Cross-sample entropy of foreign exchange time series

The correlation of foreign exchange rates in currency markets is investigated based on the empirical data of DKK/USD, NOK/USD, CAD/USD, JPY/USD, KRW/USD, SGD/USD, THB/USD and TWD/USD for a period from 1995 to 2002. Cross-SampEn (cross-sample entropy) method is used to compare the returns of every two exchange rate time series to assess their degree of asynchrony. The calculation method of confidence interval of SampEn is extended and applied to cross-SampEn. The cross-SampEn and its confidence interval for every two of the exchange rate time series in periods 1995-1998 (before the Asian currency crisis) and 1999-2002 (after the Asian currency crisis) are calculated. The results show that the cross-SampEn of every two of these exchange rates becomes higher after the Asian currency crisis, indicating a higher asynchrony between the exchange rates. Especially for Singapore, Thailand and Taiwan, the cross-SampEn values after the Asian currency crisis are significantly higher than those before the Asian currency crisis. Comparison with the correlation coefficient shows that cross-SampEn is superior to describe the correlation between time series.     

改进的相对转移熵的癫痫脑电分析

脑电信号是由脑神经活动产生并且始终存在于中枢神经系统的自发性电位活动,是一种重要的生物电信号. 脑电信号是非常微弱的且是非线性的,脑电信号也具有时间不可逆性. 本文提出了一种新的基于正向序列转移概率与逆向序列转移概率的相对熵方法即相对转移熵方法,并应用此方法研究了正常脑电与癫痫脑电的不可逆性,实验结果显示癫痫患者的脑电信号的不可逆性明显小于正常人的脑电信号的不可逆性. 这说明改进的相对转移熵可以作为一个物理过程不可逆程度的度量参数,这使得应用脑电信号区分病人是否患有癫痫疾病具有积极指导意义. 关键词： 相对转移熵 脑电信号 符号化 时间不可逆性     

Composite Multiscale Cross-Sample Entropy Analysis for Long-Term Structural Health Monitoring of Residential Buildings

This study proposesd a novel, entropy-based structural health monitoring (SHM) system for measuring microvibration signals generated by actual buildings. A structural health diagnosis interface was established for demonstration purposes. To enhance the reliability and accuracy of entropy evaluation at various scales, composite multiscale cross-sample entropy (CMSCE) was adopted to increase the number of coarse-grained time series. The degree of similarity and asynchrony between ambient vibration signals measured on adjacent floors was used as an in-dicator for structural health assessment. A residential building that has been monitored since 1994 was selected for long-term monitoring. The accumulated database, including both the earthquake and ambient vibrations in each seismic event, provided the possibility to evaluate the practicability of the CMSCE-based method. Entropy curves obtained for each of the years, as well as the stable trend of the corresponding damage index (DI) graphs, demonstrated the relia-bility of the proposed SHM system. Moreover, two large earthquake events that occurred near the monitoring site were analyzed. The results revealed that the entropy values may have been slightly increased after the earthquakes. Positive DI values were obtained for higher floors, which could provide an early warning of structural instability. The proposed SHM system is highly stable and practical.     

Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series

QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.     

A model for competitiveness level analysis in sports competitions: Application to basketball

The degree of overall competitiveness of a sport league is a complex phenomenon. It is difficult to assess and quantify all elements that yield the final standing. In this paper, we analyze the general behavior of the result matrices of each season and we use the corresponding results as a probably density. Thus, the results of previous seasons are a way to investigate the probability that each team has to reach a certain number of victories. We developed a model based on Shannon entropy using two extreme competitive structures (a hierarchical structure and a random structure), and applied this model to investigate the competitiveness of two of the best professional basketball leagues: the NBA (USA) and the ACB (Spain). Both leagues’ entropy levels are high (NBA mean 0.983; ACB mean 0.980), indicating high competitiveness, although the entropy of the ACB (from 0.986 to 0.972) demonstrated more seasonal variability than that of the NBA (from 0.985 to 0.990), a possible result of greater sporting gradients in the ACB. The use of this methodology has proven useful for investigating the competitiveness of sports leagues as well as their underlying variability across time.     

压缩真空场与原子非线性作用系统中场熵的压缩特性

利用量子信息熵理论,研究了附加克尔介质的压缩真空场与二能级原子依赖强度耦合相互作用系统中场熵的压缩特性.讨论了初始压缩真空场的压缩度以及克尔非线性作用的强度对场熵压缩特性的影响.结果表明,无克尔介质量,场熵压缩的演化具有严格的周期性.克尔介质的非线性作用的强弱可以改变场熵压缩的周期性.在初始压缩真空场的压缩度较大(r=5)时,无论克尔介质作用的强弱如何变化,场熵始终处于被压缩状态.     

Coherent control of quantum entropy via quantum interference in a four-level N-type atomic system

The time evaluation of quantum entropy in a four-level N-type atomic system is theoretically investigated. Quantum entanglement of the atom and its spontaneous emission fields is then discussed via quantum entropy. It is found that the degree of entanglement can be increased by the quantum interference induced by spontaneous emission. The phase dependence of the atom-field entanglement is also presented.     

Control of the entanglement between triple quantum dot molecule and its spontaneous emission fields via quantum entropy

The time evolution of the quantum entropy in a coherently driven triple quantum dot molecule is investigated. The entanglement of the quantum dot molecule and its spontaneous emission field is coherently controlled by the gate voltage and the rate of an incoherent pump field. The degree of entanglement between a triple quantum dot molecule and its spontaneous emission fields is decreased by increasing the tunneling parameter.     

On the Variability of Heart Rate Variability—Evidence from Prospective Study of Healthy Young College Students

Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).     

Nonlinear Measures to Evaluate Upright Postural Stability: A Systematic Review

Conventional biomechanical analyses of human movement have been generally derived from linear mathematics. While these methods can be useful in many situations, they fail to describe the behavior of the human body systems that are predominately nonlinear. For this reason, nonlinear analyses have become more prevalent in recent literature. These analytical techniques are typically investigated using concepts related to variability, stability, complexity, and adaptability. This review aims to investigate the application of nonlinear metrics to assess postural stability. A systematic review was conducted of papers published from 2009 to 2019. Databases searched were PubMed, Google Scholar, Science-Direct and EBSCO. The main inclusion consisted of: Sample entropy, fractal dimension, Lyapunov exponent used as nonlinear measures, and assessment of the variability of the center of pressure during standing using force plate. Following screening, 43 articles out of the initial 1100 were reviewed including 33 articles on sample entropy, 10 articles on fractal dimension, and 4 papers on the Lyapunov exponent. This systematic study shows the reductions in postural regularity related to aging and the disease or injures in the adaptive capabilities of the movement system and how the predictability changes with different task constraints.     

Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.     

Stochastic Order and Generalized Weighted Mean Invariance

In this paper, we present order invariance theoretical results for weighted quasi-arithmetic means of a monotonic series of numbers. The quasi-arithmetic mean, or Kolmogorov–Nagumo mean, generalizes the classical mean and appears in many disciplines, from information theory to physics, from economics to traffic flow. Stochastic orders are defined on weights (or equivalently, discrete probability distributions). They were introduced to study risk in economics and decision theory, and recently have found utility in Monte Carlo techniques and in image processing. We show in this paper that, if two distributions of weights are ordered under first stochastic order, then for any monotonic series of numbers their weighted quasi-arithmetic means share the same order. This means for instance that arithmetic and harmonic mean for two different distributions of weights always have to be aligned if the weights are stochastically ordered, this is, either both means increase or both decrease. We explore the invariance properties when convex (concave) functions define both the quasi-arithmetic mean and the series of numbers, we show its relationship with increasing concave order and increasing convex order, and we observe the important role played by a new defined mirror property of stochastic orders. We also give some applications to entropy and cross-entropy and present an example of multiple importance sampling Monte Carlo technique that illustrates the usefulness and transversality of our approach. Invariance theorems are useful when a system is represented by a set of quasi-arithmetic means and we want to change the distribution of weights so that all means evolve in the same direction.     

Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.     

非线性超声射频信号熵对乳腺结节良恶性的定征

本文提出了一种基于非线性超声射频(radio frequency, RF)信号熵对乳腺结节良恶性进行定征的方法.对306例乳腺结节样本(良性158例,恶性148例)提取了基于超声RF信号二次谐波的熵和加权熵,以及常规超声参数(图像灰度、纵横比、不规则度、乳腺结节大小、深度);采用t检验和线性分类器检测参数对乳腺结节良恶性的区分度;进一步将有效参数组合输入支持向量机对乳腺结节良恶性进行分类.结果表明:除图像灰度外,其余参数均在乳腺结节的良性与恶性间有显著差异.多参数结合输入支持向量机的良恶性分类的准确率、敏感性和特异性分别为81.4%, 78.4%和84.2%.本文工作表明非线性超声RF信号的熵可有效地定征乳腺结节的良恶性,有望成为乳腺结节良恶性定征新参量.     

非平衡统计信息理论

阐述了以表述信息演化规律的信息（熵）演化方程为核心的非平 衡统计信息理论.推导出了 Shannon信息（熵）的非线性演化方程，引入了统计物理信息并 推导出了它的非线性演化方程.这两种信息（熵）演化方程一致表明：统计信息（熵）密度 随时间的变化率是由其在坐标空间（和态变量空间）的漂移、扩散和减损（产生）三者引起 的.由此方程出发，给出了统计信息减损率和统计熵产生率的简明公式、漂移信息流和扩散 信息流的表达式，证明了非平衡系统内的统计信息减损（或增加）率等于它的统计熵产生（ 或减少）率、信息扩散与信息减损同时 关键词： 统计信息（熵）演化方程 统计信息减损率 统计熵产 生率 信息（熵）流 信息（熵）扩散 动态互信息     

Entropy as a measure of the noise extent in a two-level quantum feedback controlled system

By introducing the von Neumann entropy as a measure of the extent of noise, this paper discusses the entropy evolution in a two-level quantum feedback controlled system. The results show that the feedback control can induce the reduction of the degree of noise, and different control schemes exhibit different noise controlling ability, the extent of the reduction also related with the position of the target state on the Bloch sphere. It is shown that the evolution of entropy can provide a real time noise observation and a systematic guideline to make reasonable choice of control strategy.     

Characterizing continuously rotating detonation via nonlinear time series analysis

The sampled pressure signals in continuously rotating detonation combustors have the inherence of nonlinear dynamics. To dig out more information on combustion modes, the nonlinear time series analysis method is thus applied to the pressure-time series obtained at a hydrogen/air rotating detonation combustor by using the phase space reconstruction and wavelet entropy algorithm. The variation of wavelet entropy is approved to be associated with the periodicity variation of pressure signals. Different structures of attractor as well as wavelet entropy distribution in the phase diagram can distinguish the deflagration mode from the unstable and stable detonation modes under the conditions of different air flow rates. The limit-cycle oscillation is exhibited in the phase space diagram with a very low flow rate, here 25 g/s. The mean wavelet entropy of pressure-time series can be a quantitative index of different combustion modes occurring in the combustor. The present study is expected to enhance the understanding of the physical mechanism of continuously rotating detonation and contribute on the development of detonation propulsion technology.     

电压控制不连续导电模式DC-DC变换器的熵特性研究

结合DC-DC变换器非线性特性随反馈增益k变化的关系,以及熵能够反映序列总体统计特征的特点,提出一种基于熵估计DC-DC变换器非线性行为的新方法. 以一阶电压反馈不连续导电模式 DCM Buck和Boost变换器为例,详细分析了不同反馈增益k和初值x₀形成的数值序列及熵的分布情况. 研究结果表明：DC-DC变换器的熵由反馈增益k决定,与初值x₀无关,最终小于理论极大值log₂N （N为统计区间个数）;熵能够准确区分DC-DC变换器的倍周期分岔和混沌行为,从而得到一种新的可量化的DC-DC变换器非线性动力学行为指标;完善了该类变换器非线性动力学分析的理论和方法. 关键词： DC-DC变换器 熵 混沌 k')" href="#">反馈增益k     

多尺度多变量模糊熵分析

多尺度多变量样本熵评价同步多通道数据的多变量复杂度, 是非线性动态相互关系的一种反映, 但其统计稳定性差, 且不适用于非线性非平稳信号. 研究利用模糊隶属度函数代替模式相似判断的硬阈值准则, 并分析模糊隶属度函数形式的影响; 研究利用多变量经验模态分解算法进行多尺度化, 并对比其处理效果. 仿真试验表明, 模糊隶属度函数的引入可以有效提高算法的统计稳定性, 所构造的物理模糊隶属度函数的性能最为显著; 基于多变量经验模态分解算法的多尺度化过程可更有效地捕获信号的不同尺度成分, 从而更敏感地区分具有不同复杂度的信号. 对临床试验数据的分析支持以上结论, 且结果提示随着年龄增加或心脏疾病的发生, 心率变异性和心脏舒张间期变异性的多变量复杂度以不同的方式降低: 年龄增加会使低尺度熵值降低, 表示近程相关性的丢失; 而心脏疾病会同时影响各个尺度的熵值, 即同时丢失了近程和长时相关性. 该结论可用于指导心血管疾病的无创预警研究. 关键词： 多变量复杂度 多尺度多变量模糊熵 物理模糊隶属度函数 多变量经验模态分解     

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.