61248473
Efficient parallelization of the iterative solution of a coupled fluid–structure interaction problem: Research Articles
In this paper we consider the parallelization of the generation and iterative solution of coupled linear systems modelling the interaction of an acoustic field in a fluid medium with an elastic structure immersed in the fluid. The particular case studied is that of a hollow steel sphere in water. The aim of the work is to speed up the generation and solution of the systems. We describe the methods used, which involve special sparse storage arrangements and a novel application of a sparse approximate inverse preconditioning technique, and present results showing that the methods are very effective in terms of speeding up the generation and iterative solution of the systems. Copyright © 2007 John Wiley & Sons, Ltd.
122233994
An efficient method for evaluating the integral of a class of highly oscillatory functions
Highly oscillatory integrals require special techniques for their effective evaluation. Various studies have been conducted to find computational methods for evaluating such integrals. In this paper we present an efficient numerical method to evaluate a class of generalised Fourier integrals (on a line or a square) with integrands of the form f(x)e^i^k^g^(^x^), under the assumption that in the domain of integration, both f and g are sufficiently smooth and that g does not have any stationary/critical points. Numerical analysis and results are given to illustrate the effectiveness of our method for computing generalised Fourier integrals.
15785287
A Nonlinear Multigrid Method for Total Variation Minimization from Image Restoration
Abstract
Image restoration has been an active research topic and variational formulations are particularly effective in high quality recovery. Although there exist many modelling and theoretical results, available iterative solvers are not yet robust in solving such modeling equations. Recent attempts on developing optimisation multigrid methods have been based on first order conditions. Different from this idea, this paper proposes to use piecewise linear function spanned subspace correction to design a multilevel method for directly solving the total variation minimisation. Our method appears to be more robust than the primal-dual method (Chan et al., SIAM J. Sci. Comput. 20(6), 1964–1977, 1999) previously found reliable. Supporting numerical results are presented.

3879090
On A Recursive Schur Preconditioner For Iterative Solution Of A Class Of Dense Matrix Problems
There are currently several distinct preconditioning methods for dense matrices based on applying a wavelet transform to obtain a matrix with a large number of small entries. A sparse preconditioner for this transformed matrix can be formed by setting to zero entries that are assumed to be unimportant. The effectiveness of the preconditioner depends on retaining the most important entries and on ensuring that they are positioned conveniently within the transformed matrix. In this paper we present a new, recursive preconditioning strategy that takes into account more of the significant entries without greatly increasing cost and outperforms existing methods in certain cases.
25215706
On Two Variants of an Algebraic Wavelet Preconditioner
A recursive method of constructing preconditioning matrices for the nonsymmetric stiffness matrix in a wavelet basis is proposed for solving a class of integral and differential equations. It is based on a level-by-level application of the wavelet scales decoupling the different wavelet levels in a matrix form just as in the well-known nonstandard form. The result is a powerful iterative method with built-in preconditioning leading to two specific algebraic multilevel iteration algorithms: one with an exact Schur preconditioning and the other with an approximate Schur preconditioning. Numerical examples are presented to illustrate the efficiency of the new algorithms.
12726286
Fast Multilevel Algorithm for a Minimization Problem in Impulse Noise Removal
An effective 2-phase method for removing impulse noise was recently proposed. Its phase 1 identifies noisy pixel candidates by using median-type filters. Then in phase $2$, it restores only the noisy pixel candidates by some variational methods. The resulting method can handle salt-and-pepper noise and random-valued impulse noise at a level as high as 90% and 60%, respectively. The aim of this paper is to generalize a fast multilevel method for Gaussian denoising to solving the minimization problem arising in phase $2$ of the 2-phase method. The multilevel algorithm gives better images than standard optimization methods such as the Newton method or the conjugate gradient method. Also it can handle more general regularization functionals than the smooth ones previously considered. Supporting numerical experiments on two-dimensional gray-scale images are presented.
10775107
An Architecture Approach to Dynamic Policy in RBAC
Dynamic policy enables the system to adjust the policies according to the changing circumstance, and makes the system more flexible and adaptive. We have proposed a dynamic model and the idea is to dynamically change the policy according to a pair of states rather than one state, which provides more information for the policy decision making, thus makes policy more accurate. The dynamic policy architecture based on this model is built in this paper, and we describe the components and steps of detecting a change, deciding the pair of current and previous states and picking up the policy according to the change.
24158595
A Fractal Watermark Solution for Product Data
Copyright and integrity protection over product data is becoming an urgent requirement, because in a Distributed Networked Manufacturing environment, enterprises have to interact with each other to share product data. In this paper, we introduce a novel watermarking solution for product data based on the idea of fractal, which classifies product data watermarks into three tiers: fractal watermark, tree-structure watermark, and tree-node watermark. It embeds watermarks not only in value-carrying nodes but also in product structures that "glue" all the nodes together by embedding multiple structure watermarks in product data. Simulation results show that the proposed technique is robust against various forms of malicious attacks of product data, and naturally immune from structure transformation attacks and subtractive (sub-tree) attacks. Moreover, the solution can protect tree-structure data whose knowledge is mainly embodied in the tree structure efficiently. It is very easy to implement and popularize.
34365785
Zhang Neural Network Versus Gradient Neural Network for Online Time-Varying Quadratic Function Minimization
With the proved efficacy on solving linear time-varying matrix or vector equations, Zhang neural network (ZNN) could be generalized and developed for the online minimization of time-varying quadratic functions. The minimum of a time-varying quadratic function can be reached exactly and rapidly by using Zhang neural network, as compared with conventional gradient-based neural networks (GNN). Computer-simulation results substantiate further that ZNN models are superior to GNN models in the context of online time-varying quadratic function minimization.
1589839
Optimization of the Sliding Window Size for Protein Structure Prediction
Sliding window based methods are relatively often applied in prediction of various aspects related to protein structure. Despite their wide spread use, researchers did not establish a standard related to the size of the window, i.e., window sizes ranging between 7 and 17 residues were used in the past. To this end, this paper performs a computational study based on a probabilistic approach that aims at finding an optimal sliding window size. The results shows that formation of helical structure can be affected by amino acids (AAs) that are up to 9 positions away in the sequence, while the formation of coils and strands can be affected by AAs that are up to 3 and 6 positions away, respectively. Overall, our results suggest that a sliding window with 19 residues is optimal for secondary structure prediction, while for a specific prediction tasks, such as prediction of p-strands, a smaller window size is sufficient. Finally, the 20 AAs are categorized into five groups based on their influence of formation of the secondary structure. The finding related to the optimal window size was confirmed based on an independent experimental study related to the prediction of secondary protein structure
11647155
Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification
In practical applications of pattern recognition, there are often different features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with different features are viewed as a general problem in various application areas of pattern recognition. In this paper, a systematic investigation has been made and possible solutions are classified into three frameworks, i.e. linear opinion pools, winner-take-all and evidential reasoning. For combining multiple classifiers with different features, a novel method is presented in the framework of linear opinion pools and a modified training algorithm for associative switch is also proposed in the framework of winner-take-all. In the framework of evidential reasoning, several typical methods are briefly reviewed for use. All aforementioned methods have already been applied to text-independent speaker identification. The simulations show that results yielded by the methods described in this paper are better than not only the individual classifiers' but also ones obtained by combining multiple classifiers with the same feature. It indicates that the use of combining multiple classifiers with different features is an effective way to attack the problem of text-independent speaker identification.