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Automatic one-and three-phase re-closing power lines, after short circuit shutdown, is a very effective way to improve the reliability of power delivery. Re-closing while the fault is not cleared can be dangerous for some electrical appliances. To prevent re-closing of a short circuit a method allowing stable arc detection has developed. The method is based on the estimation of real-time parameters...
In this paper, we investigate the use of a backpropagation neural network (BPNN) to estimate the mass and depth of buried radioactive materials, i.e., depleted uranium (DU). A Lanthanum bromide(LaBr) detector is employed to collect the data for buried targets with different mass and at different depths. Due to the sparseness and randomness of a gamma spectrum, spectral transformation methods are implemented...
Probabilistic Neural Networks (PNN) learn quickly from examples in one pass and asymptotically achieve the Bayes-optimal decision boundaries. The major disadvantage of PNN is that it requires one node or neuron for each training sample. Various clustering techniques have been proposed to reduce this requirement to one node per cluster center. Decision boundaries of clustering centers are approximation...
Combining with genetic algorithm, the improved estimation of distribution algorithm (EDA) is provided. The crossover and mutation operations are added and the "elite" individuals are retained, which can keep the excellent evolution mode. The selection based on energy entropy is added, which can explore the solution space sufficiently and keep the population diversity. A neural network with...
In this paper, the relationship between injected voltages, audible noise and position estimation error is investigated for low speed high frequency injection based position sensorless control of PM synchronous motors. The modeling of noise is done using feed-forward neural network. The model is capable of predicting the audible noise. The proposed model can be used to perform optimization studies...
Image registration is amongst the most prominent problems in image processing and computer vision. Particularly in biomedical applications, automated alignment of image data from different imaging modalities has received great attention, delivering a high value added for analysis and diagnosis by integrating spatial information of two or more assays. In this context, the use of entropy based mutual...
The problem of parametrical synthesis of neural network models is considered. The evolutionary method with using the aprioristic information for neural network training is developed. Experiments on neural models synthesis for medical diagnostics are lead.
Time to market constraints pushes more and more designers to make area estimations early during the design process. Estimating the Built-In Self Test (BIST) area is only possible once the different design memories BIST are synthesized. This is time consuming and not realistic for a large circuit such as a SOC which can include hundreds of memories. In this paper we propose a push button solution for...
Using BP neural network estimate the investment of the power plant construction project is this paper's innovative points. First we give the engineering characteristic factors of power plant construction project, then give the value of each qualitative index. Then use improved BP neural network by PSO to estimate the investment. From the result, we can see that is more accuracy and speedily than BP...
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