An assessment of the accuracy of shock-capturing schemes is made for two-dimensional steady flow around a cylindrical projectile. Both a linear fourth-order method and a nonlinear third-order method are used in this s...
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An assessment of the accuracy of shock-capturing schemes is made for two-dimensional steady flow around a cylindrical projectile. Both a linear fourth-order method and a nonlinear third-order method are used in this study. This study shows, contrary to conventional wisdom, that captured two-dimensional shocks are asymptotically first order, regardless of the design accuracy of the numerical method. The practical implications of this finding are discussed in the context of the efficacy of high-order numerical methods for discontinuous flows.
It is well known that proper scaling can increase the efficiency of computational problems. In this paper, we define and show that a balancing technique can substantially improve the computational efficiency of optima...
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It is well known that proper scaling can increase the efficiency of computational problems. In this paper, we define and show that a balancing technique can substantially improve the computational efficiency of optimal-control algorithms. We also show that noncanonical scaling and balancing procedures may be used quite effectively to reduce the computational difficulty of some hard problems. These results have been used successfully for several flight and field operations at NASA and the U.S. Department of Defense. A surprising aspect of our analysis shows that it may be inadvisable to use autoscaling procedures employed in some software packages. The new results are agnostic to the specifics of the computational method;hence, they can be used to enhance the utility of any existing algorithm or software.
Complex networks depict the individual relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their...
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Complex networks depict the individual relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or clusters. However, the existing algorithms are with high complexity, which cost much computational time. In this paper, an efficient graph clustering algorithm based on spectral coarsening is proposed, to deal with the large time complexity of the traditional spectral algorithm. We first find the subset most possibly belonged to the same cluster in the original network, and merge them into a single node. The scale of the network will decrease with the network being coarsened. Then, the spectral clustering algorithm is performed on the coarsened network with the maintained advantages and the improved time efficiency. Finally, the experimental results on the multiple datasets demonstrate that the proposed algorithm, compared with the current state-of-the-art methods, has superior performance.
Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers...
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ISBN:
(纸本)9781467369985
Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from local maxima, to discriminative HMMs. The resulting method provides the posterior probabilities of the classes without explicitly determining the HMM parameters, and is able to deal with missing labels. We apply the method to Human Activity Recognition (HAR) using two different types of sensors: portable inertial sensors, and fixed, wireless binary sensor networks. Our algorithm outperforms the standard discriminative HMM learning in both complexity and accuracy.
Simultaneous attacks can cause devastating damage, breaking down communication networks into small fragments. To mitigate the risk and develop proactive responses, it is essential to assess the robustness of network i...
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ISBN:
(纸本)9781467317290
Simultaneous attacks can cause devastating damage, breaking down communication networks into small fragments. To mitigate the risk and develop proactive responses, it is essential to assess the robustness of network in the worst-case scenarios. In this paper, we propose a spectral lower-bound on the number of removed links to incur a certain level of disruption in terms of pairwise connectivity. Our lower-bound explores the latent structural information in the network Laplacian spectrum, the set of eigenvalues of the Laplacian matrix, to provide guarantees on the robustness of the network against intentional attacks. Such guarantees often cannot be found in heuristic methods for identifying critical infrastructures. For the first time, the attack-resistant proofs of large scale communication networks against link attacks are presented.
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