The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
J M Kate - One of the best experts on this subject based on the ideXlab platform.
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Strength of fabric reinforced sand under axisymmetric loading by B. Chandrasekaran, Bengt B. Broms & Kai S. Wong
Geotextiles and Geomembranes, 1991Co-Authors: F H Shamser, J M KateAbstract:Abstract The results of these studies indicated that: 1. 1. The original model at lower confining pressure and R D up to 1 only shows a good match with modified model and measured values. However, at higher confining pressures and R D > 1 it differs considerably. 2. 2. In general, the modified model and the measured values show good match. 3. 3. The value of α of 0·45 computed in the present study seems to be consistent enough to make easy prediction.
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strength of fabric reinforced sand under axisymmetric loading by b Chandrasekaran bengt b broms kai s wong
Geotextiles and Geomembranes, 1991Co-Authors: F H Shamser, J M KateAbstract:Abstract The results of these studies indicated that: 1. 1. The original model at lower confining pressure and R D up to 1 only shows a good match with modified model and measured values. However, at higher confining pressures and R D > 1 it differs considerably. 2. 2. In general, the modified model and the measured values show good match. 3. 3. The value of α of 0·45 computed in the present study seems to be consistent enough to make easy prediction.
F H Shamser - One of the best experts on this subject based on the ideXlab platform.
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Strength of fabric reinforced sand under axisymmetric loading by B. Chandrasekaran, Bengt B. Broms & Kai S. Wong
Geotextiles and Geomembranes, 1991Co-Authors: F H Shamser, J M KateAbstract:Abstract The results of these studies indicated that: 1. 1. The original model at lower confining pressure and R D up to 1 only shows a good match with modified model and measured values. However, at higher confining pressures and R D > 1 it differs considerably. 2. 2. In general, the modified model and the measured values show good match. 3. 3. The value of α of 0·45 computed in the present study seems to be consistent enough to make easy prediction.
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strength of fabric reinforced sand under axisymmetric loading by b Chandrasekaran bengt b broms kai s wong
Geotextiles and Geomembranes, 1991Co-Authors: F H Shamser, J M KateAbstract:Abstract The results of these studies indicated that: 1. 1. The original model at lower confining pressure and R D up to 1 only shows a good match with modified model and measured values. However, at higher confining pressures and R D > 1 it differs considerably. 2. 2. In general, the modified model and the measured values show good match. 3. 3. The value of α of 0·45 computed in the present study seems to be consistent enough to make easy prediction.
Shiqian Ma - One of the best experts on this subject based on the ideXlab platform.
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Alternating direction methods for latent variable gaussian graphical model selection
Neural Computation, 2013Co-Authors: Shiqian MaAbstract:Chandrasekaran, Parrilo, and Willsky 2012 proposed a convex optimization problem for graphical model selection in the presence of unobserved variables. This convex optimization problem aims to estimate an inverse covariance matrix that can be decomposed into a sparse matrix minus a low-rank matrix from sample data. Solving this convex optimization problem is very challenging, especially for large problems. In this letter, we propose two alternating direction methods for solving this problem. The first method is to apply the classic alternating direction method of multipliers to solve the problem as a consensus problem. The second method is a proximal gradient-based alternating-direction method of multipliers. Our methods take advantage of the special structure of the problem and thus can solve large problems very efficiently. A global convergence result is established for the proposed methods. Numerical results on both synthetic data and gene expression data show that our methods usually solve problems with 1 million variables in 1 to 2 minutes and are usually 5 to 35i¾ times faster than a state-of-the-art Newton-CG proximal point algorithm.
George J. Pappas - One of the best experts on this subject based on the ideXlab platform.
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Data-Driven Network Resource Allocation for Controlling Spreading Processes
IEEE Transactions on Network Science and Engineering, 2015Co-Authors: Shuo Han, Victor M. Preciado, Cameron Nowzari, George J. PappasAbstract:We propose a mathematical framework, based on conic geometric programming, to control a susceptible-infected-susceptible viral spreading process taking place in a directed contact network with unknown contact rates. We assume that we have access to time series data describing the evolution of the spreading process observed by a collection of sensor nodes over a finite time interval. We propose a data-driven robust optimization framework to find the optimal allocation of protection resources (e.g., vaccines and/or antidotes) to eradicate the viral spread at the fastest possible rate. In contrast to current network identification heuristics, in which a single network is identified to explain the observed data, we use available data to define an uncertainty set containing all networks that are coherent with empirical observations. Through Lagrange duality and convexification of the uncertainty set, we are able to relax the robust optimization problem into a conic geometric program, recently proposed by Chandrasekaran and Shah [1], which allows us to efficiently find the optimal allocation of resources to control the worst-case spread that can take place in the uncertainty set of networks. We illustrate our approach in a transportation network from which we collect partial data about the dynamics of a hypothetical epidemic outbreak over a finite period of time.
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Data-Driven Allocation of Vaccines for Controlling Epidemic Outbreaks
arXiv: Optimization and Control, 2014Co-Authors: Shuo Han, Victor M. Preciado, Cameron Nowzari, George J. PappasAbstract:We propose a mathematical framework, based on conic geometric programming, to control a susceptible-infected-susceptible viral spreading process taking place in a directed contact network with unknown contact rates. We assume that we have access to time series data describing the evolution of the spreading process observed by a collection of sensor nodes over a finite time interval. We propose a data-driven robust convex optimization framework to find the optimal allocation of protection resources (e.g., vaccines and/or antidotes) to eradicate the viral spread at the fastest possible rate. In contrast to current network identification heuristics, in which a single network is identified to explain the observed data, we use available data to define an uncertainty set containing all networks that are coherent with empirical observations. Our characterization of this uncertainty set of networks is tractable in the context of conic geometric programming, recently proposed by Chandrasekaran and Shah, which allows us to efficiently find the optimal allocation of resources to control the worst-case spread that can take place in the uncertainty set of networks. We illustrate our approach in a transportation network from which we collect partial data about the dynamics of a hypothetical epidemic outbreak over a finite period of time.
Zouhui - One of the best experts on this subject based on the ideXlab platform.
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Alternating direction methods for latent variable gaussian graphical model selection
Neural Computation, 2013Co-Authors: Mashiqian, Xuelingzhou, ZouhuiAbstract:Chandrasekaran, Parrilo, and Willsky 2012 proposed a convex optimization problem for graphical model selection in the presence of unobserved variables. This convex optimization problem aims to esti...