An effective and novel roller bearing fault diagnosis technique based on empirical mode decomposition (EMD) energy entropy and support vector machine (SVM) is put forward in this article. The vibration signal of roller bearing is decomposed by EMD and the first 5 intrinsic mode function (IMF) components are obtained. SVM served as a fault diagnosis classifier and the extracted energy features of the first 5 IMFs are taken as network input vectors, and then the fault bearing and the normal bearing can be distinguished. An technique for fault of roller bearing by SVM is evaluated against a series of fault diagnosis methods that are widely used in machinery, with particular regard to the effect of training set size on fault diagnosis accuracy. We trained the SVM using RBF kernel function. We compare our experimental results with the existing results given by SMO and SVM-light algorithms. It can be seen that the fault diagnosis method based on SVM-light is superior to that based on SMO in diagnosis accuracy of roller bearing. In addition to the SVM, the same datasets were classified using RBF NN and Hopfield NN. The experimental results show that the technique of support vector machine based on EMD energy entropy has higher fault diagnosis ability.
Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm - specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm - in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved.
Color quantization is an important process for image processing and various applications. Up to now, many color quantization methods have been proposed. The self-organizing maps (SOM) method is one of the most effective color quantization methods, which gives excellent color quantization results. However, it is slow, so it is not suitable for real-time applications. In this paper, we present a color importance{based SOM color quantization method. The proposed method dynamically adjusts the learning rate and the radius of the neighborhood using color importance. This makes the proposed method faster than the conventional SOM-based color quantization method. We compare the proposed method to 10 well-known color quantization methods to evaluate performance. The methods are compared by measuring mean absolute error (MAE), mean square error (MSE), and processing time. The experimental results show that the proposed method is effective and excellent for color quantization. Not only does the proposed method provide the best results compared to the other methods, but it uses only 67.18% of the processing time of the conventional SOM method.
The additive mixture rules have been extended for calculation of the effective longitudinal elasticity modulus of the composite (Functionally Graded Materials - FGM's) beams with both the polynomial longitudinal variation of the constituent's elasticity modulus. Stiffness matrix of the composite Bernoulli-Euler beam has been established which contains the transfer constants. These transfer constants describe very accurately the polynomial uni-axially variation of the effective longitudinal elasticity modulus, which is calculated using the extended mixture rules.
The mixture rules have been extended for calculation of the effective elasticity modulus for stretching and flexural bending of the layer-wise symmetric composite (FGM's) sandwich beam finite element as well. The polynomial longitudinal and transversally symmetric layer-wise variation of the sandwich beam stiffness has been taken into the account. Elastic behaviour of the sandwich beam will be modelled by the laminate theory. Stiffness matrix of such new sandwich beam element has been established. The nature and quality of the matrix reinforcement interface have not been considered. Four examples have been solved using the extended mixture rules and the new composite (FGM's) beam elements with varying stiffness. The obtained results are evaluated, discussed and compared. and Obsahuje seznam literatury
diagnosis. Moreover various studies can be found in medical journals dedicated to Artificial Neural Networks (ANN). In the presented study, a method was developed to learn and detect benign and malignant tumor types in contrast-enhanced breast magnetic resonance images (MRI). The backpropagation algorithm was taken as the ANN learning algorithm. The algorithm (NEUBREA) was developed in C# programming language by using Fast Artificial Neural Network Library (FANN). Having been diagnosed by radiologists, 7 cases of malignant tumor, 8 cases of benign tumor, and 3 normal cases were used as a training set. The results were tested on 34 cases that had been diagnosed by radiologists. After the comparison of the results, the overall accuracy of algorithm was defined as 92%.
When digital signals are transmitted through frequency selective communication channels, one of the problems that arise is inter-symbol interference (ISI). To compensate corruptions caused by ISI and to find original information being transmitted, an equalization process is performed at the receiver. Since communication channels are time varying and random in nature, adaptive equalizers must be used to learn and subsequently track the time varying characteristics of the channel. Traditional equalizers are based on finding the inverse of the channel and compensating the channel's influence using inverse filter technique. There exists no equalizer for non-invertible channels. Artificial Neural Networks (ANN) can be applied to this for achieving better performance than conventional methods. We have proposed a model of a neural equalizer using MLP (multi layer perceptron), which reduces the mean square error to minimum and eliminates the effects of ISI. Empirically we have found that this neural equalizer is more efficient than conventional adaptive equalizers.