Academic literature on the topic 'Scale decomposition'

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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 analysi
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Schmidt, Marie Foged, Martin Benning, and Carola-Bibiane Schönlieb. "Inverse scale space decomposition." Inverse Problems 34, no. 4 (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 (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 (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
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Moktadir, Z. "Scale decomposition of molecular beam epitaxy." Journal of Physics: Condensed Matter 20, no. 23 (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 (1996): 1–15. http://dx.doi.org/10.1007/bf02192638.

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Ji, Jingyu, Yuhua Zhang, Yongjiang Hu, et al. "Fusion of Infrared and Visible Images Based on Three-Scale Decomposition and ResNet Feature Transfer." Entropy 24, no. 10 (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
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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 doma
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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 (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 sample
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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.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 103-105).<br>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, sh
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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.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 107-112).<br>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 stoch
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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
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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 s
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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 eq
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Books on the topic "Scale decomposition"

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

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

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

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Nemoto, Jiro. Productivity, efficiency, scale economies and technical change: A new decomposition analysis. 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. Nova Science Publishers, 2001.

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

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Decentralized control and filtering in interconnected dynamical systems. 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. 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. Birkhauser, 2004.

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

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

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

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

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

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

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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), 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), 1994. http://dx.doi.org/10.2172/10170617.

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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), 1997. http://dx.doi.org/10.2172/666048.

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

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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. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160185.

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