基于改进密度峰值聚类算法的梨花密度分级

精准判断梨花疏密程度是自动疏花的基础。为了更好地判断梨花密度,该研究提出了基于改进密度峰值聚类算法的梨花密度分级方法。该方法首先提取梨花位置坐标,获取需要聚类的数据点。其次,为了实现梨花图像的密度分级,针对原有密度峰值聚类算法在梨花密度分级中的不足,结合梨花密度分级需求,改进了对聚类中心的选取方式,通过4组局部密度和中心偏移距离分割阈值将决策图划分为4部分来选取聚类中心,分别对应高、中、低密度以及无需疏花处理等4个等级,实现了对疏密合理的梨花图像的准确分级。最后,针对只有团状分布、稀疏分布及大尺度特写的梨花分布聚类分级不准确的问题,改进了两点间的距离d_(ij)参数的计算方法,统一梨花尺度大小和密度分级标准,对所有分布类型的梨花图像均能实现合理的密度分级。试验结果表明,该研究算法能够适应不同尺度大小的梨花图像,预测准确率为94.89%,密度分级准确率达到94.29%,可实现自然环境下局部花簇的密度分级,为机器智能疏花提供了技术支持。

Monotone Iterative Method for Nonlinear Discontinuous Differential Equations

A monotone iterative method for some discontinuous variational boundary problems is given, the convergence of iterative solutions is proved by the theory of partially ordered sets. It can be regarded as a generalization of the classical monotone iteration theory for continuous problems.

Dynamic Domain Decomposition Method and Its Application on Nonlinear Problem

In this paper,domain decomposition method（DDM） for numerical solutions of mathematical physics equations is improved into dynamic domain decomposition method（DDDM） . The main feature of the DDDM is that the number,shape and volume of the sub-domains are all flexibly changeable during the iterations,so it suits well to be implemented on a reconfigurable parallel computing system. Convergence analysis of the DDDM is given,while an application approach to a weak nonlinear elliptic boundary value problem and a numerical experiment are discussed.