• Nuclear Engineering and Technology
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• 제55권4호
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• pp.1507-1517
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• 2023

Centrifugal pumps are a crucial part of nuclear power plants, and their dependable and safe operation is crucial to the security of the entire facility. Cavitation will cause the centrifugal pump to violently vibration with the large number of vacuoles generated, which not only affect the hydraulic performance of the centrifugal pump but also cause structural damage to the impeller, seriously affecting the operational safety of nuclear power plants. A closed cavitation test bench of a centrifugal pump is constructed, and a method for precisely identifying the cavitation state is proposed based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Deep Residual Shrinkage Network (DRSN). First, we compared the cavitation sensitivity of pressure fluctuation, vibration, and liquid-borne noise and decomposed the liquid-borne noise by CEEMD to capture cavitation characteristics. The decomposition results are sent into a 12-layer deep residual shrinkage network (DRSN) for cavitation identification training. The results demonstrate that the liquid-borne noise signal is the most cavitation-sensitive signal, and the accuracy of CEEMD-DRSN to identify cavitation at different stages of centrifugal pumps arrives at 94.61%

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.