循环流化床颗粒浓度波动信号多重分形测度分析

由于循环流化床气－固两相流动规律相当复杂，使得至今对其流动规律的认知仍不足以直接用于循环流床的工业设计与放大。近十多年来，确定性混沌理论已被用于循环流化床动力学特性的研究，与其密切相关的分形理论也被用于流化床流动特性的研究。然而，这些研究所用均是采用单分形理论，而单分形理论对解释循环流化床复杂的非线性气－固流动行为显得能力不足。为了探讨循环流化床内颗粒流体系统非线性、非均匀性和混沌特性机理。本文引入不同测试多重分形分析的方法，用小波变换模极大值与多重分形测度分析方法相结合，检测循环流化床颗粒浓度波动信号奇异性并得出多重分形奇异谱。结果表明循环流化床颗粒浓度波动信号具有明显的多重分形特性。小波变换多重分形方法可以用来描述床内颗粒流动特性，为探讨循环流化床颗粒流体系统非均匀机制提出了一种新的方法。     

浅谈MF-DCCA在投资市场的应用

黄金,石油,汇率市场是社会资本重要的投资组成部分,本文目的是利用MF-DCCA分析法研究黄金,石油,汇率价格日收益率时间序列,结果发现该序列具有多重分形特征.且序列与序列之间具有自相关性与交叉相关性;然后利用多重分形谱方法研究风险率.分析结果表明石油,日元,欧元,黄金,风险性依次减小.最终发现MF-DCCA模型探索二组序列之间的自相关性与交叉相关性效果较好.     

时间序列中的多重分形分析

讨论了多重分形在时间序列分析中的应用，并分别针对一些噪声序列、受干扰信号序列，计算了其多重分形广义维数，试图探索一种新的方法，为时间序列的分析提供新的依据。在信号处理中，尤其在雷达信号处理中有很好的应用前景。     

多分形特性的子波分析及其在Rayleigh-Benard对流温度信号中的应用

本文采用子波分析方法,首先对一给定信号的奇异性作了分析,然后利用子波变换极大模(WTMM)理论,对三分Cantor集的分形特性作了研究,在此基础上,将WTMM理论应用于Rayleigh-Benard对流的温度信号。研究结果表明,子波方法不仅能够准确判别出信号奇异点的位置,而且还能具体给出描述该奇异强度的Holder指数h(x)的大小;且能够给出描述奇异信号的分形(包括多分形)的各种参数,如h、D(h)、)(qt等。应用于Rayleigh-Benard对流温度信号后,发现温度信号是多分形的,它的多层次,多尺度的结构完全可由多分形谱反映出来,所得结果与采用测度理论中微元覆盖的方法得到的分形参数吻合较好。     

Multifractal characterization of morphology of human red blood cells membrane skeleton

The purpose of this paper is to show applicability of multifractal analysis in investigations of the morphological changes of ultra‐structures of red blood cells (RBCs) membrane skeleton measured using atomic force microscopy (AFM). Human RBCs obtained from healthy and hypertensive donors as well as healthy erythrocytes irradiated with neutrons (45 μGy) were studied. The membrane skeleton of the cells was imaged using AFM in a contact mode. Morphological characterization of the three‐dimensional RBC surfaces was realized by a multifractal method. The nanometre scale study of human RBCs surface morphology revealed a multifractal geometry. The generalized dimensions D_q and the singularity spectrum f(α) provided quantitative values that characterize the local scale properties of their membrane skeleton organization. Surface characterization was made using areal ISO 25178‐2: 2012 topography parameters in combination with AFM topography measurement. The surface structure of human RBCs is complex with hierarchical substructures resulting from the organization of the erythrocyte membrane skeleton. The analysed AFM images confirm a multifractal nature of the surface that could be useful in histology to quantify human RBC architectural changes associated with different disease states. In case of very precise measurements when the red cell surface is not wrinkled even very fine differences can be uncovered as was shown for the erythrocytes treated with a very low dose of ionizing radiation.     

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

As a special type of denial of service (DoS) attacks, the TCP‐targeted low‐rate denial of service (LDoS) attacks have the characteristics of low average rate and strong concealment, so it is difficult to identify such attack traffic. As multifractal characteristics exist in network traffic, a new identification approach based on wavelet transform and combined neural network is proposed to classify normal network traffic and LDoS attack traffic. Wavelet energy spectrum coefficients extracted from the sampled traffic are used for multifractal analysis of traffic over different time scale. The combined neural network is designed to classify these multiscale spectrum coefficients that show different multifractal characteristics belonging to normal network traffic and LDoS attack traffic. Test results of test‐bed experiments indicate that the proposed approach can identify LDoS attack traffic accurately.     

基于瀑布过程的网络流量模型

分形或自相似模型可以很好地描述网络流量过程的长程依赖性，却无法真实地刻画网络流量过程在小尺度上的奇异性，论文从另一个角度，研究并建立了基于瀑布过程的瀑布模型，并对真实的网络流量数据进行模拟分析，发现瀑布模型从一定程度上解释了实际网络流量中的尺度特性，它能准确地刻画网络流量在小尺度上的奇异性，通过对模拟数据的尺度分析证明，瀑布模型具有刻画真实流量数据中多重分形特征的能力。     

基于多重分形和小波变换的声目标信号特征提取

研究了基于关联积分的广义维数谱的定量计算方法,提出了声目标信号的多重分形特 征,并对其特征即广义维数谱的有效性进行了分析;同时利用小波变换分析既能反映信号在变 换域特性又保留其时域信息的特点,提出基于小波变换的子空间能量及主要能量集中子空间时 域信息的特征提取方法,并通过模糊神经网络识别系统对声目标信号的广义维数谱、子空间能 量及时域信息的组合特征进行了验证.