quantum error correction known as operator quantum error correction. In standard quantum error-correcting codes, one requires the ability to apply a procedure which exactly reverses on the error-correcting subspace any correctable error. In contrast, for operator error-correcting subsystems, the correction procedure need not undo the error which has occurred, but instead one must perform corrections only modulo the subsystem structure. This does not lead to codes which differ from subspace codes, but does lead to recovery routines which explicitly make use of the subsystem structure. Here we present two examples of such operator error-correcting subsystems. These examples are motivated by simple spatially local Hamiltonians on square and cubic lattices. In three dimensions we provide evidence, in the form a simple mean field theory, that our Hamiltonian gives rise to a system which is self-correcting. Such a system will be a natural high-temperature quantum memory, robust to noise without external intervening quantum error-correction procedures

Applying recursive numerical integration techniques for solving high dimensional integrals

International Nuclear Information System (INIS)

Ammon, Andreas; Genz, Alan; Hartung, Tobias; Jansen, Karl; Volmer, Julia; Leoevey, Hernan

2016-11-01

The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

Applying recursive numerical integration techniques for solving high dimensional integrals

Energy Technology Data Exchange (ETDEWEB)

Ammon, Andreas [IVU Traffic Technologies AG, Berlin (Germany); Genz, Alan [Washington State Univ., Pullman, WA (United States). Dept. of Mathematics; Hartung, Tobias [King' s College, London (United Kingdom). Dept. of Mathematics; Jansen, Karl; Volmer, Julia [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Leoevey, Hernan [Humboldt Univ. Berlin (Germany). Inst. fuer Mathematik

2016-11-15

The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

Spectral/hp least-squares finite element formulation for the Navier-Stokes equations

International Nuclear Information System (INIS)

Pontaza, J.P.; Reddy, J.N.

2003-01-01

We consider the application of least-squares finite element models combined with spectral/hp methods for the numerical solution of viscous flow problems. The paper presents the formulation, validation, and application of a spectral/hp algorithm to the numerical solution of the Navier-Stokes equations governing two- and three-dimensional stationary incompressible and low-speed compressible flows. The Navier-Stokes equations are expressed as an equivalent set of first-order equations by introducing vorticity or velocity gradients as additional independent variables and the least-squares method is used to develop the finite element model. High-order element expansions are used to construct the discrete model. The discrete model thus obtained is linearized by Newton's method, resulting in a linear system of equations with a symmetric positive definite coefficient matrix that is solved in a fully coupled manner by a preconditioned conjugate gradient method. Spectral convergence of the L 2 least-squares functional and L 2 error norms is verified using smooth solutions to the two-dimensional stationary Poisson and incompressible Navier-Stokes equations. Numerical results for flow over a backward-facing step, steady flow past a circular cylinder, three-dimensional lid-driven cavity flow, and compressible buoyant flow inside a square enclosure are presented to demonstrate the predictive capability and robustness of the proposed formulation

Growth kinetics of borided layers: Artificial neural network and least square approaches

Science.gov (United States)

Campos, I.; Islas, M.; Ramírez, G.; VillaVelázquez, C.; Mota, C.

2007-05-01

The present study evaluates the growth kinetics of the boride layer Fe 2B in AISI 1045 steel, by means of neural networks and the least square techniques. The Fe 2B phase was formed at the material surface using the paste boriding process. The surface boron potential was modified considering different boron paste thicknesses, with exposure times of 2, 4 and 6 h, and treatment temperatures of 1193, 1223 and 1273 K. The neural network and the least square models were set by the layer thickness of Fe 2B phase, and assuming that the growth of the boride layer follows a parabolic law. The reliability of the techniques used is compared with a set of experiments at a temperature of 1223 K with 5 h of treatment time and boron potentials of 2, 3, 4 and 5 mm. The results of the Fe 2B layer thicknesses show a mean error of 5.31% for the neural network and 3.42% for the least square method.

Forecast Combination under Heavy-Tailed Errors

Directory of Open Access Journals (Sweden)

Gang Cheng

2015-11-01

Full Text Available Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student’s t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers.

Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

Directory of Open Access Journals (Sweden)

Changwei Ma

2015-01-01

Full Text Available Discrete Fourier transform- (DFT- based maximum likelihood (ML algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB, which is dependent on the number of DFT points. However, its mean square error (MSE performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR, least squares SVR (LS-SVR can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.

Study of the Integration of LIDAR and Photogrammetric Datasets by in Situ Camera Calibration and Integrated Sensor Orientation

Science.gov (United States)

Mitishita, E.; Costa, F.; Martins, M.

2017-05-01

Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO) approach was performed using Interior Orientation Parameter (IOP) values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE) of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.

Interaction and Representational Integration: Evidence from Speech Errors

Science.gov (United States)

Goldrick, Matthew; Baker, H. Ross; Murphy, Amanda; Baese-Berk, Melissa

2011-01-01

We examine the mechanisms that support interaction between lexical, phonological and phonetic processes during language production. Studies of the phonetics of speech errors have provided evidence that partially activated lexical and phonological representations influence phonetic processing. We examine how these interactive effects are modulated…

Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

Science.gov (United States)

Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.

2011-02-01

Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.

Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

International Nuclear Information System (INIS)

Olivares, A; Olivares, G; Górriz, J M; Ramírez, J

2011-01-01

Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed

A suggested procedure for resolving an anomaly in least-squares data analysis known as ''Peelle's Pertinent Puzzle'' and the general implications for nuclear data evaluation

International Nuclear Information System (INIS)

Chiba, Satoshi; Smith, D.L.

1991-09-01

Modern nuclear-data evaluation methodology is based largely on statistical inference, with the least-squares technique being chosen most often to generate best estimates for physical quantities and their uncertainties. It has been observed that those least-squares evaluations which employ covariance matrices based on absolute errors that are derived directly from the reported experimental data often tend to produce results which appear to be too low. This anomaly is discussed briefly in this report, and a procedure for resolving it is suggested. The method involves employing data uncertainties which are derived from errors expressed in percent. These percent errors are used, in conjunction with reasonable a priori estimates for the quantities to be evaluated, to derive the covariance matrices which are required for applications of the least-squares procedure. This approach appears to lead to more rational weighting of the experimental data and, thus, to more realistic evaluated results than are obtained when the errors are based on the actual data. The procedure is very straightforward when only one parameter must be estimated. However, for those evaluation exercises involving more than one parameter, this technique demands that a priori estimates be provided at the outset for all of the parameters in question. Then, the least-squares method is applied iteratively to produce a sequence of sets of estimated values which are anticipated to convergence toward a particular set of parameters which one then designates as the ''best'' evaluated results from the exercise. It is found that convergence usually occurs very rapidly when the a priori estimates approximate the final solution reasonably well

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

Science.gov (United States)

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

Non-intercepted dose errors in prescribing anti-neoplastic treatment

DEFF Research Database (Denmark)

Mattsson, T O; Holm, B; Michelsen, H

2015-01-01

BACKGROUND: The incidence of non-intercepted prescription errors and the risk factors involved, including the impact of computerised order entry (CPOE) systems on such errors, are unknown. Our objective was to determine the incidence, type, severity, and related risk factors of non-intercepted pr....... Strategies to prevent future prescription errors could usefully focus on integrated computerised systems that can aid dose calculations and reduce transcription errors between databases....

A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality

Science.gov (United States)

Cheung, KW; So, HC; Ma, W.-K.; Chan, YT

2006-12-01

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time-of-arrival (TOA), received signal strength (RSS), time-difference-of-arrival (TDOA), and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the mobile station. In this paper, we present a constrained weighted least squares (CWLS) mobile positioning approach that encompasses all the above described measurement cases. The advantages of CWLS include performance optimality and capability of extension to hybrid measurement cases (e.g., mobile positioning using TDOA and AOA measurements jointly). Assuming zero-mean uncorrelated measurement errors, we show by mean and variance analysis that all the developed CWLS location estimators achieve zero bias and the Cramér-Rao lower bound approximately when measurement error variances are small. The asymptotic optimum performance is also confirmed by simulation results.

Adjustment of Measurements with Multiplicative Errors: Error Analysis, Estimates of the Variance of Unit Weight, and Effect on Volume Estimation from LiDAR-Type Digital Elevation Models

Directory of Open Access Journals (Sweden)

Yun Shi

2014-01-01

Full Text Available Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM.

An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

Directory of Open Access Journals (Sweden)

Ming Yang

2018-03-01

Full Text Available In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment results.

Research on Error Modelling and Identification of 3 Axis NC Machine Tools Based on Cross Grid Encoder Measurement

International Nuclear Information System (INIS)

Du, Z C; Lv, C F; Hong, M S

2006-01-01

A new error modelling and identification method based on the cross grid encoder is proposed in this paper. Generally, there are 21 error components in the geometric error of the 3 axis NC machine tools. However according our theoretical analysis, the squareness error among different guide ways affects not only the translation error component, but also the rotational ones. Therefore, a revised synthetic error model is developed. And the mapping relationship between the error component and radial motion error of round workpiece manufactured on the NC machine tools are deduced. This mapping relationship shows that the radial error of circular motion is the comprehensive function result of all the error components of link, worktable, sliding table and main spindle block. Aiming to overcome the solution singularity shortcoming of traditional error component identification method, a new multi-step identification method of error component by using the Cross Grid Encoder measurement technology is proposed based on the kinematic error model of NC machine tool. Firstly, the 12 translational error components of the NC machine tool are measured and identified by using the least square method (LSM) when the NC machine tools go linear motion in the three orthogonal planes: XOY plane, XOZ plane and YOZ plane. Secondly, the circular error tracks are measured when the NC machine tools go circular motion in the same above orthogonal planes by using the cross grid encoder Heidenhain KGM 182. Therefore 9 rotational errors can be identified by using LSM. Finally the experimental validation of the above modelling theory and identification method is carried out in the 3 axis CNC vertical machining centre Cincinnati 750 Arrow. The entire 21 error components have been successfully measured out by the above method. Research shows the multi-step modelling and identification method is very suitable for 'on machine measurement'

(How) do we learn from errors? A prospective study of the link between the ward's learning practices and medication administration errors.

Science.gov (United States)

Drach-Zahavy, A; Somech, A; Admi, H; Peterfreund, I; Peker, H; Priente, O

2014-03-01

Attention in the ward should shift from preventing medication administration errors to managing them. Nevertheless, little is known in regard with the practices nursing wards apply to learn from medication administration errors as a means of limiting them. To test the effectiveness of four types of learning practices, namely, non-integrated, integrated, supervisory and patchy learning practices in limiting medication administration errors. Data were collected from a convenient sample of 4 hospitals in Israel by multiple methods (observations and self-report questionnaires) at two time points. The sample included 76 wards (360 nurses). Medication administration error was defined as any deviation from prescribed medication processes and measured by a validated structured observation sheet. Wards' use of medication administration technologies, location of the medication station, and workload were observed; learning practices and demographics were measured by validated questionnaires. Results of the mixed linear model analysis indicated that the use of technology and quiet location of the medication cabinet were significantly associated with reduced medication administration errors (estimate=.03, perrors (estimate=.04, plearning practices, supervisory learning was the only practice significantly linked to reduced medication administration errors (estimate=-.04, plearning were significantly linked to higher levels of medication administration errors (estimate=-.03, plearning was not associated with it (p>.05). How wards manage errors might have implications for medication administration errors beyond the effects of typical individual, organizational and technology risk factors. Head nurse can facilitate learning from errors by "management by walking around" and monitoring nurses' medication administration behaviors. Copyright © 2013 Elsevier Ltd. All rights reserved.

Partial Least Square with Savitzky Golay Derivative in Predicting Blood Hemoglobin Using Near Infrared Spectrum

Directory of Open Access Journals (Sweden)

Mohd Idrus Mohd Nazrul Effendy

2018-01-01

Full Text Available Near infrared spectroscopy (NIRS is a reliable technique that widely used in medical fields. Partial least square was developed to predict blood hemoglobin concentration using NIRS. The aims of this paper are (i to develop predictive model for near infrared spectroscopic analysis in blood hemoglobin prediction, (ii to establish relationship between blood hemoglobin and near infrared spectrum using a predictive model, (iii to evaluate the predictive accuracy of a predictive model based on root mean squared error (RMSE and coefficient of determination rp2. Partial least square with first order Savitzky Golay (SG derivative preprocessing (PLS-SGd1 showed the higher performance of predictions with RMSE = 0.7965 and rp2= 0.9206 in K-fold cross validation. Optimum number of latent variable (LV and frame length (f were 32 and 27 nm, respectively. These findings suggest that the relationship between blood hemoglobin and near infrared spectrum is strong, and the partial least square with first order SG derivative is able to predict the blood hemoglobin using near infrared spectral data.

Time-symmetric integration in astrophysics

Science.gov (United States)

Hernandez, David M.; Bertschinger, Edmund

2018-04-01

Calculating the long-term solution of ordinary differential equations, such as those of the N-body problem, is central to understanding a wide range of dynamics in astrophysics, from galaxy formation to planetary chaos. Because generally no analytic solution exists to these equations, researchers rely on numerical methods that are prone to various errors. In an effort to mitigate these errors, powerful symplectic integrators have been employed. But symplectic integrators can be severely limited because they are not compatible with adaptive stepping and thus they have difficulty in accommodating changing time and length scales. A promising alternative is time-reversible integration, which can handle adaptive time-stepping, but the errors due to time-reversible integration in astrophysics are less understood. The goal of this work is to study analytically and numerically the errors caused by time-reversible integration, with and without adaptive stepping. We derive the modified differential equations of these integrators to perform the error analysis. As an example, we consider the trapezoidal rule, a reversible non-symplectic integrator, and show that it gives secular energy error increase for a pendulum problem and for a Hénon-Heiles orbit. We conclude that using reversible integration does not guarantee good energy conservation and that, when possible, use of symplectic integrators is favoured. We also show that time-symmetry and time-reversibility are properties that are distinct for an integrator.

Development of an integrated system for estimating human error probabilities

Energy Technology Data Exchange (ETDEWEB)

Auflick, J.L.; Hahn, H.A.; Morzinski, J.A.

1998-12-01

This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). This project had as its main objective the development of a Human Reliability Analysis (HRA), knowledge-based expert system that would provide probabilistic estimates for potential human errors within various risk assessments, safety analysis reports, and hazard assessments. HRA identifies where human errors are most likely, estimates the error rate for individual tasks, and highlights the most beneficial areas for system improvements. This project accomplished three major tasks. First, several prominent HRA techniques and associated databases were collected and translated into an electronic format. Next, the project started a knowledge engineering phase where the expertise, i.e., the procedural rules and data, were extracted from those techniques and compiled into various modules. Finally, these modules, rules, and data were combined into a nearly complete HRA expert system.

Digital squares

DEFF Research Database (Denmark)

Forchhammer, Søren; Kim, Chul E

1988-01-01

Digital squares are defined and their geometric properties characterized. A linear time algorithm is presented that considers a convex digital region and determines whether or not it is a digital square. The algorithm also determines the range of the values of the parameter set of its preimages....... The analysis involves transforming the boundary of a digital region into parameter space of slope and y-intercept...

Optimized universal color palette design for error diffusion

Science.gov (United States)

Kolpatzik, Bernd W.; Bouman, Charles A.

1995-04-01

Currently, many low-cost computers can only simultaneously display a palette of 256 color. However, this palette is usually selectable from a very large gamut of available colors. For many applications, this limited palette size imposes a significant constraint on the achievable image quality. We propose a method for designing an optimized universal color palette for use with halftoning methods such as error diffusion. The advantage of a universal color palette is that it is fixed and therefore allows multiple images to be displayed simultaneously. To design the palette, we employ a new vector quantization method known as sequential scalar quantization (SSQ) to allocate the colors in a visually uniform color space. The SSQ method achieves near-optimal allocation, but may be efficiently implemented using a series of lookup tables. When used with error diffusion, SSQ adds little computational overhead and may be used to minimize the visual error in an opponent color coordinate system. We compare the performance of the optimized algorithm to standard error diffusion by evaluating a visually weighted mean-squared-error measure. Our metric is based on the color difference in CIE L*AL*B*, but also accounts for the lowpass characteristic of human contrast sensitivity.

Research on the Reliability Analysis of the Integrated Modular Avionics System Based on the AADL Error Model

Directory of Open Access Journals (Sweden)

Peng Wang

2018-01-01

Full Text Available In recent years, the integrated modular avionics (IMA concept has been introduced to replace the traditional federated avionics. Different avionics functions are hosted in a shared IMA platform, and IMA adopts partition technologies to provide a logical isolation among different functions. The IMA architecture can provide more sophisticated and powerful avionics functionality; meanwhile, the failure propagation patterns in IMA are more complex. The feature of resource sharing introduces some unintended interconnections among different functions, which makes the failure propagation modes more complex. Therefore, this paper proposes an architecture analysis and design language- (AADL- based method to establish the reliability model of IMA platform. The single software and hardware error behavior in IMA system is modeled. The corresponding AADL error model of failure propagation among components, between software and hardware, is given. Finally, the display function of IMA platform is taken as an example to illustrate the effectiveness of the proposed method.

Sensitivity of Multicarrier Two-Dimensional Spreading Schemes to Synchronization Errors

Directory of Open Access Journals (Sweden)

Geneviève Jourdain

2008-06-01

Full Text Available This paper presents the impact of synchronization errors on the performance of a downlink multicarrier two-dimensional spreading OFDM-CDMA system. This impact is measured by the degradation of the signal to interference and noise ratio (SINR obtained after despreading and equalization. The contribution of this paper is twofold. First, we use some properties of random matrix and free probability theories to derive a new expression of the SINR. This expression is then independent of the actual value of the spreading codes while still accounting for the orthogonality between codes. This model is validated by means of Monte Carlo simulations. Secondly, the model is exploited to derive the SINR degradation of OFDM-CDMA systems due to synchronization errors which include a timing error, a carrier frequency offset, and a sampling frequency offset. It is also exploited to compare the sensitivities of MC-CDMA and MC-DS-CDMA systems to these errors in a frequency selective channel. This work is carried out for zero-forcing and minimum mean square error equalizers.

Propagation of angular errors in two-axis rotation systems

Science.gov (United States)

Torrington, Geoffrey K.

2003-10-01

Two-Axis Rotation Systems, or "goniometers," are used in diverse applications including telescope pointing, automotive headlamp testing, and display testing. There are three basic configurations in which a goniometer can be built depending on the orientation and order of the stages. Each configuration has a governing set of equations which convert motion between the system "native" coordinates to other base systems, such as direction cosines, optical field angles, or spherical-polar coordinates. In their simplest form, these equations neglect errors present in real systems. In this paper, a statistical treatment of error source propagation is developed which uses only tolerance data, such as can be obtained from the system mechanical drawings prior to fabrication. It is shown that certain error sources are fully correctable, partially correctable, or uncorrectable, depending upon the goniometer configuration and zeroing technique. The system error budget can be described by a root-sum-of-squares technique with weighting factors describing the sensitivity of each error source. This paper tabulates weighting factors at 67% (k=1) and 95% (k=2) confidence for various levels of maximum travel for each goniometer configuration. As a practical example, this paper works through an error budget used for the procurement of a system at Sandia National Laboratories.

Application of square-root filtering for spacecraft attitude control

Science.gov (United States)

Sorensen, J. A.; Schmidt, S. F.; Goka, T.

1978-01-01

Suitable digital algorithms are developed and tested for providing on-board precision attitude estimation and pointing control for potential use in the Landsat-D spacecraft. These algorithms provide pointing accuracy of better than 0.01 deg. To obtain necessary precision with efficient software, a six state-variable square-root Kalman filter combines two star tracker measurements to update attitude estimates obtained from processing three gyro outputs. The validity of the estimation and control algorithms are established, and the sensitivity of their performance to various error sources and software parameters are investigated by detailed digital simulation. Spacecraft computer memory, cycle time, and accuracy requirements are estimated.

Use and Subtleties of Saddlepoint Approximation for Minimum Mean-Square Error Estimation

DEFF Research Database (Denmark)

Beierholm, Thomas; Nuttall, Albert H.; Hansen, Lars Kai

2008-01-01

integral representation. However, the examples also demonstrate that when two saddle points are close or coalesce, then saddle-point approximation based on isolated saddle points is not valid. A saddle-point approximation based on two close or coalesced saddle points is derived and in the examples......, the validity and accuracy of the derivation is demonstrated...

On the importance of measurement error correlations in data assimilation for integrated hydrological models

Science.gov (United States)

Camporese, Matteo; Botto, Anna

2017-04-01

Data assimilation is becoming increasingly popular in hydrological and earth system modeling, as it allows us to integrate multisource observation data in modeling predictions and, in doing so, to reduce uncertainty. For this reason, data assimilation has been recently the focus of much attention also for physically-based integrated hydrological models, whereby multiple terrestrial compartments (e.g., snow cover, surface water, groundwater) are solved simultaneously, in an attempt to tackle environmental problems in a holistic approach. Recent examples include the joint assimilation of water table, soil moisture, and river discharge measurements in catchment models of coupled surface-subsurface flow using the ensemble Kalman filter (EnKF). One of the typical assumptions in these studies is that the measurement errors are uncorrelated, whereas in certain situations it is reasonable to believe that some degree of correlation occurs, due for example to the fact that a pair of sensors share the same soil type. The goal of this study is to show if and how the measurement error correlations between different observation data play a significant role on assimilation results in a real-world application of an integrated hydrological model. The model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental hillslope. The physical model, located in the Department of Civil, Environmental and Architectural Engineering of the University of Padova (Italy), consists of a reinforced concrete box containing a soil prism with maximum height of 3.5 m, length of 6 m, and width of 2 m. The hillslope is equipped with sensors to monitor the pressure head and soil moisture responses to a series of generated rainfall events applied onto a 60 cm thick sand layer overlying a sandy clay soil. The measurement network is completed by two tipping bucket flow gages to measure the two components (subsurface and surface) of the outflow. By collecting

REGULAR METHOD FOR SYNTHESIS OF BASIC BENT-SQUARES OF RANDOM ORDER

Directory of Open Access Journals (Sweden)

A. V. Sokolov

2016-01-01

Full Text Available The paper is devoted to the class construction of the most non-linear Boolean bent-functions of any length N = 2k (k = 2, 4, 6…, on the basis of their spectral representation – Agievich bent squares. These perfect algebraic constructions are used as a basis to build many new cryptographic primitives, such as generators of pseudo-random key sequences, crypto graphic S-boxes, etc. Bent-functions also find their application in the construction of C-codes in the systems with code division multiple access (CDMA to provide the lowest possible value of Peak-to-Average Power Ratio (PAPR k = 1, as well as for the construction of error-correcting codes and systems of orthogonal biphasic signals. All the numerous applications of bent-functions relate to the theory of their synthesis. However, regular methods for complete class synthesis of bent-functions of any length N = 2k are currently unknown. The paper proposes a regular synthesis method for the basic Agievich bent squares of any order n, based on a regular operator of dyadic shift. Classification for a complete set of spectral vectors of lengths (l = 8, 16, … based on a criterion of the maximum absolute value and set of absolute values of spectral components has been carried out in the paper. It has been shown that any spectral vector can be a basis for building bent squares. Results of the synthesis for the Agievich bent squares of order n = 8 have been generalized and it has been revealed that there are only 3 basic bent squares for this order, while the other 5 can be obtained with help the operation of step-cyclic shift. All the basic bent squares of order n = 16 have been synthesized that allows to construct the bent-functions of length N = 256. The obtained basic bent squares can be used either for direct synthesis of bent-functions and their practical application or for further research in order to synthesize new structures of bent squares of orders n = 16, 32, 64, …

Nonlinear generalized synchronization of chaotic systems by pure error dynamics and elaborate nondiagonal Lyapunov function

International Nuclear Information System (INIS)

Ge Zhengming; Chang Chingming

2009-01-01

By applying pure error dynamics and elaborate nondiagonal Lyapunov function, the nonlinear generalized synchronization is studied in this paper. Instead of current mixed error dynamics in which master state variables and slave state variables are presented, the nonlinear generalized synchronization can be obtained by pure error dynamics without auxiliary numerical simulation. The elaborate nondiagonal Lyapunov function is applied rather than current monotonous square sum Lyapunov function deeply weakening the powerfulness of Lyapunov direct method. Both autonomous and nonautonomous double Mathieu systems are used as examples with numerical simulations.

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

Science.gov (United States)

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…

Internal displacement and strain measurement using digital volume correlation: a least-squares framework

International Nuclear Information System (INIS)

Pan, Bing; Wu, Dafang; Wang, Zhaoyang

2012-01-01

As a novel tool for quantitative 3D internal deformation measurement throughout the interior of a material or tissue, digital volume correlation (DVC) has increasingly gained attention and application in the fields of experimental mechanics, material research and biomedical engineering. However, the practical implementation of DVC involves important challenges such as implementation complexity, calculation accuracy and computational efficiency. In this paper, a least-squares framework is presented for 3D internal displacement and strain field measurement using DVC. The proposed DVC combines a practical linear-intensity-change model with an easy-to-implement iterative least-squares (ILS) algorithm to retrieve 3D internal displacement vector field with sub-voxel accuracy. Because the linear-intensity-change model is capable of accounting for both the possible intensity changes and the relative geometric transform of the target subvolume, the presented DVC thus provides the highest sub-voxel registration accuracy and widest applicability. Furthermore, as the ILS algorithm uses only first-order spatial derivatives of the deformed volumetric image, the developed DVC thus significantly reduces computational complexity. To further extract 3D strain distributions from the 3D discrete displacement vectors obtained by the ILS algorithm, the presented DVC employs a pointwise least-squares algorithm to estimate the strain components for each measurement point. Computer-simulated volume images with controlled displacements are employed to investigate the performance of the proposed DVC method in terms of mean bias error and standard deviation error. Results reveal that the present technique is capable of providing accurate measurements in an easy-to-implement manner, and can be applied to practical 3D internal displacement and strain calculation. (paper)

Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network

Directory of Open Access Journals (Sweden)

Golmohammadi Hassan

2013-01-01

Full Text Available A quantitative structure-property relationship (QSPR study based on partial least squares (PLS and artificial neural network (ANN was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP, ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. After optimization and training of the network according to back-propagation algorithm, a 5-5-1 neural network was generated for prediction of ferric iron precipitation. The root mean square error for the neural network calculated ferric iron precipitation for training, prediction and validation set are 32.860, 40.739 and 35.890, respectively, which are smaller than those obtained by PLS model (180.972, 165.047 and 149.950, respectively. Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ferric iron precipitation in bioleaching process.

Fast motion-including dose error reconstruction for VMAT with and without MLC tracking

DEFF Research Database (Denmark)

Ravkilde, Thomas; Keall, Paul J.; Grau, Cai

2014-01-01

of the algorithm for reconstruction of dose and motion-induced dose errors throughout the tracking and non-tracking beam deliveries was quantified. Doses were reconstructed with a mean dose difference relative to the measurements of -0.5% (5.5% standard deviation) for cumulative dose. More importantly, the root...... validate a simple model for fast motion-including dose error reconstruction applicable to intrafractional QA of MLC tracking treatments of moving targets. MLC tracking experiments were performed on a standard linear accelerator with prototype MLC tracking software guided by an electromagnetic transponder......-mean-square deviation between reconstructed and measured motion-induced 3%/3 mm γ failure rates (dose error) was 2.6%. The mean computation time for each calculation of dose and dose error was 295 ms. The motion-including dose reconstruction allows accurate temporal and spatial pinpointing of errors in absorbed dose...

Dose rate from the square volume radiation source

International Nuclear Information System (INIS)

Karpov, V.I.

1978-01-01

The expression for determining the dose rate from a three-dimensional square flat-parallel source of any dimensions is obtained. A simplified method for integrating the resultant expression is proposed. A comparison of the calculation results with the results by the Monte Carlo method has shown them to coincide within 6-8%. Since buildings and structures consist of rectangular elements, the method is recommended for practical calculations of dose rates in residential buildings

On the removal of boundary errors caused by Runge-Kutta integration of non-linear partial differential equations

Science.gov (United States)

Abarbanel, Saul; Gottlieb, David; Carpenter, Mark H.

1994-01-01

It has been previously shown that the temporal integration of hyperbolic partial differential equations (PDE's) may, because of boundary conditions, lead to deterioration of accuracy of the solution. A procedure for removal of this error in the linear case has been established previously. In the present paper we consider hyperbolic (PDE's) (linear and non-linear) whose boundary treatment is done via the SAT-procedure. A methodology is present for recovery of the full order of accuracy, and has been applied to the case of a 4th order explicit finite difference scheme.

And still, a new beginning: the Galerkin least-squares gradient method

International Nuclear Information System (INIS)

Franca, L.P.; Carmo, E.G.D. do

1988-08-01

A finite element method is proposed to solve a scalar singular diffusion problem. The method is constructed by adding to the standard Galerkin a mesh-dependent term obtained by taking the gradient of the Euler-lagrange equation and multiplying it by its least-squares. For the one-dimensional homogeneous problem the method is designed to develop nodal exact solution. An error estimate shows that the method converges optimaly for any value of the singular parameter. Numerical results demonstrate the good stability and accuracy properties of the method. (author) [pt

Disadvantage factors for square lattice cells using a collision probability method

International Nuclear Information System (INIS)

Raghav, H.P.

1976-01-01

The flux distribution in an infinite square lattice consisting of cylindrical fuel rods and moderator is calculated by using a collision probability method. Neutrons are assumed to be monoenergetic and the sources as well as scattering are assumed to be isotropic. Carlvik's method for the calculation of collision probability is used. The important features of the method are that the square boundary is treated exactly and the contribution of the surrounding cells is calculated explicitly. The method is programmed in a computer code CELLC. This carries out integration by Simpson's rule. The convergence and accuracy of CELLC is assessed by computing disadvantage factors for the well-known Thie lattices and comparing the results with Monte Carlo and other integral transport theory methods used elsewhere. It is demonstrated that it is not correct to apply the white boundary condition in the Wigner Seitz Cell for low pitch and low cross sections. (orig.) [de

Approximate critical surface of the bond-mixed square-lattice Ising model

International Nuclear Information System (INIS)

Levy, S.V.F.; Tsallis, C.; Curado, E.M.F.

1979-09-01

The critical surface of the quenched bond-mixed square-lattice spin-1/2 first-neighbour-interaction ferromagnetic Ising model (with exchange interactions J 1 and J 2 ) has been investigated. Through renormalization group and heuristical procedures, a very accurate (error inferior to 3x10 -4 in the variables t sub(i) = th (J sub(i)/k sub(b)T)) approximate numerical proposal for all points of this surface is presented. This proposal simultaneously satisfies all the available exact results concerning the surface, namely P sub(c) = 1/2, t sub(c) = √2 - 1, both limiting slopes in these points, and t 2 = (1-t 1 )/(1+t 1 ) for p = 1/2. Furthemore an analytic approximation (namely (1 - p) 1n(1 + t 1 ) + p 1n(1 + t 2 ) =(1/2)1n 2) is also proposed. In what concerns the available exact results, it only fails in reproducing one of the two limiting slopes, where there is an error of 1% in the derivative: these facts result in an estimated error less than 10 -3 (in the t-variables) for any points in the surface. (Author) [pt

Conjugate descent formulation of backpropagation error in feedforward neural networks

Directory of Open Access Journals (Sweden)

NK Sharma

2009-06-01

Full Text Available The feedforward neural network architecture uses backpropagation learning to determine optimal weights between different interconnected layers. This learning procedure uses a gradient descent technique applied to a sum-of-squares error function for the given input-output pattern. It employs an iterative procedure to minimise the error function for a given set of patterns, by adjusting the weights of the network. The first derivates of the error with respect to the weights identify the local error surface in the descent direction. Hence the network exhibits a different local error surface for every different pattern presented to it, and weights are iteratively modified in order to minimise the current local error. The determination of an optimal weight vector is possible only when the total minimum error (mean of the minimum local errors for all patterns from the training set may be minimised. In this paper, we present a general mathematical formulation for the second derivative of the error function with respect to the weights (which represents a conjugate descent for arbitrary feedforward neural network topologies, and we use this derivative information to obtain the optimal weight vector. The local error is backpropagated among the units of hidden layers via the second order derivative of the error with respect to the weights of the hidden and output layers independently and also in combination. The new total minimum error point may be evaluated with the help of the current total minimum error and the current minimised local error. The weight modification processes is performed twice: once with respect to the present local error and once more with respect to the current total or mean error. We present some numerical evidence that our proposed method yields better network weights than those determined via a conventional gradient descent approach.

Numerical prediction of augmented turbulent heat transfer in an annular fuel channel with repeated two-dimensional square ribs

International Nuclear Information System (INIS)

Takase, K.

1996-01-01

The square-ribbed fuel rod for high temperature gas-cooled reactors was designed and developed so as to enhance the turbulent heat transfer in comparison with the previous standard fuel rod. The turbulent heat transfer characteristics in an annular fuel channel with repeated two-dimensional square ribs were analysed numerically on a fully developed incompressible flow using the k-ε turbulence model and the two-dimensional axisymmetrical coordinate system. Numerical analyses were carried out under the conditions of Reynolds numbers from 3000 to 20000 and ratios of square-rib pitch to height of 10, 20 and 40 respectively. The predictions of the heat transfer coefficients agreed well within an error of 10% for the square-rib pitch to height ratio of 10, 20% for 20 and 25% for 40 respectively, with the heat transfer empirical correlations obtained from the experimental data due to the simulated square-ribbed fuel rods. Therefore it was found that the effect of heat transfer augmentation due to the square ribs could be predicted by the present numerical simulations and the mechanism could be explained by the change in the turbulence kinematic energy distribution along the flow direction. (orig.)

Baseline configuration for GNSS attitude determination with an analytical least-squares solution

International Nuclear Information System (INIS)

Chang, Guobin; Wang, Qianxin; Xu, Tianhe

2016-01-01

The GNSS attitude determination using carrier phase measurements with 4 antennas is studied on condition that the integer ambiguities have been resolved. The solution to the nonlinear least-squares is often obtained iteratively, however an analytical solution can exist for specific baseline configurations. The main aim of this work is to design this class of configurations. Both single and double difference measurements are treated which refer to the dedicated and non-dedicated receivers respectively. More realistic error models are employed in which the correlations between different measurements are given full consideration. The desired configurations are worked out. The configurations are rotation and scale equivariant and can be applied to both the dedicated and non-dedicated receivers. For these configurations, the analytical and optimal solution for the attitude is also given together with its error variance–covariance matrix. (paper)

Least-squares Minimization Approaches to Interpret Total Magnetic Anomalies Due to Spheres

Science.gov (United States)

Abdelrahman, E. M.; El-Araby, T. M.; Soliman, K. S.; Essa, K. S.; Abo-Ezz, E. R.

2007-05-01

We have developed three different least-squares approaches to determine successively: the depth, magnetic angle, and amplitude coefficient of a buried sphere from a total magnetic anomaly. By defining the anomaly value at the origin and the nearest zero-anomaly distance from the origin on the profile, the problem of depth determination is transformed into the problem of finding a solution of a nonlinear equation of the form f(z)=0. Knowing the depth and applying the least-squares method, the magnetic angle and amplitude coefficient are determined using two simple linear equations. In this way, the depth, magnetic angle, and amplitude coefficient are determined individually from all observed total magnetic data. The method is applied to synthetic examples with and without random errors and tested on a field example from Senegal, West Africa. In all cases, the depth solutions are in good agreement with the actual ones.

Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

OpenAIRE

A. Jayachitra; R. Vinodha

2014-01-01

Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating r...

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

Science.gov (United States)

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.

Numerical prediction of turbulent heat transfer augmentation in an annular fuel channel with two-dimensional square ribs

International Nuclear Information System (INIS)

Takase, Kazuyuki

1996-01-01

The square-ribbed fuel rod for high temperature gas-cooled reactors was developed in order to enhance the turbulent heat transfer in comparison with the standard fuel rod. To evaluate the heat transfer performance of the square-ribbed fuel rod, the turbulent heat transfer coefficients in an annular fuel channel with repeated two-dimensional square ribs were analyzed numerically on a fully developed incompressible flow using the k - ε turbulence model and the two-dimensional axisymmetrical coordinate system. Numerical analyses were carried out for a range of Reynolds numbers from 3000 to 20000 and ratios of square-rib pitch to height of 10, 20 and 40, respectively. The predicted values of the heat transfer coefficients agreed within an error of 10% for the square-rib pitch to height ratio of 10, 20% for 20 and 25% for 40, respectively, with the heat transfer empirical correlations obtained from the experimental data. It was concluded by the present study that the effect of the heat transfer augmentation by square ribs could be predicted sufficiently by the present numerical simulations and also a part of its mechanism could be explained by means of the change in the turbulence kinematic energy distribution along the flow direction. (author)

Elastic least-squares reverse time migration

KAUST Repository

Feng, Zongcai

2017-03-08

We use elastic least-squares reverse time migration (LSRTM) to invert for the reflectivity images of P- and S-wave impedances. Elastic LSRTMsolves the linearized elastic-wave equations for forward modeling and the adjoint equations for backpropagating the residual wavefield at each iteration. Numerical tests on synthetic data and field data reveal the advantages of elastic LSRTM over elastic reverse time migration (RTM) and acoustic LSRTM. For our examples, the elastic LSRTM images have better resolution and amplitude balancing, fewer artifacts, and less crosstalk compared with the elastic RTM images. The images are also better focused and have better reflector continuity for steeply dipping events compared to the acoustic LSRTM images. Similar to conventional leastsquares migration, elastic LSRTM also requires an accurate estimation of the P- and S-wave migration velocity models. However, the problem remains that, when there are moderate errors in the velocity model and strong multiples, LSRTMwill produce migration noise stronger than that seen in the RTM images.

Elastic least-squares reverse time migration

KAUST Repository

Feng, Zongcai; Schuster, Gerard T.

2017-01-01

We use elastic least-squares reverse time migration (LSRTM) to invert for the reflectivity images of P- and S-wave impedances. Elastic LSRTMsolves the linearized elastic-wave equations for forward modeling and the adjoint equations for backpropagating the residual wavefield at each iteration. Numerical tests on synthetic data and field data reveal the advantages of elastic LSRTM over elastic reverse time migration (RTM) and acoustic LSRTM. For our examples, the elastic LSRTM images have better resolution and amplitude balancing, fewer artifacts, and less crosstalk compared with the elastic RTM images. The images are also better focused and have better reflector continuity for steeply dipping events compared to the acoustic LSRTM images. Similar to conventional leastsquares migration, elastic LSRTM also requires an accurate estimation of the P- and S-wave migration velocity models. However, the problem remains that, when there are moderate errors in the velocity model and strong multiples, LSRTMwill produce migration noise stronger than that seen in the RTM images.

Interaction and representational integration: Evidence from speech errors

OpenAIRE

Goldrick, Matthew; Baker, H. Ross; Murphy, Amanda; Baese-Berk, Melissa

2011-01-01

We examine the mechanisms that support interaction between lexical, phonological and phonetic processes during language production. Studies of the phonetics of speech errors have provided evidence that partially activated lexical and phonological representations influence phonetic processing. We examine how these interactive effects are modulated by lexical frequency. Previous research has demonstrated that during lexical access, the processing of high frequency words is facilitated; in contr...

Lax-pair operators for squared-sum and squared-difference eigenfunctions

International Nuclear Information System (INIS)

Ichikawa, Yoshihiko; Iino, Kazuhiro.

1984-10-01

Inter-relationship between various representations of the inverse scattering transformation is established by examining eigenfunctions of Lax-pair operators of the sine-Gordon equation and the modified Korteweg-de Vries equation. In particular, it is shown explicitly that there exists Lax-pair operators for the squared-sum and squared-difference eigenfunctions of the Ablowitz-Kaup-Newell-Segur inverse scattering transformation. (author)

STUDY OF THE INTEGRATION OF LIDAR AND PHOTOGRAMMETRIC DATASETS BY IN SITU CAMERA CALIBRATION AND INTEGRATED SENSOR ORIENTATION

Directory of Open Access Journals (Sweden)

E. Mitishita

2017-05-01

Full Text Available Photogrammetric and Lidar datasets should be in the same mapping or geodetic frame to be used simultaneously in an engineering project. Nowadays direct sensor orientation is a common procedure used in simultaneous photogrammetric and Lidar surveys. Although the direct sensor orientation technologies provide a high degree of automation process due to the GNSS/INS technologies, the accuracies of the results obtained from the photogrammetric and Lidar surveys are dependent on the quality of a group of parameters that models accurately the user conditions of the system at the moment the job is performed. This paper shows the study that was performed to verify the importance of the in situ camera calibration and Integrated Sensor Orientation without control points to increase the accuracies of the photogrammetric and LIDAR datasets integration. The horizontal and vertical accuracies of photogrammetric and Lidar datasets integration by photogrammetric procedure improved significantly when the Integrated Sensor Orientation (ISO approach was performed using Interior Orientation Parameter (IOP values estimated from the in situ camera calibration. The horizontal and vertical accuracies, estimated by the Root Mean Square Error (RMSE of the 3D discrepancies from the Lidar check points, increased around of 37% and 198% respectively.

Are Low-order Covariance Estimates Useful in Error Analyses?

Science.gov (United States)

Baker, D. F.; Schimel, D.

2005-12-01

Atmospheric trace gas inversions, using modeled atmospheric transport to infer surface sources and sinks from measured concentrations, are most commonly done using least-squares techniques that return not only an estimate of the state (the surface fluxes) but also the covariance matrix describing the uncertainty in that estimate. Besides allowing one to place error bars around the estimate, the covariance matrix may be used in simulation studies to learn what uncertainties would be expected from various hypothetical observing strategies. This error analysis capability is routinely used in designing instrumentation, measurement campaigns, and satellite observing strategies. For example, Rayner, et al (2002) examined the ability of satellite-based column-integrated CO2 measurements to constrain monthly-average CO2 fluxes for about 100 emission regions using this approach. Exact solutions for both state vector and covariance matrix become computationally infeasible, however, when the surface fluxes are solved at finer resolution (e.g., daily in time, under 500 km in space). It is precisely at these finer scales, however, that one would hope to be able to estimate fluxes using high-density satellite measurements. Non-exact estimation methods such as variational data assimilation or the ensemble Kalman filter could be used, but they achieve their computational savings by obtaining an only approximate state estimate and a low-order approximation of the true covariance. One would like to be able to use this covariance matrix to do the same sort of error analyses as are done with the full-rank covariance, but is it correct to do so? Here we compare uncertainties and `information content' derived from full-rank covariance matrices obtained from a direct, batch least squares inversion to those from the incomplete-rank covariance matrices given by a variational data assimilation approach solved with a variable metric minimization technique (the Broyden-Fletcher- Goldfarb

Prediction of beef marblingusing Hyperspectral Imaging (HSI and Partial Least Squares Regression (PLSR

Directory of Open Access Journals (Sweden)

Victor Aredo

2017-01-01

Full Text Available The aim of this study was to build a model to predict the beef marbling using HSI and Partial Least Squares Regression (PLSR. Totally 58 samples of longissmus dorsi muscle were scanned by a HSI system (400 - 1000 nm in reflectance mode, using 44 samples to build t he PLSR model and 14 samples to model validation. The Japanese Beef Marbling Standard (BMS was used as reference by 15 middle - trained judges for the samples evaluation. The scores were assigned as continuous values and varied from 1.2 to 5.3 BMS. The PLSR model showed a high correlation coefficient in the prediction (r = 0.95, a low Standard Error of Calibration (SEC of 0.2 BMS score, and a low Standard Error of Prediction (SEP of 0.3 BMS score.

Making Residents Part of the Safety Culture: Improving Error Reporting and Reducing Harms.

Science.gov (United States)

Fox, Michael D; Bump, Gregory M; Butler, Gabriella A; Chen, Ling-Wan; Buchert, Andrew R

2017-01-30

Reporting medical errors is a focus of the patient safety movement. As frontline physicians, residents are optimally positioned to recognize errors and flaws in systems of care. Previous work highlights the difficulty of engaging residents in identification and/or reduction of medical errors and in integrating these trainees into their institutions' cultures of safety. The authors describe the implementation of a longitudinal, discipline-based, multifaceted curriculum to enhance the reporting of errors by pediatric residents at Children's Hospital of Pittsburgh of University of Pittsburgh Medical Center. The key elements of this curriculum included providing the necessary education to identify medical errors with an emphasis on systems-based causes, modeling of error reporting by faculty, and integrating error reporting and discussion into the residents' daily activities. The authors tracked monthly error reporting rates by residents and other health care professionals, in addition to serious harm event rates at the institution. The interventions resulted in significant increases in error reports filed by residents, from 3.6 to 37.8 per month over 4 years (P error reporting correlated with a decline in serious harm events, from 15.0 to 8.1 per month over 4 years (P = 0.01). Integrating patient safety into the everyday resident responsibilities encourages frequent reporting and discussion of medical errors and leads to improvements in patient care. Multiple simultaneous interventions are essential to making residents part of the safety culture of their training hospitals.

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

Science.gov (United States)

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.

Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation

Directory of Open Access Journals (Sweden)

Namyong Kim

2016-06-01

Full Text Available The minimum error entropy (MEE algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise, a normalized version of the MEE algorithm is proposed. The step size of the MEE algorithm is normalized with the power of input entropy that is estimated recursively for reducing its computational complexity. The proposed algorithm yields lower minimum MSE (mean squared error and faster convergence speed simultaneously than the original MEE algorithm does in the equalization simulation. On the condition of the same convergence speed, its performance enhancement in steady state MSE is above 3 dB.

Irrational Square Roots

Science.gov (United States)

Misiurewicz, Michal

2013-01-01

If students are presented the standard proof of irrationality of [square root]2, can they generalize it to a proof of the irrationality of "[square root]p", "p" a prime if, instead of considering divisibility by "p", they cling to the notions of even and odd used in the standard proof?

Approximation of the exponential integral (well function) using sampling methods

Science.gov (United States)

Baalousha, Husam Musa

2015-04-01

Exponential integral (also known as well function) is often used in hydrogeology to solve Theis and Hantush equations. Many methods have been developed to approximate the exponential integral. Most of these methods are based on numerical approximations and are valid for a certain range of the argument value. This paper presents a new approach to approximate the exponential integral. The new approach is based on sampling methods. Three different sampling methods; Latin Hypercube Sampling (LHS), Orthogonal Array (OA), and Orthogonal Array-based Latin Hypercube (OA-LH) have been used to approximate the function. Different argument values, covering a wide range, have been used. The results of sampling methods were compared with results obtained by Mathematica software, which was used as a benchmark. All three sampling methods converge to the result obtained by Mathematica, at different rates. It was found that the orthogonal array (OA) method has the fastest convergence rate compared with LHS and OA-LH. The root mean square error RMSE of OA was in the order of 1E-08. This method can be used with any argument value, and can be used to solve other integrals in hydrogeology such as the leaky aquifer integral.

SU-E-P-21: Impact of MLC Position Errors On Simultaneous Integrated Boost Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma

Energy Technology Data Exchange (ETDEWEB)

Chengqiang, L; Yin, Y; Chen, L [Shandong Cancer Hospital and Institute, 440 Jiyan Road, Jinan, 250117 (China)

2015-06-15

Purpose: To investigate the impact of MLC position errors on simultaneous integrated boost intensity-modulated radiotherapy (SIB-IMRT) for patients with nasopharyngeal carcinoma. Methods: To compare the dosimetric differences between the simulated plans and the clinical plans, ten patients with locally advanced NPC treated with SIB-IMRT were enrolled in this study. All plans were calculated with an inverse planning system (Pinnacle3, Philips Medical System{sub )}. Random errors −2mm to 2mm{sub )},shift errors{sub (} 2mm,1mm and 0.5mm) and systematic extension/ contraction errors (±2mm, ±1mm and ±0.5mm) of the MLC leaf position were introduced respectively into the original plans to create the simulated plans. Dosimetry factors were compared between the original and the simulated plans. Results: The dosimetric impact of the random and system shift errors of MLC position was insignificant within 2mm, the maximum changes in D95% of PGTV,PTV1,PTV2 were-0.92±0.51%,1.00±0.24% and 0.62±0.17%, the maximum changes in the D0.1cc of spinal cord and brainstem were 1.90±2.80% and −1.78±1.42%, the maximum changes in the Dmean of parotids were1.36±1.23% and −2.25±2.04%.However,the impact of MLC extension or contraction errors was found significant. For 2mm leaf extension errors, the average changes in D95% of PGTV,PTV1,PTV2 were 4.31±0.67%,4.29±0.65% and 4.79±0.82%, the averaged value of the D0.1cc to spinal cord and brainstem were increased by 7.39±5.25% and 6.32±2.28%,the averaged value of the mean dose to left and right parotid were increased by 12.75±2.02%,13.39±2.17% respectively. Conclusion: The dosimetric effect was insignificant for random MLC leaf position errors up to 2mm. There was a high sensitivity to dose distribution for MLC extension or contraction errors.We should pay attention to the anatomic changes in target organs and anatomical structures during the course,individual radiotherapy was recommended to ensure adaptive doses.

Estimating pole/zero errors in GSN-IRIS/USGS network calibration metadata

Science.gov (United States)

Ringler, A.T.; Hutt, C.R.; Aster, R.; Bolton, H.; Gee, L.S.; Storm, T.

2012-01-01

Mapping the digital record of a seismograph into true ground motion requires the correction of the data by some description of the instrument's response. For the Global Seismographic Network (Butler et al., 2004), as well as many other networks, this instrument response is represented as a Laplace domain pole–zero model and published in the Standard for the Exchange of Earthquake Data (SEED) format. This Laplace representation assumes that the seismometer behaves as a linear system, with any abrupt changes described adequately via multiple time-invariant epochs. The SEED format allows for published instrument response errors as well, but these typically have not been estimated or provided to users. We present an iterative three-step method to estimate the instrument response parameters (poles and zeros) and their associated errors using random calibration signals. First, we solve a coarse nonlinear inverse problem using a least-squares grid search to yield a first approximation to the solution. This approach reduces the likelihood of poorly estimated parameters (a local-minimum solution) caused by noise in the calibration records and enhances algorithm convergence. Second, we iteratively solve a nonlinear parameter estimation problem to obtain the least-squares best-fit Laplace pole–zero–gain model. Third, by applying the central limit theorem, we estimate the errors in this pole–zero model by solving the inverse problem at each frequency in a two-thirds octave band centered at each best-fit pole–zero frequency. This procedure yields error estimates of the 99% confidence interval. We demonstrate the method by applying it to a number of recent Incorporated Research Institutions in Seismology/United States Geological Survey (IRIS/USGS) network calibrations (network code IU).

A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

Directory of Open Access Journals (Sweden)

Ummuhan Basaran Filik

2016-01-01

Full Text Available A new hybrid wind speed prediction approach, which uses fast block least mean square (FBLMS algorithm and artificial neural network (ANN method, is proposed. FBLMS is an adaptive algorithm which has reduced complexity with a very fast convergence rate. A hybrid approach is proposed which uses two powerful methods: FBLMS and ANN method. In order to show the efficiency and accuracy of the proposed approach, seven-year real hourly collected wind speed data sets belonging to Turkish State Meteorological Service of Bozcaada and Eskisehir regions are used. Two different ANN structures are used to compare with this approach. The first six-year data is handled as a train set; the remaining one-year hourly data is handled as test data. Mean absolute error (MAE and root mean square error (RMSE are used for performance evaluations. It is shown for various cases that the performance of the new hybrid approach gives better results than the different conventional ANN structure.

Critical slowing down and error analysis in lattice QCD simulations

Energy Technology Data Exchange (ETDEWEB)

Schaefer, Stefan [Humboldt-Universitaet, Berlin (Germany). Inst. fuer Physik; Sommer, Rainer; Virotta, Francesco [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC

2010-09-15

We study the critical slowing down towards the continuum limit of lattice QCD simulations with Hybrid Monte Carlo type algorithms. In particular for the squared topological charge we find it to be very severe with an effective dynamical critical exponent of about 5 in pure gauge theory. We also consider Wilson loops which we can demonstrate to decouple from the modes which slow down the topological charge. Quenched observables are studied and a comparison to simulations of full QCD is made. In order to deal with the slow modes in the simulation, we propose a method to incorporate the information from slow observables into the error analysis of physical observables and arrive at safer error estimates. (orig.)

Critical slowing down and error analysis in lattice QCD simulations

International Nuclear Information System (INIS)

Schaefer, Stefan; Sommer, Rainer; Virotta, Francesco

2010-09-01

We study the critical slowing down towards the continuum limit of lattice QCD simulations with Hybrid Monte Carlo type algorithms. In particular for the squared topological charge we find it to be very severe with an effective dynamical critical exponent of about 5 in pure gauge theory. We also consider Wilson loops which we can demonstrate to decouple from the modes which slow down the topological charge. Quenched observables are studied and a comparison to simulations of full QCD is made. In order to deal with the slow modes in the simulation, we propose a method to incorporate the information from slow observables into the error analysis of physical observables and arrive at safer error estimates. (orig.)

Minimum Probability of Error-Based Equalization Algorithms for Fading Channels

Directory of Open Access Journals (Sweden)

Janos Levendovszky

2007-06-01

Full Text Available Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE and guarantee better performance than the traditional zero forcing (ZF or minimum mean square error (MMSE algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels.

[Main Components of Xinjiang Lavender Essential Oil Determined by Partial Least Squares and Near Infrared Spectroscopy].

Science.gov (United States)

Liao, Xiang; Wang, Qing; Fu, Ji-hong; Tang, Jun

2015-09-01

This work was undertaken to establish a quantitative analysis model which can rapid determinate the content of linalool, linalyl acetate of Xinjiang lavender essential oil. Totally 165 lavender essential oil samples were measured by using near infrared absorption spectrum (NIR), after analyzing the near infrared spectral absorption peaks of all samples, lavender essential oil have abundant chemical information and the interference of random noise may be relatively low on the spectral intervals of 7100~4500 cm(-1). Thus, the PLS models was constructed by using this interval for further analysis. 8 abnormal samples were eliminated. Through the clustering method, 157 lavender essential oil samples were divided into 105 calibration set samples and 52 validation set samples. Gas chromatography mass spectrometry (GC-MS) was used as a tool to determine the content of linalool and linalyl acetate in lavender essential oil. Then the matrix was established with the GC-MS raw data of two compounds in combination with the original NIR data. In order to optimize the model, different pretreatment methods were used to preprocess the raw NIR spectral to contrast the spectral filtering effect, after analysizing the quantitative model results of linalool and linalyl acetate, the root mean square error prediction (RMSEP) of orthogonal signal transformation (OSC) was 0.226, 0.558, spectrally, it was the optimum pretreatment method. In addition, forward interval partial least squares (FiPLS) method was used to exclude the wavelength points which has nothing to do with determination composition or present nonlinear correlation, finally 8 spectral intervals totally 160 wavelength points were obtained as the dataset. Combining the data sets which have optimized by OSC-FiPLS with partial least squares (PLS) to establish a rapid quantitative analysis model for determining the content of linalool and linalyl acetate in Xinjiang lavender essential oil, numbers of hidden variables of two

Adaptive Iterated Extended Kalman Filter and Its Application to Autonomous Integrated Navigation for Indoor Robot

Directory of Open Access Journals (Sweden)

Yuan Xu

2014-01-01

Full Text Available As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF which used the noise statistics estimator in the iterated extended Kalman (IEKF, and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS/wireless sensors networks (WSNs-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.

Ultrahigh Error Threshold for Surface Codes with Biased Noise

Science.gov (United States)

Tuckett, David K.; Bartlett, Stephen D.; Flammia, Steven T.

2018-02-01

We show that a simple modification of the surface code can exhibit an enormous gain in the error correction threshold for a noise model in which Pauli Z errors occur more frequently than X or Y errors. Such biased noise, where dephasing dominates, is ubiquitous in many quantum architectures. In the limit of pure dephasing noise we find a threshold of 43.7(1)% using a tensor network decoder proposed by Bravyi, Suchara, and Vargo. The threshold remains surprisingly large in the regime of realistic noise bias ratios, for example 28.2(2)% at a bias of 10. The performance is, in fact, at or near the hashing bound for all values of the bias. The modified surface code still uses only weight-4 stabilizers on a square lattice, but merely requires measuring products of Y instead of Z around the faces, as this doubles the number of useful syndrome bits associated with the dominant Z errors. Our results demonstrate that large efficiency gains can be found by appropriately tailoring codes and decoders to realistic noise models, even under the locality constraints of topological codes.

Square through tube

International Nuclear Information System (INIS)

Akita, Junji; Honma, Toei.

1975-01-01

Object: To provide a square through tube involving thermal movement in pipelines such as water supply pump driving turbine exhaust pipe (square-shaped), which is wide in freedom with respect to shape and dimension thereof for efficient installation at site. Structure: In a through tube to be airtightly retained for purpose of decontamination in an atomic power plant, comprising a seal rubber plate, a band and a bolt and a nut for securing said plate, the seal rubber plate being worked into the desired shape so that it may be placed in intimate contact with the concrete floor surface by utilization of elasticity of rubber, thereby providing airtightness at a corner portion of the square tube. (Kamimura, M.)

A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems

Directory of Open Access Journals (Sweden)

Qingzhou Mao

2015-06-01

Full Text Available In environments that are hostile to Global Navigation Satellites Systems (GNSS, the precision achieved by a mobile light detection and ranging (LiDAR system (MLS can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS. This paper proposes a novel least squares collocation (LSC-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.

Dynamic Error Analysis Method for Vibration Shape Reconstruction of Smart FBG Plate Structure

Directory of Open Access Journals (Sweden)

Hesheng Zhang

2016-01-01

Full Text Available Shape reconstruction of aerospace plate structure is an important issue for safe operation of aerospace vehicles. One way to achieve such reconstruction is by constructing smart fiber Bragg grating (FBG plate structure with discrete distributed FBG sensor arrays using reconstruction algorithms in which error analysis of reconstruction algorithm is a key link. Considering that traditional error analysis methods can only deal with static data, a new dynamic data error analysis method are proposed based on LMS algorithm for shape reconstruction of smart FBG plate structure. Firstly, smart FBG structure and orthogonal curved network based reconstruction method is introduced. Then, a dynamic error analysis model is proposed for dynamic reconstruction error analysis. Thirdly, the parameter identification is done for the proposed dynamic error analysis model based on least mean square (LMS algorithm. Finally, an experimental verification platform is constructed and experimental dynamic reconstruction analysis is done. Experimental results show that the dynamic characteristics of the reconstruction performance for plate structure can be obtained accurately based on the proposed dynamic error analysis method. The proposed method can also be used for other data acquisition systems and data processing systems as a general error analysis method.

Multivariat least-squares methods applied to the quantitative spectral analysis of multicomponent samples

International Nuclear Information System (INIS)

Haaland, D.M.; Easterling, R.G.; Vopicka, D.A.

1985-01-01

In an extension of earlier work, weighted multivariate least-squares methods of quantitative FT-IR analysis have been developed. A linear least-squares approximation to nonlinearities in the Beer-Lambert law is made by allowing the reference spectra to be a set of known mixtures, The incorporation of nonzero intercepts in the relation between absorbance and concentration further improves the approximation of nonlinearities while simultaneously accounting for nonzero spectra baselines. Pathlength variations are also accommodated in the analysis, and under certain conditions, unknown sample pathlengths can be determined. All spectral data are used to improve the precision and accuracy of the estimated concentrations. During the calibration phase of the analysis, pure component spectra are estimated from the standard mixture spectra. These can be compared with the measured pure component spectra to determine which vibrations experience nonlinear behavior. In the predictive phase of the analysis, the calculated spectra are used in our previous least-squares analysis to estimate sample component concentrations. These methods were applied to the analysis of the IR spectra of binary mixtures of esters. Even with severely overlapping spectral bands and nonlinearities in the Beer-Lambert law, the average relative error in the estimated concentration was <1%

BER analysis of regularized least squares for BPSK recovery

KAUST Repository

Ben Atitallah, Ismail; Thrampoulidis, Christos; Kammoun, Abla; Al-Naffouri, Tareq Y.; Hassibi, Babak; Alouini, Mohamed-Slim

2017-01-01

This paper investigates the problem of recovering an n-dimensional BPSK signal x0 ∈ {−1, 1}ⁿ from m-dimensional measurement vector y = Ax+z, where A and z are assumed to be Gaussian with iid entries. We consider two variants of decoders based on the regularized least squares followed by hard-thresholding: the case where the convex relaxation is from {−1, 1}ⁿ to ℝⁿ and the box constrained case where the relaxation is to [−1, 1]ⁿ. For both cases, we derive an exact expression of the bit error probability when n and m grow simultaneously large at a fixed ratio. For the box constrained case, we show that there exists a critical value of the SNR, above which the optimal regularizer is zero. On the other side, the regularization can further improve the performance of the box relaxation at low to moderate SNR regimes. We also prove that the optimal regularizer in the bit error rate sense for the unboxed case is nothing but the MMSE detector.

BER analysis of regularized least squares for BPSK recovery

KAUST Repository

Ben Atitallah, Ismail

2017-06-20

This paper investigates the problem of recovering an n-dimensional BPSK signal x0 ∈ {−1, 1}ⁿ from m-dimensional measurement vector y = Ax+z, where A and z are assumed to be Gaussian with iid entries. We consider two variants of decoders based on the regularized least squares followed by hard-thresholding: the case where the convex relaxation is from {−1, 1}ⁿ to ℝⁿ and the box constrained case where the relaxation is to [−1, 1]ⁿ. For both cases, we derive an exact expression of the bit error probability when n and m grow simultaneously large at a fixed ratio. For the box constrained case, we show that there exists a critical value of the SNR, above which the optimal regularizer is zero. On the other side, the regularization can further improve the performance of the box relaxation at low to moderate SNR regimes. We also prove that the optimal regularizer in the bit error rate sense for the unboxed case is nothing but the MMSE detector.

Round-off error in long-term orbital integrations using multistep methods

Science.gov (United States)

Quinlan, Gerald D.

1994-01-01

Techniques for reducing roundoff error are compared by testing them on high-order Stormer and summetric multistep methods. The best technique for most applications is to write the equation in summed, function-evaluation form and to store the coefficients as rational numbers. A larger error reduction can be achieved by writing the equation in backward-difference form and performing some of the additions in extended precision, but this entails a larger central processing unit (cpu) cost.

Stochastic simulation and robust design optimization of integrated photonic filters

Directory of Open Access Journals (Sweden)

Weng Tsui-Wei

2016-07-01

Full Text Available Manufacturing variations are becoming an unavoidable issue in modern fabrication processes; therefore, it is crucial to be able to include stochastic uncertainties in the design phase. In this paper, integrated photonic coupled ring resonator filters are considered as an example of significant interest. The sparsity structure in photonic circuits is exploited to construct a sparse combined generalized polynomial chaos model, which is then used to analyze related statistics and perform robust design optimization. Simulation results show that the optimized circuits are more robust to fabrication process variations and achieve a reduction of 11%–35% in the mean square errors of the 3 dB bandwidth compared to unoptimized nominal designs.

Metering error quantification under voltage and current waveform distortion

Science.gov (United States)

Wang, Tao; Wang, Jia; Xie, Zhi; Zhang, Ran

2017-09-01

With integration of more and more renewable energies and distortion loads into power grid, the voltage and current waveform distortion results in metering error in the smart meters. Because of the negative effects on the metering accuracy and fairness, it is an important subject to study energy metering combined error. In this paper, after the comparing between metering theoretical value and real recorded value under different meter modes for linear and nonlinear loads, a quantification method of metering mode error is proposed under waveform distortion. Based on the metering and time-division multiplier principles, a quantification method of metering accuracy error is proposed also. Analyzing the mode error and accuracy error, a comprehensive error analysis method is presented which is suitable for new energy and nonlinear loads. The proposed method has been proved by simulation.

Bayesian networks modeling for thermal error of numerical control machine tools

Institute of Scientific and Technical Information of China (English)

Xin-hua YAO; Jian-zhong FU; Zi-chen CHEN

2008-01-01

The interaction between the heat source location,its intensity,thermal expansion coefficient,the machine system configuration and the running environment creates complex thermal behavior of a machine tool,and also makes thermal error prediction difficult.To address this issue,a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented.The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques.Due to the effective combination of domain knowledge and sampled data,the BN method could adapt to the change of running state of machine,and obtain satisfactory prediction accuracy.Ex-periments on spindle thermal deformation were conducted to evaluate the modeling performance.Experimental results indicate that the BN method performs far better than the least squares(LS)analysis in terms of modeling estimation accuracy.

A constrained robust least squares approach for contaminant release history identification

Science.gov (United States)

Sun, Alexander Y.; Painter, Scott L.; Wittmeyer, Gordon W.

2006-04-01

Contaminant source identification is an important type of inverse problem in groundwater modeling and is subject to both data and model uncertainty. Model uncertainty was rarely considered in the previous studies. In this work, a robust framework for solving contaminant source recovery problems is introduced. The contaminant source identification problem is first cast into one of solving uncertain linear equations, where the response matrix is constructed using a superposition technique. The formulation presented here is general and is applicable to any porous media flow and transport solvers. The robust least squares (RLS) estimator, which originated in the field of robust identification, directly accounts for errors arising from model uncertainty and has been shown to significantly reduce the sensitivity of the optimal solution to perturbations in model and data. In this work, a new variant of RLS, the constrained robust least squares (CRLS), is formulated for solving uncertain linear equations. CRLS allows for additional constraints, such as nonnegativity, to be imposed. The performance of CRLS is demonstrated through one- and two-dimensional test problems. When the system is ill-conditioned and uncertain, it is found that CRLS gave much better performance than its classical counterpart, the nonnegative least squares. The source identification framework developed in this work thus constitutes a reliable tool for recovering source release histories in real applications.

Prevalence of Refractive Error and Visual Impairment among Rural ...

African Journals Online (AJOL)

Refractive error was the major cause of visual impairment accounting for 54% of all causes in the study group. No child was found wearing ... So, large scale community level screening for refractive error should be conducted and integrated with regular school eye screening programs. Effective strategies need to be devised ...

Linear least squares compartmental-model-independent parameter identification in PET

International Nuclear Information System (INIS)

Thie, J.A.; Smith, G.T.; Hubner, K.F.

1997-01-01

A simplified approach involving linear-regression straight-line parameter fitting of dynamic scan data is developed for both specific and nonspecific models. Where compartmental-model topologies apply, the measured activity may be expressed in terms of: its integrals, plasma activity and plasma integrals -- all in a linear expression with macroparameters as coefficients. Multiple linear regression, as in spreadsheet software, determines parameters for best data fits. Positron emission tomography (PET)-acquired gray-matter images in a dynamic scan are analyzed: both by this method and by traditional iterative nonlinear least squares. Both patient and simulated data were used. Regression and traditional methods are in expected agreement. Monte-Carlo simulations evaluate parameter standard deviations, due to data noise, and much smaller noise-induced biases. Unique straight-line graphical displays permit visualizing data influences on various macroparameters as changes in slopes. Advantages of regression fitting are: simplicity, speed, ease of implementation in spreadsheet software, avoiding risks of convergence failures or false solutions in iterative least squares, and providing various visualizations of the uptake process by straight line graphical displays. Multiparameter model-independent analyses on lesser understood systems is also made possible

Evaluation of Data with Systematic Errors

International Nuclear Information System (INIS)

Froehner, F. H.

2003-01-01

Application-oriented evaluated nuclear data libraries such as ENDF and JEFF contain not only recommended values but also uncertainty information in the form of 'covariance' or 'error files'. These can neither be constructed nor utilized properly without a thorough understanding of uncertainties and correlations. It is shown how incomplete information about errors is described by multivariate probability distributions or, more summarily, by covariance matrices, and how correlations are caused by incompletely known common errors. Parameter estimation for the practically most important case of the Gaussian distribution with common errors is developed in close analogy to the more familiar case without. The formalism shows that, contrary to widespread belief, common ('systematic') and uncorrelated ('random' or 'statistical') errors are to be added in quadrature. It also shows explicitly that repetition of a measurement reduces mainly the statistical uncertainties but not the systematic ones. While statistical uncertainties are readily estimated from the scatter of repeatedly measured data, systematic uncertainties can only be inferred from prior information about common errors and their propagation. The optimal way to handle error-affected auxiliary quantities ('nuisance parameters') in data fitting and parameter estimation is to adjust them on the same footing as the parameters of interest and to integrate (marginalize) them out of the joint posterior distribution afterward

Quantifying Configuration-Sampling Error in Langevin Simulations of Complex Molecular Systems

Directory of Open Access Journals (Sweden)

Josh Fass

2018-04-01

Full Text Available While Langevin integrators are popular in the study of equilibrium properties of complex systems, it is challenging to estimate the timestep-induced discretization error: the degree to which the sampled phase-space or configuration-space probability density departs from the desired target density due to the use of a finite integration timestep. Sivak et al., introduced a convenient approach to approximating a natural measure of error between the sampled density and the target equilibrium density, the Kullback-Leibler (KL divergence, in phase space, but did not specifically address the issue of configuration-space properties, which are much more commonly of interest in molecular simulations. Here, we introduce a variant of this near-equilibrium estimator capable of measuring the error in the configuration-space marginal density, validating it against a complex but exact nested Monte Carlo estimator to show that it reproduces the KL divergence with high fidelity. To illustrate its utility, we employ this new near-equilibrium estimator to assess a claim that a recently proposed Langevin integrator introduces extremely small configuration-space density errors up to the stability limit at no extra computational expense. Finally, we show how this approach to quantifying sampling bias can be applied to a wide variety of stochastic integrators by following a straightforward procedure to compute the appropriate shadow work, and describe how it can be extended to quantify the error in arbitrary marginal or conditional distributions of interest.

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

Energy Technology Data Exchange (ETDEWEB)

Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

2016-02-25

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

International Nuclear Information System (INIS)

Hao, Ming; Wang, Yanli; Bryant, Stephen H.

2016-01-01

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

POSITIONING BASED ON INTEGRATION OF MUTI-SENSOR SYSTEMS USING KALMAN FILTER AND LEAST SQUARE ADJUSTMENT

Directory of Open Access Journals (Sweden)

M. Omidalizarandi

2013-09-01

Full Text Available Sensor fusion is to combine different sensor data from different sources in order to make a more accurate model. In this research, different sensors (Optical Speed Sensor, Bosch Sensor, Odometer, XSENS, Silicon and GPS receiver have been utilized to obtain different kinds of datasets to implement the multi-sensor system and comparing the accuracy of the each sensor with other sensors. The scope of this research is to estimate the current position and orientation of the Van. The Van's position can also be estimated by integrating its velocity and direction over time. To make these components work, it needs an interface that can bridge each other in a data acquisition module. The interface of this research has been developed based on using Labview software environment. Data have been transferred to PC via A/D convertor (LabJack and make a connection to PC. In order to synchronize all the sensors, calibration parameters of each sensor is determined in preparatory step. Each sensor delivers result in a sensor specific coordinate system that contains different location on the object, different definition of coordinate axes and different dimensions and units. Different test scenarios (Straight line approach and Circle approach with different algorithms (Kalman Filter, Least square Adjustment have been examined and the results of the different approaches are compared together.

[Research on modeling method to analyze Lonicerae Japonicae Flos extraction process with online MEMS-NIR based on two types of error detection theory].

Science.gov (United States)

Du, Chen-Zhao; Wu, Zhi-Sheng; Zhao, Na; Zhou, Zheng; Shi, Xin-Yuan; Qiao, Yan-Jiang

2016-10-01

To establish a rapid quantitative analysis method for online monitoring of chlorogenic acid in aqueous solution of Lonicera Japonica Flos extraction by using micro-electromechanical near infrared spectroscopy (MEMS-NIR). High performance liquid chromatography(HPLC) was used as reference method．Kennard-Stone (K-S) algorithm was used to divide sample sets, and partial least square(PLS) regression was adopted to establish the multivariate analysis model between the HPLC analysis contents and NIR spectra. The synergy interval partial least squares (SiPLS) was used to selected modeling waveband to establish PLS models. RPD was used to evaluate the prediction performance of the models. MDLs was calculated based on two types of error detection theory, on-line analytical modeling approach of Lonicera Japonica Flos extraction process was expressed scientifically by MDL. The result shows that the model established by multiplicative scatter correction(MSC) was the best, with the root mean square with cross validation(RMSECV), root mean square error of correction(RMSEC) and root mean square error of prediction(RMSEP) of chlorogenic acid as 1.707, 1.489, 2.362, respectively, the determination coefficient of the calibration model was 0.998 5, and the determination coefficient of the prediction was 0.988 1．The value of RPD is 9.468.The MDL (0.042 15 g•L⁻¹) selected by SiPLS is less than the original,which demonstrated that SiPLS was beneficial to improve the prediction performance of the model. In this study, a more accurate expression of the prediction performance of the model from the two types of error detection theory, to further illustrate MEMS-NIR spectroscopy can be used for on-line monitoring of Lonicera Japonica Flos extraction process. Copyright© by the Chinese Pharmaceutical Association.

Doppler-shift estimation of flat underwater channel using data-aided least-square approach

Directory of Open Access Journals (Sweden)

Weiqiang Pan

2015-03-01

Full Text Available In this paper we proposed a dada-aided Doppler estimation method for underwater acoustic communication. The training sequence is non-dedicate, hence it can be designed for Doppler estimation as well as channel equalization. We assume the channel has been equalized and consider only flat-fading channel. First, based on the training symbols the theoretical received sequence is composed. Next the least square principle is applied to build the objective function, which minimizes the error between the composed and the actual received signal. Then an iterative approach is applied to solve the least square problem. The proposed approach involves an outer loop and inner loop, which resolve the channel gain and Doppler coefficient, respectively. The theoretical performance bound, i.e. the Cramer-Rao Lower Bound (CRLB of estimation is also derived. Computer simulations results show that the proposed algorithm achieves the CRLB in medium to high SNR cases.

Doppler-shift estimation of flat underwater channel using data-aided least-square approach

Science.gov (United States)

Pan, Weiqiang; Liu, Ping; Chen, Fangjiong; Ji, Fei; Feng, Jing

2015-06-01

In this paper we proposed a dada-aided Doppler estimation method for underwater acoustic communication. The training sequence is non-dedicate, hence it can be designed for Doppler estimation as well as channel equalization. We assume the channel has been equalized and consider only flat-fading channel. First, based on the training symbols the theoretical received sequence is composed. Next the least square principle is applied to build the objective function, which minimizes the error between the composed and the actual received signal. Then an iterative approach is applied to solve the least square problem. The proposed approach involves an outer loop and inner loop, which resolve the channel gain and Doppler coefficient, respectively. The theoretical performance bound, i.e. the Cramer-Rao Lower Bound (CRLB) of estimation is also derived. Computer simulations results show that the proposed algorithm achieves the CRLB in medium to high SNR cases.

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

Science.gov (United States)

Palmer, Tom M; Holmes, Michael V; Keating, Brendan J; Sheehan, Nuala A

2017-11-01

Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Error-related brain activity and error awareness in an error classification paradigm.

Science.gov (United States)

Di Gregorio, Francesco; Steinhauser, Marco; Maier, Martin E

2016-10-01

Error-related brain activity has been linked to error detection enabling adaptive behavioral adjustments. However, it is still unclear which role error awareness plays in this process. Here, we show that the error-related negativity (Ne/ERN), an event-related potential reflecting early error monitoring, is dissociable from the degree of error awareness. Participants responded to a target while ignoring two different incongruent distractors. After responding, they indicated whether they had committed an error, and if so, whether they had responded to one or to the other distractor. This error classification paradigm allowed distinguishing partially aware errors, (i.e., errors that were noticed but misclassified) and fully aware errors (i.e., errors that were correctly classified). The Ne/ERN was larger for partially aware errors than for fully aware errors. Whereas this speaks against the idea that the Ne/ERN foreshadows the degree of error awareness, it confirms the prediction of a computational model, which relates the Ne/ERN to post-response conflict. This model predicts that stronger distractor processing - a prerequisite of error classification in our paradigm - leads to lower post-response conflict and thus a smaller Ne/ERN. This implies that the relationship between Ne/ERN and error awareness depends on how error awareness is related to response conflict in a specific task. Our results further indicate that the Ne/ERN but not the degree of error awareness determines adaptive performance adjustments. Taken together, we conclude that the Ne/ERN is dissociable from error awareness and foreshadows adaptive performance adjustments. Our results suggest that the relationship between the Ne/ERN and error awareness is correlative and mediated by response conflict. Copyright © 2016 Elsevier Inc. All rights reserved.

Quantitative Analysis of Adulterations in Oat Flour by FT-NIR Spectroscopy, Incomplete Unbalanced Randomized Block Design, and Partial Least Squares

Directory of Open Access Journals (Sweden)

Ning Wang

2014-01-01

Full Text Available This paper developed a rapid and nondestructive method for quantitative analysis of a cheaper adulterant (wheat flour in oat flour by NIR spectroscopy and chemometrics. Reflectance FT-NIR spectra in the range of 4000 to 12000 cm−1 of 300 oat flour objects adulterated with wheat flour were measured. The doping levels of wheat flour ranged from 5% to 50% (w/w. To ensure the generalization performance of the method, both the oat and the wheat flour samples were collected from different producing areas and an incomplete unbalanced randomized block (IURB design was performed to include the significant variations that may be encountered in future samples. Partial least squares regression (PLSR was used to develop calibration models for predicting the levels of wheat flour. Different preprocessing methods including smoothing, taking second-order derivative (D2, and standard normal variate (SNV transformation were investigated to improve the model accuracy of PLS. The root mean squared error of Monte Carlo cross-validation (RMSEMCCV and root mean squared error of prediction (RMSEP were 1.921 and 1.975 (%, w/w by D2-PLS, respectively. The results indicate that NIR and chemometrics can provide a rapid method for quantitative analysis of wheat flour in oat flour.

Common Errors in Ecological Data Sharing

Directory of Open Access Journals (Sweden)

Robert B. Cook

2013-04-01

Full Text Available Objectives: (1 to identify common errors in data organization and metadata completeness that would preclude a “reader” from being able to interpret and re-use the data for a new purpose; and (2 to develop a set of best practices derived from these common errors that would guide researchers in creating more usable data products that could be readily shared, interpreted, and used.Methods: We used directed qualitative content analysis to assess and categorize data and metadata errors identified by peer reviewers of data papers published in the Ecological Society of America’s (ESA Ecological Archives. Descriptive statistics provided the relative frequency of the errors identified during the peer review process.Results: There were seven overarching error categories: Collection & Organization, Assure, Description, Preserve, Discover, Integrate, and Analyze/Visualize. These categories represent errors researchers regularly make at each stage of the Data Life Cycle. Collection & Organization and Description errors were some of the most common errors, both of which occurred in over 90% of the papers.Conclusions: Publishing data for sharing and reuse is error prone, and each stage of the Data Life Cycle presents opportunities for mistakes. The most common errors occurred when the researcher did not provide adequate metadata to enable others to interpret and potentially re-use the data. Fortunately, there are ways to minimize these mistakes through carefully recording all details about study context, data collection, QA/ QC, and analytical procedures from the beginning of a research project and then including this descriptive information in the metadata.

Estimation of water level and steam temperature using ensemble Kalman filter square root (EnKF-SR)

Science.gov (United States)

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.

Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea

Science.gov (United States)

Jeong, Sang-Hun; Kim, Jinah; Heo, Ki-Young; Park, Kwang-Soon

2017-04-01

In winter season, large-height swell-like waves have occurred on the East coast of Korea, causing property damages and loss of human life. It is known that those waves are generated by a local strong wind made by temperate cyclone moving to eastward in the East Sea of Korean peninsula. Because the waves are often occurred in the clear weather, in particular, the damages are to be maximized. Therefore, it is necessary to predict and forecast large-height swell-like waves to prevent and correspond to the coastal damages. In Korea, an operational oceanographic system (KOOS) has been developed by the Korea institute of ocean science and technology (KIOST) and KOOS provides daily basis 72-hours' ocean forecasts such as wind, water elevation, sea currents, water temperature, salinity, and waves which are computed from not only meteorological and hydrodynamic model (WRF, ROMS, MOM, and MOHID) but also wave models (WW-III and SWAN). In order to evaluate the model performance and guarantee a certain level of accuracy of ocean forecasts, a Skill Assessment (SA) system was established as a one of module in KOOS. It has been performed through comparison of model results with in-situ observation data and model errors have been quantified with skill scores. Statistics which are used in skill assessment are including a measure of both errors and correlations such as root-mean-square-error (RMSE), root-mean-square-error percentage (RMSE%), mean bias (MB), correlation coefficient (R), scatter index (SI), circular correlation (CC) and central frequency (CF) that is a frequency with which errors lie within acceptable error criteria. It should be utilized simultaneously not only to quantify an error but also to improve an accuracy of forecasts by providing a feedback interactively. However, in an abnormal phenomena such as high-height swell-like waves in the East coast of Korea, it requires more advanced and optimized error quantification method that allows to predict the abnormal

Insights about data assimilation frameworks for integrating GRACE with hydrological models

Science.gov (United States)

Schumacher, Maike; Kusche, Jürgen; Van Dijk, Albert I. J. M.; Döll, Petra; Schuh, Wolf-Dieter

2016-04-01

Improving the understanding of changes in the water cycle represents a challenging objective that requires merging information from various disciplines. Debates exist on selecting an appropriate assimilation technique to integrate GRACE-derived terrestrial water storage changes (TWSC) into hydrological models in order to downscale and disaggregate GRACE TWSC, overcome model limitations, and improve monitoring and forecast skills. Yet, the effect of the specific data assimilation technique in conjunction with ill-conditioning, colored noise, resolution mismatch between GRACE and model, and other complications is still unclear. Due to its simplicity, ensemble Kalman filters or smoothers (EnKF/S) are often applied. In this study, we show that modification of the filter approach might open new avenues to improve the integration process. Particularly, we discuss an improved calibration and data assimilation (C/DA) framework (Schumacher et al., 2016), which is based on the EnKF and was extended by the square root analysis scheme (SQRA) and the singular evolutive interpolated Kalman (SEIK) filter. In addition, we discuss an off-line data blending approach (Van Dijk et al., 2014) that offers the chance to merge multi-model ensembles with GRACE observations. The investigations include: (i) a theoretical comparison, focusing on similarities and differences of the conceptual formulation of the filter algorithms, (ii) a practical comparison, for which the approaches were applied to an ensemble of runs of the WaterGAP Global Hydrology Model (WGHM), as well as (iii) an impact assessment of the GRACE error structure on C/DA results. First, a synthetic experiment over the Mississippi River Basin (USA) was used to gain insights about the C/DA set-up before applying it to real data. The results indicated promising performances when considering alternative methods, e.g. applying the SEIK algorithm improved the correlation coefficient and root mean square error (RMSE) of TWSC by 0

The Versatile Magic Square.

Science.gov (United States)

Watson, Gale A.

2003-01-01

Demonstrates the transformations that are possible to construct a variety of magic squares, including modifications to challenge students from elementary grades through algebra. Presents an example of using magic squares with students who have special needs. (YDS)

Error-Free Software

Science.gov (United States)

1989-01-01

001 is an integrated tool suited for automatically developing ultra reliable models, simulations and software systems. Developed and marketed by Hamilton Technologies, Inc. (HTI), it has been applied in engineering, manufacturing, banking and software tools development. The software provides the ability to simplify the complex. A system developed with 001 can be a prototype or fully developed with production quality code. It is free of interface errors, consistent, logically complete and has no data or control flow errors. Systems can be designed, developed and maintained with maximum productivity. Margaret Hamilton, President of Hamilton Technologies, also directed the research and development of USE.IT, an earlier product which was the first computer aided software engineering product in the industry to concentrate on automatically supporting the development of an ultrareliable system throughout its life cycle. Both products originated in NASA technology developed under a Johnson Space Center contract.

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

Science.gov (United States)

Eppenhof, Koen A J; Pluim, Josien P W

2018-04-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of 0.66 mm from ground truth landmark registration errors.

A minimum bit error-rate detector for amplify and forward relaying systems

KAUST Repository

Ahmed, Qasim Zeeshan; Alouini, Mohamed-Slim; Aissa, Sonia

2012-01-01

In this paper, a new detector is being proposed for amplify-and-forward (AF) relaying system when communicating with the assistance of L number of relays. The major goal of this detector is to improve the bit error rate (BER) performance of the system. The complexity of the system is further reduced by implementing this detector adaptively. The proposed detector is free from channel estimation. Our results demonstrate that the proposed detector is capable of achieving a gain of more than 1-dB at a BER of 10 -5 as compared to the conventional minimum mean square error detector when communicating over a correlated Rayleigh fading channel. © 2012 IEEE.

A minimum bit error-rate detector for amplify and forward relaying systems

KAUST Repository

Ahmed, Qasim Zeeshan

2012-05-01

In this paper, a new detector is being proposed for amplify-and-forward (AF) relaying system when communicating with the assistance of L number of relays. The major goal of this detector is to improve the bit error rate (BER) performance of the system. The complexity of the system is further reduced by implementing this detector adaptively. The proposed detector is free from channel estimation. Our results demonstrate that the proposed detector is capable of achieving a gain of more than 1-dB at a BER of 10 -5 as compared to the conventional minimum mean square error detector when communicating over a correlated Rayleigh fading channel. © 2012 IEEE.

Transmitted wavefront error of a volume phase holographic grating at cryogenic temperature.

Science.gov (United States)

Lee, David; Taylor, Gordon D; Baillie, Thomas E C; Montgomery, David

2012-06-01

This paper describes the results of transmitted wavefront error (WFE) measurements on a volume phase holographic (VPH) grating operating at a temperature of 120 K. The VPH grating was mounted in a cryogenically compatible optical mount and tested in situ in a cryostat. The nominal root mean square (RMS) wavefront error at room temperature was 19 nm measured over a 50 mm diameter test aperture. The WFE remained at 18 nm RMS when the grating was cooled. This important result demonstrates that excellent WFE performance can be obtained with cooled VPH gratings, as required for use in future cryogenic infrared astronomical spectrometers planned for the European Extremely Large Telescope.

Stochastic and sensitivity analysis of shape error of inflatable antenna reflectors

Science.gov (United States)

San, Bingbing; Yang, Qingshan; Yin, Liwei

2017-03-01

Inflatable antennas are promising candidates to realize future satellite communications and space observations since they are lightweight, low-cost and small-packaged-volume. However, due to their high flexibility, inflatable reflectors are difficult to manufacture accurately, which may result in undesirable shape errors, and thus affect their performance negatively. In this paper, the stochastic characteristics of shape errors induced during manufacturing process are investigated using Latin hypercube sampling coupled with manufacture simulations. Four main random error sources are involved, including errors in membrane thickness, errors in elastic modulus of membrane, boundary deviations and pressure variations. Using regression and correlation analysis, a global sensitivity study is conducted to rank the importance of these error sources. This global sensitivity analysis is novel in that it can take into account the random variation and the interaction between error sources. Analyses are parametrically carried out with various focal-length-to-diameter ratios (F/D) and aperture sizes (D) of reflectors to investigate their effects on significance ranking of error sources. The research reveals that RMS (Root Mean Square) of shape error is a random quantity with an exponent probability distribution and features great dispersion; with the increase of F/D and D, both mean value and standard deviation of shape errors are increased; in the proposed range, the significance ranking of error sources is independent of F/D and D; boundary deviation imposes the greatest effect with a much higher weight than the others; pressure variation ranks the second; error in thickness and elastic modulus of membrane ranks the last with very close sensitivities to pressure variation. Finally, suggestions are given for the control of the shape accuracy of reflectors and allowable values of error sources are proposed from the perspective of reliability.

Composite Gauss-Legendre Quadrature with Error Control

Science.gov (United States)

Prentice, J. S. C.

2011-01-01

We describe composite Gauss-Legendre quadrature for determining definite integrals, including a means of controlling the approximation error. We compare the form and performance of the algorithm with standard Newton-Cotes quadrature. (Contains 1 table.)

Obtention of the parameters of the Voigt function using the least square fit method

International Nuclear Information System (INIS)

Flores Ll, H.; Cabral P, A.; Jimenez D, H.

1990-01-01

The fundamental parameters of the Voigt function are determined: lorentzian wide (Γ L ) and gaussian wide (Γ G ) with an error for almost all the cases inferior to 1% in the intervals 0.01 ≤ Γ L / Γ G ≤1 and 0.3 ≤ Γ G / Γ L ≤1. This is achieved using the least square fit method with an algebraic function, being obtained a simple method to obtain the fundamental parameters of the Voigt function used in many spectroscopies. (Author)

A predictive model of chemical flooding for enhanced oil recovery purposes: Application of least square support vector machine

Directory of Open Access Journals (Sweden)

Mohammad Ali Ahmadi

2016-06-01

Full Text Available Applying chemical flooding in petroleum reservoirs turns into interesting subject of the recent researches. Developing strategies of the aforementioned method are more robust and precise when they consider both economical point of views (net present value (NPV and technical point of views (recovery factor (RF. In the present study huge attempts are made to propose predictive model for specifying efficiency of chemical flooding in oil reservoirs. To gain this goal, the new type of support vector machine method which evolved by Suykens and Vandewalle was employed. Also, high precise chemical flooding data banks reported in previous works were employed to test and validate the proposed vector machine model. According to the mean square error (MSE, correlation coefficient and average absolute relative deviation, the suggested LSSVM model has acceptable reliability; integrity and robustness. Thus, the proposed intelligent based model can be considered as an alternative model to monitor the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible.

Fault Estimation for Fuzzy Delay Systems: A Minimum Norm Least Squares Solution Approach.

Science.gov (United States)

Huang, Sheng-Juan; Yang, Guang-Hong

2017-09-01

This paper mainly focuses on the problem of fault estimation for a class of Takagi-Sugeno fuzzy systems with state delays. A minimum norm least squares solution (MNLSS) approach is first introduced to establish a fault estimation compensator, which is able to optimize the fault estimator. Compared with most of the existing fault estimation methods, the MNLSS-based fault estimation method can effectively decrease the effect of state errors on the accuracy of fault estimation. Finally, three examples are given to illustrate the effectiveness and merits of the proposed method.

Applications of square-related theorems

Science.gov (United States)

Srinivasan, V. K.

2014-04-01

The square centre of a given square is the point of intersection of its two diagonals. When two squares of different side lengths share the same square centre, there are in general four diagonals that go through the same square centre. The Two Squares Theorem developed in this paper summarizes some nice theoretical conclusions that can be obtained when two squares of different side lengths share the same square centre. These results provide the theoretical basis for two of the constructions given in the book of H.S. Hall and F.H. Stevens , 'A Shorter School Geometry, Part 1, Metric Edition'. In page 134 of this book, the authors present, in exercise 4, a practical construction which leads to a verification of the Pythagorean theorem. Subsequently in Theorems 29 and 30, the authors present the standard proofs of the Pythagorean theorem and its converse. In page 140, the authors present, in exercise 15, what amounts to a geometric construction, whose verification involves a simple algebraic identity. Both the constructions are of great importance and can be replicated by using the standard equipment provided in a 'geometry toolbox' carried by students in high schools. The author hopes that the results proved in this paper, in conjunction with the two constructions from the above-mentioned book, would provide high school students an appreciation of the celebrated theorem of Pythagoras. The diagrams that accompany this document are based on the free software GeoGebra. The author formally acknowledges his indebtedness to the creators of this free software at the end of this document.

Square tracking sensor for autonomous helicopter hover stabilization

Science.gov (United States)

Oertel, Carl-Henrik

1995-06-01

Sensors for synthetic vision are needed to extend the mission profiles of helicopters. A special task for various applications is the autonomous position hold of a helicopter above a ground fixed or moving target. As a proof of concept for a general synthetic vision solution a restricted machine vision system, which is capable of locating and tracking a special target, was developed by the Institute of Flight Mechanics of Deutsche Forschungsanstalt fur Luft- und Raumfahrt e.V. (i.e., German Aerospace Research Establishment). This sensor, which is specialized to detect and track a square, was integrated in the fly-by-wire helicopter ATTHeS (i.e., Advanced Technology Testing Helicopter System). An existing model following controller for the forward flight condition was adapted for the hover and low speed requirements of the flight vehicle. The special target, a black square with a length of one meter, was mounted on top of a car. Flight tests demonstrated the automatic stabilization of the helicopter above the moving car by synthetic vision.

Spectrum integrated (n,He) cross section comparisons and least squares analyses for 6Li and 10B in benchmark fields

International Nuclear Information System (INIS)

Schenter, R.E.; Oliver, B.M.; Farrar, H. IV.

1986-06-01

Spectrum integrated cross sections for 6 Li and 10 B from five benchmark fast reactor neutron fields are compared with calculated values obtained using the ENDF/B-V Cross Section Files. The benchmark fields include the Coupled Fast Reactivity Measurements Facility (CFRMF) at the Idaho National Engineering Laboratory, the 10% Enriched U-235 Critical Assembly (BIG-10) at Los Alamos National Laboratory, the Sigma-Sigma and Fission Cavity fields of the BR-1 reactor at CEN/SCK, and the Intermediate Energy Standard Neutron Field (ISNF) at the National Bureau of Standards. Results from least square analyses using the FERRET computer code to obtain adjusted cross section values and their uncertainties are presented. Input to these calculations include the above five benchmark data sets. These analyses indicate a need for revision in the ENDF/B-V files for the 10 B and 6 Li cross sections for energies above 50 keV

SECOND ORDER LEAST SQUARE ESTIMATION ON ARCH(1 MODEL WITH BOX-COX TRANSFORMED DEPENDENT VARIABLE

Directory of Open Access Journals (Sweden)

Herni Utami

2014-03-01

Full Text Available Box-Cox transformation is often used to reduce heterogeneity and to achieve a symmetric distribution of response variable. In this paper, we estimate the parameters of Box-Cox transformed ARCH(1 model using second-order leastsquare method and then we study the consistency and asymptotic normality for second-order least square (SLS estimators. The SLS estimation was introduced byWang (2003, 2004 to estimate the parameters of nonlinear regression models with independent and identically distributed errors

Graphs whose complement and square are isomorphic

DEFF Research Database (Denmark)

Pedersen, Anders Sune

2014-01-01

We study square-complementary graphs, that is, graphs whose complement and square are isomorphic. We prove several necessary conditions for a graph to be square-complementary, describe ways of building new square-complementary graphs from existing ones, construct infinite families of square-compl...

Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

Methods for reduction of scattered x-ray in measuring MTF with the square chart

International Nuclear Information System (INIS)

Hatagawa, Masakatsu; Yoshida, Rie

1982-01-01

A square wave chart has been used to measure the MTF of a screen-film system. The problem is that the scattered X-ray from the chart may give rise to measurement errors. In this paper, the authors proposed two methods to reduce the scattered X-ray: the first method is the use of a Pb mask and second is to provide for an air gap between the chart and the screen-film system. In these methods, the scattered X-ray from the chart was reduced. MTFs were measured by both of the new methods and the conventional method, and MTF values of the new methods were in good agreement while that of the conventional method was not. It was concluded that these new methods are able to reduce errors in the measurement of MTF. (author)

Strong source heat transfer simulations based on a GalerKin/Gradient - least - squares method

International Nuclear Information System (INIS)

Franca, L.P.; Carmo, E.G.D. do.

1989-05-01

Heat conduction problems with temperature-dependent strong sources are modeled by an equation with a laplacian term, a linear term and a given source distribution term. When the linear-temperature-dependent source term is much larger than the laplacian term, we have a singular perturbation problem. In this case, boundary layers are formed to satisfy the Dirichlet boundary conditions. Although this is an elliptic equation, the standard Galerkin method solution is contaminated by spurious oscillations in the neighborhood of the boundary layers. Herein we employ a Galerkin/Gradient-least-squares method which eliminates all pathological phenomena of the Galerkin method. The method is constructed by adding to the Galerkin method a mesh-dependent term obtained by the least-squares form of the gradient of the Euler-Lagrange equation. Error estimates, numerical simulations in one-and multi-dimensions are given that attest the good stability and accuracy properties of the method [pt

Integrating Non-Tidal Sea Level data from altimetry and tide gauges for coastal sea level prediction

DEFF Research Database (Denmark)

Cheng, Yongcun; Andersen, Ole Baltazar; Knudsen, Per

2012-01-01

The main objective of this paper is to integrate Non-Tidal Sea Level (NSL) from the joint TOPEX, Jason-1 and Jason-2 satellite altimetry with tide gauge data at the west and north coast of the United Kingdom for coastal sea level prediction. The temporal correlation coefficient between altimetric...... NSLs and tide gauge data reaches a maximum higher than 90% for each gauge. The results show that the multivariate regression approach can efficiently integrate the two types of data in the coastal waters of the area. The Multivariate Regression Model is established by integrating the along-track NSL...... from the joint TOPEX/Jason-1/Jason-2 altimeters with that from eleven tide gauges. The model results give a maximum hindcast skill of 0.95, which means maximum 95% of NSL variance can be explained by the model. The minimum Root Mean Square Error (RMSe) between altimetric observations and model...

Least squares orthogonal polynomial approximation in several independent variables

International Nuclear Information System (INIS)

Caprari, R.S.

1992-06-01

This paper begins with an exposition of a systematic technique for generating orthonormal polynomials in two independent variables by application of the Gram-Schmidt orthogonalization procedure of linear algebra. It is then demonstrated how a linear least squares approximation for experimental data or an arbitrary function can be generated from these polynomials. The least squares coefficients are computed without recourse to matrix arithmetic, which ensures both numerical stability and simplicity of implementation as a self contained numerical algorithm. The Gram-Schmidt procedure is then utilised to generate a complete set of orthogonal polynomials of fourth degree. A theory for the transformation of the polynomial representation from an arbitrary basis into the familiar sum of products form is presented, together with a specific implementation for fourth degree polynomials. Finally, the computational integrity of this algorithm is verified by reconstructing arbitrary fourth degree polynomials from their values at randomly chosen points in their domain. 13 refs., 1 tab

A Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN

Directory of Open Access Journals (Sweden)

Wei Li

2016-11-01

Full Text Available In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI method for positioning and tracking applications using strapdown inertial navigation system (SINS and wireless sensor network (WSN. The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS, based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.

A class of integrals on squares, cubes and hypercubes

Science.gov (United States)

McCartney, Mark

2014-04-01

In this note, a class of integrals on the unit hypercube are considered and series solutions presented. The material is at a level appropriate for use with undergraduates, either as part of a course on multivariable calculus, or as a short independent research project. Suitable exercises for use in these contexts are given.

Multigrid for the Galerkin least squares method in linear elasticity: The pure displacement problem

Energy Technology Data Exchange (ETDEWEB)

Yoo, Jaechil [Univ. of Wisconsin, Madison, WI (United States)

1996-12-31

Franca and Stenberg developed several Galerkin least squares methods for the solution of the problem of linear elasticity. That work concerned itself only with the error estimates of the method. It did not address the related problem of finding effective methods for the solution of the associated linear systems. In this work, we prove the convergence of a multigrid (W-cycle) method. This multigrid is robust in that the convergence is uniform as the parameter, v, goes to 1/2 Computational experiments are included.

Application of partial least squares near-infrared spectral classification in diabetic identification

Science.gov (United States)

Yan, Wen-juan; Yang, Ming; He, Guo-quan; Qin, Lin; Li, Gang

2014-11-01

In order to identify the diabetic patients by using tongue near-infrared (NIR) spectrum - a spectral classification model of the NIR reflectivity of the tongue tip is proposed, based on the partial least square (PLS) method. 39sample data of tongue tip's NIR spectra are harvested from healthy people and diabetic patients , respectively. After pretreatment of the reflectivity, the spectral data are set as the independent variable matrix, and information of classification as the dependent variables matrix, Samples were divided into two groups - i.e. 53 samples as calibration set and 25 as prediction set - then the PLS is used to build the classification model The constructed modelfrom the 53 samples has the correlation of 0.9614 and the root mean square error of cross-validation (RMSECV) of 0.1387.The predictions for the 25 samples have the correlation of 0.9146 and the RMSECV of 0.2122.The experimental result shows that the PLS method can achieve good classification on features of healthy people and diabetic patients.

A scaled Lagrangian method for performing a least squares fit of a model to plant data

International Nuclear Information System (INIS)

Crisp, K.E.

1988-01-01

Due to measurement errors, even a perfect mathematical model will not be able to match all the corresponding plant measurements simultaneously. A further discrepancy may be introduced if an un-modelled change in conditions occurs within the plant which should have required a corresponding change in model parameters - e.g. a gradual deterioration in the performance of some component(s). Taking both these factors into account, what is required is that the overall discrepancy between the model predictions and the plant data is kept to a minimum. This process is known as 'model fitting', A method is presented for minimising any function which consists of the sum of squared terms, subject to any constraints. Its most obvious application is in the process of model fitting, where a weighted sum of squares of the differences between model predictions and plant data is the function to be minimised. When implemented within existing Central Electricity Generating Board computer models, it will perform a least squares fit of a model to plant data within a single job submission. (author)

Near Misses in Financial Trading: Skills for Capturing and Averting Error.

Science.gov (United States)

Leaver, Meghan; Griffiths, Alex; Reader, Tom

2018-05-01

The aims of this study were (a) to determine whether near-miss incidents in financial trading contain information on the operator skills and systems that detect and prevent near misses and the patterns and trends revealed by these data and (b) to explore if particular operator skills and systems are found as important for avoiding particular types of error on the trading floor. In this study, we examine a cohort of near-miss incidents collected from a financial trading organization using the Financial Incident Analysis System and report on the nontechnical skills and systems that are used to detect and prevent error in this domain. One thousand near-miss incidents are analyzed using distribution, mean, chi-square, and associative analysis to describe the data; reliability is provided. Slips/lapses (52%) and human-computer interface problems (21%) often occur alone and are the main contributors to error causation, whereas the prevention of error is largely a result of teamwork (65%) and situation awareness (46%) skills. No matter the cause of error, situation awareness and teamwork skills are used most often to detect and prevent the error. Situation awareness and teamwork skills appear universally important as a "last line" of defense for capturing error, and data from incident-monitoring systems can be analyzed in a fashion more consistent with a "Safety-II" approach. This research provides data for ameliorating risk within financial trading organizations, with implications for future risk management programs and regulation.

Errors in measuring transverse and energy jitter by beam position monitors

Energy Technology Data Exchange (ETDEWEB)

Balandin, V.; Decking, W.; Golubeva, N.

2010-02-15

The problem of errors, arising due to finite BPMresolution, in the difference orbit parameters, which are found as a least squares fit to the BPM data, is one of the standard and important problems of accelerator physics. Even so for the case of transversely uncoupled motion the covariance matrix of reconstruction errors can be calculated ''by hand'', the direct usage of obtained solution, as a tool for designing of a ''good measurement system'', does not look to be fairly straightforward. It seems that a better understanding of the nature of the problem is still desirable. We make a step in this direction introducing dynamic into this problem, which at the first glance seems to be static. We consider a virtual beam consisting of virtual particles obtained as a result of application of reconstruction procedure to ''all possible values'' of BPM reading errors. This beam propagates along the beam line according to the same rules as any real beam and has all beam dynamical characteristics, such as emittances, energy spread, dispersions, betatron functions and etc. All these values become the properties of the BPM measurement system. One can compare two BPM systems comparing their error emittances and rms error energy spreads, or, for a given measurement system, one can achieve needed balance between coordinate and momentum reconstruction errors by matching the error betatron functions in the point of interest to the desired values. (orig.)

Errors in measuring transverse and energy jitter by beam position monitors

International Nuclear Information System (INIS)

Balandin, V.; Decking, W.; Golubeva, N.

2010-02-01

The problem of errors, arising due to finite BPMresolution, in the difference orbit parameters, which are found as a least squares fit to the BPM data, is one of the standard and important problems of accelerator physics. Even so for the case of transversely uncoupled motion the covariance matrix of reconstruction errors can be calculated ''by hand'', the direct usage of obtained solution, as a tool for designing of a ''good measurement system'', does not look to be fairly straightforward. It seems that a better understanding of the nature of the problem is still desirable. We make a step in this direction introducing dynamic into this problem, which at the first glance seems to be static. We consider a virtual beam consisting of virtual particles obtained as a result of application of reconstruction procedure to ''all possible values'' of BPM reading errors. This beam propagates along the beam line according to the same rules as any real beam and has all beam dynamical characteristics, such as emittances, energy spread, dispersions, betatron functions and etc. All these values become the properties of the BPM measurement system. One can compare two BPM systems comparing their error emittances and rms error energy spreads, or, for a given measurement system, one can achieve needed balance between coordinate and momentum reconstruction errors by matching the error betatron functions in the point of interest to the desired values. (orig.)

Self-diffusion of particles interacting through a square-well or square-shoulder potential

NARCIS (Netherlands)

Wilbertz, H.; Michels, J.; Beijeren, H. van; Leegwater, J.A.

1988-01-01

The diffusion coefficient and velocity autocorrelation function for a fluid of particles interacting through a square-well or square-shoulder potential are calculated from a kinetic theory similar to the Davis-Rice-Sengers theory and the results are compared to those of computer simulations. At low

XAFS study of copper(II) complexes with square planar and square pyramidal coordination geometries

Science.gov (United States)

Gaur, A.; Klysubun, W.; Nitin Nair, N.; Shrivastava, B. D.; Prasad, J.; Srivastava, K.

2016-08-01

X-ray absorption fine structure of six Cu(II) complexes, Cu2(Clna)4 2H2O (1), Cu2(ac)4 2H2O (2), Cu2(phac)4 (pyz) (3), Cu2(bpy)2(na)2 H2O (ClO4) (4), Cu2(teen)4(OH)2(ClO4)2 (5) and Cu2(tmen)4(OH)2(ClO4)2 (6) (where ac, phac, pyz, bpy, na, teen, tmen = acetate, phenyl acetate, pyrazole, bipyridine, nicotinic acid, tetraethyethylenediamine, tetramethylethylenediamine, respectively), which were supposed to have square pyramidal and square planar coordination geometries have been investigated. The differences observed in the X-ray absorption near edge structure (XANES) features of the standard compounds having four, five and six coordination geometry points towards presence of square planar and square pyramidal geometry around Cu centre in the studied complexes. The presence of intense pre-edge feature in the spectra of four complexes, 1-4, indicates square pyramidal coordination. Another important XANES feature, present in complexes 5 and 6, is prominent shoulder in the rising part of edge whose intensity decreases in the presence of axial ligands and thus indicates four coordination in these complexes. Ab initio calculations were carried out for square planar and square pyramidal Cu centres to observe the variation of 4p density of states in the presence and absence of axial ligands. To determine the number and distance of scattering atoms around Cu centre in the complexes, EXAFS analysis has been done using the paths obtained from Cu(II) oxide model and an axial Cu-O path from model of a square pyramidal complex. The results obtained from EXAFS analysis have been reported which confirmed the inference drawn from XANES features. Thus, it has been shown that these paths from model of a standard compound can be used to determine the structural parameters for complexes having unknown structure.

Action errors, error management, and learning in organizations.

Science.gov (United States)

Frese, Michael; Keith, Nina

2015-01-03

Every organization is confronted with errors. Most errors are corrected easily, but some may lead to negative consequences. Organizations often focus on error prevention as a single strategy for dealing with errors. Our review suggests that error prevention needs to be supplemented by error management--an approach directed at effectively dealing with errors after they have occurred, with the goal of minimizing negative and maximizing positive error consequences (examples of the latter are learning and innovations). After defining errors and related concepts, we review research on error-related processes affected by error management (error detection, damage control). Empirical evidence on positive effects of error management in individuals and organizations is then discussed, along with emotional, motivational, cognitive, and behavioral pathways of these effects. Learning from errors is central, but like other positive consequences, learning occurs under certain circumstances--one being the development of a mind-set of acceptance of human error.

Evaluating and improving the representation of heteroscedastic errors in hydrological models

Science.gov (United States)

McInerney, D. J.; Thyer, M. A.; Kavetski, D.; Kuczera, G. A.

2013-12-01

Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic predictions. In particular, residual errors of hydrological models are often heteroscedastic, with large errors associated with high rainfall and runoff events. Recent studies have shown that using a weighted least squares (WLS) approach - where the magnitude of residuals are assumed to be linearly proportional to the magnitude of the flow - captures some of this heteroscedasticity. In this study we explore a range of Bayesian approaches for improving the representation of heteroscedasticity in residual errors. We compare several improved formulations of the WLS approach, the well-known Box-Cox transformation and the more recent log-sinh transformation. Our results confirm that these approaches are able to stabilize the residual error variance, and that it is possible to improve the representation of heteroscedasticity compared with the linear WLS approach. We also find generally good performance of the Box-Cox and log-sinh transformations, although as indicated in earlier publications, the Box-Cox transform sometimes produces unrealistically large prediction limits. Our work explores the trade-offs between these different uncertainty characterization approaches, investigates how their performance varies across diverse catchments and models, and recommends practical approaches suitable for large-scale applications.

Angular discretization errors in transport theory

International Nuclear Information System (INIS)

Nelson, P.; Yu, F.

1992-01-01

Elements of the information-based complexity theory are computed for several types of information and associated algorithms for angular approximations in the setting of a on-dimensional model problem. For point-evaluation information, the local and global radii of information are computed, a (trivial) optimal algorithm is determined, and the local and global error of a discrete ordinates algorithm are shown to be infinite. For average cone-integral information, the local and global radii of information are computed, the local and global error tends to zero as the underlying partition is indefinitely refined. A central algorithm for such information and an optimal partition (of given cardinality) are described. It is further shown that the analytic first-collision source method has zero error (for the purely absorbing model problem). Implications of the restricted problem domains suitable for the various types of information are discussed

Modeling coherent errors in quantum error correction

Science.gov (United States)

Greenbaum, Daniel; Dutton, Zachary

2018-01-01

Analysis of quantum error correcting codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. Here we examine the accuracy of the Pauli approximation for noise containing coherent errors (characterized by a rotation angle ɛ) under the repetition code. We derive an analytic expression for the logical error channel as a function of arbitrary code distance d and concatenation level n, in the small error limit. We find that coherent physical errors result in logical errors that are partially coherent and therefore non-Pauli. However, the coherent part of the logical error is negligible at fewer than {ε }-({dn-1)} error correction cycles when the decoder is optimized for independent Pauli errors, thus providing a regime of validity for the Pauli approximation. Above this number of correction cycles, the persistent coherent logical error will cause logical failure more quickly than the Pauli model would predict, and this may need to be combated with coherent suppression methods at the physical level or larger codes.

Error review: Can this improve reporting performance?

International Nuclear Information System (INIS)

Tudor, Gareth R.; Finlay, David B.

2001-01-01

AIM: This study aimed to assess whether error review can improve radiologists' reporting performance. MATERIALS AND METHODS: Ten Consultant Radiologists reported 50 plain radiographs, in which the diagnoses were established. Eighteen of the radiographs were normal, 32 showed an abnormality. The radiologists were shown their errors and then re-reported the series of radiographs after an interval of 4-5 months. The accuracy of the reports to the established diagnoses was assessed. Chi-square test was used to calculate the difference between the viewings. RESULTS: On re-reporting the radiographs, seven radiologists improved their accuracy score, two had a lower score and one radiologist showed no score difference. Mean accuracy pre-education was 82.2%, (range 78-92%) and post-education was 88%, (range 76-96%). Individually, two of the radiologists showed a statistically significant improvement post-education (P < 0.01,P < 0.05). Assessing the group as a whole, there was a trend for improvement post-education but this did not reach statistical significance. Assessing only the radiographs where errors were made on the initial viewing, for the group as a whole there was a 63% improvement post-education. CONCLUSION: We suggest that radiologists benefit from error review, although there was not a statistically significant improvement for the series of radiographs in total. This is partly explained by the fact that some radiologists gave incorrect responses post-education that had initially been correct, thus masking the effect of the educational intervention. Tudor, G.R. and Finlay, D.B. (2001

A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

Directory of Open Access Journals (Sweden)

Zizhou Lao

2018-05-01

Full Text Available For model-based state of charge (SOC estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF strategy is introduced to improve forgetting factor recursive least squares (FFRLS to variable forgetting factor recursive least squares (VFF-RLS. A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

CSIR Research Space (South Africa)

Ramoelo, Abel

2013-06-01

Full Text Available in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only...

Errors in causal inference: an organizational schema for systematic error and random error.

Science.gov (United States)

Suzuki, Etsuji; Tsuda, Toshihide; Mitsuhashi, Toshiharu; Mansournia, Mohammad Ali; Yamamoto, Eiji

2016-11-01

To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference. We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability. Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods. Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of error in Monte Carlo transport calculations

International Nuclear Information System (INIS)

Booth, T.E.

1979-01-01

The Monte Carlo method for neutron transport calculations suffers, in part, because of the inherent statistical errors associated with the method. Without an estimate of these errors in advance of the calculation, it is difficult to decide what estimator and biasing scheme to use. Recently, integral equations have been derived that, when solved, predicted errors in Monte Carlo calculations in nonmultiplying media. The present work allows error prediction in nonanalog Monte Carlo calculations of multiplying systems, even when supercritical. Nonanalog techniques such as biased kernels, particle splitting, and Russian Roulette are incorporated. Equations derived here allow prediction of how much a specific variance reduction technique reduces the number of histories required, to be weighed against the change in time required for calculation of each history. 1 figure, 1 table

Adaptive color halftoning for minimum perceived error using the blue noise mask

Science.gov (United States)

Yu, Qing; Parker, Kevin J.

1997-04-01

Color halftoning using a conventional screen requires careful selection of screen angles to avoid Moire patterns. An obvious advantage of halftoning using a blue noise mask (BNM) is that there are no conventional screen angle or Moire patterns produced. However, a simple strategy of employing the same BNM on all color planes is unacceptable in case where a small registration error can cause objectionable color shifts. In a previous paper by Yao and Parker, strategies were presented for shifting or inverting the BNM as well as using mutually exclusive BNMs for different color planes. In this paper, the above schemes will be studied in CIE-LAB color space in terms of root mean square error and variance for luminance channel and chrominance channel respectively. We will demonstrate that the dot-on-dot scheme results in minimum chrominance error, but maximum luminance error and the 4-mask scheme results in minimum luminance error but maximum chrominance error, while the shift scheme falls in between. Based on this study, we proposed a new adaptive color halftoning algorithm that takes colorimetric color reproduction into account by applying 2-mutually exclusive BNMs on two different color planes and applying an adaptive scheme on other planes to reduce color error. We will show that by having one adaptive color channel, we obtain increased flexibility to manipulate the output so as to reduce colorimetric error while permitting customization to specific printing hardware.

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

Science.gov (United States)

Nathwani, Jay N; Fiers, Rebekah M; Ray, Rebecca D; Witt, Anna K; Law, Katherine E; DiMarco, ShannonM; Pugh, Carla M

The purpose of this study is to coevaluate resident technical errors and decision-making capabilities during placement of a subclavian central venous catheter (CVC). We hypothesize that there would be significant correlations between scenario-based decision-making skills and technical proficiency in central line insertion. We also predict residents would face problems in anticipating common difficulties and generating solutions associated with line placement. 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 skills. This study was performed at 7 tertiary care centers. Study participants (N = 46) largely consisted of first-year research residents who could be followed longitudinally. Second-year research and clinical residents were not excluded. In total, 6 checklist errors were committed more often than anticipated. Residents committed an average of 1.9 errors, significantly more than the 1 error, at most, per person expected (t(44) = 3.82, p technical errors committed negatively correlated with the total number of commonly identified difficulties and generated solutions (r (33) = -0.429, p = 0.021, r (33) = -0.383, p = 0.044, respectively). 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. Copyright © 2016 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

KAUST Repository

Bhatia, H.; Jadhav, S.; Bremer, P.; Guoning Chen,; Levine, J. A.; Nonato, L. G.; Pascucci, V.

2012-01-01

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures. © 2012 IEEE.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

KAUST Repository

Bhatia, H.

2012-09-01

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures. © 2012 IEEE.

Neutron-induced soft errors in CMOS circuits

International Nuclear Information System (INIS)

Hazucha, P.

1999-01-01

The subject of this thesis is a systematic study of soft errors occurring in CMOS integrated circuits when being exposed to radiation. The vast majority of commercial circuits operate in the natural environment ranging from the sea level to aircraft flight altitudes (less than 20 km), where the errors are caused mainly by interaction of atmospheric neutrons with silicon. Initially, the soft error rate (SER) of a static memory was measured for supply voltages from 2V to 5V when irradiated by 14 MeV and 100 MeV neutrons. Increased error rate due to the decreased supply voltage has been identified as a potential hazard for operation of future low-voltage circuits. A novel methodology was proposed for accurate SER characterization of a manufacturing process and it was validated by measurements on a 0.6 μm process and 100 MeV neutrons. The methodology can be applied to the prediction of SER in the natural environment

Dynamic modelling of a forward osmosis-nanofiltration integrated process for treating hazardous wastewater.

Science.gov (United States)

Pal, Parimal; Das, Pallabi; Chakrabortty, Sankha; Thakura, Ritwik

2016-11-01

Dynamic modelling and simulation of a nanofiltration-forward osmosis integrated complete system was done along with economic evaluation to pave the way for scale up of such a system for treating hazardous pharmaceutical wastes. The system operated in a closed loop not only protects surface water from the onslaught of hazardous industrial wastewater but also saves on cost of fresh water by turning wastewater recyclable at affordable price. The success of dynamic modelling in capturing the relevant transport phenomena is well reflected in high overall correlation coefficient value (R 2  > 0.98), low relative error (osmosis loop at a reasonably high flux of 56-58 l per square meter per hour.

Analyzing the installation angle error of a SAW torque sensor

International Nuclear Information System (INIS)

Fan, Yanping; Ji, Xiaojun; Cai, Ping

2014-01-01

When a torque is applied to a shaft, normal strain oriented at ±45° direction to the shaft axis is at its maximum, which requires two one-port SAW resonators to be bonded to the shaft at ±45° to the shaft axis. In order to make the SAW torque sensitivity high enough, the installation angle error of two SAW resonators must be confined within ±5° according to our design requirement. However, there are few studies devoted to the installation angle analysis of a SAW torque sensor presently and the angle error was usually obtained by a manual method. Hence, we propose an approximation method to analyze the angle error. First, according to the sensitive mechanism of the SAW device to torque, the SAW torque sensitivity is deduced based on the linear piezoelectric constitutive equation and the perturbation theory. Then, when a torque is applied to the tested shaft, the stress condition of two SAW resonators mounted with an angle deviating from ±45° to the shaft axis, is analyzed. The angle error is obtained by means of the torque sensitivities of two orthogonal SAW resonators. Finally, the torque measurement system is constructed and the loading and unloading experiments are performed twice. The torque sensitivities of two SAW resonators are obtained by applying average and least square method to the experimental results. Based on the derived angle error estimation function, the angle error is estimated about 3.447°, which is close to the actual angle error 2.915°. The difference between the estimated angle and the actual angle is discussed. The validity of the proposed angle error analysis method is testified to by the experimental results. (technical design note)

Robust anti-synchronization of uncertain chaotic systems based on multiple-kernel least squares support vector machine modeling

International Nuclear Information System (INIS)

Chen Qiang; Ren Xuemei; Na Jing

2011-01-01

Highlights: Model uncertainty of the system is approximated by multiple-kernel LSSVM. Approximation errors and disturbances are compensated in the controller design. Asymptotical anti-synchronization is achieved with model uncertainty and disturbances. Abstract: In this paper, we propose a robust anti-synchronization scheme based on multiple-kernel least squares support vector machine (MK-LSSVM) modeling for two uncertain chaotic systems. The multiple-kernel regression, which is a linear combination of basic kernels, is designed to approximate system uncertainties by constructing a multiple-kernel Lagrangian function and computing the corresponding regression parameters. Then, a robust feedback control based on MK-LSSVM modeling is presented and an improved update law is employed to estimate the unknown bound of the approximation error. The proposed control scheme can guarantee the asymptotic convergence of the anti-synchronization errors in the presence of system uncertainties and external disturbances. Numerical examples are provided to show the effectiveness of the proposed method.

Frequent methodological errors in clinical research.

Science.gov (United States)

Silva Aycaguer, L C

2018-03-07

Several errors that are frequently present in clinical research are listed, discussed and illustrated. A distinction is made between what can be considered an "error" arising from ignorance or neglect, from what stems from a lack of integrity of researchers, although it is recognized and documented that it is not easy to establish when we are in a case and when in another. The work does not intend to make an exhaustive inventory of such problems, but focuses on those that, while frequent, are usually less evident or less marked in the various lists that have been published with this type of problems. It has been a decision to develop in detail the examples that illustrate the problems identified, instead of making a list of errors accompanied by an epidermal description of their characteristics. Copyright © 2018 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.

On superactivation of one-shot quantum zero-error capacity and the related property of quantum measurements

DEFF Research Database (Denmark)

Shirokov, M. E.; Shulman, Tatiana

2014-01-01

We give a detailed description of a low-dimensional quantum channel (input dimension 4, Choi rank 3) demonstrating the symmetric form of superactivation of one-shot quantum zero-error capacity. This property means appearance of a noiseless (perfectly reversible) subchannel in the tensor square...... of a channel having no noiseless subchannels. Then we describe a quantum channel with an arbitrary given level of symmetric superactivation (including the infinite value). We also show that superactivation of one-shot quantum zero-error capacity of a channel can be reformulated in terms of quantum measurement...

Preprocessing in Matlab Inconsistent Linear System for a Meaningful Least Squares Solution

Science.gov (United States)

Sen, Symal K.; Shaykhian, Gholam Ali

2011-01-01

Mathematical models of many physical/statistical problems are systems of linear equations Due to measurement and possible human errors/mistakes in modeling/data, as well as due to certain assumptions to reduce complexity, inconsistency (contradiction) is injected into the model, viz. the linear system. While any inconsistent system irrespective of the degree of inconsistency has always a least-squares solution, one needs to check whether an equation is too much inconsistent or, equivalently too much contradictory. Such an equation will affect/distort the least-squares solution to such an extent that renders it unacceptable/unfit to be used in a real-world application. We propose an algorithm which (i) prunes numerically redundant linear equations from the system as these do not add any new information to the model, (ii) detects contradictory linear equations along with their degree of contradiction (inconsistency index), (iii) removes those equations presumed to be too contradictory, and then (iv) obtain the . minimum norm least-squares solution of the acceptably inconsistent reduced linear system. The algorithm presented in Matlab reduces the computational and storage complexities and also improves the accuracy of the solution. It also provides the necessary warning about the existence of too much contradiction in the model. In addition, we suggest a thorough relook into the mathematical modeling to determine the reason why unacceptable contradiction has occurred thus prompting us to make necessary corrections/modifications to the models - both mathematical and, if necessary, physical.

36 CFR 910.67 - Square guidelines.

Science.gov (United States)

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Square guidelines. 910.67... GUIDELINES AND UNIFORM STANDARDS FOR URBAN PLANNING AND DESIGN OF DEVELOPMENT WITHIN THE PENNSYLVANIA AVENUE DEVELOPMENT AREA Glossary of Terms § 910.67 Square guidelines. Square Guidelines establish the Corporation's...

Sub-nanometer periodic nonlinearity error in absolute distance interferometers

Science.gov (United States)

Yang, Hongxing; Huang, Kaiqi; Hu, Pengcheng; Zhu, Pengfei; Tan, Jiubin; Fan, Zhigang

2015-05-01

Periodic nonlinearity which can result in error in nanometer scale has become a main problem limiting the absolute distance measurement accuracy. In order to eliminate this error, a new integrated interferometer with non-polarizing beam splitter is developed. This leads to disappearing of the frequency and/or polarization mixing. Furthermore, a strict requirement on the laser source polarization is highly reduced. By combining retro-reflector and angel prism, reference and measuring beams can be spatially separated, and therefore, their optical paths are not overlapped. So, the main cause of the periodic nonlinearity error, i.e., the frequency and/or polarization mixing and leakage of beam, is eliminated. Experimental results indicate that the periodic phase error is kept within 0.0018°.

A Note on Magic Squares

Science.gov (United States)

Williams, Horace E.

1974-01-01

A method for generating 3x3 magic squares is developed. A series of questions relating to these magic squares is posed. An invesitgation using matrix methods is suggested with some questions for consideration. (LS)

Prediction of Monte Carlo errors by a theory generalized to treat track-length estimators

International Nuclear Information System (INIS)

Booth, T.E.; Amster, H.J.

1978-01-01

Present theories for predicting expected Monte Carlo errors in neutron transport calculations apply to estimates of flux-weighted integrals sampled directly by scoring individual collisions. To treat track-length estimators, the recent theory of Amster and Djomehri is generalized to allow the score distribution functions to depend on the coordinates of two successive collisions. It has long been known that the expected track length in a region of phase space equals the expected flux integrated over that region, but that the expected statistical error of the Monte Carlo estimate of the track length is different from that of the flux integral obtained by sampling the sum of the reciprocals of the cross sections for all collisions in the region. These conclusions are shown to be implied by the generalized theory, which provides explicit equations for the expected values and errors of both types of estimators. Sampling expected contributions to the track-length estimator is also treated. Other general properties of the errors for both estimators are derived from the equations and physically interpreted. The actual values of these errors are then obtained and interpreted for a simple specific example

Democracy Squared

DEFF Research Database (Denmark)

Rose, Jeremy; Sæbø, Øystein

2005-01-01

On-line political communities, such as the Norwegian site Demokratitorget (Democracy Square), are often designed according to a set of un-reflected assumptions about the political interests of their potential members. In political science, democracy is not taken as given in this way, but can...... be represented by different models which characterize different relationships between politicians and the citizens they represent. This paper uses quantitative and qualitative content analysis to analyze the communication mediated by the Democracy Square discussion forum in the first ten months of its life......-Republican model. In the qualitative analysis the discourse is analysed as repeating genres – patterns in the communication form which also reflect the conflict of interest between citizens and politicians. Though the analysis gives insight into the nature of the discourse the site supports, little is known about...

Analysis of a Statistical Relationship Between Dose and Error Tallies in Semiconductor Digital Integrated Circuits for Application to Radiation Monitoring Over a Wireless Sensor Network

Science.gov (United States)

Colins, Karen; Li, Liqian; Liu, Yu

2017-05-01

Mass production of widely used semiconductor digital integrated circuits (ICs) has lowered unit costs to the level of ordinary daily consumables of a few dollars. It is therefore reasonable to contemplate the idea of an engineered system that consumes unshielded low-cost ICs for the purpose of measuring gamma radiation dose. Underlying the idea is the premise of a measurable correlation between an observable property of ICs and radiation dose. Accumulation of radiation-damage-induced state changes or error events is such a property. If correct, the premise could make possible low-cost wide-area radiation dose measurement systems, instantiated as wireless sensor networks (WSNs) with unshielded consumable ICs as nodes, communicating error events to a remote base station. The premise has been investigated quantitatively for the first time in laboratory experiments and related analyses performed at the Canadian Nuclear Laboratories. State changes or error events were recorded in real time during irradiation of samples of ICs of different types in a 60Co gamma cell. From the error-event sequences, empirical distribution functions of dose were generated. The distribution functions were inverted and probabilities scaled by total error events, to yield plots of the relationship between dose and error tallies. Positive correlation was observed, and discrete functional dependence of dose quantiles on error tallies was measured, demonstrating the correctness of the premise. The idea of an engineered system that consumes unshielded low-cost ICs in a WSN, for the purpose of measuring gamma radiation dose over wide areas, is therefore tenable.

Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

Science.gov (United States)

Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field

The impact of measurement errors in the identification of regulatory networks

Directory of Open Access Journals (Sweden)

Sato João R

2009-12-01

Full Text Available Abstract Background There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent and non-time series (independent data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models and dependent (autoregressive models data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error. The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Mode coupling in hybrid square-rectangular lasers for single mode operation

Energy Technology Data Exchange (ETDEWEB)

Ma, Xiu-Wen; Huang, Yong-Zhen, E-mail: yzhuang@semi.ac.cn; Yang, Yue-De; Xiao, Jin-Long; Weng, Hai-Zhong; Xiao, Zhi-Xiong [State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors and University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100083 (China)

2016-08-15

Mode coupling between a square microcavity and a Fabry-Pérot (FP) cavity is proposed and demonstrated for realizing single mode lasers. The modulations of the mode Q factor as simulation results are observed and single mode operation is obtained with a side mode suppression ratio of 46 dB and a single mode fiber coupling loss of 3.2 dB for an AlGaInAs/InP hybrid laser as a 300-μm-length and 1.5-μm-wide FP cavity connected to a vertex of a 10-μm-side square microcavity. Furthermore, tunable single mode operation is demonstrated with a continuous wavelength tuning range over 10 nm. The simple hybrid structure may shed light on practical applications of whispering-gallery mode microcavities in large-scale photonic integrated circuits and optical communication and interconnection.

A Novel Error Model of Optical Systems and an On-Orbit Calibration Method for Star Sensors

Directory of Open Access Journals (Sweden)

Shuang Wang

2015-12-01

Full Text Available In order to improve the on-orbit measurement accuracy of star sensors, the effects of image-plane rotary error, image-plane tilt error and distortions of optical systems resulting from the on-orbit thermal environment were studied in this paper. Since these issues will affect the precision of star image point positions, in this paper, a novel measurement error model based on the traditional error model is explored. Due to the orthonormal characteristics of image-plane rotary-tilt errors and the strong nonlinearity among these error parameters, it is difficult to calibrate all the parameters simultaneously. To solve this difficulty, for the new error model, a modified two-step calibration method based on the Extended Kalman Filter (EKF and Least Square Methods (LSM is presented. The former one is used to calibrate the main point drift, focal length error and distortions of optical systems while the latter estimates the image-plane rotary-tilt errors. With this calibration method, the precision of star image point position influenced by the above errors is greatly improved from 15.42% to 1.389%. Finally, the simulation results demonstrate that the presented measurement error model for star sensors has higher precision. Moreover, the proposed two-step method can effectively calibrate model error parameters, and the calibration precision of on-orbit star sensors is also improved obviously.

Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

Directory of Open Access Journals (Sweden)

Marco A

2006-01-01

Full Text Available Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non-line-of-sight (NLOS errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.. In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS, even when nearly half the measures suffered from NLOS or other coarse errors.

Robust Estimator for Non-Line-of-Sight Error Mitigation in Indoor Localization

Science.gov (United States)

Casas, R.; Marco, A.; Guerrero, J. J.; Falcó, J.

2006-12-01

Indoor localization systems are undoubtedly of interest in many application fields. Like outdoor systems, they suffer from non-line-of-sight (NLOS) errors which hinder their robustness and accuracy. Though many ad hoc techniques have been developed to deal with this problem, unfortunately most of them are not applicable indoors due to the high variability of the environment (movement of furniture and of people, etc.). In this paper, we describe the use of robust regression techniques to detect and reject NLOS measures in a location estimation using multilateration. We show how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and validate its suitability by comparing it to other methods described in the bibliography. We obtained remarkable results when using it in a real indoor positioning system that works with Bluetooth and ultrasound (BLUPS), even when nearly half the measures suffered from NLOS or other coarse errors.

Equalization of Loudspeaker and Room Responses Using Kautz Filters: Direct Least Squares Design

Directory of Open Access Journals (Sweden)

Karjalainen Matti

2007-01-01

Full Text Available DSP-based correction of loudspeaker and room responses is becoming an important part of improving sound reproduction. Such response equalization (EQ is based on using a digital filter in cascade with the reproduction channel to counteract the response errors introduced by loudspeakers and room acoustics. Several FIR and IIR filter design techniques have been proposed for equalization purposes. In this paper we investigate Kautz filters, an interesting class of IIR filters, from the point of view of direct least squares EQ design. Kautz filters can be seen as generalizations of FIR filters and their frequency-warped counterparts. They provide a flexible means to obtain desired frequency resolution behavior, which allows low filter orders even for complex corrections. Kautz filters have also the desirable property to avoid inverting dips in transfer function to sharp and long-ringing resonances in the equalizer. Furthermore, the direct least squares design is applicable to nonminimum-phase EQ design and allows using a desired target response. The proposed method is demonstrated by case examples with measured and synthetic loudspeaker and room responses.

Error begat error: design error analysis and prevention in social infrastructure projects.

Science.gov (United States)

Love, Peter E D; Lopez, Robert; Edwards, David J; Goh, Yang M

2012-09-01

Design errors contribute significantly to cost and schedule growth in social infrastructure projects and to engineering failures, which can result in accidents and loss of life. Despite considerable research that has addressed their error causation in construction projects they still remain prevalent. This paper identifies the underlying conditions that contribute to design errors in social infrastructure projects (e.g. hospitals, education, law and order type buildings). A systemic model of error causation is propagated and subsequently used to develop a learning framework for design error prevention. The research suggests that a multitude of strategies should be adopted in congruence to prevent design errors from occurring and so ensure that safety and project performance are ameliorated. Copyright © 2011. Published by Elsevier Ltd.

Edge maps: Representing flow with bounded error

KAUST Repository

Bhatia, Harsh

2011-03-01

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Many analysis techniques rely on computing streamlines, a task often hampered by numerical instabilities. Approaches that ignore the resulting errors can lead to inconsistencies that may produce unreliable visualizations and ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with linear maps defined on its boundary. This representation, called edge maps, is equivalent to computing all possible streamlines at a user defined error threshold. In spite of this error, all the streamlines computed using edge maps will be pairwise disjoint. Furthermore, our representation stores the error explicitly, and thus can be used to produce more informative visualizations. Given a piecewise-linear interpolated vector field, a recent result [15] shows that there are only 23 possible map classes for a triangle, permitting a concise description of flow behaviors. This work describes the details of computing edge maps, provides techniques to quantify and refine edge map error, and gives qualitative and visual comparisons to more traditional techniques. © 2011 IEEE.

MATLAB implementation of satellite positioning error overbounding by generalized Pareto distribution

Science.gov (United States)

Ahmad, Khairol Amali; Ahmad, Shahril; Hashim, Fakroul Ridzuan

2018-02-01

In the satellite navigation community, error overbound has been implemented in the process of integrity monitoring. In this work, MATLAB programming is used to implement the overbounding of satellite positioning error CDF. Using a trajectory of reference, the horizontal position errors (HPE) are computed and its non-parametric distribution function is given by the empirical Cumulative Distribution Function (ECDF). According to the results, these errors have a heavy-tailed distribution. Sınce the ECDF of the HPE in urban environment is not Gaussian distributed, the ECDF is overbound with the CDF of the generalized Pareto distribution (GPD).

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

Science.gov (United States)

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

Directory of Open Access Journals (Sweden)

Waddah Waheeb

Full Text Available Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN and the Dynamic Ridge Polynomial Neural Network (DRPNN. Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Propagation of error from parameter constraints in quantitative MRI: Example application of multiple spin echo T2 mapping.

Science.gov (United States)

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.

Computation of misalignment and primary mirror astigmatism figure error of two-mirror telescopes

Science.gov (United States)

Gu, Zhiyuan; Wang, Yang; Ju, Guohao; Yan, Changxiang

2018-01-01

Active optics usually uses the computation models based on numerical methods to correct misalignments and figure errors at present. These methods can hardly lead to any insight into the aberration field dependencies that arise in the presence of the misalignments. An analytical alignment model based on third-order nodal aberration theory is presented for this problem, which can be utilized to compute the primary mirror astigmatic figure error and misalignments for two-mirror telescopes. Alignment simulations are conducted for an R-C telescope based on this analytical alignment model. It is shown that in the absence of wavefront measurement errors, wavefront measurements at only two field points are enough, and the correction process can be completed with only one alignment action. In the presence of wavefront measurement errors, increasing the number of field points for wavefront measurements can enhance the robustness of the alignment model. Monte Carlo simulation shows that, when -2 mm ≤ linear misalignment ≤ 2 mm, -0.1 deg ≤ angular misalignment ≤ 0.1 deg, and -0.2 λ ≤ astigmatism figure error (expressed as fringe Zernike coefficients C5 / C6, λ = 632.8 nm) ≤0.2 λ, the misaligned systems can be corrected to be close to nominal state without wavefront testing error. In addition, the root mean square deviation of RMS wavefront error of all the misaligned samples after being corrected is linearly related to wavefront testing error.

Study of maintenance skill-work based on PSFs and error category

International Nuclear Information System (INIS)

Nagata, Manabu; Yukimachi, Takeo; Hasegawa, Toshio

2001-01-01

In this investigation, the skill-types of skill-work are clarified according to the human error data on the maintenance works at nuclear power plants. At first, the causal PSFs of the errors are extracted from the data and some of the skill-types are characterized as results from factor analysis. Moreover, the skill-work model is reexamined on the basis of the contents of the human error data and the error category corresponding to the data. Furthermore, integrating the tendency of the causal PSFs and the actual error category concerning each skill-type, an extended skill-work model was developed with a flow-chart representation as a tentative stage of the investigation. (author)

Accounting for measurement error in log regression models with applications to accelerated testing.

Science.gov (United States)

Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

2018-01-01

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

Accounting for measurement error in log regression models with applications to accelerated testing.

Directory of Open Access Journals (Sweden)

Robert Richardson

Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

Calculating the parameters of experimental data Gauss distribution using the least square fit method and evaluation of their accuracy

International Nuclear Information System (INIS)

Guseva, E.V.; Peregudov, V.N.

1982-01-01

The FITGAV program for calculation of parameters of the Gauss curve describing experimental data is considered. The calculations are based on the least square fit method. The estimations of errors in the parameter determination as a function of experimental data sample volume and their statistical significance are obtained. The curve fit using 100 points occupies less than 1 s at the SM-4 type computer

Some Theoretical Essences of Lithuania Squares Formation

Directory of Open Access Journals (Sweden)

Gintautas Tiškus

2016-04-01

Full Text Available In the Lithuanian acts of law and in the scientific literature there are no clear criteria and notions to define a square. The unbuilt city space places or the gaps between buildings are often defined as the squares, which do not have clear limits or destination. The mandatory attributes of the place which is called the square are indicated in the article, the notion of square is defined. The article deals with Lithuanian squares theme, analyses the differences between representation and representativeness. The article aims to indicate an influence of city environmental context and monument in the square on its function. The square is an independent element of city plan structure, but it is not an independent element of city spatial structure. The space and environment of the square are related to each other not only by physical, aesthetical relations, but as well as by causalities, which may be named as the essences of squares’ formation. The interdisciplinary discourse analysis method is applied in the article.

Latin Squares

Indian Academy of Sciences (India)

Admin

2012-09-07

Sep 7, 2012 ... must first talk of permutations and Latin squares. A permutation of a finite set of objects is a linear arrange- ment of ... with a special element 1 ... Of course, this has .... tion method to disprove Euler's conjecture for infinitely.

Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.

Science.gov (United States)

Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L

2012-03-06

Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

Triple-Error-Correcting Codec ASIC

Science.gov (United States)

Jones, Robert E.; Segallis, Greg P.; Boyd, Robert

1994-01-01

Coder/decoder constructed on single integrated-circuit chip. Handles data in variety of formats at rates up to 300 Mbps, correcting up to 3 errors per data block of 256 to 512 bits. Helps reduce cost of transmitting data. Useful in many high-data-rate, bandwidth-limited communication systems such as; personal communication networks, cellular telephone networks, satellite communication systems, high-speed computing networks, broadcasting, and high-reliability data-communication links.

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

Science.gov (United States)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both

Error Cost Escalation Through the Project Life Cycle

Science.gov (United States)

Stecklein, Jonette M.; Dabney, Jim; Dick, Brandon; Haskins, Bill; Lovell, Randy; Moroney, Gregory

2004-01-01

It is well known that the costs to fix errors increase as the project matures, but how fast do those costs build? A study was performed to determine the relative cost of fixing errors discovered during various phases of a project life cycle. This study used three approaches to determine the relative costs: the bottom-up cost method, the total cost breakdown method, and the top-down hypothetical project method. The approaches and results described in this paper presume development of a hardware/software system having project characteristics similar to those used in the development of a large, complex spacecraft, a military aircraft, or a small communications satellite. The results show the degree to which costs escalate, as errors are discovered and fixed at later and later phases in the project life cycle. If the cost of fixing a requirements error discovered during the requirements phase is defined to be 1 unit, the cost to fix that error if found during the design phase increases to 3 - 8 units; at the manufacturing/build phase, the cost to fix the error is 7 - 16 units; at the integration and test phase, the cost to fix the error becomes 21 - 78 units; and at the operations phase, the cost to fix the requirements error ranged from 29 units to more than 1500 units

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing

Science.gov (United States)

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.

The distribution of wind power forecast errors from operational systems

Energy Technology Data Exchange (ETDEWEB)

Hodge, Bri-Mathias; Ela, Erik; Milligan, Michael

2011-07-01

Wind power forecasting is one important tool in the integration of large amounts of renewable generation into the electricity system. Wind power forecasts from operational systems are not perfect, and thus, an understanding of the forecast error distributions can be important in system operations. In this work, we examine the errors from operational wind power forecasting systems, both for a single wind plant and for an entire interconnection. The resulting error distributions are compared with the normal distribution and the distribution obtained from the persistence forecasting model at multiple timescales. A model distribution is fit to the operational system forecast errors and the potential impact on system operations highlighted through the generation of forecast confidence intervals. (orig.)

A neural fuzzy controller learning by fuzzy error propagation

Science.gov (United States)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

An Empirical State Error Covariance Matrix Orbit Determination Example

Science.gov (United States)

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

Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

KAUST Repository

Khaki, M.

2017-07-06

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

A Fortran 77 computer code for damped least-squares inversion of Slingram electromagnetic anomalies over thin tabular conductors

Science.gov (United States)

Dondurur, Derman; Sarı, Coşkun

2004-07-01

A FORTRAN 77 computer code is presented that permits the inversion of Slingram electromagnetic anomalies to an optimal conductor model. Damped least-squares inversion algorithm is used to estimate the anomalous body parameters, e.g. depth, dip and surface projection point of the target. Iteration progress is controlled by maximum relative error value and iteration continued until a tolerance value was satisfied, while the modification of Marquardt's parameter is controlled by sum of the squared errors value. In order to form the Jacobian matrix, the partial derivatives of theoretical anomaly expression with respect to the parameters being optimised are calculated by numerical differentiation by using first-order forward finite differences. A theoretical and two field anomalies are inserted to test the accuracy and applicability of the present inversion program. Inversion of the field data indicated that depth and the surface projection point parameters of the conductor are estimated correctly, however, considerable discrepancies appeared on the estimated dip angles. It is therefore concluded that the most important factor resulting in the misfit between observed and calculated data is due to the fact that the theory used for computing Slingram anomalies is valid for only thin conductors and this assumption might have caused incorrect dip estimates in the case of wide conductors.

Practical guidance on representing the heteroscedasticity of residual errors of hydrological predictions

Science.gov (United States)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Kuczera, George

2016-04-01

Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic streamflow predictions. In particular, residual errors of hydrological predictions are often heteroscedastic, with large errors associated with high runoff events. Although multiple approaches exist for representing this heteroscedasticity, few if any studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating a range of approaches for representing heteroscedasticity in residual errors. These approaches include the 'direct' weighted least squares approach and 'transformational' approaches, such as logarithmic, Box-Cox (with and without fitting the transformation parameter), logsinh and the inverse transformation. The study reports (1) theoretical comparison of heteroscedasticity approaches, (2) empirical evaluation of heteroscedasticity approaches using a range of multiple catchments / hydrological models / performance metrics and (3) interpretation of empirical results using theory to provide practical guidance on the selection of heteroscedasticity approaches. Importantly, for hydrological practitioners, the results will simplify the choice of approaches to represent heteroscedasticity. This will enhance their ability to provide hydrological probabilistic predictions with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality).

Predicting positional error of MLC using volumetric analysis

International Nuclear Information System (INIS)

Hareram, E.S.

2008-01-01

IMRT normally using multiple beamlets (small width of the beam) for a particular field to deliver so that it is imperative to maintain the positional accuracy of the MLC in order to deliver integrated computed dose accurately. Different manufacturers have reported high precession on MLC devices with leaf positional accuracy nearing 0.1 mm but measuring and rectifying the error in this accuracy is very difficult. Various methods are used to check MLC position and among this volumetric analysis is one of the technique. Volumetric approach was adapted in our method using primus machine and 0.6cc chamber at 5 cm depth In perspex. MLC of 1 mm error introduces an error of 20%, more sensitive to other methods

Least Squares Neural Network-Based Wireless E-Nose System Using an SnO₂ Sensor Array.

Science.gov (United States)

Shahid, Areej; Choi, Jong-Hyeok; Rana, Abu Ul Hassan Sarwar; Kim, Hyun-Seok

2018-05-06

Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH₄) and carbon monoxide (CO), using an array of SnO₂ gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH₄ and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.

Computational fluid dynamics analysis and experimental study of a low measurement error temperature sensor used in climate observation.

Science.gov (United States)

Yang, Jie; Liu, Qingquan; Dai, Wei

2017-02-01

To improve the air temperature observation accuracy, a low measurement error temperature sensor is proposed. A computational fluid dynamics (CFD) method is implemented to obtain temperature errors under various environmental conditions. Then, a temperature error correction equation is obtained by fitting the CFD results using a genetic algorithm method. The low measurement error temperature sensor, a naturally ventilated radiation shield, a thermometer screen, and an aspirated temperature measurement platform are characterized in the same environment to conduct the intercomparison. The aspirated platform served as an air temperature reference. The mean temperature errors of the naturally ventilated radiation shield and the thermometer screen are 0.74 °C and 0.37 °C, respectively. In contrast, the mean temperature error of the low measurement error temperature sensor is 0.11 °C. The mean absolute error and the root mean square error between the corrected results and the measured results are 0.008 °C and 0.01 °C, respectively. The correction equation allows the temperature error of the low measurement error temperature sensor to be reduced by approximately 93.8%. The low measurement error temperature sensor proposed in this research may be helpful to provide a relatively accurate air temperature result.

Least-squares gradient calculation from multi-point observations of scalar and vector fields: methodology and applications with Cluster in the plasmasphere

Directory of Open Access Journals (Sweden)

J. De Keyser

2007-05-01

Full Text Available This paper describes a general-purpose algorithm for computing the gradients in space and time of a scalar field, a vector field, or a divergence-free vector field, from in situ measurements by one or more spacecraft. The algorithm provides total error estimates on the computed gradient, including the effects of measurement errors, the errors due to a lack of spatio-temporal homogeneity, and errors due to small-scale fluctuations. It also has the ability to diagnose the conditioning of the problem. Optimal use is made of the data, in terms of exploiting the maximum amount of information relative to the uncertainty on the data, by solving the problem in a weighted least-squares sense. The method is illustrated using Cluster magnetic field and electron density data to compute various gradients during a traversal of the inner magnetosphere. In particular, Cluster is shown to cross azimuthal density structure, and the existence of field-aligned currents in the plasmasphere is demonstrated.

Time-Series INSAR: An Integer Least-Squares Approach For Distributed Scatterers

Science.gov (United States)

Samiei-Esfahany, Sami; Hanssen, Ramon F.

2012-01-01

The objective of this research is to extend the geode- tic mathematical model which was developed for persistent scatterers to a model which can exploit distributed scatterers (DS). The main focus is on the integer least- squares framework, and the main challenge is to include the decorrelation effect in the mathematical model. In order to adapt the integer least-squares mathematical model for DS we altered the model from a single master to a multi-master configuration and introduced the decorrelation effect stochastically. This effect is described in our model by a full covariance matrix. We propose to de- rive this covariance matrix by numerical integration of the (joint) probability distribution function (PDF) of interferometric phases. This PDF is a function of coherence values and can be directly computed from radar data. We show that the use of this model can improve the performance of temporal phase unwrapping of distributed scatterers.

Errors, error detection, error correction and hippocampal-region damage: data and theories.

Science.gov (United States)

MacKay, Donald G; Johnson, Laura W

2013-11-01

This review and perspective article outlines 15 observational constraints on theories of errors, error detection, and error correction, and their relation to hippocampal-region (HR) damage. The core observations come from 10 studies with H.M., an amnesic with cerebellar and HR damage but virtually no neocortical damage. Three studies examined the detection of errors planted in visual scenes (e.g., a bird flying in a fish bowl in a school classroom) and sentences (e.g., I helped themselves to the birthday cake). In all three experiments, H.M. detected reliably fewer errors than carefully matched memory-normal controls. Other studies examined the detection and correction of self-produced errors, with controls for comprehension of the instructions, impaired visual acuity, temporal factors, motoric slowing, forgetting, excessive memory load, lack of motivation, and deficits in visual scanning or attention. In these studies, H.M. corrected reliably fewer errors than memory-normal and cerebellar controls, and his uncorrected errors in speech, object naming, and reading aloud exhibited two consistent features: omission and anomaly. For example, in sentence production tasks, H.M. omitted one or more words in uncorrected encoding errors that rendered his sentences anomalous (incoherent, incomplete, or ungrammatical) reliably more often than controls. Besides explaining these core findings, the theoretical principles discussed here explain H.M.'s retrograde amnesia for once familiar episodic and semantic information; his anterograde amnesia for novel information; his deficits in visual cognition, sentence comprehension, sentence production, sentence reading, and object naming; and effects of aging on his ability to read isolated low frequency words aloud. These theoretical principles also explain a wide range of other data on error detection and correction and generate new predictions for future test. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sporadic error probability due to alpha particles in dynamic memories of various technologies

International Nuclear Information System (INIS)

Edwards, D.G.

1980-01-01

The sensitivity of MOS memory components to errors induced by alpha particles is expected to increase with integration level. The soft error rate of a 65-kbit VMOS memory has been compared experimentally with that of three field-proven 16-kbit designs. The technological and design advantages of the VMOS RAM ensure an error rate which is lower than those of the 16-kbit memories. Calculation of the error probability for the 65-kbit RAM and comparison with the measurements show that for large duty cycles single particle hits lead to sensing errors and for small duty cycles cell errors caused by multiple hits predominate. (Auth.)

A study of the effect of measurement error in predictor variables in nondestructive assay

International Nuclear Information System (INIS)

Burr, Tom L.; Knepper, Paula L.

2000-01-01

It is not widely known that ordinary least squares estimates exhibit bias if there are errors in the predictor variables. For example, enrichment measurements are often fit to two predictors: Poisson-distributed count rates in the region of interest and in the background. Both count rates have at least random variation due to counting statistics. Therefore, the parameter estimates will be biased. In this case, the effect of bias is a minor issue because there is almost no interest in the parameters themselves. Instead, the parameters will be used to convert count rates into estimated enrichment. In other cases, this bias source is potentially more important. For example, in tomographic gamma scanning, there is an emission stage which depends on predictors (the 'system matrix') that are estimated with error during the transmission stage. In this paper, we provide background information for the impact and treatment of errors in predictors, present results of candidate methods of compensating for the effect, review some of the nondestructive assay situations where errors in predictors occurs, and provide guidance for when errors in predictors should be considered in nondestructive assay

Fabrication of MTF measurement system for a mobile phone lens using multi-square objects

Science.gov (United States)

Hong, Sung Mok; Jo, Jae Heung; Lee, Hoi Youn; Yang, Ho Soon; Lee, Yun Woo; Lee, In Won

2007-12-01

The mobile phone market grows rapidly and the performance estimation about camera module is required. Accordingly, we fabricate the MTF measurement system for a mobile phone lens having extremely small diameter and large f-number. The objective lens with the magnification of X20 for MTF measurement for high resolution lens and a detector of CCD that is pixel size of 7.4 um are adapted to the system. Also, the CCD is translated by using a linear motor to reduce measurement errors. The measurement lens is placed at the most suitable imaging point by a precise auto-focusing motor. The measuring equipment which we developed for off-axis MTF measurement of a mobile phone lens used the multi-square objects. The square objects of measuring equipment are arranged a unit in the on-axis and total 12 units (0.3 field: 4 units, 0.5 field: 4 units, 0.7 field: 4 units) in the off-axis. When the measurement is started, the linear motors of signal detection part are transferred from on-axis to off-axis. And a detected signals from the each square objects are used for MTF measurement. System driver and MTF measure are using application program that developed us. This software can be measure the on-axis and the off-axis sequentially. In addition to that it did optimization of motor transfer for measurement time shortening.

An estimation of the height system bias parameter N (0) using least squares collocation from observed gravity and GPS-levelling data

DEFF Research Database (Denmark)

Sadiq, Muhammad; Tscherning, Carl C.; Ahmad, Zulfiqar

2009-01-01

This paper deals with the analysis of gravity anomaly and precise levelling in conjunction with GPS-Levelling data for the computation of a gravimetric geoid and an estimate of the height system bias parameter N-o for the vertical datum in Pakistan by means of least squares collocation technique...... covariance parameters has facilitated to achieve gravimetric height anomalies in a global geocentric datum. Residual terrain modeling (RTM) technique has been used in combination with the EGM96 for the reduction and smoothing of the gravity data. A value for the bias parameter N-o has been estimated...... with reference to the local GPS-Levelling datum that appears to be 0.705 m with 0.07 m mean square error. The gravimetric height anomalies were compared with height anomalies obtained from GPS-Levelling stations using least square collocation with and without bias adjustment. The bias adjustment minimizes...

The incidence and types of medication errors in patients receiving antiretroviral therapy in resource-constrained settings.

Directory of Open Access Journals (Sweden)

Kenneth Anene Agu

Full Text Available This study assessed the incidence and types of medication errors, interventions and outcomes in patients on antiretroviral therapy (ART in selected HIV treatment centres in Nigeria.Of 69 health facilities that had program for active screening of medication errors, 14 were randomly selected for prospective cohort assessment. All patients who filled/refilled their antiretroviral medications between February 2009 and March 2011 were screened for medication errors using study-specific pharmaceutical care daily worksheet (PCDW. All potential or actual medication errors identified, interventions provided and the outcomes were documented in the PCDW. Interventions included pharmaceutical care in HIV training for pharmacists amongst others. Chi-square was used for inferential statistics and P0.05. The major medications errors identified were 26.4% incorrect ART regimens prescribed; 19.8% potential drug-drug interaction or contraindication present; and 16.6% duration and/or frequency of medication inappropriate. Interventions provided included 67.1% cases of prescriber contacted to clarify/resolve errors and 14.7% cases of patient counselling and education; 97.4% of potential/actual medication error(s were resolved.The incidence rate of medication errors was somewhat high; and majority of identified errors were related to prescription of incorrect ART regimens and potential drug-drug interactions; the prescriber was contacted and the errors were resolved in majority of cases. Active screening for medication errors is feasible in resource-limited settings following a capacity building intervention.

An improved algorithm for the determination of the system paramters of a visual binary by least squares

Science.gov (United States)

Xu, Yu-Lin

The problem of computing the orbit of a visual binary from a set of observed positions is reconsidered. It is a least squares adjustment problem, if the observational errors follow a bias-free multivariate Gaussian distribution and the covariance matrix of the observations is assumed to be known. The condition equations are constructed to satisfy both the conic section equation and the area theorem, which are nonlinear in both the observations and the adjustment parameters. The traditional least squares algorithm, which employs condition equations that are solved with respect to the uncorrelated observations and either linear in the adjustment parameters or linearized by developing them in Taylor series by first-order approximation, is inadequate in our orbit problem. D.C. Brown proposed an algorithm solving a more general least squares adjustment problem in which the scalar residual function, however, is still constructed by first-order approximation. Not long ago, a completely general solution was published by W.H Jefferys, who proposed a rigorous adjustment algorithm for models in which the observations appear nonlinearly in the condition equations and may be correlated, and in which construction of the normal equations and the residual function involves no approximation. This method was successfully applied in our problem. The normal equations were first solved by Newton's scheme. Practical examples show that this converges fast if the observational errors are sufficiently small and the initial approximate solution is sufficiently accurate, and that it fails otherwise. Newton's method was modified to yield a definitive solution in the case the normal approach fails, by combination with the method of steepest descent and other sophisticated algorithms. Practical examples show that the modified Newton scheme can always lead to a final solution. The weighting of observations, the orthogonal parameters and the efficiency of a set of adjustment parameters are also

Spectrum integrated (n,He) cross section comparison and least squares analysis for /sup 6/Li and /sup 10/B in benchmark fields

International Nuclear Information System (INIS)

Schenter, R.E.; Oliver, B.M.; Farrar, H. IV

1987-01-01

Spectrum integrated cross sections for /sup 6/Li and /sup 10/B from five benchmark fast reactor neutron fields are compared with calculated values obtained using the ENDF/B-V Cross Section Files. The benchmark fields include the Coupled Fast Reactivity Measurements Facility (CFRMF) at the Idaho National Engineering Laboratory, the 10% Enriched U-235 Critical Assembly (BIG-10) at Los Alamos National Laboratory, the Sigma Sigma and Fission Cavity fields of the BR-1 reactor at CEN/SCK, and the Intermediate-Energy Standard Neutron Field (ISNF) at the National Bureau of Standards. Results from least square analyses using the FERRET computer code to obtain adjusted cross section values and their uncertainties are presented. Input to these calculations include the above five benchmark data sets. These analyses indicate a need for revision in the ENDF/B-V files for the /sup 10/B cross section for energies above 50 keV

Progressive and Error-Resilient Transmission Strategies for VLC Encoded Signals over Noisy Channels

Directory of Open Access Journals (Sweden)

Guillemot Christine

2006-01-01

Full Text Available This paper addresses the issue of robust and progressive transmission of signals (e.g., images, video encoded with variable length codes (VLCs over error-prone channels. This paper first describes bitstream construction methods offering good properties in terms of error resilience and progressivity. In contrast with related algorithms described in the literature, all proposed methods have a linear complexity as the sequence length increases. The applicability of soft-input soft-output (SISO and turbo decoding principles to resulting bitstream structures is investigated. In addition to error resilience, the amenability of the bitstream construction methods to progressive decoding is considered. The problem of code design for achieving good performance in terms of error resilience and progressive decoding with these transmission strategies is then addressed. The VLC code has to be such that the symbol energy is mainly concentrated on the first bits of the symbol representation (i.e., on the first transitions of the corresponding codetree. Simulation results reveal high performance in terms of symbol error rate (SER and mean-square reconstruction error (MSE. These error-resilience and progressivity properties are obtained without any penalty in compression efficiency. Codes with such properties are of strong interest for the binarization of -ary sources in state-of-the-art image, and video coding systems making use of, for example, the EBCOT or CABAC algorithms. A prior statistical analysis of the signal allows the construction of the appropriate binarization code.

A modified backpropagation algorithm for training neural networks on data with error bars

International Nuclear Information System (INIS)

Gernoth, K.A.; Clark, J.W.

1994-08-01

A method is proposed for training multilayer feedforward neural networks on data contaminated with noise. Specifically, we consider the case that the artificial neural system is required to learn a physical mapping when the available values of the target variable are subject to experimental uncertainties, but are characterized by error bars. The proposed method, based on maximum likelihood criterion for parameter estimation, involves simple modifications of the on-line backpropagation learning algorithm. These include incorporation of the error-bar assignments in a pattern-specific learning rate, together with epochal updating of a new measure of model accuracy that replaces the usual mean-square error. The extended backpropagation algorithm is successfully tested on two problems relevant to the modelling of atomic-mass systematics by neural networks. Provided the underlying mapping is reasonably smooth, neural nets trained with the new procedure are able to learn the true function to a good approximation even in the presence of high levels of Gaussian noise. (author). 26 refs, 2 figs, 5 tabs

Effects and Correction of Closed Orbit Magnet Errors in the SNS Ring

Energy Technology Data Exchange (ETDEWEB)

Bunch, S.C.; Holmes, J.

2004-01-01

We consider the effect and correction of three types of orbit errors in SNS: quadrupole displacement errors, dipole displacement errors, and dipole field errors. Using the ORBIT beam dynamics code, we focus on orbit deflection of a standard pencil beam and on beam losses in a high intensity injection simulation. We study the correction of these orbit errors using the proposed system of 88 (44 horizontal and 44 vertical) ring beam position monitors (BPMs) and 52 (24 horizontal and 28 vertical) dipole corrector magnets. Correction is carried out numerically by adjusting the kick strengths of the dipole corrector magnets to minimize the sum of the squares of the BPM signals for the pencil beam. In addition to using the exact BPM signals as input to the correction algorithm, we also consider the effect of random BPM signal errors. For all three types of error and for perturbations of individual magnets, the correction algorithm always chooses the three-bump method to localize the orbit displacement to the region between the magnet and its adjacent correctors. The values of the BPM signals resulting from specified settings of the dipole corrector kick strengths can be used to set up the orbit response matrix, which can then be applied to the correction in the limit that the signals from the separate errors add linearly. When high intensity calculations are carried out to study beam losses, it is seen that the SNS orbit correction system, even with BPM uncertainties, is sufficient to correct losses to less than 10-4 in nearly all cases, even those for which uncorrected losses constitute a large portion of the beam.

Period, epoch, and prediction errors of ephemerides from continuous sets of timing measurements

Science.gov (United States)

Deeg, H. J.

2015-06-01

Space missions such as Kepler and CoRoT have led to large numbers of eclipse or transit measurements in nearly continuous time series. This paper shows how to obtain the period error in such measurements from a basic linear least-squares fit, and how to correctly derive the timing error in the prediction of future transit or eclipse events. Assuming strict periodicity, a formula for the period error of these time series is derived, σP = σT (12 / (N3-N))1 / 2, where σP is the period error, σT the timing error of a single measurement, and N the number of measurements. Compared to the iterative method for period error estimation by Mighell & Plavchan (2013), this much simpler formula leads to smaller period errors, whose correctness has been verified through simulations. For the prediction of times of future periodic events, usual linear ephemeris were epoch errors are quoted for the first time measurement, are prone to an overestimation of the error of that prediction. This may be avoided by a correction for the duration of the time series. An alternative is the derivation of ephemerides whose reference epoch and epoch error are given for the centre of the time series. For long continuous or near-continuous time series whose acquisition is completed, such central epochs should be the preferred way for the quotation of linear ephemerides. While this work was motivated from the analysis of eclipse timing measures in space-based light curves, it should be applicable to any other problem with an uninterrupted sequence of discrete timings for which the determination of a zero point, of a constant period and of the associated errors is needed.

Photonics-based microwave frequency measurement using a double-sideband suppressed-carrier modulation and an InP integrated ring-assisted Mach-Zehnder interferometer filter.

Science.gov (United States)

Fandiño, Javier S; Muñoz, Pascual

2013-11-01

A photonic system capable of estimating the unknown frequency of a CW microwave tone is presented. The core of the system is a complementary optical filter monolithically integrated in InP, consisting of a ring-assisted Mach-Zehnder interferometer with a second-order elliptic response. By simultaneously measuring the different optical powers produced by a double-sideband suppressed-carrier modulation at the outputs of the photonic integrated circuit, an amplitude comparison function that depends on the input tone frequency is obtained. Using this technique, a frequency measurement range of 10 GHz (5-15 GHz) with a root mean square value of frequency error lower than 200 MHz is experimentally demonstrated. Moreover, simulations showing the impact of a residual optical carrier on system performance are also provided.

The probability and the management of human error

International Nuclear Information System (INIS)

Dufey, R.B.; Saull, J.W.

2004-01-01

Embedded within modern technological systems, human error is the largest, and indeed dominant contributor to accident cause. The consequences dominate the risk profiles for nuclear power and for many other technologies. We need to quantify the probability of human error for the system as an integral contribution within the overall system failure, as it is generally not separable or predictable for actual events. We also need to provide a means to manage and effectively reduce the failure (error) rate. The fact that humans learn from their mistakes allows a new determination of the dynamic probability and human failure (error) rate in technological systems. The result is consistent with and derived from the available world data for modern technological systems. Comparisons are made to actual data from large technological systems and recent catastrophes. Best estimate values and relationships can be derived for both the human error rate, and for the probability. We describe the potential for new approaches to the management of human error and safety indicators, based on the principles of error state exclusion and of the systematic effect of learning. A new equation is given for the probability of human error (λ) that combines the influences of early inexperience, learning from experience (ε) and stochastic occurrences with having a finite minimum rate, this equation is λ 5.10 -5 + ((1/ε) - 5.10 -5 ) exp(-3*ε). The future failure rate is entirely determined by the experience: thus the past defines the future

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