Intensity Correlation of the Resonance Fluorescence Light in Quasi-monochromatic Chaotic Fields

The behaviour of the intensity correlation function of the resonance fluorescence light from a single atom is studied in the case of a quasi-monochromatic chaotic pump field of arbitrary mean intensity. In particular, it is found that the factorization of the intensity correlation function, which is impossible in the case of a narrow band chaotic pump field of low intensity, becomes a better approximation when the mean intensity of the pump field is enhanced.     

风电功率时间序列混沌特性分析及预测模型研究

为揭示风电功率序列内在的动态特性, 利用非线性方法对风电时间序列混沌特性进行识别, 为对风电功率进行预测提供了基础.首先对某风电场的风电功率时间序列的日相关性进行了分析;然后在相空间重构的基础上计算了风电序列的最大Lyapunov指数, 验证了风电时间序列的混沌特性;由于采用Volterra滤波器多步预测法对风电功率进行超短期预测误差较大, 利用局域多步预测法以及最大Lyapunov指数法的预测结果并结合加权马尔科夫链和有序算子对Volterra滤波器的预测结果进行校正.最后以某实际风电场的风电功率预测为算例, 仿真结果表明校正预测模型有效的提高了预测精度, 其为利用Volterra滤波器多步法进行风电预测提供了有益的参考.     

Chaos dynamics in semiconductor lasers with polarization-rotated optical feedback

By using polarization-rotated optical feedback from the transverse-electric (TE) mode to the transverse-magnetic (TM) mode, chaotic oscillations for both polarization modes are excited in a semiconductor laser. We find different correlations between these chaotic oscillations than those found in previous studies. In this study, the dynamics are strongly dependent on their radio-frequency (RF) components and they are divided into three RF regions. For low-pass filtered signals lower than the laser relaxation oscillation, there is an antiphase correlation between the two polarization modes. On the other hand, the two polarization modes have an in-phase correlation for the RF components of the high-pass filtered signals, which are higher than the relaxation oscillation. However, no correlations were observed between the two modes for the intermediate RF components that include the relaxation oscillation frequency. We also perform numerical calculations for the model and obtain good agreement between the theoretical and experimental results.     

一种基于非完整二维相空间分量置换的混沌检测方法

由于混沌时间序列和随机过程具有很多类似的性质, 因而在实际中很难将两者区分开来. 混沌信号检测与识别是混沌时间序列分析中一个重要的课题. 混沌信号是由确定性的混沌映射或混沌系统产生的, 相比于高斯白噪声序列, 其在非完整的二维相空间中表现出更加丰富的结构特性. 本文通过研究混沌时间序列和高斯白噪声序列在非完整二维相空间中的分布特性, 利用混沌信号的非线性动力学特性, 提出了一种基于非完整二维相空间分量置换的混沌信号检测方法. 该方法首先由接收序列得到非完整的二维相空间, 基于第一维分量大小关系实现对第二维分量的置换与分组, 进一步求得F检验统计量. 然后利用混沌系统的局部特性, 获取非完整二维相空间的动力学结构信息, 实现对混沌序列的有效检测. 在高斯白噪声条件下对多种混沌信号进行了信号检测的数值仿真. 仿真结果表明: 相比置换熵检测, 本文所提算法所需数据量小、计算简单以及具有更低的时间复杂度, 同时对噪声具有更好的鲁棒性.     

基于集合经验模态分解的类星体光变周期及其混沌特性分析

基于密歇根大学射电天文台数据库中从1965年到2012年收集的类星体3C 345,3C 273和3C 279在射电8.0 GHz的光变数据,利用集合经验模态分解方法将这些类星体的光变资料分解为周期项、趋势项和高频项,并对分解后的高频项计算其饱和关联维数、最大Lyapunov指数和Kolmogorov熵,判断是否具有混沌性.结果表明,这些类星体的光变不仅具有周期性,也具有明显的混沌特性,表明类星体光变应为产生周期性运动的物理机制和产生混沌现象的非线性机制的综合结果.     

Low-frequency wind noise correlation in microphone arrays

A three-axis orthogonal microphone array with ten sensors in each arm has been used to study wind noise in the frequency range from 0.05 to 50 Hz. Simultaneous measurements were made of the three components of the varying wind velocity. Measurements have been made for wind speeds from 4 to 7 m/s at three different sites. The frequency-dependent correlation of the wind noise over a range of wind velocities and atmospheric and environmental conditions in the downwind direction varies as exp(-3.2X)cos(27piX). For the crosswind and vertical directions, the correlation decays approximately as exp(-7Y), where X is the separation in wavelengths in the downwind direction and Y is this separation in the crosswind or vertical direction. Over a limited range of wave numbers, the power density spectra of the varying wind velocity varied as the wave number to the -(5/3) power and the pressure spectra as the -(7/3) power.     

Least mean square error difference minimum criterion for adaptive chaotic noise canceller

The least mean square error difference (LMS-ED) minimum criterion for an adaptive chaotic noise canceller is proposed in this paper. Different from traditional least mean square error minimum criterion in which the error is uncorrelated with the input vector, the proposed LMS-ED minimum criterion tries to minimize the correlation between the error difference and input vector difference. The novel adaptive LMS-ED algorithm is then derived to update the weights of adaptive noise canceller. A comparison between cancelling performances of adaptive least mean square (LMS), normalized LMS (NLMS) and proposed LMS-ED algorithms is simulated by using three kinds of chaotic noises. The simulation results clearly show that the proposed algorithm outperforms the LMS and NLMS algorithms in achieving small values of steady-state excess mean square error. Moreover, the computational complexity of the proposed LMS-ED algorithm is the same as that of the standard LMS algorithms.     

Dicke模型的量子混沌和单粒子相干动力学特性

量子化的Dicke模型在非旋波近似条件下表现为量子混沌动力学特征.利用单粒子一阶时间关联函数,通过数值计算详细考察了Dicke模型中单粒子相干动力学特性.结果表明:当初始相干态处在混沌区域时,一阶时间关联函数曲线衰减较快,而当初始相干态处在规则区域时,一阶时间关联函数曲线衰减较慢,单粒子相干动力学对初态具有较强的敏感性,经典混沌抑制量子相干.考察单粒子相干动力学在相空间的平均演化性质,得到一种较好的量子经典对应关系.最后研究了相空间单粒子相干的整体动力学性质,更好地揭示了相空间的混沌和规则结构.     

Variances of the vertical and horizontal wind measured by tower instruments and SODAR

At the Karlsruhe Nuclear Research Center, a monostatic Doppler SODAR has been operated near a 200 m high meteorological tower. Vertical profiles of the components of the wind vector and of the variances of the vertical wind and of the horizontal wind direction have been sampled continuously from the SODAR and the tower instruments as 30-min mean values. During seven episodes lasting more than 2 h, the instantaneous vertical wind speed was measured simultaneously by a sonic anemometer and the SODAR, and spectra were calculated. An intercomparison of the spectra and wind data measured directly by the SODAR and the tower instruments has been performed by a linear regression and correlation analysis referring to different height levels of measurement and stability classes.Systematic and statistically distributed differences among the data measured by the SODAR and the in situ instruments are discussed. Our investigation shows that the variance of only the vertical wind speed can be measured reliably by a SODAR. The variances of the horizontal wind direction and the elevation angle of the wind vector should not be used in a routine manner from a SODAR.     

A robust steganographic technique based on improved chaotic-range systems

This paper presents a novel chaos-based technique of steganography in spatial domain. In the last decade, chaos theory has gained utmost importance in multimedia security applications. Generally, 1-D chaotic maps are employed because of computational ease and structural simplicity but their limited chaotic range is an obstacle. In the proposed work, we model the nonlinear combinations of 1-D chaotic maps. These chaotic systems possess chaotic behavior throughout the domain. We, for the first time, propose an effective application of these improved chaotic systems in steganography. These newly synthesized systems are used to embed secret information in the least significant bits (LSBs) of the host image. By comparing with some recent models, we prove that involving improved chaotic systems in steganographic approach really produces extraordinary outcomes. We determine the strength of our steganographic algorithm through the most significant statistical analyses such as information entropy, correlation, contrast, energy, homogeneity, peak signal to noise ratio (PSNR) and mean square error (MSE). We further prove the robustness of the anticipated technique against several image processing attacks. The upshot of these analysis techniques shows that our algorithm is highly reliable and produces coherent results.     

Grazing-induced crises in hybrid dynamical systems

In hybrid dynamical systems including both continuous and discrete components, an interplay between a continuous trajectory and a discontinuity boundary can trigger a sudden qualitative change in the system dynamics. Grazing phenomena, which occur when a continuous trajectory hits a boundary tangentially, are well known as a representative of such phenomena. We demonstrate that a grazing phenomenon of a chaotic attractor can result in its sudden disappearance and initiate chaotic transients. The mechanism of this grazing-induced crisis is revealed in an illustrative example. Furthermore, we derive a formula to obtain the critical exponent of the power law on the mean duration of chaotic transients.     

Correlation dimension based nonlinear analysis of network traffics with different application protocols

This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP,FTP and SMTP.First,the phase space is reconstructed and the embedding parameters are obtained by the mutual information method.Secondly,the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature,i.e.HTTP and FTP traffics are chaotic,furthermore,the former is more complex than the later;on the other hand,SMTP traffic is stochastic.It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP,FTP and SMTP protocol network traffics.This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.     

心率变异性谱成分的非线性本质

提出了分频段相位随机化替代数据方法,与噪声滴定法相结合,观察了不同频率成分对心搏混沌的影响.结果显示心率变异性高频成分相位随机化的替代数据使混沌强度显著降低,提示谱分析的频率成分中蕴含着信号的非线性特性.提出的方法能有效分析不同频率成分对非线性的贡献,该法不仅适用于心率变异性还适用于其他确定性信号的特性分析. 关键词： 分频段相位随机化替代数据 混沌 滴定法 心率变异性     

直驱型永磁同步风力发电机组中混沌运动的反步自适应控制

以直驱型永磁同步风力发电机为研究对象, 推导了发电机在参数不确定以及风速扰动情况下的数学模型,验证了系统在某些参数及工作条件下会呈现出混沌属性. 并提出了直驱型永磁同步风力发电机混沌运动下的基于反步自适应控制算法,并对其进行了仿真, 仿真结果表明,所提出的算法对于具有参数不确定以及风速干扰的直驱型风力发电系统具有较好的鲁棒性. 关键词： 风力发电 直驱型永磁同步风力发电机组 混沌控制 反步自适应控制     

Chaos in the solar wind flow near Earth

We have done a time series analysis of daily average data of solar wind velocity, density and temperature at 1 AU measured by ACE spacecraft for a period of nine years. We have used the raw data without filtering to give a faithful representation of the nonlinear behaviour of the solar wind flow which is a novel one. The sensitivity of the results on filtering is highlighted. The attractor dimension is estimated for every parameter of the solar wind and it is found that they differ substantially. Hence a chaotic picture for the problem from different angles have been obtained. The calculated Kolmogorov entropies and Lyapunov exponents are positive showing evidences that the complex solar wind near the Earth is most likely a deterministic chaotic system.      

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

The present work reports the development of nonlinear time series prediction method of genetic algorithm(GA) with singular spectrum analysis(SSA) for forecasting the surface wind of a point station in the South China Sea(SCS) with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days(longer for some point station) in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.     

Strong perturbations in nonlinear systems

A novel case of probabilistic coupling for hybrid stochastic systems with chaotic components via Markovian switching is presented. We study its stability in the norm, in the sense of Lyapunov and present a quantitative scheme for detection of stochastic stability in the mean. In particular we examine the stability of chaotic dynamical systems in which a representative parameter undergoes a Markovian switching between two values corresponding to two qualitatively different attractors. To this end we employ, as case studies, the behaviour of two representative chaotic systems (the classic Rössler and the Thomas-Rössler models) under the influence of a probabilistic switch which modifies stochastically their parameters. A quantitative measure, based on a Lyapunov function, is proposed which detects regular or irregular motion and regimes of stability. In connection to biologically inspired models (Thomas-Rössler models), where strong fluctuations represent qualitative structural changes, we observe the appearance of stochastic resonance-like phenomena i.e. transitions that lead to orderly behavior when the noise increases. These are attributed to the nonlinear response of the system.     

基于EEMD-近似熵和储备池的风电功率混沌时间序列预测模型

针对风电功率时间序列的混沌特性,提出了一种基于集成经验模态分解(ensemble empirical mode decomposition, EEMD)-近似熵和回声状态网络(echo state network, ESN) 的风电功率混沌时间序列组合预测模型.首先为降低对风电功率局部分析的计算规模以及提高预测的准确性, 利用EEMD-近似熵将风电功率时间序列分解为一系列复杂度差异明显的风电子序列; 然后对各子序列分别建立ESN、经过高频分量正则化改进的EEMD-ESN模型和最小二乘支持向量机预测模型; 最后以某一风电场实际采集的数据为算例,仿真结果表明EEMD-ESN模型在训练速度和预测精度上优于最小二乘支持向量机模型,为实现风电功率短期预测的在线工程应用提供了新的有益参考. 关键词： 混沌时间序列 风电预测 集成经验模态分解 近似熵     

基于大脑情感学习模型和自适应遗传算法的混沌时间序列预测

针对传统神经网络预测精度不高、收敛速度慢的问题,提出一种基于大脑情感学习模型和自适应遗传算法的混沌时间序列预测方法.大脑情感学习模型模拟了哺乳动物大脑中杏仁体和眶额皮质之间的情感学习机制,具有计算复杂度低、运算速度快的特点,因此可以大大提高混沌预测的快速性.为了进一步提高大脑情感学习模型的预测精度,采用自适应遗传算法优化其参数,将待优化的权值与阈值分布在染色体基因序列上,用适应度函数选出最佳参数,从而增强了模型的逼近能力.基于Lorenz混沌时间序列和实际地磁Dst指数序列的预测结果表明,本文方法较其他传统方法在预测精度、运算速度和稳定性上均具有明显优势.     

毫米级高分辨率的混沌激光分布式光纤测温技术

近年来,随着分布式光纤传感技术在各大基础设施健康监测领域的广泛应用,人们对能够实现毫米量级精准定位和监测技术的需求日益增长.本文提出了一种基于宽线宽混沌激光的高分辨率分布式光纤测温技术.实验通过改变光反馈混沌源的偏振匹配态和反馈强度等外部参数,产生了–3 dB线宽约为7.5 GHz的宽线宽混沌激光,并在300 m传感光纤实现了空间分辨率为7.05 mm的分布式温度测量.同时,为了抑制光源线宽增加造成的布里渊增益谱恶化,在泵浦路中引入了时间门控技术,其中经脉冲调制后的泵浦光峰值功率提高了约9.5 dB,同时脉冲调制使混沌互相关锁定于脉冲持续时间内,从而布里渊增益谱的信号背景噪声比由约2.28 dB提升为4.55 dB,最终实现了空间分辨率为3.12 mm的分布式温度测量.