Cross-Correlation between Global Temperature and Atmospheric CO2 with a Temperature-Leading Time Lag

The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.

Detrended cross-correlation analysis of electroencephalogram

In the paper we use detrended cross-correlation analysis （DCCA） to study the electroencephalograms of healthy young subjects and healthy old subjects. It is found that the cross-correlation between different leads of a healthy young subject is larger than that of a healthy old subject. It was shown that the cross-correlation relationship decreases with the aging process and the phenomenon can help to diagnose whether the subject＇s brain function is healthy or not.

Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.

A Correlation Coefficient Approach for Evaluation of Stiffness Degradation of Beams Under Moving Load

This paper presents a new approach using correlation and cross-correlation coefficients to evaluate the stiffness degradation of beams under moving load.The theoretical study of identifying defects by vibration methods showed that the traditional methods derived from the vibration measurement data have not met the needs of the actual issues.We show that the correlation coefficients allow us to evaluate the degree and the effectiveness of the defects on beams.At the same time,the cross-correlation model is the basis for determining the relative position of defects.The results of this study are experimentally conducted to confirm the relationship between the correlation coefficients and the existence of the defects.In particular,the manuscript shows that the sensitivity of the correlation coefficients and cross-correlation is much higher than the parameters such as changes in stiffness(EJ)and natural frequency values(Δf).This study suggests using the above parameters to evaluate the stiffness degradation of beams by vibration measurement data in practice.

Effects of signal modulation and coloured cross-correlation of coloured noises on the diffusion of a harmonic oscillator

The diffusion in a harmonic oscillator driven by coloured noises ξ（t） and η（t） with coloured cross-correlation in which one of the noises is modulated by a biased periodic signal is investigated. The exact expression of diffusion coefficient d as a function of noise parameter, signal parameter, and oscillator frequency is derived. The findings in this paper are as follows. 1） The curves of d versus noise intensity D and d versus noises cross-correlation time z3 exist as two different phases. The transition between the two phases arises from the change of the cross-correlation coefficient A of the two Ornstein-Uhlenbeck （O-U） noises. 2） Changing the value of T3, the curves of d versus Q, the intensity of colored noise that is modulated by the signal, can transform from a phase having a minimum to a monotonic phase. 3） Changing the value of signal amplitude A, d versus Q curves can transform from a phase having a minimum to a monotonic phase. The above-mentioned results demonstrate that a like noise-induced transition appears in the model.

Research on Leak Location Method of Water Supply Pipeline Based on MVMD

At present,the leakage rate of the water distribution network in China is still high,and the waste of water resources caused by water distribution network leakage is quite serious every year.Therefore,the location of pipeline leakage is of great significance for saving water resources and reducing economic losses.Acoustic emission technology is the most widely used pipeline leak location technology.The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters,ignoring periodic noise and components unrelated to pipeline leakage.Aiming at the above problems,this paper proposes a leak location method for water supply pipelines based on a multivariate variational mode decomposition algorithm.This method combines the two parameters of the energy loss coefficient and the correlation coefficient between adjacent modes,and adaptively determines the decomposition mode number K according to the characteristics of the signal itself.According to the correlation coefficient,the effective component is selected to reconstruct the signal and the cross-correlation time delay is estimated to determine the location of the pipeline leakage point.The experimental results show that this method has higher accuracy than the cross-correlation method based on VMD and the cross-correlation method based on EMD,and the average relative positioning error is less than 2.2%.

Correlation analysis between the Aral Sea shrinkage and the Amu Darya River

The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.

实测海杂波数据空间相关性研究

针对海杂波背景下目标检测问题的实际需要,从幅度和谱两个层面研究了实测海杂波数据的空间相关性。幅度的空间相关性主要采用相关系数和消除趋势波动分析方法,重点研究了非相参条件下空间相关性随雷达距离分辨率、海况、采样率和积累四个因素的变化趋势,相参条件下空间相关系数实部、虚部和模值的特性以及不同海况时的长程空间相关性。谱的空间相关性采用相关系数和互相关系数进行表征,主要对单一频率成分的空间相关性、谱的质心和带宽与海杂波功率水平的互相关特性两个问题进行了研究。通过研究掌握了海杂波空间相关性的一般趋势及部分特性与雷达参数、环境参数间的依赖关系,为进一步理解海表面散射机理、优化目标检测算法以及海杂波仿真提供先验信息和理论指导。

一种帕金森患者行走步态的非线性特征分析

鉴于医师对帕金森患者病情严重程度的诊断存在一定的主观性,使其无法对患者进行更加有效的治疗,提出一种基于帕金森患者行走步态的非线性特征的客观定量分析方法.利用去趋势波动分析法和重复周期密度熵,对正常青年人、正常老年人以及老年帕金森患者的行走步态信号中的地面反作用力信息进行定量非线性分析,然后将所提方法的参数与传统时域参数进行相关性对比分析.实验结果表明:帕金森患者与正常人相比较,二者的步态信号在稳定性、长程相关性和重复周期性方面均有显著差别.研究结果能为帕金森患者病情的分级评估以及诊断康复,提供一种简单有效的指导方法.

基于DFA方法的股市异常波动分析

极端事件或极值事件脱离了自身的正常演化状态,是系统演化的极端状态或系统受到外界扰动而导致的异常状态.去趋势波动分析(DFA)法得到的指数是衡量系统在某一时间尺度内演化的长程相关性的参数,系数的长程相关性不受极端事件的影响或影响很小.基于这一思想,提出了利用去趋势波动分析确定极端事件的阈值方法,并验证了该方法的有效性.文章将DFA方法及基于DFA方法确定极端事件的阈值引入对中国股市异常波动的分析.

Study on S wave splitting in Dayao earth-quake sequence with M=6.2 and M=6.1 in Yunnan in 2003

The polarization direction of fast wave and the delay time between fast and slow wave were measured for two earthquake sequences occurred continuously on 21 July （M=6.2） and 16 October （M=6.1） in Dayao, Yunnan in 2003 using cross-correlation coefficient method, after determining the high-resolution hypocentral locations of the earthquake sequences using the double-difference earthquake location algorithm. The results indicated that ① The phenomena of S wave splitting are obvious in the two earthquake sequences, and the average polarization directions of fast wave in most stations are almost consistent with regional maximum horizontal compressive stress direction except the station Santai. There are bimodal fast directions in the polarization directions at station Santai and the mean polarization direction is N80°E, indicating an inconsistent phenomenon referred to regional maximum horizontal compressive stress direction. ② There is no apparent relation between delay time and focal depth in the sequences, but the polarization direction show different character in different delay time range. ③ The comparison of S wave splitting results in the two earthquake sequences show that the polarization direction in M=6.2 earthquake sequence is more scattered and its average fast direction is 20° larger than that of M=6.1 sequence, and the delay times between two sequences show a little difference. ④ The spatial variation in S wave splitting polarization direction may be due to the stress disturbance imposed by the M=6.2 and the M=6.1 mainshocks on regional background stress field.

Shibor利率间的动态关联性研究——基于多重分形的视角

通过运用基于无参数估计和非线性结构的经济物理学的方法(DCCA和MF-DCCA),克服了传统上要求的正态分布和平稳性假设条件,首次提出对Shibor利率之间的动态相关性及这种相关关系的多重分形特征进行研究。实证结果表明,两个短端Shibor利率之间具有幂律关系式的反持续性相关性,而其他任意两个短、中、长端利率之间则具有幂律关系式的持续性相关性。此外,结果还表明,任意两个Shibor利率之间的相关关系均具有多重分形性和时变的非线性依赖性,且在短期、中期和长期内具有显著的差异。

贝塔系数变动性的多重分形特征及其量化方法

针对CAPM模型中贝塔系数的时变性观点,本文提出了多重分形去趋势贝塔分析法(MFDBCA),运用该方法检验上证综合A股指数、上证综合B股指数、深圳综指、深圳综合A股指数及深圳综合B股指数的贝塔系数变动性,并对其多重分形程度进行了量化分析,分析了其在投资实践中应用。研究结果表明:它们的贝塔系数变动性呈现出多重分形特征,上证综合A股指数的多重分形程度最小,而上证综合B股指数的多重分形程度最大。本文研究为量化系统风险及利用贝塔投资实践提供了一种新方法,为改进贝塔系数提供了一种猜想。

乌鲁木齐PM_(2.5)与其他空气污染物的去趋势互相关系数分析

为了避免时间趋势对相关性的影响,深入了解乌鲁木齐市PM_(2.5)与其他空气污染物的相关关系,本文基于乌鲁木齐市2015-2018年监测站点空气质量实时监测数据,使用新方法—去趋势互相关系数分析法来探索乌市PM_(2.5)与PM_(10)、SO_(2)、NO_(2)、CO、O_(3)五种空气污染物之间的关系.一方面,通过去趋势互相关系数分析法分别对以“天”为单位和以“小时”为单位的数据进行分析,发现PM_(2.5)与PM_(10)、SO_(2)、NO_(2)、CO之间呈正相关性,与O_(3)呈负相关性,并发现CO相比其他空气污染物与PM_(2.5)的关系最为显著.另一方面,去趋势互相关系数分析法的等值线图可以直接观察到乌市PM_(2.5)与其他空气污染物在不同时间点和不同时间尺度下的相关关系变化状况,从而为乌市空气质量问题的治理,降低人类健康风险提供理论支持.

Pollen indication to source plants in the eastern desert of China

In order to investigate the indication of major pollen types to source plants, 39 surface pollen samples are collected from 39 plant communities. The results show that diversity of pollen assemblages presents in different commu-nities. Chenopodiaceae pollen percentages are highest in Kalidium communities and Ceratoides communities (higher than 45%, average is 63.4%), but high Ephedra pollen per-centages (higher than 3.3%, average is 16.5%)in Ceratoides communities make it distinguished from Kalidium communi-ties. Artemisia pollen percentages (more than 50%) are much higher than Chenopodiaceae (less than 35%) in Artemisia Communities. Nitraria pollen percentages (higher than 2%, average is 13.6%) are highest in Nitraria communities com-pared with those in other community patterns. Tamariceae pollen percentages (higher than 2%, average is 8.6%) are highest both in Tetraena and Reaumuria communities com-pared with those in other community patterns. But in Reau-muria communities, Artemisia pollen percentages are higher than Chenopodiaceae with some other pollen types such as Gramineae, Compositae, Leguminosae; on the contrary, in Tetraena communities, Artemisia pollen percentages are less than Chenopodiaceae with few other pollen types. Calli-gonum pollen only appears in Calligonum communities (higher than 2%,average is 5%). The results of cluster analysis (CLA) and detrended correspondence analysis (DCA) show that the pollen indication to source plants can be reflected by association indices (A), over-representation in-dices (O), under-representation indices (U), R-value, the av-erage pollen percentages where parent plants are absent (Xa) and the average pollen percentages where parent plants are present (Xp). Zygophyllum, Calligonum, Tetraena, Tribulus and Tamariceae can indicate source plants very well, owing to high A scores. Artemisia, Chenopodiaceae and Gramineae have high R-values, and Nitraria, Cruciferae and sedum have high O scores, so they are over-represented types, but the higher Xp and the lower Xa values make that these pollen types can also indicate source plants moderately. Allium, Leguminosae, Roseceae, Pedicularis and Compositae have low A scores (less than 0.5), high O and/or U scores, so they cannot indicate source plants clearly. Some other pollen types, such as Pinus, Picea, Betula, Quercus, Ulmus, Ephedra, Elaegnaceae, Ranunculaceae, Caryophyllaceae, Polygona-ceae, Saxifragaceae, only appear in pollen assemblages, so their indication cannot be decided. Just like the pollen per-centage ratios of Artemisia and Chenopodiaceae (A/C), the ratios of Ephedra and Artemisia (E/A), Nitraria and Ephedra (N/E) can also indicate the aridity of environment. In typical desert communities, A/C ratios are less than 0.5, E/A ratios are more than 0.1, and N/E ratios are less than 1. In steppe desert, A/C ratios are 0.5―2, E/A ratios are less than 0.1, and N/E ratios are more than 1.

DIRECT MEASUREMENT OF MULTIPLE QUANTUM RELAXATION TIME

A multiple quantum coherence (MQC) with a given order subjected to its own relaxa-tion decay in the evolution period can be transferred into observable single quantum (SQ)coherences in the detection period, the MQ relaxation time in a spin system, therefore, canbe directly measured by the 1-dimensional (1-D) Hahn echo method since the amplitudes ofSQC’s are modulated by the decayed MQC. This 1-D approach is an alternation to the 2-Dmeasurement for MQ relaxation time, the latter being somewhat time-consuming. The 1-Dapproaches can readily get rid of the inhomogeneous broadening of MQ lines. A method forselective measurement of various MQ relaxation times in AX_n systems (n=1,2, and 3) witha given MQ order is presented. Consequently, the cross correlation coefficients of the sys-tems, which are not accessible in the SQ relaxation study, can be determined.