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Artículos de revistas sobre el tema "Reduced method"

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Publicado: 14 de marzo de 2023

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1

Rizaldy, Ery M., J. M. Nursherida y Abdul Rahim Sadiq Batcha. "Reduced Dimension Lane Detection Method". International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-ICAEIT2017 (30 de noviembre de 2018): 191–96. http://dx.doi.org/10.31142/ijtsrd19136.

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2

Jain, Pankaj, Chandrani Basu y Vivek Panwar. "Reduced $pq$-Differential Transform Method and Applications". Journal of Inequalities and Special Functions 13, n.º 1 (30 de marzo de 2022): 24–40. http://dx.doi.org/10.54379/jiasf-2022-1-3.

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In this paper, Reduced Differential Transform method in the framework of (p, q)-calculus, denoted by Rp,qDT , has been introduced and applied in solving a variety of differential equations such as diffusion equation, 2Dwave equation, K-dV equation, Burgers equations and Ito system. While the diffusion equation has been studied for the special case p = 1, i.e., in the framework of q-calculus, the other equations have not been studied even in q-calculus.
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3

Maghri, Mounir El y Youssef Elboulqe. "Reduced Jacobian Method". Journal of Optimization Theory and Applications 179, n.º 3 (10 de agosto de 2018): 917–43. http://dx.doi.org/10.1007/s10957-018-1362-x.

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4

Maday, Yvon y Einar M. Rønquist. "A reduced-basis element method". Comptes Rendus Mathematique 335, n.º 2 (enero de 2002): 195–200. http://dx.doi.org/10.1016/s1631-073x(02)02427-5.

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5

Demo, Nicola, Marco Tezzele y Gianluigi Rozza. "EZyRB: Easy Reduced Basis method". Journal of Open Source Software 3, n.º 24 (11 de abril de 2018): 661. http://dx.doi.org/10.21105/joss.00661.

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6

Schell, Michael J. y Bahadur Singh. "The Reduced Monotonic Regression Method". Journal of the American Statistical Association 92, n.º 437 (marzo de 1997): 128–35. http://dx.doi.org/10.1080/01621459.1997.10473609.

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7

Lobashev, A. A. y N. N. Trunov. "The reduced semiclassical description method". Theoretical and Mathematical Physics 120, n.º 1 (julio de 1999): 896–909. http://dx.doi.org/10.1007/bf02557399.

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8

Maghri, Mounir El y Youssef Elboulqe. "Correction to: Reduced Jacobian Method". Journal of Optimization Theory and Applications 187, n.º 1 (1 de diciembre de 2018): 304. http://dx.doi.org/10.1007/s10957-018-1448-5.

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9

Barrett, A. y G. Reddien. "On the Reduced Basis Method". ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 75, n.º 7 (1995): 543–49. http://dx.doi.org/10.1002/zamm.19950750709.

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10

Degen, Denise, Karen Veroy y Florian Wellmann. "Certified reduced basis method in geosciences". Computational Geosciences 24, n.º 1 (13 de diciembre de 2019): 241–59. http://dx.doi.org/10.1007/s10596-019-09916-6.

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11

Boukouvala, Fani, Yijie Gao, Fernando Muzzio y Marianthi G. Ierapetritou. "Reduced-order discrete element method modeling". Chemical Engineering Science 95 (mayo de 2013): 12–26. http://dx.doi.org/10.1016/j.ces.2013.01.053.

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12

Wheaton, Andrew J., Arijitt Borthakur, Matthew Corbo, Sridhar R. Charagundla y Ravinder Reddy. "Method for reduced SART1?-weighted MRI". Magnetic Resonance in Medicine 51, n.º 6 (2004): 1096–102. http://dx.doi.org/10.1002/mrm.20141.

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13

Matoba, Seiichi, Xiaming Zhou, Masahiro Sekita y Takaho Ohishi. "A Reduced Admittance Matrix Method Minimizing Operations". IEEJ Transactions on Power and Energy 116, n.º 8 (1996): 932–38. http://dx.doi.org/10.1541/ieejpes1990.116.8_932.

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14

Jawad, Zahraa Ali. "Reduced Complexity SLM Method for PAPR Reduction". Journal of Engineering 25, n.º 5 (1 de mayo de 2019): 87–99. http://dx.doi.org/10.31026/j.eng.2019.05.07.

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In this paper, the computational complexity will be reduced using a revised version of the selected mapping (SLM) algorithm. Where a partial SLM is achieved to reduce the mathematical operations around 50%. Although the peak to average power ratio (PAPR) reduction gain has been slightly degraded, the dramatic reduction in the computational complexity is an outshining achievement. Matlab simulation is used to evaluate the results, where the PAPR result shows the capability of the proposed method.
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15

Zygarlicki, Jarosław y Janusz Mroczka. "Prony’s Method with Reduced Sampling - Numerical Aspects". Metrology and Measurement Systems 21, n.º 3 (21 de agosto de 2014): 521–34. http://dx.doi.org/10.2478/mms-2014-0044.

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Abstract This paper presents a new modification of the least-squares Prony’s method with reduced sampling, which allows for a significant reduction in the number of the analysed signal samples collected per unit time. The specific combination of non-uniform sampling with Prony’s method enables sampling of the analysed signals at virtually any average frequency, regardless of the Nyquist frequency, maintaining high accuracy in parameter estimation of sinusoidal signal components. This property allows using the method in measuring devices, such as for electric power quality testing equipped with low power signal processors, which in turn contributes to reducing complexity of these devices. This paper presents research on a method for selecting a sampling frequency and an analysis window length for the presented method, which provide maximum estimation accuracy for Prony’s model component parameters. This paper presents simulation tests performed in terms of the proposed method application for analysis of harmonics and interharmonics in electric power signals. Furthermore, the paper provides sensitivity analysis of the method, in terms of common interferences occurring in the actual measurement systems.
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16

Hernández, M. A. "Reduced Recurrence Relations for the Chebyshev Method". Journal of Optimization Theory and Applications 98, n.º 2 (agosto de 1998): 385–97. http://dx.doi.org/10.1023/a:1022641601991.

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17

Bakasov, A. A. y V. I. Yukalov. "Method of reduced description of coherent radiation". Theoretical and Mathematical Physics 72, n.º 1 (julio de 1987): 773–86. http://dx.doi.org/10.1007/bf01035704.

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18

Al-Amr, Mohammed O. "New applications of reduced differential transform method". Alexandria Engineering Journal 53, n.º 1 (marzo de 2014): 243–47. http://dx.doi.org/10.1016/j.aej.2014.01.003.

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19

Bastyr, Kevin J. "SYSTEM AND METHOD FOR REDUCED BAFFLE VIBRATION". Journal of the Acoustical Society of America 131, n.º 4 (2012): 3191. http://dx.doi.org/10.1121/1.4707444.

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20

Tench, D. M., D. P. Anderson, P. Jambazian, P. Kim, J. White, D. Hillman, G. K. Lucey, T. Gher y B. Piekarski. "A new reduced-oxide soldering activation method". JOM 47, n.º 6 (junio de 1995): 36–41. http://dx.doi.org/10.1007/bf03221202.

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21

Eftang, J. L., D. B. P. Huynh, D. J. Knezevic y A. T. Patera. "A Two-Step Certified Reduced Basis Method". Journal of Scientific Computing 51, n.º 1 (12 de mayo de 2011): 28–58. http://dx.doi.org/10.1007/s10915-011-9494-2.

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22

Amihud, Yakov y Clifford M. Hurvich. "Predictive Regressions: A Reduced-Bias Estimation Method". Journal of Financial and Quantitative Analysis 39, n.º 4 (diciembre de 2004): 813–41. http://dx.doi.org/10.1017/s0022109000003227.

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AbstractStandard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. See Stambaugh (1999) for the single regressor model. This paper proposes a direct and convenient method to obtain reduced-bias estimators for single and multiple regressor models by employing an augmented regression, adding a proxy for the errors in the autoregressive model. We derive bias expressions for both the ordinary least-squares and our reduced-bias estimated coefficients. For the standard errors of the estimated predictive coefficients, we develop a heuristic estimator that performs well in simulations, for both the single predictor model and an important specification of the multiple predictor model. The effectiveness of our method is demonstrated by simulations and empirical estimates of common predictive models in finance. Our empirical results show that some of the predictive variables that were significant under ordinary least squares become insignificant under our estimation procedure.
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23

Liao, Aiping. "A reduced Hessian method for constrained optimization". Computational Optimization and Applications 2, n.º 2 (octubre de 1993): 129–43. http://dx.doi.org/10.1007/bf01299153.

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24

Croll, J. G. A. y G. D. Gavrilenko. "Substantiation of the method of reduced stiffness". Strength of Materials 30, n.º 5 (septiembre de 1998): 481–96. http://dx.doi.org/10.1007/bf02522630.

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25

Szypulski, Damian, Grzegorz Fotyga y Michal Mrozowski. "An Enhanced Reduced Basis Method for Wideband Finite Element Method Simulations". IEEE Access 7 (2019): 60877–84. http://dx.doi.org/10.1109/access.2019.2915557.

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26

Kong, Ki-Bok, Jong-Sung Kim y Seong-Ook Park. "Reduced splitting error in the ADI-FDTD method using iterative method". Microwave and Optical Technology Letters 50, n.º 8 (2008): 2200–2203. http://dx.doi.org/10.1002/mop.23618.

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27

Li, Yong Hong. "Structural Analysis Based on Coefficient Reduced-Basis Method". Applied Mechanics and Materials 543-547 (marzo de 2014): 46–49. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.46.

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Efficiency and accuracy of forward problem are important in structural analysis. A real-time algorithm, called coefficient reduced-basis method, is applied to analyze a static problem. A truck frame is taken as an example. Results computed from finite element method, reduced-basis method and coefficient reduced-basis method are obtained. Comparing results from the three methods, coefficient reduced-basis method can get high-precision results quickly, which not separate the design parameters from the linear elastic operators.
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28

Okuno, R., R. T. T. Johns y K. Sepehrnoori. "Application of a Reduced Method in Compositional Simulation". SPE Journal 15, n.º 01 (12 de noviembre de 2009): 39–49. http://dx.doi.org/10.2118/119657-pa.

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Summary Simulating gas-injection processes requires a compositional model to predict the fluid properties resulting from mass transfer between reservoir fluid and injection gas. A drawback of compositional simulation is the efficiency and robustness of phase equilibrium calculations. Reduced methods for phase equilibrium calculations have been studied as a potential solution to improve the efficiency of compositional simulation. However, most of those studies have been performed only in standalone calculations, and the robustness and efficiency of a reduced method has not been confirmed in compositional simulation. In this research, we develop a robust and efficient algorithm for a reduced method and validate it in compositional simulation. We examine the efficiency and convergence property of the conventional algorithm for a reduced method and solve several implementation problems in a compositional simulator. The reduced method is implemented in UTCOMP, a compositional implicit-pressure/explicit concentration (IMPEC) simulator, to demonstrate the performance for various numbers of components and degrees of miscibility. The results show that the reduced method enables significant savings in execution time of compositional simulation without loss of accuracy compared to standard methods. Also, we observe that the reduced method exhibits improved robustness, especially for miscible processes where composition paths go near critical regions.
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29

Ganji, Hamed Faghanpour, Mohsen Jouya, Seyed Abbas Mirhosseini-Amiri y Davod Domiri Ganji. "Traveling wave solution by differential transformation method and reduced differential transformation method". Alexandria Engineering Journal 55, n.º 3 (septiembre de 2016): 2985–94. http://dx.doi.org/10.1016/j.aej.2016.04.012.

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30

YAMASHITA, Masazumi, Katsuhisa HONDA y Tadaaki WAKIMOTO. "Dioxin-reduced Small Size Incinerator Named "Ehime method"". BUNSEKI KAGAKU 61, n.º 1 (2011): 1–14. http://dx.doi.org/10.2116/bunsekikagaku.61.1.

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31

Chaudhry, Jehanzeb H., Luke N. Olson y Peter Sentz. "A Least-Squares Finite Element Reduced Basis Method". SIAM Journal on Scientific Computing 43, n.º 2 (enero de 2021): A1081—A1107. http://dx.doi.org/10.1137/20m1323552.

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32

Li, Yong Hong. "Structural Optimization Based on Inversion Reduced-Basis Method". Advanced Materials Research 255-260 (mayo de 2011): 84–88. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.84.

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Efficiency and stability of forward problem are important in structural optimum design. A real-time algorithm, called inversion reduced-basis method (IRBM), is applied to solve the forward problem in this paper. IRBM is introduced theoretically. Taking a truck frame as an example, results computed from IRBM are obtained. In the optimization process, a modified micro-genetic algorithm (μGA) is adopted to get the global optimum. In the structural reanalysis, IRBM is used, which greatly shorten the design cycle and improve work efficiency.
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33

Aldeek, Fadi y Mohamadi A. Sarkar. "Method Development and Applications for Reduced-Risk Products". Separations 9, n.º 3 (18 de marzo de 2022): 78. http://dx.doi.org/10.3390/separations9030078.

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34

Timokhin, I. V., S. A. Matveev, E. E. Tyrtyshnikov y A. P. Smirnov. "Method for Reduced Basis Discovery in Nonstationary Problems". Doklady Mathematics 103, n.º 2 (marzo de 2021): 92–94. http://dx.doi.org/10.1134/s106456242102006x.

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35

Bundith, P., P. Pimanprom, W. Janthornsirijan, C. Punkun, S. Sleesongsom y S. Bureerat. "Reduced-order modelling of unsteady vortex lattice method". IOP Conference Series: Materials Science and Engineering 1173, n.º 1 (1 de agosto de 2021): 012040. http://dx.doi.org/10.1088/1757-899x/1173/1/012040.

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36

Majowiecki, Massimo y Stefano Pinardi. "Reduced stress method for Class 4 steel section". IABSE Symposium Report 97, n.º 26 (1 de enero de 2010): 39–46. http://dx.doi.org/10.2749/222137810796024817.

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37

Keskin, Yıldıray y Galip Oturanç. "Reduced Differential Transform Method for Generalized KdV Equations". Mathematical and Computational Applications 15, n.º 3 (1 de diciembre de 2010): 382–93. http://dx.doi.org/10.3390/mca15030382.

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38

Stapelfeldt, Sina C. y Luca Di Mare. "Reduced Passage Method for Multirow Forced Response Computations". AIAA Journal 53, n.º 10 (octubre de 2015): 3049–62. http://dx.doi.org/10.2514/1.j053888.

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39

Kuroda, Eisuke, Takao Tsuji y Tsutomu Oyama. "Hybrid System Reduction Method using Reduced System Regulator". IEEJ Transactions on Power and Energy 130, n.º 4 (2010): 407–13. http://dx.doi.org/10.1541/ieejpes.130.407.

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40

Putz, Francis E. y Michelle A. Pinard. "Reduced-Impact Logging as a Carbon-Offset Method". Conservation Biology 7, n.º 4 (diciembre de 1993): 755–57. http://dx.doi.org/10.1046/j.1523-1739.1993.7407551.x.

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41

Haasdonk, B., J. Salomon y B. Wohlmuth. "A Reduced Basis Method for Parametrized Variational Inequalities". SIAM Journal on Numerical Analysis 50, n.º 5 (enero de 2012): 2656–76. http://dx.doi.org/10.1137/110835372.

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42

EVANS, D. J. y PRAGYA JAIN. "THE COUPLED REDUCED ALTERNATING GROUP EXPLICIT (CRAGE) METHOD∗". Parallel Algorithms and Applications 2, n.º 3 (enero de 1994): 193–208. http://dx.doi.org/10.1080/10637199408915416.

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43

Dang, Xunwang, Maokun Li, Fan Yang y Shenheng Xu. "Quasi-Periodic Array Modeling Using Reduced Basis Method". IEEE Antennas and Wireless Propagation Letters 16 (2017): 825–28. http://dx.doi.org/10.1109/lawp.2016.2605760.

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44

Shang-Chih Ma y Yao-Liang Chung. "A reduced-complexity implementation of trace delete method". IEEE Transactions on Consumer Electronics 50, n.º 3 (agosto de 2004): 820–22. http://dx.doi.org/10.1109/tce.2004.1341685.

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45

Yin, Ruixue, Huajun Cao, Hongcheng Li y John W. Sutherland. "A process planning method for reduced carbon emissions". International Journal of Computer Integrated Manufacturing 27, n.º 12 (28 de enero de 2014): 1175–86. http://dx.doi.org/10.1080/0951192x.2013.874585.

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46

El Majd, Badr Abou y Laurent Cordier. "Regularization method for calibrated POD reduced-order models". MATEC Web of Conferences 16 (2014): 06001. http://dx.doi.org/10.1051/matecconf/20141606001.

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47

Gabriele, G. A. y T. J. Beltracchi. "Resolving Degeneracy in the Generalized Reduced Gradient Method". Journal of Mechanisms, Transmissions, and Automation in Design 109, n.º 2 (1 de junio de 1987): 263–67. http://dx.doi.org/10.1115/1.3267449.

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The Generalized Reduced Gradient (GRG) method has proven to be one of the more robust and efficient algorithms currently available for solving nonlinear programming problems. The method divides the vector of design variables into two classes, nonbasic and basic variables, and employs the implicit function theorem to formulate a reduced, unconstrained problem in the nonbasic variables. In order to employ the implicit function theorem two assumptions are made: (1) The Jacobian matrix of the active constraints with respect to the basic variables is nonsingular; and (2) All basic variables are within their respective bounds. When the second condition is not satisfied then the current point is degenerate and further progress is not assured. Methods based on performing basis changes exist for resolving degeneracy. In this paper, we will describe a technique based on the method of feasible directions which has the advantage of requiring no basis changes to generate a new direction.
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48

Jung, N., B. Haasdonk y D. Kroner. "Reduced Basis Method for quadratically nonlinear transport equations". International Journal of Computing Science and Mathematics 2, n.º 4 (2009): 334. http://dx.doi.org/10.1504/ijcsm.2009.030912.

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49

EGI, N., H. AOKI y A. TAKAHASHI. "Objective Quality Evaluation Method for Noise-Reduced Speech". IEICE Transactions on Communications E91-B, n.º 5 (1 de mayo de 2008): 1279–86. http://dx.doi.org/10.1093/ietcom/e91-b.5.1279.

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50

Glas, Silke, Anthony T. Patera y Karsten Urban. "A reduced basis method for the wave equation". International Journal of Computational Fluid Dynamics 34, n.º 2 (8 de noviembre de 2019): 139–46. http://dx.doi.org/10.1080/10618562.2019.1686486.

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