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Готові списки джерел за темами / Extrapolation

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Добірка наукової літератури з теми "Extrapolation"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 липня 2025

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Статті в журналах з теми "Extrapolation"

1

Zhu, X., and T. Wiegelmann. "Toward a fast and consistent approach to modeling solar magnetic fields in multiple layers." Astronomy & Astrophysics 658 (January 28, 2022): A37. http://dx.doi.org/10.1051/0004-6361/202141505.

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Aims. We aim to develop a fast and consistent extrapolation method for modeling multiple layers of the solar atmosphere. Methods. The new approach combines the magnetohydrostatic (MHS) extrapolation, which models the solar low atmosphere in a flat box, together with the nonlinear force-free field (NLFFF) extrapolation, which models the solar corona with a chromospheric vector magnetogram deduced from the MHS extrapolation. We tested our code with a snapshot of a radiative magnetohydrodynamic simulation of a solar flare and we conducted quantitative comparisons based on several metrics. Results

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2

Zhang, Hui, Pan Yu, Xueying Shi, and Lin Li. "A Numerical Study of the Poisson Equation with Arbitrary Domains Based on the Extrapolation Technique." Journal of Physics: Conference Series 2455, no. 1 (2023): 012017. http://dx.doi.org/10.1088/1742-6596/2455/1/012017.

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Abstract The Poisson equation with arbitrary domains is considered in this paper using an extrapolation technique. The two-dimensional problem is primarily examined, and the extrapolation formulas O2 and O4 are thoroughly deduced. Not only the effect of different extrapolations on boundary point errors is analyzed, but a strategy for selecting extrapolation points is also provided. Finally, numerical accuracy results are presented.

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3

Zhao, Shuangren, Kang Yang, and Xintie Yang. "Reconstruction from truncated projections using mixed extrapolations of exponential and quadratic functions." Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics 19, no. 2 (2011): 155–72. http://dx.doi.org/10.3233/xst-2011-028400284.

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In computer tomography (CT), truncated projections are produced due to scanning large objects with a detector that is limited in width. Applying filtered back-projection(FBP) method directly to truncated projections, the reconstructed image will contain truncation artifacts – bright rings on the boundary of region of interest (ROI). Extrapolation algorithms can be used to reduce the truncation artifacts; however extrapolations are usually double the length of the projection data; resulting in an increased calculation time. This paper introduces mixed extrapolation, which is a combination of ex

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4

Cho, Doah, Saskia Cheyne, Sarah J. Lord, John Simes, and Chee Khoon Lee. "Extrapolating evidence for molecularly targeted therapies from common to rare cancers: a scoping review of methodological guidance." BMJ Open 12, no. 7 (2022): e058350. http://dx.doi.org/10.1136/bmjopen-2021-058350.

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ObjectivesCancer is increasingly classified according to biomarkers that drive tumour growth and therapies developed to target them. In rare biomarker-defined cancers, randomised controlled trials to adequately assess targeted therapies may be infeasible. Extrapolating existing evidence of targeted therapy from common cancers to rare cancers sharing the same biomarker may reduce evidence requirements for regulatory approval in rare cancers. It is unclear whether guidelines exist for extrapolation. We sought to identify methodological guidance for extrapolating evidence from targeted therapies

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5

Norozi, Muhammad Ali. "Faster Ranking Using Extrapolation Techniques." International Journal of Computer Vision and Image Processing 1, no. 3 (2011): 35–52. http://dx.doi.org/10.4018/ijcvip.2011070103.

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Extrapolations are techniques in linear algebra that require little additional infrastructure that must be incorporated in the existing query-dependent Link Analysis Ranking (LAR) algorithms. Extrapolations in LAR settings relies on the prior knowledge of the (iterative) process that created the existing data points (iterates) to compute the new (improved) data point, which periodically leads to the desired solution faster than the original method. In this study, the author presents novel approaches using extrapolation techniques to speed-up the convergence of query-dependent iterative methods

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6

Zhang, Jie, and George A. McMechan. "Turning wave migration by horizontal extrapolation." GEOPHYSICS 62, no. 1 (1997): 291–97. http://dx.doi.org/10.1190/1.1444130.

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Conventional migration based on depth stepping extrapolation fails to migrate turning wave energy because of its inability to propagate energy with dips beyond 90°. A viable strategy for imaging turning waves is to use horizontal, rather than depth, extrapolation. This can be implemented by a 90° rotation of the extrapolator so that the data are extrapolated horizontally rather than vertically. In this geometry, the energy associated with turned rays consistently moves in the same direction as the extrapolation, and so only one pass is necessary to image turned reflections. The viability of th

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7

Clewell, Harvey J., and Melvin E. Andersen. "Risk Assessment Extrapolations and Physiological Modeling." Toxicology and Industrial Health 1, no. 4 (1985): 111–34. http://dx.doi.org/10.1177/074823378500100408.

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The process of assessing the risk associated with human exposure to environmental chemicals inevitably relies on a number of assumptions, estimates and rationalizations. One of the more challenging aspects of risk assessment involves the need to extrapolate beyond the range of conditions used in experimental animal studies to predict anticipated human risks. The most obvious extrapolation required is that from the tested animal species to humans; but others are also generally required, including extrapolating from high dose to low dose, from one route of exposure to another and from one exposu

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8

Zhou, Hongbo, and George A. McMechan. "Rigorous absorbing boundary conditions for 3-D one‐way wave extrapolation." GEOPHYSICS 65, no. 2 (2000): 638–45. http://dx.doi.org/10.1190/1.1444760.

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Absorbing boundary conditions play an important role in one‐way wave extrapolations by reducing reflections at grid edges. Clayton and Engquist’s 2-D absorbing boundary conditions for one‐way wave extrapolation by depth stepping in the frequency domain are extended to three dimensions using paraxial approximations of the scalar wave equation. Internal consistency is retained by incorporating the interior extrapolation equation with the absorbing boundary conditions. Numerical schemes are designed to make the proposed absorbing boundary conditions both mathematically correct and efficient with

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9

Kodell, Ralph L., and David W. Gaylor. "Uncertainty of Estimates of Cancer Risks Derived by Extrapolation from High to Low Doses and from Animals to Humans." International Journal of Toxicology 16, no. 4-5 (1997): 449–60. http://dx.doi.org/10.1080/109158197227062.

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The uncertainties associated with extrapolating model-based cancer risks from high to low doses and animal-based cancer risks to humans are examined. It is argued that low-dose linear extrapolation based on statistical confidence limits calculated from animal data is designed to account for data uncertainty, model-selection uncertainty, and model-fitting instability. The intent is to err on the side of safety, that is, overstating rather than understating the true risk. The tendency toward conservatism in predicting human cancer risks from animal data based on linear extrapolation is confirmed

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10

Wang, Haijin, Zonghai Zhang, Jiguang Zhang, Yuying Shen, and Jixin Wang. "A Novel Load Extrapolation Method for Multiple Non-Stationary Loads on the Drill Pipe of a Rotary Rig." Machines 12, no. 1 (2024): 75. http://dx.doi.org/10.3390/machines12010075.

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The drill pipe of a rotary rig is subject to the dynamic influence of non-stationary loads, including rotation torque and applied force. In order to address the challenge of simultaneously extrapolating multiple non-stationary loads, a novel extrapolation framework is proposed. This framework utilizes rainflow counting to obtain mean and amplitude sequences of the loads. The extreme values of the amplitude sequence are fitted using the Generalized Pareto Distribution (GPD), while the median values are fitted using the Double Kernel Density Estimation (DKDE). By extrapolating the Inverse Cumula

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Дисертації з теми "Extrapolation"

1

Johnson, Walter William. "Studies in motion extrapolation /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487265143146004.

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2

Domingo, Salazar Carlos. "Endpoint estimates via extrapolation theory." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/396143.

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In this thesis, we study different variants of Rubio de Francia’s extrapolation that allow us to obtain estimates near L1. This theory is subsequently applied to deduce enpoint boundedness for the Bochner-Riesz operator and other classes of multipliers. We also present results related to Yano’s extrapolation on Lorentz spaces and how it can be related to the theory of weights.<br>En aquesta tesi, estudiem variants de l’extrapolació de Rubio de Francia que permetin obtenir estimacions a prop de l’espai L1. Aquesta teoria l’apliquem després per deduïr acotacions a l’extrem per l’operador de Boch

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3

Salam, Ahmed. "Extrapolation : extension et nouveaux résultats." Lille 1, 1993. http://www.theses.fr/1993LIL10191.

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4

Lin, Tim T. Y., and Felix J. Herrmann. "Compressed wavefield extrapolation with curvelets." Society of Exploration Geophysicists, 2007. http://hdl.handle.net/2429/560.

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An explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in {3-D}. By using ideas from ``compressed sensing'', we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. According {to} compressed sensing theory, signals can successfully be re

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5

Lim, Hee Jin. "Facilitatory neural dynamics for predictive extrapolation." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1759.

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6

Fang, Zhide. "Robust extrapolation designs for linear models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0035/NQ46835.pdf.

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7

Segovia, Carlos. "Extrapolation and commutators of singular integrals." Pontificia Universidad Católica del Perú, 2014. http://repositorio.pucp.edu.pe/index/handle/123456789/97397.

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l. Introduction In these notes we shall present results concerning LP inequalities with different but related weights for commutators of singular and strongly singular integrals. These commutators turn out to be controlled by commutator of fractional order of the Hardy-Littlewood maximal operator. The boundedness properties are obtained by extrapolation from infinity. These notes are based mainly on [G-H-S-T].

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8

Musielak-Mersak, Céline. "Vieillissement cognitif, apprentissage fonctionnel et extrapolation." Reims, 2005. http://theses.univ-reims.fr/exl-doc/GED00000224.pdf.

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L'objectif de ce travail est d'étudier l'effet du vieillissement sur les processus cognitifs impliqués dans l'abstraction et l'adaptation aux relations complexes de l'environnement. Un total de 208 personnes (âgées de 18-25, 40-50, 65-75 et 76-90 ans) ont participé à cette étude. Dans l'expérience 1, l'apprentissage de fonctions curvilinéaires (en forme de U et de U-inversé) est comparé à celui de fonctions linéaires (directe et inverse). Un test d'extrapolation permet d'évaluer la qualité de l'abstraction selon la fonction apprise. Les résultats montrent que les personnes âgées conservent leu

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9

Beals, Mark J. "Radar target imaging using data extrapolation." Connect to resource, 1993. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1200677949.

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10

Musielak-Mersak, Céline Chasseigne Gérard. "Vieillissement cognitif, apprentissage fonctionnel et extrapolation." Reims : Éditeur, 2005. http://scdurca.univ-reims.fr/exl-doc/GED00000224.pdf.

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Книги з теми "Extrapolation"

1

Milman, Mario. Extrapolation and Optimal Decompositions. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0073498.

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2

Brezinski, Claude, and Michela Redivo-Zaglia. Extrapolation and Rational Approximation. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58418-4.

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3

Jawerth, Björn. Extrapolation theory with applications. American Mathematical Society, 1991.

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4

Calabrese, Edward J. Principles of animal extrapolation. Lewis Publishers, 1991.

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5

Brezinski, Claude. Extrapolation methods: Theory and practice. North-Holland, 1991.

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6

Derek, Littlewood, and Stockwell Peter, eds. Impossibility fiction: Alternativity, extrapolation, speculation. Rodopi, 1996.

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7

Lakonishok, Josef. Contrarian investment, extrapolation, and risk. University of Illinois at Urbana-Champaign, 1993.

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8

Lakonishok, Josef. Contrarian investment, extrapolation, and risk. National Bureau of Economic Research, 1993.

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9

Celant, Giorgio, and Michel Broniatowski. Interpolation and Extrapolation Optimal Designs 1. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119292272.

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10

Celant, Giorgio, and Michel Broniatowski. Interpolation and Extrapolation Optimal Designs 2. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119422327.

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Частини книг з теми "Extrapolation"

1

Zhu, Peijuan, and Andrej Skerjanec. "Extrapolation." In Biosimilars. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99680-6_21.

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2

Gooch, Jan W. "Extrapolation." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15242.

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3

Kenny, Peter. "Extrapolation." In Better Business Decisions from Data. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0184-8_17.

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4

Metcalfe, Mike. "Extrapolation." In Forecasting Profit. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2255-3_7.

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5

Sandström, Rolf. "Extrapolation." In Basic Modeling and Theory of Creep of Metallic Materials. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49507-6_14.

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AbstractThe extrapolation of creep data to longer times is technically very important. The traditional way of extrapolating creep rupture data is to use time temperaturer parameters (TTPs). In this way data from several test temperatures are combined to a single master curve that can be used to assess rupture strengths at long times. Recently, there is much focus on machine learning techniques (neural networks, NNs). Both types of procedures can generate accurate results, but a detailed analysis is required. A good way to assess the quality of the results is to use the post assessment tests (P

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6

Suter, Glenn W. "Extrapolation." In Fundamentals of Environmental Assessment. CRC Press, 2023. http://dx.doi.org/10.1201/9781003156307-29.

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7

Plowright, Philip D. "Extrapolation." In Making Architecture Through Being Human. Routledge, 2019. http://dx.doi.org/10.4324/9780429261718-56.

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8

Carroll, R. J., D. Ruppert, and L. A. Stefanski. "Simulation Extrapolation." In Measurement Error in Nonlinear Models. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4477-1_4.

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9

Travis, Curtis C. "Interspecies Extrapolation." In Biologically Based Methods for Cancer Risk Assessment. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-5625-7_7.

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10

Smith, Stanley K., Jeff Tayman, and David A. Swanson. "Extrapolation Methods." In A Practitioner's Guide to State and Local Population Projections. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7551-0_8.

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Тези доповідей конференцій з теми "Extrapolation"

1

Zhang, Yiming, Nam Ho Kim, Chanyoung Park, and Raphael T. Haftka. "Function Extrapolation at One Inaccessible Point Using Converging Lines." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47689.

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Focus of this paper is on the prediction accuracy of multidimensional functions at an inaccessible point. The paper explores the possibility of extrapolating a high-dimensional function using multiple one-dimensional converging lines. The main idea is to select samples along lines towards the inaccessible point. Multi-dimensional extrapolation is thus transformed into a series of one-dimensional extrapolations that provide multiple estimates at the inaccessible point. We demonstrate the performance of converging lines using Kriging to extrapolate a two-dimensional drag coefficient function. Po

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2

Lee, Sangjin, Hyeongmin Lee, Taeoh Kim, and Sangyoun Lee. "Extrapolative-Interpolative Cycle-Consistency Learning For Video Frame Extrapolation." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191286.

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3

Hooker, Giles. "Diagnosing extrapolation." In the 2004 ACM SIGKDD international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1014052.1014121.

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4

Gaudet, Shaun R., and J. E. Donald Gauthier. "A Simple Sub-Idle Component Map Extrapolation Method." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27193.

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This paper describes a simple sub-idle component map extrapolation method. Used in conjunction with gas turbine performance models, it enables designers to estimate sub-idle gas turbine performance during engine start-up. The lack of information available regarding component maps in the sub-idle regime creates major challenges for starting system designers or control system designers as the numerical convergence of performance models decreases rapidly below idle speed. The proposed component map extrapolation method alleviates this problem by extrapolating given component map data well below i

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5

Lévy, Bruno. "Dual domain extrapolation." In ACM SIGGRAPH 2003 Papers. ACM Press, 2003. http://dx.doi.org/10.1145/1201775.882277.

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6

Woerdeman, Hugo J., and Akram Aldroubi. "Extrapolation in multiresolutions." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Andrew F. Laine and Michael A. Unser. SPIE, 1994. http://dx.doi.org/10.1117/12.188764.

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7

Mitchell, Jeff, Pontus Stenetorp, Pasquale Minervini, and Sebastian Riedel. "Extrapolation in NLP." In Proceedings of the Workshop on Generalization in the Age of Deep Learning. Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-1005.

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Zhu, Yifei, Shixiao Fu, and Jianqiao Wu. "The Analysis of the Extrapolation Method Used in Typical Structures of Jacket Platforms." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41553.

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Acquiring the hot spot stress (HSS) in dangerous points of the structure (normally the welding seams of the structure) is the foundation of the offshore platform fatigue strength design. Because of the fact that it is very difficult and unpractical to install sensors in the welding seams, the most widely used method of acquiring the HSS currently is extrapolation, a method that obtains the HSS by extrapolating from stress of distant points. In order to figure out the feasibility of the method of extrapolation as well as the best ways to implement extrapolation in real practice, it is important

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9

Eckert, Zulah, and Gary Nutt. "Parallel Program Trace Extrapolation." In 1994 International Conference on Parallel Processing (ICPP'94). IEEE, 1994. http://dx.doi.org/10.1109/icpp.1994.151.

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Duttagupta, Subhasri, and Rupinder Singh Virk. "PerfExt: Performance Extrapolation Tool." In 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2012. http://dx.doi.org/10.1109/cimsim.2012.71.

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Звіти організацій з теми "Extrapolation"

1

Barberis, Nicholas, Robin Greenwood, Lawrence Jin, and Andrei Shleifer. Extrapolation and Bubbles. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w21944.

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2

Lakonishok, Josef, Robert Vishny, and Andrei Shleifer. Contrarian Investment, Extrapolation, and Risk. National Bureau of Economic Research, 1993. http://dx.doi.org/10.3386/w4360.

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3

Xing, Tao, and Fred Stern. Factors of Safety for Richardson Extrapolation. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada498086.

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4

Jenkins, Chris. Automation and Database Collection and Extrapolation. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1814437.

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5

Munson, Miles, and Kegelmeyer, W. Philip,. Builtin vs. auxiliary detection of extrapolation risk. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1095941.

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6

Cogan, James, and Brian Reen. A Method for Extrapolation of Atmospheric Soundings. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada604460.

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Parzen G. Prediction of Long Term Stability by Extrapolation. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/1061614.

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Mullahy, John. Econometric Risk Adjustment, Endogeneity, and Extrapolation Bias. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/w12236.

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Hale, D. Stable explicit depth extrapolation of seismic wavefields. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6507600.

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Medina, Richard L., and Richard A. Albanese. Animal-to-Human Extrapolation Using Compartmental Models. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada234082.

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