Combining the characteristics of wireless sensor network, the ant colony algorithm is applied to a wireless sensor network, and a wireless sensor network route algorithm based on energy equilibrium is proposed in this...
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Combining the characteristics of wireless sensor network, the ant colony algorithm is applied to a wireless sensor network, and a wireless sensor network route algorithm based on energy equilibrium is proposed in this paper. This algorithm takes the energy factor into the consideration of selection of route based on probability and enhanced calculation of information so as to find out the optimal route from the source node to the target node with low cost and balanced energy, and it prolongs the life cycle of the whole network.
Developing new energy such as wind power and photovoltaic power is the main way to solve our energy problems. However, the volatility of wind power and photovoltaicpowerwill impact the grid. The changes of wind power ...
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Developing new energy such as wind power and photovoltaic power is the main way to solve our energy problems. However, the volatility of wind power and photovoltaicpowerwill impact the grid. The changes of wind power and photovoltaic power have complementary characteristics. It can effectively reduce the impact to the grid by combining them. This paper studies the optimal dispatch modeling problem with combination of wind power and photovoltaic power systems,establishes the optimal scheduling model of a power system including wind power and photovoltaic power considering the environmental benefits and spare capacity changing,and conducts a simulation calculation to verify the validity of the method.
The aim of this paper is to model and simulate a cantilever beam as energy harvester to expose to wind vibrations. A mathematical model describes the behavior of cantilever beam and the electromechanical coupling, usi...
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ISBN:
(纸本)9781479999828
The aim of this paper is to model and simulate a cantilever beam as energy harvester to expose to wind vibrations. A mathematical model describes the behavior of cantilever beam and the electromechanical coupling, using piezoelectric constitutive equations. An experimental setup of a fixed configuration (dimensions, materials, boundaries and shape) is performed by means of such device and the effects caused by the wind force on the cantilever are analyzed. The same device is used for a simulation, implemented with Comsol Multiphysics, in which wind force is simulated like a pressure acting on the cantilever. The comparison between simulation and experimental results validates the simulation method and allows an appropriate choice of the most suitable shape for this kind of cantilever: the choice is carried out using the optimization platform KIMEME.
Direct estimation of physiologically or biochemically important parameters from raw projection data is challenging in dynamic positron emission tomography ( PET) due to the coupling between tomographic image reconstru...
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ISBN:
(纸本)9781479923748
Direct estimation of physiologically or biochemically important parameters from raw projection data is challenging in dynamic positron emission tomography ( PET) due to the coupling between tomographic image reconstruction and nonlinear kinetic parameter estimation. optimization transfer algorithms have been previously developed to solve the complex optimization problem. These algorithms, however, can suffer from slow convergence rate. This paper proposes an accelerated iterative algorithm for direct reconstruction of kinetic parameters through variable splitting under the framework of augmented Lagrangian optimization. Similar to the optimization transfer algorithms, the proposed algorithm splits each iteration of direct reconstruction into two separate steps: dynamic image reconstruction and pixel-wise nonlinear least squares kinetic fitting. The unique advantage of the new algorithm is its flexibility to employ any existing reconstruction algorithms in the reconstruction step, which can substantially accelerate the convergence speed. Computer simulations show that the proposed direct algorithm can be efficiently implemented and achieve much faster convergence speed than the optimization transfer algorithm.
Optimally locating a transportation facility and automotive service enterprise is an interesting and important problem. In practice, many related factors, e.g., customer demands, allocations, and locations of customer...
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ISBN:
(纸本)9781467379960
Optimally locating a transportation facility and automotive service enterprise is an interesting and important problem. In practice, many related factors, e.g., customer demands, allocations, and locations of customers and facilities, are changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have addressed its stochastic time and cost issues. A new research issue arises when a) decision-makers want to minimize the transportation time of customers while minimizing their transpiration cost when locating a facility;and b) users prefer to arrive at the destination within the specific time and cost. By taking a vehicle inspection station as a typical automotive service enterprise example, this work proposes a novel stochastic multi-objective optimization approach to address it. Moreover, some regional constraints can greatly influence its solution;while vehicle velocity is an uncertain variable due to the influence of some unpredictable factors in a location process. This work builds a practical stochastic expected value multi-objective programming model of its location with regional constraints and varying velocity. A hybrid algorithm integrating stochastic simulation and Genetic algorithms (GA), namely a random weight based multi-objective GA, is proposed to solve the proposed models. A numerical example is given to illustrate the proposed models and the effectiveness of the proposed algorithm.
This paper presents a comparative study on the optimization methods in fractional PID controller design for induction motor. Choosing the best optimization algorithm for tuning fractional order controllers is crucial ...
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ISBN:
(纸本)9781467377973
This paper presents a comparative study on the optimization methods in fractional PID controller design for induction motor. Choosing the best optimization algorithm for tuning fractional order controllers is crucial in order obtain best system response. In this study genetic algorithm, local search, nonlinear sequential quadratic programming, and particle swarm optimization algorithms are used to tune a fractional PI controller for induction motors. A model of a real motor is used to design and test the controllers. The resulted fractional controllers are approximated using refined Oustaloup's recursive filter in order to be implemented using digital controllers. The controllers' speed tracking performance is tested in simulation and a comparative analysis has been conducted.
The alternating direction method of multipliers (ADMM) has emerged as a powerful technique for large-scale structured optimization. Despite many recent results on the convergence properties of ADMM, a quantitative cha...
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The alternating direction method of multipliers (ADMM) has emerged as a powerful technique for large-scale structured optimization. Despite many recent results on the convergence properties of ADMM, a quantitative characterization of the impact of the algorithm parameters on the convergence times of the method is still lacking. In this paper we find the optimal algorithm parameters that minimize the convergence factor of the ADMM iterates in the context of l(2)-regularized minimization and constrained quadratic programming. Numerical examples show that our parameter selection rules significantly outperform existing alternatives in the literature.
Single-image super-resolution (SR) is one of the most important and challenging issues in image processing. To produce a high-resolution image from a low-resolution image, one of the conventional approaches is to leve...
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Single-image super-resolution (SR) is one of the most important and challenging issues in image processing. To produce a high-resolution image from a low-resolution image, one of the conventional approaches is to leverage regularization to overcome the limitations caused by the modeling. However, conventional regularizers such as total variation always neglect the high-level structures in the data. To overcome the drawback, we propose to explore the underlying information for the images with structured edges by using directional total variation. An alternating direction method of a multiplier-based algorithm is presented to effectively solve the resulting optimization problem. Computer simulations on several texture images such as a leaf image have been used to demonstrate the effectiveness and improvement of the proposed method on SR reconstruction, both qualitatively and quantitatively. Furthermore, the effect of parameter selection is also discussed for the proposed method. (C) 2015 SPIE and IS&T
This paper describes the application of the newly introduced Continuous Ant Colony optimization algorithm (CACOA) to optimal design of sewer networks. Two alternative approaches to implement the algorithm is presented...
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This paper describes the application of the newly introduced Continuous Ant Colony optimization algorithm (CACOA) to optimal design of sewer networks. Two alternative approaches to implement the algorithm is presented and applied to a storm sewer network in which the nodal elevations of the network are considered as the decision variables of the optimization problem. In the first and unconstrained approach, a Gaussian probability density function is used to represent the pheromone concentration over the allowable range of each decision variable. The pheromone concentration function is used by each ant to randomly sample the nodal elevations of the trial networks. This method, however, will lead to solutions which may be infeasible regarding some or all of the constraints of the problem and in particular the minimum slope constraint. In the second and constrained approach, known value of the elevation at downstream node of a pipe is used to define new bounds on the elevation of the upstream node satisfying the explicit constraints on the pipe slopes. Two alternative formulations of the constrained algorithm are used to solve a test example and the results are presented and compared with those of unconstrained approach. The methods are shown to be very effective in locating the optimal solution and efficient in terms of the convergence characteristics of the resulting algorithms. The proposed algorithms are also found to be relatively insensitive to the initial colony and size of the colony used compared to the original algorithm. (C) 2009 Elsevier Ltd. All rights reserved.
This paper proposes a novel image segmentation method based on BP neural network, which is optimized by an enhanced Gravitational Search algorithm (GSA). GSA is a novel heuristic optimization algorithm based on the la...
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This paper proposes a novel image segmentation method based on BP neural network, which is optimized by an enhanced Gravitational Search algorithm (GSA). GSA is a novel heuristic optimization algorithm based on the law of gravity and mass interactions. It has been proven that the GSA has good ability to search for the global optimum, but it suffers from the premature convergence due to the rapid reduction of diversity. This work introduces a cat chaotic mapping into the steps of population initialization and iterative stage of the original GSA, which forms a new algorithm called CCMGSA. Then the CCMGSA is employed to optimize BP neural networks, which forms a combination method called CCMGSA-BP and we use it for image segmentation. To verify the efficiency of this method, the visual and performance experiments are done. The visual results using our proposed method are compared with those using other segmentation methods including an improved k-means clustering algorithm (I-K-means), a hybrid region merging method (H-Region-merging), and manual segmentation. The comparison results show that the proposed method can get good segmentation results on grayscale images with specific characteristics. And we compare the performance of our proposed method with those of IGSA-BP, CLPSO-BP and RGA-BP for image segmentation. The results indicate that the CCMGSA-BP shows better performance in terms of the convergence rate and avoidance of local minima.
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