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Large-scale nonlinear optimal power flow (OPF) problems have been solved lately by primal-dual interior point (IP) methods. In spite of their success, there are many situations in which IP-based OPF programs can fail to find a solution. On the other hand, with power systems operating heavily loaded there is an increasing need for globally convergent OPF solvers. Trust region schemes have been used...
Sparse matrix-vector and multi-vector multiplications (SpMV and SpMM) are performance bottlenecks operations in numerous HPC applications. A variety of SpMV GPU kernels using different matrix storage formats have been developed to accelerate these applications. Unlike SpMV, where matrix elements are accessed only once, multiplying by k vectors requires accessing matrix elements k times. In this paper...
The main objective of this work consists in analyzing sub-structuring method for the parallel solution of sparse linear systems with matrices arising from the discretization of partial differential equations such as finite element, finite volume and finite difference. With the success encountered by the general-purpose processing on graphics processing units (GPGPU), we develop an hybrid multi GPUs...
This paper gives an analysis and an evaluation of linear algebra operations on Graphics Processing Unit (GPU) with complex number arithmetics with double precision. Knowing the performance of these operations, iterative Krylov methods are considered to solve the acoustic problem efficiently. Numerical experiments carried out on a set of acoustic matrices arising from the modelisation of acoustic phenomena...
Gaia is a 5-year ESA (European Space Agency) cornerstone mission launched at the end of 2013. Its main goal is the production of a 5-parameter astrometric catalogue (i.e. positions, parallaxes and the two components of the proper motions) at the micro-arcsecond level for about 1 billion stars of our Galaxy by means of high-precision measurements. The main task of the code presented in this paper is...
In this paper, we present a new matrix-free timedomain method that does not require a matrix solution in nature and is independent of the element shape. The method is applicable to both structured discretizations and unstructured meshes without any need for change. Numerical simulations of both 2- and 3-D examples discretized into irregular elements have validated the proposed matrix-free method and...
A new method is proposed that is aimed to identify all global loop basis functions (GLBFs) for surfaces with at least one genus. The method relies on non-contractible circles on the geometry, based on which all GLBFs are obtained. The method has a low computational complexity of O(g2E), where g is the number of genera, and E is the number of edges. It is especially efficient for a surface with a small...
Over the past several decades, neural networks have evolved into powerful computation systems, which are able to learn complex nonlinear input-output relationship from data. However, the structure optimization problem of neural network is a big challenge for processing huge-volumed, diversified and uncertain data. This paper focuses on this problem and introduces a network pruning algorithm based...
Reconstruction of missing features promotes robustness in speaker recognition applications under noisy conditions. In this paper, we aim at enhancing the reliability of speech features for noise robust speaker identification under short training and testing sessions restrictions. Towards this direction, we apply a low-rank matrix recovery approach to reconstruct the unreliable spectrographic data...
This paper presents a novel greedy reconstruction algorithm for speech signal, named the variable step-size sparsity adaptive matching pursuit algorithm (short for VSSAMP). As the name implies, this modified algorithm achieves an improvement compared with the state-of-the-art greedy algorithm sparsity adaptive matching pursuit (SAMP). The new algorithm varies the step size, which is fixed in SAMP...
This paper presents a hierarchical approach to single image intrinsic decomposition based on non-local L0 sparsity. In contrast to previous studies using heuristic methods to well-define the ill-posed problem, our approach is able to effectively construct sparse, non-local and multiscale reflectance dependencies in an unsupervised manner, thus is less dependent on the chromaticity feature and more...
Graph matching (GM) is a fundamental problem in computer science, and it has been successfully applied to provide solutions to many problems in computer vision. In this paper, we consider GM as a clustering problem in an association graph whose nodes represent candidate correspondences between two graphs to be matched. And we take the dense subgraph as a good prior for correct correspondences, thus...
In this paper, a weighted sparse Bayesian learning algorithm for off-grid direction of arrival (DOA) estimation is proposed. By utilizing the relationship between the noise subspace and the overcomplete basis matrix, the weights are designed and treated as the hyperprior knowledge of the signals, which changes the variance of the Laplace distribution of the signal, i.e., the average power of the signal,...
Compressive sensing theory shows that sparse signals can be reconstructed from far less samples than those required by the classical Shannon-Nyquist Theorem. An optimized sensing matrix for a certain class of signals can further reduce the necessary number of samples. Additionally, in order to make the signals represented as sparse as possible, a dictionary can be optimized. In this paper, we introduce...
Sparse signals can be sensed with a reduced number of projections and then reconstructed if compressive sensing is employed. Traditionally, the projection matrix is chosen as a random matrix, but a projection sensing matrix that is optimally designed for a certain class of signals can further improve the reconstruction accuracy. This paper considers the problem of designing the projection matrix Φ...
In a military tactical network where a trust authority (e.g., a commander) makes a decision during a mission, assessing the trustworthiness of participating entities accurately is critical to mission success. In this work, we propose a trust-based reputation management scheme, called GlobalTrust, for minimizing false decisions on the reputation of nodes in the network. In the proposed scheme, nodes...
This paper presents a review of modulation and control strategies for matrix converters applied to permanent magnet synchronous generator (PMSG) in wind power generation systems. Modulation techniques already discussed in scientific literature for the command of matrix converters are mostly divided into three categories: the scalar modulations, the pulse width modulations and the predictive control...
The compressive sensing (CS) theory has shown that sparse signals can be reconstructed exactly from much fewer measurements than traditionally believed. What's more, using ℓp-norm minimization with p < 1 can do so with much fewer measurements than with p=1. In this paper, a novel algorithm is proposed for computing local minima of the nonconvex problem in the block-sparse system. A series of experiments...
Web service recommendation is of great importance when users face a large number of functionally-equivalent candidate services. To recommend Web services that best fit a user's need, QoS values which characterize the non-functional properties of those candidate services are in demand. But in reality, the QoS information of Web service is not easy to obtain, because only limited historical invocation...
Current methods of parametric model order reduction based on the interpolation of system matrices are extended in this paper to an efficient method to tackle multidimensional parameter spaces. In order to overcome the curse of dimensionality the technique of sparse grids with multiple outputs is applied. The procedure is divided into an offline and online phase. In the offline phase the weighting...
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