In response to the challenge that traditional convolutional neural networks face in effectively capturing the posture and spatial relationships of plant disease lesions on leaves, leading to issues of low recognition accuracy and poor robustness, this paper proposes a plant leaf disease image classification method that integrates capsule networks and residual networks. Firstly, by optimizing and refining the traditional residual network (ResNet), the initial convolutional layer of ResNet is enhanced by replacing its kernel with a concatenation of 3×3 small convolutional kernels, aiming to more effectively extract features of plant leaf lesions. Subsequently, a channel attention mechanism is introduced into the residual block to heighten the model’s focus on crucial features. Finally, the improved ResNet is effectively integrated with the capsule network (CapsNet). The initial pooling layer of ResNet is removed to reduce the loss of positional information. The output of the third residual module of ResNet is then connected with CapsNet, fully leveraging the strengths of both networks to enhance the model’s robustness. Train and test the model on the PlantVillage, AI Challenger 2018, and Tomato Leaf Disease datasets and conduct comparative experiments with other typical classification models. The proposed SE-SK-CapResNet model has demonstrated a remarkable ability to accurately recognize diseased leaves, achieving an accuracy rate of 98.58%, 95.08%, and 97.19%, respectively. Furthermore, this model has exhibited superior performance in image rotation transformations and classification compared to traditional network models. These experimental results suggest that the SE-SK-CapResNet model is a promising solution for the detection of diseased leaves in the field of agriculture.

Since the traditional transformer fault diagnosis method based on dissolved gas analysis (DGA) is challenging to meet today’s engineering needs, this paper proposes a multi-model fusion transformer fault diagnosis method based on TimesNet and Informer. First, the original TimesNet structure is improved by adding the MCA module to the Inception structure of the original TimesBlock to reduce the model complexity and computational burden; second, the MUSE attention mechanism is introduced into the original TimesNet to act as a bridge, so that associations can be carried out effectively among the local features, thus enhancing the modeling capability of the model; finally, when constructing the feature module, the TimesNet and Informer multilevel parallel feature extraction modules are introduced, making full use of the local features of the convolution and the global correlation of the attention mechanism module for feature summarization, so that the model learns more time-series information. To verify the effectiveness of the proposed method, the model is trained and tested on the public DGA dataset, and the model is compared and experimented with classical models such as Informer and Transformer. The experimental results show that the model has a strong learning ability for transformer fault data and has an advantage in accuracy compared with other models, which can provide a reference for transformer fault diagnosis.

This article addresses the multifrequency composite synchronization of four motors within a vibrating system. Multifrequency synchronization is commonly utilized in engineering due to its effectiveness in screening mixed materials of varying shapes and stickiness. The frequency ratio parameter n influences both the efficiency of the screening process and the overall screening results. Although multifrequency self‐synchronization motion can be realized, it can only be realized for integer frequency doubling ( n = 2 and n = 3), which limits the diversity of material screening types. By introducing the multifrequency controlled synchronization method, the multifrequency synchronization with noninteger frequencies ( n = 1.1–1.9) can be realized, which requires much cost on electrical equipment. To solve this problem, the multifrequency composite synchronization method in this article is proposed. The electromechanical coupling dynamics model of the vibration system is constructed by the Lagrange energy equation. Then, the synchronous condition and stability criteria are derived via the multiscale method by combining the speeds with phase differences. A novel fuzzy adaptive sliding model controlling method associated with a master–slave controlling strategy is introduced to realize multifrequency composite synchronization. The results show that speed errors in different frequencies are only 1000% and 3000%, respectively, and the swing response of the vibration system is small. It presents that the vibration system can not only realize the material screening stably and effectively but also reduce the cost of electrical equipment. The proposed method provides a new reference for multifrequency screening equipment.