Alternating Krylov subspace image restoration methods
National Research Council Canada - National Science Library
Abad, J.O; Morigi, S; Reichel, L; Sgallari, F
2012-01-01
... of the Krylov subspace used. However, our solution methods, suitably modified, also can be applied when no bound for the norm of η δ is known. We determine an approximation of the desired image u ˆ by so...
Image Deblurring with Krylov Subspace Methods
DEFF Research Database (Denmark)
Hansen, Per Christian
2011-01-01
Image deblurring, i.e., reconstruction of a sharper image from a blurred and noisy one, involves the solution of a large and very ill-conditioned system of linear equations, and regularization is needed in order to compute a stable solution. Krylov subspace methods are often ideally suited...... for this task: their iterative nature is a natural way to handle such large-scale problems, and the underlying Krylov subspace provides a convenient mechanism to regularized the problem by projecting it onto a low-dimensional "signal subspace" adapted to the particular problem. In this talk we consider...... the three Krylov subspace methods CGLS, MINRES, and GMRES. We describe their regularizing properties, and we discuss some computational aspects such as preconditioning and stopping criteria....
Matrix Krylov subspace methods for image restoration
Directory of Open Access Journals (Sweden)
khalide jbilou
2015-09-01
Full Text Available In the present paper, we consider some matrix Krylov subspace methods for solving ill-posed linear matrix equations and in those problems coming from the restoration of blurred and noisy images. Applying the well known Tikhonov regularization procedure leads to a Sylvester matrix equation depending the Tikhonov regularized parameter. We apply the matrix versions of the well known Krylov subspace methods, namely the Least Squared (LSQR and the conjugate gradient (CG methods to get approximate solutions representing the restored images. Some numerical tests are presented to show the effectiveness of the proposed methods.
Computing approximate (symmetric block) rational Krylov subspaces without explicit inversion
Mach, Thomas; Pranić, Miroslav S.; Vandebril, Raf
2013-01-01
It has been shown, see TW623, that approximate extended Krylov subspaces can be computed —under certain assumptions— without any explicit inversion or system solves. Instead the necessary products A-1v are obtained in an implicit way retrieved from an enlarged Krylov subspace. In this paper this approach is generalized to rational Krylov subspaces, which contain besides poles at infinite and zero also finite non-zero poles. Also an adaption of the algorithm to the block and the symmetric ...
National Research Council Canada - National Science Library
IKUNO, Soichiro; CHEN, Gong; YAMAMOTO, Susumu; ITOH, Taku; ABE, Kuniyoshi; NAKAMURA, Hiroaki
2016-01-01
Krylov subspace method and the variable preconditioned Krylov subspace method with communication avoiding technique for a linear system obtained from electromagnetic analysis are numerically investigated. In the k...
Overview of Krylov subspace methods with applications to control problems
Saad, Youcef
1989-01-01
An overview of projection methods based on Krylov subspaces are given with emphasis on their application to solving matrix equations that arise in control problems. The main idea of Krylov subspace methods is to generate a basis of the Krylov subspace Span and seek an approximate solution the the original problem from this subspace. Thus, the original matrix problem of size N is approximated by one of dimension m typically much smaller than N. Krylov subspace methods have been very successful in solving linear systems and eigenvalue problems and are now just becoming popular for solving nonlinear equations. It is shown how they can be used to solve partial pole placement problems, Sylvester's equation, and Lyapunov's equation.
On iterative processes in the Krylov-Sonneveld subspaces
Ilin, Valery P.
2016-10-01
The iterative Induced Dimension Reduction (IDR) methods are considered for solving large systems of linear algebraic equations (SLAEs) with nonsingular nonsymmetric matrices. These approaches are investigated by many authors and are charachterized sometimes as the alternative to the classical processes of Krylov type. The key moments of the IDR algorithms consist in the construction of the embedded Sonneveld subspaces, which have the decreasing dimensions and use the orthogonalization to some fixed subspace. Other independent approaches for research and optimization of the iterations are based on the augmented and modified Krylov subspaces by using the aggregation and deflation procedures with present various low rank approximations of the original matrices. The goal of this paper is to show, that IDR method in Sonneveld subspaces present an original interpretation of the modified algorithms in the Krylov subspaces. In particular, such description is given for the multi-preconditioned semi-conjugate direction methods which are actual for the parallel algebraic domain decomposition approaches.
Krylov subspace method based on data preprocessing technology
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest (SOI) component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom (DOF) of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Directory of Open Access Journals (Sweden)
Sergueï Burykh
2003-01-01
Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
Krylov-subspace acceleration of time periodic waveform relaxation
Energy Technology Data Exchange (ETDEWEB)
Lumsdaine, A. [Univ. of Notre Dame, IN (United States)
1994-12-31
In this paper the author uses Krylov-subspace techniques to accelerate the convergence of waveform relaxation applied to solving systems of first order time periodic ordinary differential equations. He considers the problem in the frequency domain and presents frequency dependent waveform GMRES (FDWGMRES), a member of a new class of frequency dependent Krylov-subspace techniques. FDWGMRES exhibits many desirable properties, including finite termination independent of the number of timesteps and, for certain problems, a convergence rate which is bounded from above by the convergence rate of GMRES applied to the static matrix problem corresponding to the linear time-invariant ODE.
Mach, Thomas; Pranić, Miroslav S.; Vandebril, Raf
2014-01-01
It has been shown that approximate extended Krylov subspaces can be computed, under certain assumptions, without any explicit inversion or system solves. Instead, the vectors spanning the extended Krylov space are retrieved in an implicit way, via unitary similarity transformations, from an enlarged Krylov subspace. In this paper this approach is generalized to rational Krylov subspaces, which aside from poles at infinity and zero, also contain finite non-zero poles. Furthermore, the algorith...
An adaptation of Krylov subspace methods to path following
Energy Technology Data Exchange (ETDEWEB)
Walker, H.F. [Utah State Univ., Logan, UT (United States)
1996-12-31
Krylov subspace methods at present constitute a very well known and highly developed class of iterative linear algebra methods. These have been effectively applied to nonlinear system solving through Newton-Krylov methods, in which Krylov subspace methods are used to solve the linear systems that characterize steps of Newton`s method (the Newton equations). Here, we will discuss the application of Krylov subspace methods to path following problems, in which the object is to track a solution curve as a parameter varies. Path following methods are typically of predictor-corrector form, in which a point near the solution curve is {open_quotes}predicted{close_quotes} by some easy but relatively inaccurate means, and then a series of Newton-like corrector iterations is used to return approximately to the curve. The analogue of the Newton equation is underdetermined, and an additional linear condition must be specified to determine corrector steps uniquely. This is typically done by requiring that the steps be orthogonal to an approximate tangent direction. Augmenting the under-determined system with this orthogonality condition in a straightforward way typically works well if direct linear algebra methods are used, but Krylov subspace methods are often ineffective with this approach. We will discuss recent work in which this orthogonality condition is imposed directly as a constraint on the corrector steps in a certain way. The means of doing this preserves problem conditioning, allows the use of preconditioners constructed for the fixed-parameter case, and has certain other advantages. Experiments on standard PDE continuation test problems indicate that this approach is effective.
Bochev, Mikhail A.; Oseledets, I.V.; Tyrtyshnikov, E.E.
The aim of this paper is two-fold. First, we propose an efficient implementation of the continuous time waveform relaxation (WR) method based on block Krylov subspaces. Second, we compare this new WR-Krylov implementation against Krylov subspace methods combined with the shift and invert (SAI)
Bochev, Mikhail A.; Oseledets, I.V.; Tyrtyshnikov, E.E.
2013-01-01
The aim of this paper is two-fold. First, we propose an efficient implementation of the continuous time waveform relaxation method based on block Krylov subspaces. Second, we compare this new implementation against Krylov subspace methods combined with the shift and invert technique.
Energy Technology Data Exchange (ETDEWEB)
Kwon, Hyuk; Kim, S. J.; Park, J. P.; Hwang, D. H. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2014-05-15
Krylov subspace method was implemented to perform the efficient whole core calculation of SMART with pin by pin subchannel model without lumping channel. The SMART core consisted of 57 fuel assemblies of 17 by 17 arrays with 264 fuel rods and 25 guide tubes and there are total 15,048 fuel rods and 16,780 subchannels. Restarted GMRES and BiCGStab methods are selected among Krylov subspace methods. For the purpose of verifying the implementation of Krylov method, whole core problem is considered under the normal operating condition. In this problem, solving a linear system Aχ = b is considered when A is nearly symmetric and when the system is preconditioned with incomplete LU factorization(ILU). The preconditioner using incomplete LU factorization are among the most effective preconditioners for solving general large, sparse linear systems arising from practical engineering problem. The Krylov subspace method is expected to improve the calculation effectiveness of MATRA code rather than direct method and stationary iteration method such as Gauss elimination and SOR. The present study describes the implementation of Krylov subspace methods with ILU into MATRA code. In this paper, we explore an improved performance of MATRA code for the SMART whole core problems by of Krylov subspace method. For this purpose, two preconditioned Krylov subspace methods, GMRES and BiCGStab, are implemented into the subchannel code MATRA. A typical ILU method is used as the preconditioner. Numerical problems examined in this study indicate that the Krylov subspace method shows the outstanding improvements in the calculation speed and easy convergence.
Domain decomposed preconditioners with Krylov subspace methods as subdomain solvers
Energy Technology Data Exchange (ETDEWEB)
Pernice, M. [Univ. of Utah, Salt Lake City, UT (United States)
1994-12-31
Domain decomposed preconditioners for nonsymmetric partial differential equations typically require the solution of problems on the subdomains. Most implementations employ exact solvers to obtain these solutions. Consequently work and storage requirements for the subdomain problems grow rapidly with the size of the subdomain problems. Subdomain solves constitute the single largest computational cost of a domain decomposed preconditioner, and improving the efficiency of this phase of the computation will have a significant impact on the performance of the overall method. The small local memory available on the nodes of most message-passing multicomputers motivates consideration of the use of an iterative method for solving subdomain problems. For large-scale systems of equations that are derived from three-dimensional problems, memory considerations alone may dictate the need for using iterative methods for the subdomain problems. In addition to reduced storage requirements, use of an iterative solver on the subdomains allows flexibility in specifying the accuracy of the subdomain solutions. Substantial savings in solution time is possible if the quality of the domain decomposed preconditioner is not degraded too much by relaxing the accuracy of the subdomain solutions. While some work in this direction has been conducted for symmetric problems, similar studies for nonsymmetric problems appear not to have been pursued. This work represents a first step in this direction, and explores the effectiveness of performing subdomain solves using several transpose-free Krylov subspace methods, GMRES, transpose-free QMR, CGS, and a smoothed version of CGS. Depending on the difficulty of the subdomain problem and the convergence tolerance used, a reduction in solution time is possible in addition to the reduced memory requirements. The domain decomposed preconditioner is a Schur complement method in which the interface operators are approximated using interface probing.
Krylov subspace methods for the solution of large systems of ODE's
DEFF Research Database (Denmark)
Thomsen, Per Grove; Bjurstrøm, Nils Henrik
1998-01-01
In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified.......In Air Pollution Modelling large systems of ODE's arise. Solving such systems may be done efficientliy by Semi Implicit Runge-Kutta methods. The internal stages may be solved using Krylov subspace methods. The efficiency of this approach is investigated and verified....
Energy Technology Data Exchange (ETDEWEB)
Druskin, V.; Lee, Ping [Schlumberger-Doll Research, Ridgefield, CT (United States); Knizhnerman, L. [Central Geophysical Expedition, Moscow (Russian Federation)
1996-12-31
There is now a growing interest in the area of using Krylov subspace approximations to compute the actions of matrix functions. The main application of this approach is the solution of ODE systems, obtained after discretization of partial differential equations by method of lines. In the event that the cost of computing the matrix inverse is relatively inexpensive, it is sometimes attractive to solve the ODE using the extended Krylov subspaces, originated by actions of both positive and negative matrix powers. Examples of such problems can be found frequently in computational electromagnetics.
A block Krylov subspace time-exact solution method for linear ordinary differential equation systems
Botchev, M.A.
2013-01-01
We propose a time-exact Krylov-subspace-based method for solving linear ordinary differential equation systems of the form $y'=-Ay+g(t)$ and $y"=-Ay+g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approximation of th
A block Krylov subspace time-exact solution method for linear ODE systems
Botchev, M.A.
2012-01-01
We propose a time-exact Krylov-subspace-based method for solving linear ODE (ordinary differential equation) systems of the form $y'=-Ay + g(t)$ and $y''=-Ay + g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approxim
Krylov subspace method for evaluating the self-energy matrices in electron transport calculations
DEFF Research Database (Denmark)
Sørensen, Hans Henrik Brandenborg; Hansen, Per Christian; Petersen, D. E.;
2008-01-01
We present a Krylov subspace method for evaluating the self-energy matrices used in the Green's function formulation of electron transport in nanoscale devices. A procedure based on the Arnoldi method is employed to obtain solutions of the quadratic eigenvalue problem associated with the infinite...
A block Krylov subspace time-exact solution method for linear ordinary differential equation systems
Bochev, Mikhail A.
2013-01-01
We propose a time-exact Krylov-subspace-based method for solving linear ordinary differential equation systems of the form $y'=-Ay+g(t)$ and $y"=-Ay+g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial approximation of
A block Krylov subspace time-exact solution method for linear ODE systems
Bochev, Mikhail A.
We propose a time-exact Krylov-subspace-based method for solving linear ODE (ordinary differential equation) systems of the form $y'=-Ay + g(t)$ and $y''=-Ay + g(t)$, where $y(t)$ is the unknown function. The method consists of two stages. The first stage is an accurate piecewise polynomial
Vorst, H.A. van der; Ye, Q.
1999-01-01
In this paper, a strategy is proposed for alternative computations of the residual vectors in Krylov subspace methods, which improves the agreement of the computed residuals and the true residuals to the level of O(u)kAkkxk. Building on earlier ideas on residual replacement and on insights in
Krylov subspace methods for complex non-Hermitian linear systems. Thesis
Freund, Roland W.
1991-01-01
We consider Krylov subspace methods for the solution of large sparse linear systems Ax = b with complex non-Hermitian coefficient matrices. Such linear systems arise in important applications, such as inverse scattering, numerical solution of time-dependent Schrodinger equations, underwater acoustics, eddy current computations, numerical computations in quantum chromodynamics, and numerical conformal mapping. Typically, the resulting coefficient matrices A exhibit special structures, such as complex symmetry, or they are shifted Hermitian matrices. In this paper, we first describe a Krylov subspace approach with iterates defined by a quasi-minimal residual property, the QMR method, for solving general complex non-Hermitian linear systems. Then, we study special Krylov subspace methods designed for the two families of complex symmetric respectively shifted Hermitian linear systems. We also include some results concerning the obvious approach to general complex linear systems by solving equivalent real linear systems for the real and imaginary parts of x. Finally, numerical experiments for linear systems arising from the complex Helmholtz equation are reported.
Adaptive coherence estimator based on the Krylov subspace technique for airborne radar
Institute of Scientific and Technical Information of China (English)
Weijian Liu; Wenchong Xie; Haibo Tong; Honglin Wang; Cui Zhou; Yongliang Wang
2015-01-01
A novel adaptive detector for airborne radar space-time adaptive detection (STAD) in partial y homogeneous environments is proposed. The novel detector combines the numerical y stable Krylov subspace technique and diagonal loading technique, and it uses the framework of the adaptive coherence estimator (ACE). It can effectively detect a target with low sample support. Compared with its natural competitors, the novel detector has higher proba-bility of detection (PD), especial y when the number of the training data is low. Moreover, it is shown to be practical y constant false alarm rate (CFAR).
Energy Technology Data Exchange (ETDEWEB)
Druskin, V.; Knizhnerman, L.
1994-12-31
The authors solve the Cauchy problem for an ODE system Au + {partial_derivative}u/{partial_derivative}t = 0, u{vert_bar}{sub t=0} = {var_phi}, where A is a square real nonnegative definite symmetric matrix of the order N, {var_phi} is a vector from R{sup N}. The stiffness matrix A is obtained due to semi-discretization of a parabolic equation or system with time-independent coefficients. The authors are particularly interested in large stiff 3-D problems for the scalar diffusion and vectorial Maxwell`s equations. First they consider an explicit method in which the solution on a whole time interval is projected on a Krylov subspace originated by A. Then they suggest another Krylov subspace with better approximating properties using powers of an implicit transition operator. These Krylov subspace methods generate optimal in a spectral sense polynomial approximations for the solution of the ODE, similar to CG for SLE.
Bloch, Jacques C R; Frommer, Andreas; Heybrock, Simon; Schaefer, Katrin; Wettig, Tilo
2009-01-01
The overlap operator in lattice QCD requires the computation of the sign function of a matrix, which is non-Hermitian in the presence of a quark chemical potential. In previous work we introduced an Arnoldi-based Krylov subspace approximation, which uses long recurrences. Even after the deflation of critical eigenvalues, the low efficiency of the method restricts its application to small lattices. Here we propose new short-recurrence methods which strongly enhance the efficiency of the computational method. Using rational approximations to the sign function we introduce two variants, based on the restarted Arnoldi process and on the two-sided Lanczos method, respectively, which become very efficient when combined with multishift solvers. Alternatively, in the variant based on the two-sided Lanczos method the sign function can be evaluated directly. We present numerical results which compare the efficiencies of a restarted Arnoldi-based method and the direct two-sided Lanczos approximation for various lattice ...
Radio astronomical image formation using constrained least squares and Krylov subspaces
Mouri Sardarabadi, Ahmad; Leshem, Amir; van der Veen, Alle-Jan
2016-04-01
Aims: Image formation for radio astronomy can be defined as estimating the spatial intensity distribution of celestial sources throughout the sky, given an array of antennas. One of the challenges with image formation is that the problem becomes ill-posed as the number of pixels becomes large. The introduction of constraints that incorporate a priori knowledge is crucial. Methods: In this paper we show that in addition to non-negativity, the magnitude of each pixel in an image is also bounded from above. Indeed, the classical "dirty image" is an upper bound, but a much tighter upper bound can be formed from the data using array processing techniques. This formulates image formation as a least squares optimization problem with inequality constraints. We propose to solve this constrained least squares problem using active set techniques, and the steps needed to implement it are described. It is shown that the least squares part of the problem can be efficiently implemented with Krylov-subspace-based techniques. We also propose a method for correcting for the possible mismatch between source positions and the pixel grid. This correction improves both the detection of sources and their estimated intensities. The performance of these algorithms is evaluated using simulations. Results: Based on parametric modeling of the astronomical data, a new imaging algorithm based on convex optimization, active sets, and Krylov-subspace-based solvers is presented. The relation between the proposed algorithm and sequential source removing techniques is explained, and it gives a better mathematical framework for analyzing existing algorithms. We show that by using the structure of the algorithm, an efficient implementation that allows massive parallelism and storage reduction is feasible. Simulations are used to compare the new algorithm to classical CLEAN. Results illustrate that for a discrete point model, the proposed algorithm is capable of detecting the correct number of sources
Development of a Burnup Module DECBURN Based on the Krylov Subspace Method
Energy Technology Data Exchange (ETDEWEB)
Cho, J. Y.; Kim, K. S.; Shim, H. J.; Song, J. S
2008-05-15
This report is to develop a burnup module DECBURN that is essential for the reactor analysis and the assembly homogenization codes to trace the fuel composition change during the core burnup. The developed burnup module solves the burnup equation by the matrix exponential method based on the Krylov Subspace method. The final solution of the matrix exponential is obtained by the matrix scaling and squaring method. To develop DECBURN module, this report includes the followings as: (1) Krylov Subspace Method for Burnup Equation, (2) Manufacturing of the DECBURN module, (3) Library Structure Setup and Library Manufacturing, (4) Examination of the DECBURN module, (5) Implementation to the DeCART code and Verification. DECBURN library includes the decay constants, one-group cross section and the fission yields. Examination of the DECBURN module is performed by manufacturing a driver program, and the results of the DECBURN module is compared with those of the ORIGEN program. Also, the implemented DECBURN module to the DeCART code is applied to the LWR depletion benchmark and a OPR-1000 pin cell problem, and the solutions are compared with the HELIOS code to verify the computational soundness and accuracy. In this process, the criticality calculation method and the predictor-corrector scheme are introduced to the DeCART code for a function of the homogenization code. The examination by a driver program shows that the DECBURN module produces exactly the same solution with the ORIGEN program. DeCART code that equips the DECBURN module produces a compatible solution to the other codes for the LWR depletion benchmark. Also the multiplication factors of the DeCART code for the OPR-1000 pin cell problem agree to the HELIOS code within 100 pcm over the whole burnup steps. The multiplication factors with the criticality calculation are also compatible with the HELIOS code. These results mean that the developed DECBURN module works soundly and produces an accurate solution
Kuprov, Ilya
2008-11-01
We extend the recently proposed state-space restriction (SSR) technique for quantum spin dynamics simulations [Kuprov et al., J. Magn. Reson. 189 (2007) 241-250] to include on-the-fly detection and elimination of unpopulated dimensions from the system density matrix. Further improvements in spin dynamics simulation speed, frequently by several orders of magnitude, are demonstrated. The proposed zero track elimination (ZTE) procedure is computationally inexpensive, reversible, numerically stable and easy to add to any existing simulation code. We demonstrate that it belongs to the same family of Krylov subspace techniques as the well-known Lanczos basis pruning procedure. The combined SSR + ZTE algorithm is recommended for simulations of NMR, EPR and Spin Chemistry experiments on systems containing between 10 and 10 4 coupled spins.
Energy Technology Data Exchange (ETDEWEB)
Lee, Yoon Hee; Cho, Nam Zin [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2010-05-15
Nowadays lattice physics codes tend to utilize a detailed burnup chain including short-lived nuclides in order to perform more accurate burnup calculations. But, since production codes, for example, ORIGEN2, take account of nuclides which have relatively long half-life, it is inappropriate for such detailed burnup chain calculation. To enhance that drawback, many matrix exponential calculation methods have been developed. Recently, a Krylov subspace method with the PADE approximation was used. In this paper, a Krylov subspace method based on spectral decomposition property of the matrix function theory with the Newton divided difference (NDD) is introduced. It is tested with a sample problem and compared with simple Taylor expansion method
Li, Liang; Huang, Ting-Zhu; Jing, Yan-Fei; Zhang, Yong
2010-02-01
The incomplete Cholesky (IC) factorization preconditioning technique is applied to the Krylov subspace methods for solving large systems of linear equations resulted from the use of edge-based finite element method (FEM). The construction of the preconditioner is based on the fact that the coefficient matrix is represented in an upper triangular compressed sparse row (CSR) form. An efficient implementation of the IC factorization is described in detail for complex symmetric matrices. With some ordering schemes our IC algorithm can greatly reduce the memory requirement as well as the iteration numbers. Numerical tests on harmonic analysis for plane wave scattering from a metallic plate and a metallic sphere coated by a lossy dielectric layer show the efficiency of this method.
Gelfgat, Alexander
2016-01-01
We propose two techniques aimed at improving the convergence rate of steady state and eigenvalue solvers preconditioned by the inverse Stokes operator and realized via time-stepping. First, we suggest a generalization of the Stokes operator so that the resulting preconditioner operator depends on several parameters and whose action preserves zero divergence and boundary conditions. The parameters can be tuned for each problem to speed up the convergence of a Krylov-subspace-based linear algebra solver. This operator can be inverted by the Uzawa-like algorithm, and does not need a time-stepping. Second, we propose to generate an initial guess of steady flow, leading eigenvalue and eigenvector using orthogonal projection on divergence-free basis satisfying all boundary conditions. The approach, including the two proposed techniques, is illustrated on the solution of the linear stability problem for laterally heated square and cubic cavities.
Yang, Taiseung; Spilker, Robert L
2007-02-01
A study was conducted on combinations of preconditioned iterative methods with matrix reordering to solve the linear systems arising from a biphasic velocity-pressure (v-p) finite element formulation used to simulate soft hydrated tissues in the human musculoskeletal system. Krylov subspace methods were tested due to the symmetric indefiniteness of our systems, specifically the generalized minimal residual (GMRES), transpose-free quasi-minimal residual (TFQMR), and biconjugate gradient stabilized (BiCGSTAB) methods. Standard graph reordering techniques were used with incomplete LU (ILU) preconditioning. Performance of the methods was compared on the basis of convergence rate, computing time, and memory requirements. Our results indicate that performance is affected more significantly by the choice of reordering scheme than by the choice of Krylov method. Overall, BiCGSTAB with one-way dissection (OWD) reordering performed best for a test problem representative of a physiological tissue layer. The preferred methods were then used to simulate the contact of the humeral head and glenoid tissue layers in glenohumeral joint of the shoulder, using a penetration-based method to approximate contact. The distribution of pressure and stress fields within the tissues shows significant through-thickness effects and demonstrates the importance of simulating soft hydrated tissues with a biphasic model.
LBAS: Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems
DEFF Research Database (Denmark)
Hansen, Per Christian; Abe, Kyniyoshi
The regularizing properties of Lanczos bidiagonalization are powerful when the underlying Krylov subspace captures the dominating components of the solution. In some applications the regularized solution can be further improved by augmenting the Krylov subspace with a low-dimensional subspace...
Deliberate Ill-Conditioning of Krylov Matrices
Brandts, J.H.
2001-01-01
This paper starts o with studying simple extrapolation methods for the classical iteration schemes such as Richardson, Jacobi and Gauss-Seidel iteration. The extrapolation procedures can be interpreted as approximate minimal residual methods in a Krylov subspace. It seems therefore logical to consid
Deliberate Ill-Conditioning of Krylov Matrices
Brandts, J.H.
2001-01-01
This paper starts o with studying simple extrapolation methods for the classical iteration schemes such as Richardson, Jacobi and Gauss-Seidel iteration. The extrapolation procedures can be interpreted as approximate minimal residual methods in a Krylov subspace. It seems therefore logical to
Krylov subspace methods for the Dirac equation
Beerwerth, Randolf; Bauke, Heiko
2015-03-01
The Lanczos algorithm is evaluated for solving the time-independent as well as the time-dependent Dirac equation with arbitrary electromagnetic fields. We demonstrate that the Lanczos algorithm can yield very precise eigenenergies and allows very precise time propagation of relativistic wave packets. The unboundedness of the Dirac Hamiltonian does not hinder the applicability of the Lanczos algorithm. As the Lanczos algorithm requires only matrix-vector products and inner products, which both can be efficiently parallelized, it is an ideal method for large-scale calculations. The excellent parallelization capabilities are demonstrated by a parallel implementation of the Dirac Lanczos propagator utilizing the Message Passing Interface standard.
Krylov subspace methods for the Dirac equation
Beerwerth, Randolf
2014-01-01
The Lanczos algorithm is evaluated for solving the time-independent as well as the time-dependent Dirac equation with arbitrary electromagnetic fields. We demonstrate that the Lanczos algorithm can yield very precise eigenenergies and allows very precise time propagation of relativistic wave packets. The Dirac Hamiltonian's property of not being bounded does not hinder the applicability of the Lanczos algorithm. As the Lanczos algorithm requires only matrix-vector and inner products, which both can be efficiently parallelized, it is an ideal method for large-scale calculations. The excellent parallelization capabilities are demonstrated by a parallel implementation of the Dirac Lanczos propagator utilizing the Message Passing Interface standard.
Minimal Krylov Subspaces for Dimension Reduction
2013-01-01
stiffness matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.4.2 Enron email corpus experiment...the Enron email corpus and eigenvalue gaps. . . . 65 4.6 Low-rank approximation errors of Enron email corpus. . . . . . . . . . . . . . . . . . . . . 66...4.7 Maximum loss of orthogonality in projection basis for Enron email corpus. . . . . . . . . 67 4.8 FLOP counts for producing low-rank
Krylov subspace acceleration of waveform relaxation
Energy Technology Data Exchange (ETDEWEB)
Lumsdaine, A.; Wu, Deyun [Univ. of Notre Dame, IN (United States)
1996-12-31
Standard solution methods for numerically solving time-dependent problems typically begin by discretizing the problem on a uniform time grid and then sequentially solving for successive time points. The initial time discretization imposes a serialization to the solution process and limits parallel speedup to the speedup available from parallelizing the problem at any given time point. This bottleneck can be circumvented by the use of waveform methods in which multiple time-points of the different components of the solution are computed independently. With the waveform approach, a problem is first spatially decomposed and distributed among the processors of a parallel machine. Each processor then solves its own time-dependent subsystem over the entire interval of interest using previous iterates from other processors as inputs. Synchronization and communication between processors take place infrequently, and communication consists of large packets of information - discretized functions of time (i.e., waveforms).
Inexact Krylov subspace methods for linear systems
Eshof, J. van den; Sleijpen, G.L.G.
There is a class of linear problems for which the computation of the matrix-vector product is very expensive since a time consuming approximation method is necessary to compute it with some prescribed relative precision. In this paper we investigate the impact of approximately computed
Inexact Krylov subspace methods for linear systems
Eshof, J. van den; Sleijpen, G.L.G.
2004-01-01
There is a class of linear problems for which the computation of the matrix-vector product is very expensive since a time consuming method is necessary to approximate it with some prescribed relative precision. In this paper we investigate the impact of approximately computed matrix-vector
Preconditioned Krylov subspace methods for eigenvalue problems
Energy Technology Data Exchange (ETDEWEB)
Wu, Kesheng; Saad, Y.; Stathopoulos, A. [Univ. of Minnesota, Minneapolis, MN (United States)
1996-12-31
Lanczos algorithm is a commonly used method for finding a few extreme eigenvalues of symmetric matrices. It is effective if the wanted eigenvalues have large relative separations. If separations are small, several alternatives are often used, including the shift-invert Lanczos method, the preconditioned Lanczos method, and Davidson method. The shift-invert Lanczos method requires direct factorization of the matrix, which is often impractical if the matrix is large. In these cases preconditioned schemes are preferred. Many applications require solution of hundreds or thousands of eigenvalues of large sparse matrices, which pose serious challenges for both iterative eigenvalue solver and preconditioner. In this paper we will explore several preconditioned eigenvalue solvers and identify the ones suited for finding large number of eigenvalues. Methods discussed in this paper make up the core of a preconditioned eigenvalue toolkit under construction.
Block-Krylov component synthesis method for structural model reduction
Craig, Roy R., Jr.; Hale, Arthur L.
1988-01-01
A new analytical method is presented for generating component shape vectors, or Ritz vectors, for use in component synthesis. Based on the concept of a block-Krylov subspace, easily derived recurrence relations generate blocks of Ritz vectors for each component. The subspace spanned by the Ritz vectors is called a block-Krylov subspace. The synthesis uses the new Ritz vectors rather than component normal modes to reduce the order of large, finite-element component models. An advantage of the Ritz vectors is that they involve significantly less computation than component normal modes. Both 'free-interface' and 'fixed-interface' component models are derived. They yield block-Krylov formulations paralleling the concepts of free-interface and fixed-interface component modal synthesis. Additionally, block-Krylov reduced-order component models are shown to have special disturbability/observability properties. Consequently, the method is attractive in active structural control applications, such as large space structures. The new fixed-interface methodology is demonstrated by a numerical example. The accuracy is found to be comparable to that of fixed-interface component modal synthesis.
Scharz Preconditioners for Krylov Methods: Theory and Practice
Energy Technology Data Exchange (ETDEWEB)
Szyld, Daniel B.
2013-05-10
Several numerical methods were produced and analyzed. The main thrust of the work relates to inexact Krylov subspace methods for the solution of linear systems of equations arising from the discretization of partial di erential equa- tions. These are iterative methods, i.e., where an approximation is obtained and at each step. Usually, a matrix-vector product is needed at each iteration. In the inexact methods, this product (or the application of a preconditioner) can be done inexactly. Schwarz methods, based on domain decompositions, are excellent preconditioners for thise systems. We contributed towards their under- standing from an algebraic point of view, developed new ones, and studied their performance in the inexact setting. We also worked on combinatorial problems to help de ne the algebraic partition of the domains, with the needed overlap, as well as PDE-constraint optimization using the above-mentioned inexact Krylov subspace methods.
Accelerating molecular property calculations with nonorthonormal Krylov space methods
Furche, Filipp; Krull, Brandon T.; Nguyen, Brian D.; Kwon, Jake
2016-05-01
We formulate Krylov space methods for large eigenvalue problems and linear equation systems that take advantage of decreasing residual norms to reduce the cost of matrix-vector multiplication. The residuals are used as subspace basis without prior orthonormalization, which leads to generalized eigenvalue problems or linear equation systems on the Krylov space. These nonorthonormal Krylov space (nKs) algorithms are favorable for large matrices with irregular sparsity patterns whose elements are computed on the fly, because fewer operations are necessary as the residual norm decreases as compared to the conventional method, while errors in the desired eigenpairs and solution vectors remain small. We consider real symmetric and symplectic eigenvalue problems as well as linear equation systems and Sylvester equations as they appear in configuration interaction and response theory. The nKs method can be implemented in existing electronic structure codes with minor modifications and yields speed-ups of 1.2-1.8 in typical time-dependent Hartree-Fock and density functional applications without accuracy loss. The algorithm can compute entire linear subspaces simultaneously which benefits electronic spectra and force constant calculations requiring many eigenpairs or solution vectors. The nKs approach is related to difference density methods in electronic ground state calculations and particularly efficient for integral direct computations of exchange-type contractions. By combination with resolution-of-the-identity methods for Coulomb contractions, three- to fivefold speed-ups of hybrid time-dependent density functional excited state and response calculations are achieved.
DEFF Research Database (Denmark)
Knudsen, Torben
2002-01-01
Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived....
DEFF Research Database (Denmark)
Knudsen, Torben
2001-01-01
Subspace identification algorithms are user friendly, numerical fast and stable and they provide a good consistent estimate of the deterministic part of a system. The weak point is the stochastic part. The uncertainty on this part is discussed below and methods to reduce it is derived....
Lattice QCD computations: Recent progress with modern Krylov subspace methods
Energy Technology Data Exchange (ETDEWEB)
Frommer, A. [Bergische Universitaet GH Wuppertal (Germany)
1996-12-31
Quantum chromodynamics (QCD) is the fundamental theory of the strong interaction of matter. In order to compare the theory with results from experimental physics, the theory has to be reformulated as a discrete problem of lattice gauge theory using stochastic simulations. The computational challenge consists in solving several hundreds of very large linear systems with several right hand sides. A considerable part of the world`s supercomputer time is spent in such QCD calculations. This paper presents results on solving systems for the Wilson fermions. Recent progress is reviewed on algorithms obtained in cooperation with partners from theoretical physics.
Application of Krylov Reduction Technique for a Machine Tool Multibody Modelling
Directory of Open Access Journals (Sweden)
M. Sulitka
2014-02-01
Full Text Available Quick calculation of machine tool dynamic response represents one of the major requirements for machine tool virtual modelling and virtual machining, aiming at simulating the machining process performance, quality, and precision of a workpiece. Enhanced time effectiveness in machine tool dynamic simulations may be achieved by employing model order reduction (MOR techniques of the full finite element (FE models. The paper provides a case study aimed at comparison of Krylov subspace base and mode truncation technique. Application of both of the reduction techniques for creating a machine tool multibody model is evaluated. The Krylov subspace reduction technique shows high quality in terms of both dynamic properties of the reduced multibody model and very low time demands at the same time.
Conformal mapping and convergence of Krylov iterations
Energy Technology Data Exchange (ETDEWEB)
Driscoll, T.A.; Trefethen, L.N. [Cornell Univ., Ithaca, NY (United States)
1994-12-31
Connections between conformal mapping and matrix iterations have been known for many years. The idea underlying these connections is as follows. Suppose the spectrum of a matrix or operator A is contained in a Jordan region E in the complex plane with 0 not an element of E. Let {phi}(z) denote a conformal map of the exterior of E onto the exterior of the unit disk, with {phi}{infinity} = {infinity}. Then 1/{vert_bar}{phi}(0){vert_bar} is an upper bound for the optimal asymptotic convergence factor of any Krylov subspace iteration. This idea can be made precise in various ways, depending on the matrix iterations, on whether A is finite or infinite dimensional, and on what bounds are assumed on the non-normality of A. This paper explores these connections for a variety of matrix examples, making use of a new MATLAB Schwarz-Christoffel Mapping Toolbox developed by the first author. Unlike the earlier Fortran Schwarz-Christoffel package SCPACK, the new toolbox computes exterior as well as interior Schwarz-Christoffel maps, making it easy to experiment with spectra that are not necessarily symmetric about an axis.
Energy Technology Data Exchange (ETDEWEB)
Starke, G. [Universitaet Karlsruhe (Germany)
1994-12-31
For nonselfadjoint elliptic boundary value problems which are preconditioned by a substructuring method, i.e., nonoverlapping domain decomposition, the author introduces and studies the concept of subspace orthogonalization. In subspace orthogonalization variants of Krylov methods the computation of inner products and vector updates, and the storage of basis elements is restricted to a (presumably small) subspace, in this case the edge and vertex unknowns with respect to the partitioning into subdomains. The author investigates subspace orthogonalization for two specific iterative algorithms, GMRES and the full orthogonalization method (FOM). This is intended to eliminate certain drawbacks of the Arnoldi-based Krylov subspace methods mentioned above. Above all, the length of the Arnoldi recurrences grows linearly with the iteration index which is therefore restricted to the number of basis elements that can be held in memory. Restarts become necessary and this often results in much slower convergence. The subspace orthogonalization methods, in contrast, require the storage of only the edge and vertex unknowns of each basis element which means that one can iterate much longer before restarts become necessary. Moreover, the computation of inner products is also restricted to the edge and vertex points which avoids the disturbance of the computational flow associated with the solution of subdomain problems. The author views subspace orthogonalization as an alternative to restarting or truncating Krylov subspace methods for nonsymmetric linear systems of equations. Instead of shortening the recurrences, one restricts them to a subset of the unknowns which has to be carefully chosen in order to be able to extend this partial solution to the entire space. The author discusses the convergence properties of these iteration schemes and its advantages compared to restarted or truncated versions of Krylov methods applied to the full preconditioned system.
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan;
2009-01-01
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace c...
Improvements in Block-Krylov Ritz Vectors and the Boundary Flexibility Method of Component Synthesis
Carney, Kelly Scott
1997-01-01
A method of dynamic substructuring is presented which utilizes a set of static Ritz vectors as a replacement for normal eigenvectors in component mode synthesis. This set of Ritz vectors is generated in a recurrence relationship, proposed by Wilson, which has the form of a block-Krylov subspace. The initial seed to the recurrence algorithm is based upon the boundary flexibility vectors of the component. Improvements have been made in the formulation of the initial seed to the Krylov sequence, through the use of block-filtering. A method to shift the Krylov sequence to create Ritz vectors that will represent the dynamic behavior of the component at target frequencies, the target frequency being determined by the applied forcing functions, has been developed. A method to terminate the Krylov sequence has also been developed. Various orthonormalization schemes have been developed and evaluated, including the Cholesky/QR method. Several auxiliary theorems and proofs which illustrate issues in component mode synthesis and loss of orthogonality in the Krylov sequence have also been presented. The resulting methodology is applicable to both fixed and free- interface boundary components, and results in a general component model appropriate for any type of dynamic analysis. The accuracy is found to be comparable to that of component synthesis based upon normal modes, using fewer generalized coordinates. In addition, the block-Krylov recurrence algorithm is a series of static solutions and so requires significantly less computation than solving the normal eigenspace problem. The requirement for less vectors to form the component, coupled with the lower computational expense of calculating these Ritz vectors, combine to create a method more efficient than traditional component mode synthesis.
Bisetti, Fabrizio
2012-06-01
Recent trends in hydrocarbon fuel research indicate that the number of species and reactions in chemical kinetic mechanisms is rapidly increasing in an effort to provide predictive capabilities for fuels of practical interest. In order to cope with the computational cost associated with the time integration of stiff, large chemical systems, a novel approach is proposed. The approach combines an exponential integrator and Krylov subspace approximations to the exponential function of the Jacobian matrix. The components of the approach are described in detail and applied to the ignition of stoichiometric methane-air and iso-octane-air mixtures, here described by two widely adopted chemical kinetic mechanisms. The approach is found to be robust even at relatively large time steps and the global error displays a nominal third-order convergence. The performance of the approach is improved by utilising an adaptive algorithm for the selection of the Krylov subspace size, which guarantees an approximation to the matrix exponential within user-defined error tolerance. The Krylov projection of the Jacobian matrix onto a low-dimensional space is interpreted as a local model reduction with a well-defined error control strategy. Finally, the performance of the approach is discussed with regard to the optimal selection of the parameters governing the accuracy of its individual components. © 2012 Copyright Taylor and Francis Group, LLC.
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan
2009-01-01
Subspace clustering and projected clustering are recent research areas for clustering in high dimensional spaces. As the field is rather young, there is a lack of comparative studies on the advantages and disadvantages of the different algorithms. Part of the underlying problem is the lack...... of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this paper, we discuss the requirements for open source evaluation software. We propose OpenSubspace, an open source framework that meets...... these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in subspace and projected clustering....
Implementation of the block-Krylov boundary flexibility method of component synthesis
Carney, Kelly S.; Abdallah, Ayman A.; Hucklebridge, Arthur A.
1993-01-01
A method of dynamic substructuring is presented which utilizes a set of static Ritz vectors as a replacement for normal eigenvectors in component mode synthesis. This set of Ritz vectors is generated in a recurrence relationship, which has the form of a block-Krylov subspace. The initial seed to the recurrence algorithm is based on the boundary flexibility vectors of the component. This algorithm is not load-dependent, is applicable to both fixed and free-interface boundary components, and results in a general component model appropriate for any type of dynamic analysis. This methodology was implemented in the MSC/NASTRAN normal modes solution sequence using DMAP. The accuracy is found to be comparable to that of component synthesis based upon normal modes. The block-Krylov recurrence algorithm is a series of static solutions and so requires significantly less computation than solving the normal eigenspace problem.
Spectral Gauss quadrature method with subspace interpolation for Kohn-Sham Density functional theory
Wang, Xin
Algorithms with linear-scaling ( (N)) computational complexity for Kohn-Sham density functional theory (K-S DFT) is crucial for studying molecular systems beyond thousands of atoms. Of the (N) methods that use a polynomial-based approximation of the density matrix, the linear-scaling spectral Gauss quadrature (LSSGQ) method (Suryanarayana et al., JMPS, 2013) has been shown to exhibit the fastest convergence. The LSSGQ method requires a Lanczos procedure at every node in a real-space mesh, leading to a large computational pre-factor. We propose a new interpolation scheme specific to the LSSGQ method that lift the need to perform a Lanczos procedure at every node in the real-mesh. This interpolation will be referred to as subspace interpolation. The key idea behind subspace interpolation is that there is a large overlap in the Krylov-subspaces produced by the Lanczos procedures of nodes that are close in real-space. The subspace interpolation scheme takes advantage of the block-Lanczos procedure to group the Krylov-subspaces from a few representative nodes to approximate the density matrix over a large collection of nodes. Subspace interpolation outperforms cubic-spline interpolation by several orders of magnitude.
On Some Extended Block Krylov Based Methods for Large Scale Nonsymmetric Stein Matrix Equations
Directory of Open Access Journals (Sweden)
Abdeslem Hafid Bentbib
2017-03-01
Full Text Available In the present paper, we consider the large scale Stein matrix equation with a low-rank constant term A X B − X + E F T = 0 . These matrix equations appear in many applications in discrete-time control problems, filtering and image restoration and others. The proposed methods are based on projection onto the extended block Krylov subspace with a Galerkin approach (GA or with the minimization of the norm of the residual. We give some results on the residual and error norms and report some numerical experiments.
Closed Loop Subspace Identification
Directory of Open Access Journals (Sweden)
Geir W. Nilsen
2005-07-01
Full Text Available A new three step closed loop subspace identifications algorithm based on an already existing algorithm and the Kalman filter properties is presented. The Kalman filter contains noise free states which implies that the states and innovation are uneorre lated. The idea is that a Kalman filter found by a good subspace identification algorithm will give an output which is sufficiently uncorrelated with the noise on the output of the actual process. Using feedback from the output of the estimated Kalman filter in the closed loop system a subspace identification algorithm can be used to estimate an unbiased model.
Institute of Scientific and Technical Information of China (English)
邹红星; 戴琼海; 赵克; 陈桂明; 李衍达
2002-01-01
The subspaces of FMmlet transform are investigated.It is shown that some of the existing transforms like the Fourier transform,short-time Fourier transform,Gabor transform,wavelet transform,chirplet transform,the mean of signal,and the FM-1let transform,and the butterfly subspace are all special cases of FMmlet transform.Therefore the FMmlet transform is more flexible for delineating both the linear and nonlinear time-varying structures of a signal.
Energy Technology Data Exchange (ETDEWEB)
Aliaga, José I., E-mail: aliaga@uji.es [Depto. Ingeniería y Ciencia de Computadores, Universitat Jaume I, Castellón (Spain); Alonso, Pedro [Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València (Spain); Badía, José M. [Depto. Ingeniería y Ciencia de Computadores, Universitat Jaume I, Castellón (Spain); Chacón, Pablo [Dept. Biological Chemical Physics, Rocasolano Physics and Chemistry Institute, CSIC, Madrid (Spain); Davidović, Davor [Rudjer Bošković Institute, Centar za Informatiku i Računarstvo – CIR, Zagreb (Croatia); López-Blanco, José R. [Dept. Biological Chemical Physics, Rocasolano Physics and Chemistry Institute, CSIC, Madrid (Spain); Quintana-Ortí, Enrique S. [Depto. Ingeniería y Ciencia de Computadores, Universitat Jaume I, Castellón (Spain)
2016-03-15
We introduce a new iterative Krylov subspace-based eigensolver for the simulation of macromolecular motions on desktop multithreaded platforms equipped with multicore processors and, possibly, a graphics accelerator (GPU). The method consists of two stages, with the original problem first reduced into a simpler band-structured form by means of a high-performance compute-intensive procedure. This is followed by a memory-intensive but low-cost Krylov iteration, which is off-loaded to be computed on the GPU by means of an efficient data-parallel kernel. The experimental results reveal the performance of the new eigensolver. Concretely, when applied to the simulation of macromolecules with a few thousands degrees of freedom and the number of eigenpairs to be computed is small to moderate, the new solver outperforms other methods implemented as part of high-performance numerical linear algebra packages for multithreaded architectures.
Extremal sizes of subspace partitions
Heden, Olof; Nastase, Esmeralda; Sissokho, Papa
2011-01-01
A subspace partition $\\Pi$ of $V=V(n,q)$ is a collection of subspaces of $V$ such that each 1-dimensional subspace of $V$ is in exactly one subspace of $\\Pi$. The size of $\\Pi$ is the number of its subspaces. Let $\\sigma_q(n,t)$ denote the minimum size of a subspace partition of $V$ in which the largest subspace has dimension $t$, and let $\\rho_q(n,t)$ denote the maximum size of a subspace partition of $V$ in which the smallest subspace has dimension $t$. In this paper, we determine the values of $\\sigma_q(n,t)$ and $\\rho_q(n,t)$ for all positive integers $n$ and $t$. Furthermore, we prove that if $n\\geq 2t$, then the minimum size of a maximal partial $t$-spread in $V(n+t-1,q)$ is $\\sigma_q(n,t)$.
Portable, parallel, reusable Krylov space codes
Energy Technology Data Exchange (ETDEWEB)
Smith, B.; Gropp, W. [Argonne National Lab., IL (United States)
1994-12-31
Krylov space accelerators are an important component of many algorithms for the iterative solution of linear systems. Each Krylov space method has it`s own particular advantages and disadvantages, therefore it is desirable to have a variety of them available all with an identical, easy to use, interface. A common complaint application programmers have with available software libraries for the iterative solution of linear systems is that they require the programmer to use the data structures provided by the library. The library is not able to work with the data structures of the application code. Hence, application programmers find themselves constantly recoding the Krlov space algorithms. The Krylov space package (KSP) is a data-structure-neutral implementation of a variety of Krylov space methods including preconditioned conjugate gradient, GMRES, BiCG-Stab, transpose free QMR and CGS. Unlike all other software libraries for linear systems that the authors are aware of, KSP will work with any application codes data structures, in Fortran or C. Due to it`s data-structure-neutral design KSP runs unchanged on both sequential and parallel machines. KSP has been tested on workstations, the Intel i860 and Paragon, Thinking Machines CM-5 and the IBM SP1.
Realistic Decoherence Free Subspaces
Romero, K M F; Terra-Cunha, M O; Nemes, M C
2003-01-01
Decoherence free subspaces (DFS) is a theoretical tool towards experimental implementation of quantum information storage and processing. However, they represent an experimental challenge, since conditions for their existence are very stringent. This work explores the situation in which a system of $N$ oscillators coupled to a bath of harmonic oscillators is close to satisfy the conditions for the existence of DFS. We show, in the Born-Markov limit and for small deviations from separability and degeneracy conditions, that there are {\\emph{weak decoherence subspaces}} which resemble the original notion of DFS.
DEFF Research Database (Denmark)
Vissing, S.; Hededal, O.
-dimensional subspace in order to establish and solve a symmetric generalized eigenvalue problem in the subspace. The algorithm is described in pseudo code and implemented in the C programming language for lower triangular matrices A and B. The implementation includes procedures for selecting initial iteration vectors......An algorithm is presented for computing the m smallest eigenvalues and corresponding eigenvectors of the generalized eigenvalue problem (A - λB)Φ = 0 where A and B are real n x n symmetric matrices. In an iteration scheme the matrices A and B are projected simultaneously onto an m...
Newton-Krylov-Schwarz methods in unstructured grid Euler flow
Energy Technology Data Exchange (ETDEWEB)
Keyes, D.E. [Old Dominion Univ., Norfolk, VA (United States)
1996-12-31
Newton-Krylov methods and Krylov-Schwarz (domain decomposition) methods have begun to become established in computational fluid dynamics (CFD) over the past decade. The former employ a Krylov method inside of Newton`s method in a Jacobian-free manner, through directional differencing. The latter employ an overlapping Schwarz domain decomposition to derive a preconditioner for the Krylov accelerator that relies primarily on local information, for data-parallel concurrency. They may be composed as Newton-Krylov-Schwarz (NKS) methods, which seem particularly well suited for solving nonlinear elliptic systems in high-latency, distributed-memory environments. We give a brief description of this family of algorithms, with an emphasis on domain decomposition iterative aspects. We then describe numerical simulations with Newton-Krylov-Schwarz methods on an aerodynamic application emphasizing comparisons with a standard defect-correction approach and subdomain preconditioner consistency.
Nonlinear Krylov acceleration of reacting flow codes
Energy Technology Data Exchange (ETDEWEB)
Kumar, S.; Rawat, R.; Smith, P.; Pernice, M. [Univ. of Utah, Salt Lake City, UT (United States)
1996-12-31
We are working on computational simulations of three-dimensional reactive flows in applications encompassing a broad range of chemical engineering problems. Examples of such processes are coal (pulverized and fluidized bed) and gas combustion, petroleum processing (cracking), and metallurgical operations such as smelting. These simulations involve an interplay of various physical and chemical factors such as fluid dynamics with turbulence, convective and radiative heat transfer, multiphase effects such as fluid-particle and particle-particle interactions, and chemical reaction. The governing equations resulting from modeling these processes are highly nonlinear and strongly coupled, thereby rendering their solution by traditional iterative methods (such as nonlinear line Gauss-Seidel methods) very difficult and sometimes impossible. Hence we are exploring the use of nonlinear Krylov techniques (such as CMRES and Bi-CGSTAB) to accelerate and stabilize the existing solver. This strategy allows us to take advantage of the problem-definition capabilities of the existing solver. The overall approach amounts to using the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) method and its variants as nonlinear preconditioners for the nonlinear Krylov method. We have also adapted a backtracking approach for inexact Newton methods to damp the Newton step in the nonlinear Krylov method. This will be a report on work in progress. Preliminary results with nonlinear GMRES have been very encouraging: in many cases the number of line Gauss-Seidel sweeps has been reduced by about a factor of 5, and increased robustness of the underlying solver has also been observed.
Operator equations and invariant subspaces
Directory of Open Access Journals (Sweden)
Valentin Matache
1994-05-01
Full Text Available Banach space operators acting on some fixed space X are considered. If two such operators A and B verify the condition A2=B2 and if A has nontrivial hyperinvariant subspaces, then B has nontrivial invariant subspaces. If A and B commute and satisfy a special type of functional equation, and if A is not a scalar multiple of the identity, the author proves that if A has nontrivial invariant subspaces, then so does B.
Energy Technology Data Exchange (ETDEWEB)
McHugh, P.R.
1995-10-01
Fully coupled, Newton-Krylov algorithms are investigated for solving strongly coupled, nonlinear systems of partial differential equations arising in the field of computational fluid dynamics. Primitive variable forms of the steady incompressible and compressible Navier-Stokes and energy equations that describe the flow of a laminar Newtonian fluid in two-dimensions are specifically considered. Numerical solutions are obtained by first integrating over discrete finite volumes that compose the computational mesh. The resulting system of nonlinear algebraic equations are linearized using Newton`s method. Preconditioned Krylov subspace based iterative algorithms then solve these linear systems on each Newton iteration. Selected Krylov algorithms include the Arnoldi-based Generalized Minimal RESidual (GMRES) algorithm, and the Lanczos-based Conjugate Gradients Squared (CGS), Bi-CGSTAB, and Transpose-Free Quasi-Minimal Residual (TFQMR) algorithms. Both Incomplete Lower-Upper (ILU) factorization and domain-based additive and multiplicative Schwarz preconditioning strategies are studied. Numerical techniques such as mesh sequencing, adaptive damping, pseudo-transient relaxation, and parameter continuation are used to improve the solution efficiency, while algorithm implementation is simplified using a numerical Jacobian evaluation. The capabilities of standard Newton-Krylov algorithms are demonstrated via solutions to both incompressible and compressible flow problems. Incompressible flow problems include natural convection in an enclosed cavity, and mixed/forced convection past a backward facing step.
Krylov subspace methods and the sign function: multishifts and deflation in the non-Hermitian case
Bloch, Jacques C R; Frommer, Andreas; Heybrock, Simon; Schäfer, Katrin; Wettig, Tilo
2009-01-01
Rational approximations of the matrix sign function lead to multishift methods. For non-Hermitian matrices long recurrences can cause storage problems, which can be circumvented with restarts. Together with deflation we obtain efficient iterative methods, as we show in numerical experiments for the overlap Dirac operator at non-vanishing quark chemical potential for lattices up to size 10^4.
A Krylov subspace algorithm for evaluating the phi-functions appearing in exponential integrators
2009-01-01
We develop an algorithm for computing the solution of a large system of linear ordinary differential equations (ODEs) with polynomial inhomogeneity. This is equivalent to computing the action of a certain matrix function on the vector representing the initial condition. The matrix function is a linear combination of the matrix exponential and other functions related to the exponential (the so-called phi-functions). Such computations are the major computational burden in the implementation of ...
A short guide to exponential Krylov subspace time integration for Maxwell's equations
Botchev, Mike A.
2012-01-01
The exponential time integration, i.e., time integration which involves the matrix exponential, is an attractive tool for solving Maxwell's equations in time. However, its application in practice often requires a substantial knowledge of numerical linear algebra algorithms, in particular, of the Kry
Krylov subspace exponential time domain solution of Maxwell’s equations in photonic crystal modeling
Bochev, Mikhail A.
The exponential time integration, i.e., time integration which involves the matrix exponential, is an attractive tool for time domain modeling involving Maxwell’s equations. However, its application in practice often requires a substantial knowledge of numerical linear algebra algorithms, such as
A short guide to exponential Krylov subspace time integration for Maxwell's equations
Bochev, Mikhail A.
The exponential time integration, i.e., time integration which involves the matrix exponential, is an attractive tool for solving Maxwell's equations in time. However, its application in practice often requires a substantial knowledge of numerical linear algebra algorithms, in particular, of the
Krylov solvers for linear algebraic systems
Broyden, Charles George
2004-01-01
The first four chapters of this book give a comprehensive and unified theory of the Krylov methods. Many of these are shown to be particular examples ofthe block conjugate-gradient algorithm and it is this observation thatpermits the unification of the theory. The two major sub-classes of thosemethods, the Lanczos and the Hestenes-Stiefel, are developed in parallel asnatural generalisations of the Orthodir (GCR) and Orthomin algorithms. Theseare themselves based on Arnoldi's algorithm and a generalised Gram-Schmidtalgorithm and their properties, in particular their stability properties,are det
Forecasting Using Random Subspace Methods
T. Boot (Tom); D. Nibbering (Didier)
2016-01-01
textabstractRandom subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two appr
Subspace clustering through attribute clustering
Institute of Scientific and Technical Information of China (English)
Kun NIU; Shubo ZHANG; Junliang CHEN
2008-01-01
Many recently proposed subspace clustering methods suffer from two severe problems. First, the algorithms typically scale exponentially with the data dimensionality or the subspace dimensionality of clusters. Second, the clustering results are often sensitive to input parameters. In this paper, a fast algorithm of subspace clustering using attribute clustering is proposed to over-come these limitations. This algorithm first filters out redundant attributes by computing the Gini coefficient. To evaluate the correlation of every two non-redundant attributes, the relation matrix of non-redundant attributes is constructed based on the relation function of two dimensional united Gini coefficients. After applying an overlapping clustering algorithm on the relation matrix, the candidate of all interesting subspaces is achieved. Finally, all subspace clusters can be derived by clustering on interesting subspaces. Experiments on both synthesis and real datasets show that the new algorithm not only achieves a significant gain of runtime and quality to find subspace clusters, but also is insensitive to input parameters.
RICE CONDITION NUMBERS OF CERTAIN CHARACTERISTIC SUBSPACES
Institute of Scientific and Technical Information of China (English)
生汉芳; 刘新国
2002-01-01
This paper proposes the Rice condition numbers for invariant subspace, singular subspaces of a matrix and deflating subspaces of a regular matrix pair. The first-order perturbation estimations for these subspaces are derived by applying perturbation expansions of orthogonal projection operators.
Average sampling theorems for shift invariant subspaces
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The sampling theorem is one of the most powerful results in signal analysis. In this paper, we study the average sampling on shift invariant subspaces, e.g. wavelet subspaces. We show that if a subspace satisfies certain conditions, then every function in the subspace is uniquely determined and can be reconstructed by its local averages near certain sampling points. Examples are given.
A Parallel Newton-Krylov-Schur Algorithm for the Reynolds-Averaged Navier-Stokes Equations
Osusky, Michal
Aerodynamic shape optimization and multidisciplinary optimization algorithms have the potential not only to improve conventional aircraft, but also to enable the design of novel configurations. By their very nature, these algorithms generate and analyze a large number of unique shapes, resulting in high computational costs. In order to improve their efficiency and enable their use in the early stages of the design process, a fast and robust flow solution algorithm is necessary. This thesis presents an efficient parallel Newton-Krylov-Schur flow solution algorithm for the three-dimensional Navier-Stokes equations coupled with the Spalart-Allmaras one-equation turbulence model. The algorithm employs second-order summation-by-parts (SBP) operators on multi-block structured grids with simultaneous approximation terms (SATs) to enforce block interface coupling and boundary conditions. The discrete equations are solved iteratively with an inexact-Newton method, while the linear system at each Newton iteration is solved using the flexible Krylov subspace iterative method GMRES with an approximate-Schur parallel preconditioner. The algorithm is thoroughly verified and validated, highlighting the correspondence of the current algorithm with several established flow solvers. The solution for a transonic flow over a wing on a mesh of medium density (15 million nodes) shows good agreement with experimental results. Using 128 processors, deep convergence is obtained in under 90 minutes. The solution of transonic flow over the Common Research Model wing-body geometry with grids with up to 150 million nodes exhibits the expected grid convergence behavior. This case was completed as part of the Fifth AIAA Drag Prediction Workshop, with the algorithm producing solutions that compare favourably with several widely used flow solvers. The algorithm is shown to scale well on over 6000 processors. The results demonstrate the effectiveness of the SBP-SAT spatial discretization, which can
Shape analysis with subspace symmetries
Berner, Alexander
2011-04-01
We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmetries whereby we characterize similarity by requiring the set of symmetric parts to form a low dimensional shape space. We present an algorithm to discover subspace symmetries based on detecting linearly correlated correspondences among graphs of invariant features. We evaluate our technique on various data sets. We show that for models with pronounced surface features, subspace symmetries can be found fully automatically. For complicated cases, a small amount of user input is used to resolve ambiguities. Our technique computes dense correspondences that can subsequently be used in various applications, such as model repair and denoising. © 2010 The Author(s).
Scalable Density-Based Subspace Clustering
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan;
2011-01-01
For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering ...
Timmerman, Marieke E.; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla
2013-01-01
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existi
Subspace Methods for Eigenvalue Problems
Hochstenbach, Michiel Erik
2003-01-01
This thesis treats a number of aspects of subspace methods for various eigenvalue problems. Vibrations and their corresponding eigenvalues (or frequencies) arise in science, engineering, and daily life. Matrix eigenvalue problems come from a large number of areas, such as chemistry, mechanics, dyn
Orthogonal two-direction multiscaling functions
Institute of Scientific and Technical Information of China (English)
XIE Changzhen; YANG Shouzhi
2006-01-01
The concept of a two-direction multiscaling functions is introduced.We investigate the existence of solutions of the two-direction matrix refinable equationΦ(x)=Σk∈ZP+kΦ(2x-k) +Σk∈ZP-kΦ&(k- 2x),where r×r matrices {P+k} and {P-k} are called the positive-direction and negative-direction masks,respectively.Necessary and sufficient conditions that the above two-direction matrix refinable equation has a compactly supported distributional solution are established.The definition of orthogonal two-direction multiscaling function is presented,and the orthogonality criteria for two-direction multiscaling function is established.An algorithm for constructing a class of two-direction multiscaling functions is obtained.In addition,the relation of both orthogonal two-direction multiscaling function and orthogonal multiscaling function is discussed.Finally,construction examples are given.
Skyline View: Efficient Distributed Subspace Skyline Computation
Kim, Jinhan; Lee, Jongwuk; Hwang, Seung-Won
Skyline queries have gained much attention as alternative query semantics with pros (e.g.low query formulation overhead) and cons (e.g.large control over result size). To overcome the cons, subspace skyline queries have been recently studied, where users iteratively specify relevant feature subspaces on search space. However, existing works mainly focuss on centralized databases. This paper aims to extend subspace skyline computation to distributed environments such as the Web, where the most important issue is to minimize the cost of accessing vertically distributed objects. Toward this goal, we exploit prior skylines that have overlapped subspaces to the given subspace. In particular, we develop algorithms for three scenarios- when the subspace of prior skylines is superspace, subspace, or the rest. Our experimental results validate that our proposed algorithm shows significantly better performance than the state-of-the-art algorithms.
Conjugate Gradient with Subspace Optimization
Karimi, Sahar
2012-01-01
In this paper we present a variant of the conjugate gradient (CG) algorithm in which we invoke a subspace minimization subproblem on each iteration. We call this algorithm CGSO for "conjugate gradient with subspace optimization". It is related to earlier work by Nemirovsky and Yudin. We apply the algorithm to solve unconstrained strictly convex problems. As with other CG algorithms, the update step on each iteration is a linear combination of the last gradient and last update. Unlike some other conjugate gradient methods, our algorithm attains a theoretical complexity bound of $O(\\sqrt{L/l} \\log(1/\\epsilon))$, where the ratio $L/l$ characterizes the strong convexity of the objective function. In practice, CGSO competes with other CG-type algorithms by incorporating some second order information in each iteration.
Subspace learning of neural networks
Cheng Lv, Jian; Zhou, Jiliu
2010-01-01
PrefaceChapter 1. Introduction1.1 Introduction1.1.1 Linear Neural Networks1.1.2 Subspace Learning1.2 Subspace Learning Algorithms1.2.1 PCA Learning Algorithms1.2.2 MCA Learning Algorithms1.2.3 ICA Learning Algorithms1.3 Methods for Convergence Analysis1.3.1 SDT Method1.3.2 DCT Method1.3.3 DDT Method1.4 Block Algorithms1.5 Simulation Data Set and Notation1.6 ConclusionsChapter 2. PCA Learning Algorithms with Constants Learning Rates2.1 Oja's PCA Learning Algorithms2.1.1 The Algorithms2.1.2 Convergence Issue2.2 Invariant Sets2.2.1 Properties of Invariant Sets2.2.2 Conditions for Invariant Sets2.
Subspace Arrangement Codes and Cryptosystems
2011-05-09
Signature Date Acceptance for the Trident Scholar Committee Professor Carl E. Wick Associate Director of Midshipmen Research Signature Date SUBSPACE...Professor William Traves. I also thank Professor Carl Wick and the Trident Scholar Committee for providing me with the opportunity to conduct this... Sagan . Why the characteristic polynomial factors. Bulletin of the American Mathematical Society, 36(2):113–133, February 1999. [16] Karen E. Smith
Random matrix improved subspace clustering
Couillet, Romain
2017-03-06
This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show in particular that our method provides high clustering performance while standard kernel choices provably fail. An application to user grouping based on vector channel observations in the context of massive MIMO wireless communication networks is provided.
Institute of Scientific and Technical Information of China (English)
莫则尧; 符尚武
2003-01-01
Two dimensional three temperatures energy equation is a kind of very impor-tant partial differential equation. In general, we discrete such equation with full implicit nine points stencil on Lagrange structured grid and generate a non-linear sparse algebraic equation including nine diagonal lines. This paper will discuss the iterative solver for such non-linear equations. We linearize the equations by fixing the coefficient matrix, and iteratively solve the linearized algebraic equation with Krylov subspace iterative method. We have applied the iterative method presented in this paper to the code Lared-Ⅰ for numerical simulation of two dimensional threetemperatures radial fluid dynamics, and have obtained efficient results.
Incremental Supervised Subspace Learning for Face Recognition
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.
Kernel based subspace projection of hyperspectral images
DEFF Research Database (Denmark)
Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten
In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...
On Subspaces of an Almost -Lagrange Space
Directory of Open Access Journals (Sweden)
P. N. Pandey
2012-01-01
Full Text Available We discuss the subspaces of an almost -Lagrange space (APL space in short. We obtain the induced nonlinear connection, coefficients of coupling, coefficients of induced tangent and induced normal connections, the Gauss-Weingarten formulae, and the Gauss-Codazzi equations for a subspace of an APL-space. Some consequences of the Gauss-Weingarten formulae have also been discussed.
Subspace learning from image gradient orientations
Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja
2012-01-01
We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data is typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities fails very often to estimate reliably the l
Jacobi method for signal subspace computation
Paul, Steffen; Goetze, Juergen
1997-10-01
The Jacobi method for singular value decomposition is well-suited for parallel architectures. Its application to signal subspace computations is well known. Basically the subspace spanned by singular vectors of large singular values are separated from subspace spanned by those of small singular values. The Jacobi algorithm computes the singular values and the corresponding vectors in random order. This requires sorting the result after convergence of the algorithm to select the signal subspace. A modification of the Jacobi method based on a linear objective function merges the sorting into the SVD-algorithm at little extra cost. In fact, the complexity of the diagonal processor cells in a triangular array get slightly larger. In this paper we present these extensions, in particular the modified algorithm for computing the rotation angles and give an example of its usefulness for subspace separation.
2012-04-20
NVIDIA, Oracle, and Samsung , U.S. DOE grants DE-SC0003959, DE-AC02-05-CH11231, Lawrence Berkeley National Laboratory, and NSF SDCI under Grant Number OCI...gradient method [19]. Van Rosendale’s implementation was motivated by exposing more parallelism using the PRAM model. Chronopoulous and Gear later created...matrix for no additional communication cost. The additional computation cost is O( s2 ) per s steps. For terms in 2. above, we have 2 choices. The rst
Explaining outliers by subspace separability
DEFF Research Database (Denmark)
Micenková, Barbora; Ng, Raymond T.; Dang, Xuan-Hong
2013-01-01
Outliers are extraordinary objects in a data collection. Depending on the domain, they may represent errors, fraudulent activities or rare events that are subject of our interest. Existing approaches focus on detection of outliers or degrees of outlierness (ranking), but do not provide a possible...... explanation of how these objects deviate from the rest of the data. Such explanations would help user to interpret or validate the detected outliers. The problem addressed in this paper is as follows: given an outlier detected by an existing algorithm, we propose a method that determines possible explanations...... for the outlier. These explanations are expressed in the form of subspaces in which the given outlier shows separability from the inliers. In this manner, our proposed method complements existing outlier detection algorithms by providing additional information about the outliers. Our method is designed to work...
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The concept of two-direction refinable functions and two-direction wavelets is introduced.We investigate the existence of distributional(or L2-stable) solutions of the two-direction refinement equation: φ(x)=∑p+kφ(mx-k)+∑p-kφ(k-mx) where m ≥ 2 is an integer. Based on the positive mask {pk+} and negative mask {p-k}, the conditions that guarantee the above equation has compactly distributional solutions or L2-stable solutions are established. Furthermore, the condition that the L2-stable solution of the above equation can generate a two-direction MRA is given. The support interval of φ(x) is discussed amply. The definition of orthogonal two-direction refinable function and orthogonal two-direction wavelets is presented, and the orthogonality criteria for two-direction refinable functions are established. An algorithm for constructing orthogonal two-direction refinable functions and their two-direction wavelets is presented. Another construction algorithm for two-direction L2-refinable functions, which have nonnegative symbol masks and possess high approximation order and regularity, is presented. Finally, two construction examples are given.
Energy Technology Data Exchange (ETDEWEB)
Bakhos, Tania, E-mail: taniab@stanford.edu [Institute for Computational and Mathematical Engineering, Stanford University (United States); Saibaba, Arvind K. [Department of Electrical and Computer Engineering, Tufts University (United States); Kitanidis, Peter K. [Institute for Computational and Mathematical Engineering, Stanford University (United States); Department of Civil and Environmental Engineering, Stanford University (United States)
2015-10-15
We consider the problem of estimating parameters in large-scale weakly nonlinear inverse problems for which the underlying governing equations is a linear, time-dependent, parabolic partial differential equation. A major challenge in solving these inverse problems using Newton-type methods is the computational cost associated with solving the forward problem and with repeated construction of the Jacobian, which represents the sensitivity of the measurements to the unknown parameters. Forming the Jacobian can be prohibitively expensive because it requires repeated solutions of the forward and adjoint time-dependent parabolic partial differential equations corresponding to multiple sources and receivers. We propose an efficient method based on a Laplace transform-based exponential time integrator combined with a flexible Krylov subspace approach to solve the resulting shifted systems of equations efficiently. Our proposed solver speeds up the computation of the forward and adjoint problems, thus yielding significant speedup in total inversion time. We consider an application from Transient Hydraulic Tomography (THT), which is an imaging technique to estimate hydraulic parameters related to the subsurface from pressure measurements obtained by a series of pumping tests. The algorithms discussed are applied to a synthetic example taken from THT to demonstrate the resulting computational gains of this proposed method.
The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspace.
Wang, Bing; Mueller, Klaus
2017-02-23
Analyzing high-dimensional data and finding hidden patterns is a difficult problem and has attracted numerous research efforts. Automated methods can be useful to some extent but bringing the data analyst into the loop via interactive visual tools can help the discovery process tremendously. An inherent problem in this effort is that humans lack the mental capacity to truly understand spaces exceeding three spatial dimensions. To keep within this limitation, we describe a framework that decomposes a high-dimensional data space into a continuum of generalized 3D subspaces. Analysts can then explore these 3D subspaces individually via the familiar trackball interface while using additional facilities to smoothly transition to adjacent subspaces for expanded space comprehension. Since the number of such subspaces suffers from combinatorial explosion, we provide a set of data-driven subspace selection and navigation tools which can guide users to interesting subspaces and views. A subspace trail map allows users to manage the explored subspaces, keep their bearings, and return to interesting subspaces and views. Both trackball and trail map are each embedded into a word cloud of attribute labels which aid in navigation. We demonstrate our system via several use cases in a diverse set of application areas - cluster analysis and refinement, information discovery, and supervised training of classifiers. We also report on a user study that evaluates the usability of the various interactions our system provides.
Sinusoidal Order Estimation Using Angles between Subspaces
Directory of Open Access Journals (Sweden)
Søren Holdt Jensen
2009-01-01
Full Text Available We consider the problem of determining the order of a parametric model from a noisy signal based on the geometry of the space. More specifically, we do this using the nontrivial angles between the candidate signal subspace model and the noise subspace. The proposed principle is closely related to the subspace orthogonality property known from the MUSIC algorithm, and we study its properties and compare it to other related measures. For the problem of estimating the number of complex sinusoids in white noise, a computationally efficient implementation exists, and this problem is therefore considered in detail. In computer simulations, we compare the proposed method to various well-known methods for order estimation. These show that the proposed method outperforms the other previously published subspace methods and that it is more robust to the noise being colored than the previously published methods.
Face recognition with L1-norm subspaces
Maritato, Federica; Liu, Ying; Colonnese, Stefania; Pados, Dimitris A.
2016-05-01
We consider the problem of representing individual faces by maximum L1-norm projection subspaces calculated from available face-image ensembles. In contrast to conventional L2-norm subspaces, L1-norm subspaces are seen to offer significant robustness to image variations, disturbances, and rank selection. Face recognition becomes then the problem of associating a new unknown face image to the "closest," in some sense, L1 subspace in the database. In this work, we also introduce the concept of adaptively allocating the available number of principal components to different face image classes, subject to a given total number/budget of principal components. Experimental studies included in this paper illustrate and support the theoretical developments.
Kernel based subspace projection of hyperspectral images
DEFF Research Database (Denmark)
Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten
In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF). Th......). The MAF projection exploits the fact that interesting phenomena in images typically exhibit spatial autocorrelation. The analysis is based on nearinfrared hyperspectral images of maize grains demonstrating the superiority of the kernelbased MAF method....
An extended EM algorithm for subspace clustering
Institute of Scientific and Technical Information of China (English)
Lifei CHEN; Qingshan JIANG
2008-01-01
Clustering high dimensional data has become a challenge in data mining due to the curse of dimension-ality. To solve this problem, subspace clustering has been defined as an extension of traditional clustering that seeks to find clusters in subspaces spanned by different combinations of dimensions within a dataset. This paper presents a new subspace clustering algorithm that calcu-lates the local feature weights automatically in an EM-based clustering process. In the algorithm, the features are locally weighted by using a new unsupervised weight-ing method, as a means to minimize a proposed cluster-ing criterion that takes into account both the average intra-clusters compactness and the average inter-clusters separation for subspace clustering. For the purposes of capturing accurate subspace information, an additional outlier detection process is presented to identify the pos-sible local outliers of subspace clusters, and is embedded between the E-step and M-step of the algorithm. The method has been evaluated in clustering real-world gene expression data and high dimensional artificial data with outliers, and the experimental results have shown its effectiveness.
4DVar Assimilation in the Unstable Subspace : existence of an optimal subspace dimension
Trevisan, Anna; D'Isidoro, Massimo; Talagrand, Olivier
2010-05-01
The nonlinear stability properties of a chaotic system are exploited to formulate a reduced subspace 4-dimensional assimilation algorithm, 4DVar-AUS (Assimilation in the Unstable Subspace). The key result is the existence of an optimal subspace dimension for the assimilation that is directly related to the unstable subspace dimension. Theoretical arguments suggest that the optimal subspace dimension is equal to N++1, where N+ is the number of nonnegative Lyapunov exponents. In support of the theory, numerical experiments are performed in a simple model with a variable number of positive exponents: the results show that, in the presence of observational error, the confinement of the assimilation increment in the unstable subspace of the system reduces the RMS analysis error with respect to standard 4DVar. The standard 4DVar solution, while being closer to the observations, is further away from the truth. The explanation of this result is that, assimilating in the unstable subspace, errors in the stable directions are naturally damped: because of observational error, assimilating the whole space otherwise prevents this decay. In agreement with this interpretation, if observations are perfect standard 4DVar gives the best results. These results are in agreement with an independent theoretical study of the Extended Kalman Filter, which show that the error concentrates in the unstable subspace.
Random subspaces in quantum information theory
Hayden, Patrick
2005-03-01
The selection of random unitary transformations plays a role in quantum information theory analogous to the role of random hash functions in classical information theory. Recent applications have included protocols achieving the quantum channel capacity and methods for extending superdense coding from bits to qubits. In addition, the corresponding random subspaces have proved useful for studying the structure of bipartite and multipartite entanglement. In quantum information theory, we're fond of saying that Hilbert space is a big place, the implication being that there's room for the unexpected to occur. The goal of this talk is to further bolster this homespun wisdowm. I'm going to present a number of results in quantum information theory that stem from the initially counterintuitive geometry of high-dimensional vector spaces, where subspaces with highly extremal properties are the norm rather than the exception. Peter Shor has shown, for example, that randomly selected subspaces can be used to send quantum information through a noisy quantum channel at the highest possible rate, that is, the quantum channel capacity. More recently, Debbie Leung, Andreas Winter and I demonstrated that a randomly chosen subspace of a bipartite quantum system will likely contain nothing but nearly maximally entangled states, even if the subspace is nearly as large as the original system in qubit terms. This observation has implications for communication, especially superdense coding.
Nocera, A.; Alvarez, G.
2016-11-01
Frequency-dependent correlations, such as the spectral function and the dynamical structure factor, help illustrate condensed matter experiments. Within the density matrix renormalization group (DMRG) framework, an accurate method for calculating spectral functions directly in frequency is the correction-vector method. The correction vector can be computed by solving a linear equation or by minimizing a functional. This paper proposes an alternative to calculate the correction vector: to use the Krylov-space approach. This paper then studies the accuracy and performance of the Krylov-space approach, when applied to the Heisenberg, the t-J, and the Hubbard models. The cases studied indicate that the Krylov-space approach can be more accurate and efficient than the conjugate gradient, and that the error of the former integrates best when a Krylov-space decomposition is also used for ground state DMRG.
Nocera, A; Alvarez, G
2016-11-01
Frequency-dependent correlations, such as the spectral function and the dynamical structure factor, help illustrate condensed matter experiments. Within the density matrix renormalization group (DMRG) framework, an accurate method for calculating spectral functions directly in frequency is the correction-vector method. The correction vector can be computed by solving a linear equation or by minimizing a functional. This paper proposes an alternative to calculate the correction vector: to use the Krylov-space approach. This paper then studies the accuracy and performance of the Krylov-space approach, when applied to the Heisenberg, the t-J, and the Hubbard models. The cases studied indicate that the Krylov-space approach can be more accurate and efficient than the conjugate gradient, and that the error of the former integrates best when a Krylov-space decomposition is also used for ground state DMRG.
Monomial codes seen as invariant subspaces
Directory of Open Access Journals (Sweden)
García-Planas María Isabel
2017-08-01
Full Text Available It is well known that cyclic codes are very useful because of their applications, since they are not computationally expensive and encoding can be easily implemented. The relationship between cyclic codes and invariant subspaces is also well known. In this paper a generalization of this relationship is presented between monomial codes over a finite field and hyperinvariant subspaces of n under an appropriate linear transformation. Using techniques of Linear Algebra it is possible to deduce certain properties for this particular type of codes, generalizing known results on cyclic codes.
Comparing Subspace Methods for Closed Loop Subspace System Identification by Monte Carlo Simulations
Directory of Open Access Journals (Sweden)
David Di Ruscio
2009-10-01
Full Text Available A novel promising bootstrap subspace system identification algorithm for both open and closed loop systems is presented. An outline of the SSARX algorithm by Jansson (2003 is given and a modified SSARX algorithm is presented. Some methods which are consistent for closed loop subspace system identification presented in the literature are discussed and compared to a recently published subspace algorithm which works for both open as well as for closed loop data, i.e., the DSR_e algorithm as well as the bootstrap method. Experimental comparisons are performed by Monte Carlo simulations.
Bounds on Subspace Codes Based on Subspaces of Type (m,1 in Singular Linear Space
Directory of Open Access Journals (Sweden)
You Gao
2014-01-01
Full Text Available The Sphere-packing bound, Singleton bound, Wang-Xing-Safavi-Naini bound, Johnson bound, and Gilbert-Varshamov bound on the subspace codes n+l,M,d,(m,1q based on subspaces of type (m,1 in singular linear space Fq(n+l over finite fields Fq are presented. Then, we prove that codes based on subspaces of type (m,1 in singular linear space attain the Wang-Xing-Safavi-Naini bound if and only if they are certain Steiner structures in Fq(n+l.
Quantum Computing in Decoherence-Free Subspace Constructed by Triangulation
Directory of Open Access Journals (Sweden)
Qiao Bi
2010-01-01
Full Text Available A formalism for quantum computing in decoherence-free subspaces is presented. The constructed subspaces are partial triangulated to an index related to environment. The quantum states in the subspaces are just projected states which are ruled by a subdynamic kinetic equation. These projected states can be used to perform ideal quantum logical operations without decoherence.
Biomarkers spectral subspace for cancer detection.
Sun, Yi; Pu, Yang; Yang, Yuanlong; Alfano, Robert R
2012-10-01
A novel approach to cancer detection in biomarkers spectral subspace (BSS) is proposed. The basis spectra of the subspace spanned by fluorescence spectra of biomarkers are obtained by the Gram-Schmidt method. A support vector machine classifier (SVM) is trained in the subspace. The spectrum of a sample tissue is projected onto and is classified in the subspace. In addition to sensitivity and specificity, the metrics of positive predictivity, Score1, maximum Score1, and accuracy (AC) are employed for performance evaluation. The proposed BSS using SVM is applied to breast cancer detection using four biomarkers: collagen, NADH, flavin, and elastin, with 340-nm excitation. It is found that the BSS SVM outperforms the approach based on multivariate curve resolution (MCR) using SVM and achieves the best performance of principal component analysis (PCA) using SVM among all combinations of PCs. The descent order of efficacy of the four biomarkers in the breast cancer detection of this experiment is collagen, NADH, elastin, and flavin. The advantage of BSS is twofold. First, all diagnostically useful information of biomarkers for cancer detection is retained while dimensionality of data is significantly reduced to obviate the curse of dimensionality. Second, the efficacy of biomarkers in cancer detection can be determined.
Subspace System Identification of the Kalman Filter
Directory of Open Access Journals (Sweden)
David Di Ruscio
2003-07-01
Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.
Index formulae for subspaces of Krein spaces
Dijksma, A; Gheondea, A
1996-01-01
For a subspace S of a Krein space K and an arbitrary fundamental decomposition K = K-[+]K+ of K, we prove the index formula k(-)(S) + dim(S-perpendicular to boolean AND K-+) = k(+)(S-perpendicular to)+ dim(S boolean AND K-), were k(+/-)(S) stands for the positive/negative signature of S. The differe
Interference subspace rejection in wideband CDMA:
DEFF Research Database (Denmark)
Hansen, Henrik; Affes, Sofiene; Mermelstein, Paul
2001-01-01
This paper extends our study on a multi-user receiver structure for base-station receivers with antenna arrays in multicellular systems. The receiver employs a beamforming structure with constraints that nulls the signal component in appropriate interference subspaces. Here we introduce a new mod...
Partial interference subspace rejection in CDMA systems
DEFF Research Database (Denmark)
Hansen, Henrik; Affes, Sofiene; Mewelstein, Paul
2001-01-01
Previously presented interference subspace rejection (ISR) proposed a family of new efficient multiuser detectors for CDMA. We reconsider in this paper the modes of ISR using decision feedback (DF). DF modes share similarities with parallel interference cancellation (PIC) but attempt to cancel...
Robust Latent Subspace Learning for Image Classification.
Fang, Xiaozhao; Teng, Shaohua; Lai, Zhihui; He, Zhaoshui; Xie, Shengli; Wong, Wai Keung
2017-05-10
This paper proposes a novel method, called robust latent subspace learning (RLSL), for image classification. We formulate an RLSL problem as a joint optimization problem over both the latent SL and classification model parameter predication, which simultaneously minimizes: 1) the regression loss between the learned data representation and objective outputs and 2) the reconstruction error between the learned data representation and original inputs. The latent subspace can be used as a bridge that is expected to seamlessly connect the origin visual features and their class labels and hence improve the overall prediction performance. RLSL combines feature learning with classification so that the learned data representation in the latent subspace is more discriminative for classification. To learn a robust latent subspace, we use a sparse item to compensate error, which helps suppress the interference of noise via weakening its response during regression. An efficient optimization algorithm is designed to solve the proposed optimization problem. To validate the effectiveness of the proposed RLSL method, we conduct experiments on diverse databases and encouraging recognition results are achieved compared with many state-of-the-arts methods.
Joint Local Quasinilpotence and Common Invariant Subspaces
Indian Academy of Sciences (India)
A Fernández Valles
2006-08-01
In this article we obtain some positive results about the existence of a common nontrivial invariant subspace for -tuples of not necessarily commuting operators on Banach spaces with a Schauder basis. The concept of joint quasinilpotence plays a basic role. Our results complement recent work by Kosiek [6] and Ptak [8].
Compressive Detection of Random Subspace Signals
Razavi, Alireza; Valkama, Mikko; Cabric, Danijela
2016-08-01
The problem of compressive detection of random subspace signals is studied. We consider signals modeled as $\\mathbf{s} = \\mathbf{H} \\mathbf{x}$ where $\\mathbf{H}$ is an $N \\times K$ matrix with $K \\le N$ and $\\mathbf{x} \\sim \\mathcal{N}(\\mathbf{0}_{K,1},\\sigma_x^2 \\mathbf{I}_K)$. We say that signal $\\mathbf{s}$ lies in or leans toward a subspace if the largest eigenvalue of $\\mathbf{H} \\mathbf{H}^T$ is strictly greater than its smallest eigenvalue. We first design a measurement matrix $\\mathbf{\\Phi}=[\\mathbf{\\Phi}_s^T,\\mathbf{\\Phi}_o^T]^T$ comprising of two sub-matrices $\\mathbf{\\Phi}_s$ and $\\mathbf{\\Phi}_o$ where $\\mathbf{\\Phi}_s$ projects the signals to the strongest left-singular vectors, i.e., the left-singular vectors corresponding to the largest singular values, of subspace matrix $\\mathbf{H}$ and $\\mathbf{\\Phi}_o$ projects it to the weakest left-singular vectors. We then propose two detectors which work based on the difference in energies of the samples measured by two sub-matrices $\\mathbf{\\Phi}_s$ and $\\mathbf{\\Phi}_o$ and prove their optimality. Simplified versions of the proposed detectors for the case when the variance of noise is known are also provided. Furthermore, we study the performance of the detector when measurements are imprecise and show how imprecision can be compensated by employing more measurement devices. The problem is then re-formulated for the case when the signal lies in the union of a finite number of linear subspaces instead of a single linear subspace. Finally, we study the performance of the proposed methods by simulation examples.
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
Ni, Yuzhao; Yuan, Xiaotong; Yan, Shuicheng; Cheong, Loong-Fah
2010-01-01
Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group) {Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably.} high-dimensional structural data such as those (approximately) lying on subspaces {We follow \\cite{liu2010robust} and use the term "subspace" to denote both linear subspaces and affine subspaces. There is a trivial conversion between linear subspaces and affine subspaces as mentioned therein.} or low-dimensional manifolds. By learning the affinity matrix in the form of sparse reconstruction, techniques proposed in this vein often considerably boost the performance in subspace settings where traditional SC can fail. Despite the success, there are fundamental problems that have been left unsolved: the spectrum property of the learned affinity matrix cannot be gauged in advance, and there is often one ugly symmetrization step that post-processes the affinity for SC input. Hence we advocate to enforce...
Newton-Krylov-BDDC solvers for nonlinear cardiac mechanics
Pavarino, L.F.
2015-07-18
The aim of this work is to design and study a Balancing Domain Decomposition by Constraints (BDDC) solver for the nonlinear elasticity system modeling the mechanical deformation of cardiac tissue. The contraction–relaxation process in the myocardium is induced by the generation and spread of the bioelectrical excitation throughout the tissue and it is mathematically described by the coupling of cardiac electro-mechanical models consisting of systems of partial and ordinary differential equations. In this study, the discretization of the electro-mechanical models is performed by Q1 finite elements in space and semi-implicit finite difference schemes in time, leading to the solution of a large-scale linear system for the bioelectrical potentials and a nonlinear system for the mechanical deformation at each time step of the simulation. The parallel mechanical solver proposed in this paper consists in solving the nonlinear system with a Newton-Krylov-BDDC method, based on the parallel solution of local mechanical problems and a coarse problem for the so-called primal unknowns. Three-dimensional parallel numerical tests on different machines show that the proposed parallel solver is scalable in the number of subdomains, quasi-optimal in the ratio of subdomain to mesh sizes, and robust with respect to tissue anisotropy.
Notes on Newton-Krylov based Incompressible Flow Projection Solver
Energy Technology Data Exchange (ETDEWEB)
Robert Nourgaliev; Mark Christon; J. Bakosi
2012-09-01
The purpose of the present document is to formulate Jacobian-free Newton-Krylov algorithm for approximate projection method used in Hydra-TH code. Hydra-TH is developed by Los Alamos National Laboratory (LANL) under the auspices of the Consortium for Advanced Simulation of Light-Water Reactors (CASL) for thermal-hydraulics applications ranging from grid-to-rod fretting (GTRF) to multiphase flow subcooled boiling. Currently, Hydra-TH is based on the semi-implicit projection method, which provides an excellent platform for simulation of transient single-phase thermalhydraulics problems. This algorithm however is not efficient when applied for very slow or steady-state problems, as well as for highly nonlinear multiphase problems relevant to nuclear reactor thermalhydraulics with boiling and condensation. These applications require fully-implicit tightly-coupling algorithms. The major technical contribution of the present report is the formulation of fully-implicit projection algorithm which will fulfill this purpose. This includes the definition of non-linear residuals used for GMRES-based linear iterations, as well as physics-based preconditioning techniques.
On a subspace of dual Zariski topology
Ćeken, Seçil
2017-07-01
Let R be a commutative ring with identity and S pecs(M) (resp. Min(M)) denote the set of all second (resp. minimal) submodules of a non-zero R-module M. In this paper, we investigate several properties of the subspace topology on Min(M) induced by the dual Zariski on S pecs(M) and determine some cases in which Min(M) is a max-spectral space.
New learning subspace method for image feature extraction
Institute of Scientific and Technical Information of China (English)
CAO Jian-hai; LI Long; LU Chang-hou
2006-01-01
A new method of Windows Minimum/Maximum Module Learning Subspace Algorithm(WMMLSA) for image feature extraction is presented. The WMMLSM is insensitive to the order of the training samples and can regulate effectively the radical vectors of an image feature subspace through selecting the study samples for subspace iterative learning algorithm,so it can improve the robustness and generalization capacity of a pattern subspace and enhance the recognition rate of a classifier. At the same time,a pattern subspace is built by the PCA method. The classifier based on WMMLSM is successfully applied to recognize the pressed characters on the gray-scale images. The results indicate that the correct recognition rate on WMMLSM is higher than that on Average Learning Subspace Method,and that the training speed and the classification speed are both improved. The new method is more applicable and efficient.
Invariant Subspaces of the Two-Dimensional Nonlinear Evolution Equations
Directory of Open Access Journals (Sweden)
Chunrong Zhu
2016-11-01
Full Text Available In this paper, we develop the symmetry-related methods to study invariant subspaces of the two-dimensional nonlinear differential operators. The conditional Lie–Bäcklund symmetry and Lie point symmetry methods are used to construct invariant subspaces of two-dimensional differential operators. We first apply the multiple conditional Lie–Bäcklund symmetries to derive invariant subspaces of the two-dimensional operators. As an application, the invariant subspaces for a class of two-dimensional nonlinear quadratic operators are provided. Furthermore, the invariant subspace method in one-dimensional space combined with the Lie symmetry reduction method and the change of variables is used to obtain invariant subspaces of the two-dimensional nonlinear operators.
Indoor Subspacing to Implement Indoorgml for Indoor Navigation
Jung, H.; Lee, J.
2015-10-01
According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.
INDOOR SUBSPACING TO IMPLEMENT INDOORGML FOR INDOOR NAVIGATION
Directory of Open Access Journals (Sweden)
H. Jung
2015-10-01
Full Text Available According to an increasing demand for indoor navigation, there are great attempts to develop applicable indoor network. Representation for a room as a node is not sufficient to apply complex and large buildings. As OGC established IndoorGML, subspacing to partition the space for constructing logical network is introduced. Concerning subspacing for indoor network, transition space like halls or corridors also have to be considered. This study presents the subspacing process for creating an indoor network in shopping mall. Furthermore, categorization of transition space is performed and subspacing of this space is considered. Hall and squares in mall is especially defined for subspacing. Finally, implementation of subspacing process for indoor network is presented.
Subspace decomposition-based correlation matrix multiplication
Institute of Scientific and Technical Information of China (English)
Cheng Hao; Guo Wei; Yu Jingdong
2008-01-01
The correlation matrix, which is widely used in eigenvalue decomposition (EVD) or singular value decomposition (SVD), usually can be denoted by R = E[yiy'i]. A novel method for constructing the correlation matrix R is proposed. The proposed algorithm can improve the resolving power of the signal eigenvalues and overcomes the shortcomings of the traditional subspace methods, which cannot be applied to low SNR. Then the proposed method is applied to the direct sequence spread spectrum (DSSS) signal's signature sequence estimation.The performance of the proposed algorithm is analyzed, and some illustrative simulation results are presented.
Reliability-based concurrent subspace optimization method
Institute of Scientific and Technical Information of China (English)
FAN Hui; LI Wei-ji
2008-01-01
To avoid the high computational cost and much modification in the process of applying traditional re-liability-based design optimization method, a new reliability-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing muhidisciplinary optimization techniques and reli-ability assessment methods. It is shown through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current de-terministic optimization process.
Subspace Signal Processing in Structured Noise
1990-12-01
subspace spanned by a matrix of the form: :0 .. .0 Hrt (2 .1) Such a matrLx is called a Vandermonde matriz when m = n [GVL89]. We follow Demeure [Dem89] in...of Squared Bias. We now consider how he squared bias may bc estimated from the data for a given low rank projection P. Reca!l h!nt ie squared !ia is...mathematics used here is not new. Since it is mostly of a linear algebraic nature, it can be found in such books as the classic Matriz Compufalzons by Golub
A Nonlinera Krylov Accelerator for the Boltzmann k-Eigenvalue Problem
Calef, Matthew T; Warsa, James S; Berndt, Markus; Carlson, Neil N
2011-01-01
We compare variants of Anderson Mixing with the Jacobian-Free Newton-Krylov and Broyden methods applied to the k-eigenvalue formulation of the linear Boltzmann transport equation. We present evidence that one variant of Anderson Mixing finds solutions in the fewest number of iterations. We examine and strengthen theoretical results of Anderson Mixing applied to linear problems.
Newton-Raphson preconditioner for Krylov type solvers on GPU devices.
Kushida, Noriyuki
2016-01-01
A new Newton-Raphson method based preconditioner for Krylov type linear equation solvers for GPGPU is developed, and the performance is investigated. Conventional preconditioners improve the convergence of Krylov type solvers, and perform well on CPUs. However, they do not perform well on GPGPUs, because of the complexity of implementing powerful preconditioners. The developed preconditioner is based on the BFGS Hessian matrix approximation technique, which is well known as a robust and fast nonlinear equation solver. Because the Hessian matrix in the BFGS represents the coefficient matrix of a system of linear equations in some sense, the approximated Hessian matrix can be a preconditioner. On the other hand, BFGS is required to store dense matrices and to invert them, which should be avoided on modern computers and supercomputers. To overcome these disadvantages, we therefore introduce a limited memory BFGS, which requires less memory space and less computational effort than the BFGS. In addition, a limited memory BFGS can be implemented with BLAS libraries, which are well optimized for target architectures. There are advantages and disadvantages to the Hessian matrix approximation becoming better as the Krylov solver iteration continues. The preconditioning matrix varies through Krylov solver iterations, and only flexible Krylov solvers can work well with the developed preconditioner. The GCR method, which is a flexible Krylov solver, is employed because of the prevalence of GCR as a Krylov solver with a variable preconditioner. As a result of the performance investigation, the new preconditioner indicates the following benefits: (1) The new preconditioner is robust; i.e., it converges while conventional preconditioners (the diagonal scaling, and the SSOR preconditioners) fail. (2) In the best case scenarios, it is over 10 times faster than conventional preconditioners on a CPU. (3) Because it requries only simple operations, it performs well on a GPGPU. In
A simple subspace approach for speech denoising.
Manfredi, C; Daniello, M; Bruscaglioni, P
2001-01-01
For pathological voices, hoarseness is mainly due to airflow turbulence in the vocal tract and is often referred to as noise. This paper focuses on the enhancement of speech signals that are supposedly degraded by additive white noise. Speech enhancement is performed in the time-domain, by means of a fast and reliable subspace approach. A low-order singular value decomposition (SVD) allows separating the signal and the noise contribution in subsequent data frames of the analysed speech signal. The noise component is thus removed from the signal and the filtered signal is reconstructed along the directions spanned by the eigenvectors associated with the signal subspace eigenvalues only, thus giving enhanced voice quality. This approach was tested on synthetic data, showing higher performance in terms of increased SNR when compared with linear prediction (LP) filtering. It was also successfully applied to real data, from hoarse voices of patients that had undergone partial cordectomisation. The simple structure of the proposed technique allows a real-time implementation, suitable for portable device realisation, as an aid to dysphonic speakers. It could be useful for reducing the effort in speaking, which is closely related to social problems due to awkwardness of voice.
Robust Recovery of Subspace Structures by Low-Rank Representation
Liu, Guangcan; Yan, Shuicheng; Sun, Ju; Yu, Yong; Ma, Yi
2010-01-01
Data that arises from computer vision and image processing is often characterized by a mixture of multiple linear (or affine) subspaces, leading to the challenging problem of subspace segmentation. We observe that the heart of segmentation is to deal with the data that may not strictly follow subspace structures, i.e., to handle the data corrupted by noise. In this work we therefore address the subspace recovery problem. Given a set of data samples approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible noise as well, i.e., our goal is to recover the subspace structures from corrupted data. To this end, we propose low-rank representation (LRR) for recovering a low-rank data matrix from corrupted observations. The recovery is performed by seeking the lowest-rank representation among all the candidates that can represent the data vectors as linear combinations of the basis in a given dictionary. LRR fits well the subspac...
Single-trial subspace-based approach for VEP extraction.
Kamel, Nidal; Yusoff, Mohd Zuki; Hani, Ahmad Fadzil Mohamad
2011-05-01
A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a prewhitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with the recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P(100), P(200), and P(300) of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital, Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P(100) is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.
Classes of Invariant Subspaces for Some Operator Algebras
Hamhalter, Jan; Turilova, Ekaterina
2014-10-01
New results showing connections between structural properties of von Neumann algebras and order theoretic properties of structures of invariant subspaces given by them are proved. We show that for any properly infinite von Neumann algebra M there is an affiliated subspace such that all important subspace classes living on are different. Moreover, we show that can be chosen such that the set of σ-additive measures on subspace classes of are empty. We generalize measure theoretic criterion on completeness of inner product spaces to affiliated subspaces corresponding to Type I factor with finite dimensional commutant. We summarize hitherto known results in this area, discuss their importance for mathematical foundations of quantum theory, and outline perspectives of further research.
Subspace methods for pattern recognition in intelligent environment
Jain, Lakhmi
2014-01-01
This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
Robust adaptive subspace detection in impulsive noise
Atitallah, Ismail Ben
2016-09-13
This paper addresses the design of the Adaptive Subspace Matched Filter (ASMF) detector in the presence of compound Gaussian clutters and a mismatch in the steering vector. In particular, we consider the case wherein the ASMF uses the regularized Tyler estimator (RTE) to estimate the clutter covariance matrix. Under this setting, a major question that needs to be addressed concerns the setting of the threshold and the regularization parameter. To answer this question, we consider the regime in which the number of observations used to estimate the RTE and their dimensions grow large together. Recent results from random matrix theory are then used in order to approximate the false alarm and detection probabilities by deterministic quantities. The latter are optimized in order to maximize an upper bound on the asymptotic detection probability while keeping the asymptotic false alarm probability at a fixed rate. © 2016 IEEE.
Robust video hashing via multilinear subspace projections.
Li, Mu; Monga, Vishal
2012-10-01
The goal of video hashing is to design hash functions that summarize videos by short fingerprints or hashes. While traditional applications of video hashing lie in database searches and content authentication, the emergence of websites such as YouTube and DailyMotion poses a challenging problem of anti-piracy video search. That is, hashes or fingerprints of an original video (provided to YouTube by the content owner) must be matched against those uploaded to YouTube by users to identify instances of "illegal" or undesirable uploads. Because the uploaded videos invariably differ from the original in their digital representation (owing to incidental or malicious distortions), robust video hashes are desired. We model videos as order-3 tensors and use multilinear subspace projections, such as a reduced rank parallel factor analysis (PARAFAC) to construct video hashes. We observe that, unlike most standard descriptors of video content, tensor-based subspace projections can offer excellent robustness while effectively capturing the spatio-temporal essence of the video for discriminability. We introduce randomization in the hash function by dividing the video into (secret key based) pseudo-randomly selected overlapping sub-cubes to prevent against intentional guessing and forgery. Detection theoretic analysis of the proposed hash-based video identification is presented, where we derive analytical approximations for error probabilities. Remarkably, these theoretic error estimates closely mimic empirically observed error probability for our hash algorithm. Furthermore, experimental receiver operating characteristic (ROC) curves reveal that the proposed tensor-based video hash exhibits enhanced robustness against both spatial and temporal video distortions over state-of-the-art video hashing techniques.
An alternative subspace approach to EEG dipole source localization
Energy Technology Data Exchange (ETDEWEB)
Xu Xiaoliang [KC Science and Technologies Inc., Naperville, IL 60565 (United States); Xu, Bobby [Illinois Mathematics and Science Academy, Aurora, IL 60506 (United States); He Bin [Department of Bioengineering, University of Illinois, Chicago, IL 60607 (United States)
2004-01-21
In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.
A New Inexact Inverse Subspace Iteration for Generalized Eigenvalue Problems
Directory of Open Access Journals (Sweden)
Fatemeh Mohammad
2014-05-01
Full Text Available In this paper, we represent an inexact inverse subspace iteration method for computing a few eigenpairs of the generalized eigenvalue problem $Ax = \\lambda Bx$[Q.~Ye and P.~Zhang, Inexact inverse subspace iteration for generalized eigenvalue problems, Linear Algebra and its Application, 434 (2011 1697-1715]. In particular, the linear convergence property of the inverse subspace iteration is preserved.
Newton-Krylov-Schwarz algorithms for the 2D full potential equation
Energy Technology Data Exchange (ETDEWEB)
Cai, Xiao-Chuan [Univ. of Colorado, Boulder, CO (United States); Gropp, W.D. [Argonne National Lab., IL (United States); Keyes, D.E. [Old Dominion Univ. Norfolk, VA (United States)] [and others
1996-12-31
We study parallel two-level overlapping Schwarz algorithms for solving nonlinear finite element problems, in particular, for the full potential equation of aerodynamics discretized in two dimensions with bilinear elements. The main algorithm, Newton-Krylov-Schwarz (NKS), employs an inexact finite-difference Newton method and a Krylov space iterative method, with a two-level overlapping Schwarz method as a preconditioner. We demonstrate that NKS, combined with a density upwinding continuation strategy for problems with weak shocks, can be made robust for this class of mixed elliptic-hyperbolic nonlinear partial differential equations, with proper specification of several parameters. We study upwinding parameters, inner convergence tolerance, coarse grid density, subdomain overlap, and the level of fill-in in the incomplete factorization, and report favorable choices for numerical convergence rate and overall execution time on a distributed-memory parallel computer.
Inexact Krylov iterations and relaxation strategies with fast-multipole boundary element method
Layton, Simon K
2015-01-01
Boundary element methods produce dense linear systems that can be accelerated via multipole expansions. Solved with Krylov methods, this implies computing the matrix-vector products within each iteration with some error, at an accuracy controlled by the order of the expansion, $p$. We take advantage of a unique property of Krylov iterations that allow lower accuracy of the matrix-vector products as convergence proceeds, and propose a relaxation strategy based on progressively decreasing $p$. Via extensive numerical tests, we show that the relaxed Krylov iterations converge with speed-ups of between 2x and 4x for Laplace problems and between 3.5x and 4.5x for Stokes problems. We include an application to Stokes flow around red blood cells, computing with up to 64 cells and problem size up to 131k boundary elements and nearly 400k unknowns. The study was done with an in-house multi-threaded C++ code, on a quad-core CPU.
A Geometric Analysis of Subspace Clustering with Outliers
Soltanolkotabi, Mahdi
2011-01-01
This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named {\\em sparse subspace clustering} (SSC) \\cite{Elhamifar09}, which significantly broadens the range of problems where it is provably effective. For instance, we show that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension. We also prove that SSC can correctly cluster data points even when the subspaces of interest intersect. Further, we develop an extension of SSC that succeeds when the data set is corrupted with possibly overwhelmingly many outliers. Underlying our analysis are clear geometric insights, which may bear on other sparse recovery problems. A numerical study complements our theoretica...
Review of Decoherence Free Subspaces, Noiseless Subsystems, and Dynamical Decoupling
Lidar, Daniel A
2012-01-01
Quantum information requires protection from the adverse affects of decoherence and noise. This review provides an introduction to the theory of decoherence-free subspaces, noiseless subsystems, and dynamical decoupling. It addresses quantum information preservation as well protected computation.
New results in subspace-stabilization control theory
Directory of Open Access Journals (Sweden)
C. D. Johnson
2000-01-01
Full Text Available Subspace-stabilization is a generalization of the classical idea of stabilizing motions of a dynamical system to an equilibrium state. The concept of subspace-stabilization and a theory for designing subspace-stabilizing control laws was introduced in a previously published paper. In the present paper, two new alternative methods for designing control laws that achieve subspace-stabilization are presented. These two alternative design methods are based on: (i a novel application of existing Linear Quadratic Regulator optimal-control theory, and (ii an algebraic method in which the control-law is expressed as a linear feedback of certain “canonical variables.” In some cases, these new design methods may be more effective than existing ones. The results are illustrated by worked examples.
Projections onto Invariant Subspaces of Some Banach Algebras
Institute of Scientific and Technical Information of China (English)
Ali GHAFFARI
2008-01-01
In this paper,among other things,the author studies the weak*-closed left translation invariant complemented subspace of semigroup algebras and group algebras.Also,the author studiesthe relationships between projections and amenability.
Modeling Sampling in Tensor Products of Unitary Invariant Subspaces
Directory of Open Access Journals (Sweden)
Antonio G. García
2016-01-01
Full Text Available The use of unitary invariant subspaces of a Hilbert space H is nowadays a recognized fact in the treatment of sampling problems. Indeed, shift-invariant subspaces of L2(R and also periodic extensions of finite signals are remarkable examples where this occurs. As a consequence, the availability of an abstract unitary sampling theory becomes a useful tool to handle these problems. In this paper we derive a sampling theory for tensor products of unitary invariant subspaces. This allows merging the cases of finitely/infinitely generated unitary invariant subspaces formerly studied in the mathematical literature; it also allows introducing the several variables case. As the involved samples are identified as frame coefficients in suitable tensor product spaces, the relevant mathematical technique is that of frame theory, involving both finite/infinite dimensional cases.
Unsupervised Spike Sorting Based on Discriminative Subspace Learning
Keshtkaran, Mohammad Reza
2014-01-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separab...
Manifold learning-based subspace distance for machinery damage assessment
Sun, Chuang; Zhang, Zhousuo; He, Zhengjia; Shen, Zhongjie; Chen, Binqiang
2016-03-01
Damage assessment is very meaningful to keep safety and reliability of machinery components, and vibration analysis is an effective way to carry out the damage assessment. In this paper, a damage index is designed by performing manifold distance analysis on vibration signal. To calculate the index, vibration signal is collected firstly, and feature extraction is carried out to obtain statistical features that can capture signal characteristics comprehensively. Then, manifold learning algorithm is utilized to decompose feature matrix to be a subspace, that is, manifold subspace. The manifold learning algorithm seeks to keep local relationship of the feature matrix, which is more meaningful for damage assessment. Finally, Grassmann distance between manifold subspaces is defined as a damage index. The Grassmann distance reflecting manifold structure is a suitable metric to measure distance between subspaces in the manifold. The defined damage index is applied to damage assessment of a rotor and the bearing, and the result validates its effectiveness for damage assessment of machinery component.
Wavelet Subspaces Invariant Under Groups of Translation Operators
Indian Academy of Sciences (India)
Biswaranjan Behera; Shobha Madan
2003-05-01
We study the action of translation operators on wavelet subspaces. This action gives rise to an equivalence relation on the set of all wavelets. We show by explicit construction that each of the associated equivalence classes is non-empty.
AN IMPROVED SUBSPACE TRACKING ALGORITHM FOR BLIND ADAPTIVE MULTIUSER DETECTION
Institute of Scientific and Technical Information of China (English)
Xu Changqing; Wang Hongyang; Song Wentao
2004-01-01
As the Projection Approximation Subspace Tracking with deflation(PASTd) algorithm is sensitive to impulsive noise, an improved subspace tracking algorithm is proposed and applied to blind adaptive multi-user detection. Simulation results show that the improved PASTd algorithm not only remains the properties of the conventional PASTdalgorithm, but also has good Bit Error Rate(BER) performance in impulsive noise environment, thus it can effectively improve the system performance.
Evaluating Clustering in Subspace Projections of High Dimensional Data
DEFF Research Database (Denmark)
Müller, Emmanuel; Günnemann, Stephan; Assent, Ira
2009-01-01
Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation and co...... and create a common baseline for future developments and comparable evaluations in the field. For repeatability, all implementations, data sets and evaluation measures are available on our website....
Experimental Comparison of Signal Subspace Based Noise Reduction Methods
DEFF Research Database (Denmark)
Hansen, Peter Søren Kirk; Hansen, Per Christian; Hansen, Steffen Duus
1999-01-01
The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimato....... Experimental results show that the signal subspace approach may lead to a significant enhancement of the signal to noise ratio of the output signal....
Experimental Comparison of Signal Subspace Based Noise Reduction Methods
Hansen, Peter Søren Kirk; Hansen, Per Christian; Hansen, Steffen Duus; Sørensen, John Aasted
1999-01-01
The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the signal subspace approach may lead to a significant enhancement of the signal to noise ratio of the output signal.
On the structure of finitely generated shift-invariant subspaces
Kazarian, K. S.
2016-01-01
A characterization of finitely generated shift-invariant subspaces is given when generators are g-minimal. An algorithm is given for the determination of the coefficients in the well known representation of the Fourier transform of an element of the finitely generated shift-invariant subspace as a linear combination of Fourier transformations of generators. An estimate for the norms of those coefficients is derived. For the proof a sort of orthogonalization procedure for generators is used wh...
EVD Dualdating Based Online Subspace Learning
Directory of Open Access Journals (Sweden)
Bo Jin
2014-01-01
Full Text Available Conventional incremental PCA methods usually only discuss the situation of adding samples. In this paper, we consider two different cases: deleting samples and simultaneously adding and deleting samples. To avoid the NP-hard problem of downdating SVD without right singular vectors and specific position information, we choose to use EVD instead of SVD, which is used by most IPCA methods. First, we propose an EVD updating and downdating algorithm, called EVD dualdating, which permits simultaneous arbitrary adding and deleting operation, via transforming the EVD of the covariance matrix into a SVD updating problem plus an EVD of a small autocorrelation matrix. A comprehensive analysis is delivered to express the essence, expansibility, and computation complexity of EVD dualdating. A mathematical theorem proves that if the whole data matrix satisfies the low-rank-plus-shift structure, EVD dualdating is an optimal rank-k estimator under the sequential environment. A selection method based on eigenvalues is presented to determine the optimal rank k of the subspace. Then, we propose three incremental/decremental PCA methods: EVDD-IPCA, EVDD-DPCA, and EVDD-IDPCA, which are adaptive to the varying mean. Finally, plenty of comparative experiments demonstrate that EVDD-based methods outperform conventional incremental/decremental PCA methods in both efficiency and accuracy.
Subspace Expanders and Matrix Rank Minimization
Khajehnejad, Amin; Hassibi, Babak
2011-01-01
Matrix rank minimization (RM) problems recently gained extensive attention due to numerous applications in machine learning, system identification and graphical models. In RM problem, one aims to find the matrix with the lowest rank that satisfies a set of linear constraints. The existing algorithms include nuclear norm minimization (NNM) and singular value thresholding. Thus far, most of the attention has been on i.i.d. Gaussian measurement operators. In this work, we introduce a new class of measurement operators, and a novel recovery algorithm, which is notably faster than NNM. The proposed operators are based on what we refer to as subspace expanders, which are inspired by the well known expander graphs based measurement matrices in compressed sensing. We show that given an $n\\times n$ PSD matrix of rank $r$, it can be uniquely recovered from a minimal sampling of $O(nr)$ measurements using the proposed structures, and the recovery algorithm can be cast as matrix inversion after a few initial processing s...
IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD
Institute of Scientific and Technical Information of China (English)
ZHANG Zhiyi; FAN Jiangling; HUA Hongxing
2006-01-01
An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix. In combination with component energy index (CEI) which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio (SNR)and gives a reliable separation of spurious and physical estimates.
Working mechanism of two-direction reinforced composite foundation
Institute of Scientific and Technical Information of China (English)
ZHANG Ling; ZHAO Ming-hua; HE Wei
2007-01-01
Based on the discussion about working mechanism of horizontal reinforcement and that of vertical reinforcement,respectively, the working mechanism of two-direction reinforced composite foundation was studied. The enhancing effect of horizontal reinforcement on vertical reinforced composite foundation was analyzed. A simplified calculation method for such two-direction reinforced working system was presented. A model experiment was carried out to validate the proposed method. In the experiment, geocell reinforcement worked as the horizontal reinforcement, while gravel pile composite foundation worked as the vertical reinforcement. The results show that the calculated curve is close to the measured one. The installation of geosynthetic reinforcement can increase the bearing capacity of composite foundation by nearly 68% at normal foundation settlement, which suggests that the enhancing effect by geosynthetic reinforcement should be taken into account in current design/analysis methods.
Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis.
Fang, Shu; Li, Jia; Tian, Yonghong; Huang, Tiejun; Chen, Xiaowu
2017-05-01
In visual saliency estimation, one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes. With the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces, we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors. Toward this end, we first conduct principal component analysis on massive randomly selected image patches. The principal components, which correspond to the largest eigenvalues, are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors. By projecting images onto various subspaces, we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions. In this manner, the probable targets often have the highest responses, while the responses at background regions become very low. Based on such random contrasts, an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces. Experimental results on two public benchmarks show that the proposed approach outperforms 16 state-of-the-art methods in human fixation prediction.
Numerical Validation of the Delaunay Normalization and the Krylov-Bogoliubov-Mitropolsky Method
Directory of Open Access Journals (Sweden)
David Ortigosa
2014-01-01
Full Text Available A scalable second-order analytical orbit propagator programme based on modern and classical perturbation methods is being developed. As a first step in the validation and verification of part of our orbit propagator programme, we only consider the perturbation produced by zonal harmonic coefficients in the Earth’s gravity potential, so that it is possible to analyze the behaviour of the mathematical expressions involved in Delaunay normalization and the Krylov-Bogoliubov-Mitropolsky method in depth and determine their limits.
Semi-Classical field theory as Decoherence Free Subspaces
Varela, Jaime
2014-01-01
We formulate semi-classical field theory as an approximate decoherence-free-subspace of a finite-dimensional quantum-gravity hilbert space. A complementarity construction can be realized as a unitary transformation which changes the decoherence-free-subspace. This can be translated to signify that field theory on a global slice, in certain space-times, is the simultaneous examination of two different superselected sectors of a gauge theory. We posit that a correct course graining procedure of quantum gravity should be WKB states propagating in a curved background in which particles exiting a horizon have imaginary components to their phases. The field theory appears non-unitary, but it is due to the existence of approximate decoherence free sub-spaces. Furthermore, the importance of operator spaces in the course-graining procedure is discussed. We also briefly touch on Firewalls.
Subspace Correction Methods for Total Variation and $\\ell_1$-Minimization
Fornasier, Massimo
2009-01-01
This paper is concerned with the numerical minimization of energy functionals in Hilbert spaces involving convex constraints coinciding with a seminorm for a subspace. The optimization is realized by alternating minimizations of the functional on a sequence of orthogonal subspaces. On each subspace an iterative proximity-map algorithm is implemented via oblique thresholding, which is the main new tool introduced in this work. We provide convergence conditions for the algorithm in order to compute minimizers of the target energy. Analogous results are derived for a parallel variant of the algorithm. Applications are presented in domain decomposition methods for degenerate elliptic PDEs arising in total variation minimization and in accelerated sparse recovery algorithms based on 1-minimization. We include numerical examples which show e.cient solutions to classical problems in signal and image processing. © 2009 Society for Industrial and Applied Physics.
Poset pinball, GKM-compatible subspaces, and Hessenberg varieties
Harada, Megumi
2010-01-01
This paper has three main goals. First, we set up a general framework to address the problem of constructing module bases for the equivariant cohomology of certain subspaces of GKM spaces. To this end we introduce the notion of a GKM-compatible subspace of an ambient GKM space. We also discuss poset-upper-triangularity, a key combinatorial notion in both GKM theory and more generally in localization theory in equivariant cohomology. With a view toward other applications, we present parts of our setup in a general algebraic and combinatorial framework. Second, motivated by our central problem of building module bases, we introduce a combinatorial game which we dub poset pinball and illustrate with several examples. Finally, as first applications, we apply the perspective of GKM-compatible subspaces and poset pinball to construct explicit and computationally convenient module bases for the $S^1$-equivariant cohomology of all Peterson varieties of classical Lie type, and subregular Springer varieties of Lie type...
Subspace Properties of Network Coding and their Applications
Siavoshani, Mahdi Jafari; Diggavi, Suhas
2011-01-01
Systems that employ network coding for content distribution convey to the receivers linear combinations of the source packets. If we assume randomized network coding, during this process the network nodes collect random subspaces of the space spanned by the source packets. We establish several fundamental properties of the random subspaces induced in such a system, and show that these subspaces implicitly carry topological information about the network and its state that can be passively collected and inferred. We leverage this information towards a number of applications that are interesting in their own right, such as topology inference, bottleneck discovery in peer-to-peer systems and locating Byzantine attackers. We thus argue that, randomized network coding, apart from its better known properties for improving information delivery rate, can additionally facilitate network management and control.
ON THE STABILITY OF FUSION FRAMES (FRAMES OF SUBSPACES)
Institute of Scientific and Technical Information of China (English)
Mohammad Sadegh Asgari
2011-01-01
A frame is an orthonormal basis-like collection of vectors in a Hilbert space,but need not be a basis or orthonormal.A fusion frame (frame of subspaces) is a frame-like collection of subspaces in a Hilbert space,thereby constructing a frame for the whole space by joining sequences of frames for subspaces.Moreover the notion of fusion frames provide a framework for applications and providing efficient and robust information processing algorithms.In this paper we study the conditions under which removing an element from a fusion frame,again we obtain another fusion frame.We give another proof of[5,Corollary 3.3(iii)]with extra information about the bounds.
EFFICIENT SUBSPACE CLUSTERING FOR HIGHER DIMENSIONAL DATA USING FUZZY ENTROPY
Institute of Scientific and Technical Information of China (English)
C.PALANISAMY; S.SELVAN
2009-01-01
In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual disbution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.
Roller Bearing Monitoring by New Subspace-Based Damage Indicator
Directory of Open Access Journals (Sweden)
G. Gautier
2015-01-01
Full Text Available A frequency-band subspace-based damage identification method for fault diagnosis in roller bearings is presented. Subspace-based damage indicators are obtained by filtering the vibration data in the frequency range where damage is likely to occur, that is, around the bearing characteristic frequencies. The proposed method is validated by considering simulated data of a damaged bearing. Also, an experimental case is considered which focuses on collecting the vibration data issued from a run-to-failure test. It is shown that the proposed method can detect bearing defects and, as such, it appears to be an efficient tool for diagnosis purpose.
A subspace preconditioning algorithm for eigenvector/eigenvalue computation
Energy Technology Data Exchange (ETDEWEB)
Bramble, J.H.; Knyazev, A.V.; Pasciak, J.E.
1996-12-31
We consider the problem of computing a modest number of the smallest eigenvalues along with orthogonal bases for the corresponding eigen-spaces of a symmetric positive definite matrix. In our applications, the dimension of a matrix is large and the cost of its inverting is prohibitive. In this paper, we shall develop an effective parallelizable technique for computing these eigenvalues and eigenvectors utilizing subspace iteration and preconditioning. Estimates will be provided which show that the preconditioned method converges linearly and uniformly in the matrix dimension when used with a uniform preconditioner under the assumption that the approximating subspace is close enough to the span of desired eigenvectors.
Research on spatial association rules mining in two-direction
Institute of Scientific and Technical Information of China (English)
XUE Li-xia; WANG Zuo-cheng
2007-01-01
In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and two-direction association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During two-direction spatial association rules mining, an algorithm is proposed to get non-spatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into non-spatial associations and the non-spatial itemsets were gotten. Based on the non-spatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.
New Estimates for the Rate of Convergence of the Method of Subspace Corrections
Institute of Scientific and Technical Information of China (English)
Durkbin Cho; Jinchao Xu; Ludmil Zikatanov
2008-01-01
We discuss estimates for the rate of convergence of the method of successive subspace corrections in terms of condition number estimate for the method of parallel subspace corrections. We provide upper bounds and in a special case, a lower bound for preconditioners defined via the method of successive subspace corrections.
Asgharzadeh, Hafez; Borazjani, Iman
2017-02-01
The explicit and semi-implicit schemes in flow simulations involving complex geometries and moving boundaries suffer from time-step size restriction and low convergence rates. Implicit schemes can be used to overcome these restrictions, but implementing them to solve the Navier-Stokes equations is not straightforward due to their non-linearity. Among the implicit schemes for non-linear equations, Newton-based techniques are preferred over fixed-point techniques because of their high convergence rate but each Newton iteration is more expensive than a fixed-point iteration. Krylov subspace methods are one of the most advanced iterative methods that can be combined with Newton methods, i.e., Newton-Krylov Methods (NKMs) to solve non-linear systems of equations. The success of NKMs vastly depends on the scheme for forming the Jacobian, e.g., automatic differentiation is very expensive, and matrix-free methods without a preconditioner slow down as the mesh is refined. A novel, computationally inexpensive analytical Jacobian for NKM is developed to solve unsteady incompressible Navier-Stokes momentum equations on staggered overset-curvilinear grids with immersed boundaries. Moreover, the analytical Jacobian is used to form a preconditioner for matrix-free method in order to improve its performance. The NKM with the analytical Jacobian was validated and verified against Taylor-Green vortex, inline oscillations of a cylinder in a fluid initially at rest, and pulsatile flow in a 90 degree bend. The capability of the method in handling complex geometries with multiple overset grids and immersed boundaries is shown by simulating an intracranial aneurysm. It was shown that the NKM with an analytical Jacobian is 1.17 to 14.77 times faster than the fixed-point Runge-Kutta method, and 1.74 to 152.3 times (excluding an intensively stretched grid) faster than automatic differentiation depending on the grid (size) and the flow problem. In addition, it was shown that using only the
General Scheme for the Construction of a Protected Qubit Subspace
DEFF Research Database (Denmark)
Aharon, N.; Drewsen, M.; Retzker, A.
2013-01-01
We present a new robust decoupling scheme suitable for half integer angular momentum states. The scheme is based on continuous dynamical decoupling techniques by which we create a protected qubit subspace. Our scheme predicts a coherence time of ~1 second, as compared to typically a few...
Robust Visual Tracking via Sparsity-Induced Subspace Learning.
Sui, Yao; Zhang, Shunli; Zhang, Li
2015-12-01
Target representation is a necessary component for a robust tracker. However, during tracking, many complicated factors may make the accumulated errors in the representation significantly large, leading to tracking drift. This paper aims to improve the robustness of target representation to avoid the influence of the accumulated errors, such that the tracker only acquires the information that facilitates tracking and ignores the distractions. We observe that the locally mutual relations between the feature observations of temporally obtained targets are beneficial to the subspace representation in visual tracking. Thus, we propose a novel subspace learning algorithm for visual tracking, which imposes joint row-wise sparsity structure on the target subspace to adaptively exclude distractive information. The sparsity is induced by exploiting the locally mutual relations between the feature observations during learning. To this end, we formulate tracking as a subspace sparsity inducing problem. A large number of experiments on various challenging video sequences demonstrate that our tracker outperforms many other state-of-the-art trackers.
New Characterizations of Fusion Frames (Frames of Subspaces)
Indian Academy of Sciences (India)
Mohammad Sadegh Asgari
2009-06-01
In this article, we give new characterizations of fusion frames, on the properties of their synthesis operators, on the behavior of fusion frames under bounded operators with closed range, and on erasures of subspaces of fusion frames. Furthermore we show that every fusion frame is the image of an orthonormal fusion basis under a bounded surjective operator.
Decoherence free subspaces of a quantum Markov semigroup
Energy Technology Data Exchange (ETDEWEB)
Agredo, Julián, E-mail: jaagredoe@unal.edu.co [Centro de Análisis Estocástico, Facultad de Ingeniería, Universidad Católica de Chile, Santiago, Chile and Departamento de Matemáticas, Universidad Nacional de Colombia, Manizales (Colombia); Fagnola, Franco, E-mail: franco.fagnola@polimi.it [Dipartimento di Matematica, Politecnico di Milano, Milano (Italy); Rebolledo, Rolando, E-mail: rrebolle@uc.cl [Centro de Análisis Estocástico, Facultad de Ingeniería, Facultad de Matemáticas, Universidad Católica de Chile, Santiago (Chile)
2014-11-15
We give a full characterisation of decoherence free subspaces of a given quantum Markov semigroup with generator in a generalised Lindbald form which is valid also for infinite-dimensional systems. Our results, extending those available in the literature concerning finite-dimensional systems, are illustrated by some examples.
Amendment on DPM and OJA Class Subspace Tracking Methods
Cheng, Zhu; Liu, Haitao; Ahmadi, Majid
2012-01-01
After analysis of the updating formula of DPM and OJA class of subspace tracking method, the reason for the spark in the stead state projector error power is discovered. The spark problem was fixed by the application of a limiter on update stepsize. The simulation confirmed the elimination of the overtune error.
Contractively complemented subspaces of pre-symmetric spaces
Neal, Matthew
2008-01-01
In 1965, Ron Douglas proved that if $X$ is a closed subspace of an $L^1$-space and $X$ is isometric to another $L^1$-space, then $X$ is the range of a contractive projection on the containing $L^1$-space. In 1977 Arazy-Friedman showed that if a subspace $X$ of $C_1$ is isometric to another $C_1$-space (possibly finite dimensional), then there is a contractive projection of $C_1$ onto $X$. In 1993 Kirchberg proved that if a subspace $X$ of the predual of a von Neumann algebra $M$ is isometric to the predual of another von Neumann algebra, then there is a contractive projection of the predual of $M$ onto $X$. We widen significantly the scope of these results by showing that if a subspace $X$ of the predual of a $JBW^*$-triple $A$ is isometric to the predual of another $JBW^*$-triple $B$, then there is a contractive projection on the predual of $A$ with range $X$, as long as $B$ does not have a direct summand which is isometric to a space of the form $L^\\infty(\\Omega,H)$, where $H$ is a Hilbert space of dimensio...
Problem of invariant subspaces in C*-algebras
Institute of Scientific and Technical Information of China (English)
张伦传
2002-01-01
Let A be a separable simple C*-algebra. For each a(≠0) in A, there exists a separable faithful and irreducible * representation (π, Hπ) on A such that π(a) has a non-trivial invariant subspace in Hπ.
Subspace Iteration Algorithms in FORTRAN 77 and FORTRAN 8x
1988-12-30
AFWAL-TR-88-3120 SUBSPACE ITERATION ?ALGORITHMS IN N FORTRAN 77 AND Cy FORTRAN 8x Q0 Paul J. Nikolai AFWAL/ FIBRA AUG a 289 December 1988 U Final...employed by your organization please notify AFWAI FIBRA . WPAFB, OH 45433-6553 to help us maintain a current mailing list . Copies of this report
Active Subspace Methods for Data-Intensive Inverse Problems
Energy Technology Data Exchange (ETDEWEB)
Wang, Qiqi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
2017-04-27
The project has developed theory and computational tools to exploit active subspaces to reduce the dimension in statistical calibration problems. This dimension reduction enables MCMC methods to calibrate otherwise intractable models. The same theoretical and computational tools can also reduce the measurement dimension for calibration problems that use large stores of data.
Statistical tests for associations between two directed acyclic graphs.
Directory of Open Access Journals (Sweden)
Robert Hoehndorf
Full Text Available Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs. However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs. Our method takes the topology of the input graphs and the specificity and relevance of associations between nodes into consideration. We apply our method to the extraction of associations between biomedical ontologies in an extensive use-case. Through a manual and an automatic evaluation, we show that our tests discover biologically relevant relations. The suite of statistical tests we develop for this purpose is implemented and freely available for download.
Online Categorical Subspace Learning for Sketching Big Data with Misses
Shen, Yanning; Mardani, Morteza; Giannakis, Georgios B.
2017-08-01
With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to address in this context include the sheer volume of data that may consist of categorical samples, the typically streaming format of acquisition, and the possibly missing entries. To cope with these challenges, the present paper develops a novel categorical subspace learning approach to unravel the latent structure for three prominent categorical (bilinear) models, namely, Probit, Tobit, and Logit. The deterministic Probit and Tobit models treat data as quantized values of an analog-valued process lying in a low-dimensional subspace, while the probabilistic Logit model relies on low dimensionality of the data log-likelihood ratios. Leveraging the low intrinsic dimensionality of the sought models, a rank regularized maximum-likelihood estimator is devised, which is then solved recursively via alternating majorization-minimization to sketch high-dimensional categorical data `on the fly.' The resultant procedure alternates between sketching the new incomplete datum and refining the latent subspace, leading to lightweight first-order algorithms with highly parallelizable tasks per iteration. As an extra degree of freedom, the quantization thresholds are also learned jointly along with the subspace to enhance the predictive power of the sought models. Performance of the subspace iterates is analyzed for both infinite and finite data streams, where for the former asymptotic convergence to the stationary point set of the batch estimator is established, while for the latter sublinear regret bounds are derived for the empirical cost. Simulated tests with both synthetic and real-world datasets corroborate the merits of the novel schemes for real-time movie recommendation and chess-game classification.
Subspaces indexing model on Grassmann manifold for image search.
Wang, Xinchao; Li, Zhu; Tao, Dacheng
2011-09-01
Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribution we are trying to model is typically nonlinear. Moreover, these algorithms fail to incorporate local variations of the intrinsic sample distribution manifold. Therefore, these algorithms are ineffective when applied on large scale datasets. Kernel versions of these approaches can alleviate the problem to certain degree but face a serious computational challenge when data set is large, where the computing involves Eigen/QP problems of size N × N. When N is large, kernel versions are not computationally practical. To tackle the aforementioned problems and improve recognition/searching performance, especially on large scale image datasets, we propose a novel local subspace indexing model for image search termed Subspace Indexing Model on Grassmann Manifold (SIM-GM). SIM-GM partitions the global space into local patches with a hierarchical structure; the global model is, therefore, approximated by piece-wise linear local subspace models. By further applying the Grassmann manifold distance, SIM-GM is able to organize localized models into a hierarchy of indexed structure, and allow fast query selection of the optimal ones for classification. Our proposed SIM-GM enjoys a number of merits: 1) it is able to deal with a large number of training samples efficiently; 2) it is a query-driven approach, i.e., it is able to return an effective local space model, so the recognition performance could be significantly improved; 3) it is a common framework, which can incorporate many learning algorithms. Theoretical analysis and extensive experimental results confirm the validity of this model.
Institute of Scientific and Technical Information of China (English)
LI Chao; LIU Wenju
2012-01-01
Although the signal subspace approach has been studied extensively for speech enhancement, no good solution has been found to identify signal subspace dimension in multi- channel situation. This paper presents a signal subspace dimension estimator based on F-norm of correlation matrix, with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input signal to noise ratio （SNR）. Experiments demonstrate the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.
Hayes, Charles E.; McClellan, James H.; Scott, Waymond R.; Kerr, Andrew J.
2016-05-01
This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most powerful invariant detector.
Two-direction nonlocal model for image denoising.
Zhang, Xuande; Feng, Xiangchu; Wang, Weiwei
2013-01-01
Similarities inherent in natural images have been widely exploited for image denoising and other applications. In fact, if a cluster of similar image patches is rearranged into a matrix, similarities exist both between columns and rows. Using the similarities, we present a two-directional nonlocal (TDNL) variational model for image denoising. The solution of our model consists of three components: one component is a scaled version of the original observed image and the other two components are obtained by utilizing the similarities. Specifically, by using the similarity between columns, we get a nonlocal-means-like estimation of the patch with consideration to all similar patches, while the weights are not the pairwise similarities but a set of clusterwise coefficients. Moreover, by using the similarity between rows, we also get nonlocal-autoregression-like estimations for the center pixels of the similar patches. The TDNL model leads to an alternative minimization algorithm. Experiments indicate that the model can perform on par with or better than the state-of-the-art denoising methods.
A Newton-Krylov solver for fast spin-up of online ocean tracers
Lindsay, Keith
2017-01-01
We present a Newton-Krylov based solver to efficiently spin up tracers in an online ocean model. We demonstrate that the solver converges, that tracer simulations initialized with the solution from the solver have small drift, and that the solver takes orders of magnitude less computational time than the brute force spin-up approach. To demonstrate the application of the solver, we use it to efficiently spin up the tracer ideal age with respect to the circulation from different time intervals in a long physics run. We then evaluate how the spun-up ideal age tracer depends on the duration of the physics run, i.e., on how equilibrated the circulation is.
Chen, Guangye; Leibs, Christopher A; Knoll, Dana A; Taitano, William
2013-01-01
A recent proof-of-principle study proposes an energy- and charge-conserving, nonlinearly implicit electrostatic particle-in-cell (PIC) algorithm in one dimension [Chen et al, J. Comput. Phys., 230 (2011) 7018]. The algorithm in the reference employs an unpreconditioned Jacobian-free Newton-Krylov method, which ensures nonlinear convergence at every timestep (resolving the dynamical timescale of interest). Kinetic enslavement, which is one key component of the algorithm, not only enables fully implicit PIC a practical approach, but also allows preconditioning the kinetic solver with a fluid approximation. This study proposes such a preconditioner, in which the linearized moment equations are closed with moments computed from particles. Effective acceleration of the linear GMRES solve is demonstrated, on both uniform and non-uniform meshes. The algorithm performance is largely insensitive to the electron-ion mass ratio. Numerical experiments are performed on a 1D multi-scale ion acoustic wave test problem.
Krylov methods preconditioned with incompletely factored matrices on the CM-2
Berryman, Harry; Saltz, Joel; Gropp, William; Mirchandaney, Ravi
1989-01-01
The performance is measured of the components of the key interative kernel of a preconditioned Krylov space interative linear system solver. In some sense, these numbers can be regarded as best case timings for these kernels. Sweeps were timed over meshes, sparse triangular solves, and inner products on a large 3-D model problem over a cube shaped domain discretized with a seven point template. The performance of the CM-2 is highly dependent on the use of very specialized programs. These programs mapped a regular problem domain onto the processor topology in a careful manner and used the optimized local NEWS communications network. The rather dramatic deterioration in performance was documented when these ideal conditions no longer apply. A synthetic workload generator was developed to produce and solve a parameterized family of increasingly irregular problems.
SKRYN: A fast semismooth-Krylov-Newton method for controlling Ising spin systems
Ciaramella, G.; Borzì, A.
2015-05-01
The modeling and control of Ising spin systems is of fundamental importance in NMR spectroscopy applications. In this paper, two computer packages, ReHaG and SKRYN, are presented. Their purpose is to set-up and solve quantum optimal control problems governed by the Liouville master equation modeling Ising spin-1/2 systems with pointwise control constraints. In particular, the MATLAB package ReHaG allows to compute a real matrix representation of the master equation. The MATLAB package SKRYN implements a new strategy resulting in a globalized semismooth matrix-free Krylov-Newton scheme. To discretize the real representation of the Liouville master equation, a norm-preserving modified Crank-Nicolson scheme is used. Results of numerical experiments demonstrate that the SKRYN code is able to provide fast and accurate solutions to the Ising spin quantum optimization problem.
Krylov iterative methods and synthetic acceleration for transport in binary statistical media
Energy Technology Data Exchange (ETDEWEB)
Fichtl, Erin D [Los Alamos National Laboratory; Warsa, James S [Los Alamos National Laboratory; Prinja, Anil K [Los Alamos National Laboratory
2008-01-01
In particle transport applications there are numerous physical constructs in which heterogeneities are randomly distributed. The quantity of interest in these problems is the ensemble average of the flux, or the average of the flux over all possible material 'realizations.' The Levermore-Pomraning closure assumes Markovian mixing statistics and allows a closed, coupled system of equations to be written for the ensemble averages of the flux in each material. Generally, binary statistical mixtures are considered in which there are two (homogeneous) materials and corresponding coupled equations. The solution process is iterative, but convergence may be slow as either or both materials approach the diffusion and/or atomic mix limits. A three-part acceleration scheme is devised to expedite convergence, particularly in the atomic mix-diffusion limit where computation is extremely slow. The iteration is first divided into a series of 'inner' material and source iterations to attenuate the diffusion and atomic mix error modes separately. Secondly, atomic mix synthetic acceleration is applied to the inner material iteration and S{sup 2} synthetic acceleration to the inner source iterations to offset the cost of doing several inner iterations per outer iteration. Finally, a Krylov iterative solver is wrapped around each iteration, inner and outer, to further expedite convergence. A spectral analysis is conducted and iteration counts and computing cost for the new two-step scheme are compared against those for a simple one-step iteration, to which a Krylov iterative method can also be applied.
Manipulating quantum information on the controllable systems or subspaces
Zhang, Ming
2010-01-01
In this paper, we explore how to constructively manipulate quantum information on the controllable systems or subspaces. It is revealed that one can make full use of distinguished properties of Pauli operators to design control Hamiltonian based on the geometric parametrization of quantum states. It is demonstrated in this research that Bang-Bang controls, triangle-function controls and square-function control can be utilized to manipulate controllable qubits or encoded qubits on controllable subspace for both open quantum dynamical systems and uncontrollable closed quantum dynamical systems. Furthermore, we propose a new kind of time-energy performance index to trade-off time and energy resource cost, and comprehensively discuss how to design control magnitude to minimize a kind of time-energy performance. A comparison has been made among these three kind of optimal control. It is underlined in this research that the optimal time performance can be always expressed as J^{*} =\\lamda{\\cdot}t^{*}_{f} +E^{*} for...
Evaluating Clustering in Subspace Projections of High Dimensional Data
DEFF Research Database (Denmark)
Müller, Emmanuel; Günnemann, Stephan; Assent, Ira
2009-01-01
of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention. In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering......Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation...... and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects...
A Target Recognition Approach to Projecting HRR Profiles onto Subspace
Institute of Scientific and Technical Information of China (English)
裴炳南; 保铮
2003-01-01
A array of the azimuthally averaged range-profile vectors and the inter-class and intra-class divergence matrixes are constructed iwth many frames of the high resolution range profiles which result from radar echoes of airplanes. Taking the methods of whitening transformation and SVD produces a system of subspace vectors for target recognition. Whereupon, a template library for target recognition is built by the projection of a class-mean vector made from the radar data onto the subspace for recognition. By Euclidean distance, a comparison is made between the above projection and each template in the library, to decide which class the target belongs to. Finally, simulations with the experimental radar data arte given to show that the proposed method is robust to variation in azimuth and immune to additive Gaussian noise when SNR≥5dB.
Unified Model in Identity Subspace for Face Recognition
Institute of Scientific and Technical Information of China (English)
Pin Liao; Li Shen; Yi-Qiang Chen; Shu-Chang Liu
2004-01-01
Human faces have two important characteristics: (1) They are similar objects and the specific variations of each face are similar to each other; (2) They are nearly bilateral symmetric. Exploiting the two important properties, we build a unified model in identity subspace (UMIS) as a novel technique for face recognition from only one example image per person. An identity subspace spanned by bilateral symmetric bases, which compactly encodes identity information, is presented. The unified model, trained on an obtained training set with multiple samples per class from a known people group A, can be generalized well to facial images of unknown individuals,and can be used to recognize facial images from an unknown people group B with only one sample per subject.Extensive experimental results on two public databases (the Yale database and the Bern database) and our own database (the ICT-JDL database) demonstrate that the UMIS approach is significantly effective and robust for face recognition.
View subspaces for indexing and retrieval of 3D models
Dutagaci, Helin; Sankur, Bulent; Yemez, Yücel
2011-01-01
View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We a...
User Scheduling for Heterogeneous Multiuser MIMO Systems: A Subspace Viewpoint
Yi, Xinping
2011-01-01
In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, users are practically heterogeneous in nature. However, most of the existing user scheduling algorithms are designed with an implicit assumption that the users are homogeneous. In this paper, we revisit the problem by exploring the characteristics of heterogeneous users from a subspace point of view. With an objective of minimizing interference non-orthogonality among users, three new angular-based user scheduling criteria that can be applied in various user scheduling algorithms are proposed. While the first criterion is heuristically determined by identifying the incapability of largest principal angle to characterize the subspace correlation and hence the interference non-orthogonality between users, the second and third ones are derived by using, respectively, the sum rate capacity bounds with block diagonalization and the change in capacity by adding a new user into an existing user subset. Aiming at capturing fairness among heteroge...
Integrated Phoneme Subspace Method for Speech Feature Extraction
Directory of Open Access Journals (Sweden)
Park Hyunsin
2009-01-01
Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.
Eigenvoice-based MAP adaptation within correlation subspace
Institute of Scientific and Technical Information of China (English)
LUO Jun; OU Zhi-jian; WANG Zuo-ying
2006-01-01
In recent years,the eigenvoice approach has proven to be an efficient method for rapid speaker adaptation,which directs the adaptation according to the analysis of full speaker vector space.In this article,we developed a new algorithm for eigenspace-based adaptation restricting eigenvoices in clustered subspaces, and maximum likelihood (ML) criterion was replaced with maximum aposteriori (MAP) criterion for better parameter estimation.Experiments show that even with one sentence adaptation data this algorithm would result in 6.45 % error ratio reduction relatively,which overcomes the instability of maximum likelihood linear regression (MLLR) with limited data and is much faster than traditional MAP method.This algorithm is not highly-dependent on subspace number of division,thus it proved to be a robust adaptation algorithm.
Mining visual collocation patterns via self-supervised subspace learning.
Yuan, Junsong; Wu, Ying
2012-04-01
Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.
MODELING INTRAPERSONAL DEFORMATION SUBSPACE USING GMM FOR PALMPRINT IDENTIFICATION
Institute of Scientific and Technical Information of China (English)
Li Qiang; Qiu Zhengding; Sun Dongmei
2006-01-01
In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person,we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.
Noise Robust Joint Sparse Recovery using Compressive Subspace Fitting
Kim, Jong Min; Ye, Jong Chul
2011-01-01
We study a multiple measurement vector (MMV) problem where multiple signals share a common sparse support set and are sampled by a common sensing matrix. Although we can expect that joint sparsity can improve the recovery performance over a single measurement vector (SMV) problem, compressive sensing (CS) algorithms for MMV exhibit performance saturation as the number of multiple signals increases. Recently, to overcome these drawbacks of CS approaches, hybrid algorithms that optimally combine CS with sensor array signal processing using a generalized MUSIC criterion have been proposed. While these hybrid algorithms are optimal for critically sampled cases, they are not efficient in exploiting the redundant sampling to improve noise robustness. Hence, in this work, we introduce a novel subspace fitting criterion that extends the generalized MUSIC criterion so that it exhibits near-optimal behaviors for various sampling conditions. In addition, the subspace fitting criterion leads to two alternative forms of c...
Face recognition based on LDA in manifold subspace
Directory of Open Access Journals (Sweden)
Hung Phuoc Truong
2016-05-01
Full Text Available Although LDA has many successes in dimensionality reduction and data separation, it also has disadvantages, especially the small sample size problem in training data because the "within-class scatter" matrix may not be accurately estimated. Moreover, this algorithm can only operate correctly with labeled data in supervised learning. In practice, data collection is very huge and labeling data requires high-cost, thus the combination of a part of labeled data and unlabeled data for this algorithm in Manifold subspace is a novelty research. This paper reports a study that propose a semi-supervised method called DSLM, which aims at overcoming all these limitations. The proposed method ensures that the discriminative information of labeled data and the intrinsic geometric structure of data are mapped to new optimal subspace. Results are obtained from the experiments and compared to several related methods showing the effectiveness of our proposed method.
The Lie Algebras in which Every Subspace s Its Subalgebra
Institute of Scientific and Technical Information of China (English)
WU MING-ZHONG
2009-01-01
In this paper, we study the Lie algebras in which every subspace is its subalgebra (denoted by HB Lie algebras). We get that a nonabelian Lie algebra is an HB Lie algebra if and only if it is isomorphic to g+Cidg, where g is an abelian Lie algebra. Moreover we show that the derivation algebra and the holomorph of a nonabelian HB Lie algebra are complete.
SUBSPACE SEARCH METHOD FOR A CLASS OF LEAST SQUARES PROBLEM
Institute of Scientific and Technical Information of China (English)
Zi-Luan Wei
2000-01-01
A subspace search method for solving a class of least squares problem is pre sented in the paper. The original problem is divided into many independent sub problems, and a search direction is obtained by solving each of the subproblems, as well as a new iterative point is determined by choosing a suitable steplength such that the value of residual norm is decreasing. The convergence result is also given. The numerical test is also shown for a special problem,
Index Formulae for Subspaces of Kreĭn Spaces
Dijksma, Aad; Gheondea, Aurelian
1996-01-01
For a subspace S of a Kreĭn space K and an arbitrary fundamental decomposition K = K-[+]K+ of K, we prove the index formula κ-(S) + dim(S⊥ ∩ K+) = κ+(S⊥) + dim(S ∩ K-), where κ±(S) stands for the positive/negative signature of S. The difference dim(S ∩ K-) - dim(S⊥ ∩ K+), provided it is well defined
Propagator-based methods for recursive subspace model identification
Mercère, Guillaume; Bako, Laurent; Lecoeuche, Stéphane
2008-01-01
International audience; The problem of the online identification of multi-input multi-output (MIMO) state-space models in the framework of discrete-time subspace methods is considered in this paper. Several algorithms, based on a recursive formulation of the MIMO output error state-space (MOESP) identification class, are developed. The main goals of the proposed methods are to circumvent the huge complexity of eigenvalues or singular values decomposition techniques used by the offline algorit...
Improved Stochastic Subspace System Identification for Structural Health Monitoring
Chang, Chia-Ming; Loh, Chin-Hsiung
2015-07-01
Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.
Classification of Polarimetric SAR Image Based on the Subspace Method
Xu, J.; Li, Z.; Tian, B.; Chen, Q.; Zhang, P.
2013-07-01
Land cover classification is one of the most significant applications in remote sensing. Compared to optical sensing technologies, synthetic aperture radar (SAR) can penetrate through clouds and have all-weather capabilities. Therefore, land cover classification for SAR image is important in remote sensing. The subspace method is a novel method for the SAR data, which reduces data dimensionality by incorporating feature extraction into the classification process. This paper uses the averaged learning subspace method (ALSM) method that can be applied to the fully polarimetric SAR image for classification. The ALSM algorithm integrates three-component decomposition, eigenvalue/eigenvector decomposition and textural features derived from the gray-level cooccurrence matrix (GLCM). The study site, locates in the Dingxing county, in Hebei Province, China. We compare the subspace method with the traditional supervised Wishart classification. By conducting experiments on the fully polarimetric Radarsat-2 image, we conclude the proposed method yield higher classification accuracy. Therefore, the ALSM classification method is a feasible and alternative method for SAR image.
A basis in an invariant subspace of analytic functions
Energy Technology Data Exchange (ETDEWEB)
Krivosheev, A S [Institute of Mathematics with Computing Centre, Ufa Science Centre, Russian Academy of Sciences, Ufa (Russian Federation); Krivosheeva, O A [Bashkir State University, Ufa (Russian Federation)
2013-12-31
The existence problem for a basis in a differentiation-invariant subspace of analytic functions defined in a bounded convex domain in the complex plane is investigated. Conditions are found for the solvability of a certain special interpolation problem in the space of entire functions of exponential type with conjugate diagrams lying in a fixed convex domain. These underlie sufficient conditions for the existence of a basis in the invariant subspace. This basis consists of linear combinations of eigenfunctions and associated functions of the differentiation operator, whose exponents are combined into relatively small clusters. Necessary conditions for the existence of a basis are also found. Under a natural constraint on the number of points in the groups, these coincide with the sufficient conditions. That is, a criterion is found under this constraint that a basis constructed from relatively small clusters exists in an invariant subspace of analytic functions in a bounded convex domain in the complex plane. Bibliography: 25 titles.
LESS: a model-based classifier for sparse subspaces.
Veenman, Cor J; Tax, David M J
2005-09-01
In this paper, we specifically focus on high-dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standpoint, such small sample size problems have some interesting challenges. The first challenge is to find, from all hyperplanes that separate the classes, a separating hyperplane which generalizes well for future data. A second important task is to determine which features are required to distinguish the classes. To attack these problems, we propose the LESS (Lowest Error in a Sparse Subspace) classifier that efficiently finds linear discriminants in a sparse subspace. In contrast with most classifiers for high-dimensional data sets, the LESS classifier incorporates a (simple) data model. Further, by means of a regularization parameter, the classifier establishes a suitable trade-off between subspace sparseness and classification accuracy. In the experiments, we show how LESS performs on several high-dimensional data sets and compare its performance to related state-of-the-art classifiers like, among others, linear ridge regression with the LASSO and the Support Vector Machine. It turns out that LESS performs competitively while using fewer dimensions.
A CLASS OF MULTIWAVELETS AND PROJECTED FRAMES FROM TWO-DIRECTION WAVELETS
Institute of Scientific and Technical Information of China (English)
李尤发; 杨守志
2014-01-01
This article aims at studying two-direction refinable functions and two-direction wavelets in the setting Rs, s>1. We give a sufficient condition for a two-direction refinable function belonging to L2(Rs). Then, two theorems are given for constructing biorthogonal (orthogonal) two-direction refinable functions in L2(Rs) and their biorthogonal (orthogo-nal) two-direction wavelets, respectively. From the constructed biorthogonal (orthogonal) two-direction wavelets, symmetric biorthogonal (orthogonal) multiwaveles in L2(Rs) can be obtained easily. Applying the projection method to biorthogonal (orthogonal) two-direction wavelets in L2(Rs), we can get dual (tight) two-direction wavelet frames in L2(Rm), where m≤s. From the projected dual (tight) two-direction wavelet frames in L2(Rm), symmetric dual (tight) frames in L2(Rm) can be obtained easily. In the end, an example is given to illustrate theoretical results.
Constraining Torsion in Maximally symmetric (sub)spaces
Sur, Sourav
2013-01-01
We look into the general aspects of space-time symmetries in presence of torsion, and how the latter is affected by such symmetries. Focusing in particular to space-times which either exhibit maximal symmetry on their own, or could be decomposed to maximally symmetric subspaces, we work out the constraints on torsion in two different theoretical schemes. We show that at least for a completely antisymmetric torsion tensor (for e.g. the one motivated from string theory), an equivalence is set between these two schemes, as the non-vanishing independent torsion tensor components turn out to be the same.
FAST PARALLELIZABLE METHODS FOR COMPUTING INVARIANT SUBSPACES OF HERMITIAN MATRICES
Institute of Scientific and Technical Information of China (English)
Zhenyue Zhang; Hongyuan Zha; Wenlong Ying
2007-01-01
We propose a quadratically convergent algorithm for computing the invariant subspaces of an Hermitian matrix.Each iteration of the algorithm consists of one matrix-matrix multiplication and one QR decomposition.We present an accurate convergence analysis of the algorithm without using the big O notation.We also propose a general framework based on implicit rational transformations which allows us to make connections with several existing algorithms and to derive classes of extensions to our basic algorithm with faster convergence rates.Several numerical examples are given which compare some aspects of the existing algorithms and the new Mgorithms.
Genome classification by gene distribution: An overlapping subspace clustering approach
Directory of Open Access Journals (Sweden)
Halgamuge Saman K
2008-04-01
Full Text Available Abstract Background Genomes of lower organisms have been observed with a large amount of horizontal gene transfers, which cause difficulties in their evolutionary study. Bacteriophage genomes are a typical example. One recent approach that addresses this problem is the unsupervised clustering of genomes based on gene order and genome position, which helps to reveal species relationships that may not be apparent from traditional phylogenetic methods. Results We propose the use of an overlapping subspace clustering algorithm for such genome classification problems. The advantage of subspace clustering over traditional clustering is that it can associate clusters with gene arrangement patterns, preserving genomic information in the clusters produced. Additionally, overlapping capability is desirable for the discovery of multiple conserved patterns within a single genome, such as those acquired from different species via horizontal gene transfers. The proposed method involves a novel strategy to vectorize genomes based on their gene distribution. A number of existing subspace clustering and biclustering algorithms were evaluated to identify the best framework upon which to develop our algorithm; we extended a generic subspace clustering algorithm called HARP to incorporate overlapping capability. The proposed algorithm was assessed and applied on bacteriophage genomes. The phage grouping results are consistent overall with the Phage Proteomic Tree and showed common genomic characteristics among the TP901-like, Sfi21-like and sk1-like phage groups. Among 441 phage genomes, we identified four significantly conserved distribution patterns structured by the terminase, portal, integrase, holin and lysin genes. We also observed a subgroup of Sfi21-like phages comprising a distinctive divergent genome organization and identified nine new phage members to the Sfi21-like genus: Staphylococcus 71, phiPVL108, Listeria A118, 2389, Lactobacillus phi AT3, A2
Accurate Excited State Geometries within Reduced Subspace TDDFT/TDA.
Robinson, David
2014-12-09
A method for the calculation of TDDFT/TDA excited state geometries within a reduced subspace of Kohn-Sham orbitals has been implemented and tested. Accurate geometries are found for all of the fluorophore-like molecules tested, with at most all valence occupied orbitals and half of the virtual orbitals included but for some molecules even fewer orbitals. Efficiency gains of between 15 and 30% are found for essentially the same level of accuracy as a standard TDDFT/TDA excited state geometry optimization calculation.
Image segmentation by using the localized subspace iteration algorithm
Institute of Scientific and Technical Information of China (English)
2008-01-01
An image segmentation algorithm called"segmentation based on the localized subspace iterations"(SLSI)is proposed in this paper.The basic idea is to combine the strategies in Ncut algorithm by Shi and Malik in 2000 and the LSI by E,Li and Lu in 2007.The LSI is applied to solve an eigenvalue problem associated with the affinity matrix of an image,which makes the overall algorithm linearly scaled.The choices of the partition number,the supports and weight functions in SLSI are discussed.Numerical experiments for real images show the applicability of the algorithm.
Subspace Distribution Clustering HMM for Chinese Digit Speech Recognition
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
As a kind of statistical method, the technique of Hidden Markov Model (HMM) is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model (SDCHMM), derived from the Continuous Density Hidden Markov Model (CDHMM), is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits.
Directory of Open Access Journals (Sweden)
M. Saifur Rahman
2012-12-01
Full Text Available Recently, a unified Krylov-Bogoliubov-Mitropolskii method has been presented (by Shamsul \\cite{1} for solving an $n$-th, $n=2$ or $n>2$, order nonlinear differential equation. Instead of amplitude(s and phase(s, a set of variables is used in \\cite{1} to obtain a general formula in which the nonlinear differential equations can be solved. By a simple variables transformation the usual form solutions (i.e., in terms of amplitude(s and phase(s have been found. In this paper a perturbation technique is developed to calculate the initial values of the variables used in \\cite{1}. By the noted transformation the initial amplitude(s and phase(s can be calculated quickly. Usually the conditional equations are nonlinear algebraic or transcendental equations; so that a numerical method is used to solve them. Rink \\cite{7} earlier employed an asymptotic method for solving the conditional equations of a second-order differential equation; but his derived results were not so good. The new results agree with their exact values (or numerical results nicely. The method can be applied whether the eigen-values of the unperturbed equation are purely imaginary, complex conjugate or real. Thus the derived solution is a general one and covers the three cases, i.e., un-damped, under-damped and over-damped.
Jacobian-free Newton-Krylov methods with GPU acceleration for computing nonlinear ship wave patterns
Pethiyagoda, Ravindra; Moroney, Timothy J; Back, Julian M
2014-01-01
The nonlinear problem of steady free-surface flow past a submerged source is considered as a case study for three-dimensional ship wave problems. Of particular interest is the distinctive wedge-shaped wave pattern that forms on the surface of the fluid. By reformulating the governing equations with a standard boundary-integral method, we derive a system of nonlinear algebraic equations that enforce a singular integro-differential equation at each midpoint on a two-dimensional mesh. Our contribution is to solve the system of equations with a Jacobian-free Newton-Krylov method together with a banded preconditioner that is carefully constructed with entries taken from the Jacobian of the linearised problem. Further, we are able to utilise graphics processing unit acceleration to significantly increase the grid refinement and decrease the run-time of our solutions in comparison to schemes that are presently employed in the literature. Our approach provides opportunities to explore the nonlinear features of three-...
A Jacobian-free Newton Krylov method for mortar-discretized thermomechanical contact problems
Hansen, Glen
2011-07-01
Multibody contact problems are common within the field of multiphysics simulation. Applications involving thermomechanical contact scenarios are also quite prevalent. Such problems can be challenging to solve due to the likelihood of thermal expansion affecting contact geometry which, in turn, can change the thermal behavior of the components being analyzed. This paper explores a simple model of a light water reactor nuclear fuel rod, which consists of cylindrical pellets of uranium dioxide (UO 2) fuel sealed within a Zircalloy cladding tube. The tube is initially filled with helium gas, which fills the gap between the pellets and cladding tube. The accurate modeling of heat transfer across the gap between fuel pellets and the protective cladding is essential to understanding fuel performance, including cladding stress and behavior under irradiated conditions, which are factors that affect the lifetime of the fuel. The thermomechanical contact approach developed here is based on the mortar finite element method, where Lagrange multipliers are used to enforce weak continuity constraints at participating interfaces. In this formulation, the heat equation couples to linear mechanics through a thermal expansion term. Lagrange multipliers are used to formulate the continuity constraints for both heat flux and interface traction at contact interfaces. The resulting system of nonlinear algebraic equations are cast in residual form for solution of the transient problem. A Jacobian-free Newton Krylov method is used to provide for fully-coupled solution of the coupled thermal contact and heat equations.
Active Sonar Detection in Reverberation via Signal Subspace Extraction Algorithm
Directory of Open Access Journals (Sweden)
Ma Xiaochuan
2010-01-01
Full Text Available This paper presents a new algorithm called Signal Subspace Extraction (SSE for detecting and estimating target echoes in reverberation. The new algorithm can be taken as an extension of the Principal Component Inverse (PCI and maintains the benefit of PCI algorithm and moreover shows better performance due to a more reasonable reverberation model. In the SSE approach, a best low-rank estimate of a target echo is extracted by decomposing the returns into short duration subintervals and by invoking the Eckart-Young theorem twice. It was assumed that CW is less efficiency in lower Doppler than broadband waveforms in spectrum methods; however, the subspace methods show good performance in detection whatever the respective Doppler is. Hence, the signal emitted by active sonar is CW in the new algorithm which performs well in detection and estimation even when low Doppler is low. Further, a block forward matrix is proposed to extend the algorithm to the sensor array problem. The comparison among the block forward matrix, the conventional matrix, and the three-mode array is discussed. Echo separation is also provided by the new algorithm. Examples are presented using both real, active-sonar data and simulated data.
A Subspace Method for Dynamical Estimation of Evoked Potentials
Directory of Open Access Journals (Sweden)
Stefanos D. Georgiadis
2007-01-01
Full Text Available It is a challenge in evoked potential (EP analysis to incorporate prior physiological knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by means of estimated signal subspace and eigenvalue decomposition. Then for those situations that dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman filtering and smoothing. We demonstrate that a few dominant eigenvectors of the data correlation matrix are able to model trend-like changes of some component of the EPs, and that Kalman smoother algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal subspace and EP estimates by means of independent component analysis applied as a prepossessing step on the multichannel measurements.
Conformal Laplace superintegrable systems in 2D: polynomial invariant subspaces
Escobar-Ruiz, M. A.; Miller, Willard, Jr.
2016-07-01
2nd-order conformal superintegrable systems in n dimensions are Laplace equations on a manifold with an added scalar potential and 2n-1 independent 2nd order conformal symmetry operators. They encode all the information about Helmholtz (eigenvalue) superintegrable systems in an efficient manner: there is a 1-1 correspondence between Laplace superintegrable systems and Stäckel equivalence classes of Helmholtz superintegrable systems. In this paper we focus on superintegrable systems in two-dimensions, n = 2, where there are 44 Helmholtz systems, corresponding to 12 Laplace systems. For each Laplace equation we determine the possible two-variate polynomial subspaces that are invariant under the action of the Laplace operator, thus leading to families of polynomial eigenfunctions. We also study the behavior of the polynomial invariant subspaces under a Stäckel transform. The principal new results are the details of the polynomial variables and the conditions on parameters of the potential corresponding to polynomial solutions. The hidden gl 3-algebraic structure is exhibited for the exact and quasi-exact systems. For physically meaningful solutions, the orthogonality properties and normalizability of the polynomials are presented as well. Finally, for all Helmholtz superintegrable solvable systems we give a unified construction of one-dimensional (1D) and two-dimensional (2D) quasi-exactly solvable potentials possessing polynomial solutions, and a construction of new 2D PT-symmetric potentials is established.
Outlier Ranking via Subspace Analysis in Multiple Views of the Data
DEFF Research Database (Denmark)
Muller, Emmanuel; Assent, Ira; Iglesias, Patricia
2012-01-01
, a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its...
AN ITERATED-SUBSPACE MINIMIZATION METHODS WITH SYMMETRIC RANK-ONE UPDATING
Institute of Scientific and Technical Information of China (English)
徐徽宁; 孙麟平
2004-01-01
We consider an Iterated-Subspace Minimization(ISM) method for solving large-scale unconstrained minimization problems. At each major iteration of the method,a two-dimensional manifold, the iterated subspace, is constructed and an approximate minimizer of the objective function in this manifold then determined, and a symmetric rank-one updating is used to solve the inner minimization problem.
Crystallizing highly-likely subspaces that contain an unknown quantum state of light
Teo, Yong Siah; Mogilevtsev, Dmitri; Mikhalychev, Alexander; Řeháček, Jaroslav; Hradil, Zdeněk
2016-12-01
In continuous-variable tomography, with finite data and limited computation resources, reconstruction of a quantum state of light is performed on a finite-dimensional subspace. In principle, the data themselves encode all information about the relevant subspace that physically contains the state. We provide a straightforward and numerically feasible procedure to uniquely determine the appropriate reconstruction subspace by extracting this information directly from the data for any given unknown quantum state of light and measurement scheme. This procedure makes use of the celebrated statistical principle of maximum likelihood, along with other validation tools, to grow an appropriate seed subspace into the optimal reconstruction subspace, much like the nucleation of a seed into a crystal. Apart from using the available measurement data, no other assumptions about the source or preconceived parametric model subspaces are invoked. This ensures that no spurious reconstruction artifacts are present in state reconstruction as a result of inappropriate choices of the reconstruction subspace. The procedure can be understood as the maximum-likelihood reconstruction for quantum subspaces, which is an analog to, and fully compatible with that for quantum states.
Wawro, Megan; Sweeney, George F.; Rabin, Jeffrey M.
2011-01-01
This paper reports on a study investigating students' ways of conceptualizing key ideas in linear algebra, with the particular results presented here focusing on student interactions with the notion of subspace. In interviews conducted with eight undergraduates, we found students' initial descriptions of subspace often varied substantially from…
A Hybrid, Parallel Krylov Solver for MODFLOW using Schwarz Domain Decomposition
Sutanudjaja, E.; Verkaik, J.; Hughes, J. D.
2015-12-01
In order to support decision makers in solving hydrological problems, detailed high-resolution models are often needed. These models typically consist of a large number of computational cells and have large memory requirements and long run times. An efficient technique for obtaining realistic run times and memory requirements is parallel computing, where the problem is divided over multiple processor cores. The new Parallel Krylov Solver (PKS) for MODFLOW-USG is presented. It combines both distributed memory parallelization by the Message Passing Interface (MPI) and shared memory parallelization by Open Multi-Processing (OpenMP). PKS includes conjugate gradient and biconjugate gradient stabilized linear accelerators that are both preconditioned by an overlapping additive Schwarz preconditioner in a way that: a) subdomains are partitioned using the METIS library; b) each subdomain uses local memory only and communicates with other subdomains by MPI within the linear accelerator; c) is fully integrated in the MODFLOW-USG code. PKS is based on the unstructured PCGU-solver, and supports OpenMP. Depending on the available hardware, PKS can run exclusively with MPI, exclusively with OpenMP, or with a hybrid MPI/OpenMP approach. Benchmarks were performed on the Cartesius Dutch supercomputer (https://userinfo.surfsara.nl/systems/cartesius) using up to 144 cores, for a synthetic test (~112 million cells) and the Indonesia groundwater model (~4 million 1km cells). The latter, which includes all islands in the Indonesian archipelago, was built using publically available global datasets, and is an ideal test bed for evaluating the applicability of PKS parallelization techniques to a global groundwater model consisting of multiple continents and islands. Results show that run time reductions can be greatest with the hybrid parallelization approach for the problems tested.
Reynolds, Daniel R.; Samtaney, Ravi; Tiedeman, Hilari C.
2012-01-01
Single-fluid resistive magnetohydrodynamics (MHD) is a fluid description of fusion plasmas which is often used to investigate macroscopic instabilities in tokamaks. In MHD modeling of tokamaks, it is often desirable to compute MHD phenomena to resistive time scales or a combination of resistive-Alfvén time scales, which can render explicit time stepping schemes computationally expensive. We present recent advancements in the development of preconditioners for fully nonlinearly implicit simulations of single-fluid resistive tokamak MHD. Our work focuses on simulations using a structured mesh mapped into a toroidal geometry with a shaped poloidal cross-section, and a finite-volume spatial discretization of the partial differential equation model. We discretize the temporal dimension using a fully implicit θ or the backwards differentiation formula method, and solve the resulting nonlinear algebraic system using a standard inexact Newton-Krylov approach, provided by the sundials library. The focus of this paper is on the construction and performance of various preconditioning approaches for accelerating the convergence of the iterative solver algorithms. Effective preconditioners require information about the Jacobian entries; however, analytical formulae for these Jacobian entries may be prohibitive to derive/implement without error. We therefore compute these entries using automatic differentiation with OpenAD. We then investigate a variety of preconditioning formulations inspired by standard solution approaches in modern MHD codes, in order to investigate their utility in a preconditioning context. We first describe the code modifications necessary for the use of the OpenAD tool and sundials solver library. We conclude with numerical results for each of our preconditioning approaches in the context of pellet-injection fueling of tokamak plasmas. Of these, our optimal approach results in a speedup of a factor of 3 compared with non-preconditioned implicit tests
Reynolds, Daniel R.
2012-01-01
Single-fluid resistive magnetohydrodynamics (MHD) is a fluid description of fusion plasmas which is often used to investigate macroscopic instabilities in tokamaks. In MHD modeling of tokamaks, it is often desirable to compute MHD phenomena to resistive time scales or a combination of resistive-Alfvén time scales, which can render explicit time stepping schemes computationally expensive. We present recent advancements in the development of preconditioners for fully nonlinearly implicit simulations of single-fluid resistive tokamak MHD. Our work focuses on simulations using a structured mesh mapped into a toroidal geometry with a shaped poloidal cross-section, and a finite-volume spatial discretization of the partial differential equation model. We discretize the temporal dimension using a fully implicit or the backwards differentiation formula method, and solve the resulting nonlinear algebraic system using a standard inexact Newton-Krylov approach, provided by the sundials library. The focus of this paper is on the construction and performance of various preconditioning approaches for accelerating the convergence of the iterative solver algorithms. Effective preconditioners require information about the Jacobian entries; however, analytical formulae for these Jacobian entries may be prohibitive to derive/implement without error. We therefore compute these entries using automatic differentiation with OpenAD. We then investigate a variety of preconditioning formulations inspired by standard solution approaches in modern MHD codes, in order to investigate their utility in a preconditioning context. We first describe the code modifications necessary for the use of the OpenAD tool and sundials solver library. We conclude with numerical results for each of our preconditioning approaches in the context of pellet-injection fueling of tokamak plasmas. Of these, our optimal approach results in a speedup of a factor of 3 compared with non-preconditioned implicit tests, with
First Applications of the New Parallel Krylov Solver for MODFLOW on a National and Global Scale
Verkaik, J.; Hughes, J. D.; Sutanudjaja, E.; van Walsum, P.
2016-12-01
Integrated high-resolution hydrologic models are increasingly being used for evaluating water management measures at field scale. Their drawbacks are large memory requirements and long run times. Examples of such models are The Netherlands Hydrological Instrument (NHI) model and the PCRaster Global Water Balance (PCR-GLOBWB) model. Typical simulation periods are 30-100 years with daily timesteps. The NHI model predicts water demands in periods of drought, supporting operational and long-term water-supply decisions. The NHI is a state-of-the-art coupling of several models: a 7-layer MODFLOW groundwater model ( 6.5M 250m cells), a MetaSWAP model for the unsaturated zone (Richards emulator of 0.5M cells), and a surface water model (MOZART-DM). The PCR-GLOBWB model provides a grid-based representation of global terrestrial hydrology and this work uses the version that includes a 2-layer MODFLOW groundwater model ( 4.5M 10km cells). The Parallel Krylov Solver (PKS) speeds up computation by both distributed memory parallelization (Message Passing Interface) and shared memory parallelization (Open Multi-Processing). PKS includes conjugate gradient, bi-conjugate gradient stabilized, and generalized minimal residual linear accelerators that use an overlapping additive Schwarz domain decomposition preconditioner. PKS can be used for both structured and unstructured grids and has been fully integrated in MODFLOW-USG using METIS partitioning and in iMODFLOW using RCB partitioning. iMODFLOW is an accelerated version of MODFLOW-2005 that is implicitly and online coupled to MetaSWAP. Results for benchmarks carried out on the Cartesius Dutch supercomputer (https://userinfo.surfsara.nl/systems/cartesius) for the PCRGLOB-WB model and on a 2x16 core Windows machine for the NHI model show speedups up to 10-20 and 5-10, respectively.
Hausdorff hyperspaces of $R^m$ and their dense subspaces
Kubis, Wieslaw
2007-01-01
Let $CLB_H(X)$ denote the hyperspace of closed bounded subsets of a metric space $X$, endowed with the Hausdorff metric topology. We prove, among others, that natural dense subspaces of $CLB_H(R^m)$ of all nowhere dense closed sets, of all perfect sets, of all Cantor sets and of all Lebesgue measure zero sets are homeomorphic to the Hilbert space $\\ell_2$. Moreover, we investigate the hyperspace $CL_H(R)$ of all nonempty closed subsets of the real line $R$ with the Hausdorff (infinite-valued) metric. We show that a nonseparable component of $CL_H(R)$ is homeomorphic to the Hilbert space $\\ell_2(2^{\\aleph_0})$ as long as it does not contain any of the sets $R, [0,\\infty), (-\\infty,0]$.
Spatial Bell-State Generation without Transverse Mode Subspace Postselection
Kovlakov, E. V.; Bobrov, I. B.; Straupe, S. S.; Kulik, S. P.
2017-01-01
Spatial states of single photons and spatially entangled photon pairs are becoming an important resource in quantum communication. This additional degree of freedom provides an almost unlimited information capacity, making the development of high-quality sources of spatial entanglement a well-motivated research direction. We report an experimental method for generation of photon pairs in a maximally entangled spatial state. In contrast to existing techniques, the method does not require postselection of a particular subspace of spatial modes and allows one to use the full photon flux from the nonlinear crystal, providing a tool for creating high-brightness sources of pure spatially entangled photons. Such sources are a prerequisite for emerging applications in free-space quantum communication.
Universal quantum computation in waveguide QED using decoherence free subspaces
Paulisch, V.; Kimble, H. J.; González-Tudela, A.
2016-04-01
The interaction of quantum emitters with one-dimensional photon-like reservoirs induces strong and long-range dissipative couplings that give rise to the emergence of the so-called decoherence free subspaces (DFSs) which are decoupled from dissipation. When introducing weak perturbations on the emitters, e.g., driving, the strong collective dissipation enforces an effective coherent evolution within the DFS. In this work, we show explicitly how by introducing single-site resolved drivings, we can use the effective dynamics within the DFS to design a universal set of one and two-qubit gates within the DFS of an ensemble of two-level atom-like systems. Using Liouvillian perturbation theory we calculate the scaling with the relevant figures of merit of the systems, such as the Purcell factor and imperfect control of the drivings. Finally, we compare our results with previous proposals using atomic Λ systems in leaky cavities.
Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble
Directory of Open Access Journals (Sweden)
Pham Tuan D
2011-04-01
Full Text Available Abstract Background Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM, which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting. Results Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The
Analysis and Improvement of Low Rank Representation for Subspace segmentation
Siming, Wei
2011-01-01
We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data. We prove that for the noiseless case, the optimization model of LRR has a unique solution, which is the shape interaction matrix (SIM) of the data matrix. So in essence LRR is equivalent to factorization methods. We also prove that the minimum value of the optimization model of LRR is equal to the rank of the data matrix. For the noisy case, we show that LRR can be approximated as a factorization method that combines noise removal by column sparse robust PCA. We further propose an improved version of LRR, called Robust Shape Interaction (RSI), which uses the corrected data as the dictionary instead of the noisy data. RSI is more robust than LRR when the corruption in data is heavy. Experiments on both synthetic and real data testify to the improved robustness of RSI.
Universal Fault-Tolerant Computation on Decoherence-Free Subspaces
Bacon, D J; Lidar, D A; Whaley, K B
2000-01-01
A general scheme to perform universal quantum computation fault-tolerantly within decoherence-free subspaces (DFSs) of a system's Hilbert space is derived. This scheme leads to the first fault-tolerant realization of universal quantum computation on DFSs with the properties that (i) only one- and two-qubit interactions are required, and (ii) the system remains within the DFS throughout the entire implementation of a quantum gate. We show explicitly how to perform universal computation on clusters of the four-qubit DFS encoding one logical qubit each under "collective decoherence" (qubit-permutation-invariant system-bath coupling). Our results have immediate relevance to a number of proposed quantum computer implementations, in particular those in which the internal system Hamiltonian is of the Heisenberg type, such as spin-spin coupled quantum dots.
Subspace identification and classification of healthy human gait.
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Vinzenz von Tscharner
Full Text Available PURPOSE: The classification between different gait patterns is a frequent task in gait assessment. The base vectors were usually found using principal component analysis (PCA is replaced by an iterative application of the support vector machine (SVM. The aim was to use classifyability instead of variability to build a subspace (SVM space that contains the information about classifiable aspects of a movement. The first discriminant of the SVM space will be compared to a discriminant found by an independent component analysis (ICA in the SVM space. METHODS: Eleven runners ran using shoes with different midsoles. Kinematic data, representing the movements during stance phase when wearing the two shoes, was used as input to a PCA and SVM. The data space was decomposed by an iterative application of the SVM into orthogonal discriminants that were able to classify the two movements. The orthogonal discriminants spanned a subspace, the SVM space. It represents the part of the movement that allowed classifying the two conditions. The data in the SVM space was reconstructed for a visual assessment of the movement difference. An ICA was applied to the data in the SVM space to obtain a single discriminant. Cohen's d effect size was used to rank the PCA vectors that could be used to classify the data, the first SVM discriminant or the ICA discriminant. RESULTS: The SVM base contains all the information that discriminates the movement of the two shod conditions. It was shown that the SVM base contains some redundancy and a single ICA discriminant was found by applying an ICA in the SVM space. CONCLUSIONS: A combination of PCA, SVM and ICA is best suited to extract all parts of the gait pattern that discriminates between the two movements and to find a discriminant for the classification of dichotomous kinematic data.
Error analysis of Padé iterations for computing matrix invariant subspaces
Institute of Scientific and Technical Information of China (English)
Zhenyue ZHANG; Rui HE
2009-01-01
The method of Padé matrix iteration is commonly used for computing matrix sign function and invariant subspaces of a real or complex matrix. In this paper, a detailed rounding error analysis is given for two classical schemes of the Padé matrix iteration, using basic matrix floating point arithmetics. Error estimations of computing invariant sub-spaces by the Padé sign iteration are also provided. Numerical experiments are given to show the numerical behaviors of the Padé iterations and the corresponding subspace computation.
DETECTION OF CHANGES OF THE SYSTEM TECHNICAL STATE USING STOCHASTIC SUBSPACE OBSERVATION METHOD
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Andrzej Puchalski
2014-03-01
Full Text Available System diagnostics based on vibroacoustics signals, carried out by means of stochastic subspace methods was undertaken in the hereby paper. Subspace methods are the ones based on numerical linear algebra tools. The considered solutions belong to diagnostic methods according to data, leading to the generation of residuals allowing failure recognition of elements and assemblies in machines and devices. The algorithm of diagnostics according to the subspace observation method was applied – in the paper – for the estimation of the valve system of the spark ignition engine.
The four-qubit singlet state and decoherence-free subspaces
Cabello, A
2002-01-01
It is pointed out that the recent experimental preparation of the four-qubit singlet state by Weinfurter's group is a fundamental achievement for the encoding of quantum information in decoherence-free (DF) subspaces. This state is the DF state orthogonal to the tensor product of two two-qubit singlet states, whose DF properties were experimentally checked by P. G. Kwiat et al. [Science 290, 498 (2000)], and thus provides the missing state for the simplest nontrivial encoding of quantum information in a DF subspace. An experiment to study this DF subspace is suggested.
SUBSPACE-BASED NOISE VARIANCE AND SNR ESTIMATION FOR MIMO OFDM SYSTEMS
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This paper proposes a subspace-based noise variance and Signal-to-Noise Ratio (SNR) estimation algorithm for Multi-Input Multi-Output (MIMO) wireless Orthogonal Frequency Division Multiplexing (OFDM) systems. The special training sequences with the property of orthogonality and phase shift orthogonality are used in pilot tones to obtain the estimated channel correlation matrix. Partitioning the observation space into a delay subspace and a noise subspace, we achieve the measurement of noise variance and SNR.Simulation results show that the proposed estimator can obtain accurate and real-time measurements of the noise variance and SNR for various multipath fading channels, demonstrating its strong robustness against different channels.
Zimmerling, J.T.; Wei, L.; Urbach, H.P.; Remis, R.F.
2016-01-01
We present a Krylov model-order reduction approach to efficiently compute the spontaneous decay (SD) rate of arbitrarily shaped 3D nanosized resonators. We exploit the symmetry of Maxwell’s equations to efficiently construct so-called reduced-order models that approximate the SD rate of a quantum
MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING
National Aeronautics and Space Administration — MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract....
Face Recognition of Illumination Tolerance in 2D Subspace Based on the Optimum Correlation Filter
National Research Council Canada - National Science Library
Yi Xu
2014-01-01
... subspace based on the optimal correlation filter. Firstly, through the use of a particular class 2D-PCA the face image is reconstructed and by using the optimum projecting image correlation filter (OPICF...
Closed and Open Loop Subspace System Identification of the Kalman Filter
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David Di Ruscio
2009-04-01
Full Text Available Some methods for consistent closed loop subspace system identification presented in the literature are analyzed and compared to a recently published subspace algorithm for both open as well as for closed loop data, the DSR_e algorithm. Some new variants of this algorithm are presented and discussed. Simulation experiments are included in order to illustrate if the algorithms are variance efficient or not.
A Subspace Embedding Method in L2 Norm via Fast Cauchy Transform
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Xu Xiang
2013-01-01
Full Text Available We propose a subspace embedding method via Fast Cauchy Transform (FCT in L2 norm. It is motivated by and complements the work of the subspace embedding method in Lp norm, for all p∈[1,∞] except p = 2, by K. L. Clarkson (ACM-SIAM, 2013. Unlike the traditionally used orthogonal basis in Johnson-Lindenstrauss (JL embedding, we employ the well-conditioned basis in L2 norm to obtain concentration property of FCT in L2 norm.
A New Subspace Correction Method for Nonlinear Unconstrained Convex Optimization Problems
Institute of Scientific and Technical Information of China (English)
Rong-liang CHEN; Jin-ping ZENG
2012-01-01
This paper gives a new subspace correction algorithm for nonlinear unconstrained convex optimization problems based on the multigrid approach proposed by S.Nash in 2000 and the subspace correction algorithm proposed by X.Tai and J.Xu in 2001.Under some reasonable assumptions,we obtain the convergence as well as a convergence rate estimate for the algorithm.Numerical results show that the algorithm is effective.
THE CODIMENSION FORMULA ON QUASI-INVARIANT SUBSPACES OF THE FOCK SPACE
Institute of Scientific and Technical Information of China (English)
侯绳照; 胡俊云
2003-01-01
Let M be an approximately finite codimensional quasi-invariant subspace of the Fock space.This paper gives a formula to calculate the codimension of such spaces and uses this formulato study the structure of quasi-invariant subspaces of the Fock space. Especially, as one ofapplications, it is showed that the analogue of Beurling's theorem is not true for the Fock spaceL2a (Cn ) in the case of n ≥ 2.
Fast filtering false active subspaces for efficient high dimensional similarity processing
Institute of Scientific and Technical Information of China (English)
WANG GuoRen; YU Ge; XIN JunChang; ZHAO YuHai; ZHANG EnDe
2009-01-01
The query space of a similarity query is usually narrowed down by pruning inactive query subspaces which contain no query results and keeping active query subspaces which may contain objects corre-sponding to the request. However, some active query subspaces may contain no query results at all, those are called false active query subspaces. It is obvious that the performance of query processing degrades in the presence of false active query subspaces. Our experiments show that this problem becomes seriously when the data are high dimensional and the number of accesses to false active sub-spaces increases as the dimensionality increases. In order to solve this problem, this paper proposes a space mapping approach to reducing such unnecessary accesses. A given query space can be re-fined by filtering within its mapped space. To do so, a mapping strategy called maxgap is proposed to improve the efficiency of the refinement processing. Based on the mapping strategy, an index structure called MS-tree and algorithms of query processing are presented in this paper. Finally, the performance of MS-tree is compared with that of other competitors in terms of range queries on a real data set.
Subspace Leakage Analysis and Improved DOA Estimation With Small Sample Size
Shaghaghi, Mahdi; Vorobyov, Sergiy A.
2015-06-01
Classical methods of DOA estimation such as the MUSIC algorithm are based on estimating the signal and noise subspaces from the sample covariance matrix. For a small number of samples, such methods are exposed to performance breakdown, as the sample covariance matrix can largely deviate from the true covariance matrix. In this paper, the problem of DOA estimation performance breakdown is investigated. We consider the structure of the sample covariance matrix and the dynamics of the root-MUSIC algorithm. The performance breakdown in the threshold region is associated with the subspace leakage where some portion of the true signal subspace resides in the estimated noise subspace. In this paper, the subspace leakage is theoretically derived. We also propose a two-step method which improves the performance by modifying the sample covariance matrix such that the amount of the subspace leakage is reduced. Furthermore, we introduce a phenomenon named as root-swap which occurs in the root-MUSIC algorithm in the low sample size region and degrades the performance of the DOA estimation. A new method is then proposed to alleviate this problem. Numerical examples and simulation results are given for uncorrelated and correlated sources to illustrate the improvement achieved by the proposed methods. Moreover, the proposed algorithms are combined with the pseudo-noise resampling method to further improve the performance.
Sekihara, Kensuke; Kawabata, Yuya; Ushio, Shuta; Sumiya, Satoshi; Kawabata, Shigenori; Adachi, Yoshiaki; Nagarajan, Srikantan S.
2016-06-01
Objective. In functional electrophysiological imaging, signals are often contaminated by interference that can be of considerable magnitude compared to the signals of interest. This paper proposes a novel algorithm for removing such interferences that does not require separate noise measurements. Approach. The algorithm is based on a dual definition of the signal subspace in the spatial- and time-domains. Since the algorithm makes use of this duality, it is named the dual signal subspace projection (DSSP). The DSSP algorithm first projects the columns of the measured data matrix onto the inside and outside of the spatial-domain signal subspace, creating a set of two preprocessed data matrices. The intersection of the row spans of these two matrices is estimated as the time-domain interference subspace. The original data matrix is projected onto the subspace that is orthogonal to this interference subspace. Main results. The DSSP algorithm is validated by using the computer simulation, and using two sets of real biomagnetic data: spinal cord evoked field data measured from a healthy volunteer and magnetoencephalography data from a patient with a vagus nerve stimulator. Significance. The proposed DSSP algorithm is effective for removing overlapped interference in a wide variety of biomagnetic measurements.
Occlusion Handling via Random Subspace Classifiers for Human Detection.
Marín, Javier; Vázquez, David; López, Antonio M; Amores, Jaume; Kuncheva, Ludmila I
2014-03-01
This paper describes a general method to address partial occlusions for human detection in still images. The random subspace method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach's capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labeling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes.
Face image modeling by multilinear subspace analysis with missing values.
Geng, Xin; Smith-Miles, Kate; Zhou, Zhi-Hua; Wang, Liang
2011-06-01
Multilinear subspace analysis (MSA) is a promising methodology for pattern-recognition problems due to its ability in decomposing the data formed from the interaction of multiple factors. The MSA requires a large training set, which is well organized in a single tensor, which consists of data samples with all possible combinations of the contributory factors. However, such a "complete" training set is difficult (or impossible) to obtain in many real applications. The missing-value problem is therefore crucial to the practicality of the MSA but has been hardly investigated up to present. To solve the problem, this paper proposes an algorithm named M(2)SA, which is advantageous in real applications due to the following: 1) it inherits the ability of the MSA to decompose the interlaced semantic factors; 2) it does not depend on any assumptions on the data distribution; and 3) it can deal with a high percentage of missing values. M(2)SA is evaluated by face image modeling on two typical multifactorial applications, i.e., face recognition and facial age estimation. Experimental results show the effectiveness of M(2) SA even when the majority of the values in the training tensor are missing.
Optimal Design of Large Dimensional Adaptive Subspace Detectors
Ben Atitallah, Ismail
2016-05-27
This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.
MODAL TRACKING of A Structural Device: A Subspace Identification Approach
Energy Technology Data Exchange (ETDEWEB)
Candy, J. V. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Franco, S. N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ruggiero, E. L. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Emmons, M. C. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Lopez, I. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Stoops, L. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-03-20
Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging processing problem. To detect/classify a device subsystem from noisy data, it is necessary to identify unique signatures or particular features. An obvious feature would be resonant (modal) frequencies emitted during its normal operation. In this report, we discuss a model-based approach to incorporate these physical features into a dynamic structure that can be used for such an identification. The approach we take after pre-processing the raw vibration data and removing any extraneous disturbances is to obtain a representation of the structurally unknown device along with its subsystems that capture these salient features. One approach is to recognize that unique modal frequencies (sinusoidal lines) appear in the estimated power spectrum that are solely characteristic of the device under investigation. Therefore, the objective of this effort is based on constructing a black box model of the device that captures these physical features that can be exploited to “diagnose” whether or not the particular device subsystem (track/detect/classify) is operating normally from noisy vibrational data. Here we discuss the application of a modern system identification approach based on stochastic subspace realization techniques capable of both (1) identifying the underlying black-box structure thereby enabling the extraction of structural modes that can be used for analysis and modal tracking as well as (2) indicators of condition and possible changes from normal operation.
Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator
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Ronald Phlypo
2007-01-01
Full Text Available To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power. It is shown that the JSSE is able to estimate a component from simulated data that is superior with respect to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms used for blind source separation (BSS. Interference and distortion suppression are of comparable order when compared with the above-mentioned methods. Results on patient data demonstrate that the method is able to suppress blinking and saccade artefacts in a fully automated way.
Controllable subspace of edge dynamics in complex networks
Pang, Shao-Peng; Hao, Fei
2017-09-01
For the edge dynamics in some real networks, it may be neither feasible nor necessary to be fully controlled. An accompanying issue is that, when the external signal is applied to a few nodes or even a single node, how many edges can be controlled? In this paper, for the edge dynamics system, we propose a theoretical framework to determine the controllable subspace and calculate its generic dimension based on the integer linear programming. This framework allows us not only to analyze the control centrality, i.e., the ability of a node to control, but also to uncover the controllable centrality, i.e., the propensity of an edge to be controllable. The simulation results and analytic calculation show that dense and homogeneous networks tend to have larger control centrality of nodes and controllable centrality of edges, but the negatively correlated in- and out-degrees of nodes or edges can reduce the two centrality. The positive correlation between the control centrality of node and its out-degree leads to that the distribution of control centrality, instead of that of controllable centrality, is encoded by the out-degree distribution of networks. Meanwhile, the positive correlation indicates that the nodes with high out-degree tend to play more important roles in control.
Subspace-based identification of discrete time-delay system
Institute of Scientific and Technical Information of China (English)
Qiang LIU; Jia-chen MA
2016-01-01
We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant (LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search (ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.
KRYLOV’S SUBSPACES ITERATIVE METHODS TO EVALUATE ELECTROSTATIC PARAMETERS
Directory of Open Access Journals (Sweden)
Mario Versaci
2014-01-01
Full Text Available Most of the electromagnetic problems can be stated in terms of an inhomogeneous equation Af = g in which A is a differential, integral or integro-differential operator, g in the exitation source and f is the unknown function to be determined. Methods of Moments (MoM is a procedure to solve the equation above and, by means of an appropriate choice of the Basis/Testing (B/T, the problem can be translated into an equivalent linear system even of bigger dimensions. In this work we investigate on how the performances of the major Krylov’s subspace iterative solvers are affected by different choice of these sets of functions. More specifically, as a test case, we consider the algebric linear system of equations obtained by an electrostatic problem of evaluation of the capacitance and electrostatic charge distribution in a cylindrical conductor of finite length. Results are compared in terms of analytical/computational complexity and speed of convergence by exploiting three leading iterative methods (GMRES, CGS, BibGStab and B/T functions of Pulse/Pulse (P/P and Pulse/Delta (P/D type.
Generalized shift-invariant systems and frames for subspaces
DEFF Research Database (Denmark)
Christensen, Ole; Eldar, Y.C.
2005-01-01
)(j is an element of J,k is an element of Z) are Bessel sequences, we are interested in expansions [GRAPHICS] Our main result gives an equivalent condition for this to hold in a more general setting than described here, where translation by k is an element of Z(d) is replaced by translation via the action......Let T-k denote translation by k is an element of Z(d). Given countable collections of functions {phi(j)}(j is an element of J), {(phi) over bar (j)}(j is an element of J) subset of L-2(R-d) and assuming that {T(k)phi(j)}(j is an element of J,k is an element of Z)(d) and {T-k(phi) over bar (j)} (d...... of a matrix. As special cases of our result we find conditions for shift-invariant systems, Gabor systems, and wavelet systems to generate a subspace frame with a corresponding dual having the same structure....
Supervised orthogonal discriminant subspace projects learning for face recognition.
Chen, Yu; Xu, Xiao-Hong
2014-02-01
In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP.
AXISYMMETRIC BENDING OF TWO-DIRECTIONAL FUNCTIONALLY GRADED CIRCULAR AND ANNULAR PLATES
Institute of Scientific and Technical Information of China (English)
Guojun Nie; Zheng Zhong
2007-01-01
Assuming the material properties varying with an exponential law both in the thickness and radial directions, axisymmetric bending of two-directional functionally graded circular and annular plates is studied using the semi-analytical numerical method in this paper. The deflections and stresses of the plates are presented. Numerical results show the well accuracy and convergence of the method. Compared with the finite element method, the semi-analytical numerical method is with great advantage in the computational efficiency. Moreover, study on axisymmetric bending of two-directional functionally graded annular plate shows that such plates have better performance than those made of isotropic homogeneous materials or one-directional functionally graded materials. Two-directional functionally graded material is a potential alternative to the one-directional functionally graded material. And the integrated design of materials and structures can really be achieved in two-directional functionally graded materials.
Conjunctive patches subspace learning with side information for collaborative image retrieval.
Zhang, Lining; Wang, Lipo; Lin, Weisi
2012-08-01
Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.
Single-shot realization of nonadiabatic holonomic quantum gates in decoherence-free subspaces
Zhao, P. Z.; Xu, G. F.; Ding, Q. M.; Sjöqvist, Erik; Tong, D. M.
2017-06-01
Nonadiabatic holonomic quantum computation in decoherence-free subspaces has attracted increasing attention recently, as it allows for high-speed implementation and combines both the robustness of holonomic gates and the coherence stabilization of decoherence-free subspaces. Since the first protocol of nonadiabatic holonomic quantum computation in decoherence-free subspaces, a number of schemes for its physical implementation have been put forward. However, all previous schemes require two noncommuting gates to realize an arbitrary one-qubit gate, which doubles the exposure time of gates to error sources as well as the resource expenditure. In this paper, we propose an alternative protocol for nonadiabatic holonomic quantum computation in decoherence-free subspaces, in which an arbitrary one-qubit gate in decoherence-free subspaces is realized by a single-shot implementation. The present protocol not only maintains the merits of the original protocol but also avoids the extra work of combining two gates to implement an arbitrary one-qubit gate and thereby reduces the exposure time to various error sources.
Institute of Scientific and Technical Information of China (English)
GUYanfeng; ZHANGYe; QUANTaifan
2003-01-01
A challenging problem in using hyper-spectral data is to eliminate redundancy and preserve useful spectral information for applications. In this pa-per, a kernel-based nonlinear subspace projection (KNSP)method is proposed for feature extraction and dimension-ality reduction in hyperspectral images. The proposed method includes three key steps: subspace partition of hyperspectral data, feature extraction using kernel-based principal component analysis (KPCA) and feature selec-tion based on class separability in the subspaces. Accord-ing to the strong correlation between neighboring bands,the whole data space is partitioned to requested subspaces.In each subspace, the KPCA method is used to effectively extract spectral feature and eliminate redundancies. A criterion function based on class discrimination and sepa-rability is used for the transformed feature selection. For the purpose of testifying its effectiveness, the proposed new method is compared with the classical principal component analysis (PCA) and segmented principal component trans-formation (SPCT). A hyperspectral image classification is performed on AVIRIS data. which have 224 svectral bands.Experimental results show that KNSP is very effective for feature extraction and dimensionality reduction of hyper-spectral data and provides significant improvement over classical PCA and current SPCT technique.
Zhao, P. Z.; Xu, G. F.; Tong, D. M.
2016-12-01
Nonadiabatic geometric quantum computation in decoherence-free subspaces has received increasing attention due to the merits of its high-speed implementation and robustness against both control errors and decoherence. However, all the previous schemes in this direction have been based on the conventional geometric phases, of which the dynamical phases need to be removed. In this paper, we put forward a scheme of nonadiabatic geometric quantum computation in decoherence-free subspaces based on unconventional geometric phases, of which the dynamical phases do not need to be removed. Specifically, by using three physical qubits undergoing collective dephasing to encode one logical qubit, we realize a universal set of geometric gates nonadiabatically and unconventionally. Our scheme not only maintains all the merits of nonadiabatic geometric quantum computation in decoherence-free subspaces, but also avoids the additional operations required in the conventional schemes to cancel the dynamical phases.
Crevecoeur, Guillaume; Yitembe, Bertrand; Dupre, Luc; Van Keer, Roger
2013-01-01
This paper proposes a modification of the subspace correlation cost function and the Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) method for electroencephalography (EEG) source analysis in epilepsy. This enables to reconstruct neural source locations and orientations that are less degraded due to the uncertain knowledge of the head conductivity values. An extended linear forward model is used in the subspace correlation cost function that incorporates the sensitivity of the EEG potentials to the uncertain conductivity value parameter. More specifically, the principal vector of the subspace correlation function is used to provide relevant information for solving the EEG inverse problems. A simulation study is carried out on a simplified spherical head model with uncertain skull to soft tissue conductivity ratio. Results show an improvement in the reconstruction accuracy of source parameters compared to traditional methodology, when using conductivity ratio values that are different from the actual conductivity ratio.
Estimation of direction of arrival of a moving target using subspace based approaches
Ghosh, Ripul; Das, Utpal; Akula, Aparna; Kumar, Satish; Sardana, H. K.
2016-05-01
In this work, array processing techniques based on subspace decomposition of signal have been evaluated for estimation of direction of arrival of moving targets using acoustic signatures. Three subspace based approaches - Incoherent Wideband Multiple Signal Classification (IWM), Least Square-Estimation of Signal Parameters via Rotation Invariance Techniques (LS-ESPRIT) and Total Least Square- ESPIRIT (TLS-ESPRIT) are considered. Their performance is compared with conventional time delay estimation (TDE) approaches such as Generalized Cross Correlation (GCC) and Average Square Difference Function (ASDF). Performance evaluation has been conducted on experimentally generated data consisting of acoustic signatures of four different types of civilian vehicles moving in defined geometrical trajectories. Mean absolute error and standard deviation of the DOA estimates w.r.t. ground truth are used as performance evaluation metrics. Lower statistical values of mean error confirm the superiority of subspace based approaches over TDE based techniques. Amongst the compared methods, LS-ESPRIT indicated better performance.
A real-time cardiac surface tracking system using Subspace Clustering.
Singh, Vimal; Tewfik, Ahmed H; Gowreesunker, B
2010-01-01
Catheter based radio frequency ablation of atrial fibrillation requires real-time 3D tracking of cardiac surfaces with sub-millimeter accuracy. To best of our knowledge, there are no commercial or non-commercial systems capable to do so. In this paper, a system for high-accuracy 3D tracking of cardiac surfaces in real-time is proposed and results applied to a real patient dataset are presented. Proposed system uses Subspace Clustering algorithm to identify the potential deformation subspaces for cardiac surfaces during the training phase from pre-operative MRI scan based training set. In Tracking phase, using low-density outer cardiac surface samples, active deformation subspace is identified and complete inner & outer cardiac surfaces are reconstructed in real-time under a least squares formulation.
Rational time-frequency multi-window subspace Gabor frames and their Gabor duals
Institute of Scientific and Technical Information of China (English)
ZHANG Yan; LI YunZhang
2014-01-01
This paper addresses the theory of multi-window subspace Gabor frame with rational time-frequency parameter products.With the help of a suitable Zak transform matrix,we characterize multi-window subspace Gabor frames,Riesz bases,orthonormal bases and the uniqueness of Gabor duals of type I and type II.Using these characterizations we obtain a class of multi-window subspace Gabor frames,Riesz bases,orthonormal bases,and at the same time we derive an explicit expression of their Gabor duals of type I and type II.As an application of the above results,we obtain characterizations of multi-window Gabor frames,Riesz bases and orthonormal bases for L2（R）,and derive a parametric expression of Gabor duals for multi-window Gabor frames in L2（R）.
Robust subspace estimation using low-rank optimization theory and applications
Oreifej, Omar
2014-01-01
Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book,?the authors?discuss fundame
The Many Faces of the Subspace Theorem (after Adamczewski, Bugeaud, Corvaja, Zannier...)
Bilu, Yuri
2009-01-01
Séminaire Bourbaki, Exposé 967, 59ème année (2006-2007); Astérisque 317 (2008), 1-38.; During the last decade the Subspace Theorem found several quite unexpected applications, mainly in the Diophantine Analysis and in the Transcendence Theory. Among the great variety of spectacular results, I have chosen several which are technically simpler and which allow one to appreciate how miraculously does the Subspace Theorem emerge in numerous situations, implying beautiful solutions to difficult pro...
Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace
Institute of Scientific and Technical Information of China (English)
解翔; 侍洪波
2012-01-01
For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.
Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2007-01-01
We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated...... with working Matlab code and applications in speech processing....
Atomic GHZ States Prepared in Two Directly Coupled Cavities with Virtual Excitations in One Step
Institute of Scientific and Technical Information of China (English)
杨榕灿; 黄志平; 郭强; 张鹏飞; 钟纯勇; 张天才
2011-01-01
A scheme for one-step preparation of atomic GHZ states in two directly coupled cavities via virtual excitations is proposed. In the whole procedure, the information is carried only in two ground states of A-type atoms, while the excited states of atoms and cavity modes are virtually excited, leading the system to be insensitive to atomic spontaneous emission and photon loss.
Comparing the Effectiveness of Two Directive Styles in the Academic Counseling of Foreign Students.
Merta, Rod J.; And Others
1992-01-01
Examined effectiveness of two directive academic counseling styles (authoritative versus collaborative) on Asian foreign students' (n=50) ratings of peer counselor effectiveness. High-acculturated students rated authoritative peer counselors higher in overall effectiveness, whereas low-acculturated students rated collaborative peer counselors…
A Survey on Sparse Subspace Clustering%稀疏子空间聚类综述
Institute of Scientific and Technical Information of China (English)
王卫卫; 李小平; 冯象初; 王斯琪
2015-01-01
Sparse subspace clustering (SSC) is a newly developed spectral clustering-based framework for data clustering. High-dimensional data usually lie in a union of several low-dimensional subspaces, which allows sparse representation of high-dimensional data with an appropriate dictionary. Sparse subspace clustering methods pursue a sparse representation of high-dimensional data and use it to build the aﬃnity matrix. The subspace clustering result of the data is finally obtained by means of spectral clustering. The key to sparse subspace clustering is to design a good representation model which can reveal the real subspace structure of high-dimensional data. More importantly, the obtained representation coeﬃcient and the aﬃnity matrix are more beneficial to accurate subspace clustering. Sparse subspace clustering has been successfully applied to different research fields, including machine learning, computer vision, image processing, system identification and others, but there is still a vast space to develop. In this paper, the fundamental models, algorithms and applications of sparse subspace clustering are reviewed in detail. Limitations existing in available methods are analyzed. Problems for further research on sparse subspace clustering are discussed.%稀疏子空间聚类(Sparse subspace clustering, SSC)是一种基于谱聚类的数据聚类框架。高维数据通常分布于若干个低维子空间的并上,因此高维数据在适当字典下的表示具有稀疏性。稀疏子空间聚类利用高维数据的稀疏表示系数构造相似度矩阵,然后利用谱聚类方法得到数据的子空间聚类结果。其核心是设计能够揭示高维数据真实子空间结构的表示模型,使得到的表示系数及由此构造的相似度矩阵有助于精确的子空间聚类。稀疏子空间聚类在机器学习、计算机视觉、图像处理和模式识别等领域已经得到了广泛的研究和应用,但仍有很大的发展空间。本文
Energy Technology Data Exchange (ETDEWEB)
Luanjing Guo; Chuan Lu; Hai Huang; Derek R. Gaston
2012-06-01
Systems of multicomponent reactive transport in porous media that are large, highly nonlinear, and tightly coupled due to complex nonlinear reactions and strong solution-media interactions are often described by a system of coupled nonlinear partial differential algebraic equations (PDAEs). A preconditioned Jacobian-Free Newton-Krylov (JFNK) solution approach is applied to solve the PDAEs in a fully coupled, fully implicit manner. The advantage of the JFNK method is that it avoids explicitly computing and storing the Jacobian matrix during Newton nonlinear iterations for computational efficiency considerations. This solution approach is also enhanced by physics-based blocking preconditioning and multigrid algorithm for efficient inversion of preconditioners. Based on the solution approach, we have developed a reactive transport simulator named RAT. Numerical results are presented to demonstrate the efficiency and massive scalability of the simulator for reactive transport problems involving strong solution-mineral interactions and fast kinetics. It has been applied to study the highly nonlinearly coupled reactive transport system of a promising in situ environmental remediation that involves urea hydrolysis and calcium carbonate precipitation.
Caplan, R. M.; Mikić, Z.; Linker, J. A.; Lionello, R.
2017-05-01
We explore the performance and advantages/disadvantages of using unconditionally stable explicit super time-stepping (STS) algorithms versus implicit schemes with Krylov solvers for integrating parabolic operators in thermodynamic MHD models of the solar corona. Specifically, we compare the second-order Runge-Kutta Legendre (RKL2) STS method with the implicit backward Euler scheme computed using the preconditioned conjugate gradient (PCG) solver with both a point-Jacobi and a non-overlapping domain decomposition ILU0 preconditioner. The algorithms are used to integrate anisotropic Spitzer thermal conduction and artificial kinematic viscosity at time-steps much larger than classic explicit stability criteria allow. A key component of the comparison is the use of an established MHD model (MAS) to compute a real-world simulation on a large HPC cluster. Special attention is placed on the parallel scaling of the algorithms. It is shown that, for a specific problem and model, the RKL2 method is comparable or surpasses the implicit method with PCG solvers in performance and scaling, but suffers from some accuracy limitations. These limitations, and the applicability of RKL methods are briefly discussed.
Yuan, Xuefei
2012-07-01
Numerical simulations of the four-field extended magnetohydrodynamics (MHD) equations with hyper-resistivity terms present a difficult challenge because of demanding spatial resolution requirements. A time-dependent sequence of . r-refinement adaptive grids obtained from solving a single Monge-Ampère (MA) equation addresses the high-resolution requirements near the . x-point for numerical simulation of the magnetic reconnection problem. The MHD equations are transformed from Cartesian coordinates to solution-defined curvilinear coordinates. After the application of an implicit scheme to the time-dependent problem, the parallel Newton-Krylov-Schwarz (NKS) algorithm is used to solve the system at each time step. Convergence and accuracy studies show that the curvilinear solution requires less computational effort than a pure Cartesian treatment. This is due both to the more optimal placement of the grid points and to the improved convergence of the implicit solver, nonlinearly and linearly. The latter effect, which is significant (more than an order of magnitude in number of inner linear iterations for equivalent accuracy), does not yet seem to be widely appreciated. © 2012 Elsevier Inc.
Kernel based subspace projection of near infrared hyperspectral images of maize kernels
DEFF Research Database (Denmark)
Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben;
2009-01-01
In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...
Fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections
Hong Liang
2015-01-01
Fuzzy ordered linear spaces, Riesz spaces, fuzzy Archimedean spaces and $\\sigma$-complete fuzzy Riesz spaces were defined and studied in several works. Following the efforts along this line, we define fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections and establish their fundamental properties.
A Subspace Approach to Blind Multiuser Detection for Ultra-Wideband Communication Systems
Directory of Open Access Journals (Sweden)
Liu Ping
2005-01-01
Full Text Available Impulse radio-based ultra-wideband (UWB communication systems allow multiple users to access channels simultaneously by assigning unique time-hopping codes to individual users, while each user's information stream is modulated by pulse-position modulation (PPM. However, transmitted signals undergo fading from a number of propagation paths in a dense multipath environment and meanwhile suffer from multiuser interference (MUI. Although RAKE receiver can be employed to maximally exploit path diversity, it is a single-user receiver. Multiuser receiver can significantly improve detection performance. Each of these receivers requires channel parameters. Existing maximum likelihood channel estimators treat MUI as Gaussian noise. In this paper, we derive a blind subspace channel estimator first and then design linear receivers. Following a channel input/output model that transforms a PPM signal into a sum of seemingly pulse-amplitude modulated signals, a structure similar to a code-division multiple-access (CDMA system is observed. Code matrices for each user are identified. After considering unique statistical properties of new inputs such as mean and covariance, the model is further transformed to ensure that all signature waveforms lie in the signal subspace and are orthogonal to the noise subspace. Consequently, a subspace technique is applicable to estimate each channel. Then minimum mean square error receivers of two different versions are designed, suitable for both uplink and downlink. Asymptotic performance of both the channel estimator and receivers is studied. Closed-form bit error rate is also derived.
Recursive subspace identification of linear and non-linear Wiener state-space models
Lovera, Marco; Gustafsson, Tony; Verhaegen, M.H.G.
2000-01-01
The problem of MIMO recursive identification is analyzed within the framework of subspace model identification (SMI) and the use of recent signal processing algorithms for the recursive update of the singular value decomposition (SVD) is proposed. To accommodate for arbitrary correlation of the dist
Active constraints selection based semi-supervised dimensionality in ensemble subspaces
Institute of Scientific and Technical Information of China (English)
Jie Zeng; Wei Nie; Yong Zhang
2015-01-01
Semi-supervised dimensionality reduction (SSDR) has attracted an increasing amount of attention in this big-data era. Many algorithms have been developed with a smal number of pairwise constraints to achieve performances comparable to those of ful y supervised methods. However, one chal enging problem with semi-supervised approaches is the appropriate choice of the constraint set, including the cardinality and the composition of the constraint set, which to a large extent, affects the performance of the resulting algorithm. In this work, we address the problem by incorporating ensemble subspace and active learning into dimen-sionality reduction and propose a new algorithm, termed as global and local scatter based SSDR with active pairwise constraints selection in ensemble subspaces (SSGL-ESA). Unlike traditional methods that select the supervised information in one subspace, we pick up pairwise constraints in ensemble subspace, where a novel active learning algorithm is designed with both exploration and filtering to generate informative pairwise constraints. The auto-matic constraint selection approach proposed in this paper can be generalized to be used with al constraint-based semi-supervised learning algorithms. Comparative experiments are conducted on two face database and the results validate the effectiveness of the proposed method.
Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2007-01-01
We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using bo...... with working Matlab code and applications in speech processing....
Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using bo...... with working Matlab code and applications in speech processing....
Kernel based subspace projection of near infrared hyperspectral images of maize kernels
DEFF Research Database (Denmark)
Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben
2009-01-01
In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...
Practical Low Data-Complexity Subspace-Trail Cryptanalysis of Round-Reduced PRINCE
DEFF Research Database (Denmark)
Grassi, Lorenzo; Rechberger, Christian
2016-01-01
of a 2.5 rounds subspace trail of PRINCE, we present several (truncated differential) attacks up to 6 rounds of PRINCE. This includes a very practical attack with the lowest data complexity of only 8 plaintexts for 4 rounds, which co-won the final round of the PRINCE challenge in the 4-round chosen...
Consistency analysis of subspace identification methods based on a linear regression approach
DEFF Research Database (Denmark)
Knudsen, Torben
2001-01-01
In the literature results can be found which claim consistency for the subspace method under certain quite weak assumptions. Unfortunately, a new result gives a counter example showing inconsistency under these assumptions and then gives new more strict sufficient assumptions which however does n...
On the Maximal Dimension of a Completely Entangled Subspace for Finite Level Quantum Systems
Indian Academy of Sciences (India)
K R Parthasarathy
2004-11-01
Let $\\mathcal{H}_i$ be a finite dimensional complex Hilbert space of dimension $d_i$ associated with a finite level quantum system $A_i$ for $i=1, 2,\\ldots,k$. A subspace $S\\subset\\mathcal{H} = \\mathcal{H}_{A_1 A_2\\ldots A_k} = \\mathcal{H}_1 \\otimes \\mathcal{H}_2 \\otimes\\cdots\\otimes \\mathcal{H}_k$ is said to be completely entangled if it has no non-zero product vector of the form $u_1 \\otimes u_2 \\otimes\\cdots\\otimes u_k$ with $u_i$ in $\\mathcal{H}_i$ for each . Using the methods of elementary linear algebra and the intersection theorem for projective varieties in basic algebraic geometry we prove that $$\\max\\limits_{S\\in\\mathcal{E}}\\dim S=d_1 d_2\\ldots d_k-(d_1+\\cdots +d_k)+k-1,$$ where $\\mathcal{E}$ is the collection of all completely entangled subspaces. When $\\mathcal{H}_1 = \\mathcal{H}_2$ and $k = 2$ an explicit orthonormal basis of a maximal completely entangled subspace of $\\mathcal{H}_1 \\otimes \\mathcal{H}_2$ is given. We also introduce a more delicate notion of a perfectly entangled subspace for a multipartite quantum system, construct an example using the theory of stabilizer quantum codes and pose a problem.
Incomplete Phase Space Reconstruction Method Based on Subspace Adaptive Evolution Approximation
Directory of Open Access Journals (Sweden)
Tai-fu Li
2013-01-01
Full Text Available The chaotic time series can be expanded to the multidimensional space by phase space reconstruction, in order to reconstruct the dynamic characteristics of the original system. It is difficult to obtain complete phase space for chaotic time series, as a result of the inconsistency of phase space reconstruction. This paper presents an idea of subspace approximation. The chaotic time series prediction based on the phase space reconstruction can be considered as the subspace approximation problem in different neighborhood at different time. The common static neural network approximation is suitable for a trained neighborhood, but it cannot ensure its generalization performance in other untrained neighborhood. The subspace approximation of neural network based on the nonlinear extended Kalman filtering (EKF is a dynamic evolution approximation from one neighborhood to another. Therefore, in view of incomplete phase space, due to the chaos phase space reconstruction, we put forward subspace adaptive evolution approximation method based on nonlinear Kalman filtering. This method is verified by multiple sets of wind speed prediction experiments in Wulong city, and the results demonstrate that it possesses higher chaotic prediction accuracy.
A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery
Directory of Open Access Journals (Sweden)
Kwon Heesung
2007-01-01
Full Text Available Several linear and nonlinear detection algorithms that are based on spectral matched (subspace filters are compared. Nonlinear (kernel versions of these spectral matched detectors are also given and their performance is compared with linear versions. Several well-known matched detectors such as matched subspace detector, orthogonal subspace detector, spectral matched filter, and adaptive subspace detector are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The expression for each detection algorithm is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. Experimental results based on simulated toy examples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.
Experimental Study of Generalized Subspace Filters for the Cocktail Party Situation
DEFF Research Database (Denmark)
Christensen, Knud Bank; Christensen, Mads Græsbøll; Boldt, Jesper B.
2016-01-01
This paper investigates the potential performance of generalized subspace filters for speech enhancement in cocktail party situations with very poor signal/noise ratio, e.g. down to -15 dB. Performance metrics output signal/noise ratio, signal/ distortion ratio, speech quality rating and speech...
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan
2011-01-01
In high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few c...
Subspace-Based Algorithms for Structural Identification, Damage Detection, and Sensor Data Fusion
Directory of Open Access Journals (Sweden)
Goursat Maurice
2007-01-01
Full Text Available This paper reports on the theory and practice of covariance-driven output-only and input/output subspace-based identification and detection algorithms. The motivating and investigated application domain is vibration-based structural analysis and health monitoring of mechanical, civil, and aeronautic structures.
Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
Improved Detection of Local Earthquakes in the Vienna Basin (Austria), using Subspace Detectors
Apoloner, Maria-Theresia; Caffagni, Enrico; Bokelmann, Götz
2016-04-01
The Vienna Basin in Eastern Austria is densely populated and highly-developed; it is also a region of low to moderate seismicity, yet the seismological network coverage is relatively sparse. This demands improving our capability of earthquake detection by testing new methods, enlarging the existing local earthquake catalogue. This contributes to imaging tectonic fault zones for better understanding seismic hazard, also through improved earthquake statistics (b-value, magnitude of completeness). Detection of low-magnitude earthquakes or events for which the highest amplitudes slightly exceed the signal-to-noise-ratio (SNR), may be possible by using standard methods like the short-term over long-term average (STA/LTA). However, due to sparse network coverage and high background noise, such a technique may not detect all potentially recoverable events. Yet, earthquakes originating from the same source region and relatively close to each other, should be characterized by similarity in seismic waveforms, at a given station. Therefore, waveform similarity can be exploited by using specific techniques such as correlation-template based (also known as matched filtering) or subspace detection methods (based on the subspace theory). Matching techniques basically require a reference or template event, usually characterized by high waveform coherence in the array receivers, and high SNR, which is cross-correlated with the continuous data. Instead, subspace detection methods overcome in principle the necessity of defining template events as single events, but use a subspace extracted from multiple events. This approach theoretically should be more robust in detecting signals that exhibit a strong variability (e.g. because of source or magnitude). In this study we scan the continuous data recorded in the Vienna Basin with a subspace detector to identify additional events. This will allow us to estimate the increase of the seismicity rate in the local earthquake catalogue
Lyapunov vectors and assimilation in the unstable subspace: theory and applications
Palatella, Luigi; Carrassi, Alberto; Trevisan, Anna
2013-06-01
Based on a limited number of noisy observations, estimation algorithms provide a complete description of the state of a system at current time. Estimation algorithms that go under the name of assimilation in the unstable subspace (AUS) exploit the nonlinear stability properties of the forecasting model in their formulation. Errors that grow due to sensitivity to initial conditions are efficiently removed by confining the analysis solution in the unstable and neutral subspace of the system, the subspace spanned by Lyapunov vectors with positive and zero exponents, while the observational noise does not disturb the system along the stable directions. The formulation of the AUS approach in the context of four-dimensional variational assimilation (4DVar-AUS) and the extended Kalman filter (EKF-AUS) and its application to chaotic models is reviewed. In both instances, the AUS algorithms are at least as efficient but simpler to implement and computationally less demanding than their original counterparts. As predicted by the theory when error dynamics is linear, the optimal subspace dimension for 4DVar-AUS is given by the number of positive and null Lyapunov exponents, while the EKF-AUS algorithm, using the same unstable and neutral subspace, recovers the solution of the full EKF algorithm, but dealing with error covariance matrices of a much smaller dimension and significantly reducing the computational burden. Examples of the application to a simplified model of the atmospheric circulation and to the optimal velocity model for traffic dynamics are given. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Lyapunov analysis: from dynamical systems theory to applications’.
Using CUDA Technology for Defining the Stiffness Matrix in the Subspace of Eigenvectors
Directory of Open Access Journals (Sweden)
Yu. V. Berchun
2015-01-01
Full Text Available The aim is to improve the performance of solving a problem of deformable solid mechanics through the use of GPGPU. The paper describes technologies for computing systems using both a central and a graphics processor and provides motivation for using CUDA technology as the efficient one.The paper also analyses methods to solve the problem of defining natural frequencies and design waveforms, i.e. an iteration method in the subspace. The method includes several stages. The paper considers the most resource-hungry stage, which defines the stiffness matrix in the subspace of eigenforms and gives the mathematical interpretation of this stage.The GPU choice as a computing device is justified. The paper presents an algorithm for calculating the stiffness matrix in the subspace of eigenforms taking into consideration the features of input data. The global stiffness matrix is very sparse, and its size can reach tens of millions. Therefore, it is represented as a set of the stiffness matrices of the single elements of a model. The paper analyses methods of data representation in the software and selects the best practices for GPU computing.It describes the software implementation using CUDA technology to calculate the stiffness matrix in the subspace of eigenforms. Due to the input data nature, it is impossible to use the universal libraries of matrix computations (cuSPARSE and cuBLAS for loading the GPU. For efficient use of GPU resources in the software implementation, the stiffness matrices of elements are built in the block matrices of a special form. The advantages of using shared memory in GPU calculations are described.The transfer to the GPU computations allowed a twentyfold increase in performance (as compared to the multithreaded CPU-implementation on the model of middle dimensions (degrees of freedom about 2 million. Such an acceleration of one stage speeds up defining the natural frequencies and waveforms by the iteration method in a subspace
Institute of Scientific and Technical Information of China (English)
XUGuoxing; GANLiangcai; ZHANGXuliang; HUANGTiaxi
2005-01-01
In this paper, we consider subspace based channel estimation methods for Turbo parallel interference cancellation and decoding integrating frequency diversity combining (Turbo FDC-PIC/decoding) over convolutionally coded multi-carrier Direct-sequence Code-division multiple access (DS-CDMA). Applying Turbo principle, we propose blind subspace iterative channel estimation, and apply it to Turbo FDC-PIC/decoding to perform joint channel estimation, detection and decoding. The simu- lation results show that Turbo FDC-PIC/decoding with blind subspace iterative channel estimation has greater performance improvement than that with blind subspace noniterative channel estimation or Pilot symbol aided (PSA) iterative channel estimation, and after a number of iterations, can even obtain performance close to Turbo FDC-PIC/decoding with ideal channel estimation. For example, with the chosen simulation parameter and for the fourth iteration, at the Signal-to-noise rate (SNR) of 7dB, Turbo FDC-PIC/decoding with blind subspace iterative channel estimation acquires the bit error rate of 9×10-4, nearly one order of magnitude lower than that of Turbo FDC-PIC/decoding with PSA iterative channel estimation or with blind subspace noniterative channel estimation. Besides, for blind subspace iterative channel estimation, pilot symbols aren't needed to insert in coded symbols, and therefore data rate is not lowered.
Kovalevsky, L
2016-01-01
The Variational Theory of Complex Rays (VTCR) is an indirect Trefftz method designed to study systems governed by Helmholtz-like equations. It uses wave functions to represent the solution inside elements, which reduces the dispersion error compared to classical polynomial approaches but the resulting system is prone to be ill conditioned. This paper gives a simple and original presentation of the VTCR using the discontinuous Galerkin framework and it traces back the ill-conditioning to the accumulation of eigenvalues near zero for the formulation written in terms of wave amplitude. The core of this paper presents an efficient solving strategy that overcomes this issue. The key element is the construction of a search subspace where the condition number is controlled at the cost of a limited decrease of attainable precision. An augmented LSQR solver is then proposed to solve efficiently and accurately the complete system. The approach is successfully applied to different examples.
Chen, Dan; Guo, Lin-yuan; Wang, Chen-hao; Ke, Xi-zheng
2017-07-01
Equalization can compensate channel distortion caused by channel multipath effects, and effectively improve convergent of modulation constellation diagram in optical wireless system. In this paper, the subspace blind equalization algorithm is used to preprocess M-ary phase shift keying (MPSK) subcarrier modulation signal in receiver. Mountain clustering is adopted to get the clustering centers of MPSK modulation constellation diagram, and the modulation order is automatically identified through the k-nearest neighbor (KNN) classifier. The experiment has been done under four different weather conditions. Experimental results show that the convergent of constellation diagram is improved effectively after using the subspace blind equalization algorithm, which means that the accuracy of modulation recognition is increased. The correct recognition rate of 16PSK can be up to 85% in any kind of weather condition which is mentioned in paper. Meanwhile, the correct recognition rate is the highest in cloudy and the lowest in heavy rain condition.
Hyperspectral Image Kernel Sparse Subspace Clustering with Spatial Max Pooling Operation
Zhang, Hongyan; Zhai, Han; Liao, Wenzhi; Cao, Liqin; Zhang, Liangpei; Pižurica, Aleksandra
2016-06-01
In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.
Zhang, Yungang; Zhang, Bailing; Lu, Wenjin
2011-06-01
Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co-occurence Matrix (GLCM) and Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.
On the Kalman Filter error covariance collapse into the unstable subspace
Trevisan, A.; Palatella, L.
2011-03-01
When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N+ + N0 where N+ and N0 are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS) are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.
Maximal Dimension of Invariant Subspaces to Systems of Nonlinear Evolution Equations
Institute of Scientific and Technical Information of China (English)
Shoufeng SHEN; ChangZheng QU; Yongyang JIN; Lina JI
2012-01-01
In this paper,the dimension of invariant subspaces admitted by nonlinear systems is estimated under certain conditions.It is shown that if the two-component nonlinear vector differential operator F =(F1,F2) with orders {k1,k2} (k1 ≥ k2) preserves the invariant subspace W1n1 × W2n2 (n1 ≥ n2),then n1 - n2 ≤ k2,n1 ≤ 2(k1 + k2) + 1,where Wqnq is the space generated by solutions of a linear ordinary differential equation of order nq (q =1,2).Several examples including the (1+1)-dimensional diffusion system and It(o)'s type,Drinfel'd-Sokolov-Wilson's type and Whitham-Broer-Kaup's type equations are presented to illustrate the result.Furthermore,the estimate of dimension for m-component nonlinear systems is also given.
Active subspace approach to reliability and safety assessments of small satellite separation
Hu, Xingzhi; Chen, Xiaoqian; Zhao, Yong; Tuo, Zhouhui; Yao, Wen
2017-02-01
Ever-increasing launch of small satellites demands an effective and efficient computer-aided analysis approach to shorten the ground test cycle and save the economic cost. However, the multiple influencing factors hamper the efficiency and accuracy of separation reliability assessment. In this study, a novel evaluation approach based on active subspace identification and response surface construction is established and verified. The formulation of small satellite separation is firstly derived, including equations of motion, separation and gravity forces, and quantity of interest. The active subspace reduces the dimension of uncertain inputs with minimum precision loss and a 4th degree multivariate polynomial regression (MPR) using cross validation is hand-coded for the propagation and error analysis. A common spring separation of small satellites is employed to demonstrate the accuracy and efficiency of the approach, which exhibits its potential use in widely existing needs of satellite separation analysis.
Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption
Directory of Open Access Journals (Sweden)
Zheping Yan
2014-01-01
Full Text Available A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.
The Many Faces of the Subspace Theorem (after Adamczewski, Bugeaud, Corvaja, Zannier...)
Bilu, Yuri
2009-01-01
During the last decade the Subspace Theorem found several quite unexpected applications, mainly in the Diophantine Analysis and in the Transcendence Theory. Among the great variety of spectacular results, I have chosen several which are technically simpler and which allow one to appreciate how miraculously does the Subspace Theorem emerge in numerous situations, implying beautiful solutions to difficult problems hardly anybody hoped to solve so easily. The three main topics discussed in this article are: the work of Adamczewski and Bugeaud on complexity of algebraic numbers; the work of Corvaja and Zannier on Diophantine equations with power sums; the work of Corvaja and Zannier on integral points on curves and surfaces, and the subsequent development due to Levin and Autissier. In particular, we give a complete proof of the beautiful theorem of Levin and Autissier: an affine surface with 4 (or more) properly intersecting ample divisors at infinity cannot have a Zariski dense set of integral points.
Quantum gate between logical qubits in decoherence-free subspace implemented with trapped ions
Ivanov, Peter A; Singer, Kilian; Schmidt-Kaler, Ferdinand
2009-01-01
We propose an efficient technique for the implementation of a geometric phase gate in a decoherence-free subspace with trapped ions. In this scheme, the quantum information is encoded in the Zeeman sublevels of the ground state and two physical qubits are used to make up one logical qubit with ultra long coherence time. The physical realization of a geometric phase gate between two logic qubits is performed with four ions in a linear crystal simultaneously interacting with single laser beam. We investigate in detail the robustness of the scheme with respect to the right choice of the trap frequency and provide a detailed analysis of error sources, taking into account the experimental conditions. Furthermore, possible applications for the generation of cluster states for larger numbers of ions within the decoherence-free subspace are presented.
Cumulant-Based Coherent Signal Subspace Method for Bearing and Range Estimation
Directory of Open Access Journals (Sweden)
Bourennane Salah
2007-01-01
Full Text Available A new method for simultaneous range and bearing estimation for buried objects in the presence of an unknown Gaussian noise is proposed. This method uses the MUSIC algorithm with noise subspace estimated by using the slice fourth-order cumulant matrix of the received data. The higher-order statistics aim at the removal of the additive unknown Gaussian noise. The bilinear focusing operator is used to decorrelate the received signals and to estimate the coherent signal subspace. A new source steering vector is proposed including the acoustic scattering model at each sensor. Range and bearing of the objects at each sensor are expressed as a function of those at the first sensor. This leads to the improvement of object localization anywhere, in the near-field or in the far-field zone of the sensor array. Finally, the performances of the proposed method are validated on data recorded during experiments in a water tank.
Subspace identification for continuous-time errors-in-variables model from sampled data
Institute of Scientific and Technical Information of China (English)
Ping WU; Chun-jie YANG; Zhi-huan SONG
2009-01-01
We study the subspace identification for the continuous-time errors-in-variables model from sampled data. First, the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification. The generalized Poisson moment functional is focused. A total least squares equation based on this filtering approach is derived. Inspired by the idea of discrete-time subspace identification based on principal component analysis, we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables. Order determination and other instrumental variables are discussed. The usefulness of the proposed algorithms is illustrated through numerical simulation.
Universal holonomic quantum gates in decoherence-free subspace on superconducting circuits
Xue, Zheng-Yuan; Zhou, Jian; Wang, Z. D.
2015-08-01
To implement a set of universal quantum logic gates based on non-Abelian geometric phases, it is conventional wisdom that quantum systems beyond two levels are required, which is extremely difficult to fulfill for superconducting qubits and appears to be a main reason why only single-qubit gates were implemented in a recent experiment [A. A. Abdumalikov, Jr. et al., Nature (London) 496, 482 (2013), 10.1038/nature12010]. Here we propose to realize nonadiabatic holonomic quantum computation in decoherence-free subspace on circuit QED, where one can use only the two levels in transmon qubits, a usual interaction, and a minimal resource for the decoherence-free subspace encoding. In particular, our scheme not only overcomes the difficulties encountered in previous studies but also can still achieve considerably large effective coupling strength, such that high-fidelity quantum gates can be achieved. Therefore, the present scheme makes realizing robust holonomic quantum computation with superconducting circuits very promising.
Time-dependent global sensitivity analysis with active subspaces for a lithium ion battery model
Constantine, Paul G
2016-01-01
Renewable energy researchers use computer simulation to aid the design of lithium ion storage devices. The underlying models contain several physical input parameters that affect model predictions. Effective design and analysis must understand the sensitivity of model predictions to changes in model parameters, but global sensitivity analyses become increasingly challenging as the number of input parameters increases. Active subspaces are part of an emerging set of tools to reveal and exploit low-dimensional structures in the map from high-dimensional inputs to model outputs. We extend a linear model-based heuristic for active subspace discovery to time-dependent processes and apply the resulting technique to a lithium ion battery model. The results reveal low-dimensional structure that a designer may exploit to efficiently study the relationship between parameters and predictions.
Sahadevan, R.; Prakash, P.
2017-01-01
We show how invariant subspace method can be extended to time fractional coupled nonlinear partial differential equations and construct their exact solutions. Effectiveness of the method has been illustrated through time fractional Hunter-Saxton equation, time fractional coupled nonlinear diffusion system, time fractional coupled Boussinesq equation and time fractional Whitman-Broer-Kaup system. Also we explain how maximal dimension of the time fractional coupled nonlinear partial differential equations can be estimated.
Energy Technology Data Exchange (ETDEWEB)
Fattebert, J
2008-07-29
We describe an iterative algorithm to solve electronic structure problems in Density Functional Theory. The approach is presented as a Subspace Accelerated Inexact Newton (SAIN) solver for the non-linear Kohn-Sham equations. It is related to a class of iterative algorithms known as RMM-DIIS in the electronic structure community. The method is illustrated with examples of real applications using a finite difference discretization and multigrid preconditioning.
Quantum computing in decoherence-free subspaces with superconducting charge qubits
Energy Technology Data Exchange (ETDEWEB)
Feng Zhibo [National Laboratory of Solid State Microstructures, Department of Physics, Nanjing University, Nanjing 210093 (China); Institute for Condensed Matter Physics, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510631 (China); Zhang Xinding [Institute for Condensed Matter Physics, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510631 (China)], E-mail: xdzhang2000@gmail.com
2007-12-10
Taking into account the main noises in superconducting charge qubits (SCQs), we propose a feasible scheme to realize quantum computing (QC) in a specially-designed decoherence-free subspace (DFS). In our scheme two physical qubits are connected with a common inductance to form a strong coupling subsystem, which acts as a logical qubit. Benefiting from the well-designed DFS, our scheme is helpful to suppress certain decoherence effects.
Grothendieck-Lidskii theorem for subspaces and factor spaces of L_p-spaces
Reinov, Oleg
2011-01-01
In 1955, A. Grothendieck has shown that if the linear operator $T$ in a Banach subspace of an $L_\\infty$-space is 2/3-nuclear then the trace of $T$ is well defined and is equal to the sum of all eigenvalues $\\{\\mu_k(T)\\}$ of $T.$ V.B. Lidski\\v{\\i}, in 1959, proved his famous theorem on the coincidence of the trace of the $S_1$-operator in $L_2(\
Subspace based adaptive denoising of surface EMG from neurological injury patients
Liu, Jie; Ying, Dongwen; Zev Rymer, William; Zhou, Ping
2014-10-01
Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.
Cavity quantum networks for quantum information processing in decoherence-free subspace
Institute of Scientific and Technical Information of China (English)
Hua WEI; Zhi-jiao DENG; Wan-li YANG; Fei ZHOU
2009-01-01
We give a brief review on the quantum infor- mation processing in decoherence-free subspace (DFS). We show how to realize the initialization of the entangled quantum states, information transfer and teleportation of quantum states, two-qubit Grover search and how to construct the quantum network in DFS, within the cav- ity QED regime based on a cavity-assisted interaction by single-photon pulses.
Decoherence free in subspace using Na at C{sub 60} as quantum qubit
Energy Technology Data Exchange (ETDEWEB)
Zeng Xianghua; Bi Qiao; Guo Guangcan; Ruda, H.E
2003-06-23
An approach of quantum computing based on endohedral metallofullerenes has been discussed, which includes the construction of c{sup n}-Not logic gates and decoherence-free in the projected subspace to protect against the decoherence from the interaction with environment. As the special structure of Na at C{sub 60}, symmetrically alignment of n Na at C{sub 60} along z direction, we can construct the multiqubits under the control of the STM setups.
Yu, Hang; Xu, Luping; Feng, Dongzhu; He, Xiaochuan
2015-01-01
Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time.
A Rank-Constrained Matrix Representation for Hypergraph-Based Subspace Clustering
Directory of Open Access Journals (Sweden)
Yubao Sun
2015-01-01
Full Text Available This paper presents a novel, rank-constrained matrix representation combined with hypergraph spectral analysis to enable the recovery of the original subspace structures of corrupted data. Real-world data are frequently corrupted with both sparse error and noise. Our matrix decomposition model separates the low-rank, sparse error, and noise components from the data in order to enhance robustness to the corruption. In order to obtain the desired rank representation of the data within a dictionary, our model directly utilizes rank constraints by restricting the upper bound of the rank range. An alternative projection algorithm is proposed to estimate the low-rank representation and separate the sparse error from the data matrix. To further capture the complex relationship between data distributed in multiple subspaces, we use hypergraph to represent the data by encapsulating multiple related samples into one hyperedge. The final clustering result is obtained by spectral decomposition of the hypergraph Laplacian matrix. Validation experiments on the Extended Yale Face Database B, AR, and Hopkins 155 datasets show that the proposed method is a promising tool for subspace clustering.
Directory of Open Access Journals (Sweden)
Forooz Shahbazi Avarvand
2012-01-01
Full Text Available To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method “RAP-MUSIC” to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.
Shahbazi Avarvand, Forooz; Ewald, Arne; Nolte, Guido
2012-01-01
To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method "RAP-MUSIC" to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a) RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b) conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c) the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.
Operational Modal Analysis Based on Subspace Algorithm with an Improved Stabilization Diagram Method
Directory of Open Access Journals (Sweden)
Shiqiang Qin
2016-01-01
Full Text Available Subspace-based algorithms for operational modal analysis have been extensively studied in the past decades. In the postprocessing of subspace-based algorithms, the stabilization diagram is often used to determine modal parameters. In this paper, an improved stabilization diagram is proposed for stochastic subspace identification. Specifically, first, a model order selection method based on singular entropy theory is proposed. The singular entropy increment is calculated from nonzero singular values of the output covariance matrix. The corresponding model order can be selected when the variation of singular entropy increment approaches to zero. Then, the stabilization diagram with confidence intervals which is established using the uncertainty of modal parameter is presented. Finally, a simulation example of a four-story structure and a full-scale cable-stayed footbridge application is employed to illustrate the improved stabilization diagram method. The study demonstrates that the model order can be reasonably determined by the proposed method. The stabilization diagram with confidence intervals can effectively remove the spurious modes.
Alcohol Consumption during Pregnancy: Analysis of Two Direct Metabolites of Ethanol in Meconium.
Sanvisens, Arantza; Robert, Neus; Hernández, José María; Zuluaga, Paola; Farré, Magí; Coroleu, Wifredo; Serra, Montserrat; Tor, Jordi; Muga, Robert
2016-03-22
Alcohol consumption in young women is a widespread habit that may continue during pregnancy and induce alterations in the fetus. We aimed to characterize prevalence of alcohol consumption in parturient women and to assess fetal ethanol exposure in their newborns by analyzing two direct metabolites of ethanol in meconium. This is a cross-sectional study performed in September 2011 and March 2012 in a series of women admitted to an obstetric unit following childbirth. During admission, socio-demographic and substance use (alcohol, tobacco, cannabis, cocaine, and opiates) during pregnancy were assessed using a structured questionnaire and clinical charts. We also recorded the characteristics of pregnancy, childbirth, and neonates. The meconium analysis was performed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to detect the presence of ethyl glucuronide (EtG) and ethyl sulfate (EtS). Fifty-one parturient and 52 neonates were included and 48 meconium samples were suitable for EtG and EtS detection. The median age of women was 30 years (interquartile range (IQR): 26-34 years); EtG was present in all meconium samples and median concentration of EtG was 67.9 ng/g (IQR: 36.0-110.6 ng/g). With respect to EtS, it was undetectable (alcohol consumption during pregnancy in face-to-face interviews. However, prevalence of fetal exposure to alcohol through the detection of EtG and EtS was 4.2% and 16.7%, respectively. Prevention of alcohol consumption during pregnancy and the detection of substance use with markers of fetal exposure are essential components of maternal and child health.
Ogawa, Takahiro; Haseyama, Miki
2016-10-10
This paper presents adaptive subspace-based inverse projections via division into multiple sub-problems (ASIP-DIMS) for missing image data restoration. In the proposed method, a target problem for estimating missing image data is divided into multiple sub-problems, and each sub-problem is iteratively solved with constraints of other known image data. By projection into a subspace model of image patches, the solution of each subproblem is calculated, where we call this procedure "subspacebased inverse projection" for simplicity. The proposed method can use higher-dimensional subspaces for finding unique solutions in each sub-problem, and successful restoration becomes feasible since a high level of image representation performance can be preserved. This is the biggest contribution of this paper. Furthermore, the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch. In this way, the proposed method realizes missing image data restoration using ASIP-DIMS. Since our method can estimate any kind of missing image data, its potential in two image restoration tasks, image inpainting and super-resolution, based on several methods for multivariate analysis is also shown in this paper.
Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis
Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan
2017-09-01
It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus
Directory of Open Access Journals (Sweden)
Y. Xu
2016-06-01
Full Text Available This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM. The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.
Investigation into on-road vehicle parameter identification based on subspace methods
Dong, Guangming; Chen, Jin; Zhang, Nong
2014-12-01
The randomness of road-tyre excitations can excite the low frequency ride vibrations of bounce, pitch and roll modes of an on-road vehicle. In this paper, modal parameters and mass moments of inertia of an on-road vehicle are estimated with an acceptable accuracy only by measuring accelerations of vehicle sprung mass and unsprung masses, which is based on subspace identification methods. The vehicle bounce, pitch and roll modes are characterized by their large damping (damping ratio 0.2-0.3). Two kinds of subspace identification methods, one that uses input/output data and the other that uses output data only, are compared for the highly damped modes. It is shown that, when the same data length is given, larger error of modal identification results can be clearly observed for the method using output data only; while additional use of input data will significantly reduce estimation variance. Instead of using tyre forces as inputs, which are difficult to be measured or estimated, vertical accelerations of unsprung masses are used as inputs. Theoretical analysis and Monte Carlo experiments show that, when the vehicle speed is not very high, subspace identification method using accelerations of unsprung masses as inputs can give more accurate results compared with the method using road-tyre forces as inputs. After the modal parameters are identified, and if vehicle mass and its center of gravity are pre-determined, roll and pitch moments of inertia of an on-road vehicle can be directly computed using the identified frequencies only, without requiring accurate estimation of mode shape vectors and multi-variable optimization algorithms.
Xu, Y.; Tuttas, S.; Heogner, L.; Stilla, U.
2016-06-01
This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.
THE STRESS SUBSPACE OF HYBRID STRESS ELEMENT AND THE DIAGONALIZATION METHOD FOR FLEXIBILITY MATRIX H
Institute of Scientific and Technical Information of China (English)
张灿辉; 冯伟; 黄黔
2002-01-01
The following is proved: 1 ) The linear independence of assumed stress modes is the necessary and sufficient condition for the nonsingular fiexibility matrix; 2) The equivalent assumed stress modes lead to the identical hybrid element. The Hilbert stress subspace of the assumed stress modes is established. So, it is easy to derive the equivalent orthogonal normal stress modes by Schmidt 's method. Because of the resulting diagonal fiexibility matrix, the identical hybrid element is free from the complex matrix inversion so that the hybrid efficiency is improved greatly. The numerical examples show that the method is effective.
Two stage DOA and Fundamental Frequency Estimation based on Subspace Techniques
DEFF Research Database (Denmark)
Zhou, Zhenhua; Christensen, Mads Græsbøll; So, Hing-Cheung
2012-01-01
optimally weighted harmonic multiple signal classification (MCOW-HMUSIC) estimator is devised for the estimation of fundamental frequencies. Secondly, the spatio- temporal multiple signal classification (ST-MUSIC) estimator is proposed for the estimation of DOA with the estimated frequencies. Statistical......In this paper, the problem of fundamental frequency and direction-of-arrival (DOA) estimation for multi-channel harmonic sinusoidal signal is addressed. The estimation procedure consists of two stages. Firstly, by making use of the subspace technique and Markov-based eigenanalysis, a multi- channel...... evaluation with synthetic signals shows the high accuracy of the proposed methods compared with their non-weighting versions....
A Subspace Identification Method for Detecting Abnormal Behavior in HVAC Systems
Directory of Open Access Journals (Sweden)
Dimitris Sklavounos
2015-01-01
Full Text Available A method for the detection of abnormal behavior in HVAC systems is presented. The method combines deterministic subspace identification for each zone independently to create a system model that produces the anticipated zone’s temperature and the sequential test CUSUM algorithm to detect drifts of the rate of change of the difference between the real and the anticipated measurements. Simulation results regarding the detection of infiltration heat losses and the detection of exogenous heat gains such as fire demonstrate the effectiveness of the proposed method.
Dai, Kaoshan; Wang, Ying; Lu, Wensheng; Ren, Xiaosong; Huang, Zhenhua
2017-04-01
Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.
A subspace-based parameter estimation algorithm for Nakagami-m fading channels
Dianat, Sohail; Rao, Raghuveer
2010-04-01
Estimation of channel fading parameters is an important task in the design of communication links such as maximum ratio combining (MRC). The MRC weights are directly related to the fading channel coefficients. In this paper, we propose a subspace based parameter estimation algorithm for the estimation of the parameters of Nakagami-m fading channels in the presence of additive white Gaussian noise. Comparisons of our proposed approach are made with other techniques available in the literature. The performance of the algorithm with respect to the Cramer-Rao bound (CRB) is investigated. Computer simulation results for different signal to noise ratios (SNR) are presented.
CARRIER FREQUENCY OFFSET ESTIMATION FOR INTERLEAVED OFDMA UPLINK BASED ON SUBSPACE PROCESSING
Institute of Scientific and Technical Information of China (English)
Fan Da; Cao Zhigang
2007-01-01
This paper investigates Carrier Frequency Offset (CFO), estimation in the uplink of the Orthogonal Frequency-Division Multiple Access (OFDMA) systems with the interleaved subcarrier assignment. CFOs between the transmitters and the uplink receiver will destroy orthogonality among different subcarriers, hence resulting in inter-carrier interference and multiuser interference. A two-stage frequency offset estimation algorithm based on subspace processing is proposed. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. Compared with the previously known methods, it not only has a relatively low implementation complexity but is also suitable for random subchannel assignment.
MAXIMAL SUBSPACES FOR SOLUTIONS OF THE SECOND ORDER ABSTRACT CAUCHY PROBLEM
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
For a continuous, increasing function ω: R+ → R+\\{0} of finite exponential type, this paper introduces the set Z(A, ω) of all x in a Banach space X for which the second order abstract differential equation (2) has a mild solution such that [ω(t)]-1u(t,x) is uniformly continues on R+, and show that Z(A, ω) is a maximal Banach subspace continuously embedded in X, where A ∈ B(X) is closed. Moreover, A|z(A,ω) generates an O(ω(t))strongly continuous cosine operator function family.
The wide-band coherent signal-subspace processing based on propagator method
Institute of Scientific and Technical Information of China (English)
ZHI Wanjun; LI Zhishun
2000-01-01
The narrow band propagator method is introduced to the wide-band coherent signal-subspace processing in the direction finding problem. A new technique that needs no direction pre-estimation or matrix decomposition is presented to compute the focusing matrices, so the focusing matrices are robust and the computation. is simple. Then, the propagator method is extended to the focused covariance matrix to find the directions of the sources. The whole estimation process avoids the rather expensive matrix decomposition, and the results of simulations proved the effectiveness of the new method.
A Comfort-Aware Energy Efficient HVAC System Based on the Subspace Identification Method
Directory of Open Access Journals (Sweden)
O. Tsakiridis
2016-01-01
Full Text Available A proactive heating method is presented aiming at reducing the energy consumption in a HVAC system while maintaining the thermal comfort of the occupants. The proposed technique fuses time predictions for the zones’ temperatures, based on a deterministic subspace identification method, and zones’ occupancy predictions, based on a mobility model, in a decision scheme that is capable of regulating the balance between the total energy consumed and the total discomfort cost. Simulation results for various occupation-mobility models demonstrate the efficiency of the proposed technique.
Institute of Scientific and Technical Information of China (English)
WANG Yi-Min; ZHOU Yan-Li; LIANG Lin-Mei; LI Cheng-Zu
2009-01-01
We propose a feasible scheme to achieve universal quantum gate operations in decoherence-free subspace with superconducting charge qubits placed in a microwave cavity.Single-logic-qubit gates can be realized with cavity assisted interaction, which possesses the advantages of unconventional geometric gate operation.The two-logic-qubit controlled-phase gate between subsystems can be constructed with the help of a variable electrostatic transformer, The collective decoherence can be successfully avoided in our well-designed system.Moreover, GHZ state for logical qubits can also be easily produced in this system.
Prewhitening for Rank-Deficient Noise in Subspace Methods for Noise Reduction
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2005-01-01
A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, e.g., when the noise...... also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....
Prewhitening for Narrow-Band Noise in Subspace Methods for Noise Reduction
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2004-01-01
A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, typically when...... that works also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....
SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS
Institute of Scientific and Technical Information of China (English)
Liu Yulin; Peng Qicong
2002-01-01
A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.
SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME—VARYING CHANNELS
Institute of Scientific and Technical Information of China (English)
LiuYulin; PengQicong
2002-01-01
A new blind method is proposed for identification of CDMA Time-Varyin(TV) channels in this paper.By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant(TI) coordingates is proposed.Unlike existing basis expansion methods, this new algorithm does not require estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols.The algorithm offers definite explanation of the expansion coordinates.Simulation demonstrates the effectiveness of the algorithm.
DEFF Research Database (Denmark)
Tatu, Aditya Jayant
defined subspace, the N-links bicycle chain space, i.e. the space of curves with equidistant neighboring landmark points. This in itself is a useful shape space for medical image analysis applications. The Histogram of Gradient orientation based features are many in number and are widely used......This thesis deals with two unrelated issues, restricting curve evolution to subspaces and computing image patches in the equivalence class of Histogram of Gradient orientation based features using nonlinear projection methods. Curve evolution is a well known method used in various applications like...... specific requirements like shape priors or a given data model, and due to limitations of the computer, the computed curve evolution forms a path in some finite dimensional subspace of the space of curves. We give methods to restrict the curve evolution to a finite dimensional linear or implicitly defined...
Estimating the directions of arrival based on multi-subarray subspace fitting
Institute of Scientific and Technical Information of China (English)
ZHU Weiqing; LIU Xiaodong; ZHANG Dongsheng; LIAO Zheng; ZHANG Fangsheng
2006-01-01
A multi-subarray subspace fitting method which take mutual coupling among array elements into account to estimate the directions of arrival was presented. The mutual coupling matrix of uniform linear arrays is modeled with a banded symmetric Toeplitz matrix. According to the DOF (Degree of Freedom) of mutual coupling matrix, part of the array elements at the two sides are neglected. The theoretical characteristics of ESPRIT algorithm, one of the subspace fitting algorithms, are studied. For large N and uniform linear array, the MCLS-ESPRIT estimation error is asymptotically jointly Gaussian distributed with zero means, and its covariance expression is obtained. It is known from the simulation that there exists a subarray with lowest estimation error for certain number of the array elements and the signal sources, and its performance of estimating the directions of arrival is close to the ideal situation when mutual coupling does not exist while its variance has some increase. The method has been applied to bathymetric sidescan sonar with high resolution, and good results have been obtained. At the cost of increasing the number of the array elements, the method can reduce the affection of the mutual coupling among array elements.
Stochastic subspace identification for operational modal analysis of an arch bridge
Loh, Chin-Hsiung; Chen, Ming-Che; Chao, Shu-Hsien
2012-04-01
In this paer the application of output-only system identification technique, known as Stochastic Subspace Identification (SSI) algorithms, for civil infrastructures is carried out. The ability of covariance driven stochastic subspace identification (SSI-COV) was proved through the analysis of the ambient data of an arch bridge under operational condition. A newly developed signal processing technique, Singular Spectrum analysis (SSA), capable to smooth noisy signals, is adopted for pre-processing the recorded data before the SSI. The conjunction of SSA and SSICOV provides a useful criterion for the system order determination. With the aim of estimating accurate modal parameters of the structure in off-line analysis, a stabilization diagram is constructed by plotting the identified poles of the system with increasing the size of data Hankel matrix. Identification task of a real structure, Guandu Bridge, is carried out to identify the system natural frequencies and mode shapes. The uncertainty of the identified model parameters from output-only measurement of the bridge under operation condition, such as temperature and traffic loading conditions, is discussed.
Institute of Scientific and Technical Information of China (English)
Mathu Soothana S.Kumar Retna Swami; Muneeswaran Karuppiah
2013-01-01
An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis (PCA) and multiple discriminant analysis (MDA) is presented in this paper.In this framework,features are extracted from the optimal random image components using greedy approach.These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems.The design of Gabor filters,PCA and MDA are crucial processes used for facial feature extraction.The FERET,ORL and YALE face databases are used to generate the results.Experiments show that optimal random image component selection (ORICS) plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA.Our method achieves 96.25％,99.44％ and 100％ recognition accuracy on the FERET,ORL and YALE databases for 30％ training respectively.This is a considerably improved performance compared with other standard methodologies described in the literature.
Energy Technology Data Exchange (ETDEWEB)
Abdel-Khalik, Hany S. [North Carolina State Univ., Raleigh, NC (United States); Zhang, Qiong [North Carolina State Univ., Raleigh, NC (United States)
2014-05-20
The development of hybrid Monte-Carlo-Deterministic (MC-DT) approaches, taking place over the past few decades, have primarily focused on shielding and detection applications where the analysis requires a small number of responses, i.e. at the detector locations(s). This work further develops a recently introduced global variance reduction approach, denoted by the SUBSPACE approach is designed to allow the use of MC simulation, currently limited to benchmarking calculations, for routine engineering calculations. By way of demonstration, the SUBSPACE approach is applied to assembly level calculations used to generate the few-group homogenized cross-sections. These models are typically expensive and need to be executed in the order of 10^{3} - 10^{5} times to properly characterize the few-group cross-sections for downstream core-wide calculations. Applicability to k-eigenvalue core-wide models is also demonstrated in this work. Given the favorable results obtained in this work, we believe the applicability of the MC method for reactor analysis calculations could be realized in the near future.
Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan
2016-12-07
In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.
On the Kalman Filter error covariance collapse into the unstable subspace
Directory of Open Access Journals (Sweden)
A. Trevisan
2011-03-01
Full Text Available When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N^{+} + N^{0} where N^{+} and N^{0} are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.
Shirkhodaie, Amir; Poshtyar, Azin; Chan, Alex; Hu, Shuowen
2016-05-01
In many military and homeland security persistent surveillance applications, accurate detection of different skin colors in varying observability and illumination conditions is a valuable capability for video analytics. One of those applications is In-Vehicle Group Activity (IVGA) recognition, in which significant changes in observability and illumination may occur during the course of a specific human group activity of interest. Most of the existing skin color detection algorithms, however, are unable to perform satisfactorily in confined operational spaces with partial observability and occultation, as well as under diverse and changing levels of illumination intensity, reflection, and diffraction. In this paper, we investigate the salient features of ten popular color spaces for skin subspace color modeling. More specifically, we examine the advantages and disadvantages of each of these color spaces, as well as the stability and suitability of their features in differentiating skin colors under various illumination conditions. The salient features of different color subspaces are methodically discussed and graphically presented. Furthermore, we present robust and adaptive algorithms for skin color detection based on this analysis. Through examples, we demonstrate the efficiency and effectiveness of these new color skin detection algorithms and discuss their applicability for skin detection in IVGA recognition applications.
Park, Suhyung; Kim, Eung Yeop; Sohn, Chul-Ho; Park, Jaeseok
2017-02-01
Dynamic contrast-enhanced magnetic resonance angiography (DCE MRA) has been widely used as a clinical routine for diagnostic assessment of vascular morphology and hemodynamics. It requires high spatial and temporal resolution to capture rapid variation of DCE signals within a limited imaging time. Subtraction-based approaches are typically employed to selectively delineate arteries while eliminating unwanted background signals. Nevertheless, in the presence of subject motion with time, conventional subtraction approaches suffer from incomplete background suppression that impairs the detectability of arteries. In this work, we propose a novel, DCE MRA method that exploits subspace projection (SP) based angiogram separation for robust background suppression. A new, SP-based DCE signal model is introduced, in which images are decomposed into stationary background tissues, motion-induced artifacts, and DCE angiograms of interest. Constrained image reconstruction with sparsity priors is performed to project motion-induced artifacts onto the predefined subspace while extracting DCE angiograms of interest. Simulations and experimental studies validate that the proposed method outperforms existing techniques with increasing reduction factors in suppressing artifacts and noise.
Variance analysis for model updating with a finite element based subspace fitting approach
Gautier, Guillaume; Mevel, Laurent; Mencik, Jean-Mathieu; Serra, Roger; Döhler, Michael
2017-07-01
Recently, a subspace fitting approach has been proposed for vibration-based finite element model updating. The approach makes use of subspace-based system identification, where the extended observability matrix is estimated from vibration measurements. Finite element model updating is performed by correlating the model-based observability matrix with the estimated one, by using a single set of experimental data. Hence, the updated finite element model only reflects this single test case. However, estimates from vibration measurements are inherently exposed to uncertainty due to unknown excitation, measurement noise and finite data length. In this paper, a covariance estimation procedure for the updated model parameters is proposed, which propagates the data-related covariance to the updated model parameters by considering a first-order sensitivity analysis. In particular, this propagation is performed through each iteration step of the updating minimization problem, by taking into account the covariance between the updated parameters and the data-related quantities. Simulated vibration signals are used to demonstrate the accuracy and practicability of the derived expressions. Furthermore, an application is shown on experimental data of a beam.
Hachem, Walid; Mestre, Xavier; Najim, Jamal; Vallet, Pascal
2011-01-01
In array processing, a common problem is to estimate the angles of arrival of $K$ deterministic sources impinging on an array of $M$ antennas, from $N$ observations of the source signal, corrupted by gaussian noise. The problem reduces to estimate a quadratic form (called "localization function") of a certain projection matrix related to the source signal empirical covariance matrix. Recently, a new subspace estimation method (called "G-MUSIC") has been proposed, in the context where the number of available samples $N$ is of the same order of magnitude than the number of sensors $M$. In this context, the traditional subspace methods tend to fail because the empirical covariance matrix of the observations is a poor estimate of the source signal covariance matrix. The G-MUSIC method is based on a new consistent estimator of the localization function in the regime where $M$ and $N$ tend to $+\\infty$ at the same rate. However, the consistency of the angles estimator was not adressed. The purpose of this paper is ...
Energy Technology Data Exchange (ETDEWEB)
Renaut, R.; He, Q. [Arizona State Univ., Tempe, AZ (United States)
1994-12-31
In a new parallel iterative algorithm for unconstrained optimization by multisplitting is proposed. In this algorithm the original problem is split into a set of small optimization subproblems which are solved using well known sequential algorithms. These algorithms are iterative in nature, e.g. DFP variable metric method. Here the authors use sequential algorithms based on an inexact subspace search, which is an extension to the usual idea of an inexact fine search. Essentially the idea of the inexact line search for nonlinear minimization is that at each iteration the authors only find an approximate minimum in the line search direction. Hence by inexact subspace search, they mean that, instead of finding the minimum of the subproblem at each interation, they do an incomplete down hill search to give an approximate minimum. Some convergence and numerical results for this algorithm will be presented. Further, the original theory will be generalized to the situation with a singular Hessian. Applications for nonlinear least squares problems will be presented. Experimental results will be presented for implementations on an Intel iPSC/860 Hypercube with 64 nodes as well as on the Intel Paragon.
Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images
Zhai, Han; Zhang, Hongyan; Zhang, Liangpei; Li, Pingxiang
2016-10-01
Considering the inevitable obstacles faced by the pixel-based clustering methods, such as salt-and-pepper noise, high computational complexity, and the lack of spatial information, a reweighted mass center based object-oriented sparse subspace clustering (RMC-OOSSC) algorithm for hyperspectral images (HSIs) is proposed. First, the mean-shift segmentation method is utilized to oversegment the HSI to obtain meaningful objects. Second, a distance reweighted mass center learning model is presented to extract the representative and discriminative features for each object. Third, assuming that all the objects are sampled from a union of subspaces, it is natural to apply the SSC algorithm to the HSI. Faced with the high correlation among the hyperspectral objects, a weighting scheme is adopted to ensure that the highly correlated objects are preferred in the procedure of sparse representation, to reduce the representation errors. Two widely used hyperspectral datasets were utilized to test the performance of the proposed RMC-OOSSC algorithm, obtaining high clustering accuracies (overall accuracy) of 71.98% and 89.57%, respectively. The experimental results show that the proposed method clearly improves the clustering performance with respect to the other state-of-the-art clustering methods, and it significantly reduces the computational time.
High Resolution DOA Estimation Using Unwrapped Phase Information of MUSIC-Based Noise Subspace
Ichige, Koichi; Saito, Kazuhiko; Arai, Hiroyuki
This paper presents a high resolution Direction-Of-Arrival (DOA) estimation method using unwrapped phase information of MUSIC-based noise subspace. Superresolution DOA estimation methods such as MUSIC, Root-MUSIC and ESPRIT methods are paid great attention because of their brilliant properties in estimating DOAs of incident signals. Those methods achieve high accuracy in estimating DOAs in a good propagation environment, but would fail to estimate DOAs in severe environments like low Signal-to-Noise Ratio (SNR), small number of snapshots, or when incident waves are coming from close angles. In MUSIC method, its spectrum is calculated based on the absolute value of the inner product between array response and noise eigenvectors, means that MUSIC employs only the amplitude characteristics and does not use any phase characteristics. Recalling that phase characteristics plays an important role in signal and image processing, we expect that DOA estimation accuracy could be further improved using phase information in addition to MUSIC spectrum. This paper develops a procedure to obtain an accurate spectrum for DOA estimation using unwrapped and differentiated phase information of MUSIC-based noise subspace. Performance of the proposed method is evaluated through computer simulation in comparison with some conventional estimation methods.
Directory of Open Access Journals (Sweden)
Søren Holdt Jensen
2007-01-01
Full Text Available We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both diagonal (eigenvalue and singular value decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV, and ULLIV. In addition, we show how the subspace-based algorithms can be analyzed and compared by means of simple FIR filter interpretations. The algorithms are illustrated with working Matlab code and applications in speech processing.
La Cour, Brian R.; Ostrove, Corey I.
2017-01-01
This paper describes a novel approach to solving unstructured search problems using a classical, signal-based emulation of a quantum computer. The classical nature of the representation allows one to perform subspace projections in addition to the usual unitary gate operations. Although bandwidth requirements will limit the scale of problems that can be solved by this method, it can nevertheless provide a significant computational advantage for problems of limited size. In particular, we find that, for the same number of noisy oracle calls, the proposed subspace projection method provides a higher probability of success for finding a solution than does an single application of Grover's algorithm on the same device.
Subspace Dimensionality: A Tool for Automated QC in Seismic Array Processing
Rowe, C. A.; Stead, R. J.; Begnaud, M. L.
2013-12-01
Because of the great resolving power of seismic arrays, the application of automated processing to array data is critically important in treaty verification work. A significant problem in array analysis is the inclusion of bad sensor channels in the beamforming process. We are testing an approach to automated, on-the-fly quality control (QC) to aid in the identification of poorly performing sensor channels prior to beam-forming in routine event detection or location processing. The idea stems from methods used for large computer servers, when monitoring traffic at enormous numbers of nodes is impractical on a node-by node basis, so the dimensionality of the node traffic is instead monitoried for anomalies that could represent malware, cyber-attacks or other problems. The technique relies upon the use of subspace dimensionality or principal components of the overall system traffic. The subspace technique is not new to seismology, but its most common application has been limited to comparing waveforms to an a priori collection of templates for detecting highly similar events in a swarm or seismic cluster. In the established template application, a detector functions in a manner analogous to waveform cross-correlation, applying a statistical test to assess the similarity of the incoming data stream to known templates for events of interest. In our approach, we seek not to detect matching signals, but instead, we examine the signal subspace dimensionality in much the same way that the method addresses node traffic anomalies in large computer systems. Signal anomalies recorded on seismic arrays affect the dimensional structure of the array-wide time-series. We have shown previously that this observation is useful in identifying real seismic events, either by looking at the raw signal or derivatives thereof (entropy, kurtosis), but here we explore the effects of malfunctioning channels on the dimension of the data and its derivatives, and how to leverage this effect for
Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine
2016-01-01
Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the
EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces
Ashari, Rehab Bahaaddin
Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only
Pehlevan, Cengiz; Hu, Tao; Chklovskii, Dmitri B
2015-07-01
Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis, by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function, these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state, which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step toward an algorithmic theory of neural computation.
Ruth, van S.M.; Boscaini, E.; Mayr, D.; Pugh, J.; Posthumus, M.A.
2003-01-01
Three gas chromatography methods and two direct mass spectrometry techniques were compared for the analysis of the aroma of rehydrated diced red bell peppers. Gas chromatography methods included systems with olfactometry detection (GC-O), flame ionisation detection (GC-FID) and mass spectrometry (GC
Efficient Structural System Reliability Updating with Subspace-Based Damage Detection Information
DEFF Research Database (Denmark)
Döhler, Michael; Thöns, Sebastian
modelling is introduced building upon the non-destructive testing reliability which applies to structural systems and DDS containing a strategy to overcome the high computational efforts for the pre-determination of the DDS reliability. This approach takes basis in the subspace-based damage detection method......Damage detection systems and algorithms (DDS and DDA) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. However, the potential of utilizing DDS information for the structural integrity assessment...... and prognosis is hardly exploited nor treated in scientific literature up to now. In order to utilize the information provided by DDS for the structural performance, usually high computational efforts for the pre-determination of DDS reliability are required. In this paper, an approach for the DDS performance...
Siddiqui, Bilal A.
2016-07-26
In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.
Al-Jabery, Khalid; Obafemi-Ajayi, Tayo; Olbricht, Gayla R; Takahashi, T Nicole; Kanne, Stephen; Wunsch, Donald
2016-08-01
Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.
Cho, Soojin; Park, Jong-Woong; Sim, Sung-Han
2015-04-08
Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.
Initial Results in Power System Identification from Injected Probing Signals Using a Subspace Method
Energy Technology Data Exchange (ETDEWEB)
Zhou, Ning; Pierre, John W.; Hauer, John F.
2006-08-01
In this paper, the authors use the Numerical algorithm for Subspace State Space System IDentification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals including Low-Level Pseudo-Random Noise (LLPRN) and Single-Mode Square Wave (SMSW) injected at the Celilo terminal of the Pacific HVDC In-tertie (PDCI). An identified model is validated using a cross vali-dation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar op-erating conditions. The consistent results show that methods in this class can be highly effective even when the probing signal is small.
Gao, Heng-zhen; Wan, Jian-wei; Zhu, Zhen-zhen; Wang, Li-bao; Nian, Yong-jian
2011-05-01
The present paper proposes a novel hyperspectral image classification algorithm based on LS-SVM (least squares support vector machine). The LS-SVM uses the features extracted from subspace of bands (SOB). The maximum noise fraction (MNF) method is adopted as the feature extraction method. The spectral correlations of the hyperspectral image are used in order to divide the feature space into several SOBs. Then the MNF is used to extract characteristic features of the SOBs. The extracted features are combined into the feature vector for classification. So the strong bands correlation is avoided and the spectral redundancies are reduced. The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved. The proposed method optimizes spectral information by feature extraction and reduces the spectral noise. The classifier performance is improved. Experimental results show the superiorities of the proposed algorithm.
Quantum computation with prethreshold superconducting qubits: Single-excitation subspace approach
Galiautdinov, Andrei
2011-01-01
We describe an alternative approach to quantum computation that is ideally suited for today's sub-threshold-fidelity qubits, and which can be applied to a family of hardware models that includes superconducting qubits with tunable coupling. In this approach, the computation on an n-qubit processor is carried out in the n-dimensional single-excitation subspace (SES) of the full 2^n-dimensional Hilbert space. Because any real Hamiltonian can be directly generated in the SES [E. J. Pritchett et al., arXiv:1008.0701], high-dimensional unitary operations can be carried out in a single step, bypassing the need to decompose into single- and two-qubit gates. Although technically nonscalable and unsuitable for applications (including Shor's) requiring enormous Hilbert spaces, this approach would make practical a first-generation quantum computer capable of achieving significant quantum speedup.
Joint DOA and multi-pitch estimation based on subspace techniques
Xi Zhang, Johan; Christensen, Mads Græsbøll; Jensen, Søren Holdt; Moonen, Marc
2012-12-01
In this article, we present a novel method for high-resolution joint direction-of-arrivals (DOA) and multi-pitch estimation based on subspaces decomposed from a spatio-temporal data model. The resulting estimator is termed multi-channel harmonic MUSIC (MC-HMUSIC). It is capable of resolving sources under adverse conditions, unlike traditional methods, for example when multiple sources are impinging on the array from approximately the same angle or similar pitches. The effectiveness of the method is demonstrated on a simulated an-echoic array recordings with source signals from real recorded speech and clarinet. Furthermore, statistical evaluation with synthetic signals shows the increased robustness in DOA and fundamental frequency estimation, as compared with to a state-of-the-art reference method.
A new damage diagnosis approach for NC machine tools based on hybrid Stationary subspace analysis
Gao, Chen; Zhou, Yuqing; Ren, Yan
2017-05-01
This paper focused on the damage diagnosis for NC machine tools and put forward a damage diagnosis method based on hybrid Stationary subspace analysis (SSA), for improving the accuracy and visibility of damage identification. First, the observed single sensor signal was reconstructed to multi-dimensional signals by the phase space reconstruction technique, as the inputs of SSA. SSA method was introduced to separate the reconstructed data into stationary components and non-stationary components without the need for independency and prior information of the origin signals. Subsequently, the selected non-stationary components were analysed for training LS-SVM (Least Squares Support Vector Machine) classifier model, in which several statistic parameters in the time and frequency domains were exacted as the sample of LS-SVM. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy of the proposed approach.
Quantum computation in decoherence-free subspace via cavity-decay-assisted adiabatic passage
Directory of Open Access Journals (Sweden)
FENG Xunli
2015-08-01
Full Text Available In this work,a scheme for quantum computation based on cavity QED in a decoherence-free subspaces via using the technique of stimulated Raman adiabatic passage (STIRAP is proposed.To implement universal quantum logic gates that form basic blocks of quantum computation,we suppose two atoms are trapped in a single-mode cavity with large decay rates and are driven by the laser fields.The relatively large cavity decay can be used for the continuous detection of the cavity mode as so-called ″cavity-decay-induced quantum Zeno effect″.The results show that,decoherence induced by the atomic spontaneous emission and cavity decay can be efficiently suppress with the STIRAP technique and the quantum Zeno effect.
Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays
Directory of Open Access Journals (Sweden)
Xin Zhang
2014-01-01
Full Text Available Small aperture microphone arrays provide many advantages for portable devices and hearing aid equipment. In this paper, a subspace based localization method is proposed for acoustic source using small aperture arrays. The effects of array aperture on localization are analyzed by using array response (array manifold. Besides array aperture, the frequency of acoustic source and the variance of signal power are simulated to demonstrate how to optimize localization performance, which is carried out by introducing frequency error with the proposed method. The proposed method for 5 mm array aperture is validated by simulations and experiments with MEMS microphone arrays. Different types of acoustic sources can be localized with the highest precision of 6 degrees even in the presence of wind noise and other noises. Furthermore, the proposed method reduces the computational complexity compared with other methods.
Huang, Jian; Yuen, Pong C; Chen, Wen-Sheng; Lai, Jian Huang
2007-08-01
This paper addresses the problem of automatically tuning multiple kernel parameters for the kernel-based linear discriminant analysis (LDA) method. The kernel approach has been proposed to solve face recognition problems under complex distribution by mapping the input space to a high-dimensional feature space. Some recognition algorithms such as the kernel principal components analysis, kernel Fisher discriminant, generalized discriminant analysis, and kernel direct LDA have been developed in the last five years. The experimental results show that the kernel-based method is a good and feasible approach to tackle the pose and illumination variations. One of the crucial factors in the kernel approach is the selection of kernel parameters, which highly affects the generalization capability and stability of the kernel-based learning methods. In view of this, we propose an eigenvalue-stability-bounded margin maximization (ESBMM) algorithm to automatically tune the multiple parameters of the Gaussian radial basis function kernel for the kernel subspace LDA (KSLDA) method, which is developed based on our previously developed subspace LDA method. The ESBMM algorithm improves the generalization capability of the kernel-based LDA method by maximizing the margin maximization criterion while maintaining the eigenvalue stability of the kernel-based LDA method. An in-depth investigation on the generalization performance on pose and illumination dimensions is performed using the YaleB and CMU PIE databases. The FERET database is also used for benchmark evaluation. Compared with the existing PCA-based and LDA-based methods, our proposed KSLDA method, with the ESBMM kernel parameter estimation algorithm, gives superior performance.
Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing
Directory of Open Access Journals (Sweden)
Majid Shakhsi Dastgahian
2016-11-01
Full Text Available Millimeter-wave communication (mmWC is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS and mobile sets (MS. Unlike the conventional MIMO systems, Millimeter-wave (mmW systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.
Bayesian estimation of Karhunen-Loève expansions; A random subspace approach
Chowdhary, Kenny; Najm, Habib N.
2016-08-01
One of the most widely-used procedures for dimensionality reduction of high dimensional data is Principal Component Analysis (PCA). More broadly, low-dimensional stochastic representation of random fields with finite variance is provided via the well known Karhunen-Loève expansion (KLE). The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L2 sense, i.e., which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition) on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build probabilistic Karhunen-Loève expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.
高效多子空间Skyline查询处理算法%Efficient Algorithm for Multiple Subspace Skyline Queries Processing
Institute of Scientific and Technical Information of China (English)
王潇逸; 秦小麟; 王宁; 史文浩
2016-01-01
As Skyline queries are widely used, subspace Skyline query processing has attracted lots of attention. Aiming at meeting the need that users want to evaluate a dataset from multiple perspectives, this paper makes a full study of multiple subspace Skyline queries. Motivated by the deficiency of existing algorithms, this paper proposes the struc-ture of subspace skycube group, and puts forward an efficient method called MSSC (multiple subspace skycube) based on that structure. The MSSC algorithm can efficiently process any number of subspace Skyline queries simultaneously. Firstly, the MSSC algorithm uses subspace candidate sets to share the results of different subspace Skyline queries in the subspace Skycube group. Then it adopts sum filter and max-value filter to cut and filter data, which further im-proves the performance of the MSSC algorithm. At last, the experiments conducted on both synthetic data sets and a real-life data set demonstrate that the MSSC algorithm can solve the multiple subspace Skyline queries problem efficiently.%随着Skyline查询应用的增多，子空间Skyline查询成为热点。针对实际应用中用户从多角度审视某一数据集的需求，充分研究了多子空间Skyline查询问题。在分析现有子空间Skyline查询算法解决该问题不足的基础上，提出了子空间立方体群（subspace skycube group，SSG）结构，并给出了基于该结构的同时计算任意多个子空间Skyline查询的MSSC（multiple subspace skycube）算法。该算法采用子空间候选集（subspace can-didate sets，SCS），并充分利用了子空间立方体群结构中各子空间Skyline结果间的共享关系；在此基础上，算法采用求和过滤以及最大值过滤等方法，对数据集进行剪枝和过滤，从而进一步提高算法效率。最后，分别用人造数据和真实数据对算法进行实验，并与现有算法进行比较，结果表明MSSC算法可以高效地解决多子空间Skyline查询问题。
Energy Technology Data Exchange (ETDEWEB)
Gardner, David [Lawrence Livermore National Laboratory (LLNL); Woodward, Carol S. [Lawrence Livermore National Laboratory (LLNL); Evans, Katherine J [ORNL
2015-01-01
Efficient solution of global climate models requires effectively handling disparate length and time scales. Implicit solution approaches allow time integration of the physical system with a time step dictated by accuracy of the processes of interest rather than by stability governed by the fastest of the time scales present. Implicit approaches, however, require the solution of nonlinear systems within each time step. Usually, a Newton s method is applied for these systems. Each iteration of the Newton s method, in turn, requires the solution of a linear model of the nonlinear system. This model employs the Jacobian of the problem-defining nonlinear residual, but this Jacobian can be costly to form. If a Krylov linear solver is used for the solution of the linear system, the action of the Jacobian matrix on a given vector is required. In the case of spectral element methods, the Jacobian is not calculated but only implemented through matrix-vector products. The matrix-vector multiply can also be approximated by a finite-difference which may show a loss of accuracy in the overall nonlinear solver. In this paper, we review the advantages and disadvantages of finite-difference approximations of these matrix-vector products for climate dynamics within the spectral-element based shallow-water dynamical-core of the Community Atmosphere Model (CAM).
Pak, Chan-gi; Lung, Shu
2009-01-01
Modern airplane design is a multidisciplinary task which combines several disciplines such as structures, aerodynamics, flight controls, and sometimes heat transfer. Historically, analytical and experimental investigations concerning the interaction of the elastic airframe with aerodynamic and in retia loads have been conducted during the design phase to determine the existence of aeroelastic instabilities, so called flutter .With the advent and increased usage of flight control systems, there is also a likelihood of instabilities caused by the interaction of the flight control system and the aeroelastic response of the airplane, known as aeroservoelastic instabilities. An in -house code MPASES (Ref. 1), modified from PASES (Ref. 2), is a general purpose digital computer program for the analysis of the closed-loop stability problem. This program used subroutines given in the International Mathematical and Statistical Library (IMSL) (Ref. 3) to compute all of the real and/or complex conjugate pairs of eigenvalues of the Hessenberg matrix. For high fidelity configuration, these aeroelastic system matrices are large and compute all eigenvalues will be time consuming. A subspace iteration method (Ref. 4) for complex eigenvalues problems with nonsymmetric matrices has been formulated and incorporated into the modified program for aeroservoelastic stability (MPASES code). Subspace iteration method only solve for the lowest p eigenvalues and corresponding eigenvectors for aeroelastic and aeroservoelastic analysis. In general, the selection of p is ranging from 10 for wing flutter analysis to 50 for an entire aircraft flutter analysis. The application of this newly incorporated code is an experiment known as the Aerostructures Test Wing (ATW) which was designed by the National Aeronautic and Space Administration (NASA) Dryden Flight Research Center, Edwards, California to research aeroelastic instabilities. Specifically, this experiment was used to study an instability
Kasai, Kenta; Sakaniwa, Kohichi
2012-01-01
We study LDPC codes for the channel with $2^m$-ary input $\\underline{x}\\in \\GF(2)^m$ and output $\\underline{y}=\\underline{x}+\\underline{z}\\in \\GF(2)^m$. The receiver knows a subspace $V\\subset \\GF(2)^m$ from which $\\underline{z}=\\underline{y}-\\underline{x}$ is uniformly chosen. Or equivalently, the receiver receives an affine subspace $\\underline{y}-V$ where $\\underline{x}$ lies. We consider a joint iterative decoder involving the channel detector and the LDPC decoder. The decoding system considered in this paper can be viewed as a simplified model of the joint iterative decoder over non-binary modulated signal inputs e.g., $2^m$-QAM. We evaluate the performance of binary spatially-coupled MacKay-Neal code by density evolution. EXIT-like function curve calculations reveal that iterative decoding threshold values are very close to the Shannon limit.
Constantine, Paul; Larsson, Johan; Iaccarino, Gianluca
2014-01-01
We present a computational analysis of the reactive flow in a hypersonic scramjet engine with emphasis on effects of uncertainties in the operating conditions. We employ a novel methodology based on active subspaces to characterize the effects of the input uncertainty on the scramjet performance. The active subspace re-parameterizes the operating conditions from seven well characterized physical parameters to a single derived active variable. This dimension reduction enables otherwise intractable---given the cost of the simulation---computational studies to quantify uncertainty; bootstrapping provides confidence intervals on the studies' results. In particular we (i) identify the parameters that contribute the most to the variation in the output quantity of interest, (ii) compute a global upper and lower bound on the quantity of interest, and (iii) classify sets of operating conditions as safe or unsafe corresponding to a threshold on the output quantity of interest. We repeat this analysis for two values of ...
Energy Technology Data Exchange (ETDEWEB)
Zhang, Peng; Zhou, Ning; Abdollahi, Ali
2013-09-10
A Generalized Subspace-Least Mean Square (GSLMS) method is presented for accurate and robust estimation of oscillation modes from exponentially damped power system signals. The method is based on orthogonality of signal and noise eigenvectors of the signal autocorrelation matrix. Performance of the proposed method is evaluated using Monte Carlo simulation and compared with Prony method. Test results show that the GSLMS is highly resilient to noise and significantly dominates Prony method in tracking power system modes under noisy environments.
Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Zi, Yanyang; Yan, Ruqiang
2017-09-01
The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.
2014-12-01
impact of various signal frequencies, bandwidths, and signal to noise ratios present in the source signals received by a sparse array using the multiple...signals classification ( MUSIC ) subspace direction-finding algorithm are evaluated in this thesis. Additionally, two performance enhancements are...presented: one that reduces the MUSIC computational load and one that provides a method of utilizing collector motion to resolve DOA ambiguities.
Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y
2014-05-01
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.
Xu, Y; Li, N
2014-09-01
Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.
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Kohei Fujita
2017-08-01
Full Text Available A system identification (SI problem of high-rise buildings is investigated under restricted data environments. The shear and bending stiffnesses of a shear-bending model (SB model representing the high-rise buildings are identified via the smart combination of the subspace and inverse-mode methods. Since the shear and bending stiffnesses of the SB model can be identified in the inverse-mode method by using the lowest mode of horizontal displacements and floor rotation angles, the lowest mode of the objective building is identified first by using the subspace method. Identification of the lowest mode is performed by using the amplitude of transfer functions derived in the subspace method. Considering the resolution in measuring the floor rotation angles in lower stories, floor rotation angles in most stories are predicted from the floor rotation angle at the top floor. An empirical equation of floor rotation angles is proposed by investigating those for various building models. From the viewpoint of application of the present SI method to practical situations, a non-simultaneous measurement system is also proposed. In order to investigate the reliability and accuracy of the proposed SI method, a 10-story building frame subjected to micro-tremor is examined.
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Seth H. Weinberg
2012-01-01
Full Text Available Cardiac myocyte calcium signaling is often modeled using deterministic ordinary differential equations (ODEs and mass-action kinetics. However, spatially restricted “domains” associated with calcium influx are small enough (e.g., 10−17 liters that local signaling may involve 1–100 calcium ions. Is it appropriate to model the dynamics of subspace calcium using deterministic ODEs or, alternatively, do we require stochastic descriptions that account for the fundamentally discrete nature of these local calcium signals? To address this question, we constructed a minimal Markov model of a calcium-regulated calcium channel and associated subspace. We compared the expected value of fluctuating subspace calcium concentration (a result that accounts for the small subspace volume with the corresponding deterministic model (an approximation that assumes large system size. When subspace calcium did not regulate calcium influx, the deterministic and stochastic descriptions agreed. However, when calcium binding altered channel activity in the model, the continuous deterministic description often deviated significantly from the discrete stochastic model, unless the subspace volume is unrealistically large and/or the kinetics of the calcium binding are sufficiently fast. This principle was also demonstrated using a physiologically realistic model of calmodulin regulation of L-type calcium channels introduced by Yue and coworkers.
Reynders, Edwin; Maes, Kristof; Lombaert, Geert; De Roeck, Guido
2016-01-01
Identified modal characteristics are often used as a basis for the calibration and validation of dynamic structural models, for structural control, for structural health monitoring, etc. It is therefore important to know their accuracy. In this article, a method for estimating the (co)variance of modal characteristics that are identified with the stochastic subspace identification method is validated for two civil engineering structures. The first structure is a damaged prestressed concrete bridge for which acceleration and dynamic strain data were measured in 36 different setups. The second structure is a mid-rise building for which acceleration data were measured in 10 different setups. There is a good quantitative agreement between the predicted levels of uncertainty and the observed variability of the eigenfrequencies and damping ratios between the different setups. The method can therefore be used with confidence for quantifying the uncertainty of the identified modal characteristics, also when some or all of them are estimated from a single batch of vibration data. Furthermore, the method is seen to yield valuable insight in the variability of the estimation accuracy from mode to mode and from setup to setup: the more informative a setup is regarding an estimated modal characteristic, the smaller is the estimated variance.
De Filippis, G.; Noël, J. P.; Kerschen, G.; Soria, L.; Stephan, C.
2017-09-01
The introduction of the frequency-domain nonlinear subspace identification (FNSI) method in 2013 constitutes one in a series of recent attempts toward developing a realistic, first-generation framework applicable to complex structures. If this method showed promising capabilities when applied to academic structures, it is still confronted with a number of limitations which needs to be addressed. In particular, the removal of nonphysical poles in the identified nonlinear models is a distinct challenge. In the present paper, it is proposed as a first contribution to operate directly on the identified state-space matrices to carry out spurious pole removal. A modal-space decomposition of the state and output matrices is examined to discriminate genuine from numerical poles, prior to estimating the extended input and feedthrough matrices. The final state-space model thus contains physical information only and naturally leads to nonlinear coefficients free of spurious variations. Besides spurious variations due to nonphysical poles, vibration modes lying outside the frequency band of interest may also produce drifts of the nonlinear coefficients. The second contribution of the paper is to include residual terms, accounting for the existence of these modes. The proposed improved FNSI methodology is validated numerically and experimentally using a full-scale structure, the Morane-Saulnier Paris aircraft.
Closed subspaces and some basic topological properties of noncommutative Orlicz spaces
Indian Academy of Sciences (India)
LINING JIANG; ZHENHUA MA
2017-06-01
In this paper, we study the noncommutative Orlicz space $L_{\\varphi}( \\tilde{\\cal M}, \\tau)$,which generalizes the concept of noncommutative $L^p$ space, where $\\cal M$ is a von Neumann algebra, and $\\varphi$ is an Orlicz function. As a modular space, the space $L_{\\varphi}( \\tilde{\\cal M}, \\tau)$ possesses the Fatou property, and consequently, it is a Banach space. In addition, a new description of the subspace $E_{\\varphi}( \\tilde{\\cal M}, \\tau)$ =$\\overline{\\cal {M}\\bigcap L_{\\varphi}( \\tilde{\\cal M}, \\tau)}$ in $L_{\\varphi}( \\tilde{\\cal M}, \\tau)$, which is closed under the norm topology and dense under the measure topology, is given. Moreover, if the Orlicz function $\\varphi$ satisfies the $\\Delta_2$-condition, then $L_{\\varphi}( \\tilde{\\cal M}, \\tau)$ is uniformly monotone, and convergence in the norm topology and measure topology coincide onthe unit sphere. Hence, $E_{\\varphi}( \\tilde{\\cal M}, \\tau)$ = $L_{\\varphi}( \\tilde{\\cal M}, \\tau)$ if $\\varphi$ satisfies the $\\Delta_2$-condition.
Subspace Alignment Chains and the Degrees of Freedom of the Three-User MIMO Interference Channel
Wang, Chenwei; Jafar, Syed A
2011-01-01
We show that the 3 user M_T x M_R MIMO interference channel has d(M,N)=min(M/(2-1/k),N/(2+1/k)) degrees of freedom (DoF) normalized by time, frequency, and space dimensions, where M=min(M_T,M_R), N=max(M_T,M_R), k=ceil{M/(N-M)}. While the DoF outer bound is established for every M_T, M_R value, the achievability is established in general subject to normalization with respect to spatial-extensions. Given spatial-extensions, the achievability relies only on linear beamforming based interference alignment schemes with no need for time/frequency extensions. In the absence of spatial extensions, we show through examples how essentially the same scheme may be applied over time/frequency extensions. The central new insight to emerge from this work is the notion of subspace alignment chains as DoF bottlenecks. The DoF value d(M,N) is a piecewise linear function of M,N, with either M or N being the bottleneck within each linear segment. The corner points of these piecewise linear segments correspond to A={1/2,2/3,3/4,...
Subspace weighted ℓ 2,1 minimization for sparse signal recovery
Zheng, Chundi; Li, Gang; Liu, Yimin; Wang, Xiqin
2012-12-01
In this article, we propose a weighted ℓ 2,1 minimization algorithm for jointly-sparse signal recovery problem. The proposed algorithm exploits the relationship between the noise subspace and the overcomplete basis matrix for designing weights, i.e., large weights are appointed to the entries, whose indices are more likely to be outside of the row support of the jointly sparse signals, so that their indices are expelled from the row support in the solution, and small weights are appointed to the entries, whose indices correspond to the row support of the jointly sparse signals, so that the solution prefers to reserve their indices. Compared with the regular ℓ 2,1 minimization, the proposed algorithm can not only further enhance the sparseness of the solution but also reduce the requirements on both the number of snapshots and the signal-to-noise ratio (SNR) for stable recovery. Both simulations and experiments on real data demonstrate that the proposed algorithm outperforms the ℓ 1-SVD algorithm, which exploits straightforwardly ℓ 2,1 minimization, for both deterministic basis matrix and random basis matrix.
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Henrik Hansen
2004-05-01
Full Text Available We proposed recently a new technique for multiuser detection in CDMA networks, denoted by interference subspace rejection (ISR, and evaluated its performance on the uplink. This paper extends its application to the downlink (DL. On the DL, the information about the interference is sparse, for example, spreading factor (SF and modulation of interferers may not be known, which makes the task much more challenging. We present three new ISR variants which require no prior knowledge of interfering users. The new solutions are applicable to MIMO systems and can accommodate any modulation, coding, SF, and connection type. We propose a new code allocation scheme denoted by DACCA which significantly reduces the complexity of our solution at the receiving mobile. We present estimates of user capacities and data rates attainable under practically reasonable conditions regarding interferences identified and suppressed in a multicellular interference-limited system. We show that the system capacity increases linearly with the number of antennas despite the existence of interference. Our new DL multiuser receiver consistently provides an Erlang capacity gain of at least 3Ã¢Â€Â‰db over the single-user detector.
Transferring multiqubit entanglement onto memory qubits in a decoherence-free subspace
He, Xiao-Ling; Yang, Chui-Ping
2017-03-01
Different from the previous works on generating entangled states, this work is focused on how to transfer the prepared entangled states onto memory qubits for protecting them against decoherence. We here consider a physical system consisting of n operation qubits and 2 n memory qubits placed in a cavity or coupled to a resonator. A method is presented for transferring n-qubit Greenberger-Horne-Zeilinger (GHZ) entangled states from the operation qubits (i.e., information processing cells) onto the memory qubits (i.e., information memory elements with long decoherence time). The transferred GHZ states are encoded in a decoherence-free subspace against collective dephasing and thus can be immune from decoherence induced by a dephasing environment. In addition, the state transfer procedure has nothing to do with the number of qubits, the operation time does not increase with the number of qubits, and no measurement is needed for the state transfer. This proposal can be applied to a wide range of hybrid qubits such as natural atoms and artificial atoms (e.g., various solid-state qubits).
Shape Detection from Raw LiDAR Data with Subspace Modeling.
Wang, Jun; Xu, Kevin Kai
2016-08-31
LiDAR scanning has become a prevalent technique for digitalizing large-scale outdoor scenes. However, the raw LiDAR data often contain imperfections, e.g., missing large regions, anisotropy of sampling density, and contamination of noise and outliers, which are the major obstacles that hinder its more ambitious and higher level applications in digital city modeling. Observing that 3D urban scenes can be locally described with several low dimensional subspaces, we propose to locally classify the neighborhoods of the scans to model the substructures of the scenes. The key enabler is the adaptive kernel-scale scoring, filtering and clustering of substructures, making it possible to recover the local structures at all points simultaneously, even in the presence of severe data imperfections. Integrating the local analyses leads to robust shape detection from raw LiDAR data. On this basis, we develop several urban scene applications and verify them on a number of LiDAR scans with various complexities and styles, which demonstrates the effectiveness and robustness of our methods.
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Soojin Cho
2015-04-01
Full Text Available Wireless sensor networks (WSNs facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI technique is selected for system identification, and SSI-based decentralized system identification (SDSI is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.
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Wilfried B. Krätzig
2014-01-01
Full Text Available This paper applies recent research on structural damage description to earthquake-resistant design concepts. Based on the primary design aim of life safety, this work adopts the necessity of additional protection aims for property, installation, and equipment. This requires the definition of damage indicators, which are able to quantify the arising structural damage. As in present design, it applies nonlinear quasistatic (pushover concepts due to code provisions as simplified dynamic design tools. Substituting so nonlinear time-history analyses, seismic low-cycle fatigue of RC structures is approximated in similar manner. The treatment will be embedded into a finite element environment, and the tangential stiffness matrix KT in tangential subspaces then is identified as the most general entry for structural damage information. Its spectra of eigenvalues λi or natural frequencies ωi of the structure serve to derive damage indicators Di, applicable to quasistatic evaluation of seismic damage. Because det KT=0 denotes structural failure, such damage indicators range from virgin situation Di=0 to failure Di=1 and thus correspond with Fema proposals on performance-based seismic design. Finally, the developed concept is checked by reanalyses of two experimentally investigated RC frames.
Ma, Junshui; Bayram, Sevinç; Tao, Peining; Svetnik, Vladimir
2011-03-15
After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion.
Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation.
Xu, Yong; Fang, Xiaozhao; Wu, Jian; Li, Xuelong; Zhang, David
2016-02-01
In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.
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Do-Sik Yoo
2015-01-01
Full Text Available We propose a low complexity subspace-based direction-of-arrival (DOA estimation algorithm employing a direct signal space construction method (DSPCM by subsampling the autocorrelation matrix of a uniform linear array (ULA. Three major contributions of this paper are as follows. First of all, we introduce the method of autocorrelation matrix subsampling which enables us to employ a low complexity algorithm based on a ULA without computationally complex eigenvalue decomposition or singular-value decomposition. Secondly, we introduce a signal vector separation method to improve the distinguishability among signal vectors, which can greatly improve the performance, particularly, in low signal-to-noise ratio (SNR regime. Thirdly, we provide a root finding (RF method in addition to a spectral search (SS method as the angle finding scheme. Through simulations, we illustrate that the performance of the proposed scheme is reasonably close to computationally much more expensive MUSIC- (MUltiple SIgnal Classification- based algorithms. Finally, we illustrate that the computational complexity of the proposed scheme is reduced, in comparison with those of MUSIC-based schemes, by a factor of O(N2/K, where K is the number of sources and N is the number of antenna elements.
A Sub-Space Method to Detect Multiple Wireless Microphone Signals in TV Band White Space
Dhillon, Harpreet S; Datla, Dinesh; Benonis, Michael; Buehrer, R Michael; Reed, Jeffrey H
2011-01-01
The main hurdle in the realization of dynamic spectrum access (DSA) systems from physical layer perspective is the reliable sensing of low power licensed users. One such scenario shows up in the unlicensed use of TV bands where the TV Band Devices (TVBDs) are required to sense extremely low power wireless microphones (WMs). The lack of technical standard among various wireless manufacturers and the resemblance of certain WM signals to narrow-band interference signals, such as spurious emissions, further aggravate the problem. Due to these uncertainties, it is extremely difficult to abstract the features of WM signals and hence develop robust sensing algorithms. To partly counter these challenges, we develop a two-stage sub-space algorithm that detects multiple narrow-band analog frequency-modulated signals generated by WMs. The performance of the algorithm is verified by using experimentally captured low power WM signals with received power ranging from -100 to -105 dBm. The problem of differentiating between...
A Note on the Topology of a Generic Subspace of Riem
Gomes, Henrique de A
2009-01-01
For Riem(M) the space of Riemannian metrics over a compact 3-manifold without boundary $M$, we study topological properties of the dense open subspace Riem'(M) of metrics which possess no Killing vectors. Given the stratification of Riem(M), we work under the condition that, in a sense defined in the text, the connected components of each stratum do not accumulate. Given this condition we find that one of the most fundamental results regarding the topology of Riem(M), namely that it has trivial homotopy groups, would still be true for Riem'(M). This would make the topology of Riem'(M) completely understood. Coupled with the fact that for Riem'(M), we have a proper principal fibration with the group of diffeomorphisms, which makes Riem'(M)/Diff(M) a proper manifold (as opposed to Riem(M)/Diff(M)), we would have that the homotopy groups of the quotient are given by the homotopy groups of Diff(M), which reflects the topology of $M$. These results would render the space of metrics with no symmetries subject to th...
Preservation of local linearity by neighborhood subspace scaling for solving the pre-image problem
Institute of Scientific and Technical Information of China (English)
Sheng-kai YANG; Jian-yi MENG; Hai-bin SHEN
2014-01-01
An important issue involved in kernel methods is the pre-image problem. However, it is an ill-posed problem, as the solution is usually nonexistent or not unique. In contrast to direct methods aimed at minimizing the distance in feature space, indirect methods aimed at constructing approximate equivalent models have shown outstanding performance. In this paper, an indirect method for solving the pre-image problem is proposed. In the proposed algorithm, an inverse mapping process is constructed based on a novel framework that preserves local linearity. In this framework, a local nonlinear transformation is implicitly conducted by neighborhood subspace scaling transformation to preserve the local linearity between feature space and input space. By extending the inverse mapping process to test samples, we can obtain pre-images in input space. The proposed method is non-iterative, and can be used for any kernel functions. Experimental results based on image denoising using kernel principal component analysis (PCA) show that the proposed method outperforms the state-of-the-art methods for solving the pre-image problem.
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Remus Oşan
Full Text Available Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA, Principal Components Analysis (PCA and Artificial Neural Networks (ANN, and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD. Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD.
A subspace-based coil combination method for phased-array magnetic resonance imaging.
Gol Gungor, Derya; Potter, Lee C
2016-02-01
Coil-by-coil reconstruction methods are followed by coil combination to obtain a single image representing a spin density map. Typical coil combination methods, such as square-root sum-of-squares and adaptive coil combining, yield images that exhibit spatially varying modulation of image intensity. Existing practice is to first combine coils according to a signal-to-noise criterion, then postprocess to correct intensity inhomogeneity. If inhomogeneity is severe, however, intensity correction methods can yield poor results. The purpose of this article is to present an alternative optimality criterion for coil combination; the resulting procedure yields reduced intensity inhomogeneity while preserving contrast. A minimum mean squared error criterion is adopted for combining coils via a subspace decomposition. Techniques are compared using both simulated and in vivo data. Experimental results for simulated and in vivo data demonstrate lower bias, higher signal-to-noise ratio (about 7×) and contrast-to-noise ratio (about 2×), compared to existing coil combination techniques. The proposed coil combination method is noniterative and does not require estimation of coil sensitivity maps or image mask; the method is particularly suited to cases where intensity inhomogeneity is too severe for existing approaches. © 2015 Wiley Periodicals, Inc.
Data processing in subspace identification and modal parameter identification of an arch bridge
Fan, Jiangling; Zhang, Zhiyi; Hua, Hongxing
2007-05-01
A data-processing method concerning subspace identification is presented to improve the identification of modal parameters from measured response data only. The identification procedure of this method consists of two phases, first estimating frequencies and damping ratios and then extracting mode shapes. Elements of Hankel matrices are specially rearranged to enhance the identifiability of weak characteristics and the robustness to noise contamination. Furthermore, an alternative stabilisation diagram in combination with component energy index is adopted to effectively separate spurious and physical modes. On the basis of identified frequencies, mode shapes are extracted from the signals obtained by filtering measured data with a series of band-pass filters. The proposed method was tested with a concrete-filled steel tubular arch bridge, which was subjected to ambient excitation. Gabor representation was also employed to process measured signals before conducting parameter identification. Identified results show that the proposed method can give a reliable separation of spurious and physical modes as well as accurate estimates of weak modes only from response signals.
Dimensions of subspaces of a Hilbert space and index of the semi-Fredholm operator
Institute of Scientific and Technical Information of China (English)
马吉溥
1996-01-01
Let 2" denote the set of all closed subspaces of the Hilbert space H. The generalized dimension, dim gH0 for any , is introduced. Then an order is defined in [2H], the set of generalized dimensions of 2H. It makes [2H] totally ordered such that 0
Energy Technology Data Exchange (ETDEWEB)
Vrugt, Jasper A [Los Alamos National Laboratory; Hyman, James M [Los Alamos National Laboratory; Robinson, Bruce A [Los Alamos National Laboratory; Higdon, Dave [Los Alamos National Laboratory; Ter Braak, Cajo J F [NETHERLANDS; Diks, Cees G H [UNIV OF AMSTERDAM
2008-01-01
Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.
Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon
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Moumita Saha
2016-01-01
Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.
Fast Second-Order Orthogonal Tensor Subspace Analysis for Face Recognition
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Yujian Zhou
2014-01-01
Full Text Available Tensor subspace analysis (TSA and discriminant TSA (DTSA are two effective two-sided projection methods for dimensionality reduction and feature extraction of face image matrices. However, they have two serious drawbacks. Firstly, TSA and DTSA iteratively compute the left and right projection matrices. At each iteration, two generalized eigenvalue problems are required to solve, which makes them inapplicable for high dimensional image data. Secondly, the metric structure of the facial image space cannot be preserved since the left and right projection matrices are not usually orthonormal. In this paper, we propose the orthogonal TSA (OTSA and orthogonal DTSA (ODTSA. In contrast to TSA and DTSA, two trace ratio optimization problems are required to be solved at each iteration. Thus, OTSA and ODTSA have much less computational cost than their nonorthogonal counterparts since the trace ratio optimization problem can be solved by the inexpensive Newton-Lanczos method. Experimental results show that the proposed methods achieve much higher recognition accuracy and have much lower training cost.
Application of Subspace Detection to the 6 November 2011 M5.6 Prague, Oklahoma Aftershock Sequence
McMahon, N. D.; Benz, H.; Johnson, C. E.; Aster, R. C.; McNamara, D. E.
2015-12-01
Subspace detection is a powerful tool for the identification of small seismic events. Subspace detectors improve upon single-event matched filtering techniques by using multiple orthogonal waveform templates whose linear combinations characterize a range of observed signals from previously identified earthquakes. Subspace detectors running on multiple stations can significantly increasing the number of locatable events, lowering the catalog's magnitude of completeness and thus providing extraordinary detail on the kinematics of the aftershock process. The 6 November 2011 M5.6 earthquake near Prague, Oklahoma is the largest earthquake instrumentally recorded in Oklahoma history and the largest earthquake resultant from deep wastewater injection. A M4.8 foreshock on 5 November 2011 and the M5.6 mainshock triggered tens of thousands of detectable aftershocks along a 20 km splay of the Wilzetta Fault Zone known as the Meeker-Prague fault. In response to this unprecedented earthquake, 21 temporary seismic stations were deployed surrounding the seismic activity. We utilized a catalog of 767 previously located aftershocks to construct subspace detectors for the 21 temporary and 10 closest permanent seismic stations. Subspace detection identified more than 500,000 new arrival-time observations, which associated into more than 20,000 locatable earthquakes. The associated earthquakes were relocated using the Bayesloc multiple-event locator, resulting in ~7,000 earthquakes with hypocentral uncertainties of less than 500 m. The relocated seismicity provides unique insight into the spatio-temporal evolution of the aftershock sequence along the Wilzetta Fault Zone and its associated structures. We find that the crystalline basement and overlying sedimentary Arbuckle formation accommodate the majority of aftershocks. While we observe aftershocks along the entire 20 km length of the Meeker-Prague fault, the vast majority of earthquakes were confined to a 9 km wide by 9 km deep
Karabanov, Alexander; van der Drift, Anniek; Edwards, Luke J; Kuprov, Ilya; Köckenberger, Walter
2012-02-28
A strategy is described for simulations of solid effect dynamic nuclear polarisation that reduces substantially the dimension of the quantum mechanical problem. Averaging the Hamiltonian in the doubly rotating frame is used to confine the active space to the zero quantum coherence subspace. A further restriction of the Liouville space is made by truncating higher spin order states, which are weakly populated due to the presence of relaxation processes. Based on a dissipative transport equation, which is used to estimate the transport of the magnetisation starting from single spin order to higher spin order states, a minimal spin order for the states is calculated that needs to be taken into account for the spin dynamics simulation. The strategy accelerates individual spin calculations by orders of magnitude, thus making it possible to simulate the polarisation dynamics of systems with up to 25 nuclear spins.
Reynders, Edwin; Roeck, Guido De
2008-04-01
The modal analysis of mechanical or civil engineering structures consists of three steps: data collection, system identification and modal parameter estimation. The system identification step plays a crucial role in the quality of the modal parameters, that are derived from the identified system model, as well as in the number of modal parameters that can be determined. This explains the increasing interest in sophisticated system identification methods for both experimental and operational modal analysis. In purely operational or output-only modal analysis, absolute scaling of the obtained mode shapes is not possible and the frequency content of the ambient forces could be narrow banded so that only a limited number of modes are obtained. This drives the demand for system identification methods that take both artificial and ambient excitation into account so that the amplitude of the artificial excitation can be small compared to that of the ambient excitation. An accurate, robust and efficient system identification method that meets this requirements is combined deterministic-stochastic subspace identification. It can be used both for experimental modal analysis and for operational modal analysis with deterministic inputs. In this paper, the method is generalized to a reference-based version which is faster and, if the chosen reference outputs have the highest SNR values, more accurate than the classical algorithm. The algorithm is validated with experimental data from the Z24 bridge that overpassing the A1 highway between Bern and Zurich in Switzerland, that have been proposed as a benchmark for the assessment of system identification methods for the modal analysis of large structures. With the presented algorithm, the most complete set of modes reported so far is obtained.
Subspace-based Identification Algorithm for characterizing causal networks in resting brain.
Kadkhodaeian Bakhtiari, Shahab; Hossein-Zadeh, Gholam-Ali
2012-04-02
The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity (FC). However the other main stream of brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored. Inherent complexity of brain activities in resting-state, as observed in BOLD (Blood Oxygenation-Level Dependant) fluctuations, calls for exploratory methods for characterizing these causal networks. On the other hand, the inevitable effects that hemodynamic system imposes on causal inferences in fMRI data, lead us toward the methods in which causal inferences can take place in latent neuronal level, rather than observed BOLD time-series. To simultaneously satisfy these two concerns, in this paper, we introduce a novel state-space system identification approach for studying causal interactions among brain regions in the absence of explicit cognitive task. This algorithm is a geometrically inspired method for identification of stochastic systems, purely based on output observations. Using extensive simulations, three aspects of our proposed method are investigated: ability in discriminating existent interactions from non-existent ones, the effect of observation noise, and downsampling on algorithm performance. Our simulations demonstrate that Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably uncover the underlying causal interactions of resting-state fMRI. Furthermore, in contrast to previously established state-space approaches in Effective Connectivity studies, this method is able to characterize causal networks with large number of brain regions. In addition, we utilized the proposed algorithm for identification of causal relationships underlying anti-correlation of default-mode and Dorsal Attention Networks during the rest, using fMRI. We observed that Default-Mode Network places in a
Goher, K M; Fadlallah, S O
2017-01-01
This paper presents the performance of utilizing a bacterial foraging optimization algorithm on a PID control scheme for controlling a five DOF two-wheeled robotic machine with two-directional handling mechanism. The system under investigation provides solutions for industrial robotic applications that require a limited-space working environment. The system nonlinear mathematical model, derived using Lagrangian modeling approach, is simulated in MATLAB/Simulink(®) environment. Bacterial foraging-optimized PID control with decoupled nature is designed and implemented. Various working scenarios with multiple initial conditions are used to test the robustness and the system performance. Simulation results revealed the effectiveness of the bacterial foraging-optimized PID control method in improving the system performance compared to the PID control scheme.
Banerjee, Amartya S.; Lin, Lin; Hu, Wei; Yang, Chao; Pask, John E.
2016-10-01
The Discontinuous Galerkin (DG) electronic structure method employs an adaptive local basis (ALB) set to solve the Kohn-Sham equations of density functional theory in a discontinuous Galerkin framework. The adaptive local basis is generated on-the-fly to capture the local material physics and can systematically attain chemical accuracy with only a few tens of degrees of freedom per atom. A central issue for large-scale calculations, however, is the computation of the electron density (and subsequently, ground state properties) from the discretized Hamiltonian in an efficient and scalable manner. We show in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) can be used to address this issue and push the envelope in large-scale materials simulations in a discontinuous Galerkin framework. We describe how the subspace filtering steps can be performed in an efficient and scalable manner using a two-dimensional parallelization scheme, thanks to the orthogonality of the DG basis set and block-sparse structure of the DG Hamiltonian matrix. The on-the-fly nature of the ALB functions requires additional care in carrying out the subspace iterations. We demonstrate the parallel scalability of the DG-CheFSI approach in calculations of large-scale two-dimensional graphene sheets and bulk three-dimensional lithium-ion electrolyte systems. Employing 55 296 computational cores, the time per self-consistent field iteration for a sample of the bulk 3D electrolyte containing 8586 atoms is 90 s, and the time for a graphene sheet containing 11 520 atoms is 75 s.
Institute of Scientific and Technical Information of China (English)
WANG Lu; LI Ning; LI Shao-Yuan
2013-01-01
In this paper,a historical objective function benchmark is proposed to monitor the performance of data-driven subspace predictive control systems.A new criterion for selection of the historical data set can be used to monitor the controller's performance,instead of using traditional methods based on prior knowledge.Under this monitoring framework,users can define their own index based on different demands and can also obtain the historical benchmark with a better sensitivity.Finally,a distillation column simulation example is used to illustrate the validity of the proposed algorithms.
DEFF Research Database (Denmark)
Najafi, Nadia; Panah, Mohammad Esmail Aryaee; Schmidt Paulsen, Uwe
2015-01-01
in the analysis are very short because of limitations in the image acquisition system. Short time series are not fully qualified for OMA and analyzing the data needs a proper method. Covariance driven Stochastic Subspace Identification method (SSI-cov) has been used for short time series like earthquakes....... In the SSI-cov method, a block Toeplitz matrix is formed which contains output correlation functions. 10 displacement time series have been recorded with 187 Hz sampling rate, and about 3 time series were chosen to be analyzed. The block Toeplitz matrix of 3 time series are averaged out and the procedure how...
Achar, N. S.; Gaonkar, G. H.
1994-01-01
Floquet eigenanalysis requires a few dominant eigenvalues of the Floquet transition matrix (FTM). Although the QR method is used almost exclusively, it is expensive for such partial eigenanalysis; the operation counts and, thereby, the approximate machine-time grow cubically with the matrix order. Accordingly, for Floquet eigenanalysis, the Arnold-Saad method, a subspace iteration method, is investigated as an alternative to the QR method. The two methods are compared for machine-time efficiency and the residual errors of the corresponding eigenpairs. The Arnolds-Saad method takes much less machine-time than the QR method with comparable computational reliability and offers promise fpr large-scale Floquet eigenanalysis.
Directory of Open Access Journals (Sweden)
T. Janesupasaeree
2009-01-01
Full Text Available Problem statement: Flutter derivatives are the essential parameters in the estimations of the flutter critical wind velocity and the responses of long-span cable supported bridges. These derivatives can be experimentally estimated from wind tunnel test results. Generally, wind tunnel test methods can be divided into free decay test and buffeting test. Compared with the free decay method, the buffeting test is simpler but its outputs appear random-like. This makes the flutter derivatives extraction from its outputs more difficult and then a more advanced system identification is required. Most of previous studies have used deterministic system identification techniques, in which buffeting forces and responses are considered as noises. These previous techniques were applicable only to the free decay method. They also confronted some difficulties in extracting flutter derivatives at high wind speeds and under turbulence flow cases where the buffeting responses dominate. Approach: In this study, the covariance-driven stochastic subspace identification technique (SSI-COV was presented to extract the flutter derivatives of bridge decks from the buffeting test results. An advantage of this method is that it considers the buffeting forces and responses as inputs rather than as noises. Numerical simulations and wind tunnel tests of a streamlined thin plate model conducted under smooth flow by the free decay and the buffeting tests were used to validate the applicability of the SSI-COV method. Then, wind tunnel tests of a two-edge girder blunt type of Industrial-Ring-Road Bridge deck (IRR were conducted under smooth and turbulence flow. Results: The identified flutter derivatives of the thin plate model by the SSI-COV technique agree well with those obtained theoretically. The results from the thin plate and the IRR Bridge deck validated the reliability and applicability of the SSI-COV technique to various experimental methods and conditions of wind flow
Contractions without non-trivial invariant subspaces satisfying a positivity condition
Directory of Open Access Journals (Sweden)
Bhaggy Duggal
2016-04-01
Full Text Available Abstract An operator A ∈ B ( H $A\\in B(\\mathcal{H}$ , the algebra of bounded linear transformations on a complex infinite dimensional Hilbert space H $\\mathcal{H}$ , belongs to class A ( n $\\mathcal{A}(n$ (resp., A ( ∗ − n $\\mathcal{A}(*-n$ if | A | 2 ≤ | A n + 1 | 2 n + 1 $\\vert A\\vert^{2}\\leq\\vert A^{n+1}\\vert^{\\frac{2}{n+1}}$ (resp., | A ∗ | 2 ≤ | A n + 1 | 2 n + 1 $\\vert A^{*}\\vert^{2}\\leq \\vert A^{n+1}\\vert^{\\frac{2}{n+1}}$ for some integer n ≥ 1 $n\\geq1$ , and an operator A ∈ B ( H $A\\in B(\\mathcal{H}$ is called n-paranormal, denoted A ∈ P ( n $A\\in \\mathcal{P}(n$ (resp., ∗ − n $*-n$ -paranormal, denoted A ∈ P ( ∗ − n $A\\in \\mathcal{P}(*-n$ if ∥ A x ∥ n + 1 ≤ ∥ A n + 1 x ∥ ∥ x ∥ n $\\Vert Ax\\Vert ^{n+1}\\leq \\Vert A^{n+1}x\\Vert \\Vert x\\Vert ^{n}$ (resp., ∥ A ∗ x ∥ n + 1 ≤ ∥ A n + 1 x ∥ ∥ x ∥ n $\\Vert A^{*}x\\Vert ^{n+1}\\leq \\Vert A^{n+1}x\\Vert \\Vert x\\Vert ^{n}$ for some integer n ≥ 1 $n\\geq 1$ and all x ∈ H $x \\in\\mathcal{H}$ . In this paper, we prove that if A ∈ { A ( n ∪ P ( n } $A\\in\\{\\mathcal{A}(n\\cup \\mathcal{P}(n\\}$ (resp., A ∈ { A ( ∗ − n ∪ P ( ∗ − n } $A\\in\\{\\mathcal{A}(*-n\\cup \\mathcal{P}(*-n\\}$ is a contraction without a non-trivial invariant subspace, then A, | A n + 1 | 2 n + 1 − | A | 2 $\\vert A^{n+1}\\vert^{\\frac{2}{n+1}}-\\vert A\\vert^{2}$ and | A n + 1 | 2 − n + 1 n | A | 2 + 1 $\\vert A^{n+1}\\vert^{2}- {\\frac{n+1}{n}}\\vert A\\vert^{2}+ 1$ (resp., A, | A n + 1 | 2 n + 1 − | A ∗ | 2 $\\vert A^{n+1}\\vert^{\\frac{2}{n+1}}-\\vert A^{*}\\vert^{2}$ and | A n + 2 | 2 − n + 1 n | A | 2 + 1 ≥ 0 $\\vert A^{n+2}\\vert^{2}- {\\frac{n+1}{n}}\\vert A\\vert^{2}+ 1\\geq0$ are proper contractions.
Aboudi, Jacob; Pindera, Marek-Jerzy; Arnold, Steven M.
1995-01-01
A recently developed micromechanical theory for the thermoelastic response of functionally graded composites with nonuniform fiber spacing in the through-thickness direction is further extended to enable analysis of material architectures characterized by arbitrarily nonuniform fiber spacing in two directions. In contrast to currently employed micromechanical approaches applied to functionally graded materials, which decouple the local and global effects by assuming the existence of a representative volume element at every point within the composite, the new theory explicitly couples the local and global effects. The analytical development is based on volumetric averaging of the various field quantities, together with imposition of boundary and interfacial conditions in an average sense. Results are presented that illustrate the capability of the derived theory to capture local stress gradients at the free edge of a laminated composite plate due to the application of a uniform temperature change. It is further shown that it is possible to reduce the magnitude of these stress concentrations by a proper management of the microstructure of the composite plies near the free edge. Thus by an appropriate tailoring of the microstructure it is possible to reduce or prevent the likelihood of delamination at free edges of standard composite laminates.
Hu, Weiming; Li, Xi; Luo, Wenhan; Zhang, Xiaoqin; Maybank, Stephen; Zhang, Zhongfei
2012-12-01
Object appearance modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.
Abdallah, Saeed; Psaromiligkos, Ioannis N.
2012-03-01
We analyze the mean-squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing binary phase-shift-keying (BPSK) modulation when the covariance matrix is estimated using a finite number of samples. The conventional estimator suffers from a phase ambiguity that reduces to a sign ambiguity for the WL estimator. We derive closed-form expressions for the MSE of the two estimators under four different ambiguity resolution scenarios. The first scenario is optimal resolution, which minimizes the Euclidean distance between the channel estimate and the actual channel. The second scenario assumes that a randomly chosen coefficient of the actual channel is known and the third assumes that the one with the largest magnitude is known. The fourth scenario is the more realistic case where pilot symbols are used to resolve the ambiguities. Our work demonstrates that there is a strong relationship between the accuracy of ambiguity resolution and the relative performance of WL and conventional subspace-based estimators, and shows that the less information available about the actual channel for ambiguity resolution, or the lower the accuracy of this information, the higher the performance gap in favor of the WL estimator.
2013-01-01
Background Boolean models are increasingly used to study biological signaling networks. In a Boolean network, nodes represent biological entities such as genes, proteins or protein complexes, and edges indicate activating or inhibiting influences of one node towards another. Depending on the input of activators or inhibitors, Boolean networks categorize nodes as either active or inactive. The formalism is appealing because for many biological relationships, we lack quantitative information about binding constants or kinetic parameters and can only rely on a qualitative description of the type “A activates (or inhibits) B”. A central aim of Boolean network analysis is the determination of attractors (steady states and/or cycles). This problem is known to be computationally complex, its most important parameter being the number of network nodes. Various algorithms tackle it with considerable success. In this paper we present an algorithm, which extends the size of analyzable networks thanks to simple and intuitive arguments. Results We present lnet, a software package which, in fully asynchronous updating mode and without any network reduction, detects the fixed states of Boolean networks with up to 150 nodes and a good part of any present cycles for networks with up to half the above number of nodes. The algorithm goes through a complete enumeration of the states of appropriately selected subspaces of the entire network state space. The size of these relevant subspaces is small compared to the full network state space, allowing the analysis of large networks. The subspaces scanned for the analyses of cycles are larger, reducing the size of accessible networks. Importantly, inherent in cycle detection is a classification scheme based on the number of non-frozen nodes of the cycle member states, with cycles characterized by fewer non-frozen nodes being easier to detect. It is further argued that these detectable cycles are also the biologically more important ones
Velazquez, Antonio; Swartz, R. Andrew
2013-04-01
Wind energy is becoming increasingly important worldwide as an alternative renewable energy source. Economical, maintenance and operation are critical issues for large slender dynamic structures, especially for remote offshore wind farms. Health monitoring systems are very promising instruments to assure reliability and good performance of the structure. These sensing and control technologies are typically informed by models based on mechanics or data-driven identification techniques in the time and/or frequency domain. Frequency response functions are popular but are difficult to realize autonomously for structures of higher order and having overlapping frequency content. Instead, time-domain techniques have shown powerful advantages from a practical point of view (e.g. embedded algorithms in wireless-sensor networks), being more suitable to differentiate closely-related modes. Customarily, time-varying effects are often neglected or dismissed to simplify the analysis, but such is not the case for wind loaded structures with spinning multibodies. A more complex scenario is constituted when dealing with both periodic mechanisms responsible for the vibration shaft of the rotor-blade system, and the wind tower substructure interaction. Transformations of the cyclic effects on the vibration data can be applied to isolate inertia quantities different from rotating-generated forces that are typically non-stationary in nature. After applying these transformations, structural identification can be carried out by stationary techniques via data-correlated Eigensystem realizations. In this paper an exploration of a periodic stationary or cyclo-stationary subspace identification technique is presented here by means of a modified Eigensystem Realization Algorithm (ERA) via Stochastic Subspace Identification (SSI) and Linear Parameter Time-Varying (LPTV) techniques. Structural response is assumed under stationary ambient excitation produced by a Gaussian (white) noise assembled
Farahania, N Darestani
2015-01-01
A formulation of a multi-input single-output closed-loop subspace system identification method is employed for the purpose of obtaining control-relevant model of the vacuum-plasma response in Damavand tokamak. Such a model is particularly well suited for robust controller design. The accuracy of the estimate of the plant dynamics is estimated by different experiments. The method described in this paper is a worst-case identification technique, in that it aims to minimize the error between the identified model and the true plant. The identified model fitness around defined operating point is more than 90% and with comparison by physical-based model it has better root mean square measure of the goodness of the fit.
Darestani Farahani, N.; Abbasi Davani, F.
2016-02-01
The formulation of a multi-input single-output closed-loop subspace method for system identification has been employed for the purpose of obtaining control-relevant model of the open loop response for plasma vertical movement in the Damavand tokamak. Such a model is particularly well suited for the robust controller design. The method described in this paper is a kind of worst-case identification technique, aiming to minimize the error between the identified model and the true plant. The accuracy of the estimation of the plant dynamics has been tested by different experiments. The fitness of the identified model around the defined operating point has been more than 90%, and compared to the physical-based model, it has better root mean squared error (RMSE) measure of the goodness of fitting.
Energy Technology Data Exchange (ETDEWEB)
Okada, M. [The University of Tokyo, Tokyo (Japan); Sugie, T. [Kyoto University, Kyoto (Japan)
1998-03-31
Recently, the importance of the joint design of identification and control has been recognized, and several controller design methods based on the iteration of identification and controller re-design have been proposed. In these methods, the frequency weighted identification plays an important role. On the other hand, as a powerful identification method, subspace state-space system identification (4SID) method has been proposed. However, it is difficult to use the frequency weight in conventional 4SID methods. Therefore in this paper, we propose a frequency weighted 4SID method for the joint design of identification and control, and choose the appropriate frequency weighting function for identification considering the cost function of the controller design. Furthermore, the effectiveness of the proposed method is evaluated by numerical examples. 18 refs., 7 figs.
Energy Technology Data Exchange (ETDEWEB)
Olofsson, K. Erik J., E-mail: erik.olofsson@ee.kth.se [School of Electrical Engineering (EES), Royal Institute of Technology (KTH), Stockholm (Sweden); Brunsell, Per R.; Drake, James R. [School of Electrical Engineering (EES), Royal Institute of Technology (KTH), Stockholm (Sweden)
2012-12-15
Highlights: Black-Right-Pointing-Pointer Unstable plasma response safely measured using special signal processing techniques. Black-Right-Pointing-Pointer Prediction-capable MIMO models obtained. Black-Right-Pointing-Pointer Computational statistics employed to show physical content of these models. Black-Right-Pointing-Pointer Multifold cross-validation applied for the supervised learning problem. - Abstract: A multibatch formulation of a multi-input multi-output closed-loop subspace system identification method is employed for the purpose of obtaining control-relevant models of the vacuum-plasma response in the magnetic confinement fusion experiment EXTRAP T2R. The accuracy of the estimate of the plant dynamics is estimated by computing bootstrap replication statistics of the dataset. It is seen that the thus identified models exhibit both predictive capabilities and physical spectral properties.
Directory of Open Access Journals (Sweden)
Davide Barbieri
2016-12-01
Full Text Available This is a joint work with E. Hernández, J. Parcet and V. Paternostro. We will discuss the structure of bases and frames of unitary orbits of discrete groups in invariant subspaces of separable Hilbert spaces. These invariant spaces can be characterized, by means of Fourier intertwining operators, as modules whose rings of coefficients are given by the group von Neumann algebra, endowed with an unbounded operator valued pairing which defines a noncommutative Hilbert structure. Frames and bases obtained by countable families of orbits have noncommutative counterparts in these Hilbert modules, given by countable families of operators satisfying generalized reproducing conditions. These results extend key notions of Fourier and wavelet analysis to general unitary actions of discrete groups, such as crystallographic transformations on the Euclidean plane or discrete Heisenberg groups.
Banerjee, Amartya S; Hu, Wei; Yang, Chao; Pask, John E
2016-01-01
The Discontinuous Galerkin (DG) electronic structure method employs an adaptive local basis set to solve the equations of density functional theory in a discontinuous Galerkin framework. The methodology is implemented in the Discontinuous Galerkin Density Functional Theory (DGDFT) code for large-scale parallel electronic structure calculations. In DGDFT, the basis is generated on-the-fly to capture the local material physics, and can systematically attain chemical accuracy with only a few tens of degrees of freedom per atom. Hence, DGDFT combines the key advantage of planewave basis sets in terms of systematic improvability with that of localized basis sets in reducing basis size. A central issue for large-scale calculations, however, is the computation of the electron density from the discretized Hamiltonian in an efficient and scalable manner. We show in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) can be used to address this issue and push the envelope in large-scale materials si...
McMahon, Nicole D; Aster, Richard C.; Yeck, William; McNamara, Daniel E.; Benz, Harley M.
2017-01-01
The 6 November 2011 Mw 5.7 earthquake near Prague, Oklahoma is the second largest earthquake ever recorded in the state. A Mw 4.8 foreshock and the Mw 5.7 mainshock triggered a prolific aftershock sequence. Utilizing a subspace detection method, we increase by fivefold the number of precisely located events between 4 November and 5 December 2011. We find that while most aftershock energy is released in the crystalline basement, a significant number of the events occur in the overlying Arbuckle Group, indicating that active Meeker-Prague faulting extends into the sedimentary zone of wastewater disposal. Although the number of aftershocks in the Arbuckle Group is large, comprising ~40% of the aftershock catalog, the moment contribution of Arbuckle Group earthquakes is much less than 1% of the total aftershock moment budget. Aftershock locations are sparse in patches that experienced large slip during the mainshock.
Xie, Yong; Liu, Pan; Cai, Guo-Ping
2016-08-01
In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the coupled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochastic subspace identification (SSI-COV) algorithm is adopted to identify the modal parameters of the system. In this algorithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decomposition on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally, numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indicate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.
一种基于双子空间的人脸美感分析方法%Dual Subspace Algorithm for Facial Attractiveness Analysis
Institute of Scientific and Technical Information of China (English)
段红帅; 朱振峰; 赵耀
2012-01-01
Subspace technique is an efficient method ior automatic iacial attractiveness analysis. To enhance the intrinsic description for facial attractiveness, a dual subspace method on the subspaces of principal component analysis (PCA) and generalized low rank approximation matrix (GLRAM) is proposed. Thus, their individual characteristics in characterizing the global and local intrinsic description of facial attractiveness can be collaboratively boosted. Besides, the Gaussian field (GF) model is applied to reflect the geometric structure in sample space. The experiment is performed on a challenging database. It takes on significant variations in the aspects of illumination, background, facial expression, age, race, and so on. Experimental results show the advantages of the proposed dual subspace method for facial attractiveness analysis over the individual subspace one.%子空间技术是一种有效的人脸美感本征描述方法.为了克服单一子空间在人脸图像美感描述方面的不足,提出了一种基于主成分分析(PCA)与广义矩阵低秩逼近(Generalized low rank approximation matrix,GLRAM)双子空间的自动人脸美感分析方法.通过组合PCA和GLRAM子空间获取人脸美感特性的全局及局部本征描述,并利用高斯场模型(Gaussian field model,GF)构造组合子空间的内在几何结构关系.实验选用了一个光照、背景、表情、年龄和种族等变化比较显著的数据库,结果表明,提出的基于双子空间算法优于基于单一子空间的人脸美感分析方法.
Analysis of performance of DOA estimation based on partial noise subspace%基于部分噪声子空间DOA估计的性能分析
Institute of Scientific and Technical Information of China (English)
张宏谋; 施锦文; 闫剑虹
2013-01-01
基于子空间DOA估计的MUSIC算法，是将阵列输出数据的协方差矩阵进行特征分解后，利用与信号分量相正交的噪声子空间来估计信号波达方向。然而在进行谱估计之前，需要对信号源的数目进行估计，以确定信号子空间和噪声子空间的维数，这将增大DOA估计的复杂度。对利用部分噪声子空间进行谱估计的方法进行了阐述，由于其不需要进行信源数目的估计，因此可以减小谱估计的复杂度。计算机仿真实验和性能分析验证了该方法的性能。%The principle of MUSIC algorithm based on the subspace DOA estimation is to use the noise subspace orthogo⁃nal to signal component to estimate the direction of the signal wave,throught eigen decomposition of the covariance matrix with array output data. However,before the spectrum estimation,the number of the signal source should be estimated to determine the dimensions of the signal subspace and noise subspace,Which would increase the complexity of DOA estimation. The paper illustrates the method of using the partial noise subspace to estimate the DOA. As the estimation of the signal number is unneces⁃sary,the complexity of the spectrum estimation is reduced. The simulation and performance analysis demonstrate the validity and feasibility of the partial noise algorithm.
Restarted FOM Augmented with Ritz Vectors for Shifted Linear Systems
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The restarted FOM method presented by Simoncini [7] according to the natural collinearity of all residuals is an efficient method for solving shifted systems, which generates the same Krylov subspace when the shifts are handled simultaneously. However, restarting slows down the convergence. We present a practical method for solving the shifted systems by adding some Ritz vectors into the Krylov subspace to form an augmented Krylov subspace.Numerical experiments illustrate that the augmented FOM approach (restarted version) can converge more quickly than the restarted FOM method.
DOA Estimation Based on Eigenvalue Reconstruction of Noise Subspace%基于噪声子空间特征值重构的DOA估计算法
Institute of Scientific and Technical Information of China (English)
方庆园; 韩勇; 金铭; 宋立众; 乔晓林
2014-01-01
This paper proposes an Eigenvalue Reconstruction method in Noise Subspace (ERNS) for Direction of Arrival DOA estimation with high resolution, provided that the powers of sources are different. The noise subspace eigenvalues belonging to the covariance matrix of received signals, obtained by EigenValue Decomposition (EVD), are modified to construct a new covariance matrix with respect to virtual source. The noise subspace eigenvalues corresponding to the new covariance matrix remain the same as before they are modified. The invariance of the noise subspace is utilized to estimate the DOA of emitters. The theory and process of ERNS algorithm are provided, at the same time, the theory and performance of ERNS algorithm is validated by computer simulations. The simulation results show that the ERNS algorithm has a better performance in successful probability of weak signal estimation compared with other subspace methods and MUSIC algorithm.%该文针对非等功率信号波达方向(DOA)估计问题，提出一种基于噪声子空间特征值重构(Eigenvalue Reconstruction of Noise Subspace, ERNS)的超分辨算法。算法对接收信号自相关矩阵进行特征值分解，通过重构噪声空间特征值以及引入虚拟信源来构造新的接收信号自相关矩阵，对该矩阵进行特征值分解得到新的噪声空间特征值。当虚拟信源与实际信源入射方向相同时，新噪声空间特征值与重构后噪声空间特征值保持不变，利用这一特性来估计信源入射方向。该文给出算法的原理及实现步骤，并通过仿真进行原理验证与性能分析，仿真结果表明与其他子空间算法和MUSIC 算法相比，ERNS算法能够提高弱信号估计成功的概率。
Soft Subspace Clustering Based on Particle Swarm Optimization%基于粒子群优化的软子空间聚类算法
Institute of Scientific and Technical Information of China (English)
邱云飞; 杨倩; 唐晓亮
2015-01-01
目标函数和子空间搜索策略决定软子空间聚类算法的性能，而聚类有效性分析是衡量其性能的主要指标。针对子空间聚类性能，提出基于粒子群优化的软子空间聚类算法(SC-WPSO)。首先，利用 K 均值类型框架，结合类间分散度和特征权重，提出模糊加权软子空间聚类目标函数。然后，为跳出局部最优，将带惯性权重的粒子群算法作为子空间的搜索策略。最后，根据提出的聚类有效性函数，选取最佳聚类数目。在数据集上的实验证实 SC-PSO能提高聚类准确度，同时自动确定最佳聚类数目。%The performance of soft subspace clustering depends on the objective function and subspace search strategy, and cluster validity analysis is the main indicator of its performance. Aiming at the subspace clustering performance, a soft subspace clustering algorithm based on particle swarm optimization (SC-PSO) is proposed. Firstly, combining inter-cluster separation with feature weight based on K means-type clustering framework, a fuzzy weighting soft subspace objective function is designed. Then, particle swarm optimization with inertia weight is used as a subspace search strategy to jump out of the local optimum. Finally, the optimal cluster number is selected by the proposed cluster validity function. The experimental results demonstrate that SC-PSO improves the clustering accuracy and automatically determines the optimal cluster number.
Gavryushin, S. S.; Nikolaeva, A. S.
2016-05-01
The theoretical foundations, methods, and algorithms developed to analyze the stability and postbuckling behavior of thin elastic axisymmetric shells are discussed. The algorithm for numerically studying the processes of nonlinear deformation of thin-walled axisymmetric shells by the solution parametric continuation method is generalized to solving the practical problem of design of mechanical actuators of discrete action. The synthesis algorithm is based on the method of changing the subspace of control parameters, which is supplemented with the procedure of smooth transition in changing the subspaces. The efficiency of the proposed algorithm is illustrated by an example of synthesis of a thermobimetallic actuator of discrete action. The procedure of determining an isolated solution, whose existencewas predicted byV. I. Feodosiev, is considered in the framework of studying the process of nonlinear deformation of a corrugated membrane loaded by an external pressure.
Directory of Open Access Journals (Sweden)
Nelson Adam
2005-08-01
Full Text Available Abstract Background: Many polyhydroxylated piperidines are inhibitors of the oligosaccharide processing enzymes, glycosidases and glycosyltransferases. Aza-C-linked disaccharide mimetics are compounds in which saturated polyhydroxylated nitrogen and oxygen heterocycles are linked by an all-carbon tether. The saturated oxygen heterocycle has the potential to mimic the departing sugar in a glycosidase-catalysed reaction and aza-C-linked disaccharide mimetics may, therefore, be more potent inhibitors of these enzymes. Results: The scope, limitations and diastereoselectivity of the dihydroxylation of stereoisomeric 2-butyl-1-(toluene-4-sulfonyl-1,2,3,6-tetrahydro-pyridin-3-ols is discussed. In the absence of a 6-substituent on the piperidine ring, the Upjohn (cat. OsO4, NMO, acetone-water and Donohoe (OsO4, TMEDA, CH2Cl2 conditions allow complementary diastereoselective functionalisation of the alkene of the (2R*,3R* diastereoisomer. However, in the presence of a 6-substituent, the reaction is largely controlled by steric effects with both reagents. The most synthetically useful protocols were exploited in the two-directional synthesis of aza-C-linked disaccharide analogues. A two-directional oxidative ring expansion was used to prepare bis-enones such as (2R,6S,2'S-6-methoxy-2-(6-methoxy-3-oxo-3,6-dihydro-2H-pyran-2-ylmethyl-1-(toluene-4-sulfonyl-1,6-dihydro-2H-pyridin-3-one from the corresponding difuran. Selective substitution of its N,O acetal was possible. The stereochemical outcome of a two-directional Luche reduction step was different in the two heterocyclic rings, and depended on the conformation of the ring. Finally, two-directional diastereoselective dihydroxylation yielded seven different aza-C-linked disaccharide analogues. Conclusion: A two-directional approach may be exploited in the synthesis of aza-C-linked disaccharide mimetics. Unlike previous approaches to similar molecules, neither of the heterocyclic rings is directly derived
二维PCA非参数子空间分析的人脸识别算法%Face Recognition Algorithm of 2DPCA Nonparametric Subspace Analysis
Institute of Scientific and Technical Information of China (English)
王美; 梁久祯
2011-01-01
This paper proposes a novel face recognition algorithm of 2D Nonparametric Subspace Analysis(2DNSA) based on 2D Principal Componet Analysis(2DPCA) subspace. The original face matrices are performed to have feature dimension reduction, and the reduced feature matrices are used as a new training set, which can be conducted by 2D non-parametric subspace analysis. This method not only can reduce feature dimensions by 2DPCA, but also consider the impact of boundary samples for classification by taking full advantage of classification capacity of 2DNSA, which avoids the irrationality of using class centers to measure the distances of different classes. Experimental results on the two face databases(namely Yale and LARGE) show the improvements of the developed new algorithm over the traditional subspace methods such as (2D)2PCA, 2DPCA, (2D)2LDA, 2DLDA, 2DPCA+2DLDA, 2DNSA, etc.%提出一种结合二维PCA(2DPCA)的二维非参数子空间分析(2DNSA)人脸识别算法.利用2DPCA对原始图像矩阵进行特征降维,以降维后的特征为训练样本,进行二维非参数判别分析,并综合考虑类边界样本对分类的影响,采用2DNSA实现更合理的特征提取.基于Yale、LARGE人脸数据库的实验结果表明,与(2D)2pCA、2DPCA、(2D)2LDA、2DLDA、2DPCA+2DLDA、2DNSA算法相比,该算法性能更优.
基于联合子空间的宽带弱信号测向算法%Wideband DOA Estimation of Weak Signals Based on Joint Subspace
Institute of Scientific and Technical Information of China (English)
苏成晓; 罗景青; 樊甫华
2013-01-01
In order to correctly estimate weak signals' directions with strong jamming for wideband beam forming system,a super-resolution direction finding algorithm for weak signals based on jamming-noise joint subspace was proposed.Eigenvectors that represent strong jamming were combined with noise subspace to construct jamming-noise joint subspace,which was used to estimate weak signals' directions of arrival (DOA) instead of noise subspace.Jamming suppression and weak signals' DOA estimation were done simultaneously.Analysis and computer simulations showed that the proposed algorithm could suppress strong jamming effectively and got the correct DOA estimation of nearby weak signals,and the resolution probability of weak signals was improved.%为了解决强干扰环境下宽带多波束系统中弱信号的测向问题,提出了一种基于干扰-噪声联合子空间的弱信号超分辨测向算法.该算法将强干扰对应特征向量并入噪声子空间,构造干扰-噪声联合子空间代替噪声子空间进行估计,干扰抑制与弱信号测向同步进行.分析和仿真实验表明:算法能够抑制强干扰的影响,实现邻近弱信号的正确测向,提高了弱信号的分辨概率.
Hund, Michael; Böhm, Dominic; Sturm, Werner; Sedlmair, Michael; Schreck, Tobias; Ullrich, Torsten; Keim, Daniel A; Majnaric, Ljiljana; Holzinger, Andreas
2016-12-01
Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.
Institute of Scientific and Technical Information of China (English)
马光同
2013-01-01
该文阐述了Jacobian-free Newton-Krylov (JFNK)法的基本原理及其双端预处理形式的迭代格式，选择处于时变(非均匀)外磁场中的二维超导体为研究对象，建立了基于矢量磁位法的控制超导体电磁特性的偏微分方程及相关的非线性有限元矩阵方程和数值迭代策略。以时变外磁场中具有高尺寸比的超导薄带的交流损耗问题和永磁外场中块状高温超导体的磁悬浮问题为计算实例，在肯定计算程序有效性的基础上，检验了预处理JFNK法求解这2类典型问题时的计算性能，证实了预处理JFNK法能较为快速地求解大型超导非线性电磁场问题，可作为开发超导电磁场数值计算程序的优选方法。%The principal basis of the Jacobian-free Newton-Krylov (JFNK) algorithm was firstly introduced in conjunction with its iterative scheme using the split preconditioning technique, and then the partial differential equation with magnetic vector potential as the state variable for governing the electromagnetic properties of a 2-D superconductor (SC) subjected to time-varying/nonuniform magnetic fields was established, and the related nonlinear systems of finite element equations plus the adopted strategy for numerical iteration were released. Taking the ac loss problems of a high-aspect-ratio SC strip in a time-varying field and the maglev problems of a bulk high temperature superconductor (HTS) above a magnetic track as the studied cases, the computational performance of the preconditioned JFNK algorithm was tested on the basis of the validated program. It was found by this investigation that the preconditioned JFNK algorithm has the ability to rapidly solve the large nonlinear electromagnetic problems of SC, and is thus an advanced approach for developing the program to solve the electromagnetic problems of SC.
Institute of Scientific and Technical Information of China (English)
Yong Xie; Pan Liu; Guo-Ping Cai
2016-01-01
In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the cou-pled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochas-tic subspace identification (SSI-COV) algorithm is adopted to identify the modal parameters of the system. In this algo-rithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decom-position on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally, numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indi-cate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.
Mellinger, Philippe; Döhler, Michael; Mevel, Laurent
2016-09-01
An important step in the operational modal analysis of a structure is to infer on its dynamic behavior through its modal parameters. They can be estimated by various modal identification algorithms that fit a theoretical model to measured data. When output-only data is available, i.e. measured responses of the structure, frequencies, damping ratios and mode shapes can be identified assuming that ambient sources like wind or traffic excite the system sufficiently. When also input data is available, i.e. signals used to excite the structure, input/output identification algorithms are used. The use of input information usually provides better modal estimates in a desired frequency range. While the identification of the modal mass is not considered in this paper, we focus on the estimation of the frequencies, damping ratios and mode shapes, relevant for example for modal analysis during in-flight monitoring of aircrafts. When identifying the modal parameters from noisy measurement data, the information on their uncertainty is most relevant. In this paper, new variance computation schemes for modal parameters are developed for four subspace algorithms, including output-only and input/output methods, as well as data-driven and covariance-driven methods. For the input/output methods, the known inputs are considered as realizations of a stochastic process. Based on Monte Carlo validations, the quality of identification, accuracy of variance estimations and sensor noise robustness are discussed. Finally these algorithms are applied on real measured data obtained during vibrations tests of an aircraft.
Jia, Zhongxiao
2011-01-01
We give a quantitative analysis of the Shift-Invert Residual Arnoldi (SIRA) method and the Jacobi--Davidson (JD) method for computing a simple eigenvalue nearest to a target $\\sigma$ and/or the associated eigenvector. In SIRA and JD, subspace expansion vectors at each step are obtained by solving certain (different) inner linear systems, respectively. We show that (i) SIRA and the JD method with the fixed target $\\sigma$ are mathematically equivalent when the inner linear systems are solved exactly and (ii) the inexact SIRA is asymptotically equivalent to the JD method when the inner linear systems in them are solved with the same accuracy. Remarkably, we prove that the inexact SIRA and JD methods mimic the exact SIRA well provided that the inner linear systems are iteratively solved with a fixed {\\em low} or {\\em modest} accuracy. It is opposed to the inexact Shift-Invert Arnoldi (SIA) method, where the inner linear system involved must be solved with very high accuracy whenever the approximate eigenpair is ...
Hara S; Akazawa H; Mitani S; Oda K; Inoue H
2002-01-01
Two-directional arthrographic findings made during conservative treatment of developmental dislocation of the hip were compared with the femoral-head configurations and radiological results obtained from long-term follow-up examinations in this retrospective study. Sixty hips were followed until at least age 14. Arthrography was carried out according to Terazawa's method. The shape of the superior, anterior, and posterior limbus was evaluated based on a modified Fujii's classification. The fe...
Iterative solution of linear systems
Freund, Roland W.; Golub, Gene H.; Nachtigal, Noel M.
1992-01-01
Recent advances in the field of iterative methods for solving large linear systems are reviewed. The main focus is on developments in the area of conjugate gradient-type algorithms and Krylov subspace methods for nonHermitian matrices.
Approximate inverse preconditioning of iterative methods for nonsymmetric linear systems
Energy Technology Data Exchange (ETDEWEB)
Benzi, M. [Universita di Bologna (Italy); Tuma, M. [Inst. of Computer Sciences, Prague (Czech Republic)
1996-12-31
A method for computing an incomplete factorization of the inverse of a nonsymmetric matrix A is presented. The resulting factorized sparse approximate inverse is used as a preconditioner in the iterative solution of Ax = b by Krylov subspace methods.
Robust Principal Component Analysis for Face Subspace Recovery%基于鲁棒主成分分析的人脸子空间重构方法
Institute of Scientific and Technical Information of China (English)
江明阳; 封举富
2012-01-01
Subspace method is one of the classical methods in face recognition, which assumes that face images lie in a low-rank subspace. However, due to illumination variation, shadows, occlusion, specularities and corruption, real face images seldom reveal such low-rank structure. We propose a face subspace recovery method based on the Robust Principal Component Analysis. The face image matrix is modeled as the sum of a low-rank matrix and a deviation matrix, in which the low-rank matrix reveals the ideal subspace structure and the deviation matrix accounts for the illumination variation, shadows, occlusion, specularities and corruption. By using the robust principal component analysis, the low-rank matrix and deviation matrix can be recovered efficiently. The experimental results show that this method is efficient in recovering the low-rank face subspaces.%子空间方法是人脸识别中的经典方法,其基本假设是人脸图像处于高维图像空间的低维子空间中.但是,由于光照变化、阴影、遮挡、局部镜面反射、图像噪声等因素的影响,使得子空间假设难以满足.为此,提出一种基于鲁棒主成分分析的人脸子空间重构方法.该方法将人脸图像数据矩阵表示为满足子空间假设的低秩矩阵和表征光照变化、阴影、遮挡、局部镜面反射、图像噪声等因素的误差矩阵之和,利用鲁棒主成分分析法求解低秩矩阵和误差矩阵.实验结果表明,文中方法能够有效地重构人脸图像的低维子空间.
Modified MUSIC approach with weighted pseudo-noise subspace projection%加权伪噪声子空间投影的修正MUSIC算法
Institute of Scientific and Technical Information of China (English)
杨志伟; 贺顺; 廖桂生
2011-01-01
多重信号分类(multiple signal classification:MUSIC)方法通过计算搜索导向矢量与噪声或信号子空间的距离来估计波达方向,对采样协方差矩阵的依赖性较大.在小快拍或存在强弱临近信号条件下,采样协方差矩阵的估计值与真实值通常存在较大差异,导致估计的噪声或信号子空间发生畸变,严重恶化了MUSIC方法的波达角估计性能.针对该问题,本文提出采用加权伪噪声子空间投影的改进方法(称为wpnMUSIC).该方法在修正数据相关矩阵的基础上估计与搜索导向矢量对应的伪噪声子空间并利用其在伪噪声子空间的投影值对MUSIC空间谱进行加权处理,在保持子空间处理方法高分辨能力的同时改善了对小快拍和强弱信号的稳健性.理论分析和仿真实验表明本文方法对强弱临近目标的分辨能力优于MUSIC方法.%The direction-of-arrival (DOA) can he estimated as measured the distance between each search steering vector and the noise subspace or signal subspace with MUSIC algorithm. Therefore, the subspace deviation which associated with the correlation matrix will deteriorate the performance of MUSIC algorithm. To alleviate this decreasing in DOA estimation with secondary data deficient scenario and/or strong and weak signal coexistence, a new method based on pseudo-noise subspace projection is presented. The approach is performed in two stages. First, we employ a modified correlation matrix at each search steering vector to calculate the pseudonoise subspace, then, the spatial spectrum can be obtained as weighted the MUSIC spectrum with the projection value of the search steering vector on the corresponding pseudo-noise subspace. The high-resolution of subspace processing is remained and the robustness against small sample support and in the presence of strong signal and weak signal is improved. Theoretical analysis and numerical simulation indicate that its performance is better than that of
Chen, Tianwen; Ryali, Srikanth; Qin, Shaozheng; Menon, Vinod
2013-11-15
Intrinsic functional connectivity analysis using resting-state functional magnetic resonance imaging (rsfMRI) has become a powerful tool for examining brain functional organization. Global artifacts such as physiological noise pose a significant problem in estimation of intrinsic functional connectivity. Here we develop and test a novel random subspace method for functional connectivity (RSMFC) that effectively removes global artifacts in rsfMRI data. RSMFC estimates the partial correlation between a seed region and each target brain voxel using multiple subsets of voxels sampled randomly across the whole brain. We evaluated RSMFC on both simulated and experimental rsfMRI data and compared its performance with standard methods that rely on global mean regression (GSReg) which are widely used to remove global artifacts. Using extensive simulations we demonstrate that RSMFC is effective in removing global artifacts in rsfMRI data. Critically, using a novel simulated dataset we demonstrate that, unlike GSReg, RSMFC does not artificially introduce anti-correlations between inherently uncorrelated networks, a result of paramount importance for reliably estimating functional connectivity. Furthermore, we show that the overall sensitivity, specificity and accuracy of RSMFC are superior to GSReg. Analysis of posterior cingulate cortex connectivity in experimental rsfMRI data from 22 healthy adults revealed strong functional connectivity in the default mode network, including more reliable identification of connectivity with left and right medial temporal lobe regions that were missed by GSReg. Notably, compared to GSReg, negative correlations with lateral fronto-parietal regions were significantly weaker in RSMFC. Our results suggest that RSMFC is an effective method for minimizing the effects of global artifacts and artificial negative correlations, while accurately recovering intrinsic functional brain networks.
Carvajal, Gonzalo; Figueroa, Miguel
2014-07-01
Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods.
Energy Technology Data Exchange (ETDEWEB)
Zhou, Ping; Song, Heda; Wang, Hong; Chai, Tianyou
2017-09-01
Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multi-phase and multi-field coupling and large time delay occur during its operation. In BF operation, the molten iron temperature (MIT) as well as Si, P and S contents of molten iron are the most essential molten iron quality (MIQ) indices, whose measurement, modeling and control have always been important issues in metallurgic engineering and automation field. This paper develops a novel data-driven nonlinear state space modeling for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques. First, to improve modeling efficiency, a data-driven hybrid method combining canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modeling inputs from multitudinous factors would affect the MIQ indices. Then, a Hammerstein model for the prediction of MIQ indices is established using the LS-SVM based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method to fit the complex nonlinear kernel function. Compared to the original Hammerstein model, this simplified model can not only significantly reduce the computational complexity, but also has almost the same reliability and accuracy for a stable prediction of MIQ indices. Last, in order to verify the practicability of the developed model, it is applied in designing a genetic algorithm based nonlinear predictive controller for multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approach.
Directory of Open Access Journals (Sweden)
Alcaraz Marie-Lyne
2008-01-01
Full Text Available Abstract Background Hippodamine is a volatile defence alkaloid isolated from ladybird beetles which holds potential as an agrochemical agent and was the subject of a synthesis by our group in 2005. Results Two enhancements to our previous syntheses of (±-hippodamine and (±-epi-hippodamine are presented which are able to shorten the syntheses by up to two steps. Conclusion Key advances include a two-directional homologation by cross metathesis and a new tandem reductive amination/double intramolecular Michael addition which generates 6 new bonds, 2 stereogenic centres and two rings, giving a single diastereomer in 74% yield.
Institute of Scientific and Technical Information of China (English)
曲庆国; 徐大举
2012-01-01
研究了计算大型稀疏对称矩阵的若干个最大或最小特征值的问题,首先引入了求解大型对称特征值问题的预处理子空间迭代法和Chebyshev迭代法,并对其作了理论分析.为了加速顶处理子空间迭代法的收敛性,笔者采用组合Chebyshev迭代法和预处理子空间选代法,提出了计算大型对称稀疏矩阵的几个最大或最小特征值的Chebyshev预处理子空间迭代法.数值结果表明,该方法比预处理子空间方法优越.%The problem of computing a few of the largest (or smallest) eigenvalues of a large symmetric sparse matrix is dealt with. This paper considers the preconditioning subspace iteration method and the Chebyshev iteration, and analyzes them. In order to accelerate the convergence rate of the preconditioning subspace iteration method,a new method, i. e. Chebyshev -PSI(the preconditioning subspace iteration) method, is presented for computing the extreme eigenvalues of a large symmetric sparse matrix. The new method combines the Chebyshev iteration with the PSI method. Numerical experiments show that the Chebyshev - PS1 metod is very effective for computing the extreme eigenvalues of a large symmetric sparse matrix.
DOA estimation based on distributed subspace method%基于分布式子空间方法的DOA估计
Institute of Scientific and Technical Information of China (English)
郭俊颖; 刘庆华
2013-01-01
针对集中式子空间方法的DOA估计需要融合中心的问题,采用分布式子空间的方法进行DOA估计.分布式算法无需设置用于将所有节点的数据传递到数据融合中心的路由,仅有相邻节点之间进行通信,每个传感器仅仅估计其在子空间矩阵中所对应的行.仿真结果表明,分布式算法可以达到与集中式算法相似的性能,即能很好地跟踪信号协方差矩阵的主子空间,将其应用于DOA估计,能准确分辨出几个源信号.分布式算法解决了集中式算法数据集中处理所带来的难题,为大规模传感器网络的一些重要问题提供了解决方法.%DOA Estimation based on centralized subspace method need a fusion center.A decentralized subspace estimation method is proposed.The distributed algorithm via near-neighbor communication need not set routes to transmit the data to the fusion center.Each sensor estimates only the corresponding row of the subspace matrix.The simulation results indicate that the decentralized algorithm can achieve the similar performance as centralized algorithm and can track the principal subspace of a signal's covariance matrix well.In addition,the several source signals are distinguished accurately when the proposed method is applied to estimate the direction of arrival.The decentralized algorithm solves the problem of processing data at a fusion center and provides solutions for important problems in large sensor networks.
非线性阵列Khatri-Rao子空间宽带DOA估计%Khatri-Rao Subspace Wideband DOA Estimation for Nonlinear Array
Institute of Scientific and Technical Information of China (English)
潘捷; 周建江
2013-01-01
A Khatri-Rao subspace based wideband direction-of-arrive (DOA) estimation algorithm for nonlinear arrays without preliminary angle estimation is proposed. Prom steering vectors of the Khatri-Rao subspace virtual array, the wideband focusing matrix regardless of DOAs is constructed with a manifold separation technique. Benefited from the increased dimensions of the Khatri-Rao subspace virtual array, preliminary angle estimation can be avoided and the algorithm still performs well. On the other hand, by using Root-MUSIC, this method can avoid expensive spectrum searching used in conventional methods so as to reduce the computational burden. Simulations show that performance of the proposed method is close to the preliminary angle estimation needed Khatri-Rao subspace wideband DOA estimation algorithm, FKR-RSS. The proposed method performs better than FKR-RSS when the number of sources is more than the number of sensors.%针对非线性阵列,基于Khatri-Rao子空间概念提出一种新的无预估角宽带到达角(direction-of-arrive,DOA)估计方法.从Khatri-Rao子空间虚拟阵列导向矢量出发,利用虚拟阵列所增加的维数,以流形分离技术构造与到达角无关的宽带聚焦矩阵,无需预估角且估计性能良好.采用Root-MUSIC算法避免传统算法中的谱峰搜索过程,降低了计算量.仿真结果表明,该方法与需要预估角的已有Khatri-Rao子空间宽带DOA估计方法FKR-RSS相比,具有相近的估计精度和目标分辨力.在信号源数大于阵元数的情况下,其性能优于FKR-RSS.
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Günnemann, Stephan
2009-01-01
. We prove that computation of this model is NP-hard. For RESCU, we propose an approximative solution that shows high accuracy with respect to our relevance model. Thorough experiments on synthetic and real world data show that RESCU successfully reduces the result to manageable sizes. It reliably...... achieves top clustering quality while competing approaches show greatly varying performance....
Subspace Iteration for Eigenproblems
Vorst, H.A. van der
2001-01-01
We discuss a novel approach for the computation of a number of eigenvalues and eigenvectors of the standard eigenproblem Ax = x. Our method is based on a combination of the Jacobi-Davidson method and the QR-method. For that reason we refer to the method as JDQR. The eectiveness of the method is illu
Understanding Stochastic Subspace Identification
DEFF Research Database (Denmark)
Brincker, Rune; Andersen, Palle
2006-01-01
to follow and to understand for people with a classical background in structural dynamics. Also the connection to the classical correlation driven time domain techniques is not well established. The purpose of this paper is to explain the different steps in the SSI techniques of importance for modal...
Generalized subspace correction methods
Energy Technology Data Exchange (ETDEWEB)
Kolm, P. [Royal Institute of Technology, Stockholm (Sweden); Arbenz, P.; Gander, W. [Eidgenoessiche Technische Hochschule, Zuerich (Switzerland)
1996-12-31
A fundamental problem in scientific computing is the solution of large sparse systems of linear equations. Often these systems arise from the discretization of differential equations by finite difference, finite volume or finite element methods. Iterative methods exploiting these sparse structures have proven to be very effective on conventional computers for a wide area of applications. Due to the rapid development and increasing demand for the large computing powers of parallel computers, it has become important to design iterative methods specialized for these new architectures.
Balajewicz, Maciej; Dowell, Earl
2015-01-01
For a projection-based reduced order model (ROM) to be stable and accurate, the dynamics of the truncated subspace must be taken into account. This paper proposes an approach for stabilizing and enhancing projection-based fluid ROMs in which truncated modes are accounted for \\textit{a priori} via a minimal rotation of the projection subspace. Attention is focused on the full non-linear compressible Navier-Stokes equations in specific volume form as a step toward a more general formulation for problems with generic non-linearities. Unlike traditional approaches, no empirical turbulence modeling terms are required, and consistency between the ROM and the full order model from which the ROM is derived is maintained. Mathematically, the approach is formulated as a quadratic matrix program on the Stiefel manifold. The reproductive as well as predictive capabilities of the method are evaluated on several compressible flow problems, including a problem involving laminar flow over an airfoil with a high angle of atta...
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Directory of Open Access Journals (Sweden)
Zhiqiang Guo
Full Text Available In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D2PCA and a Radial Basis Function Neural Network (RBFNN to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA and independent component analysis (ICA. The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Directory of Open Access Journals (Sweden)
Hara S
2002-04-01
Full Text Available Two-directional arthrographic findings made during conservative treatment of developmental dislocation of the hip were compared with the femoral-head configurations and radiological results obtained from long-term follow-up examinations in this retrospective study. Sixty hips were followed until at least age 14. Arthrography was carried out according to Terazawa's method. The shape of the superior, anterior, and posterior limbus was evaluated based on a modified Fujii's classification. The femoral-head configuration was classified into 4 groups, and the radiological results were evaluated using Severin's classification at the final observation. There was a statistically significant relationship between the shape of the anterior limbus, the number of portions of deformed limbus (superior, anterior, posterior, and the femoral-head configuration. Also, a statistically significant relationship between the shape of the limbus and Severin's classification was observed. These results suggest that the deformed limbus seems to play an important role in triggering femoral-head deformities, possibly via mechanical compression, and negatively affects development of the hip joint.
Hara, Seinosuke; Akazawa, Hirofumi; Mitani, Shigeru; Oda, Ko; Inoue, Hajime
2002-04-01
Two-directional arthrographic findings made during conservative treatment of developmental dislocation of the hip were compared with the femoral-head configurations and radiological results obtained from long-term follow-up examinations in this retrospective study. Sixty hips were followed until at least age 14. Arthrography was carried out according to Terazawa's method. The shape of the superior, anterior, and posterior limbus was evaluated based on a modified Fujii's classification. The femoral-head configuration was classified into 4 groups, and the radiological results were evaluated using Severin's classification at the final observation. There was a statistically significant relationship between the shape of the anterior limbus, the number of portions of deformed limbus (superior, anterior, posterior), and the femoral-head configuration. Also, a statistically significant relationship between the shape of the limbus and Severin's classification was observed. These results suggest that the deformed limbus seems to play an important role in triggering femoral-head deformities, possibly via mechanical compression, and negatively affects development of the hip joint.
Velazquez, Antonio; Swartz, R. Andrew
2015-02-01
Economical maintenance and operation are critical issues for rotating machinery and spinning structures containing blade elements, especially large slender dynamic beams (e.g., wind turbines). Structural health monitoring systems represent promising instruments to assure reliability and good performance from the dynamics of the mechanical systems. However, such devices have not been completely perfected for spinning structures. These sensing technologies are typically informed by both mechanistic models coupled with data-driven identification techniques in the time and/or frequency domain. Frequency response functions are popular but are difficult to realize autonomously for structures of higher order, especially when overlapping frequency content is present. Instead, time-domain techniques have shown to possess powerful advantages from a practical point of view (i.e. low-order computational effort suitable for real-time or embedded algorithms) and also are more suitable to differentiate closely-related modes. Customarily, time-varying effects are often neglected or dismissed to simplify this analysis, but such cannot be the case for sinusoidally loaded structures containing spinning multi-bodies. A more complex scenario is constituted when dealing with both periodic mechanisms responsible for the vibration shaft of the rotor-blade system and the interaction of the supporting substructure. Transformations of the cyclic effects on the vibrational data can be applied to isolate inertial quantities that are different from rotation-generated forces that are typically non-stationary in nature. After applying these transformations, structural identification can be carried out by stationary techniques via data-correlated eigensystem realizations. In this paper, an exploration of a periodic stationary or cyclo-stationary subspace identification technique is presented here for spinning multi-blade systems by means of a modified Eigensystem Realization Algorithm (ERA) via
基于小波子空间法的MIMO系统辨识研究%Identification of MIMO System Based on Subspace Method in Wavelet Domain
Institute of Scientific and Technical Information of China (English)
李振强
2013-01-01
For the LTI multi-input multi-output (MIMO) system with the noise corrupted output data,an identification method of MIMO system was proposed by using the input-output data in wavelet domain directly.The subspace state space system identification method is a main method for MIMO system in time domain.Through the projection of data matrices,we took the QR decomposition and singular value decomposition of data matrices,and identified the order of the MIMO system,then obtained the estimated system matrices of the state equation.By means of wavelet transform,the signal was become a signal in wavelet domain.The MIMO system was identified by the wavelet subspace state space system identifcation method.Compared with the subspace state space system identification method in time domain,the proposed method is feasible and effective by the simulation.%针对线性时不变多输人多输出(MIMO)系统的输出存在随机噪声情况下,提出直接利用小波域的输入输出数据,辨识MIMO系统的方法.子空间状态空间法是时域辨识MIMO系统的主要方法,通过数据矩阵投影,对数据矩阵进行QR分解和奇异值分解,辨识出系统的阶数和系统的状态方程矩阵.运用小波变换,将时域信号转换为小波域的信号,利用小波子空间状态空间辨识算法对MIMO系统辨识,通过仿真,得到辨识的结果与时域子空间状态空间法相比较,证明提出方法是有效的.
Energy Technology Data Exchange (ETDEWEB)
Weston, Brian T. [Univ. of California, Davis, CA (United States)
2017-05-17
This dissertation focuses on the development of a fully-implicit, high-order compressible ow solver with phase change. The work is motivated by laser-induced phase change applications, particularly by the need to develop large-scale multi-physics simulations of the selective laser melting (SLM) process in metal additive manufacturing (3D printing). Simulations of the SLM process require precise tracking of multi-material solid-liquid-gas interfaces, due to laser-induced melting/ solidi cation and evaporation/condensation of metal powder in an ambient gas. These rapid density variations and phase change processes tightly couple the governing equations, requiring a fully compressible framework to robustly capture the rapid density variations of the ambient gas and the melting/evaporation of the metal powder. For non-isothermal phase change, the velocity is gradually suppressed through the mushy region by a variable viscosity and Darcy source term model. The governing equations are discretized up to 4th-order accuracy with our reconstructed Discontinuous Galerkin spatial discretization scheme and up to 5th-order accuracy with L-stable fully implicit time discretization schemes (BDF2 and ESDIRK3-5). The resulting set of non-linear equations is solved using a robust Newton-Krylov method, with the Jacobian-free version of the GMRES solver for linear iterations. Due to the sti nes associated with the acoustic waves and thermal and viscous/material strength e ects, preconditioning the GMRES solver is essential. A robust and scalable approximate block factorization preconditioner was developed, which utilizes the velocity-pressure (vP) and velocity-temperature (vT) Schur complement systems. This multigrid block reduction preconditioning technique converges for high CFL/Fourier numbers and exhibits excellent parallel and algorithmic scalability on classic benchmark problems in uid dynamics (lid-driven cavity ow and natural convection heat transfer) as well as for laser
Kouakou, Matthias
2010-01-01
In this article, we describe the right ideals of $A_1:=k[t,\\partial]$, the first Weyl agebra, over any field $k$ of characteristic zero. For this, we define the notion of primary decomposable subspaces of $k[t]$. This description generalizes a result of Cannings and Holland obtained for an algebraically closed field $k$. Dans cet article, on d\\'ecrit les id\\'eaux \\`a droite de $A_1$ sur un corps quelconque de caract\\'eristique nulle. Pour cela on d\\'efinit la notion de sous-espaces d\\'ecomposables primaires de $k[t]$. Cette description g\\'en\\'eralise un r\\'esultat de Cannings et Holland obtenu pour un corps $k$ alg\\'ebriquement clos.
基于信号子空间的语音增强方法%Speech enhancement method based on the signal subspace
Institute of Scientific and Technical Information of China (English)
曹玉萍
2012-01-01
基于子空间的语音增强算法不同于基于信号处理和统计估计的经典语音增强算法，其核心思想就是将带噪语音信号映射到信号子空间和噪声子空间中，并在信号子空间中估计原始信号。本文提出的算法是以线性代数和矩阵分析为基础，利用对语音信号和噪声协方差矩阵同时对角变换的条件，对混有加性白噪声和粉红噪声的语音信号进行增强处理。经过实验分析及与传统的语音增强算法相比较，语音失真较小，增强效果较好，能够在极大限度地抑制背景噪声的同时减少频谱失真和残余噪声。%The speech enhancement algorithm based on the signal subspace is different from the classic speech enhancement algorithm based on signal processing and statistical estimates. This paper proposes a speech enhancement method based on signal subspace. This method is based on linear algebra and matrix analysis, uses conditions of speech signal and noise covariance matrix and diagonal transform, processes the enhancement of mixture of white noise and pink noise voice signal. Compared through experimental analysis and enhancement algorithms with the traditional voice, it has a smaller distortion, and a better enhancement effect, greatly limits to suppress background noise while reducing the spectral distortion and residual noise
Rooting time delay estimation based on noise subspace approximation%基于逼近噪声子空间的求根时延估计算法
Institute of Scientific and Technical Information of China (English)
巴斌; 胡捍英; 郑娜娥; 任修坤
2016-01-01
多重信号分类(MUSIC)时延估计算法需要多径数估计，且其特征分解和谱峰搜索的计算复杂度较高。针对此问题，给出了一种基于逼近噪声子空间的求根时延估计算法。该算法利用协方差矩阵逆的高次幂逼近噪声子空间与其自身共轭转置的积，并构造多项式等式，以多项式求根的方式避免谱峰搜索，从而降低了计算复杂度。仿真结果表明，在无需多径数估计和复杂度低于MUSIC算法的条件下，所提算法的性能与MUSIC算法的性能相当，并且逼近克拉美罗界。%The Multiple Signal Classification(MUSIC) algorithm requires multipath number estimation. The eigenvalue decomposition and spectral peak searching feature high computational complexity. To address the issues, a new root time delay estimation based on noise subspace approximation is proposed. The proposed algorithm uses the high power inverse matrix to approach the product of both noise subspace and its conjugate transpose. The polynomial is constructed for estimating time delay. The polynomial rooting avoids the spectral peak searching and reduces the computational complexity. Simulation results show that the proposed algorithm has the similar performance as the MUSIC algorithm and approaches the Cramer-Rao Bound(CRB) without multipath number estimation; and the computational complexity of the proposed algorithm is lower than that of the MUSIC algorithm.
R3GMRES: including prior information in GMRES-type methods for discrete inverse problems
DEFF Research Database (Denmark)
Dong, Yiqiu; Garde, Henrik; Hansen, Per Christian
2014-01-01
Lothar Reichel and his collaborators proposed several iterative algorithms that augment the underlying Krylov subspace with an additional low-dimensional subspace in order to produce improved regularized solutions. We take a closer look at this approach and investigate a particular Regularized Ra...... Range-Restricted GMRES method, R3GMRES, with a subspace that represents prior information about the solution. We discuss the implementation of this approach and demonstrate its advantage by means of several test problems....
Radial Basis Function Neural Network Modeling Using Fuzzy Subspace Clustering%模糊子空间聚类的径向基函数神经网络建模
Institute of Scientific and Technical Information of China (English)
张江滨; 邓赵红; 王士同
2015-01-01
传统径向基函数(radial basis function,RBF)神经网络模型在处理噪声环境下的数据时,会因缺乏去除噪音特征的机制而使得受训模型的泛化性能下降.针对此缺陷,根据模糊子空间聚类(fuzzy subspace clus-tering,FSC)算法的子空间特性,为RBF神经网络添加特征抽取机制,提出了一种模糊子空间聚类RBF神经网络建模新方法(RBF neural network modeling using fuzzy subspace clustering,FSC-RBF-NN).与传统RBF神经网络建模方法相比,FSC-RBF-NN方法可根据FSC的子空间特性和特征抽取机制,为不同的隐含层节点选取不同的特征子空间.当训练数据中含有大量噪音特征时,FSC-RBF-NN方法可通过特征抽取机制去除噪音特征,只保留对建模有积极作用的特征,使模型能保持良好的泛化性能.模拟和真实数据集上的实验结果亦验证了FSC-RBF-NN方法在噪声环境下具有更好的鲁棒性.%When training data in the noisy environment, the generalization performance of traditional RBF (radial basis function) neural network is degraded because of the deficiency of feature extraction mechanism. This paper pro-poses a novel modeling method, i.e., RBF neural network modeling using fuzzy subspace clustering (FSC-RBF-NN) which adds feature extraction mechanism to overcome this challenge. Compared with traditional RBF neural network modeling, the proposed method can extract different subspace features for different nodes in hidden layer according to the subspace features of FSC (fuzzy subspace clustering) method and the feature extraction mechanism. When the training data contain lots of noise features, the proposed method can still keep good generalization performance by using the feature extraction mechanism to remove noise features. The experimental results on the synthetic and real-world datasets prove that the FSC-RBF-NN method has strong robustness in the noisy environment.
基于子空间旋转变换的低复杂度波达角估计算法%Low-complexity DOA Estimation via Subspace Rotation Technique
Institute of Scientific and Technical Information of China (English)
闫锋刚; 齐晓辉; 刘帅; 沈毅; 金铭
2016-01-01
多重信号分选(MUltiple SIgnal Classification,MUSIC)算法是波达方向(Direction-Of-Arrival,DOA)估计的最重要算法之一,但庞大的计算量使其工程实用性大打折扣.为降低MUSIC的计算量,该文基于子空间旋转(Subspace Rotation Technique,SRT)变换思想提出了一种高效改进算法,即SRT-MUSIC算法.SRT-MUSIC利用秩亏特性对噪声子空间矩阵按行分块并以旋转变换得到降维噪声子空间,进而基于该降维噪声子空间与导向矢量的正交性构造空间谱估计信号DOA.理论分析表明:SRT-MUSIC能有效避免空间谱搜索中的冗余运算,从而成倍降低算法的计算量.对于大阵元、少信号情况,所提算法计算效率优势更为明显.仿真实验证明了SRT-MUSIC的有效性和高效性.%The MUltiple SIgnal Classification (MUSIC) algorithm is one of the most important techniques for Direction-Of-Arrival (DOA) estimate. However, this method is found expensive in practical applications, due to the heavy computational cost involved. To reduce the complexity, a novel efficient estimator based on Subspace Rotation Technique (STR) is proposed. The key idea is to divide the noise subspace matrix along its row direction into two sub-matrices, and perform STR to get a new rotated sub-noise subspace with reduced dimensions. As this rotated sub-noise subspace is also orthogonal to the signal subspace, a new cost function is finally derived to es-timate DOAs. Theoretical analysis indicates that redundancy computations in spectral search are efficiently avoided by the proposed method as compared to MUSIC, especially in scenarios where large numbers of sensors are applied to locate small numbers of signals. Simulation results verify the effectiveness and efficiency of the new technique.
子空间与维纳滤波相结合的语音增强方法%Speech enhancement method based on combination of subspace and Winner filter.
Institute of Scientific and Technical Information of China (English)
张雪英; 贾海蓉; 靳晨升
2011-01-01
In view of the musical noise after the enhancement of speech corrupted by complicated additive noise, a speech enhancement method based on the combination of subspace and Winner filter is proposed. This method has following steps. By KL transformation the noisy speech is transformed into subspace domain,and the noisy speech eigenvalue is estimated.A Winner filter is formed by using the Signal-Noise-Ratio(SNR) formula in subspace domain. The estimated eigenvalue is filtered by the Winner filter. Thereby the new clean speech eigenvalue is gained. The clean speech is gained by KL reverse transformation. Simulation results show that under the background of white and train noise,the SNR in this method is more excellent than that in traditional subspace method. Meanwhile the musical noise after the enhancement is depressed effectively.%针对复杂背景噪声下语音增强后带有音乐噪声的问题,提出一种子空间与维纳滤波相结合的语音增强方法.对带噪语音进行KL变换,估计出纯净语音的特征值,再利用子空间域中的信噪比计算公式构成一个维纳滤波器,使该特征值通过这个滤波器,从而得到新的纯净语音特征值,由KL逆变换还原出纯净语音.仿真结果表明,在白噪声和火车噪声的背景下,信噪比都比传统予空间方法有明显提高,并有效抑制了增强后产生的音乐噪声.
Institute of Scientific and Technical Information of China (English)
高育新; 孙阔
2014-01-01
Baseline correction for spectrum processing , using subspace pattern recognition principle , selecting the Raman spectra of reactants ( salicylic acid , acetic anhydride ) and catalyst ( sulfamic acid ) as the vector-subspace , the Raman spectra of reaction time as a concerned vector , radian values between concerned vector and the vector -subspace were obtained , reaction end -point can be determined by analyzing changes in radian values with time series analysis model, when experience threshold value was 0.5 and conversion of salicylic acid was 97.8%.%拉曼光谱经过基线校正处理后，运用子空间模式识别原理，反应物水杨酸、醋酸酐以及催化剂氨基磺酸的拉曼光谱作为子空间，各个反应时刻的拉曼光谱作为被关注向量，求取被关注与子空间夹角，通过时间序列分析模型对空间向量夹角变动分析体系内组分变化情况设经验阀值δ（ti）小于0.5判定为反应终点，此时水杨酸的转化率为97.8％。
Institute of Scientific and Technical Information of China (English)
徐兴; 陈特; 陈龙; 王吴杰
2016-01-01
In order to realize the control and coordinated allocation of tire longitudinal force for motorized wheels driving electric vehicle,a longitudinal force estimation method was proposed based on improved closed-loop subspace identification.The characteristics of electric drive system of motorized wheels driving vehicle was analyzed to propose a longitudinal force estimation model.The road simulation test on chassis dynamometer was carried out, and the experimental data were collected. The subspace identification algorithm N4SID was deviated when model input and noise were correlated.To solve the problem,an improved closed-loop subspace identification method was investigated.The results show that compared with N4SID identification method,the improved closed-loop subspace identification method has better anti-interference ability with higher longitudinal force estimation accuracy and better real-time tracking capability,which can meet the requirements of driving force model predictive control based on data driving.%为实现电动轮汽车轮胎纵向力的控制与协调分配，提出了基于改进闭环子空间辨识的电动轮汽车纵向力估计方法。分析了电动轮汽车电驱动系统特性，在此基础上提出了用于辨识的纵向力估计模型。进行底盘测功机道路模拟试验并采集数据。模型输入与噪声相关时，子空间 N4SID （nu-merical algorithm for subspace identification）辨识算法是有偏的，针对这一问题，研究了一种改进闭环子空间辨识算法。结果表明：对比子空间 N4SID 辨识算法，改进闭环子空间辨识算法辨识出的模型具有更好的抗干扰性，纵向力估计精度更高，实时跟踪效果更好，满足基于数据驱动的驱动力模型预测控制的需求。
Smart video crime detection based on 3D model spatio-temporal subspace%三维模型时空子空间引导的智能视频侦查系统
Institute of Scientific and Technical Information of China (English)
卢涤非; 斯进; 王秋
2016-01-01
In order to overcome the problem of the "semantic gap" faced by the traditional video processing technology, this paper proposes a three-dimensional model of spatio-temporal subspace guided smart video detecting technology. Its core idea is that the video data is processed and analyzed with the information contained in the 3D model of spatio-temporal subspace. This paper include: 1, matching 3D target model with the video under the guidance of the shape subspace; 2, 3D model of spatio-temporal subspace guided extraction of video events: monitor object video + spatio-temporal subspace of 3D model → 3D monitored object movement; 3, Comparison of movements in 3D event Library: Sports data + 3D event library → video type and nature. This paper is related to graphics, video processing and criminal technology. It establishes new channels for the use of 3D graphics technology to solve the problem of video detection and has an important academic significance.%为了克服传统视频处理技术面临的“语义鸿沟”等难题，借助三维模型时空子空间所蕴含的信息进行视频处理分析，提出了三维模型时空子空间引导的智能视频侦查技术。①在体形子空间的引导下从视频中匹配三维目标模型。②三维模型时空子空间引导下提取视频事件：监控对象视频+三维模型时空子空间→监控对象三维动作。③三维事件库中的动作比对分类：运动数据+三维事件库→视频类型和性质。文章涉及图形学、视频处理和刑事技术，探索了使用三维图形学技术解决视频侦查难题的新渠道。
A CG Method for Multiple Right Hand Sides and Multiple Shifts in Lattice QCD Calculations
Birk, Sebastian
2012-01-01
We consider the task of computing solutions of linear systems that only differ by a shift with the identity matrix as well as linear systems with several different right hand sides. In the past Krylov subspace methods have been developed which exploit either the need for solutions to multiple right hand sides (e.g. deflation type methods and block methods) or multiple shifts (e.g. shifted CG) with some success. In this paper we present a block Krylov subspace method which, based on a block Lanczos process, exploits both features - shifts and multiple right hand sides - at once. Such situations arise, for example, in lattice QCD simulations within the Rational Hybrid Monte Carlo algorithm. We give numerical evidence that our method is superior to applying other iterative methods to each of the systems individually as well as, in some cases, to shifted or block Krylov subspace methods.
Institute of Scientific and Technical Information of China (English)
谢志刚; 陈自力
2011-01-01
Unmanned Powered Parafoil( UPP)of special flight characteristics is studied, and establish a 9-DOF nonlinear dynamic e-quation. A observer/kalman filter identification(OKID)method and advanced subspace observer/kalman filter identification(OKID)is researched. Using the flight data, the longitudinal slate space models of UPP is identified and derived, and the identified model pitch angle response characters and identified precision are also analysed. The consistent and effective of the identification method are verified in a simulation experiment, and the validity of the identified longitudinal model is also tested in the fly.%对具有独特飞行特性的无人动力伞(Unmanned Powered Parafoil,UPP)进行了研究,建立了无人动力伞九自由度非线性动力学方程,研究了观测器/卡尔曼滤波辨识算法和改进的子空间观测器/卡尔曼滤波辨识算法.根据系统的飞行数据,辨识得到系统的纵向状态空间模型,分析了两种辨识模型的俯仰角响应特性和辨识精度.仿真结果表明子空间观测器/卡尔曼滤波辨识算法的一致和有效估计,能有效辨识无人动力伞的纵向动态模型.