Gearbox bearings play an important role in wind power generation system. Their regular and stable operation will increase wind turbine power generation and improve the economic efficiency of wind farms. They often fail because they work under complex wind conditions. Therefore, it is necessary to find the fault early. The vibration signal of the gearbox bearing has the characteristics of volatility and continuity. Traditional bearing fault diagnosis methods are often based on signal analysis and feature selection, and the process is relatively complex. Deep learning methods can extract and select features automatically, thereby reducing the workload. A fault diagnosis method based on deep learning is proposed in this study. This method combines a one-dimensional convolutional neural network (1DCNN), support vector machine (SVM) classifier, and 1DCNN adaptively extracts features. The extracted features are input into the SVM classifier, and particle swarm optimization (PSO) is used to optimize the SVM classifier. The results show that the proposed fault diagnosis method is effective for fault diagnosis of wind turbine gearbox bearings. This method improves the precision and accuracy of diagnosis when compared to other methods. INDEX TERMS Wind power, gearbox bearings, deep learning, fault diagnosis

The millionaires' problem is the basis of secure multiparty computation and has many applications. Using a vectorization method and the Paillier encryption scheme, we first propose a secure two-party solution to the millionaires' problem, which can determine = , < , or > in one execution. Subsequently, using the vectorization and secret splitting methods, we propose an information-theoretically secure protocol to solve the multiparty millionaires' problem (a.k.a. secure sorting problem), and this protocol can resist collusion attacks. We analyze the accuracy and security of our protocols in the semihonest model and compare the computational and communication complexities between the proposed protocols and the existing ones.

Social network data publishing is dynamic, and attackers can perform association attacks based on social network directed graph data at different times. The existing social network privacy protection technology has low performance in dealing with large-scale dynamic social network directed graph data, and anonymous data publishing does not meet the needs of community structure analysis. A Dynamic Social Network Directed Graph K-In&Out-Degree Anonymity (DSNDG-KIODA) method to protect community structure is proposed. The method is based on the dynamic grouping anonymity rule to anonymize the dynamic K-in&out-degree sequence, and the virtual node distribution is added in parallel to construct an anonymous graph. The node information is transmitted based on the GraphX, and the virtual node pairs are selected and deleted according to the change of the directed graph modularity to reduce information loss. The experimental results show that the DSNDG-KIODA method improves the efficiency of processing large-scale dynamic social network directed graph data, and ensures the availability of community structure analysis when data is released.

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The real-time processing of the image segmentation method with accuracy is very important in the application of the flame image detection system. This paper considers a novel method for flame image segmentation. It is the bee colony algorithm with characteristics enhancement of Levy flights against the problems of the algorithm during segmentation, including long calculation time and poor stability. By introducing the idea of Levy flights, this method designs a new local search strategy. By setting the current optimal value and based on the collaboration between the populations, it reinforces the overall convergence speed. By adopting the new fitness evaluation method and combining it with the two-dimensional entropy multithreshold segmentation principle, this paper develops a threshold segmentation test of the flame image. Test results show that this method has some advantages in terms of accuracy of threshold selection and calculation time. The robustness of the algorithm meets the actual demands in the engineering application.