Relevant bibliographies by topics / Scale decomposition

Academic literature on the topic 'Scale decomposition'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Scale decomposition"

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Mendez, M. A., M. Balabane, and J. M. Buchlin. "Multi-scale proper orthogonal decomposition of complex fluid flows." Journal of Fluid Mechanics 870 (May 15, 2019): 988–1036. http://dx.doi.org/10.1017/jfm.2019.212.

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Data-driven decompositions are becoming essential tools in fluid dynamics, allowing for tracking the evolution of coherent patterns in large datasets, and for constructing low-order models of complex phenomena. In this work, we analyse the main limits of two popular decompositions, namely the proper orthogonal decomposition (POD) and the dynamic mode decomposition (DMD), and we propose a novel decomposition which allows for enhanced feature detection capabilities. This novel decomposition is referred to as multi-scale proper orthogonal decomposition (mPOD) and combines multi-resolution analysis (MRA) with a standard POD. Using MRA, the mPOD splits the correlation matrix into the contribution of different scales, retaining non-overlapping portions of the correlation spectra; using the standard POD, the mPOD extracts the optimal basis from each scale. After introducing a matrix factorization framework for data-driven decompositions, the MRA is formulated via one- and two-dimensional filter banks for the dataset and the correlation matrix respectively. The validation of the mPOD, and a comparison with the discrete Fourier transform (DFT), DMD and POD are provided in three test cases. These include a synthetic test case, a numerical simulation of a nonlinear advection–diffusion problem and an experimental dataset obtained by the time-resolved particle image velocimetry (TR-PIV) of an impinging gas jet. For each of these examples, the decompositions are compared in terms of convergence, feature detection capabilities and time–frequency localization.
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Schmidt, Marie Foged, Martin Benning, and Carola-Bibiane Schönlieb. "Inverse scale space decomposition." Inverse Problems 34, no. 4 (March 13, 2018): 045008. http://dx.doi.org/10.1088/1361-6420/aab0ae.

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Aluie, Hussein. "Scale decomposition in compressible turbulence." Physica D: Nonlinear Phenomena 247, no. 1 (March 2013): 54–65. http://dx.doi.org/10.1016/j.physd.2012.12.009.

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Camps, O. I., T. Kanungo, and R. M. Haralick. "Gray-scale structuring element decomposition." IEEE Transactions on Image Processing 5, no. 1 (January 1996): 111–20. http://dx.doi.org/10.1109/83.481675.

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Führ, Hartmut, and Azita Mayeli. "Homogeneous Besov Spaces on Stratified Lie Groups and Their Wavelet Characterization." Journal of Function Spaces and Applications 2012 (2012): 1–41. http://dx.doi.org/10.1155/2012/523586.

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We establish wavelet characterizations of homogeneous Besov spaces on stratified Lie groups, both in terms of continuous and discrete wavelet systems. We first introduce a notion of homogeneous Besov spaceB˙p,qsin terms of a Littlewood-Paley-type decomposition, in analogy to the well-known characterization of the Euclidean case. Such decompositions can be defined via the spectral measure of a suitably chosen sub-Laplacian. We prove that the scale of Besov spaces is independent of the precise choice of Littlewood-Paley decomposition. In particular, different sub-Laplacians yield the same Besov spaces. We then turn to wavelet characterizations, first via continuous wavelet transforms (which can be viewed as continuous-scale Littlewood-Paley decompositions), then via discretely indexed systems. We prove the existence of wavelet frames and associated atomic decomposition formulas for all homogeneous Besov spacesB˙p,qswith1≤p,q<∞ands∈ℝ.
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Moktadir, Z. "Scale decomposition of molecular beam epitaxy." Journal of Physics: Condensed Matter 20, no. 23 (May 13, 2008): 235240. http://dx.doi.org/10.1088/0953-8984/20/23/235240.

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Bertsekas, D. P. "Thevenin decomposition and large-scale optimization." Journal of Optimization Theory and Applications 89, no. 1 (April 1996): 1–15. http://dx.doi.org/10.1007/bf02192638.

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Ji, Jingyu, Yuhua Zhang, Yongjiang Hu, Yongke Li, Changlong Wang, Zhilong Lin, Fuyu Huang, and Jiangyi Yao. "Fusion of Infrared and Visible Images Based on Three-Scale Decomposition and ResNet Feature Transfer." Entropy 24, no. 10 (September 24, 2022): 1356. http://dx.doi.org/10.3390/e24101356.

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Image fusion technology can process multiple single image data into more reliable and comprehensive data, which play a key role in accurate target recognition and subsequent image processing. In view of the incomplete image decomposition, redundant extraction of infrared image energy information and incomplete feature extraction of visible images by existing algorithms, a fusion algorithm for infrared and visible image based on three-scale decomposition and ResNet feature transfer is proposed. Compared with the existing image decomposition methods, the three-scale decomposition method is used to finely layer the source image through two decompositions. Then, an optimized WLS method is designed to fuse the energy layer, which fully considers the infrared energy information and visible detail information. In addition, a ResNet-feature transfer method is designed for detail layer fusion, which can extract detailed information such as deeper contour structures. Finally, the structural layers are fused by weighted average strategy. Experimental results show that the proposed algorithm performs well in both visual effects and quantitative evaluation results compared with the five methods.
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Li, Ming Jing, Xiao Li Wang, and Yu Bing Dong. "Research and Development of Multi-Scale to Pixel-Level Image Fusion." Applied Mechanics and Materials 448-453 (October 2013): 3625–28. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3625.

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Image fusion method based on image multi-scale decomposition is a kind of fusion method of multi-scale, multi-resolution image fusion. Its fusion process realize in different scales and different spatial resolution and different decomposition layer. Fusion effects based on multi-scale decomposition algorithm can obviously improve compared to the simple fusion methods. Among the fusion algorithm based on multi-scale to pixel-level image fusion, Pyramid decomposition and wavelet decomposition are widely used, the original image is decomposed to convert the original image domain to transform domain, and then, fusion process realized in transform domain according to certain rules of image fusion. Basic principle of fusion process was introduced in detail in this paper, and pixel level fusion algorithm at present was summed up. Simulation results on fusion are presented to illustrate the proposed fusion scheme. In practice, fusion algorithm was selected according to imaging characteristics being retained.
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Abolfazl Hajisami and Dario Pompili. "MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel." ITU Journal on Future and Evolving Technologies 1, no. 1 (December 11, 2020): 25–35. http://dx.doi.org/10.52953/psmv3163.

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Multi-scale decomposition is a signal description method in which the signal is decomposed into multiple scales, which has been shown to be a valuable method in information preservation. Much focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this article, a new powerful method to perform multi-scale decomposition exploiting Independent Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple statistically independent scales. It is proven that extracting the independent components of the even and odd samples of a digital signal results in the decomposition of the same into approximation and detail. It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure. Performance results of MSICA in signal denoising are presented; also, the statistical independency of the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel noisy transmissions aimed at improving communication reliability by exploiting channel diversity.
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Dissertations / Theses on the topic "Scale decomposition"

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Hawley, Stephen Dwyer. "Adaptive time-scale decomposition for multiscale systems /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/6009.

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Finney, John D. "Decomposition and decentralized output control of large-scale systems." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/15606.

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Shankar, Jayashree. "Analysis of a nonhierarchical decomposition algorithm." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09192009-040336/.

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Sanneman, Lindsay (Lindsay Michelle). "Decomposition techniques for large-scale optimization in the supply chain." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118674.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 103-105).
Integrated supply chain models provide an opportunity to optimize costs and production times in the supply chain while taking into consideration the many steps in the production and delivery process and the many constraints on time, shared resources, and throughput capabilities. In this work, mixed integer linear programming (MILP) models are developed to describe the manufacturing plant, consolidation transport, and distribution center components of the supply chain. Initial optimization results are obtained for each of these models. Additionally, an integrated model including a single plant, multiple consolidation transport vehicles, and a single distribution center is formulated and initial results are obtained. All models are implemented and optimized for their given objectives using a standard MILP solver. Initial optimization results suggest that it is intractable to solve problems of relevant scale using standard MILP solvers. The natural hierarchical structure in the supply chain problem lends itself well to application of decomposition techniques intended to speed up solution time. Exact techniques, such as Benders decomposition, are explored as a baseline. Classical Benders decomposition is applied to the manufacturing plant model, and results indicate that Benders decomposition on its own will not improve solve times for the manufacturing plant problem and instead leads to longer solve times for the problems that are solved. This is likely due to the large number of discrete variables in manufacturing plant model. To improve upon solve times for the manufacturing plant model, an approximate decomposition technique is developed, applied to the plant model, and evaluated. The approximate algorithm developed in this work decomposes the problem into a three-level hierarchical structure and integrates a heuristic approach at two of the three levels in order to solve abstracted versions of the larger problem and guide towards high-quality solutions. Results indicate that the approximate technique solves problems faster than those solved by the standard MILP solver and all solutions are within approximately 20% of the true optimal solutions. Additionally, the approximate technique can solve problems twice the size of those solved by the standard MILP solver within a one hour timeframe.
by Lindsay Sanneman.
S.M.
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Becker, Adrian Bernard Druke. "Decomposition methods for large scale stochastic and robust optimization problems." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68969.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 107-112).
We propose new decomposition methods for use on broad families of stochastic and robust optimization problems in order to yield tractable approaches for large-scale real world application. We introduce a new type of a Markov decision problem named the Generalized Rest less Bandits Problem that encompasses a broad generalization of the restless bandit problem. For this class of stochastic optimization problems, we develop a nested policy heuristic which iteratively solves a series of sub-problems operating on smaller bandit systems. We also develop linear-optimization based bounds for the Generalized Restless Bandit problem and demonstrate promising computational performance of the nested policy heuristic on a large-scale real world application of search term selection for sponsored search advertising. We further study the distributionally robust optimization problem with known mean, covariance and support. These optimization models are attractive in their real world applications as they require the model consumer to only rely on those statistics of uncertainty that are known with relative confidence rather than making arbitrary assumptions about the exact dynamics of the underlying distribution of uncertainty. Known to be AP - hard, current approaches invoke tractable but often weak relaxations for real-world applications. We develop a decomposition method for this family of problems which recursively derives sub-policies along projected dimensions of uncertainty and provides a sequence of bounds on the value of the derived policy. In the development of this method, we prove that non-convex quadratic optimization in n-dimensions over a box in two-dimensions is efficiently solvable. We also show that this same decomposition method yields a promising heuristic for the MAXCUT problem. We then provide promising computational results in the context of a real world fixed income portfolio optimization problem. The decomposition methods developed in this thesis recursively derive sub-policies on projected dimensions of the master problem. These sub-policies are optimal on relaxations which admit "tight" projections of the master problem; that is, the projection of the feasible region for the relaxation is equivalent to the projection of that of master problem along the dimensions of the sub-policy. Additionally, these decomposition strategies provide a hierarchical solution structure that aids in solving large-scale problems.
by Adrian Bernard Druke Becker.
Ph.D.
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Ortiz, Diaz Camilo. "Block-decomposition and accelerated gradient methods for large-scale convex optimization." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53438.

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In this thesis, we develop block-decomposition (BD) methods and variants of accelerated *9gradient methods for large-scale conic programming and convex optimization, respectively. The BD methods, discussed in the first two parts of this thesis, are inexact versions of proximal-point methods applied to two-block-structured inclusion problems. The adaptive accelerated methods, presented in the last part of this thesis, can be viewed as new variants of Nesterov's optimal method. In an effort to improve their practical performance, these methods incorporate important speed-up refinements motivated by theoretical iteration-complexity bounds and our observations from extensive numerical experiments. We provide several benchmarks on various important problem classes to demonstrate the efficiency of the proposed methods compared to the most competitive ones proposed earlier in the literature. In the first part of this thesis, we consider exact BD first-order methods for solving conic semidefinite programming (SDP) problems and the more general problem that minimizes the sum of a convex differentiable function with Lipschitz continuous gradient, and two other proper closed convex (possibly, nonsmooth) functions. More specifically, these problems are reformulated as two-block monotone inclusion problems and exact BD methods, namely the ones that solve both proximal subproblems exactly, are used to solve them. In addition to being able to solve standard form conic SDP problems, the latter approach is also able to directly solve specially structured non-standard form conic programming problems without the need to add additional variables and/or constraints to bring them into standard form. Several ingredients are introduced to speed-up the BD methods in their pure form such as: adaptive (aggressive) choices of stepsizes for performing the extragradient step; and dynamic updates of scaled inner products to balance the blocks. Finally, computational results on several classes of SDPs are presented showing that the exact BD methods outperform the three most competitive codes for solving large-scale conic semidefinite programming. In the second part of this thesis, we present an inexact BD first-order method for solving standard form conic SDP problems which avoids computations of exact projections onto the manifold defined by the affine constraints and, as a result, is able to handle extra large-scale SDP instances. In this BD method, while the proximal subproblem corresponding to the first block is solved exactly, the one corresponding to the second block is solved inexactly in order to avoid finding the exact solution of a linear system corresponding to the manifolds consisting of both the primal and dual affine feasibility constraints. Our implementation uses the conjugate gradient method applied to a reduced positive definite dual linear system to obtain inexact solutions of the latter augmented primal-dual linear system. In addition, the inexact BD method incorporates a new dynamic scaling scheme that uses two scaling factors to balance three inclusions comprising the optimality conditions of the conic SDP. Finally, we present computational results showing the efficiency of our method for solving various extra large SDP instances, several of which cannot be solved by other existing methods, including some with at least two million constraints and/or fifty million non-zero coefficients in the affine constraints. In the last part of this thesis, we consider an adaptive accelerated gradient method for a general class of convex optimization problems. More specifically, we present a new accelerated variant of Nesterov's optimal method in which certain acceleration parameters are adaptively (and aggressively) chosen so as to: preserve the theoretical iteration-complexity of the original method; and substantially improve its practical performance in comparison to the other existing variants. Computational results are presented to demonstrate that the proposed adaptive accelerated method performs quite well compared to other variants proposed earlier in the literature.
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Scott, Drew. "Decomposition Methods for Routing and Planning of Large-Scale Aerospace Systems." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617108065278479.

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Prescott, Thomas Paul. "Large-scale layered systems and synthetic biology : model reduction and decomposition." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:205a18fb-b21f-4148-ba7d-3238f4b1f25b.

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This thesis is concerned with large-scale systems of Ordinary Differential Equations that model Biomolecular Reaction Networks (BRNs) in Systems and Synthetic Biology. It addresses the strategies of model reduction and decomposition used to overcome the challenges posed by the high dimension and stiffness typical of these models. A number of developments of these strategies are identified, and their implementation on various BRN models is demonstrated. The goal of model reduction is to construct a simplified ODE system to closely approximate a large-scale system. The error estimation problem seeks to quantify the approximation error; this is an example of the trajectory comparison problem. The first part of this thesis applies semi-definite programming (SDP) and dissipativity theory to this problem, producing a single a priori upper bound on the difference between two models in the presence of parameter uncertainty and for a range of initial conditions, for which exhaustive simulation is impractical. The second part of this thesis is concerned with the BRN decomposition problem of expressing a network as an interconnection of subnetworks. A novel framework, called layered decomposition, is introduced and compared with established modular techniques. Fundamental properties of layered decompositions are investigated, providing basic criteria for choosing an appropriate layered decomposition. Further aspects of the layering framework are considered: we illustrate the relationship between decomposition and scale separation by constructing singularly perturbed BRN models using layered decomposition; and we reveal the inter-layer signal propagation structure by decomposing the steady state response to parametric perturbations. Finally, we consider the large-scale SDP problem, where large scale SDP techniques fail to certify a system’s dissipativity. We describe the framework of Structured Storage Functions (SSF), defined where systems admit a cascaded decomposition, and demonstrate a significant resulting speed-up of large-scale dissipativity problems, with applications to the trajectory comparison technique discussed above.
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Chan, Chi-keung, and 陳志強. "Minimum bounding boxes and volume decomposition of CAD models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29947340.

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Kemenov, Konstantin A. "A New Two-Scale Decomposition Approach for Large-Eddy Simulation of Turbulent Flows." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11520.

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A novel computational approach, Two Level Simulation (TLS), was developed based on the explicit reconstruction of the small-scale velocity by solving the small-scale governing equations on the domain with reduced dimension representing a collection of one-dimensional lines embedded in the three-dimensional flow domain. A coupled system of equations, that is not based on an eddy-viscosity hypothesis, was derived based on the decomposition of flow variables into the large-scale and the small-scale components without introducing the concept of filtering. Simplified treatment of the small-scale equations was proposed based on modeling of the small-scale advective derivatives and the small-scale dissipative terms in the directions orthogonal to the lines. TLS approach was tested to simulate benchmark cases of turbulent flows, including forced isotropic turbulence, mixing layers and well-developed channel flow, and demonstrated good capabilities to capture turbulent flow features using relatively coarse grids.
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Books on the topic "Scale decomposition"

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Large-scale optimization: Problems and methods. Dordrecht: Kluwer Academic Publishers, 2001.

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Sakawa, Masatoshi. Large Scale Interactive Fuzzy Multiobjective Programming: Decomposition Approaches. Heidelberg: Physica-Verlag HD, 2000.

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Suvrajeet, Sen, ed. Stochastic decomposition: A statistical method for large scale stochastic linear programming. Dordrecht: Kluwer, 1996.

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Nemoto, Jiro. Productivity, efficiency, scale economies and technical change: A new decomposition analysis. Cambridge, MA: National Bureau of Economic Research, 2005.

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L, Tourrette, and Halpern Laurence, eds. Absorbing boundaries and layers, domain decomposition methods: Applications to large scale computers. Huntington, N.Y: Nova Science Publishers, 2001.

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Lange, Heinrich. Solution of large-scale multicommodity network flow problems via a logarithmic barrier function decomposition. Monterey, California: Naval Postgraduate School, 1988.

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Decentralized control and filtering in interconnected dynamical systems. Boca Raton: CRC Press, 2010.

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Nemoto, Jiro. Productivity, efficiency, scale economies and technical change: A new decomposition analysis of TFP applied to the Japanese prefectures. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Hogan, Jeffrey A. Time-frequency and time-scale methods: Adaptive decompositions, uncertainty principles, and sampling. Boston, MA: Birkhauser, 2004.

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1963-, Lakey Joseph D., ed. Time-frequency and time-scale methods: Adaptive decompositions, uncertainty principles, and sampling. Boston: Birkhauser, 2005.

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Book chapters on the topic "Scale decomposition"

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Jütte, Silke. "The Divide-and-Price Decomposition Algorithm." In Large-Scale Crew Scheduling, 57–89. Wiesbaden: Springer Fachmedien Wiesbaden, 2012. http://dx.doi.org/10.1007/978-3-658-24360-9_4.

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Horst, Reiner, and Hoang Tuy. "Decomposition of Large Scale Problems." In Global Optimization, 371–433. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7_8.

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Heurtaux, Frédéric, Fabrice Planchon, and Mladen Victor Wickerhauser. "Scale decomposition in Burgers' equation." In Wavelets, 505–23. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003210450-17.

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Horst, Reiner, and Hoang Tuy. "Decomposition of Large Scale Problems." In Global Optimization, 371–433. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3_8.

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Horst, Reiner, and Hoang Tuy. "Decomposition of Large Scale Problems." In Global Optimization, 381–446. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5_8.

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Martin, Richard Kipp. "Projection: Benders’ Decomposition." In Large Scale Linear and Integer Optimization: A Unified Approach, 349–67. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4975-8_10.

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Sakawa, Masatoshi. "The Dantzig-Wolfe Decomposition Method." In Large Scale Interactive Fuzzy Multiobjective Programming, 41–52. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1851-2_3.

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Sakawa, Masatoshi. "Genetic Algorithms with Decomposition Procedures." In Large Scale Interactive Fuzzy Multiobjective Programming, 117–34. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1851-2_7.

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Litvinchev, Igor, and Vladimir Tsurkov. "Iterative Aggregation-Decomposition in Optimization Problems." In Aggregation in Large-Scale Optimization, 61–161. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9154-6_2.

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Sen, Suvrajeet, Jason Mai, and Julia L. Higle. "Solution of Large Scale Stochastic Programs with Stochastic Decomposition Algorithms." In Large Scale Optimization, 388–410. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3632-7_19.

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Conference papers on the topic "Scale decomposition"

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Alsam, Ali, and Hans Jakob Rivertz. "Fast scale space image decomposition." In 2015 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2015. http://dx.doi.org/10.1109/iwssip.2015.7313926.

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de Almeida, Andre L. F., and Alain Y. Kibangou. "Distributed large-scale tensor decomposition." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853551.

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Makarov, Dmitry, and Vladimir Sobolev. "Decomposition of Multiple Time-Scale Systems." In 2021 14th International Conference Management of large-scale system development (MLSD). IEEE, 2021. http://dx.doi.org/10.1109/mlsd52249.2021.9600157.

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Mendez, Miguel Alfonso, Mikhael Balabane, and Jean Marie Buchlin. "Multi-scale proper orthogonal decomposition (mPOD)." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5043720.

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Rao, Raghuveer M., and Harold H. Szu. "Progress toward three-scale biorthogonal decomposition." In Aerospace/Defense Sensing and Controls, edited by Harold H. Szu. SPIE, 1996. http://dx.doi.org/10.1117/12.235982.

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Bespalov, Dmitriy, Ali Shokoufandeh, William C. Regli, and Wei Sun. "Local Feature Extraction Using Scale-Space Decomposition." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57702.

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In our recent work we have introduced a framework for extracting features from solid of mechanical artifacts in polyhedral representation based on scale-space feature decomposition [1]. Our approach used recent developments in efficient hierarchical decomposition of metric data using its spectral properties. In that work, through spectral decomposition, we were able to reduce the problem of matching to that of computing a mapping and distance measure between vertex-labeled rooted trees. This work discusses how Scale-Space decomposition frame-work could be extended to extract features from CAD models in polyhedral representation in terms of surface triangulation. First, we give an overview of the Scale-Space decomposition approach that is used to extract these features. Second, we discuss the performance of the technique used to extract features from CAD data in polyhedral representation. Third, we show the feature extraction process on noisy data — CAD models that were constructed using a 3D scanner. Finally, we conclude with discussion of future work.
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Zalozhnev, Alexey Yu. "Large-Scale Railway Network Design and Decomposition." In 2018 Eleventh International Conference "Management of large-scale system development" (MLSD 2018). IEEE, 2018. http://dx.doi.org/10.1109/mlsd.2018.8551788.

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Cheng, Lechao, Chengyi Zhang, and Zicheng Liao. "Intrinsic Image Transformation via Scale Space Decomposition." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00075.

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Can, Azime, Ervin Sejdic, and Luis F. Chaparro. "An asynchronous scale decomposition for biomedical signals." In 2011 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2011. http://dx.doi.org/10.1109/spmb.2011.6120107.

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Li, Xin‐Gong, and Tadeuss J. Ulrych. "Multi‐scale attribute analysis and trace decomposition." In SEG Technical Program Expanded Abstracts 1996. Society of Exploration Geophysicists, 1996. http://dx.doi.org/10.1190/1.1826439.

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Reports on the topic "Scale decomposition"

1

Rohlicek, J. R., and A. S. Willsky. Structural Decomposition of Multiple Time Scale Markov Processes,. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada189739.

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2

Yao, Y. Decomposition of Large Scale Semantic Graphsvia an Efficient Communities Algorithm. Office of Scientific and Technical Information (OSTI), February 2008. http://dx.doi.org/10.2172/926011.

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3

Dantzig, George B., and Gerd Infanger. Large-Scale Stochastic Linear Programs: Importance Sampling and Benders Decomposition. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada234962.

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4

Sen, Suvrajeet, and Yang Yuan. Decomposition Algorithms for Very Large Scale Stochastic Mixed-Integer Programs. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada481382.

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5

Nemoto, Jiro, and Mika Goto. Productivity, Efficiency, Scale Economies and Technical Change: A New Decomposition Analysis. Cambridge, MA: National Bureau of Economic Research, May 2005. http://dx.doi.org/10.3386/w11373.

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6

Cena, R. J., C. B. Thorsness, T. T. Coburn, and B. E. Watkins. Second test of base hydrolysate decomposition in a 0.04 gallon per minute scale reactor. Office of Scientific and Technical Information (OSTI), October 1994. http://dx.doi.org/10.2172/105852.

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Cena, R. J., C. B. Thorsness, T. Coburn, and B. E. Watkins. LLNL demonstration of base hydrolysate decomposition in a 0.035 gallon per minute scale reactor. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/10170617.

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8

Mukerji, Sudip. Turbulence computations with 3-D small-scale additive turbulent decomposition and data-fitting using chaotic map combinations. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/666048.

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9

Hussain, Fazle. Vortex Core Dynamics, Complex Helical Wave Decomposition, Organization of Fine-Scale Turbulence and Other Related Theoretical/Numerical Studies. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada299198.

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10

Lou, X. C., R. Rohlicek, P. G. Coxson, G. C. Verghese, and A. S. Willsky. Time Scale Decomposition: The Role of Scaling in Linear Systems and Transient States in Finite-State Markov Processes. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada160185.

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