The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance the accuracy. The one-dimensional convolution neural network (1D-CNN) method can not only diagnose bearing faults accurately, but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. In this paper, the 1D-CNN network architecture is proposed in order to effectively improve the accuracy of the diagnosis of rolling bearing, and the number of convolution kernels decreases with the reduction of the convolution kernel size. The method obtains high accuracy and improves the generalizing ability by introducing the dropout operation. The experimental results show 99.2% of the average accuracy under a single load and 98.83% under different loads.

Plant diseases such as drought stress and pest diseases significantly impact crops’ growth and yield levels. By detecting the surface characteristics of plant leaves, we can judge the growth state of plants and whether diseases occur. Traditional manual detection methods are limited by the professional knowledge and practical experience of operators. In recent years, a detection method based on deep learning has been applied to improve detection accuracy and reduce detection time. In this paper, we propose a disease detection method using a convolutional neural network (CNN) with multi-scale feature fusion for maize leaf disease detection. Based on the one-stage plant disease network YoLov5s, the coordinate attention (CA) attention module is added, along with a key feature weight to enhance the effective information of the feature map, and the spatial pyramid pooling (SSP) module is modified by data augmentation to reduce the loss of feature information. Three experiments are conducted under complex conditions such as overlapping occlusion, sparse distribution of detection targets, and similar textures and backgrounds of disease areas. The experimental results show that the average accuracy of the MFF-CNN is higher than that of currently used methods such as YoLov5s, Faster RCNN, CenterNet, and DETR, and the detection time is also reduced. The proposed method provides a feasible solution not only for the diagnosis of maize leaf diseases, but also for the detection of other plant diseases.

With the continuous development of communication technology, the wireless communication environment becomes more and more complex with various intentional and unintentional signals. Radio signals are modulated in different ways. The traditional radio modulation recognition technology cannot recognize the modulation modes accurately. Consequently, the communication system has embraced Deep Learning (DL) models as they can automatically recognize the modulation modes and have better accuracy. This paper systematically summarizes the related contents of radio Automatic Modulation Recognition (AMR) based on DL over the last seven years. First, we summarize the current research status of modulation recognition and the necessity of AMR research based on DL. Then, we review current radio AMR methods based on DL. In addition, we also propose a network model of AMR based on Convolutional Neural Network (CNN) and prove its effectiveness. Finally, we highlight existing challenges and research directions of radio AMR based on DL.

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

The issue of limited energy resources is crucial for underwater wireless sensor networks (UWSNs) because these networks operate in remote and harsh environments where access to power sources is limited. Overcoming the energy constraints is necessary to ensure the long-term functionality and sustainability of UWSN, enabling continuous data collection and communication for various applications such as environmental monitoring and surveillance. To solve the problems of limited energy and the difficulty of battery replacement in UWSN, a path planning and energy-saving scheme for charging underwater sensor nodes using AUVs (autonomous underwater vehicles) is proposed. Applying multiple AUVs to charge the sensing network nodes will maximize the size of the underwater sensing network as well as meet the transmission reliability, and the optimal path of AUVs is solved by using a genetic algorithm. Simulation results show that the AUV path planning scheme convergence is faster than that of conventional algorithms, and the lifetime of UWSN is prolonged while energy balancing according to the network size and node density. In high-density networks, the average energy consumption generated by AUVs for exploration is reduced by 15 percent for each additional AUV with our path planning.