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Journal articles on the topic 'Linear error'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Lelboy, Natalia Emanuela, Selestina Nahak, and Justin Eduardo Simarmata. "ANALISIS KESALAHAN DALAM MENYELESAIKAN SOAL MATEMATIKA SISTEM PERSAMAAN LINEAR TIGA VARIABEL." MES: Journal of Mathematics Education and Science 7, no. 1 (2021): 10–20. http://dx.doi.org/10.30743/mes.v7i1.4347.

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This study aims to identify and describe the erros experienced by students in solfing comparison questions and to determine the causal factors in completing the comparison material for studenst of SMA Stella Gratia Atambua. The research method used is descriptive qualitative method. The subjects in this study were students of class X SMA Stella Gratia Atambua for the 2020/2021 school year. The data collection techniques used were tests and interviews. The instruments in this study was a test item in the form of a description with 3 numbers of questions and an interview guide. Based on the resu

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Rudnyi, E. B. "Statistical model of systematic errors: linear error model." Chemometrics and Intelligent Laboratory Systems 34, no. 1 (1996): 41–54. http://dx.doi.org/10.1016/0169-7439(96)00004-4.

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Feng, Keqin, Lanju Xu, and Fred J. Hickernell. "Linear error-block codes." Finite Fields and Their Applications 12, no. 4 (2006): 638–52. http://dx.doi.org/10.1016/j.ffa.2005.03.006.

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4

Tian, Yudong, Grey S. Nearing, Christa D. Peters-Lidard, Kenneth W. Harrison, and Ling Tang. "Performance Metrics, Error Modeling, and Uncertainty Quantification." Monthly Weather Review 144, no. 2 (2016): 607–13. http://dx.doi.org/10.1175/mwr-d-15-0087.1.

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Abstract A common set of statistical metrics has been used to summarize the performance of models or measurements—the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model shou

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Zulhendri, Z. Mawardi Effendi, and Darmansyah. "Analysis Of Student’s Error In Solving Linear Inequality." East Asian Journal of Multidisciplinary Research 1, no. 4 (2022): 559–70. http://dx.doi.org/10.55927/eajmr.v1i4.354.

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The purpose of this study is to describe the analyses of errors that students make in solving Linear Inequality. The method used in this study is a qualitative descriptive method. Questions were given to 3 students of Mathematic program of Pahlawan University. The data collection techniques in this study were the results of students' written tests and the results of interviews related to the results of students' written tests. Based on the results of the research conducted, it is known that the types of student mistakes math in solving the linear inequality problem are: a) Conceptual errors ma

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Zhang, Penghai, Tao Wang, and Jun Zha. "A study on accuracy of linear ball guide." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, no. 7 (2022): 3293–312. http://dx.doi.org/10.1177/09544062211023069.

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The accuracy of linear motion guide greatly affects the form accuracy of the processed parts in precision machine tools. For the linear ball guide, it is an important issue that how to improve accuracy by optimizing structure parameters based on the geometric errors. Due to the diversity of geometric errors and structure parameters, it requires a quantitative indicator of error averaging ability to judge whether a linear ball guide has higher accuracy. In this article, based on the newly built accuracy model and the newly defined averaging coefficient, the influence of rail profile errors and

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Chen, Bor Nan, and Yinyi Lin. "Hybrid Error Concealment Using Linear Interpolation." ECTI Transactions on Electrical Engineering, Electronics, and Communications 6, no. 2 (2007): 117–25. http://dx.doi.org/10.37936/ecti-eec.200862.171773.

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In this paper a hybrid error concealment algorithm using linear interpolation is proposed. In the proposed hybrid algorithm, the selective motion field interpolation (SMFI) is employed to conceal the erroneous macroblock. The spatial and temporal boundary-matched errors are then used to check whether the SMFI conceals the erroneous macroblock properly. If the temporally recovered macroblock is reconstructed incorrectly, the spatial error concealment using linear interpolation is employed to conceal the damaged macroblock instead of SMFI.It can achieve better performance subjectively as well as

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Santoso, Mutiara Winda, Dinawati Trapsilasiwi, and Randi Pratama Murtikusuma. "The ANALISIS KESALAHAN SISWA DALAM MENYELESAIKAN SOAL CERITA SPLDV BERDASARKAN TAHAPAN NEWMAN DITINJAU DARI TIPE KEPRIBADIAN FLORENCE LITTAUER." KadikmA 12, no. 2 (2021): 48. http://dx.doi.org/10.19184/kdma.v12i2.25014.

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The aim of this qualitative research is to describe the types of student errors in solving the two-variable linear equation system story problem based on Newman's error analysis in terms of Florence Littauer's personality type. The data sources consisted of 8 students of grade IX C SMP Nuris Jember who had been taught the material of two-variable linear equation systems. The data taken were the results of the questionnaire used to group students into four categories of personality types, the results of the student's story problem solving test results, and the results of the interviews of the s

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Dass, B. K., and Rashmi Verma. "REPEATED BURST ERROR CORRECTING LINEAR CODES." Asian-European Journal of Mathematics 01, no. 03 (2008): 303–35. http://dx.doi.org/10.1142/s1793557108000278.

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Many kinds of errors in coding theory have been dealt with for which codes have been constructed to combat such errors. Though there is a long history concerning the growth of the subject and many of the codes developed have found applications in numerous areas of practical interest, one of the areas of practical importance in which a parallel growth of the subject took place is that of burst error detecting and correcting codes. The nature of burst errors differ from channel to channel depending upon the behaviour of channels or the kind of errors which occur during the process of data transm

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10

Lasser, Rupert, and Sebastian Walcher. "Error Estimatesfor Linear Compartmental Systems." SIAM Journal on Matrix Analysis and Applications 23, no. 4 (2002): 1013–24. http://dx.doi.org/10.1137/s0895479800374522.

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11

Clear, Robert, and Sam Berman. "Estimation of Linear Interpolation Error." Journal of the Illuminating Engineering Society 18, no. 2 (1989): 32–39. http://dx.doi.org/10.1080/00994480.1989.10748758.

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12

Rosenqvist, Fredrik, and Anders Karlström. "Piecewise-Linear Output-Error Models." IFAC Proceedings Volumes 36, no. 16 (2003): 1795–800. http://dx.doi.org/10.1016/s1474-6670(17)35020-6.

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13

Nghiem, Linh H., Michael C. Byrd, and Cornelis J. Potgieter. "Estimation in linear errors-in-variables models with unknown error distribution." Biometrika 107, no. 4 (2020): 841–56. http://dx.doi.org/10.1093/biomet/asaa025.

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Summary Parameter estimation in linear errors-in-variables models typically requires that the measurement error distribution be known or estimable from replicate data. A generalized method of moments approach can be used to estimate model parameters in the absence of knowledge of the error distributions, but it requires the existence of a large number of model moments. In this paper, parameter estimation based on the phase function, a normalized version of the characteristic function, is considered. This approach requires the model covariates to have asymmetric distributions, while the error d

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Islamiyah, Anna Citra, Sudi Prayitno, and Amrullah Amrullah. "Analisis Kesalahan Siswa SMP pada Penyelesaian Masalah Sistem Persamaan Linear Dua Variabel." Jurnal Didaktik Matematika 5, no. 1 (2018): 66–76. http://dx.doi.org/10.24815/jdm.v5i1.10035.

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Background of this research is many student errors at solving linear equation systems with two variables problems. The research aim is to know the kinds and level of errors at solving linear equation systems with two variables problems. This research is descriptive quantitative. The subject of this study is 30 students grade 7 from one of junior high school at Mataram, West Nusa Tenggara. A data collection done by tested and interviewed. The error of the problem solving that could analyze were a reading error, comprehension, transformation, process skills and encoding. Result of the research i

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Tsai, Hsiu-An, and Yu-Lung Lo. "An Approach to Measure Tilt Motion, Straightness and Position of Precision Linear Stage with a 3D Sinusoidal-Groove Linear Reflective Grating and Triangular Wave-Based Subdivision Method." Sensors 19, no. 12 (2019): 2816. http://dx.doi.org/10.3390/s19122816.

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This work presents a novel and compact method for simultaneously measuring errors in linear displacement and vertical straightness of a moving linear air-bearing stage using 3D sinusoidal-groove linear reflective grating and a novel triangular wave-based sequence signal analysis method. The new scheme is distinct from the previous studies as it considers two signals to analyze linear displacement and vertical straightness. In addition, the tilt motion of the precision linear stage could also be measured using the 3D sinusoidal-groove linear reflective grating. The proposed system is similar to

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16

Tzeng, Jengnan. "Linear Regression to Minimize the Total Error of the Numerical Differentiation." East Asian Journal on Applied Mathematics 7, no. 4 (2017): 810–26. http://dx.doi.org/10.4208/eajam.161016.300517a.

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AbstractIt is well known that numerical derivative contains two types of errors. One is truncation error and the other is rounding error. By evaluating variables with rounding error, together with step size and the unknown coefficient of the truncation error, the total error can be determined. We also know that the step size affects the truncation error very much, especially when the step size is large. On the other hand, rounding error will dominate numerical error when the step size is too small. Thus, to choose a suitable step size is an important task in computing the numerical differentia

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17

Zimmerman, John W., and Roger A. Powell. "Radiotelemetry error: location error method compared with error polygons and confidence ellipses." Canadian Journal of Zoology 73, no. 6 (1995): 1123–33. http://dx.doi.org/10.1139/z95-134.

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We assert that researchers should use statistics derived from the linear distances between actual and estimated locations of test transmitters to estimate location error in radiotelemetry data. We call this approach the location error method. We used the distribution of such linear distances from a test data set from a study on black bears (Ursus americanus) in the mountains of North Carolina to predict error statistics for another test data set. We then compared the predicted with the actual error statistics. We also predicted error statistics for the second test data set using the error poly

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Elsa, Hanne Ayuningtias, and Eyus Sudihartinih. "Error Analysis of High School Students on Linear Program Topics Based on Newman Error Analysis." Mathematics Education Journal 4, no. 1 (2020): 7. http://dx.doi.org/10.22219/mej.v4i1.11466.

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This study aims to obtain a description of the errors of high school students on linear program topics by using the Newman Error Analysis. This type of research is a descriptive study with a qualitative approach. This research was conducted by giving a test to participants consisting of a three-word problem on the topic of a linear program then conducting interviews as a data collection technique. Participants in this study were five female students of class XI in one of the senior high schools in Bandung, including four people who were students majoring in Sciences studies and one person who

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19

Kløve, Torleiv, and Moshe Schwartz. "Linear covering codes and error-correcting codes for limited-magnitude errors." Designs, Codes and Cryptography 73, no. 2 (2014): 329–54. http://dx.doi.org/10.1007/s10623-013-9917-1.

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20

Balkema, Guus, and Paul Embrechts. "Linear Regression for Heavy Tails." Risks 6, no. 3 (2018): 93. http://dx.doi.org/10.3390/risks6030093.

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There exist several estimators of the regression line in the simple linear regression: Least Squares, Least Absolute Deviation, Right Median, Theil–Sen, Weighted Balance, and Least Trimmed Squares. Their performance for heavy tails is compared below on the basis of a quadratic loss function. The case where the explanatory variable is the inverse of a standard uniform variable and where the error has a Cauchy distribution plays a central role, but heavier and lighter tails are also considered. Tables list the empirical sd and bias for ten batches of one hundred thousand simulations when the exp

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21

Lin, Jian, and Mu Lan Wang. "Error Modeling and Compensation of Linear Motors Positioning Stage Based on RBF Network." Advanced Materials Research 139-141 (October 2010): 1744–48. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1744.

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The error measuring, modeling and compensation techniques for the positioning stage driven by NC linear motors are studied. The error source of the positioning stage is analyzed, the positioning errors are measured by the laser interferometer, and the neural network error model is set up by RBF algorithm. In order to evaluate the accuracy of RBF network prediction method, part of the error samples are used to test. A DSP-core linear motor experimental platform is built up, the error compensation experiments are conducted, the real-time requirement is proved to be met. The simulation and experi

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22

Roy, Surupa, and Tathagata Banerjee. "Generalized Linear Measurement-Error Models with Multivariate t-Measurement Error." Calcutta Statistical Association Bulletin 51, no. 3-4 (2001): 191–204. http://dx.doi.org/10.1177/0008068320010303.

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23

Falcao, D. M., and S. M. de Assis. "Linear programming state estimation: error analysis and gross error identification." IEEE Transactions on Power Systems 3, no. 3 (1988): 809–15. http://dx.doi.org/10.1109/59.14526.

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24

Veeser, Andreas. "Positivity Preserving Gradient Approximation with Linear Finite Elements." Computational Methods in Applied Mathematics 19, no. 2 (2019): 295–310. http://dx.doi.org/10.1515/cmam-2018-0017.

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AbstractPreserving positivity precludes that linear operators onto continuous piecewise affine functions provide near best approximations of gradients. Linear interpolation thus does not capture the approximation properties of positive continuous piecewise affine functions. To remedy, we assign nodal values in a nonlinear fashion such that their global best error is equivalent to a suitable sum of local best errors with positive affine functions. As one of the applications of this equivalence, we consider the linear finite element solution to the elliptic obstacle problem and derive that its e

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25

Haihua Cui, Haihua Cui, Wenhe Liao Wenhe Liao, Ning Dai Ning Dai, and Xiaosheng Cheng Xiaosheng Cheng. "Linear sinusoidal phase-shifting method resistant to non-sinusoidal phase error." Chinese Optics Letters 10, no. 3 (2012): 031201–31204. http://dx.doi.org/10.3788/col201210.031201.

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26

Kulmuminov, Olimjon Khurramovich. "MEASUREMENT METHODS FOR ERROR ANALYSIS." Multidisciplinary Journal of Science and Technology 4, no. 12 (2024): 467–73. https://doi.org/10.5281/zenodo.14531507.

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<em>It is necessary to reduce the individual measurement error as much as possible. For this purpose, it is necessary to know the description of the measurement error and at least a list of the main factors that affect the amount of error in the measurement result.</em> <em>Measuring a quantity is the process of comparing it with another quantity that is accepted as a unit of measurement that is homogeneous with it. For example, when measuring wear, an operation is performed to find out how many times the decrease or increase in diameter, thickness, or other linear dimension (including the len

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WOLFF, GREGORY J., DAVID G. STORK, and ART OWEN. "EMPIRICAL ERROR-CONFIDENCE CURVES FOR NEURAL NETWORK AND GAUSSIAN CLASSIFIERS." International Journal of Neural Systems 07, no. 03 (1996): 263–71. http://dx.doi.org/10.1142/s0129065796000245.

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“Error-Confidence” measures the probability that the proportion of errors made by a classifier will be within ∊ of EB, the optimal (Bayes) error. Probably Almost Bayes (PAB) theory attempts to quantify how this confidence increases with the number of training samples. We investigate the relationship empirically by comparing average error versus number of training patterns (m) for linear and neural network classifiers. On Gaussian problems, the resulting EC curves demonstrate that the PAB bounds are extremely conservative. Asymptotic statistics predicts a linear relationship between the logarit

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Kirby, William. "Analysis of quantum Krylov algorithms with errors." Quantum 8 (August 29, 2024): 1457. http://dx.doi.org/10.22331/q-2024-08-29-1457.

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This work provides a nonasymptotic error analysis of quantum Krylov algorithms based on real-time evolutions, subject to generic errors in the outputs of the quantum circuits. We prove upper and lower bounds on the resulting ground state energy estimates, and the error associated to the upper bound is linear in the input error rates. This resolves a misalignment between known numerics, which exhibit approximately linear error scaling, and prior theoretical analysis, which only provably obtained scaling with the error rate to the power 23. Our main technique is to express generic errors in term

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29

Staudenmayer, John, and John P. Buonaccorsi. "Measurement Error in Linear Autoregressive Models." Journal of the American Statistical Association 100, no. 471 (2005): 841–52. http://dx.doi.org/10.1198/016214504000001871.

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30

Liang, Hua, and Haobo Ren. "Generalized Partially Linear Measurement Error Models." Journal of Computational and Graphical Statistics 14, no. 1 (2005): 237–50. http://dx.doi.org/10.1198/106186005x37481.

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31

DASS, B. K., and SURBHI MADAN. "REPEATED BURST ERROR LOCATING LINEAR CODES." Discrete Mathematics, Algorithms and Applications 02, no. 02 (2010): 181–88. http://dx.doi.org/10.1142/s1793830910000553.

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This paper deals with derivation of bounds for linear codes that are able to detect and locate errors which occur during the process of transmission. The kind of errors considered are known as repeated burst errors. An illustration for such kind of a code has also been provided.

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Fan Pingzhi, Chen Zhi, and Jin Fan. "Linear unequal error-protection array codes." Electronics Letters 24, no. 6 (1988): 333. http://dx.doi.org/10.1049/el:19880225.

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33

Olver, F. W. J. "Error bounds for linear recurrence relations." Mathematics of Computation 50, no. 182 (1988): 481. http://dx.doi.org/10.1090/s0025-5718-1988-0929547-9.

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34

Meidl, Wilfried, Harald Niederreiter, and Ayineedi Venkateswarlu. "Error linear complexity measures for multisequences." Journal of Complexity 23, no. 2 (2007): 169–92. http://dx.doi.org/10.1016/j.jco.2006.10.005.

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35

Piriou, F., S. Clenet, and G. Marques. "Error estimators in 3D linear magnetostatics." IEEE Transactions on Magnetics 36, no. 4 (2000): 1588–91. http://dx.doi.org/10.1109/20.877743.

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Marmin, F., S. Clénet, P. Bussy, and F. Piriou. "Error estimator in linear magnetostatic 2D." European Physical Journal Applied Physics 1, no. 2 (1998): 203–9. http://dx.doi.org/10.1051/epjap:1998138.

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37

Jiwen Wang, Xiangui Yu, N. K. Loh, Zuxu Qin, and W. C. Miller. "Solving linear algebraic equations without error." IEEE Signal Processing Letters 1, no. 3 (1994): 58–60. http://dx.doi.org/10.1109/97.295324.

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38

Sturm, Jos F. "Error Bounds for Linear Matrix Inequalities." SIAM Journal on Optimization 10, no. 4 (2000): 1228–48. http://dx.doi.org/10.1137/s1052623498338606.

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Rady, El-Houssainy. "Partitioning Error Spaces in Linear Models." Egyptian Statistical Journal 39, no. 1 (1995): 51–64. http://dx.doi.org/10.21608/esju.1995.314796.

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刘, 玉玲. "Backward Error of Complex Linear System." Pure Mathematics 13, no. 06 (2023): 1677–88. http://dx.doi.org/10.12677/pm.2023.136171.

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41

Yanushkevichiene, Olga, and Romanas Yanushkevichius. "Apie vieną stabiliųjų dėsnių charakterizaciją ir jos stabilumo įtvertį." Lietuvos matematikos rinkinys 41 (December 17, 2001): 626–31. http://dx.doi.org/10.15388/lmr.2001.34743.

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As early as 1923, Georg P6lya wrote: ``The Gaussian error law possesses the property that it remains valid under a linear combination of errors. The Gaussian error law can be characterized by this property to some extent – it is the only law that admits steadiness with respect to linear combinations of errors''. The idea of using linear combinations of random variables to characterize the stable distributions has been extended by P. Levy. We investigate the stability of this characterization.

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Zhang, Hong Tao, Jian Guo Yang, Kai Guo Fan, and Yi Zhang. "Error Decoupling and Linkage-Compensation on Five-Axis NC Machine Tools." Advanced Materials Research 154-155 (October 2010): 1502–7. http://dx.doi.org/10.4028/www.scientific.net/amr.154-155.1502.

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The mathematical model of five-axis NC machine tools was established based on the transformation matrix. A new decoupling and linkage-compensation method for five-axis NC machine tools is proposed. The error caused by linear axes and rotary axes was compensated by using the linkage-compensation approach. In the real-time error compensation process, the rotary axes error was compensated firstly, and then the linear error caused by linear axes and rotary axes was compensated. The new decoupling method can effectively compensate machining errors for five-axis NC machine tools, which was verified

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Saragih, Agung Shamsuddin, and Tae Jo Ko. "Extracting Single Source Geometric Error Value from a Double Ballbar Measurement Error Map." Applied Mechanics and Materials 284-287 (January 2013): 754–57. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.754.

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The double ballbar (DBB) test is a well-known way to check the geometric error of axis interaction. The DBB test captures actual data from multiple error origins. Here, we define the DBB measurement result as the sinusoid error map model plus noise. Using this concept, we extract a single source geometric error value from the DBB error map by LS fitting. We considered the “noise” as mix error from other sources. To ensure the quality of a numerical fitting, we used a sinusoid model of each geometric error that was generated by simulation of axis movement based on homogeneous transformation mat

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Luo, Ji Man, Ming Shan Zhang, Yu Zhen An, and Xiao Wei Sun. "Study on Error in Interpolation Algorithm of the-5-DOF-Hybrid Robot." Applied Mechanics and Materials 385-386 (August 2013): 695–98. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.695.

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In order to improve the accuracy of the-5-DOF-hybrid robot motion control, it is necessary to establish a mathematical model of the error in the interpolation process. On the basis of the structure characteristics of five degrees of freedom hybrid robot and the algorithm of linear interpolation and circular interpolation, the error calculation patterns about the concave surface machining and convex surface machining are set up respectively in the paper. Then, examples are analyzed. The results show that the nonlinear error caused by using the straight-line interpolation method is much smaller

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Hou, Meng Han, Wu Mei Lin, and Zhen Jie Fan. "A Research on Improving the Precision of Rotating-Wave Plate Polarization Measurement." Applied Mechanics and Materials 742 (March 2015): 105–10. http://dx.doi.org/10.4028/www.scientific.net/amm.742.105.

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The device errors of a rotating-wave-plate-based polarization measurement system can be mainly categorized as the angle error of the polarization prism, the fast axis angle error of wave-plate and the retardation error. By applying Fourier analysis to solve Stokes vectors, we obtain the formulas to calculate the three errors mentioned above, using linear 0° and 45° polarized light to illuminate the system for the error solving. We analyze the measurement errors of the degree of polarization, the polarization purity and the intensity of polarization state, under certain simulation conditions. T

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Kurosawa, K., F. Sato, T. Sakata, and W. Kishimoto. "A relationship between linear complexity and k-error linear complexity." IEEE Transactions on Information Theory 46, no. 2 (2000): 694–98. http://dx.doi.org/10.1109/18.825845.

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Van Schaeybroeck, B., and S. Vannitsem. "Post-processing through linear regression." Nonlinear Processes in Geophysics 18, no. 2 (2011): 147–60. http://dx.doi.org/10.5194/npg-18-147-2011.

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Abstract. Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regr

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Otsuka, Jiro, Toshiharu Tanaka, and Ikuro Masuda. "Sub-Nanometer Positioning Combining New Linear Motor with Linear Motion Ball Guide Ways." International Journal of Automation Technology 3, no. 3 (2009): 241–48. http://dx.doi.org/10.20965/ijat.2009.p0241.

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A new type of linear motor described in this paper has some advantages compared with the usual types of motors. The attractive magnetic force between the stator (permanent magnets) and mover (armature) is diminished almost to zero. The efficiency is better because the magnetic flux leakage is very small, the size of motor is smaller and detent (force ripple) is smaller than the general motors. Therefore, we think that this motor is greatly suitable for ultra-precision positioning as an actuator. An ultra-precision positioning device using this motor and liner motion ball guide ways is newly de

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ODEN, J. TINSLEY, SERGE PRUDHOMME, TIM WESTERMANN, JON BASS, and MARK E. BOTKIN. "ERROR ESTIMATION OF EIGENFREQUENCIES FOR ELASTICITY AND SHELL PROBLEMS." Mathematical Models and Methods in Applied Sciences 13, no. 03 (2003): 323–44. http://dx.doi.org/10.1142/s0218202503002520.

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In this paper, a method for deriving computable estimates of the approximation error in eigenvalues or eigenfrequencies of three-dimensional linear elasticity or shell problems is presented. The analysis for the error estimator follows the general approach of goal-oriented error estimation for which the error is estimated in so-called quantities of interest, here the eigenfrequencies, rather than global norms. A general theory is developed and is then applied to the linear elasticity equations. For the shell analysis, it is assumed that the shell model is not completely known and additional er

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Li, Ji Zhong, and Yan Zhao Li. "Eliminating Linear Positioning Bias with Polynomial Approximation in AGPS Method." Applied Mechanics and Materials 263-266 (December 2012): 383–86. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.383.

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An AGPS (assisted global positioning system) positioning method was researched with polynomial approximation in this paper. Linear positioning errors were found through the analysis of an AGPS positioning algorithm with mobile station (MS) clock error. These errors were engendered to induce a bias of reference moment. The value of an objective function can be quadratically related to the deviation of reference time. Thus, the objective function curve was fitted with three reference time values, and the lowest point of curve to amend the reference time is determined. This method can eliminate l

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