This study aims to understand the response of the ring stiffened cylinders made up of hybrid composites subjected to buckling loads by using the concepts of Design of Experiments (DOE) and optimization by using Finite...
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This study aims to understand the response of the ring stiffened cylinders made up of hybrid composites subjected to buckling loads by using the concepts of Design of Experiments (DOE) and optimization by using Finite Element Method (FEM) simulation software Ansys workbench V15. Carbon epoxy and E-glass epoxy composites were used in the hybrid composite. This hybrid composite was analyzed by using different layup angles. Central composite design (CCD) was used to perform design of experiments (D.O.E) and kriging method was used to generate a response surface. The response surface optimization (RSO) was performed by using the method of the multi-objective genetic algorithm (MOGA). After optimization, the best candidate was chosen and applied to the ring stiffened cylinder and eigenvalue buckling analysis was performed to understand the buckling behavior. Best laminate candidates with high buckling strength have been identified. A generalized procedure of the laminate optimization and analysis have been shown.
Resource allocation strategy directly affects project time and cost. Considering an accurate method of planning is required to evaluate the effect of resources, and to optimize resource allocation plan. Current schedu...
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Robust modeling of estimation error in a distributed sensor network under random sensing environment is a challenging problem. In this paper, we propose a novel methodology based on order statistics to statistically m...
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ISBN:
(纸本)9798350344868;9798350344851
Robust modeling of estimation error in a distributed sensor network under random sensing environment is a challenging problem. In this paper, we propose a novel methodology based on order statistics to statistically model scaling behavior of the mean-squared error (MSE) for distributed estimation in a wireless sensor network. In particular, by leveraging order statistics of the random signal-to-noise ratios (SNRs) over the entire network, we derive and compute cumulative distribution functions of average MSE for distributed estimation. In addition, we develop a novel approach of expressing the scaling of the maximum of independent and identically distributed (i.i.d.) sensors' random SNRs by deriving the distribution function of the estimation error. simulation results validate the close gap between the proposed method and the empirically obtained result.
Big data storage and sharing are becoming the major demand of the community. To overcome such issues, virtually unified data facilities are being presented with geo-distributed data centers by providing the user with ...
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ISBN:
(纸本)9781509036530
Big data storage and sharing are becoming the major demand of the community. To overcome such issues, virtually unified data facilities are being presented with geo-distributed data centers by providing the user with the single unified namespace. These unified data storage facilities lack efficient storage and analysis of data. To address these shortcomings in such unified data facilities, we designed and implemented time optimization model which minimizes the job execution time whereby selecting optimal data center with constraints of storage, computational and network bandwidth among all data centers. Our extensive simulation results show that our model provides the optimal decision that leads to minimal end-to-end data placement and analysis times.
Efficient Global optimization (EGO) method with Kriging model is rapid, stable and effective for a complex black-box function. However, How to get a more global optimal point on the basis of saving some computation ha...
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ISBN:
(纸本)9783038352938
Efficient Global optimization (EGO) method with Kriging model is rapid, stable and effective for a complex black-box function. However, How to get a more global optimal point on the basis of saving some computation has been concerned in simulation-based design optimization. In order to better solve a black-box unconstrained optimization problem, this paper introduces a new EGO method called improved generalized EGO (IGEGO). In this algorithm, generalized expected improvement (GEI: a new infill sampling criterion) which round off Euclidean norm of. to replace parameter g may better balance global and local search in IGEGO method. Several numerical tests are given to illustrate the applicability, effectiveness and reliability of the proposed methods.
Dynamic precision scaling is a promising technique to reduce power consumption in Digital Signal Processing (DSP) systems. Power savings are achieved by dynamically adapting word lengths to a time-varying environment....
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To make effective fault diagnosis of grounding grid, a new method using Self-Adaptive Particle Swarm optimization (SAPSO) is proposed. Firstly, the grounding grid can be handled as a resistive network to establish fau...
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ISBN:
(纸本)9783037859124
To make effective fault diagnosis of grounding grid, a new method using Self-Adaptive Particle Swarm optimization (SAPSO) is proposed. Firstly, the grounding grid can be handled as a resistive network to establish fault diagnosis equations. Then the objective function based on minimum energy principle is added to lower the ill-condition of diagnostic equation. Next, according to optimization techniques, a new method of SAPSO is proposed to solve the corrosion diagnosis equations. The method takes advantage of the high global searching ability of SAPSO to obtain the optimal solution to the diagnosis model. By means of the analysis of the simulation, the correctness and reliability of the method have been verified.
In this paper, we present a novel active contour model, in which the traditional gradient descent optimization is replaced by graph cut optimization. The basic idea is to first define an energy function according to c...
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The proceedings contain 290 papers. The topics discussed include: natural hazards in a changing climate: impacts, adaptation and risk management;struggling with epistemic uncertainties in environmental modeling of nat...
ISBN:
(纸本)9780784413609
The proceedings contain 290 papers. The topics discussed include: natural hazards in a changing climate: impacts, adaptation and risk management;struggling with epistemic uncertainties in environmental modeling of natural hazards;vulnerability and risk analysis of critical infrastructures;robust simulation: why and when needed and what should be qualified;probabilistic parameters in the MR hyperbolic tangent damper model;risk-consistent design approach for designing innovative hazard-resistant structures;reliability-based optimization of updated dynamical systems;reliability and redundancy of two-girder, steel-concrete composite bridges under uncertainty;optimal performance-based design of non-linear stochastic dynamical systems;and adaptive implementation of importance sampling in optimization under uncertainty.
Particle swarm optimization (PSO) is a global algorithm which is inspired by birds flocking and fish schooling. PSO has shown good search ability in many complex optimization problems, but premature convergence is sti...
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ISBN:
(纸本)9783037858585
Particle swarm optimization (PSO) is a global algorithm which is inspired by birds flocking and fish schooling. PSO has shown good search ability in many complex optimization problems, but premature convergence is still a main problem. A novel hybrid PSO(NHPSO) was proposed, which employed hybrid strategies, including dynamic step length (DSL) and opposition-based learning (OBL). DSL is helpful to enhance local search ability of PSO, and OBL is beneficial for improving the quality of candidate solutions. In order to verify the performance of NHPSO, we test it on several benchmark functions. The simulation results demonstrate the effectiveness and efficiency of our approach.
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