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In this work, first part of this study, the high resolution numerical schemes of Lax and Wend off, of Yee, Warming and Harten, of Yee, and of Harten and Osher are applied to the solution of the Euler and Navier-Stokes equations in two-dimensions. With the exception of the Lax and Wend off and of the Yee schemes, which are symmetrical ones, all others are flux difference splitting algorithms. All schemes...
To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold...
In this paper we consider distributed localization in a wireless peer-to-peer network. Each node is required to estimate the geographical configuration of the whole network (i.e., the positions of all nodes regardless of link availability) based on local processing and iterated information exchange with neighbors. We propose a new weighted-average consensus method based on received-signal-strength...
The application of compressive sensing to computational electromagnetics is demonstrated by considering the radiation from large polygon-shaped apertures. For a well-behaved aperture distribution, it is known that the radiation may be is found in terms of virtual sources located at the vertices. This sparse representation enables the use of sub-Nyquist sampling to obtain the coefficients of the expansion...
Curvilinear, higher-order, triangular elements are often used in the Method of Moments (MoM). In such cases quadrature schemes based on near-singularity cancellation transformations have shown great promise. This paper focuses on the field evaluation integral involving the scalar Green function gradient kernel grad(1/R). Schemes based on splitting into three sub-triangles, followed by near-singularity...
Localization, the determination of geographical location of sensors is a fundamental problem in wireless sensor networks. In this paper we consider a static wireless network in which the reference nodes are static-non moving. We propose a positioning method known as hybrid optimizing algorithm (HOA) which combines steepest decent method and Taylor series expansion method. The steepest decent method...
In this paper we address the problem of recovering a matrix, with inherent low rank structure, from its lower dimensional projections. This problem is frequently encountered in wide range of areas including pattern recognition, wireless sensor networks, control systems, recommender systems, image/video reconstruction etc. Both in theory and practice, the most optimal way to solve the low rank matrix...
In this paper, the particle swarm optimization algorithm (PSO) for reservoir optimal operation is studied. A new algorithm which is suitable for reservoir optimal operation called multiple groups of gradient particle swarm optimization algorithm (MGPSO) is proposed to avoid the shortcomings of PSO including premature convergence, poor search accuracy and easily falling into local optimal solution...
This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an...
Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a popular choice. However, the performance of the RF can be degraded if the local patch...
Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition methods update a small subset of dual variables by solving a restricted optimization problem. In this paper, we propose an exact and efficient method for solving the restricted problem. In our method, the restricted problem is reduced...
Large scale image classification requires efficient scalable learning methods with linear complexity in the number of samples. Although Stochastic Gradient Descent is an efficient alternative to classical Support Vector Machine, this method suffers from slow convergence. In this paper, our contribution is two folds. First we consider the minimization of specific calibrated losses, for which we show...
Monte Carlo (MC) sequential simulation is capable of providing system chronological information. It is a very useful tool for power system reliability analysis and planning, especially for systems involving time-dependent sources. In this paper, a new sequential simulation approach is proposed for reliability evaluation of composite power systems. The main idea is to apply Latin Hypercube sampling...
This paper deals with a new scheme for coupling phasor-mode and electromagnetic transients simulations. In each simulation, an iteratively updated linear equivalent is used to represent the effect of the subsystem treated by the other simulation. Time interpolation and phasor extraction methods adapted to this scheme are presented and compared to existing methods. Finally, simulation results obtained...
We design the control laws which can be used to prepare operators based on quantum Lyapunov control method for two level Markovian open quantum systems in this paper. For the operator preparation, an often used Lyapunov function is the distance Vdis between the evolution operator U (t) and the target operator Uf. We construct a novel Lyapunov function V which is from the matrix logarithm function...
This paper proposes a novel unsupervised cosegmentation method which automatically segments the common objects in multiple images. It designs a simple superpixel matching algorithm to explore the inter-image similarity. It then constructs the object mask for each image using the matched superpixels. This object mask is a convex hull potentially containing the common objects and some backgrounds. Finally,...
PLL (phase-locked loop) is widely used in radar, navigation equipment and space technology, affecting the performance of the device directly. It's essential to study parts of the PLL to improve the performance of PLL. PD (Phase discriminator) is a kind of phase comparison device, its performance directly affects the performance of the entire loop. This paper studies PD from four aspects of calculated...
In indoor environment, there are gross errors in random measured values of base station, which has effect on generalization ability of BP neural network and then results in low location accuracy. In order to improve location accuracy, location algorithm of BP Neural Network based on residual analysis is proposed, namely conducting pretreatment on measured values separately in training phase and location...
The simultaneous localization and mapping (SLAM) based on a conventional centralized filter reconfigures the entire state vectors in every necessary cycle as the number of landmarks changes, which is result in an exponential growth in computation quantities and hard to isolate potential faults. For that, SLAM system using distributed particle filter was presented to cope with these problems. In this...
A new modification of the popular finite-time-convergent robust exact sliding-mode-based differentiator is proposed. Such nth-order differentiator provides for the fast global convergence of its outputs to the first n exact derivatives of its input, provided a time-variable local Lipschitz constant of the input's nth derivative is available and has a bounded logarithmic derivative. It features the...
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