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Relevant bibliographies by topics / Error propagation

Academic literature on the topic 'Error propagation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 April 2023

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Journal articles on the topic "Error propagation"

1

Kubáček, Lubomír. "Nonlinear error propagation law." Applications of Mathematics 41, no. 5 (1996): 329–45. http://dx.doi.org/10.21136/am.1996.134330.

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Seiler, Fritz A. "Error Propagation for Large Errors." Risk Analysis 7, no. 4 (December 1987): 509–18. http://dx.doi.org/10.1111/j.1539-6924.1987.tb00487.x.

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Singh, Arvind, and Priyanka Chaturvedi. "Error Propagation." Resonance 26, no. 6 (June 2021): 853–61. http://dx.doi.org/10.1007/s12045-021-1185-1.

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Tellinghuisen, Joel. "Statistical Error Propagation." Journal of Physical Chemistry A 105, no. 15 (April 2001): 3917–21. http://dx.doi.org/10.1021/jp003484u.

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Nelson, Lloyd S. "Propagation of Error." Journal of Quality Technology 24, no. 4 (October 1992): 232–35. http://dx.doi.org/10.1080/00224065.1992.11979404.

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van Laar, A. "The Quality of Information: Errors and Error Propagation." South African Forestry Journal 132, no. 1 (March 1, 1985): 22–25. http://dx.doi.org/10.1080/00382167.1985.9629545.

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Asbjornsen, O. A. "Error in the propagation of error formula." AIChE Journal 32, no. 2 (February 1986): 332–34. http://dx.doi.org/10.1002/aic.690320225.

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Meija, Juris. "Random error propagation challenge." Analytical and Bioanalytical Chemistry 395, no. 1 (July 21, 2009): 5–6. http://dx.doi.org/10.1007/s00216-009-2936-0.

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Fowler, Austin G., David S. Wang, and Lloyd C. L. Hollenberg. "Surface code quantum error correction incorporating accurate error propagation." Quantum Information and Computation 11, no. 1&2 (January 2011): 8–18. http://dx.doi.org/10.26421/qic11.1-2-2.

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The surface code is a powerful quantum error correcting code that can be defined on a 2-D square lattice of qubits with only nearest neighbor interactions. Syndrome and data qubits form a checkerboard pattern. Information about errors is obtained by repeatedly measuring each syndrome qubit after appropriate interaction with its four nearest neighbor data qubits. Changes in the measurement value indicate the presence of chains of errors in space and time. The standard method of determining operations likely to return the code to its error-free state is to use the minimum weight matching algorithm to connect pairs of measurement changes with chains of corrections such that the minimum total number of corrections is used. Prior work has not taken into account the propagation of errors in space and time by the two-qubit interactions. We show that taking this into account leads to a quadratic improvement of the logical error rate.

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Huang, Lian-Jie, and Michael C. Fehler. "Accuracy analysis of the split-step Fourier propagator: Implications for seismic modeling and migration." Bulletin of the Seismological Society of America 88, no. 1 (February 1, 1998): 18–29. http://dx.doi.org/10.1785/bssa0880010018.

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Abstract The split-step Fourier propagator is a one-way wave propagation method that has been widely used to simulate primary forward and backward (reflected) deterministic/random wave propagation due to its fast computational speed and limited computer memory requirement. The method is useful for rapid modeling of seismic-wave propagation in heterogeneous media where forward scattered waveforms can be considered to be dominant or reverberations can be ignored. The method is based on a solution to the one-way wave equation that requires expanding the square root of an operator and splitting of the resulting noncommutative operators to allow calculation by transferring wave fields between the space and wavenumber domains. Previous analysis of the accuracy of the method has focused on the error related to only a portion of the approximations involved in the propagator. To better understand the accuracy of the propagator, we present a complete formal and numerical accuracy analyses. Our formal analysis indicates that the dominant error of the propagator increases as the first order in the marching interval. We show that nonsymmetrically and symmetrically split-step marching solutions have the same first-order error term. Their second- and third-order error terms are similar. Therefore, the differences between the accuracy of different split-step marching solutions are insignificant. This conclusion is confirmed by our numerical tests. The relation among the phase error of the split-step Fourier propagator, relative velocity perturbation, and propagation angle is numerically studied. The results suggest that the propagator is accurate for up to a 60° propagation angle from the main propagation direction for media with small relative velocity perturbations (10%).

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Dissertations / Theses on the topic "Error propagation"

1

Robinson, Anthony John. "Dynamic error propagation networks." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303145.

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Lu, Chun. "The error propagation in robots." Thesis, University of Ottawa (Canada), 1990. http://hdl.handle.net/10393/5871.

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The accuracy of a robot manipulator has been receiving scrutinity since the widespread acceptance of robot manipulators. The relationship between two consecutive joint coordinate frames of a robot manipulator can be completely defined by five link parameters; one is the joint variable and the other four are the geometric parameters. The basis for the open-loop manipulator control is often the relationship between the Cartesian coordinates of the end-effector and the joint coordinates; therefore, the accuracy of the Cartesian position and orientation of the end-effector with regard to the real world depends on the errors of the five link parameters for each link. For design optimization and robot calibration, it is very important to develop a model for quantitative characterization and evaluation of the positioning and orientational errors of the end-effector. A static error propagation model is developed in order to describe the relationships between the six Cartesian errors and the five independent kinematic errors for each link. In this thesis, a general method for evaluating the end-effector errors produced by a mix of arbitrarily distributed errors is presented. Based on this method, any different combinations of biased and mixed error distributions can be dealt with directly to give a quantative error propagation analysis. Numerical results are presented for one, two and three degrees-of-freedom robot manipulators. Comparison of the results of the proposed model with other published model are presented and analyzed.

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Deslandes, Jeffrey E. "Error propagation through digital demultiplexers." Thesis, University of Essex, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280874.

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Garufi, David (David J. ). "Error propagation in concurrent product development." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118550.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 68).
System dynamics modelling is used to explore varying levels of concurrency in a typical design-build-produce project introducing a new product. Faster product life-cycles and demanding schedules have introduced the importance of beginning downstream work (build/manufacturing) while upstream work (design) is incomplete. Conceivably, this project concurrency improves project schedule and cost by forcing rework to be discovered and completed earlier in the project life. Depending on the type of project, some design errors may only be discoverable once the build phase has begun its work. Namely, systemic errors and assembly errors that cannot be easily discovered within the design phase. Pushing build activity earlier in the project allows the rework to be discovered earlier in the project, shortening the overall effort required to complete the project. A mathematical simulation, created using Vensim@ system modeling software, was created by James Lyneis to simulate two-phase rework cycles. The model was tuned to match data based on a disguised real project. Various start dates (as a function of project percentage complete) for downstream phases were explored to find optimal levels of concurrency. Project types were varied by exploring three levels of "rework discoverable within the design phase" to cover a range of project types. The simulation found that for virtually all project types, significant schedule and effort benefits can be gained by introducing the downstream phase as early as 30% to 40% into the project progress and ramping downstream effort over an extended period of time.
by David Garufi.
S.M. in Engineering and Management

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Utcke, Sven. "Error propagation in geometry-based grouping." [S.l. : s.n.], 2006.

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Dumont, Pascal. "Error propagation calculation in groundwater vulnerability models." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27212.

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Blukacz, Edyta Agnes. "Error propagation in ecology, an aquatic example." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58714.pdf.

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Morgan, Keith S. "SEU-Induced Persistent Error Propagation in FPGAs." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1377.pdf.

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Alboabidallah, Ahmed Hussein Hamdullah. "Error propagation analysis for remotely sensed aboveground biomass." Thesis, University of Plymouth, 2018. http://hdl.handle.net/10026.1/13074.

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Above-Ground Biomass (AGB) assessment using remote sensing has been an active area of research since the 1970s. However, improvements in the reported accuracy of wide scale studies remain relatively small. Therefore, there is a need to improve error analysis to answer the question: Why is AGB assessment accuracy still under doubt? This project aimed to develop and implement a systematic quantitative methodology to analyse the uncertainty of remotely sensed AGB, including all perceptible error types and reducing the associated costs and computational effort required in comparison to conventional methods. An accuracy prediction tool was designed based on previous study inputs and their outcome accuracy. The methodology used included training a neural network tool to emulate human decision making for the optimal trade-off between cost and accuracy for forest biomass surveys. The training samples were based on outputs from a number of previous biomass surveys, including 64 optical data based studies, 62 Lidar data based studies, 100 Radar data based studies, and 50 combined data studies. The tool showed promising convergent results of medium production ability. However, it might take many years until enough studies will be published to provide sufficient samples for accurate predictions. To provide field data for the next steps, 38 plots within six sites were scanned with a Leica ScanStation P20 terrestrial laser scanner. The Terrestrial Laser Scanning (TLS) data analysis used existing techniques such as 3D voxels and applied allometric equations, alongside exploring new features such as non-plane voxel layers, parent-child relationships between layers and skeletonising tree branches to speed up the overall processing time. The results were two maps for each plot, a tree trunk map and branch map. An error analysis tool was designed to work on three stages. Stage 1 uses a Taylor method to propagate errors from remote sensing data for the products that were used as direct inputs to the biomass assessment process. Stage 2 applies a Monte Carlo method to propagate errors from the direct remote sensing and field inputs to the mathematical model. Stage 3 includes generating an error estimation model that is trained based on the error behaviour of the training samples. The tool was applied to four biomass assessment scenarios, and the results show that the relative error of AGB represented by the RMSE of the model fitting was high (20-35% of the AGB) in spite of the relatively high correlation coefficients. About 65% of the RMSE is due to the remote sensing and field data errors, with the remaining 35% due to the ill-defined relationship between the remote sensing data and AGB. The error component that has the largest influence was the remote sensing error (50-60% of the propagated error), with both the spatial and spectral error components having a clear influence on the total error. The influence of field data errors was close to the remote sensing data errors (40-50% of the propagated error) and its spatial and non-spatial Overall, the study successfully traced the errors and applied certainty-scenarios using the software tool designed for this purpose. The applied novel approach allowed for a relatively fast solution when mapping errors outside the fieldwork areas.

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Pan, Zhao. "Error Propagation Dynamics of PIV-based Pressure Field Calculation." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6353.

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Particle Image Velocimetry (PIV) based pressure field calculation is becoming increasingly popular in experimental fluid dynamics due to its non-intrusive nature. Errors propagated from PIV results to pressure field calculations are unavoidable, and in most cases, non-negligible. However, the specific dynamics of this error propagation process have not been unveiled. This dissertation examines both why and how errors in the experimental data are propagated to the pressure field by direct analysis of the pressure Poisson equation. Error in the pressure calculations are bounded with the error level of the experimental data. The error bounds quantitatively explain why and how many factors (i.e., geometry and length scale of the flow domain, type of boundary conditions) determine the resulting error propagation. The reason that the type of flow and profile of the error matter to the error propagation is also qualitatively illustrated. Numerical and experimental validations are conducted to verify these results. The results and framework introduced in this research can be used to guide the optimization of the experimental design, and potentially estimate the error in the reconstructed pressure field before performing PIV experiments.

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Books on the topic "Error propagation"

1

Error propagation in environmental modelling with GIS. London: Taylor & Francis, 1998.

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United States. National Aeronautics and Space Administration., ed. Error propagation in a digital avionic mini processor. Urbana, IL: University of Illinoios at Urbana-Champaign, 1988.

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Heuvelink, Gerard B. M. Error propagation in quantitative spatial modelling: Applications in geographical information systems. [Amsterdam]: Koninklijk Nederlands Aardrijkskundig Genootschap, 1993.

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4

K, Iyer R., and United States. National Aeronautics and Space Administration, eds. Error propagation in a digital avionic processor: A simulation-based study. Urbana, Ill: Computer Systems Group, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1986.

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J, Gurdak Jason. Estimating prediction uncertainty from geographical information system raster processing: A user's manual for the Raster error propagation tool (REPTool). Reston, Va: U.S. Geological Survey, 2009.

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J, Gurdak Jason. Estimating prediction uncertainty from geographical information system raster processing: A user's manual for the Raster error propagation tool (REPTool). Reston, Va: U.S. Geological Survey, 2009.

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United States. National Aeronautics and Space Administration., ed. Asymptotic boundary conditions for dissipative waves: General theory. [Washington, D.C.]: NASA, 1990.

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8

Alfano, Roberto. Astrometria Fotografica: Photographic Astrometry. Genoa, Italy: Genoa Astronomical Observatory, 1988.

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Kharaghani, Gholam A. Propagation of refraction errors in trigonometric height traversing and geodetic levelling. Fredericton, N.B: Dept. of Surveying Engineering, University of New Brunswick, 1987.

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Santerre, Rock. GPS satellite sky distribution: Impact on the propagation of some important errors in precise relative positioning. Fredericton, N.B: Dept. of Surveying Engineering, University of New Brunswick, 1989.

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Book chapters on the topic "Error propagation"

1

Shekhar, Shashi, and Hui Xiong. "Error Propagation." In Encyclopedia of GIS, 287. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_366.

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Schnizer, Pierre. "Error Propagation." In Springer Tracts in Modern Physics, 133–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65666-3_9.

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Grabe, Michael. "Error Propagation, Two Variables." In Measurement Uncertainties in Science and Technology, 81–96. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04888-8_6.

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Grabe, Michael. "Error Propagation, m Variables." In Measurement Uncertainties in Science and Technology, 97–104. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04888-8_7.

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Weinzierl, Tobias. "Round-Off Error Propagation." In Undergraduate Topics in Computer Science, 61–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76194-3_6.

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Wagner, Armin, Bernd Reckmann, Kerstin Hagen-Mann, and Gerhard Krauss. "Error Production and Error Propagation During PCR." In PCR Topics, 69–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-75924-6_14.

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Sutherland, Stuart. "On-detect pulse error propagation." In Verilog — 2001, 94–95. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1713-9_40.

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Bivand, Roger. "Error Propagation in Spatial Prediction." In Encyclopedia of GIS, 1–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_367-2.

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Gertsbakh, Ilya. "Measurement Uncertainty: Error Propagation Formula." In Measurement Theory for Engineers, 87–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-08583-7_5.

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Bivand, Roger. "Error Propagation in Spatial Prediction." In Encyclopedia of GIS, 552–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_367.

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Conference papers on the topic "Error propagation"

1

Al-Bassam, S., and R. Venkatesan. "Error propagation codes." In Proceedings Pacific Rim International Symposium on Fault Tolerant Systems. IEEE, 1991. http://dx.doi.org/10.1109/rfts.1991.212965.

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Stelzer, Andreas, and Christian G. Diskus. "Six-port error propagation." In International Symposium on Optical Science and Technology, edited by Cam Nguyen. SPIE, 2000. http://dx.doi.org/10.1117/12.390658.

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Wang, Jun, Yichun Tang, Hao Sun, and Satoshi Goto. "Error concealment considering error propagation inside a frame." In 2010 IEEE 12th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2010. http://dx.doi.org/10.1109/mmsp.2010.5662053.

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Jurik, M. "Back error propagation - A critique." In COMPCON Spring 88. IEEE, 1988. http://dx.doi.org/10.1109/cmpcon.1988.4895.

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Kellerer, Aglaé, Mark Chun, and Christ Ftaclas. "Error propagation in curvature sensors." In SPIE Astronomical Telescopes + Instrumentation, edited by Brent L. Ellerbroek, Michael Hart, Norbert Hubin, and Peter L. Wizinowich. SPIE, 2010. http://dx.doi.org/10.1117/12.856641.

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Li, Guanpeng. "Modeling Error Propagation in Programs." In 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W). IEEE, 2017. http://dx.doi.org/10.1109/dsn-w.2017.24.

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Haase, O., and A. Henrich. "Error propagation in distributed databases." In the fourth international conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/221270.221650.

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Ni, Wei. "Minimized error propagation location method based on error estimation." In Sixth International Conference on Electronics and Information Engineering, edited by Qiang Zhang. SPIE, 2015. http://dx.doi.org/10.1117/12.2208931.

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Lister, R. "Error functions, error signals, and conjugate gradient back propagation." In 4th International Conference on Artificial Neural Networks. IEE, 1995. http://dx.doi.org/10.1049/cp:19950532.

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Alkhateeb, Mojahed, Jeremy L. Rickli, and Nicholas J. Christoforou. "Error Propagation in Digital Additive Remanufacturing Process Planning." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-3009.

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Abstract A point cloud is a digital representation of a part that consists of a set of data points in space. Typically point clouds are produced by 3D scanners that hover above a part and records points in a large number that represent the external surface of a part. Additive remanufacturing offers a sustainable solution to end-of-use (EoU) core disposal and recovery and requires quantification of part damage or wear that requires reprocessing. This paper proposes an error propagation approach that models the interaction of each step of the additive remanufacturing process. This proposed model is formulated, and the results of the errors generated from the parameters of the scanner and point cloud smoothing are presented. Smoothing is an important step to reduce the noises generated from scanning, knowing the right smoothing factor is important since over smoothing results in dimensional inaccuracies and errors, especially in cores with smaller degrees of damage. It is important to know the error generated from scanning and point cloud smoothing to compensate in the following steps and generate appropriate material deposition paths. Inaccuracies in the 3D model renders can impact the remainder of the additive remanufacturing accuracy, especially because there are multiple steps in the process. Sources of error from smoothing, meshing, slicing, and material deposition are proposed in the error propagation model for additive remanufacturing. Results of efforts to quantify the scanning and smoothing steps within this model are presented.

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Reports on the topic "Error propagation"

1

Pack, D. J., and D. J. Downing. NLO error propagation exercise: statistical results. Office of Scientific and Technical Information (OSTI), September 1985. http://dx.doi.org/10.2172/5053984.

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Smith, D. L., and L. P. Geraldo. A vector model for error propagation. Office of Scientific and Technical Information (OSTI), March 1989. http://dx.doi.org/10.2172/6402022.

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Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. Learning Internal Representations by Error Propagation. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada164453.

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Hickman, D. P., S. Maclean, D. Shepley, and R. K. Shaw. Inductively Coupled Plasma Mass Spectrometry Uranium Error Propagation. Office of Scientific and Technical Information (OSTI), July 2001. http://dx.doi.org/10.2172/15006257.

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Pascucci, V., and Z. Li. Advanced Visualization of Silent Error Propagation in HPC Applications. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1660503.

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Li, Z., and V. Pascucci. Advanced Visualization of Silent Error Propagation in HPC Applications. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1840137.

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Solem, J. C. Error propagation in conductivity measurements for the isentropic compression experiment. Office of Scientific and Technical Information (OSTI), February 1997. http://dx.doi.org/10.2172/431153.

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McEliece, Robert, and Padhraic Smyth. Turbo Decoding of High Performance Error-Correcting Codes via Belief Propagation. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada386835.

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Clark, E. L. Error propagation equations and tables for estimating the uncertainty in high-speed wind tunnel test results. Office of Scientific and Technical Information (OSTI), August 1993. http://dx.doi.org/10.2172/10178382.

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Wendelberger, James G. Non-Destructive Assay (NDA) Uncertainties Impact on Physical Inventory Difference (ID) and Material Balance Determination: Sources of Error, Precision/Accuracy, and ID/Propagation of Error (POV). Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1304800.

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