Fault Diagnosis of Wind Turbine Generator with Stacked Noise Reduction Autoencoder Based on Group Normalization

In order to improve the condition monitoring and fault diagnosis of wind turbines,a stacked noise reduction autoencoding network based on group normalization is proposed in this paper.The network is based on SCADA data of wind turbine operation,firstly,the group normalization(GN)algorithm is added to solve the problems of stack noise reduction autoencoding network training and slow convergence speed,and the RMSProp algorithm is used to update the weight and the bias of the autoenccoder,which further optimizes the problem that the loss function swings too much during the update process.Finally,in the last layer of the network,the softmax activation function is used to classify the results,and the output of the network is transformed into a probability distribution.The selected wind turbine SCADA data was substituted into the pre-improved and improved stacked denoising autoencoding(SDA)networks for comparative training and verification.The results show that the stacked denoising autoencoding network based on group normalization is more accurate and effective for wind turbine condition monitoring and fault diagnosis,and also provides a reference for wind turbine fault identification.

Behavior recognition based on the fusion of 3D-BN-VGG and LSTM network

In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity.

On the Group of Units for the Ring of Linear Transformations

In this paper, we introduce a practical method for obtaining the structure of thegroup of units for the ring of linear transformations of a vector space over an arbitrary field,and we give a further generalization of the result in [3].

Class-Preserving Coleman Automorphisms of Finite Groups with Prescribed Centralizers

Let G be a finite group. It is proved that any class-preserving Coleman automorphism of G is an inner automorphism whenever G belongs to one of the following two classes of groups： （1） CN-groups, i.e., groups in which the centralizer of any element is nilpotent; （2） CIT-groups, i.e., groups of even order in which the centralizer of any involution is a 2-group. In particular, the normalizer conjecture holds for both CN-groups and CIT-groups. Additionally, some other results are also obtained.

Complex eigenvalues and group velocities of normal modes in shallow water with a lossy bottom

For the stratified shallow water with a lossy bottom, the distribution and asymptotic behavior of mode eigenvalues in the complex plane are discussed on the basis of the Pekeris cut. The analysis shows that even in the shallow water with a low-speed lossy bottom there may be the proper modes which satisfy the radiation condition at infinite depth. It is also shown that when the ratio between the densities of the seawater and seabottom is close to one, there exist only a finite number of improper modes . An iterative method for evaluating the complex eigenvalues and group velocities of normal modes is presented and some numerical results are given.