Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300−1400 nm and secondarily concentrated at approximately 1050−1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.

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

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Most of the existing multi-recipient signcryption schemes do not take the anonymity of recipients into consideration because the list of the identities of all recipients must be included in the ciphertext as a necessary element for decryption. Although the signer’s anonymity has been taken into account in several alternative schemes, these schemes often suffer from the cross-comparison attack and joint conspiracy attack. That is to say, there are few schemes that can achieve complete anonymity for both the signer and the recipient. However, in many practical applications, such as network conference, both the signer’s and the recipient’s anonymity should be considered carefully. Motivated by these concerns, we propose a novel multi-recipient signcryption scheme with complete anonymity. The new scheme can achieve both the signer’s and the recipient’s anonymity at the same time. Each recipient can easily judge whether the received ciphertext is from an authorized source, but cannot determine the real identity of the sender, and at the same time, each participant can easily check decryption permission, but cannot determine the identity of any other recipient. The scheme also provides a public verification method which enables anyone to publicly verify the validity of the ciphertext. Analyses show that the proposed scheme is more efficient in terms of computation complexity and ciphertext length and possesses more advantages than existing schemes, which makes it suitable for practical applications. The proposed scheme could be used for network conferences, paid-TV or DVD broadcasting applications to solve the secure communication problem without violating the privacy of each participant.Key words: Multi-recipient signcryption; Signcryption; Complete Anonymity; Public verification.