A copper tube of 3.3m in length and 8mm in inner diameter, acting as an acoustic pressure amplifier, was incorporated in a standing-wave thermoacoustically driven pulse tube refrigerator system. The enhancement of pressure ratio from 1.129 to 1.152 by the acoustic pressure amplifier has been obtained. As a result, a cooling temperature as low as 79.7K and a tripled coefficient of performance improvement at 120K were reached.
Triboelectric nanogenerator (TENG) technologies have explosive development in the field of energy harvesting and self‐powered sensing. As the key element of triboelectric devices, dielectric polymers have obtained much attention in recent years. The dielectric properties of polymer determine the output performance of TENG. In this paper, we take silicone rubber as an example of dielectric polymers, to study the properties of molecular structure influence on the dielectric properties and mechanical properties by the molecular dynamics simulation method. The free volume fraction, dielectric constant, and mechanical properties of silicone rubbers with different branch chains were calculated. The dielectric constant is highly related to the free volume distribution and the dipole moments of silicone rubbers with different amounts of branch chains. For fewer branch chains silicone rubber, the free volume distribution contributes most to the dielectric constant; for more branch chains silicone rubber, the dipole moment dominates the dielectric constant. Therefore, the silicone rubber ratio has a great influence on the dielectric constant of silicone rubber. With the increase of temperature, the dielectric constant of 2‐chain silicone rubber increases at first and then decreases, and the maximum value is obtained near 300 K. Therefore, it is necessary to control the temperature when silicone rubber is used as a dielectric material. This work can be a guide for improving the dielectric properties of silicone rubber, and it provides a new approach to the optimal design of high‐performance triboelectric nanogenerators.
Accurate recognition method of pitaya in natural environment provides technical support for automatic picking. Aiming at the intricate spatial position relationship between pitaya fruits and branches, a pitaya recognition method based on improved YOLOv4 was proposed. GhostNet feature extraction network was used instead of CSPDarkNet53 as the backbone network of YOLOv4. A structure of generating a large number of feature maps through a small amount of calculation was used, and the redundant information in feature layer was obtained with lower computational cost, which can reduce the number of parameters and computation of the model. Coordinate attention was introduced to enhance the extraction of fine-grained feature of targets. An improved combinational convolution module was designed to save computing power and prevent the loss of effective features and improve the recognition accuracy. The Ghost Module was referenced in Yolo Head to improve computing speed and reduce delay. Precision, Recall, F1, AP, detection speed and weight size were selected as performance evaluation indexes of recognition model. 8800 images of pitaya fruit in different environments were used as the dataset, which were randomly divided into the training set, the validation set and the test set according to the ratio of 7:1:2. The research results show that the recognition accuracy of the improved YOLOv4 model for pitaya fruit is 99.23%. Recall, F1 and AP are 95.10%, 98% and 98.94%, respectively. The detection speed is 37.2 frames·s-1, and the weight size is 59.4MB. The improved YOLOv4 recognition algorithm can meet the requirements for the accuracy and the speed of pitaya fruit recognition in natural environment, which will ensure the rapid and accurate operation of the picking robot.
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