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The emergence of nanotechnology and surface engineering techniques provides new opportunities for designing self-lubricant coatings with enhanced properties. In recent years, green coating technologies have played a vital role in environmental preservation. This article mainly reviews five typical types of self-lubricant coatings including MoN coatings, VN coatings, WN coatings and TMN (Transition Metal Nitride) soft-metal coatings, and DLC (Diamond-like Carbon) with lubricant agents deposited by PVD (Physical Vapor Deposition) for the demanding tribological applications, which is the latest research into the green lubricant coatings. Furthermore, it is of great significance for designing the green self-lubricant coatings to adapt the demanding tribological applications to meet the industrial requirements. [ABSTRACT FROM AUTHOR]

The fruit and vegetable classification problem is an inseparable branch in the field of image recognition. GoogLeNet provides a more ideal solution for the fruit and vegetable classification problem. We use the GoogLeNet network to classify apples, lemons, oranges, pomegranates, tomatoes, and colored peppers. Through experiments, we obtained the training accuracy of GoogLeNet as 96.88%, the testing accuracy as 96%, and the training speed as 11.38 sheets/second. The recognition accuracy of this model can meet the basic recognition requirements, but the training speed is low. Therefore, we decided to optimize GoogLeNet to significantly improve the training speed and further enhance the recognition accuracy of GoogLeNet. We reduced the number of convolutional kernels of GoogLeNet and adjusted the structure of Inception, which reduced the number of parameters of GoogLeNet by nearly 48% and increased the training speed of GoogLeNet from 11.38 to 33.68 sheets/second. To further improve its recognition accuracy, we tried two methods: 1) introducing a new activation function Swish; 2) between convolutional layers, we introduced DropBlock layer; these two methods improved the testing accuracy of GoogLeNet by 2%. Finally, we introduce AlexNet, VGGNet, ResNet18, DenseNet121, and Inception-ResNet to compare with our optimized GoogLeNet. By comparison, we found that our model has incredible performance in ACC, AUC, FPS, Recall, etc.

In order to solve the problems of domestic and foreign vegetable precision metering devices that easily damage seeds and have low seeding efficiency and poor qualification rate, this paper designs a one-machine for two-row pneumatic combination seeder, which is mainly composed of a pneumatic seeding mechanism and a honeycomb seeding mechanism. The mechanism and other components and theoretical analysis of the working process of the combined seed metering device clarifies the minimum negative pressure for adsorbing a seed during the suction stage, along with the negative pressure required for the seed-carrying stage. It also establishes the seeding speed and movement during planting with the trajectory parameter equation and uses ADAMS software to conduct virtual experiments on the influence of the number of holes, diameters, and heights of different types of honeycomb seeding disks on the success rate of seeding. Based on research on previous pneumatic seeding mechanisms, single factor tests, orthogonal tests, and high-speed photography tests of the honeycomb seeding disk of the combined seeder were carried out on the number of holes, the diameter of holes, and the height of seeding. The results of tests conducted on okra seeds showed that when the rotating speed of the pneumatic seeding disc was 18 r/min, the diameter of the pneumatic seeding disc hole was 2.4 mm, the vacuum degree was 3.5 kPa, the number of honeycomb seeding disc holes was 24, the hole diameter was 13 mm, and the seed height was 60 mm, the working performance of the combined seed meter reached the optimal level, which provides a reference for the development of a small seed vegetable precision seeder. [ABSTRACT FROM AUTHOR]