The hilly farmland in China is characterized by small farmland areas and dense farmland distribution, and the working environment is three-dimensional topographic farmland, so the working conditions in the field are relatively complex. In this working environment, the coverage path planning technique of a farmland autonomous task is harder than that of 2D farmland autonomous task. Generally, the path planning problem of 2D farmland is to construct the path cost model to realize the planning of agricultural machinery driving route, while for the path planning problem of three-dimensional terrain farmland in the hilly region, this paper proposes a covering path planning scheme that meets the requirements of autonomous work. Based on the energy consumption model, the scheme searches the optimal driving angle of agricultural machinery, prioritizes solutions to the problem of covering path planning within the scattered fields in the working area, and then searches through the genetic algorithm for the optimal order of traversing the paths of each field to complete the coverage path planning in the working area. On the one hand, the scheme optimizes the planning route in the fields from the angle of optimal energy consumption; on the other hand, through the genetic algorithm, the fields are connected in an orderly manner, which solves the comprehensive problems brought by the unique agricultural environment and farming system in China’s hilly areas to the agricultural machinery operation. The algorithm program is developed according to the research content, and a series of simulation experiments are carried out based on the program using actual farmland data and agricultural machinery parameters. The results show that the planned path obtained at the cost of energy consumption has a total energy consumption of 4771897.17J, which is 17.4% less energy consumption than the optimal path found by the path cost search; the optimization effect is evident.

As an efficient tool, radial basis function (RBF) has been widely used for the multivariate approximation, interpolating continuous, and the solution of the particle differential equations. However, ill-conditioned interpolation matrix may be encountered when the interpolation points are very dense or irregularly arranged. To avert this problem, RBFs with variable shape parameters are introduced, and several new variation strategies are proposed. Comparison with the RBF with constant shape parameters are made, and the results show that the condition number of the interpolation matrix grows much slower with our strategies. As an application, an improved collocation meshless method is formulated by employing the new RBF. In addition, the Hermite-type interpolation is implemented to handle the Neumann boundary conditions and an additional sine/cosine basis is introduced for the Helmlholtz equation. Then, two interior acoustic problems are solved with the presented method; the results demonstrate the robustness and effectiveness of the method.

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Although the bandwidth of the high-resolution panchromatic (HR PAN) image is wide, it is narrow in each band of the low-resolution multispectral (LR MS) image. Hence, the spatial resolution of the HR PAN image is much higher than that of the LR MS image. However, HR PAN image only has a single band. The purpose of the Pan-sharpening algorithm is to make the Pan-sharpened image with both high spatial resolution and good spectral information. In this paper, a novel learning interpolation method for Pan-sharpening is proposed by expanding the sketch information in the HR PAN image. The sketch information contains the edges and lines features of the image, and each segment of the sketch information has its own direction. According to the primal sketch graph of the HR PAN image, a regional map is obtained by a designed geometrical template. Since the size of the HR PAN image is different from that of the LR MS image, the LR MS image is interpolated into an interpolated multispectral (IMS) image by the nearest interpolation method. In addition, the IMS image can be mapped into the structure and the nonstructure regions by this regional map. The nonstructure regions are divided into the smooth and the texture regions by a variance value. For the structure and texture regions, the interpolated pixels in the IMS image are relearned and readjusted by the proposed structure and texture learning interpolation method, respectively. Experimental results show that the proposed Pan-sharpening method can provide superior performance in both visual effect and quality metrics, particularly for the images with a large spectral difference.

This paper investigates the plane deformation of periodic nano-inclusions of arbitrary shape embedded in a homogeneous isotropic material. A representative unit cell (RUC) with periodic boundary conditions imposed on its edges is used to represent the periodicity of the structure. Residual interface tension is incorporated into the deformation model so that the normal and tangential stresses have to jump across the matrix–inclusion interface, despite that the displacement can generally be treated as continuous across that interface. The stress field in the entire RUC is obtained by using the complex variable methods with the assistance of conformal mapping, series expansion, and collocation techniques. Numerical examples are presented for three different inclusion shapes. The results show that the interface tension-induced stress field can be greatly influenced by the shape, elastic modulus, and volume fraction of the inclusions.