Relevant bibliographies by topics / Generalized Subspace Model

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Academic literature on the topic 'Generalized Subspace Model'

Author: Grafiati
Published: 28 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Generalized Subspace Model"

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Tulkin, Tulkin, and Shokhida Nematova. "INVESTIGATION OF THE SPECTRUM OF A GENERALIZED FRIEDRICHS MODEL: NON-INTEGRAL LATTICE CASE." Scientific Reports of Bukhara State University 3, no. 1 (January 30, 2019): 5–11. http://dx.doi.org/10.52297/2181-1466/2019/3/1/1.

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The article investigates the essential and discrete spectrum of the self-adjoint generalized Friedrichs model. This model corresponds to a system consisting of no more than two particles on a non-integral lattice, and operates in a truncated subspace of Fock space. The number and location of eigenvalues is determined according to the "interaction parameter". Anobvious form of the eigenvectors is found
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Wang, Xinsheng, Chenxu Wang, and Mingyan Yu. "The Minimum Norm Least-Squares Solution in Reduction by Krylov Subspace Methods." Journal of Circuits, Systems and Computers 26, no. 01 (October 4, 2016): 1750006. http://dx.doi.org/10.1142/s0218126617500062.

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In recent years, model order reduction (MOR) of interconnect system has become an important technique to reduce the computation complexity and improve the verification efficiency in the nanometer VLSI design. The Krylov subspaces techniques in existing MOR methods are efficient, and have become the methods of choice for generating small-scale macro-models of the large-scale multi-port RCL networks that arise in VLSI interconnect analysis. Although the Krylov subspace projection-based MOR methods have been widely studied over the past decade in the electrical computer-aided design community, all of them do not provide a best optimal solution in a given order. In this paper, a minimum norm least-squares solution for MOR by Krylov subspace methods is proposed. The method is based on generalized inverse (or pseudo-inverse) theory. This enables a new criterion for MOR-based Krylov subspace projection methods. Two numerical examples are used to test the PRIMA method based on the method proposed in this paper as a standard model.
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KIM, W. T., B. H. CHO, and D. K. PARK. "HAMILTONIAN FORMULATION OF CHIRAL SCHWINGER MODEL IN FOUR DIMENSIONS." Modern Physics Letters A 04, no. 26 (December 10, 1989): 2531–37. http://dx.doi.org/10.1142/s0217732389002835.

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The four dimensional chiral Schwinger model can be quantized through the generalized point splitting method in Schrödinger representation. It satisfies the consistency and unitarity in the physical subspace.
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GANGULY, NILOY, PRADIPTA MAJI, BIPLAB K. SIKDAR, and P. PAL CHAUDHURI. "GENERALIZED MULTIPLE ATTRACTOR CELLULAR AUTOMATA (GMACA) MODEL FOR ASSOCIATIVE MEMORY." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (November 2002): 781–95. http://dx.doi.org/10.1142/s0218001402001988.

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This paper reports an efficient technique of evolving Cellular Automata (CA) as an associative memory model. The evolved CA termed as GMACA (Generalized Multiple Attractor Cellular Automata), acts as a powerful pattern recognizer. Detailed analysis of GMACA rules establishes the fact that the rule subspace of the pattern recognizing CA lies at the edge of chaos — believed to be capable of executing complex computation.
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Reynders, Edwin, and Guido De Roeck. "Subspace identification of (AR)ARMAX, Box-Jenkins, and generalized model structures." IFAC Proceedings Volumes 42, no. 10 (2009): 868–73. http://dx.doi.org/10.3182/20090706-3-fr-2004.00144.

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Zhang, Zhiyi. "3D resistivity mapping of airborne EM data." GEOPHYSICS 68, no. 6 (November 2003): 1896–905. http://dx.doi.org/10.1190/1.1635042.

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A 3D resistivity mapping technique has been developed to provide fast estimates of resistivity distributions in airborne electromagnetic surveys. This proposed 3D mapping method consists of an approximate 3D linear inverse operator and a generalized subspace solver. The 3D inverse operator can be generated using any forward approximation that is linear in resistivity. The generalized subspace method is an alternative to the conjugate gradient method, and it reduces the original large linear system of equations to a much smaller but nonlinear one that is solved iteratively. The major benefit of using generalized subspace methods is that subspace vectors can be built based upon physical principles such as skin and investigation depths. Since the 3D mapping is a linear inverse problem, no iteration, and thus no forward modeling nor sensitivity updating, is needed. The 3D resistivity‐mapping technique can be used directly to estimate 3D resistivity distribution or to provide a model update during an intermediate iteration in a nonlinear 3D inversion. Synthetic and field data examples indicate that the 3D mapping can provide quantitative information about the resistivity and spatial distributions of the 3D targets.
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Zhao, Yuhui, Jinlong Yu, Peng Shan, Ziheng Zhao, Xueying Jiang, and Shuli Gao. "PLS Subspace-Based Calibration Transfer for Near-Infrared Spectroscopy Quantitative Analysis." Molecules 24, no. 7 (April 2, 2019): 1289. http://dx.doi.org/10.3390/molecules24071289.

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In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results.
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Mojaveri, B., A. Dehghani, M. A. Fasihi, and T. Mohammadpour. "Ground state and thermal entanglement between two two-level atoms interacting with a nondegenerate parametric amplifier: Different sub-spaces." International Journal of Modern Physics B 33, no. 06 (March 10, 2019): 1950035. http://dx.doi.org/10.1142/s0217979219500358.

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In this paper, a Hamiltonian model that includes interaction of two coupled two-level atoms with a nondegenerate parametric amplifier in a cavity is introduced. By using the two-mode squeezing operator and under a certain condition, the introduced Hamiltonian is reduced to a generalized Jaynes–Cummings Hamiltonian. The constants of motion of system imply the existence of a decomposition of the system’s Hilbert space [Formula: see text] into a direct sum of three infinite dimensional sub-spaces, as [Formula: see text]. This decomposition enables us to study ground and thermally induced entanglement between the atoms in each of the sub-spaces as well as whole Hilbert space. The effect of atom–atom and atom–photon couplings on the degree of ground state and thermal entanglement are also investigated using the concurrence measure. It is found that in the subspaces [Formula: see text] and [Formula: see text] for all experimental values of parameters of the system, the atoms are disentangled. It is also observed that in the total space [Formula: see text] the ground state entanglement is always zero, while in the subspace [Formula: see text] it is at its maximum value. Moreover, it is found that in the subspace [Formula: see text] thermal entanglement between the atoms is robust against temperature.
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Wu, Riheng, Yangyang Dong, Zhenhai Zhang, and Le Xu. "Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology." International Journal of Antennas and Propagation 2019 (December 24, 2019): 1–11. http://dx.doi.org/10.1155/2019/3924569.

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We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost.
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Liu, Xian-xia, Jiao-fen Li, and Xi-Yan Hu. "Generalized inverse problems for part symmetric matrices on a subspace in structural dynamic model updating." Mathematical and Computer Modelling 53, no. 1-2 (January 2011): 110–21. http://dx.doi.org/10.1016/j.mcm.2010.07.024.

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Conference papers on the topic "Generalized Subspace Model"

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D’Souza, Kiran, and Bogdan I. Epureanu. "Minimum Rank Generalized Subspace Updating Approach for Nonlinear Systems." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80135.

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An algorithm for analyzing a nonlinear system as an augmented linear system is presented. The method uses a nonlinear discrete model of the system and the form of the nonlinearities to create an augmented linear model of the system. A linear modal analysis technique that uses forcing that is known but not prescribed is then used to solve for the modal properties of the augmented linear system after the onset of damage. Due to the specialized form of the augmentation, nonlinear damage causes asymmetric damage in the updated matrices. A generalized minimum rank perturbation theory, which requires knowledge of both right and left eigenvectors, is developed to handle the asymmetric damage scenarios. The damage extent algorithm becomes an iterative process when an incomplete set of right eigenvectors are known. The method is demonstrated using numerical data from nonlinear 3-bay truss structures. Various damage scenarios of the nonlinear systems are used to demonstrate the effectiveness of the augmentation and the generalized minimum rank perturbation theory, and the effect of random noise on the technique. The nonlinearities included in the 3-bay truss are cubic springs.
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Wang, Yan. "Solving Interval Master Equation in Simulation of Jump Processes Under Uncertainties." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12740.

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Two types of uncertainty are generally recognized in modeling and simulation, including variability caused by inherent randomness and incertitude due to the lack of perfect knowledge. Generalized interval probability is able to model both uncertainty components simultaneously, where epistemic uncertainty is quantified by the generalized interval in addition to the probabilistic measure. With the conditioning, independence, and Markovian property uniquely defined, the calculus structures in generalized interval probability resembles those in the classical probability theory. An imprecise Markov chain model is proposed with the ease of computation. A Krylov subspace projection method is developed to solve the interval master equation to simulate jump processes with finite state transitions under uncertainties. The state transitions with interval-valued probabilities can be simulated, which provides the lower and upper bound information of evolving distributions as an alternative to the traditional sensitivity analysis.
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Bellizzi, Sergio, and Rubens Sampaio. "Smooth Decomposition Analysis and Order Reduction of Nonlinear Mechanical Systems Under Random Excitation." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87496.

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This paper presents a possible alternative procedure to the Karhunen-Loève approach to construct reduced order models which capture accurately the dynamics of nonlinear discrete mechanical systems under random excitation. This procedure combines the Smooth Decomposition method and the Petrov-Galerkin approximation. The smooth decomposition method is a multivariate-data analysis method characterizing coherent structures (the smooth modes) as the eigenvectors of the generalized eigenproblem defined from the covariance matrix of the displacement field and the covariance matrix of the velocity field. The Petrov-Galerkin approximation is used to project the dynamics in a subspace generated by a set of the smooth modes. The Petrov-Galerkin approximation preserves the second order structure of the equations of motion. The procedure is considered for a mechanical system including a strongly nonlinear end-attachment. The efficiency of the approach is analyzed comparing the power spectral density functions of the reduced-order model and of the original system.
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Blomquist, Matthew, and Abhijit Mukherjee. "Performance Improvements of Krylov Subspace Methods in Numerical Heat Transfer and Fluid Flow Simulations." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12174.

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Abstract In recent years, advancements in computational hardware have enabled massive parallelism that can significantly reduce the duration of many numerical simulations. However, many high-fidelity simulations use serial algorithms to solve large systems of linear equations and are not well suited to exploit the parallelism of modern hardware. The Tri-Diagonal Matrix Algorithm (TDMA) is one such example of a serial algorithm that is ubiquitous in numerical simulations of heat transfer and fluid flow. Krylov subspace methods for solving linear systems, such as the Bi-Conjugate Gradients (BiCG) algorithm, can offer an ideal solution to improve the performance of numerical simulations as these methods can exploit the massive parallelism of modern hardware. In the present work, Krylov-based linear solvers of Bi-Conjugate Gradients (BCG), Generalized Minimum Residual (GMRES), and Bi-Conjugate Gradients Stabilized (BCGSTAB) have been incorporated into the SIMPLER algorithm to solve a three-dimensional Rayleigh-Bénard Convection model. The incompressible Navier-Stoke’s equations, along with the continuity and energy equations, are solved using the SIMPLER method. The computational duration and numerical accuracy for the Krylov-solvers are compared with that of the TDMA. The results show that Krylov methods can improve the speed of convergence for the SIMPLER method by factors up to 7.7 while maintaining equivalent numerical accuracy to the TDMA.
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Lloyd, George M. "Optimal Design of a JT Cryocooler With Stochastic Constraints—Part I: Formulation." In ASME 2009 Heat Transfer Summer Conference collocated with the InterPACK09 and 3rd Energy Sustainability Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/ht2009-88169.

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Cyrocoolers are notorious for being difficult to design and optimize. Reasons for this include subsystem complexity, large unknowns associated with material and transport parameters, and high sensitivity to manufacturing tolerances. The purpose of this paper is to address this topic by incorporating design uncertainty itself as a constraint during the optimization of a Joule-Thomson sorption cryocooler. In our method a Markov Chain Monte Carlo sampler is used as the means to develop a suitable ensemble from a practical set of computational results which circumscribe the power/efficiency characteristics of a cryocooler as a function of several dimensionless stochastic optimization parameters. The ensemble is used to estimate the covariance structure of the design uncertainty, which is then projected into the best low rank subspace where tests of hypothesis under the dominant generalized parameters can be formulated; growth in fluctuations of the generalized parameters along optimization trajectories becomes clearly evident and quantifiable. The method results in a classical power/efficiency diagram, with the addition of quantified design uncertainty. The utility of these diagrams is that they enable rapid-prototyping efforts to target the best cooler design that is most likely to function as expected either for model validation or production. This paper will present the methodogy and a comprehensive computation model of a JT metal hydride cryocooler and demonstrate its application.
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Lloyd, George M., and K. J. Kim. "Power/Efficiency Optimization of a Sorption Cooler Under Quantified Design Uncertainty." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43742.

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While the design paradigm in engineering of searching for the optimum system has proven fruitful (and given a good model relatively straightforward, in principle), the desired end result of engineering development is rarely a model (even the optimum one), but a system. In this regard it has frequently been observed (generally with some disappointment) that what one can specify is not always what one gets. It is frequently the case that realized systems, no matter how carefully constructed according to specifications derived from verified and validated models, frequently depart from the designed-for behaviour, due to parametric incertitude. Given this not uncommon circumstance, a somewhat more useful question one might seek to answer during an optimization process is “what is the best system under the constraints which I can reasonably hope to build?” Design optimization under incertitude approaches based on intrusive modifications to the deterministic model, such as stochastic finite elements and chaos expansions, are tedious to apply, computationally expensive, and fraught with convergence issues. The simplest nonintrusive approach—direct Monte Carlo sampling— is far too slow to efficiently sample the joint response distribution of complex thermophysics transient models. The purpose of this paper is to address this topic by incorporating design uncertainty itself as a constraint during the optimization of a sorption cooler. In our method a Markov Chain Monte Carlo sampler is used as the means to develop a suitable ensemble from a practical set of computational results which circumscribe the power/efficiency characteristics of a cooler as a function of several dimensionless stochastic optimization parameters. The ensemble is used to estimate the covariance structure of the design uncertainty, which is then projected into the best low rank subspace where tests of hypothesis under the dominant generalized parameters can be formulated; growth in fluctuations of the generalized parameters along optimization trajectories becomes clearly evident and quantifiable. The method results in a classical power/efficiency diagram, with the addition of quantified design uncertainty. The utility of these diagrams is that they enable rapid-prototyping efforts to target the best cooler design that is most likely to function as expected.
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Yu, Chengpu, Jie Chen, Lennart Ljung, and Michel Verhaegen. "Subspace identification of continuous-time models using generalized orthonormal bases." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264440.

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Lloyd, George M. "A Kalman Filter Framework for High-Dimensional Sensor Fusion Using Stochastic Non-Linear Networks." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37834.

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The textbook Kalman Filter (LKF) seeks to estimate the state of a linear system based on having two things in hand: a.) a reasonable state-space model of the underlying process and its noise components; b.) imperfect (noisy) measurements obtained from the process via one or more sensors. The LKF approach results in a predictor-corrector algorithm which can be applied recursively to correct predictions from the state model so as to yield posterior estimates of the current process state, as new sensor data are made available. The LKF can be shown to be optimal in a Gaussian setting and is eminently useful in practical settings when the models and measurements are stochastic and non-stationary. Numerous extensions of the KF filter have been proposed for the non-linear problem, such as extended Kalman Filters (EKF) and ‘ensemble’ filters (EnKF). Proofs of optimality are difficult to obtain but for many problems where the ‘physics’ is of modest complexity EKF’s yield algorithms which function well in a practical sense; the EnKF also shows promise but is limited by the requirement for sampling the random processes. In multi-physics systems, for example, several complications arise, even beyond non-Gaussianity. On the one hand, multi-physics effects may include multi-scale responses and path dependency, which may be poorly sampled by a sensor suite (tending to favor low gains). One the other hand, as more multi-physics effects are incorporated into a model, the model itself becomes a less and less perfect model of reality (tending to favor high gains). For reasons such as these suitable estimates of the joint system response are difficult to obtain, as are corresponding joint estimates of the sensor ensemble. This paper will address these issues in a two-fold way — first by a generalized process model representation based on regularized stochastic non-linear networks (Snn), and second by transformation of the process itself by an adaptive low-dimensional subspace in which the update step on the residual can be performed in a space commensurate with the available information content of the process and measured response.
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