When the Galactic Cosmic Rays (GCRs) entering the heliosphere, they encounter the solar wind plasma, and their intensity is reduced, so called solar modulation. The modulation is caused by the combination of a few factors, such as the particle energies, solar activity and solar disturbance. In this work, a 2D numerical method is adopted to simulate the propagation of GCRs in the heliosphere with SOLARPROP, and to overcome the time consuming issue, the machine learning technique is also applied. With the obtained proton local interstellar spectra (LIS) based on the observation from Voyager 1 and AMS-02, the solar modulation parameters during the solar maximum activity of cycle 24 have been found. It shows the normalization and index of diffusion coefficient indeed reach a maximal value in February 2014. However after taking into account the travel time of particles with different energies, the peak time found postponed to November 2014 as expected. The nine-month late is so called time lag.
The evaluation of grassland degradation is an important part of ecological conservation research, and rodent infestation is a significant factor in grassland degradation. The presence of a large number of mouseholes means that the environmental balance of grassland has been destroyed, so the coverage of mouseholes can be used as an evaluation method for grassland degradation levels. In this paper, the image segmentation method is used to segment the mousehole images, Upernet is used as the segmentation network, and Swin Transformer as the Backbone. FAM and FSM modules are added to the Upernet network to solve the target misalignment problem when upsampling the network. The mIoU is improved by 5.3% according to the experimental results.
Studies by grassland workers have shown that the occurrence of degradation indicator plants in grassland is an important sign of grassland degradation. The detection of degradation indicator plants can provide a certain data basis for the study of grassland degradation. In this paper, a target detection algorithm for improving YOLOv5 model is proposed to detect the degradation indicator plants (wolfsbane) in grassland. Firstly, the target detection dataset of the grassland degradation indicator plant(wolfsbane)is constructed, and then the backbone network is optimized by adding a coordinate attention mechanism on the basis of the original YOLOv5 model; The original feature pyramid module in the feature fusion module is replaced by a weighted bidirectional feature pyramid (BiFPN) network structure, which realizes effective weighted feature fusion and bi-directional cross-scale connection; A small target detection layer is also added to further improve the detection accuracy of small targets. Experimental results show that the proposed improved algorithm achieves an average precision (AP) of 80.4%, which is 3.4% better than the original YOLOv5 model, and verifies the effectiveness of the improved model for the detection of degraded indicator plants (wolfsbane).
The quality of the data determines the quality of the model. In this paper, the grassland degradation data in the Headwaters of the Three Rivers were processed in the early stage and labeled with multiple classification. Guided clustering and semi-supervised clustering were used for comparison. The two methods were combined to classify and label the data, so as to improve the accuracy and completeness of the classification data.
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