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.

In this paper, a high-isolation multiple-input/multiple-output (MIMO) microstrip monopole antenna array is investigated. To reduce the mutual coupling between antenna elements, a novel composite parasitic element constituted by a T-shaped ground branch and an isolated branch was designed and analyzed. The proposed composite parasitic element is capable of generating a unique three-dimensional weak electric field, which can effectively suppress the mutual coupling between the antenna elements. To give an intuitive illustration about the design principle and decoupling strategy of the proposed antenna, the antenna design procedure was ingeniously divided into four steps, and three types of decoupling structures during the antenna evolution were meticulously analyzed at both the theoretical and the physical level. To validate the proposed decoupling concept, the antenna prototype was fabricated, measured, and evaluated. The reflection coefficient, transmission coefficient, radiation pattern, and antenna gain were studied, and remarkable consistency between the measured and simulated results was observed. The simulations showed that the antenna has a peak gain of 3.5 dBi, a low envelope correlation coefficient (ECC < 0.001), and a high radiation efficiency (radiation efficiency > 0.9). Parameters of the proposed MIMO antenna including electrical dimension, highest isolation level, and 20 dB isolation bandwidth were evaluated. Compared with the previous similar designs, the proposed antenna exhibits attractive features including compressed dimension (0.55λ₀ × 0.46λ₀), extremely high isolation level (approximately 43 dB), fabulous 20 dB isolation bandwidth (3.11–3.78 GHz, 19.4%), a high diversity gain (DG > 9.99 dB), an appropriate mean effective gain (−3.5 dB < MEG < −3 dB), and low design complexity.

To realize the multiple-degree-of-freedom motion, in this article, we propose a new spherical reluctance motor (SPRM). For the nonlinear characteristics of flux linkage and excessively complex torque calculation, this article proposes a new adaptive analytical model for SPRM based on hybrid trigonometric function–power function. A sixth-degree polynomial is used to represent the five coefficients of this model, and these coefficients are calculated from flux-linkage–rotor angle–phase current data using numerical curve fitting with nonlinear least square. Based on the criterion of minimizing the sum of squared error between the flux linkage obtained by finite-element analysis and the flux linkage calculated by analytical model, the optimal trigonometric function angular frequency coefficient and the optimal power function exponent are adaptively obtained by iterative algorithm. The feasibility and accuracy of this model are validated by comparing with the existing models. Furthermore, the experimental results show that the calculated torque has good agreement with the measured torque, which further verifies the validity of the proposed analytical model. The versatility of the proposed model for SPRMs with different structure parameters is discussed as well.