Relevant bibliographies by topics / Second-order method

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Second-order method'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Second-order method"

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Bradji, Abdallah, and Jürgen Fuhrmann. "Some new error estimates for finite element methods for second order hyperbolic equations using the Newmark method." Mathematica Bohemica 139, no. 2 (2014): 125–36. http://dx.doi.org/10.21136/mb.2014.143843.

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Zhao, Yan-Gang, Tetsuro Ono, and Masahiro Kato. "Second-Order Third-Moment Reliability Method." Journal of Structural Engineering 128, no. 8 (August 2002): 1087–90. http://dx.doi.org/10.1061/(asce)0733-9445(2002)128:8(1087).

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Lee, Seok, Heung-Jae Lie, Kyu-Min Song, and Chong-Jeanne Lim. "A second-order particle tracking method." Ocean Science Journal 40, no. 4 (December 2005): 201–8. http://dx.doi.org/10.1007/bf03023519.

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Qiu, Zhi-Yong, Yao-Lin Jiang, and Jia-Wei Yuan. "Interpolatory Model Order Reduction Method for Second Order Systems." Asian Journal of Control 20, no. 1 (June 5, 2017): 312–22. http://dx.doi.org/10.1002/asjc.1550.

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Xie, Zong Wu, Cao Li, and Hong Liu. "Cosine Second Order Robot Trajectory Planning Method." Applied Mechanics and Materials 80-81 (July 2011): 1075–80. http://dx.doi.org/10.4028/www.scientific.net/amm.80-81.1075.

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A new joint space trajectory planning method for the series robot is proposed. Comparing with the traditional path planning methods which can only guarantee the planned trajectory velocity or acceleration continuous, the proposed trajectory planning algorithm can also ensure the derivative of acceleration (Jerk) continuous within a limit threshold. At the end of this paper, the proposed path planning algorithm is validated of having a great performance on robot trajectory tracking.
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Liu, Miao, and Bin Wang. "A Web Second-Order Vulnerabilities Detection Method." IEEE Access 6 (2018): 70983–88. http://dx.doi.org/10.1109/access.2018.2881070.

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Teng, Zhongming, Linzhang Lu, and Xiaoqian Niu. "Restartable Generalized Second Order Krylov Subspace Method." Journal of Computational and Theoretical Nanoscience 12, no. 11 (November 1, 2015): 4584–92. http://dx.doi.org/10.1166/jctn.2015.4405.

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Azarenok, Boris N. "Realization of a second-order Godunov's method." Computer Methods in Applied Mechanics and Engineering 189, no. 3 (September 2000): 1031–52. http://dx.doi.org/10.1016/s0045-7825(00)00194-8.

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Corradi, Gianfranco. "A second order method for unconstrained optimization." International Journal of Computer Mathematics 20, no. 3-4 (January 1986): 253–60. http://dx.doi.org/10.1080/00207168608803547.

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Wei, Haixin, Ray Luo, and Ruxi Qi. "An efficient second‐order poisson–boltzmann method." Journal of Computational Chemistry 40, no. 12 (February 18, 2019): 1257–69. http://dx.doi.org/10.1002/jcc.25783.

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Dissertations / Theses on the topic "Second-order method"

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Auffredic, Jérémy. "A second order Runge–Kutta method for the Gatheral model." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-49170.

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In this thesis, our research focus on a weak second order stochastic Runge–Kutta method applied to a system of stochastic differential equations known as the Gatheral Model. We approximate numerical solutions to this system and investigate the rate of convergence of our method. Both call and put options are priced using Monte-Carlo simulation to investigate the order of convergence. The numerical results show that our method is consistent with the theoretical order of convergence of the Monte-Carlo simulation. However, in terms of the Runge-Kutta method, we cannot accept the consistency of our method with the theoretical order of convergence without further research.
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Mashalaba, Qaphela. "Implementation of numerical Fourier method for second order Taylor schemes." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/30978.

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The problem of pricing contingent claims in a complete market has received a significant amount of attention in literature since the seminal work of Black, Fischer and Scholes, Myron (1973). It was also in 1973 that the theory of backward stochastic differential equations (BSDEs) was developed by Bismut, Jean-Michel (1973), but it was much later in the literature that BSDEs developed links to contingent claim pricing. This dissertation is a thorough exposition of the survey paper Ruijter, Marjon J and Oosterlee, Cornelis W (2016) in which a highly accurate and efficient Fourier pricing technique compatible with BSDEs is developed and implemented. We prove our understanding of this technique by reproducing some of the numerical experiments and results in Ruijter, Marjon J and Oosterlee, Cornelis W (2016), and outlining some key implementationl considerations.
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Ben, Romdhane Mohamed. "Higher-Degree Immersed Finite Elements for Second-Order Elliptic Interface Problems." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39258.

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A wide range of applications involve interface problems. In most of the cases, mathematical modeling of these interface problems leads to partial differential equations with non-smooth or discontinuous inputs and solutions, especially across material interfaces. Different numerical methods have been developed to solve these kinds of problems and handle the non-smooth behavior of the input data and/or the solution across the interface. The main focus of our work is the immersed finite element method to obtain optimal numerical solutions for interface problems. In this thesis, we present piecewise quadratic immersed finite element (IFE) spaces that are used with an immersed finite element (IFE) method with interior penalty (IP) for solving two-dimensional second-order elliptic interface problems without requiring the mesh to be aligned with the material interfaces. An analysis of the constructed IFE spaces and their dimensions is presented. Shape functions of Lagrange and hierarchical types are constructed for these spaces, and a proof for the existence is established. The interpolation errors in the proposed piecewise quadratic spaces yield optimal O(h³) and O(h²) convergence rates, respectively, in the L² and broken H¹ norms under mesh refinement. Furthermore, numerical results are presented to validate our theory and show the optimality of our quadratic IFE method. Our approach in this thesis is, first, to establish a theory for the simplified case of a linear interface. After that, we extend the framework to quadratic interfaces. We, then, describe a general procedure for handling arbitrary interfaces occurring in real physical practical applications and present computational examples showing the optimality of the proposed method. Furthermore, we investigate a general procedure for extending our quadratic IFE spaces to p-th degree and construct hierarchical shape functions for p=3.
Ph. D.
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Beamis, Christopher Paul 1960. "Solution of second order differential equations using the Godunov integration method." Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/277319.

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This MS Thesis proposes the use of an integration technique due to Godunov for the direct numerical solution of systems of second order differential equations. This method is to be used instead of the conventional technique of separating each second order equation into two first order equations and then solving the resulting system with one of the many methods available for systems of first order differential equations. Stability domains and expressions for the truncation error will be developed for this method when it is used to solve the wave equation, a passive mechanical system, and a passive electrical circuit. It will be shown both analytically and experimentally that the Godunov method compares favorably with the Adams-Bashforth third order method when used to solve both the wave equation and the mechanical system, but that there are potential problems when this method is used to simulate electrical circuits which result in integro-differential equations.
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Dzacka, Charles Nunya. "A Variation of the Carleman Embedding Method for Second Order Systems." Digital Commons @ East Tennessee State University, 2009. https://dc.etsu.edu/etd/1877.

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The Carleman Embedding is a method that allows us to embed a finite dimensional system of nonlinear differential equations into a system of infinite dimensional linear differential equations. This technique works well when dealing with first-order nonlinear differential equations. However, for higher order nonlinear ordinary differential equations, it is difficult to use the Carleman Embedding method. This project will examine the Carleman Embedding and a variation of the method which is very convenient in applying to second order systems of nonlinear equations.
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Vie, Jean-Léopold. "Second-order derivatives for shape optimization with a level-set method." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1072/document.

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Le but de cette thèse est de définir une méthode d'optimisation de formes qui conjugue l'utilisation de la dérivée seconde de forme et la méthode des lignes de niveaux pour la représentation d'une forme.On considèrera d'abord deux cas plus simples : un cas d'optimisation paramétrique et un cas d'optimisation discrète.Ce travail est divisé en quatre parties.La première contient le matériel nécessaire à la compréhension de l'ensemble de la thèse.Le premier chapitre rappelle des résultats généraux d'optimisation, et notamment le fait que les méthodes d'ordre deux ont une convergence quadratique sous certaines hypothèses.Le deuxième chapitre répertorie différentes modélisations pour l'optimisation de formes, et le troisième se concentre sur l'optimisation paramétrique puis l'optimisation géométrique.Les quatrième et cinquième chapitres introduisent respectivement la méthode des lignes de niveaux (level-set) et la méthode des éléments-finis.La deuxième partie commence par les chapitres 6 et 7 qui détaillent des calculs de dérivée seconde dans le cas de l'optimisation paramétrique puis géométrique.Ces chapitres précisent aussi la structure et certaines propriétés de la dérivée seconde de forme.Le huitième chapitre traite du cas de l'optimisation discrète.Dans le neuvième chapitre on introduit différentes méthodes pour un calcul approché de la dérivée seconde, puis on définit un algorithme de second ordre dans un cadre général.Cela donne la possibilité de faire quelques premières simulations numériques dans le cas de l'optimisation paramétrique (Chapitre 6) et dans le cas de l'optimisation discrète (Chapitre 7).La troisième partie est consacrée à l'optimisation géométrique.Le dixième chapitre définit une nouvelle notion de dérivée de forme qui prend en compte le fait que l'évolution des formes par la méthode des lignes de niveaux, grâce à la résolution d'une équation eikonale, se fait toujours selon la normale.Cela permet de définir aussi une méthode d'ordre deux pour l'optimisation.Le onzième chapitre détaille l'approximation d'intégrales de surface et le douzième chapitre est consacré à des exemples numériques.La dernière partie concerne l'analyse numérique d'algorithmes d'optimisation de formes par la méthode des lignes de niveaux.Le Chapitre 13 détaille la version discrète d'un algorithme d'optimisation de formes.Le Chapitre 14 analyse les schémas numériques relatifs à la méthodes des lignes de niveaux.Enfin le dernier chapitre fait l'analyse numérique complète d'un exemple d'optimisation de formes en dimension un, avec une étude des vitesses de convergence
The main purpose of this thesis is the definition of a shape optimization method which combines second-order differentiationwith the representation of a shape by a level-set function. A second-order method is first designed for simple shape optimization problems : a thickness parametrization and a discrete optimization problem. This work is divided in four parts.The first one is bibliographical and contains different necessary backgrounds for the rest of the work. Chapter 1 presents the classical results for general optimization and notably the quadratic rate of convergence of second-order methods in well-suited cases. Chapter 2 is a review of the different modelings for shape optimization while Chapter 3 details two particular modelings : the thickness parametrization and the geometric modeling. The level-set method is presented in Chapter 4 and Chapter 5 recalls the basics of the finite element method.The second part opens with Chapter 6 and Chapter 7 which detail the calculation of second-order derivatives for the thickness parametrization and the geometric shape modeling. These chapters also focus on the particular structures of the second-order derivative. Then Chapter 8 is concerned with the computation of discrete derivatives for shape optimization. Finally Chapter 9 deals with different methods for approximating a second-order derivative and the definition of a second-order algorithm in a general modeling. It is also the occasion to make a few numerical experiments for the thickness (defined in Chapter 6) and the discrete (defined in Chapter 8) modelings.Then, the third part is devoted to the geometric modeling for shape optimization. It starts with the definition of a new framework for shape differentiation in Chapter 10 and a resulting second-order method. This new framework for shape derivatives deals with normal evolutions of a shape given by an eikonal equation like in the level-set method. Chapter 11 is dedicated to the numerical computation of shape derivatives and Chapter 12 contains different numerical experiments.Finally the last part of this work is about the numerical analysis of shape optimization algorithms based on the level-set method. Chapter 13 is concerned with a complete discretization of a shape optimization algorithm. Chapter 14 then analyses the numerical schemes for the level-set method, and the numerical error they may introduce. Finally Chapter 15 details completely a one-dimensional shape optimization example, with an error analysis on the rates of convergence
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Andrade, Prashant William. "Implementation of second-order absorbing boundary conditions in frequency-domain computations /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Zhang, Chun Yang. "A second order ADI method for 2D parabolic equations with mixed derivative." Thesis, University of Macau, 2012. http://umaclib3.umac.mo/record=b2592940.

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Dunn, Kyle George. "An Integral Equation Method for Solving Second-Order Viscoelastic Cell Motility Models." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/578.

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For years, researchers have studied the movement of cells and mathematicians have attempted to model the movement of the cell using various methods. This work is an extension of the work done by Zheltukhin and Lui (2011), Mathematical Biosciences 229:30-40, who simulated the stress and displacement of a one-dimensional cell using a model based on viscoelastic theory. The report is divided into three main parts. The first part considers viscoelastic models with a first-order constitutive equation and uses the standard linear model as an example. The second part extends the results of the first to models with second-order constitutive equations. In this part, the two examples studied are Burger model and a Kelvin-Voigt element connected with a dashpot in series. In the third part, the effects of substrate with variable stiffness are explored. Here, the effective adhesion coefficient is changed from a constant to a spatially-dependent function. Numerical results are generated using two different functions for the adhesion coefficient. Results of this thesis show that stress on the cell varies greatly across each part of the cell depending on the constitute equation we use, while the position and velocity of the cell remain essentially unchanged from a large-scale point of view.
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Chibi, Ahmed-Salah. "Defect correction and Galerkin's method for second-order elliptic boundary value problems." Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/47378.

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Books on the topic "Second-order method"

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Phase transitions of the second order: Collective variables method. Singapore: World Scientific, 1987.

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Deshpande, Suresh M. A second-order accurate kinetic-theory-based method for inviscid compressible flows. Hampton, Va: Langley Research Center, 1986.

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Shepherd, Adrian J. Second-Order Methods for Neural Networks. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0953-2.

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Otmani, Zoulikha Zaidi ep. Numerical methods for second order parabolic partial differential equations. Uxbridge: Brunel University, 1986.

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Krispin, J. Second-order Godunov methods and self-similar steady supersonic three-dimensional flowfields. Washington, D. C: American Institute of Aeronautics and Astronautics, 1991.

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Shepherd, Adrian J. Second-order methods for neural networks: Fast and reliable training methods for multi-layer perceptrons. London: Springer, 1997.

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Joost, Engelfriet, ed. Graph structure and monadic second-order logic: A language-theoretic approach. Cambridge: Cambridge University Press, 2012.

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Heinrich, Bernd. Finite difference methods on irregular networks: A generalized approach to second order elliptic problems. Berlin: Akademie-Verlag, 1987.

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Heinrich, Bernd. Finite difference methods on irregular networks: A generalized approach to second order elliptic problems. Basel: Birkhäuser Verlag, 1987.

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International Conference on Spectral and High Order Methods (2nd 1992 Montpellier, France). ICOSAHOM'92: Selected papers from the second International Conference on Spectral and High Order Methods, Montpellier, France, 22-26 June 1992. Amsterdam: North-Holland, 1994.

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Book chapters on the topic "Second-order method"

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Pytlak, Radosław. "Second order method." In Lecture Notes in Mathematics, 81–128. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0097249.

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Vacher, Morgane, David Mendive-Tapia, Michael J. Bearpark, and Michael A. Robb. "The second-order Ehrenfest method." In Highlights in Theoretical Chemistry, 325–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-48148-6_29.

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Han, Houde, and Xiaonan Wu. "Global ABCs for Second Order Elliptic Equations." In Artificial Boundary Method, 9–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35464-9_2.

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Chen, W. F., and I. Sohal. "Equilibrium Method." In Plastic Design and Second-Order Analysis of Steel Frames, 157–222. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4613-8428-1_4.

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Chen, W. F., and I. Sohal. "Work Method." In Plastic Design and Second-Order Analysis of Steel Frames, 223–330. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4613-8428-1_5.

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Evtushenko, Yuri G., E. S. Zasuhina, and V. I. Zubov. "FAD Method to Compute Second Order Derivatives." In Automatic Differentiation of Algorithms, 327–33. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0075-5_39.

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Attouch, H., and P. Redont. "The Second-order in Time Continuous Newton Method." In Approximation, Optimization and Mathematical Economics, 25–36. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57592-1_2.

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Zeidler, Eberhard. "Second-Order Evolution Equations and the Galerkin Method." In Nonlinear Functional Analysis and its Applications, 919–57. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-0981-2_9.

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Godinez, Humberto C., and Dacian N. Daescu. "A Second Order Adjoint Method to Targeted Observations." In Lecture Notes in Computer Science, 322–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01973-9_36.

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Ilmonen, Pauliina, Klaus Nordhausen, Hannu Oja, and Fabian Theis. "An Affine Equivariant Robust Second-Order BSS Method." In Latent Variable Analysis and Signal Separation, 328–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_38.

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Conference papers on the topic "Second-order method"

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Du, Xiaoping, and Junfu Zhang. "Second-Order Reliability Method With First-Order Efficiency." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28178.

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The widely used First Order Reliability Method (FORM) is efficient, but may not be accurate for nonlinear limit-state functions. The Second Order Reliability Method (SORM) is more accurate but less efficient. To maintain both high accuracy and efficiency, we propose a new second order reliability analysis method with first order efficiency. The method first performs the FORM and identifies the Most Probable Point (MPP). Then the associated limit-state function is decomposed into additive univariate functions at the MPP. Each univariate function is further approximated as a quadratic function, which is created with the gradient information at the MPP and one more point near the MPP. The cumulant generating function of the approximated limit-state function is then available so that saddlepoint approximation can be easily applied for computing the probability of failure. The accuracy of the new method is comparable to that of the SORM, and its efficiency is in the same order of magnitude as the FORM.
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Zhang Limei. "The second-order explicit RKC method." In 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE 2010). IEEE, 2010. http://dx.doi.org/10.1109/cmce.2010.5610529.

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Gupta, M., J. M. Slezak, F. Alalhareth, S. Roy, and H. V. Kojouharov. "Second-order nonstandard explicit Euler method." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 12th International On-line Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’20. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0033534.

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Ping, Chen. "A second-order SQL injection detection method." In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2017. http://dx.doi.org/10.1109/itnec.2017.8285104.

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Ismail, Ainathon, and Faranak Rabiei. "Multivalue-multistage method for second-order ODEs." In PROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Mathematical Sciences Exploration for the Universal Preservation. Author(s), 2017. http://dx.doi.org/10.1063/1.4995927.

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Miettinen, Pekka, Mikko Honkala, Janne Roos, and Martti Valtonen. "Partitioning-based second-order model-order reduction method for RC circuits." In 2009 European Conference on Circuit Theory and Design (ECCTD 2009). IEEE, 2009. http://dx.doi.org/10.1109/ecctd.2009.5275044.

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Glancy, Charles G., and Kenneth W. Chase. "A Second-Order Method for Assembly Tolerance Analysis." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8707.

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Abstract Linear analysis and Monte Carlo simulation are two well-established methods for statistical tolerance analysis of mechanical assemblies. Both methods have advantages and disadvantages. The Linearized Method, a form of linear analysis, provides fast analysis, tolerance allocation, and the capability to solve closed loop constraints. However, the Linearized Method does not accurately approximate nonlinear geometric effects or allow for non-normally distributed input or output distributions. Monte Carlo simulation, on the other hand, does accurately model nonlinear effects and allow for non-normally distributed input and output distributions. Of course, Monte Carlo simulation can be computationally expensive and must be re-run when any input variable is modified. The second-order tolerance analysis (SOTA) method attempts to combine the advantages of the Linearized Method with the advantages of Monte Carlo simulation. The SOTA method applies the Method of System Moments to implicit variables of a system of nonlinear equations. The SOTA method achieves the benefits of speed, tolerance allocation, closed-loop constraints, non-linear geometric effects and non-normal input and output distributions. The SOTA method offers significant benefits as a nonlinear analysis tool suitable for use in design iteration. A comparison was performed between the Linearized Method, Monte Carlo simulation, and the SOTA method. The SOTA method provided a comparable nonlinear analysis to Monte Carlo simulation with 106 samples. The analysis time of the SOTA method was comparable to the Linearized Method.
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Berinde, Vasile. "A Method For Solving Second Order Difference Equations." In Proceedings of the Third International Conference on Difference Equations. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.4324/9780203745854-6.

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Mokhtari, Aryan, Wei Shi, Qing Ling, and Alejandro Ribeiro. "A decentralized Second-Order Method for Dynamic Optimization." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7799196.

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Hu, Zhen. "A second-order screening method for Preventive regions." In 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG). IEEE, 2019. http://dx.doi.org/10.1109/igbsg.2019.8886313.

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Reports on the topic "Second-order method"

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Manzini, Gianmarco. Annotations on the virtual element method for second-order elliptic problems. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1338710.

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Colella, P., D. T. Graves, and J. A. Greenough. A second-order method for interface reconstruction in orthogonal coordinate systems. Office of Scientific and Technical Information (OSTI), January 2002. http://dx.doi.org/10.2172/834475.

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Xia, Yidong, Joshua Hansel, Ray A. Berry, David Andrs, and Richard C. Martineau. Preliminary Study on the Suitability of a Second-Order Reconstructed Discontinuous Galerkin Method for RELAP-7 Thermal-Hydraulic Modeling. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1468483.

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Petersson, N., and B. Sjogreen. Serpentine: Finite Difference Methods for Wave Propagation in Second Order Formulation. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1046802.

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Mitchell, Jason W. Implementing Families of Implicit Chebyshev Methods with Exact Coefficients for the Numerical Integration of First- and Second-Order Differential Equations. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada404958.

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Rutan, S. C. Enhancement of fluorescence detection in chromatographic methods by computer analysis of second order data. Progress report, August 1, 1990--October 1, 1993. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10163516.

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Job, Jacob. Mesa Verde National Park: Acoustic monitoring report. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286703.

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Abstract:
In 2015, the Natural Sounds and Night Skies Division (NSNSD) received a request to collect baseline acoustical data at Mesa Verde National Park (MEVE). Between July and August 2015, as well as February and March 2016, three acoustical monitoring systems were deployed throughout the park, however one site (MEVE002) stopped recording after a couple days during the summer due to wildlife interference. The goal of the study was to establish a baseline soundscape inventory of backcountry and frontcountry sites within the park. This inventory will be used to establish indicators and thresholds of soundscape quality that will support the park and NSNSD in developing a comprehensive approach to protecting the acoustic environment through soundscape management planning. Additionally, results of this study will help the park identify major sources of noise within the park, as well as provide a baseline understanding of the acoustical environment as a whole for use in potential future comparative studies. In this deployment, sound pressure level (SPL) was measured continuously every second by a calibrated sound level meter. Other equipment included an anemometer to collect wind speed and a digital audio recorder collecting continuous recordings to document sound sources. In this document, “sound pressure level” refers to broadband (12.5 Hz–20 kHz), A-weighted, 1-second time averaged sound level (LAeq, 1s), and hereafter referred to as “sound level.” Sound levels are measured on a logarithmic scale relative to the reference sound pressure for atmospheric sources, 20 μPa. The logarithmic scale is a useful way to express the wide range of sound pressures perceived by the human ear. Sound levels are reported in decibels (dB). A-weighting is applied to sound levels in order to account for the response of the human ear (Harris, 1998). To approximate human hearing sensitivity, A-weighting discounts sounds below 1 kHz and above 6 kHz. Trained technicians calculated time audible metrics after monitoring was complete. See Methods section for protocol details, equipment specifications, and metric calculations. Median existing (LA50) and natural ambient (LAnat) metrics are also reported for daytime (7:00–19:00) and nighttime (19:00–7:00). Prominent noise sources at the two backcountry sites (MEVE001 and MEVE002) included vehicles and aircraft, while building and vehicle predominated at the frontcountry site (MEVE003). Table 1 displays time audible values for each of these noise sources during the monitoring period, as well as ambient sound levels. In determining the current conditions of an acoustical environment, it is informative to examine how often sound levels exceed certain values. Table 2 reports the percent of time that measured levels at the three monitoring locations were above four key values.
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