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.     

一类广义L-统计量的极限分布

假定(X1,Y1),(X2,Y2),…,(Xn,Yn)是取自二维随机向量(X,Y)的独立同分布样本.记X(1)≤X(2)≤…≤X(n)是X1,X2,…,Xn产生的次序统计量,Y[1],Y[2],…,Y[n]是诱导的次序统计量.讨论广义L-统计量Tn=n-1 n∑i=1 J(i/n+1)Y[i]的极限分布,式中J(x)是由实际问题的需要而选取的权函数.在较弱的条件下,证明了Tn的渐近正态性,同时给出了Tn的期望和方差的渐近公式.     

基于探索性分析法仿真的建模方法研究

针对探索性分析法的主要特点,探讨建立适合探索性分析法仿真的模型问题.提出了通过分解和校准建立多分辨率模型的方法,该方法包括对低分辨率模型的校准、对高分辨率模型的分解以及输入参数的取值方法,很好地解决了模型的实用以及模型的有效性问题,为探索性分析法仿真的应用提供了具体的方法指导.     

基于VMD和SVM的舰船辐射噪声特征提取及分类识别

针对复杂海洋背景下舰船声频辐射噪声特征提取困难的问题,提出一种基于变分模态分解、中心频率、复杂度特征和支持向量机的舰船辐射噪声特征提取及分类识别方法。对四类舰船辐射噪声信号使用变分模态方法分解,得到一定数量的固有模态函数。通过比较提取能量最大的固有模态函数中心频率和排列熵作为特征参数,并利用支持向量机方法对四类舰船信号样本进行分类识别。实验结果表明,该方法可以实现对舰船辐射噪声的特征提取,与已有方法对比,该方法具有较高的识别率。     

基于不同能量值特征优选的水声目标识别

采用Bartlet平均周期图、小波变换和经验模态分解方法分别提取信号的频段能量值特征、IMF能量值特征,重点对IMF能量值特征进行特征优选。通过设计BP神经网络分类器,对实测的四类舰船目标的辐射噪声信号进行测试,取得了较好的识别效果。     

基于极球面投影的极区格网坐标表示方法?

随着全球气候变暖,北极冰盖的快速消融使北极航道的夏季全线通航成为可能,但由于极区经线的快速收敛,导致基于地理坐标的传统航海导航方法误差过大而不能使用。针对这一问题引发的极区导航问题进行研究,首先对常用海图及传统航海导航方法在极区使用存在的问题进行分析,然后采用极球面投影下的极区格网坐标表示方法,给出了极球面投影格网坐标与地理坐标的转换关系,该方法能够使在极区航行的船舶便捷、准确地确定其在地球的位置,为极区航海作业提供方便。     

An eikonal equation based path planning method using polygon decomposition and curve evolution

Path planning is a key technique of autonomous navigation for robots, and the velocity field is an important part. Constructing velocity field in a complex workspace is still challenging. In this paper, an inner normal guided segmentation algorithm in a complex polygon is proposed to decompose the complex workspace in this paper. The artificial potential field model based on probability theory is then used to calculate the potential field of the decomposed workspace, and the velocity field is obtained by utilizing the potential field of this workspace. Path optimization is implemented by curve evolution, during which the internal force generated in the smoothing process of the initial path by a mean filter and the external force is obtained from the gradient of the workspace potential field. The parameter selection principle is deduced by analyzing the influence of several parameters on the path length and smoothness. Simulation results show that the designed polygon decomposition algorithm can effectively segment complex workspace and that the path optimization algorithm can shorten and smoothen paths.     

寿命周期费用分析中的数据采集问题探讨

寿命周期费用数据采集是寿命周期费用分析的关键问题之一。简要介绍了寿命周期费用的概念,探讨了寿命周期费用数据采集的目的与要求、费用数据的来源、内容、采集形式,以及对费用数据的处理等与寿命周期费用数据采集有关的问题。目的在于为寿命周期费用的数据采集提供一般性指导方法。     

A nested benders decomposition approach for telecommunication network planning

Despite its ability to result in more effective network plans, the telecommunication network planning problem with signal‐to‐interference ratio constraints gained less attention than the power‐based one because of its complexity. In this article, we provide an exact solution method for this class of problems that combines combinatorial Benders decomposition, classical Benders decomposition, and valid cuts in a nested way. Combinatorial Benders decomposition is first applied, leading to a binary master problem and a mixed integer subproblem. The subproblem is then decomposed using classical Benders decomposition. The algorithm is enhanced using valid cuts that are generated at the classical Benders subproblem and are added to the combinatorial Benders master problem. The valid cuts proved efficient in reducing the number of times the combinatorial Benders master problem is solved and in reducing the overall computational time. More than 120 instances of the W‐CDMA network planning problem ranging from 20 demand points and 10 base stations to 140 demand points and 30 base stations are solved to optimality. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

基于提升小波包的漏磁信号去噪研究

为了更好地解决对原始漏磁信号的噪声抑制问题,在讨论提升小波包变换的基本原理及特点的基础上,提出应用这种方法对漏磁信号进行分解和重构。与传统小波包变换相比较,该方法具有更高的分辨率和近似优化能力,研究结果表明,它能在保留原始漏磁信号有效成分的同时,更好地去除噪声。