Relevant bibliographies by topics / Statistical decomposition measures

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

Academic literature on the topic 'Statistical decomposition measures'

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

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Journal articles on the topic "Statistical decomposition measures"

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ANTONIOU, IOANNIS, COSTAS KARANIKAS, and STANISLAV SHKARIN. "DECOMPOSITIONS OF SPACES OF MEASURES." Infinite Dimensional Analysis, Quantum Probability and Related Topics 11, no. 01 (March 2008): 119–26. http://dx.doi.org/10.1142/s0219025708003014.

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Let 𝔐 be the Banach space of σ-additive complex-valued measures on an abstract measurable space. We prove that any closed, with respect to absolute continuity norm-closed, linear subspace L of 𝔐 is complemented and describe the unique complement, projection onto L along which has norm 1. Using this fact we prove a decomposition theorem, which includes the Jordan decomposition theorem, the generalized Radon–Nikodým theorem and the decomposition of measures into decaying and non-decaying components as particular cases. We also prove an analog of the Jessen–Wintner purity theorem for our decompositions.
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Alberts, Tom, and Hugo Duminil-Copin. "Bridge Decomposition of Restriction Measures." Journal of Statistical Physics 140, no. 3 (June 5, 2010): 467–93. http://dx.doi.org/10.1007/s10955-010-9999-3.

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Dębowski, Łukasz. "Approximating Information Measures for Fields." Entropy 22, no. 1 (January 9, 2020): 79. http://dx.doi.org/10.3390/e22010079.

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We supply corrected proofs of the invariance of completion and the chain rule for the Shannon information measures of arbitrary fields, as stated by Dębowski in 2009. Our corrected proofs rest on a number of auxiliary approximation results for Shannon information measures, which may be of an independent interest. As also discussed briefly in this article, the generalized calculus of Shannon information measures for fields, including the invariance of completion and the chain rule, is useful in particular for studying the ergodic decomposition of stationary processes and its links with statistical modeling of natural language.
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Carrington, M. E., R. Kobes, G. Kunstatter, D. Ostapchuk, and G. Passante. "Geometric measures of entanglement and the Schmidt decomposition." Journal of Physics A: Mathematical and Theoretical 43, no. 31 (July 6, 2010): 315302. http://dx.doi.org/10.1088/1751-8113/43/31/315302.

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Xie, Yongjian, Aili Yang, and Fang Ren. "Super quantum measures on effect algebras with the Riesz decomposition properties." Journal of Mathematical Physics 56, no. 10 (October 2015): 103509. http://dx.doi.org/10.1063/1.4933324.

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Capobianco, Enrico. "Statistical Embedding in Complex Biosystems." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 90–108. http://dx.doi.org/10.1515/jib-2006-30.

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Summary Complex high-dimensional systems represent an important area of interdisciplinary research in systems biology. Gene expression values obtained by microarray data represent a good example, owing to their various features that depend on biological network dynamics. This work emphasizes the role of blind source separation for dealing with dimensionality reduction and feature selection, and their useful combination with fuzzy rules, embedding principles and entropic measures. In particular, entropy and embedding are useful tools for controlling the robustness and stability of the decomposition of a system with larger than intrinsic dimensionality. As a result, the convergence to a small intrinsic dimensionality occurs by the means of least dependent components, seen as a minimal number of salient features.
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SMOLYANOV, O. G., and H. v. WEIZSÄCKER. "SMOOTH PROBABILITY MEASURES AND ASSOCIATED DIFFERENTIAL OPERATORS." Infinite Dimensional Analysis, Quantum Probability and Related Topics 02, no. 01 (March 1999): 51–78. http://dx.doi.org/10.1142/s0219025799000047.

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We compare different notions of differentiability of a measure along a vector field on a locally convex space. We consider in the L2-space of a differentiable measure the analog of the classical concepts of gradient, divergence and Laplacian (which coincides with the Ornstein–Uhlenbeck operator in the Gaussian case). We use these operators for the extension of the basic results of Malliavin and Stroock on the smoothness of finite dimensional image measures under certain nonsmooth mappings to the case of non-Gaussian measures. The proof of this extension is quite straight forward and does not use any Chaos-decomposition. Finally, the role of this Laplacian in the procedure of quantization of anharmonic oscillators is discussed.
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ACCARDI, LUIGI, ROMUALD LENCZEWSKI, and RAFAŁ SAŁAPATA. "DECOMPOSITIONS OF THE FREE PRODUCT OF GRAPHS." Infinite Dimensional Analysis, Quantum Probability and Related Topics 10, no. 03 (September 2007): 303–34. http://dx.doi.org/10.1142/s0219025707002750.

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We study the free product of rooted graphs and its various decompositions using quantum probabilistic methods. We show that the free product of rooted graphs is canonically associated with free independence, which completes the proof of the conjecture that there exists a product of rooted graphs canonically associated with each notion of noncommutative independence which arises in the axiomatic theory. Using the orthogonal product of rooted graphs, we decompose the branches of the free product of rooted graphs as "alternating orthogonal products". This leads to alternating decompositions of the free product itself, with the star product or the comb product followed by orthogonal products. These decompositions correspond to the recently studied decompositions of the free additive convolution of probability measures in terms of boolean and orthogonal convolutions, or monotone and orthogonal convolutions. We also introduce a new type of quantum decomposition of the free product of graphs, where the distance partition of the set of vertices is taken with respect to a set of vertices instead of a single vertex. We show that even in the case of widely studied graphs this yields new and more complete information on their spectral properties, like spectral measures of a (usually infinite) set of cyclic vectors under the action of the adjacency matrix.
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Osborne, T. J. "Convex hulls of varieties and entanglement measures based on the roof construction." Quantum Information and Computation 7, no. 3 (March 2007): 209–27. http://dx.doi.org/10.26421/qic7.3-3.

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In this paper we study the problem of calculating the convex hull of certain affine algebraic varieties. As we explain, the motivation for considering this problem is that certain pure-state measures of quantum entanglement, which we call \emph{polynomial entanglement measures}, can be represented as affine algebraic varieties. We consider the evaluation of certain mixed-state extensions of these polynomial entanglement measures, namely \emph{convex and concave roofs}. We show that the evaluation of a roof-based mixed-state extension is equivalent to calculating a hyperplane which is multiply tangent to the variety in a number of places equal to the number of terms in an optimal decomposition for the measure. In this way we provide an \emph{implicit} representation of optimal decompositions for mixed-state entanglement measures based on the roof construction.
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LEE, YUH-JIA, and HSIN-HUNG SHIH. "AN APPLICATION OF THE SEGAL–BARGMANN TRANSFORM TO THE CHARACTERIZATION OF LÉVY WHITE NOISE MEASURES." Infinite Dimensional Analysis, Quantum Probability and Related Topics 13, no. 02 (June 2010): 191–221. http://dx.doi.org/10.1142/s0219025710004012.

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Being inspired by the observation that the Stein's identity is closely connected to the quantum decomposition of probability measures and the Segal–Bargmann transform, we are able to characterize the Lévy white noise measures on the space [Formula: see text] of tempered distributions associated with a Lévy spectrum having finite second moment. The results not only extends the Stein and Chen's lemma for Gaussian and Poisson distributions to infinite dimensions but also to many other infinitely divisible distributions such as Gamma and Pascal distributions and corresponding Lévy white noise measures on [Formula: see text].
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Dissertations / Theses on the topic "Statistical decomposition measures"

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Metawe, Saad Abdel-Hamid. "The Prediction of Industrial Bond Rating Changes: a Multiple Discriminant Model Versus a Statistical Decomposition Model." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc332370/.

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The purpose of this study is to investigate the usefulness of statistical decomposition measures in the prediction of industrial bond rating changes. Further, the predictive ability of decomposition measures is compared with multiple discriminant analysis on the same sample. The problem of this study is twofold. It stems in general from the statistical problems associated with current techniques employed in the study of bond ratings and in particular from the lack of attention to the study of bond rating changes. Two main hypotheses are tested in this study. The first is that bond rating changes can be predicted through the use of financial statement data. The second is that decomposition analysis can achieve the same performance as multiple discriminant analysis in duplicating and predicting industrial bond rating changes. To explain and predict industrial bond rating changes, statistical decomposition measures were computed for each company in the sample. Based on these decomposition measures, the two types of analyses performed were (a) a univariate analysis where each decomposition measure was compared with an industry average decomposition measure, and (b) a multivariate analysis where decomposition measures were used as independent variables in a probability linear model. In addition to statistical decomposition analysis, multiple discriminant analysis was used in duplicating and predicting bond rating changes. Finally, a comparison was made between the predictive abilities of decomposition analysis and discriminant analysis.
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Löhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-38267.

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Many complexity measures are defined as the size of a minimal representation in a specific model class. One such complexity measure, which is important because it is widely applied, is statistical complexity. It is defined for discrete-time, stationary stochastic processes within a theory called computational mechanics. Here, a mathematically rigorous, more general version of this theory is presented, and abstract properties of statistical complexity as a function on the space of processes are investigated. In particular, weak-* lower semi-continuity and concavity are shown, and it is argued that these properties should be shared by all sensible complexity measures. Furthermore, a formula for the ergodic decomposition is obtained. The same results are also proven for two other complexity measures that are defined by different model classes, namely process dimension and generative complexity. These two quantities, and also the information theoretic complexity measure called excess entropy, are related to statistical complexity, and this relation is discussed here. It is also shown that computational mechanics can be reformulated in terms of Frank Knight's prediction process, which is of both conceptual and technical interest. In particular, it allows for a unified treatment of different processes and facilitates topological considerations. Continuity of the Markov transition kernel of a discrete version of the prediction process is obtained as a new result.
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Löhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures." Doctoral thesis, Max Planck Institut für Mathematik in den Naturwissenschaften, 2009. https://ul.qucosa.de/id/qucosa%3A11017.

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Many complexity measures are defined as the size of a minimal representation in a specific model class. One such complexity measure, which is important because it is widely applied, is statistical complexity. It is defined for discrete-time, stationary stochastic processes within a theory called computational mechanics. Here, a mathematically rigorous, more general version of this theory is presented, and abstract properties of statistical complexity as a function on the space of processes are investigated. In particular, weak-* lower semi-continuity and concavity are shown, and it is argued that these properties should be shared by all sensible complexity measures. Furthermore, a formula for the ergodic decomposition is obtained. The same results are also proven for two other complexity measures that are defined by different model classes, namely process dimension and generative complexity. These two quantities, and also the information theoretic complexity measure called excess entropy, are related to statistical complexity, and this relation is discussed here. It is also shown that computational mechanics can be reformulated in terms of Frank Knight''s prediction process, which is of both conceptual and technical interest. In particular, it allows for a unified treatment of different processes and facilitates topological considerations. Continuity of the Markov transition kernel of a discrete version of the prediction process is obtained as a new result.
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Mertens, Bart Josepha August. "Efficient cross-validatory computations and influence measures for principal component and partial least squares decompositions with applications in chemometrics." Thesis, University College London (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321683.

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Books on the topic "Statistical decomposition measures"

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Barndorff-Nielsen, O. E. Decomposition and invariance of measures, and statistical transformation models. New York: Springer-Verlag, 1989.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. Decomposition and Invariance of Measures, and Statistical Transformation Models. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5.

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Barndorff-Nielsen, O. E. Decomposition, factorization and invariance of measures, with a view to applications in statistics. Aarhus C, Denmark: Dept. of Theoretical Statistics, Institute of Mathematics, University of Aarhus, 1985.

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Barndorff-Nielsen, O. E. Decomposition and Invariance of Measures, and Statistical Transformation Models. Springer, 2011.

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Ramsay, James. Curve registration. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.9.

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This article deals with curve registration, which refers to methods for aligning prominent features in a set of curves by transforming their abscissa variables. It first illustrates the concepts of amplitude and phase variation schematically and with real data before defining the time-warping functions and their functional inverse. It then describes the decomposition of total mean squared variation into separate amplitude and phase components, along with an R2 measure of the proportion of functional variation due to phase in a sample of curves. It also considers landmark registration, novel ways of defining curve features, continuous registration, and methods based on structured models for amplitude and phase variation combined with more statistically oriented fitting methods such as maximum likelihood or Bayesian estimation. The article concludes with a brief survey of software resources for registration.
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Book chapters on the topic "Statistical decomposition measures"

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Statistical transformation models." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 74–112. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_8.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Decomposition and factorization of measures." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 41–53. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_5.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Construction of invariant measures." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 53–66. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_6.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Invariant, relatively invariant, and quasi-invariant measures." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 28–41. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_4.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Introduction." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 1–2. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_1.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Topological groups and actions." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 2–14. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_2.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Matrix Lie groups." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 15–27. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_3.

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Barndorff-Nielsen, Ole E., Preben Blæsild, and Poul Svante Eriksen. "Exterior calculus." In Decomposition and Invariance of Measures, and Statistical Transformation Models, 66–74. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3682-5_7.

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Štĕpán, Josef, and Viktor Beneš. "Moment decompositions of measure spaces." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 328–36. Hayward, CA: Institute of Mathematical Statistics, 1996. http://dx.doi.org/10.1214/lnms/1215452629.

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Biswas, Mainak, and Debangshu Dey. "Bi-dimensional Statistical Empirical Mode Decomposition-Based Video Analysis for Detecting Colon Polyps Using Composite Similarity Measure." In Advances in Intelligent Systems and Computing, 297–308. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2009-1_35.

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Conference papers on the topic "Statistical decomposition measures"

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Nagaria, Baluram, Mohammad Farukh Hashmi, Imran Hussain, and Ravijeet Singh Chauhan. "Comparative Analysis of an Optimal Image Compression Using FDWT at Various Decomposition Level with Different Statistical Numerical Measures for Different Pixel Frame." In 2011 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2011. http://dx.doi.org/10.1109/cicn.2011.53.

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Gadallah, Mohamed H. "Tolerance Optimization: A Decomposition Scheme, Variance Reduction and Fractional Approximation." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/ied-21222.

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Abstract Development of involved optimization algorithms is not an easy task for several reasons: First, every analyst is interested in a specific problem; Second, the capabilities of these methods may not be fully understood a priori; Third, coding of multi-purpose and more involved algorithms is not an easy job. In this paper, the optimization problem employing the near to global optimum algorithm is studied (Gadallah, M.H., 2000). The focus is to exploit 2 ideas: First, the algorithm can be modified to act as a variance reduction technique; Second, the algorithm can be modified to tackle the problem of system decomposition. Both ideas are novel within the context of statistical design of experiments. The first, if fully proved experimentally could yield the simultaneous integration of nominal and variance optimization possible. The second, can be extended to deal with multi-dimensional highly constrained systems with ease. These two ideas are explained wife the use of a simple example to illustrate the idea. An algorithm is developed that deal with the problem in several stages according to a predetermined decomposition scheme. The original objective and constraint functions are dealt with to suit each stage. Accordingly, all NP hard problems can ideally be transformed into NP complete ones with a consequence on the number of stages resulting from decomposition. Several decomposition scenarios are used and their results are compared numerically. Two orthogonal arrays and four composite arrays are used to plan experimentation; these are L27OA and L54OA and their subfamilies. These arrays are compared with respect to their statistical measures. The algorithm as such, is very promising optimization tool, especially for coupling system decomposition and variance reduction. Past work focused on either decomposition or statistical optimization. This work offers both capabilities. Several studies are reviewed and conclusions are drawn.
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Hofleitner, A., and A. Bayen. "Optimal decomposition of travel times measured by probe vehicles using a statistical traffic flow model." In 2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/itsc.2011.6083050.

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De Giorgi, Maria Grazia, Marco Tarantino, and Antonio Ficarella. "A New Hybrid Method for Wind Power Forecasting Based on Wavelet Decomposition and Artificial Neural Networks." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46382.

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Depending on their input, wind power forecasting models are classified as physical or statistical approaches or a combination of both. Physical models use physical considerations, as meteorological information (Numerical Weather Prediction) and technical characteristics of the wind turbines (hub height, power curve, thrust coefficient). Statistical models use explanatory variables and online measurements, usually employing recursive techniques, like recursive least squares or artificial neural networks (ANNs) which perform a non-linear mapping and provide a robust approach for wind prediction. In this paper a new hybrid method (mixing physical and statistical approaches) is proposed, based on the wavelet decomposition technique and on artificial neural networks, in order to predict power production of a wind farm in different time horizons: 1, 3, 6, 12 and 24 hours. In particular, two approaches are compared, both based on the time series of on-line measured wind power and on the Numerical Weather Predictions; in the first approach, the forecast is carried out only through the training of a neural network which, in the second approach is, instead, used in combination with the wavelet decomposition technique, improving the performance especially over the short time horizons. The error of the different forecast systems is investigated for various forecasting horizons and statistical distributions of the error are calculated and presented.
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Xiaogang Hu, Nina L. Suresh, Brian Jeon, Henry Shin, and William Z. Rymer. "Statistics of inter-spike intervals as a routine measure of accuracy in automatic decomposition of surface electromyogram." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944387.

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Ghiocel, Dan M. "Refined Stochastic Field Models for Jet Engine Vibration and Fault Diagnostics." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0654.

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The paper proposes a refined stochastic fault classifier for jet engine vibration diagnostic based on advanced stochastic concepts. The statistical data to be analyzed are the spectrum profiles of vibration measured at different locations in the engine. The statistical spectrum profiles are idealized by non-homogeneous stochastic fields with non-Gaussian probability distributions. The proposed stochastic classifier is based on the decomposition of the statistical correlation matrix of spectrum profiles using a Karhunen-Loeve (KL) expansion. Two stochastic classifiers are proposed, namely a “global” and a “specific” fault classifier. The “global” KL classifier, which is a scalar quantity, is an efficient anomaly/novelty detection tool for identifying incipient fault diagnosis with small amplitude fluctuations. The “specific” KL classifier, which is a vector quantity, is a refined diagnostic tool for identifying the engine malfunction causes. An illustrative example of a turbofan engine is included.
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Murphy, Alexander R., Henry D. Banks, Robert L. Nagel, and Julie S. Linsey. "Graduate Students’ Mental Models: An Investigation Into the Role of Function in Systems Understanding." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98383.

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Abstract Elicited student mental models reveal students’ understanding of a given system as well as their ability to communicate knowledge of that system to others. Understanding how students form and developmental models of systems is critical to the progress of engineering education. In this work, graduate students’ mental models of common household products are measured before and after instruction on functional modeling and functional decomposition. These mental models are measured using previously published, but still developing mental model instruments. The included systems are a hair dryer, clothes dryer, and vacuum cleaner with accompanying scoring rubrics. Results show statistically significant improvements on average mental model rubric scores on all three given systems after the functional modeling intervention. These results suggest that curriculum content on functional modeling and decomposition likely improves students’ mental models of engineering systems and their ability to communicate their knowledge about those systems. As we improve our understanding of how students form, change, and communicate their mental models of engineering systems, educators can shape curriculum to facilitate the skills necessary for comprehensive systems understanding.
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Zhang, Guangjian, Ilyass Khlifa, and Olivier Coutier-Delgosha. "Experimental Investigation of Turbulent Cavitating Flows in a Small Venturi Nozzle." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-4781.

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Abstract The cavitating flows created in a small Venturi tube with throat cross section 4 × 15.34 mm2 are investigated based on ultra-fast x-ray imaging. The instantaneous velocities of the liquid and vapor are measured simultaneously by tracking seeding particles and vapor structures respectively while the vapor volume fraction is derived from the different x-ray attenuation. Wavelet decomposition with appropriate thresholds is used to separate seeding particles from vapor structures, so that image cross-correlations could be applied on the two phases separately. This study presents data on mean velocity and void ratio field, statistical turbulent quantities in three different cavitation levels with the same reference velocity. A type of cavitation associated with a weak but persistent re-entrant jet is described. The comparison between the cavitation and the noncavitating flow shows that the averaged flow field is significantly altered by the presence of cavitation and the vapor formation near the throat area is observed to suppress velocity fluctuations.
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Durgesh, Vibhav, and Jonathan W. Naughton. "Multi-Time-Delay LSE-POD Complementary Approach Applied to Wake Flow Behind a Bluff Body." In ASME/JSME 2007 5th Joint Fluids Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/fedsm2007-37175.

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An understanding of the near wake dynamics of a bluff body is desired to better link base drag reduction observed on these bodies with the coherent structures in the wake. This investigation explores different Linear Stochastic Estimation-Proper Orthogonal Decomposition (LSE-POD) methods that can be employed to estimate the dynamics of the energy containing structure. Statistically independent two-dimensional PIV measurements and time-resolved surface pressure measurements are used to determine spatial POD modes and LSE coefficients for estimating the time-varying POD coefficients using measured surface pressures. These results are used with the time-resolved surface pressure measurements to estimate the time-varying POD coefficients that may be used for a low-order, time-resolved reconstruction of the flow field. The multi-time LSE approach formulated in the time domain (multi-time-delay LSE) is found to be successful in capturing the important near wake dynamics.
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Song, Miao, David B. Segala, Jonathan B. Dingwell, and David Chelidze. "Slow-Time Changes in Human Muscle Fatigue Are Fully Represented in Movement Kinematics." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35173.

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Muscle fatigue is exhibited in individuals who partake in repetitive movements for an extensive duration of time. The objective of this study was to identify correlation, if any, between movement kinematics and muscle electrical activity (EMG). Movement kinematics and EMG signals were measured and recorded from the lower extremities of stationary elite cyclists. Standard statistical metrics (SSM) and phase space warping (PSW) based features were extracted from the recorded fast-time time series for consecutive intermediate time intervals. The SSM based features were composed of higher moments, fractal dimension, correlation sum, power spectral density, and cross-correlation. The PSW based features were subject to quantifying short-time differences between the fatigued and unfatigued reconstructed phase spaces. The slow-time manifolds describing global fatigue dynamics in these feature spaces were extracted using smooth orthogonal decomposition (SOD). Mean and median frequencies from the EMG data were calculated to describe the local fatigue dynamics in each muscle. There were very close correlations between the EMG and kinematics data based global fatigue features. Also, the 4 and 5 dimensional slow time manifolds (corresponding to PSW and SSM based features, respectively) fully represented the local fatigue dynamics in all the muscles as described by the EMG data. Therefore, for this particular context, the fatigue information present in the standard EMG analysis was fully represented in the SOD based slow-time features extracted from the kinematic data. Furthermore, the SOD based analysis gave estimates of effective dimensionality of muscle fatigue dynamics.
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Reports on the topic "Statistical decomposition measures"

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Huang, Norden E. Development a Statistical Measure for Empirical Mode Decomposition and Hilbert Spectral Analysis and its Applications to Oceanographic Data. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada634054.

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