In the field of vehicle system dynamics, revealing the dynamic characteristics of nonlinear vehicle models with high degrees of freedom is a key point in current research. In the classic 2-DOF (degrees of freedom) model, the dynamics is analysed and represented in two-dimensional state space. However, it is necessary to reveal the three-dimensional state characteristics of the system when analysing high-DOF vehicle models. The concept of equilibrium points in two-dimensional space will be extended to curves in three-dimensional space, including stable curves and unstable curves, which can qualitatively reveal the evolvement characteristics of the system under different control parameters. In addition, because the dissipation of energy method can quantitatively reveal the global dynamic characteristics of the vehicle, this paper combines the (un)stable curves in the three-dimensional space with dissipation of energy to better analyse the global dynamics characteristics and mutually verify the correctness of conclusions. In this paper, first, the connection between (un)stable curves and region boundary is established. Second, the theoretical analysis of relationship between dissipation of energy and phase trajectories convergence characteristics is perfected. Third, the static and dynamic characteristics of vehicle system are analysed. Therefore, this paper proposed the concept of stable and unstable curves to intuitively reveal the global dynamics characteristics and system evolvement characteristics in three-dimensional state space.
Grasslands, as an important part of terrestrial ecosystems, are facing serious threats of land degradation. Therefore, the remote monitoring of grasslands is an important tool to control degradation and protect grasslands. However, the existing methods are often disturbed by clouds and fog, which makes it difficult to achieve all-weather and all-time grassland remote sensing monitoring. Synthetic aperture radar (SAR) data can penetrate clouds, which is helpful for solving this problem. In this study, we verified the advantages of the fusion of multi-spectral (MS) and SAR data for improving classification accuracy, especially for cloud-covered areas. We also proposed an adaptive feature fusion method (the SK-like method) based on an attention mechanism, and tested two types of patch construction strategies, single-size and multi-size patches. Experiments have shown that the proposed SK-like method with single-size patches obtains the best results, with 93.12% accuracy and a 0.91 average f1-score, which is a 1.02% accuracy improvement and a 0.01 average f1-score improvement compared with the commonly used feature concatenation method. Our results show that the all-weather, all-time remote sensing monitoring of grassland is possible through the fusion of MS and SAR data with suitable feature fusion methods, which will effectively enhance the regulatory capability of grassland resources.
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