Features of the optical medium of the surface of plant seeds

It was found that the integumentary tissue of plant seeds has the property of enhancing luminescence light. This phenomenon manifests itself in both the time and frequency domain. The gain factor (in power), depending on the type of seed, varies from 1.7 (beans) to 2.6 (rye). To determine it, the optical medium of the plant seed surface was represented as a dynamic link having an input and output. The impulse (time) characteristic of the link was found by the relaxation curve of luminescence. The dependence of the gain on temperature is expressed in the fact that when it decreases from 50 oC to –30 oC, the gain increases from 1.73 to 2.48. This phenomenon can be explained by the fact that at the stage of relaxation of luminescence, the absorption coefficient of the optical medium of the seed surface takes a negative value

ABB robot data collection based on dynamic link library

At present, with the development of the times and technology, in order to meet the needs of industrial production, robots have begun to be widely used in factories. Although the production efficiency of the robot is very high and relatively cheap compared to the labor cost, when an industrial robot fails, it will have a serious impact on the production capacity of the entire production line. Therefore, it is necessary to monitor various data of the robot. If some abnormal data can be found, it can perform effective predictive maintenance on the robot. This article mainly proposes a method for various data acquisition of ABB robots. The data acquisition interface is developed using C# and the dynamic link library is called. The data of ABB robots can be read in real time. It can realize real-time monitoring of each data of the robot.

Deep Segmentation of Point Clouds of Wheat

The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.