In order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (cg) algorith...
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In order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (cg) algorithm and the Levenberg-Marquardt (LM) algorithm, the cg-BPNN and LM-BPNN models of small signal for gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) are obtained and analyzed here. At first, the scattering parameters (S-parameters) of GaAs pHEMT are divided into training set and test set randomly. Experimental results show that the cg-BPNN model is better than another S-parameters when predicting ImS(12) with mean square error (MSE) of 7.6632e-06, while LM-BPNN model predicts ImS(12) with MSE of 2.4672e-06. Meanwhile, the MSE of cg-BPNN model is higher than LM-BPNN model when predicting all the S-parameters. In addition, it shows a smaller fluctuation range for the error curve of LM-BPNN model, which is more stable than the cg-BPNN model. Therefore, the double hidden layer LM-BPNN model is the better choice to characterize the small signal of GaAs pHEMT.
This article proposes an optimal strategy by using line hardening and energy storage system (ESS) deployment to enhance the distribution system (DS) resilience in natural disasters. A multi-stage multi-zone DS line st...
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This article proposes an optimal strategy by using line hardening and energy storage system (ESS) deployment to enhance the distribution system (DS) resilience in natural disasters. A multi-stage multi-zone DS line state set is established, based on the conceptual resilience quantification, to reflect spatial and temporal characteristics of the severe contingencies. To address random contingencies, the proposed problem is formulated as a defender-attacker-defender (DAD) model that minimizes the weighted load shedding in the worst-case scenario, and the column-and-constraint generation (C&cg) algorithm is used to solve the model efficiently. The effectiveness of the proposed strategy is verified on the modified IEEE 33-node DS and a modified power grid representing a province in China.
Frequent natural disasters have a serious impact on the power system. It is urgent to ensure the normal operation of the distribution network under disaster events. This paper divides the stages of improving the resil...
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Frequent natural disasters have a serious impact on the power system. It is urgent to ensure the normal operation of the distribution network under disaster events. This paper divides the stages of improving the resilience of the distribution network into two aspects: long-term investment planning and short-term response. We combine the decision-making process with the chronological order of natural disasters, and a N-k uncertainty set is applied to describe the uncertainty of distribution network damage. We propose a line hardening and distributed genera-tions configuration strategy for the purpose of enhancing the resilience of distribution networks and countering extreme natural disasters. The problem is expressed as a tri-level robust optimization formulation in order to minimize the load shedding under the worst line damaged scheme. The first level corresponds to the long-term investment planning and preparation of pre-disaster stage. The second level represents the stage of line damage, and the third level matches the optimal scheduling during the post-disaster period. On the basis, this mixed integer linear programming problem is transformed into a solvable form, which is calculated by a modified Column and Constraint Generation (C & cg) algorithm. Case studies show that our proposed model can reduce load shedding to 70% of the original level, and the running time of the algorithm is less than 60 s, which verifies the model can effectively enhance the resilience of the distribution network and enhance its ability to respond to natural disasters.
The increased penetration of natural gas-fired generation units in electricity systems calls for coordination of natural gas and electricity systems. Moreover, concerns over security of coordinated gas-electricity sys...
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The increased penetration of natural gas-fired generation units in electricity systems calls for coordination of natural gas and electricity systems. Moreover, concerns over security of coordinated gas-electricity systems are spreading in recent years. Notably, both energy systems are vulnerable to malicious cyber-attacks. Within this context, this article (1) proposes a coordination scheme for the day-ahead schedule of natural gas and electricity systems;(2) defines a new class of false data injection (FDI) cyber-attacks on the gas demand and scheduling information of the natural gas system;and (3) investigates the impacts of such attacks on the operation of both energy systems. The coordination mechanism consists of three nested optimization problems. The natural gas system scheduling step in the mechanism is modeled as a bi-level max-min optimization problem with consideration of attacks. We use a column and constraint generation (C&cg) algorithm to solve this bi-level problem. The coordination mechanism and bi-level attack problem are examined using a case study based on the IEEE 24-node test system and a 9-node gas system, as well as a case study based on the IEEE 118-node test system and a 25-node gas system.
Location estimation is a very useful concept in the real world. The assessment of the localization system is based on the wireless technology used, positioning algorithm, complexity, scalability, accuracy, power consu...
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ISBN:
(纸本)9781479918546
Location estimation is a very useful concept in the real world. The assessment of the localization system is based on the wireless technology used, positioning algorithm, complexity, scalability, accuracy, power consumption, size and cost. GPS based localization are inconvenient due to cost and poor signal coverage area. Modern localization system use techniques like AoA, ToA, TDoA & RSS. Among them RSS is an attractive approach since it provides low cost and easy implementation. This survey paper mainly focuses the estimation of received signal strength (RSS) based LBS and it's attributes like accuracy, cost etc.
Xeon Phi is a high performance co-processor launched by Intel in 2012. Though Phi is specifically designed for Exascale super computer, the task assignment for Phi is yet to be studied. Based on the special needs of t...
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ISBN:
(纸本)9781479941698
Xeon Phi is a high performance co-processor launched by Intel in 2012. Though Phi is specifically designed for Exascale super computer, the task assignment for Phi is yet to be studied. Based on the special needs of task assignment for Phi, this paper presents an algorithm evolved from graph bisection algorithm: a graph is formed based on the memory dependence of tasks, by traversal the graph with an assuming cut point, iteratively finds out the groups of tasks with least dependence on tasks outside group, this algorithm can provide a task assignment solution between CPU and Phi aiming at memory optimization. Experiment reveals that this algorithm can significantly reduce the total memory usage of the job, also increase the efficiency of the execution. By reducing the memory usage, this algorithm can eliminates the memory bottleneck of Phi and expand the using range of Phi.
Xeon Phi is a high performance co-processor launched by Intel in 2012. Though Phi is specifically designed for Exascale super computer, the task assignment for Phi is yet to be studied. Based on the special needs of t...
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Xeon Phi is a high performance co-processor launched by Intel in 2012. Though Phi is specifically designed for Exascale super computer, the task assignment for Phi is yet to be studied. Based on the special needs of task assignment for Phi, this paper presents an algorithm evolved from graph bisection algorithm: a graph is formed based on the memory dependence of tasks, by traversal the graph with an assuming cut point, iteratively finds out the groups of tasks with least dependence on tasks outside group, this algorithm can provide a task assignment solution between CPU and Phi aiming at memory optimization. Experiment reveals that this algorithm can significantly reduce the total memory usage of the job, also increase the efficiency of the execution. By reducing the memory usage, this algorithm can eliminates the memory bottleneck of Phi and expand the using range of Phi.
Xeon Phi is a high performance co-processor launched by Intel in *** Phi is specifically designed for Exascale super computer,the task assignment for Phi is yet to be *** on the special needs of task assignment for Ph...
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ISBN:
(纸本)9781479941681
Xeon Phi is a high performance co-processor launched by Intel in *** Phi is specifically designed for Exascale super computer,the task assignment for Phi is yet to be *** on the special needs of task assignment for Phi,this paper presents an algorithm evolved from graph bisection algorithm: a graph is formed based on the memory dependence of tasks,by traversal the graph with an assuming cut point,iteratively finds out the groups of tasks with least dependence on tasks outside group,this algorithm can provide a task assignment solution between CPU and Phi aiming at memory *** reveals that this algorithm can significantly reduce the total memory usage of the job,also increase the efficiency of the *** reducing the memory usage,this algorithm can eliminates the memory bottleneck of Phi and expand the using range of Phi.
A two-step factorised sparse approximation inverse and symmetric successive over relaxation preconditioned conjugate gradient (cg) algorithm is proposed to solve the large system of linear equations resulted from the ...
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A two-step factorised sparse approximation inverse and symmetric successive over relaxation preconditioned conjugate gradient (cg) algorithm is proposed to solve the large system of linear equations resulted from the hierarchical implicit time-domain finite-element method (TDFEM). Convergence properties and CPU time of the proposed algorithm are compared with those of other preconditioned cg schemes. Numerical results demonstrate that the present approach is efficient for solving the large sparse system from hierarchical implicit TDFEM.
In this paper, we describe a new approach to combine the conjugate gradient method and the multigrid method. This approach simultaneously constructs conjugate new correction directions based on restricted gradients. T...
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In this paper, we describe a new approach to combine the conjugate gradient method and the multigrid method. This approach simultaneously constructs conjugate new correction directions based on restricted gradients. The computational amount is O(N), where N is the number of unknowns. The algorithm is easy to implement. It only requires restriction and prolongation operators, matrix vector multiplications on several levels, and scalar products. Therefore, the algorithm can be applied to accelerate a multilevel algorithm with slow convergence. Numerical results for Poisson's equation with jumping coefficients and a Stokes type equation are presented. 2007 IMACS. Published by Elsevier B.V. All rights reserved.
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