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Relevant bibliographies by topics / SVD real-normalization of complex modal vectors

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Academic literature on the topic 'SVD real-normalization of complex modal vectors'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "SVD real-normalization of complex modal vectors"

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LaTorraca, G. A., T. R. Madden, and J. Korringa. "An analysis of the magnetotelluric impedance for three‐dimensional conductivity structures." GEOPHYSICS 51, no. 9 (1986): 1819–29. http://dx.doi.org/10.1190/1.1442228.

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The eigenstate analysis of Lanczos, also known as singular value decomposition (SVD), is used to define eight parameters which uniquely describe the magnetotelluric impedance Z. These parameters are independent of a priori assumptions about Z and can be interpreted in terms of three‐dimensional conductivity structures. Through SVD, the impedance is represented by two characteristic states. These states consist of two pairs (E and H) of complex vectors and two corresponding, real, singular values which together describe the extremal properties of Z. The singular values are the maximum and minim

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Antonacci, Yuri, Ludovico Minati, Luca Faes, et al. "Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators." PeerJ Computer Science 7 (May 18, 2021): e429. http://dx.doi.org/10.7717/peerj-cs.429.

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One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by so

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Dissertations / Theses on the topic "SVD real-normalization of complex modal vectors"

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Hiremaglur, Rajeev. "Real-Normalization of Experimental Complex Modal Vectors with Modal Vector Contamination." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1415283945.

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Conference papers on the topic "SVD real-normalization of complex modal vectors"

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Schacter, David, Christopher Wang, Goldie Nejat, and Beno Benhabib. "The Development of a Facial-Affect Recognition System for Application in Human-Robot Interaction Scenarios." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48195.

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This paper presents a non-contact unique automated affect recognition system that identifies human facial expressions and classifies them using Support Vector Regression (SVR) into affective states based on a pleasure-arousal two-dimensional model of affect. By utilizing a continuous two-dimensional model, rather than a traditional discrete categorical model for affect, the proposed system captures complex and ambiguous emotions that are prevalent in real-world scenarios. Our aim is to incorporate the proposed recognition system in robots engaged in human-robot interaction (HRI) scenarios. Nam

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Anguita, Davide, Fabio Rivieccio, Marcello Canova, Paolo Casoli, and Agostino Gambarotta. "A Learning-Machine Based Method for the Simulation of Combustion Process in Automotive I.C. Engines." In ASME 2003 Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/ices2003-0682.

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In automotive applications problems related to management and diagnostics play an important role to improve engine performance and to reduce fuel consumption and pollutant emissions. In the design of control systems the use of theoretical models for the simulation of engine behaviour proved to be very useful, and it is apparent from the literature. However, since automotive engines have become very complex plants, their modelling requires a comprehensive description of the behaviour of many processes and components. Combustion process has a strong influence on performance and emissions, but it

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