With the equivalent dielectric parameters of an artificial gradient structure consisting of a kind of dielectric material as inputs of FDTD multi-layer equivalent simulation, there are big nonuniform differences betwe...
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With the equivalent dielectric parameters of an artificial gradient structure consisting of a kind of dielectric material as inputs of FDTD multi-layer equivalent simulation, there are big nonuniform differences between the S-curve of retrieval methods and the corresponding full structure. In order to decease these differences, here, a boundary restricted geneticalgorithm is proposed. In our method, Smith S method is employed to find the rough values of dielectric parameters, and at the same time, the up and low limit cases are introduced to calculate the boundary parameter values for each layer of the artificial structure and form the searching areas for geneticalgorithm to get high-precision inversion of S-curve. The FDTD S-curve of the retrieval parameters and full structure of cone gradient and moth eye were performed experimentally, the maximum deviation of inversion S21 curves corresponding to the cone and moth eye with full structure is limited within 0.0028 and 0.0024 in the X-band (8-12 GHz) range, respectively, which shows us the promising application of our method in dielectric parameter retrieval and may be helpful for electromagnetic field analysis.
In order to solve the problem that the(OS-ELM) is used in the fault diagnosis of the transformer, the geneticalgorithm(algorithmgenetic) is applied to the on-line extreme learning machine, and a new method of transf...
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In order to solve the problem that the(OS-ELM) is used in the fault diagnosis of the transformer, the geneticalgorithm(algorithmgenetic) is applied to the on-line extreme learning machine, and a new method of transformer fault diagnosis is proposed. In this method, the number of hidden layer neurons of the Block L, the data set size N, and the hidden layer activation function are selected by the algorithmgeneticoptimizationalgorithm. Through simulation test, the fault diagnosis of transformer is 99.56%, and the test time is 0.0024 s. Compared with the optimization, the diagnostic accuracy and the test time of the transformer fault are improved obviously.
Atomic clock frequency difference prediction is the key step in atomic clock time scale calculation and atomic clock control. Precise prediction algorithm can accurately predict the future operation state of atomic cl...
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Atomic clock frequency difference prediction is the key step in atomic clock time scale calculation and atomic clock control. Precise prediction algorithm can accurately predict the future operation state of atomic clock which can be used to improve the accuracy of atomic time. In order to further improve the prediction accuracy of atomic clock frequency difference,a genetic wavelet neural network (GAWNN) atomic clock frequency difference prediction algorithm is proposed in this paper, which is based on wavelet neural network (WNN) atomic clock frequency difference prediction algorithm. The geneticalgorithm is used to optimize the wavelet neural network so as to select the appropriate number of hidden layers and the number of training points to construct the atomic clock frequency difference prediction model. In this paper, the algorithm is validated by the hydrogen clock and cesium clock actual frequency difference data of the National Institute of Metrology, and the results show that the algorithm improves the prediction accuracy of hydrogen clock and cesium clock frequency difference data.
genetic algorithm optimization BP neural network to establish a diagnosis of ovarian cancer classification model of ovarian cancer cells to extract images from Zunyi Medical College affiliated hospital clinical, using...
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genetic algorithm optimization BP neural network to establish a diagnosis of ovarian cancer classification model of ovarian cancer cells to extract images from Zunyi Medical College affiliated hospital clinical, using Mat lab image processing toolbox and programmed to select BP algorithm eigenvectors, compared to forecast data by using BP neural network and genetic algorithm optimization BP network prediction error curve, results showed that GA-BP algorithm enables network model predictions and experimental values after training error is small, to improve the simulation model the prediction accuracy of the classification system for the diagnosis of ovarian cancer patients with positive clinical significance of grading and staging.
Electric vehicle battery modeling and state-of-charge prediction has gained a lot of importance with the growing range anxiety among electric vehicle users. The future of battery implementation in electric vehicles mi...
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ISBN:
(纸本)9781479917631
Electric vehicle battery modeling and state-of-charge prediction has gained a lot of importance with the growing range anxiety among electric vehicle users. The future of battery implementation in electric vehicles might be in their customized design according to specific journey conditions. As such, this paper highlights the application of a novel concept - Journey Mapping for a Ford Focus Electric 2012's battery characterization and SOC prediction with the help of geneticalgorithm and the Recursive Least Squares techniques respectively. The Journey Mapping concept, which re-defines driving cycles in order to better capture the journey of a vehicle by including various external conditions such as weather, terrain, traffic, driver behavior, road, aerodynamic and vehicle proved to be a a more accurate testing bed for electric vehicle battery modeling.
This paper presents a novel adaptive robust proportional-integral-derivative (PID) controller for under-actuated dynamical systems via employing the advantages of the PID control and sliding surfaces. The related cont...
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This paper presents a novel adaptive robust proportional-integral-derivative (PID) controller for under-actuated dynamical systems via employing the advantages of the PID control and sliding surfaces. The related control gains as adjustable parameters are computed during the control process using gradient descent techniques and the chain derivative rule. Based on the design of control rules, the genetic algorithm optimization is utilized in order to find the optimal values of the learning rates and initial conditions of the control gains. The suggested strategy is implemented successfully to stabilize the cart-pole and ball-beam systems. Lastly, the simulation results demonstrate the efficacy of the proposed controller to control such under-actuated dynamical systems as well as its superiority in comparison with other recently introduced methods.
Recently the plug-in hybrid electric vehicles (PHEVs) have increasingly been used for transportation due to less petrol consumption and low carbon emission. The well-known disadvantages of this vehicle are charging ti...
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ISBN:
(纸本)9781728111834;9781728111827
Recently the plug-in hybrid electric vehicles (PHEVs) have increasingly been used for transportation due to less petrol consumption and low carbon emission. The well-known disadvantages of this vehicle are charging time and mile range compared to gas-powered vehicles. Moreover, charging a vast number of vehicles directly from the grid may cause damage to the local grid due to due to high current draws. Thus off-grid sources may be employed to share amount of power for charging PHEVs. This paper focuses on the design of a grid connected 30 kW photovoltaic powered PHEV charging station with optional battery storage units it is indispensable to design such system with low cost operation with respect to grid utility price. This is achieved by optimizing charging time of PHEVs through demand side management strategies such as load shifting, valley filling etc. using the real-coded geneticalgorithms.
This paper presents a safe and economic grounding system design for the substation of Ain El-Melh located in M'Sila city (Algeria) using genetic algorithm optimization (GAO). The main objective is to minimize the ...
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ISBN:
(纸本)9781467366748
This paper presents a safe and economic grounding system design for the substation of Ain El-Melh located in M'Sila city (Algeria) using genetic algorithm optimization (GAO). The main objective is to minimize the cost function of the considered grounding system in accordance with the security restrictions required by the ANSI/IEEE Std. 80-2000. A new mathematical model has been proposed for the cost function. This latter includes number of conductors, conductor dimension, number of rods, length of rods and total area of revetment. The study results have showed the effectiveness of the established GAO algorithm in designing of grounding system in economic view. A good accordance has been obtained when comparing the results found by GAO algorithm and the calculation code CYMGrd ones.
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