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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...
A stochastic approximation with averaging is applied to a consensus algorithm for multi-agent systems and the convergence of the algorithm is analyzed. The consensus is considered with respect to the time average of the states of agents. For the fixed network structure and time-varying structure, the relation between the number of iterations of the algorithm and consensus accuracy is explicitly clarified...
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems with linear measurement. The certain conditions to ensure that the estimation error of CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large estimation error. Accordingly, a modified CKF (MCKF)...
Many combinatorial problems in fields such as object tracking involve reasoning over correspondence, e.g., calculating the probability that a measurement belongs to a particular track. Recent studies have shown that loopy belief propagation (LBP) provides a highly desirable option in the trade-off between accuracy and computational complexity in this task. LBP can be understood as a particular method...
The science gateway MoSGrid (Molecular Simulation Grid) is a valuable and user-friendly tool to submit and process molecular simulation studies on a large scale. With regard to the needs of the users, we focus on the comparability of simulations using two prominent quantum chemical codes, Gaussian09 and NWChem. At first sight, the definition of functionals and basis sets seems to be sufficient to...
Due to the explosive increment of data in big data era, it is a challenging task to analyze and extract meaningful data for users. Data needs to be timely operated because of the time sensitivity, so it faces enormous pressure in storage and computing. To deal with the problem that it is hard to achieve valuable information from out-of-order streams over big data in short time, a model-matching algorithm...
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