Search

Spectrum auction is considered a suitable approach to efficiently allocate spectrum among unlicensed users. In this paper, for the first time ever, we study this problem from Pattern Recognit. point of view, where all wireless users could be classified based on their economic capability, the interests of the channel for the auction, and the interference they need to suffer during communication. These factors from wireless users are multimodal data ranging from linguistic to numerical data. How to make analysis and integration of such multimodal data is a key for multi-slot spectrum auction. We adopt the Deep Feedforward Network algorithm to perform multimodal data analysis and integration for multi-slot spectrum auction. Simulation results are presented to verify the effectiveness of the proposed algorithm in the small cell network. Our approach is essentially a Pattern Recognit. approach where spectrum auction decision is made based on deep learning and different user patterns. [ABSTRACT FROM AUTHOR]

• A fault diagnosis method for power electronics converters based on deep feedforward network and wavelet compression with transient features is proposed. • The transient historical data after wavelet compression are used to realize the training of fault diagnosis classifier in this paper, and the proposed method reduces the dependence on the fault mathematical model of the power electronics converters. • The fault diagnosis classifier based DFN and wavelet compression has the advantages of good classification ability, high computational efficiency and implementation simplicity. And the proposed method can locate the location of open-circuit fault in IGBT accurately. A fault diagnosis method for power electronics converters based on deep feedforward network and wavelet compression is proposed in this paper. The transient historical data after wavelet compression are used to realize the training of fault diagnosis classifier. Firstly, the correlation analysis of the voltage or current data running in various fault states is performed to remove the redundant features and the sampling point. Secondly, the wavelet transform is used to remove the redundant data of the features, and then the training sample data is greatly compressed. The deep feedforward network is trained by the low frequency component of the features, while the training speed is greatly accelerated. The average accuracy of fault diagnosis classifier can reach over 97%. Finally, the fault diagnosis classifier is tested, and final diagnosis result is determined by multiple-groups transient data, by which the reliability of diagnosis results is improved. The experimental result proves that the classifier has strong generalization ability and can accurately locate the open-circuit faults in IGBTs. Image, graphical abstract [ABSTRACT FROM AUTHOR]