生成n元数的全体排列的一个算法

许多组合问题可以表达为minf(q),其中q为1,2,…,n的任一排列。关于排列生成的各种算法,文献[1]作了全面的介绍,并且进行了比较分析。本文从集合映射出发,得到了一个算法。在这个算法的基础上,对每个排列可以得到一个序号,对每个序号可以得到一个排列,并且可以从任一给定的排列开始生成全体排列。     

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing, which is significant for signal detection, classification, and recognition. The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields. Reverse dispersion entropy (RDE) proposed by us recently, as a nonlinear dynamic analysis method, has the advantages of fast computing speed and strong anti-noise ability, which is more suitable for measuring the complexity of signal than traditional permutation entropy (PE) and dispersion entropy (DE). Empirical wavelet transform (EWT), based on the theory of wavelet analysis, can decompose a complex non-stationary signal into a number of empirical wavelet functions (EWFs) with compact support set spectrum, which has better decomposition performance than empirical mode decomposition (EMD) and its improved algorithms. Considering the advantages of RDE and EWT, on the one hand, we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy; on the other hand, we use RDE as the features of EWFs to improve the signal separability and stability. Finally, we propose a novel signal feature extraction technology based on EWT and RDE in this paper. Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals. Moreover, it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.     

基于排列法的目标威胁评估模型

目标威胁评估是一个推理、决策的行为,结合现代防空作战的特点和指挥自动化系统的工作过程,分析了影响目标威胁评估的几个重要因素,应用多属性决策中的一种改进排列法建立了数学模型,验证了其可行性。