To address the problems that most convolutional neural network-based image defogging algorithm models capture incomplete global feature information and incomplete defogging, this paper proposes an end-to-end convolutional neural network and vision transformer hybrid image defogging algorithm. First, the shallow features of the haze image were extracted by a preprocessing module. Then, a symmetric network structure including a convolutional neural network (CNN) branch and a vision transformer branch was used to capture the local features and global features of the haze image, respectively. The mixed features were fused using convolutional layers to cover the global representation while retaining the local features. Finally, the features obtained by the encoder and decoder were fused to obtain richer feature information. The experimental results show that the proposed defogging algorithm achieved better defogging results in both the uniform and non-uniform haze datasets, solves the problems of dark and distorted colors after image defogging, and the recovered images are more natural for detail processing.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.

The nonlinear characteristics of the pneumatic servo system are the main factors limiting its control accuracy. A new mathematical model of the nonlinear system of the valve control cylinder is proposed in order to improve the control accuracy of the pneumatic servo system. Firstly, the mass flow equation of the gas flowing through each port is established by analyzing the physical structure of the proportional directional control valve. Then, the dynamic equation of the system is set up by applying the Stribeck friction model for the friction model of the valve control cylinder and building a pneumatic circuit experiment to identify the friction model parameters. Finally, the correctness of the mathematical model is verified by the inflation and deflation experiment of the fixed capacitive chamber and the servo controls experiment based on PID position. The Simulink simulation of the mathematical model better reflects the characteristics of the pneumatic position servo system.

With the rapid development of the semiconductor field, the application of micro-electro-mechanical systems embedded in smart devices and driverless vehicles is gradually widespread, and the problem of the self-powered of devices has gradually been attention. Hence, this study propose a novel hydro-pneumatic suspension system by embedding piezoelectric energy harvester into hydro-pneumatic suspensions, which makes the suspension structure simple and achieves the effect of self-powered for the monitoring and the diagnostics of a vehicle or its subsystems. The mathematical model of intermediate fixed piezoelectric energy harvester with hydro-pneumatic suspension was established. The influence of hydraulic component and exitation parameters of suspension system on damping force and power is analyzed. Numerical simulation proves that the novel hydro-pneumatic suspension with piezoelectric energy harvester is justified to achieve self-powered. The bench test proves the accuracy of some of the conclusions of the simulation and the feasibility of the structure.

To address the problem that piezoelectric energy harvesters are difficult to apply in certain environments, this paper establishes the theoretical study of the intermediate fixed disc piezoelectric energy harvester (IFDPEH) based on the unimorph under concentrated force. The reliability of the model was indirectly verified by numerical simulation and Computer-Aided Engineering (CAE) simulation. The effects of load, radius ratio (piezoelectric layer/intermediate support), thickness ratio (piezoelectric layer/total thickness), and elastic modulus ratio (substrate/piezoelectric layer) on electrical energy were studied. The results indicate that the radius/thickness ratios of the IFDPEH based on aluminum and beryllium bronze are 0.05/0.31 and 0.05/0.48, respectively. In addition, through parameter comparison, it is found that the most important parameters affecting IFDPEH power are radius ratio and large load. The results are demonstrated to be meaningful for broadening the application of piezoelectric energy harvesters by the derived closed-form equations for the electrical energy along the diameters of the piezoelectric discs in the z-direction.