RESUMO
Accurate and efficient extraction of cultivated land data is of great significance for agricultural resource monitoring and national food security. Deep-learning-based classification of remote-sensing images overcomes the two difficulties of traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) when extracting the cultivated land: (1) the limited performance when extracting the same land-cover type with the high intra-class spectral variation, such as cultivated land with both vegetation and non-vegetation cover, and (2) the limited generalization ability for handling a large dataset to apply the model to different locations. However, the "pooling" process in most deep convolutional networks, which attempts to enlarge the sensing field of the kernel by involving the upscale process, leads to significant detail loss in the output, including the edges, gradients, and image texture details. To solve this problem, in this study we proposed a new end-to-end extraction algorithm, a high-resolution U-Net (HRU-Net), to preserve the image details by improving the skip connection structure and the loss function of the original U-Net. The proposed HRU-Net was tested in Xinjiang Province, China to extract the cultivated land from Landsat Thematic Mapper (TM) images. The result showed that the HRU-Net achieved better performance (Acc: 92.81%; kappa: 0.81; F1-score: 0.90) than the U-Net++ (Acc: 91.74%; kappa: 0.79; F1-score: 0.89), the original U-Net (Acc: 89.83%; kappa: 0.74; F1-score: 0.86), and the Random Forest model (Acc: 76.13%; kappa: 0.48; F1-score: 0.69). The robustness of the proposed model for the intra-class spectral variation and the accuracy of the edge details were also compared, and this showed that the HRU-Net obtained more accurate edge details and had less influence from the intra-class spectral variation. The model proposed in this study can be further applied to other land cover types that have more spectral diversity and require more details of extraction.
RESUMO
The design of the crescent block is a key factor in the high-pressure operation of the internal meshing gear pump. In order to increase the output pressure of the pump, this article designs a new type of separable crescent plate. Then, taking a certain type of high-pressure internal meshing gear pump as the research object, a nonlinear differential equation for the internal flow field of the gear pump was established, and the pressure distribution law in the transition zone of a cycle was derived. A mathematical model of the device was established based on the equilibrium conditions of the internal and external crescent block forces. Finally, experimental research was conducted on the design parameters of the separation crescent plate. The results showed that under the conditions of displacement of 100.5 ml/r, pressure of 20.5 MPa, and rotational speed of 1800 RPM, the compensation chamber angle range was 31.23°, and the pump's volumetric efficiency could reach 94.6%. There were no abnormal phenomena during the entire operation of the pump, and there was no jamming or jamming of the friction pair.