The relationships of 42 species of ground moss with six environmental factors in 41 sites on Changbai Mountain Biosphere Reserve were analyzed. Four site groups and four groups of ground moss ecological species were i...The relationships of 42 species of ground moss with six environmental factors in 41 sites on Changbai Mountain Biosphere Reserve were analyzed. Four site groups and four groups of ground moss ecological species were identified using the method of Two-way Indicator Species Analysis (TWINSPAN). The results of Detrended Canonical. Correspondence Analysis (DCCA) showed that altitude, soil sand content, soil acidity, forest canopy coverage and soil water content are the five major environmental factors influencing the distributional patterns of the moss species. The four groups of ecological species, which correspond well with the four site groups, are projected on the species-environment biplot of DCCA. Group 1 dominated in the bogs of Larix olgensis forest, group 2 in the alpine tundra, group 3 in the dense conifer forest, and group 4 mainly in the Betula ermanii community and the Betula ermanii-Larix olgensis forest in sub-alpine respectively.展开更多
In this paper,a novel algorithm is proposed to detect and classify the power quality(PQ)disturbances for distribution networks with distributed generation.First,a distribution system with photovoltaic and wind power g...In this paper,a novel algorithm is proposed to detect and classify the power quality(PQ)disturbances for distribution networks with distributed generation.First,a distribution system with photovoltaic and wind power generation is built as a test platform.Then,nine types of power quality disturbances in the distribution network are decomposed by variational mode decomposition(VMD)and the noise is filtered.Meanwhile,the mode functions containing characteristic information are extracted as input signals of detrended fluctuation analysis(DFA).Power quality disturbances are classified from the view of their distributed energy operational status,and three types of windows are set up to deal with different frequency disturbances.The two-dimensional and three-dimensional scatter plots of each type under three windows are depicted,and the criteria are determined to distinguish the disturbances under the different operating conditions.The simulations show that the algorithm is simpler,more accurate and feasible.It provides an approach for online real-time detection of embedded systems.展开更多
Types and structure of plant communities in the Yellow River Delta were investigated by using detrended canonical correspondence analyses （DCCAs） and a two-way indicator species analysis （TWINSPAN）. The distributi...Types and structure of plant communities in the Yellow River Delta were investigated by using detrended canonical correspondence analyses （DCCAs） and a two-way indicator species analysis （TWINSPAN）. The distribution pattern and influential factors of the plant communities were also analyzed by testing elevation, slope, soil characteristics, longitude and latitude of 134 vegetation samples collected by representative plot sampling methods. Results showed that all the 134 vegetation samples could be divided into seven vegetation groups, separately dominated by Robinia pseucdoacacia, Imperata cylindrical, Miscanthus saccharifleus, Suaeda salsa, Aeluropus sinensis, Phragmites australis and Tamarix chinensis. The vegetation distribution pattern was mainly related to elevation, ground water depth and soil characteristics such as salinity and soluble potassium. Among the factors affecting distribution pattern of the plant communities, the species matrix explained by non-spatial environmental variation accounts for 45.2% of total variation. Spatial variation and spatial-structured environmental variation explain 11.8%, and 2.2%, respectively. Remained 40.8% of undetermined variation is attributed to biological and stochastic factors.展开更多
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 o...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.展开更多
When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year...When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MFDFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data.展开更多
Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and los...Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and loss of consciousness to blank staring, lip smacking, or jerking movements of arms and legs. If early warning signals of an upcoming seizure (diagnosis of preictal period) are detected, proper treatment can be applied to the patient to help prevent the seizure. In this research, an epileptic disorder has been divided into three subsets: Normal, Preictal (just before the seizure), and Ictal (during seizure). By using Detrended Fluctuation Analysis (DFA), Bispectral Analysis (BIS), and Standard Deviation (SD) three features from single-channel EEG signals have been derived in the foresaid groups. A fuzzy classifier is used to separate the three groups which can successfully separate them with a separation degree of 100% and further a fuzzy inference engine is used to extract a Seizure Intensity Index (SII) from the Electroencephalogram (EEG) signals of the three different states. One can apparently see the distinction of SII amounts between the three states. It is more important when one remembers that these results are just from single-channel EEG signal.展开更多
Types and structure of plant communities in the Yellow River Delta were investigated by using detrended canonical correspon-dence analyses(DCCAs) and a two-way indicator species analysis(TWINSPAN).The distribution pat...Types and structure of plant communities in the Yellow River Delta were investigated by using detrended canonical correspon-dence analyses(DCCAs) and a two-way indicator species analysis(TWINSPAN).The distribution pattern and influential factors of the plant communities were also analyzed by testing elevation, slope, soil characteristics, longitude and latitude of 134 vegetation samples collected by representative plot sampling methods.Results showed that all the 134 vegetation samples could be divided into seven vegetation groups, separately dominated by Robinia pseucdoacacia, Imperata cylindrical, Miscanthus saccharifleus, Suaeda salsa, Aeluropus sinensis, Phragmites australis and Tamarix chinensis.The vegetation distribution pattern was mainly related to elevation, ground water depth and soil characteristics such as salinity and soluble potassium.Among the factors affecting distribution pattern of the plant communities, the species matrix explained by non-spatial environmental variation accounts for 45.2% of total variation.Spatial variation and spatial-structured environmental variation explain 11.8%, and 2.2%, respectively.Remained 40.8% of undetermined variation is attributed to biological and stochastic factors.展开更多
Topography and soil factors are known to play crucial roles in the species composition of plant communities in subtropical evergreen-deciduous broadleaved mixed forests.In this study,we used a systematic quantitative ...Topography and soil factors are known to play crucial roles in the species composition of plant communities in subtropical evergreen-deciduous broadleaved mixed forests.In this study,we used a systematic quantitative approach to classify plant community types in the subtropical forests of Hubei Province(central China),and then quantified the relative contribution of drivers responsible for variation in species composition and diversity.We classified the subtropical forests in the study area into 12 community types.Of these,species diversity indices of three communities were significantly higher than those of others.In each community type,species richness,abundance,basal area and importance values of evergreen and deciduous species were different.In most community types,deciduous species richness was higher than that of evergreen species.Linear regression analysis showed that the dominant factors that affect species composition in each community type are elevation,slope,aspect,soil nitrogen content,and soil phosphorus content.Furthermore,structural equation modeling analysis showed that the majority of variance in species composition of plant communities can be explained by elevation,aspect,soil water content,litterfall,total nitrogen,and total phosphorus.Thus,the major factors that affect evergreen and deciduous species distribution across the 12 community types in subtropical evergreendeciduous broadleaved mixed forests include elevation,slope and aspect,soil total nitrogen content,soil total phosphorus content,soil available nitrogen content and soil available phosphorus content.展开更多
Massive seamounts have been surveyed and documented in the last decades.However,the morphologies of seamounts are usually described in qualitative manners,yet few quantitative detections have been carried out.Here,bas...Massive seamounts have been surveyed and documented in the last decades.However,the morphologies of seamounts are usually described in qualitative manners,yet few quantitative detections have been carried out.Here,based on the high-re solution multi-beam bathymetric data,we report a recentlysurveyed guy ot on the Caroline Ridge in the West Pacific,and the large-scale volcanic structures and smallscale erosive-depositional landforms in the guyot area have been identified.The multifractal features of the guyot are characterized for the first time by applying multifractal detrended fluctuation analysis on the surveyed bathymetric data.The results indicate that the multifractal spectrum parameters of the seafloor have strong spatial dependency on the fluctuations of local landforms.Both small-and large-scale components contribute to the degree of asymmetry of the multifractal spectrum(B),while the fluctuations of B are mostly attributed to the changes in small-scale roughness.The maximum singularity strength(α0)correlates well with the roughness of large-scale landforms and likely reflects the large-scale topographic irregularity.Comparing to traditional roughness parameters or monofractal exponents,multifractal spectra are able to depict not only the multiscale characteristics of submarine landforms,but also the spatial variations of scaling behaviors.Although more comparative works are required for various seamounts,we hope this study,as a case of quantifying geomorphological characters and multiscale behaviors of seamounts,can encourage further studies on seamounts concerning geomorphological processes,ocean bottom circulations,and seamount ecosystems.展开更多
Pollen analysis of 23 surface samples in the east of Qaidam Basin reveals the characteristics of pollen assemblages and their relationships with vegetation and climate. In pollen assemblages, Chenopodiaceae and Artemi...Pollen analysis of 23 surface samples in the east of Qaidam Basin reveals the characteristics of pollen assemblages and their relationships with vegetation and climate. In pollen assemblages, Chenopodiaceae and Artemisia are preponderant types in all the samples, and Ephedra, Gramineae and Compositae are common types. The results of DCA （Detrended Correspondance Analysis） and Correlation Analysis show different pollen assemblages indicate different vegetations, coincided with respective vegetation types. A/C （Artemisia/Chenopodiaceae） in the desert can indicate the aridity. Depending on the aridity, the vegetation communities are divided into four groups： severe drought group, moderate drought group, slight drought group and tropophilous group. A/C value is less 0.2 in the severe drought group, 0.2-0.5 in the moderate drought group, 1.63 in the slight drought group and 5.72 slight-wetness group.展开更多
A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness...A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.展开更多
Multifractal detrended fluctuation analysis （MF-DFA） is a relatively new method of multifractal analysis. It is extended from detrended fluctuation analysis （DFA）, which was developed for detecting the long-range ...Multifractal detrended fluctuation analysis （MF-DFA） is a relatively new method of multifractal analysis. It is extended from detrended fluctuation analysis （DFA）, which was developed for detecting the long-range correlation and the fractal properties in stationary and non-stationary time series. Although MF-DFA has become a widely used method, some relationships among the exponents established in the original paper seem to be incorrect under the general situation. In this paper, we theoretically and experimentally demonstrate the invalidity of the expression r（q） = qh（q） - 1 stipulating the relationship between the multifractal exponent T（q） and the generalized Hurst exponent h（q）. As a replacement, a general relationship is established on the basis of the universal multifractal formalism for the stationary series as .t-（q） = qh（q） - qH - 1, where H is the nonconservation parameter in the universal multifractal formalism. The singular spectra, a and f（a）, are also derived according to this new relationship.展开更多
The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been us...The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been used successfully to extract the time-domain Hurst exponent of sea-clutter series. Since the frequency of the sea clutter mainly concentrates around Doppler center so that we consider to extract frequency-do- main fractal characterization and then detect a weak target within sea clutter by using the difference of frequency-domain fractal characterization. The generalized detrended fluctuation analysis （GDFA） is more flexible than traditional DFA owing to its smoothing action for the clutters. In this paper, we apply the GDFA to evaluate the generalized Hurst exponent of sea-clutter series in the frequency domain. The difference of generalized Hurst exponents between different sea-clutter range bins would be used to determine whether the target exists. Moreover, some simulations with the real IPIX radar data have also been demonstrated in order to suooort this conclusion.展开更多
We study the dynamical properties of heart rate variability （HRV） in sleep by analysing the scaling behaviour with the multifractal detrended fluctuation analysis method. It is well known that heart rate is regulate...We study the dynamical properties of heart rate variability （HRV） in sleep by analysing the scaling behaviour with the multifractal detrended fluctuation analysis method. It is well known that heart rate is regulated by the interaction of two branches of the autonomic nervous system： the parasympathetic and sympathetic nervous systems. By investigating the multifractal properties of light, deep, rapid-eye-movement （REM） sleep and wake stages, we firstly find an increasing multifractal behaviour during REM sleep which may be caused by augmented sympathetic activities relative to non-REM sleep. In addition, the investigation of long-range correlations of HRV in sleep with second order detrended fluctuation analysis presents irregular phenomena. These findings may be helpful to understand the underlying regulating mechanism of heart rate by autonomic nervous system during wake-sleep transitions.展开更多
We analyze the correlation properties of the Erd6s-Rdnyi random graph （RG） and the Barabdsi-Albert scale-free network （SF） under the attack and repair strategy with detrended fluctuation analysis （DFA）. The maxi...We analyze the correlation properties of the Erd6s-Rdnyi random graph （RG） and the Barabdsi-Albert scale-free network （SF） under the attack and repair strategy with detrended fluctuation analysis （DFA）. The maximum degree kmax, representing the local property of the system, shows similar scaling behaviors for random graphs and scale-free networks. The fluctuations are quite random at short time scales but display strong anticorrelation at longer time scales under the same system size N and different repair probability pre. The average degree 〈k〉, revealing the statistical property of the system, exhibits completely different scaling behaviors for random graphs and scale-free networks. Random graphs display long-range power-law correlations. Scale-free networks are uncorrelated at short time scales; while anticorrelated at longer time scales and the anticorrelation becoming stronger with the increase of pre.展开更多
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 Duffin...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.展开更多
This paper presents fault detection,classification,and location for a PV-Wind-based DC ring microgrid in the MATLAB/SIMULINK platform.Initially,DC fault signals are collected from local measurements to examine the out...This paper presents fault detection,classification,and location for a PV-Wind-based DC ring microgrid in the MATLAB/SIMULINK platform.Initially,DC fault signals are collected from local measurements to examine the outcomes of the proposed system.Accurate detection is carried out for all faults,(i.e.,cable and arc faults)under two cases of fault resistance and distance variation,with the assistance of primary and secondary detection techniques,i.e.second-order differential current derivatived2I3 dt2and sliding mode window-based Pearson’s correlation coefficient.For fault classification a novel approach using modified multifractal detrended fluctuation analysis(M-MFDFA)is presented.The advantage of this method is its ability to estimate the local trends of any order polynomial function with the help of polynomial and trigonometric functions.It also doesn’t require any signal processing algorithm for decomposition resulting and this results in a reduction of computational burden.The detected fault signals are directly passed through the M-MFDFA classifier for fault type classification.To enhance the performance of the proposed classifier,statistical data is obtained from the M-MFDFA feature vectors,and the obtained data is plotted in 2-D and 3-D scatter plots for better visualization.Accurate fault distance estimation is carried out for all types of faults in the DC ring bus microgrid with the assistance of recursive least squares with a forgetting factor(FF-RLS).To verify the performance and superiority of the proposed classifier,it is compared with existing classifiers in terms of features,classification accuracy(CA),and relative computational time(RCT).展开更多
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 se...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.展开更多
Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (...Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (EEG) channel for the purpose of building an automated sleep staging system based on the hybrid prediction engine model. The testing results of the model were promising as the classification accuracies were 98.85%, 92.26%, 94.4%, 95.16% and 93.68% for the wake, non-rapid eye movement S1, non-rapid eye movement S2, non-rapid eye movement S3 and rapid eye movement sleep stages, respectively. The overall classification accuracy was 85.18%. We concluded that it might be possible to employ this approach to build an industrial sleep assessment system that reduced the number of channels that affected the sleep quality and the effort excreted by sleep specialists through the process of the sleep scoring.展开更多
Detrended fluctuation analysis （DFA） is a method foro estimating the long-range power-law correlation exponent in noisy signals. It has been used successfully in many different fields, especially in the research of ...Detrended fluctuation analysis （DFA） is a method foro estimating the long-range power-law correlation exponent in noisy signals. It has been used successfully in many different fields, especially in the research of physiological signals. As an inherent part of these studies, quantization of continuous signals is inevitable. In addition, coarse-graining, to transfer original signals into symbol series in symbolic dynamic analysis, can also be considered as a quantization-like operation. Therefore, it is worth considering whether the quantization of signal has any effect on the result of DFA and if so, how large the effect will be. In this paper we study how the quantized degrees for three types of noise series （anti-correlated, uncorrelated and long-range power-law correlated signals） affect the results of DFA and find that their effects are completely different. The conclusion has an essential value in choosing the resolution of data acquisition instrument and in the processing of coarse-graining of signals.展开更多
文摘The relationships of 42 species of ground moss with six environmental factors in 41 sites on Changbai Mountain Biosphere Reserve were analyzed. Four site groups and four groups of ground moss ecological species were identified using the method of Two-way Indicator Species Analysis (TWINSPAN). The results of Detrended Canonical. Correspondence Analysis (DCCA) showed that altitude, soil sand content, soil acidity, forest canopy coverage and soil water content are the five major environmental factors influencing the distributional patterns of the moss species. The four groups of ecological species, which correspond well with the four site groups, are projected on the species-environment biplot of DCCA. Group 1 dominated in the bogs of Larix olgensis forest, group 2 in the alpine tundra, group 3 in the dense conifer forest, and group 4 mainly in the Betula ermanii community and the Betula ermanii-Larix olgensis forest in sub-alpine respectively.
基金This work was supported by China Scholarship Council and National Natural Science Foundation of China(Grant Number 51507091)。
文摘In this paper,a novel algorithm is proposed to detect and classify the power quality(PQ)disturbances for distribution networks with distributed generation.First,a distribution system with photovoltaic and wind power generation is built as a test platform.Then,nine types of power quality disturbances in the distribution network are decomposed by variational mode decomposition(VMD)and the noise is filtered.Meanwhile,the mode functions containing characteristic information are extracted as input signals of detrended fluctuation analysis(DFA).Power quality disturbances are classified from the view of their distributed energy operational status,and three types of windows are set up to deal with different frequency disturbances.The two-dimensional and three-dimensional scatter plots of each type under three windows are depicted,and the criteria are determined to distinguish the disturbances under the different operating conditions.The simulations show that the algorithm is simpler,more accurate and feasible.It provides an approach for online real-time detection of embedded systems.
基金Foundation project： This study was financially supported by the Na- tional Natural Science Foundation of China （No. 40771172） and the orientation project of the Chinese Academy of Sciences （No. kzcx2-yw-308）
文摘Types and structure of plant communities in the Yellow River Delta were investigated by using detrended canonical correspondence analyses （DCCAs） and a two-way indicator species analysis （TWINSPAN）. The distribution pattern and influential factors of the plant communities were also analyzed by testing elevation, slope, soil characteristics, longitude and latitude of 134 vegetation samples collected by representative plot sampling methods. Results showed that all the 134 vegetation samples could be divided into seven vegetation groups, separately dominated by Robinia pseucdoacacia, Imperata cylindrical, Miscanthus saccharifleus, Suaeda salsa, Aeluropus sinensis, Phragmites australis and Tamarix chinensis. The vegetation distribution pattern was mainly related to elevation, ground water depth and soil characteristics such as salinity and soluble potassium. Among the factors affecting distribution pattern of the plant communities, the species matrix explained by non-spatial environmental variation accounts for 45.2% of total variation. Spatial variation and spatial-structured environmental variation explain 11.8%, and 2.2%, respectively. Remained 40.8% of undetermined variation is attributed to biological and stochastic factors.
基金supported by the Science Foundation of Jiangsu Province of China (Grant No.BK2011759)
文摘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.
基金Supported by the NSFC-Shandong Joint Fund “Study on the DisasterCausing Mechanism and Disaster Prevention Countermeasures of MultiSource Storm Surges”(No.U1706226)the National Natural Science Foundation of China “Coastal Engineering and Risk Assessment Based on a Four-Layer Nested Multi-Objective Probability Model”(No.51379195)+1 种基金the Natural Science Foundation of Shandong Province “Three-Layer Nested Multi-Objective Probability Prediction and Risk Assessment in Coastal Engineering”(No.ZR2013EEM034)the Program of Promotion Plan for Postgraduates’ Educational Quality “Paying More Attention to the Study on the Cultivation Mode of Mathematical Modeling for Engineering Postgraduates”(No.861801232417)
文摘When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MFDFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data.
文摘Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and loss of consciousness to blank staring, lip smacking, or jerking movements of arms and legs. If early warning signals of an upcoming seizure (diagnosis of preictal period) are detected, proper treatment can be applied to the patient to help prevent the seizure. In this research, an epileptic disorder has been divided into three subsets: Normal, Preictal (just before the seizure), and Ictal (during seizure). By using Detrended Fluctuation Analysis (DFA), Bispectral Analysis (BIS), and Standard Deviation (SD) three features from single-channel EEG signals have been derived in the foresaid groups. A fuzzy classifier is used to separate the three groups which can successfully separate them with a separation degree of 100% and further a fuzzy inference engine is used to extract a Seizure Intensity Index (SII) from the Electroencephalogram (EEG) signals of the three different states. One can apparently see the distinction of SII amounts between the three states. It is more important when one remembers that these results are just from single-channel EEG signal.
基金supported by the Na-tional Natural Science Foundation of China (No. 40771172)the orientation project of the Chinese Academy of Sciences (No. kzcx2-yw-308)
文摘Types and structure of plant communities in the Yellow River Delta were investigated by using detrended canonical correspon-dence analyses(DCCAs) and a two-way indicator species analysis(TWINSPAN).The distribution pattern and influential factors of the plant communities were also analyzed by testing elevation, slope, soil characteristics, longitude and latitude of 134 vegetation samples collected by representative plot sampling methods.Results showed that all the 134 vegetation samples could be divided into seven vegetation groups, separately dominated by Robinia pseucdoacacia, Imperata cylindrical, Miscanthus saccharifleus, Suaeda salsa, Aeluropus sinensis, Phragmites australis and Tamarix chinensis.The vegetation distribution pattern was mainly related to elevation, ground water depth and soil characteristics such as salinity and soluble potassium.Among the factors affecting distribution pattern of the plant communities, the species matrix explained by non-spatial environmental variation accounts for 45.2% of total variation.Spatial variation and spatial-structured environmental variation explain 11.8%, and 2.2%, respectively.Remained 40.8% of undetermined variation is attributed to biological and stochastic factors.
基金the National Natural Science Foundation of China(51809250)Hubei Provincial Natural Science Foundation for Innovation Groups(No.2019CFA019).
文摘Topography and soil factors are known to play crucial roles in the species composition of plant communities in subtropical evergreen-deciduous broadleaved mixed forests.In this study,we used a systematic quantitative approach to classify plant community types in the subtropical forests of Hubei Province(central China),and then quantified the relative contribution of drivers responsible for variation in species composition and diversity.We classified the subtropical forests in the study area into 12 community types.Of these,species diversity indices of three communities were significantly higher than those of others.In each community type,species richness,abundance,basal area and importance values of evergreen and deciduous species were different.In most community types,deciduous species richness was higher than that of evergreen species.Linear regression analysis showed that the dominant factors that affect species composition in each community type are elevation,slope,aspect,soil nitrogen content,and soil phosphorus content.Furthermore,structural equation modeling analysis showed that the majority of variance in species composition of plant communities can be explained by elevation,aspect,soil water content,litterfall,total nitrogen,and total phosphorus.Thus,the major factors that affect evergreen and deciduous species distribution across the 12 community types in subtropical evergreendeciduous broadleaved mixed forests include elevation,slope and aspect,soil total nitrogen content,soil total phosphorus content,soil available nitrogen content and soil available phosphorus content.
基金the Senior User Project of R/V Kexue(No.KEXUE2018G11)the Science and Technology Basic Resources Investigation Program ofChina(No.2017FY100801)the Open Fund of the Key Laboratoryof Marine Geology and Environment,Chinese Academy of Sciences(No.MGE2018KG02)。
文摘Massive seamounts have been surveyed and documented in the last decades.However,the morphologies of seamounts are usually described in qualitative manners,yet few quantitative detections have been carried out.Here,based on the high-re solution multi-beam bathymetric data,we report a recentlysurveyed guy ot on the Caroline Ridge in the West Pacific,and the large-scale volcanic structures and smallscale erosive-depositional landforms in the guyot area have been identified.The multifractal features of the guyot are characterized for the first time by applying multifractal detrended fluctuation analysis on the surveyed bathymetric data.The results indicate that the multifractal spectrum parameters of the seafloor have strong spatial dependency on the fluctuations of local landforms.Both small-and large-scale components contribute to the degree of asymmetry of the multifractal spectrum(B),while the fluctuations of B are mostly attributed to the changes in small-scale roughness.The maximum singularity strength(α0)correlates well with the roughness of large-scale landforms and likely reflects the large-scale topographic irregularity.Comparing to traditional roughness parameters or monofractal exponents,multifractal spectra are able to depict not only the multiscale characteristics of submarine landforms,but also the spatial variations of scaling behaviors.Although more comparative works are required for various seamounts,we hope this study,as a case of quantifying geomorphological characters and multiscale behaviors of seamounts,can encourage further studies on seamounts concerning geomorphological processes,ocean bottom circulations,and seamount ecosystems.
基金Scientific research fund of Hebei Normal University, No.L2004B14 National Key Basic Research Program, No.2005CB422005+3 种基金 National Natural Science Foundation of China, No.90202012 No.40171095 Natural Science Foundation of Hebei Province, No.402615 Knowledge Innovation Project of CAS, No.KZCX3-SW-339
文摘Pollen analysis of 23 surface samples in the east of Qaidam Basin reveals the characteristics of pollen assemblages and their relationships with vegetation and climate. In pollen assemblages, Chenopodiaceae and Artemisia are preponderant types in all the samples, and Ephedra, Gramineae and Compositae are common types. The results of DCA （Detrended Correspondance Analysis） and Correlation Analysis show different pollen assemblages indicate different vegetations, coincided with respective vegetation types. A/C （Artemisia/Chenopodiaceae） in the desert can indicate the aridity. Depending on the aridity, the vegetation communities are divided into four groups： severe drought group, moderate drought group, slight drought group and tropophilous group. A/C value is less 0.2 in the severe drought group, 0.2-0.5 in the moderate drought group, 1.63 in the slight drought group and 5.72 slight-wetness group.
基金This work was supported by the National Nature Science Foundation of China under Grant No. 60571019 and No. 30525030.
文摘A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.
基金Project supported by the National Natural Science Foundation of China (Grant No.11071282)the Chinese Program for New Century Excellent Talents in University (Grant No.NCET-08-06867)
文摘Multifractal detrended fluctuation analysis （MF-DFA） is a relatively new method of multifractal analysis. It is extended from detrended fluctuation analysis （DFA）, which was developed for detecting the long-range correlation and the fractal properties in stationary and non-stationary time series. Although MF-DFA has become a widely used method, some relationships among the exponents established in the original paper seem to be incorrect under the general situation. In this paper, we theoretically and experimentally demonstrate the invalidity of the expression r（q） = qh（q） - 1 stipulating the relationship between the multifractal exponent T（q） and the generalized Hurst exponent h（q）. As a replacement, a general relationship is established on the basis of the universal multifractal formalism for the stationary series as .t-（q） = qh（q） - qH - 1, where H is the nonconservation parameter in the universal multifractal formalism. The singular spectra, a and f（a）, are also derived according to this new relationship.
基金The National Natural Science Foundation of China Project under contract Nos 41276187 and 41076119the Scientific Research Foundation for Introducing Talents,Nanjing University of Information Science and Technology under contract No.20110310Jiangsu Natural Science Foundation under contract No.BK2011008
文摘The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been used successfully to extract the time-domain Hurst exponent of sea-clutter series. Since the frequency of the sea clutter mainly concentrates around Doppler center so that we consider to extract frequency-do- main fractal characterization and then detect a weak target within sea clutter by using the difference of frequency-domain fractal characterization. The generalized detrended fluctuation analysis （GDFA） is more flexible than traditional DFA owing to its smoothing action for the clutters. In this paper, we apply the GDFA to evaluate the generalized Hurst exponent of sea-clutter series in the frequency domain. The difference of generalized Hurst exponents between different sea-clutter range bins would be used to determine whether the target exists. Moreover, some simulations with the real IPIX radar data have also been demonstrated in order to suooort this conclusion.
基金Supported by the National Science Foundation of China under Grant Nos 60471057 and 70571075, and the Foundation for Graduate Student of USTC under Grant No KD2006046.
文摘We study the dynamical properties of heart rate variability （HRV） in sleep by analysing the scaling behaviour with the multifractal detrended fluctuation analysis method. It is well known that heart rate is regulated by the interaction of two branches of the autonomic nervous system： the parasympathetic and sympathetic nervous systems. By investigating the multifractal properties of light, deep, rapid-eye-movement （REM） sleep and wake stages, we firstly find an increasing multifractal behaviour during REM sleep which may be caused by augmented sympathetic activities relative to non-REM sleep. In addition, the investigation of long-range correlations of HRV in sleep with second order detrended fluctuation analysis presents irregular phenomena. These findings may be helpful to understand the underlying regulating mechanism of heart rate by autonomic nervous system during wake-sleep transitions.
基金The project supported by National Natural Science Foundation of China under Grant Nos. 70271067 and 70401020 and the Science Foundation of the Ministry of Education of China under Grant No. 03113
文摘We analyze the correlation properties of the Erd6s-Rdnyi random graph （RG） and the Barabdsi-Albert scale-free network （SF） under the attack and repair strategy with detrended fluctuation analysis （DFA）. The maximum degree kmax, representing the local property of the system, shows similar scaling behaviors for random graphs and scale-free networks. The fluctuations are quite random at short time scales but display strong anticorrelation at longer time scales under the same system size N and different repair probability pre. The average degree 〈k〉, revealing the statistical property of the system, exhibits completely different scaling behaviors for random graphs and scale-free networks. Random graphs display long-range power-law correlations. Scale-free networks are uncorrelated at short time scales; while anticorrelated at longer time scales and the anticorrelation becoming stronger with the increase of pre.
基金Project supported by the National Natural Science Foundation of China (Grant No. 51175316)the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20103108110006)
文摘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.
文摘This paper presents fault detection,classification,and location for a PV-Wind-based DC ring microgrid in the MATLAB/SIMULINK platform.Initially,DC fault signals are collected from local measurements to examine the outcomes of the proposed system.Accurate detection is carried out for all faults,(i.e.,cable and arc faults)under two cases of fault resistance and distance variation,with the assistance of primary and secondary detection techniques,i.e.second-order differential current derivatived2I3 dt2and sliding mode window-based Pearson’s correlation coefficient.For fault classification a novel approach using modified multifractal detrended fluctuation analysis(M-MFDFA)is presented.The advantage of this method is its ability to estimate the local trends of any order polynomial function with the help of polynomial and trigonometric functions.It also doesn’t require any signal processing algorithm for decomposition resulting and this results in a reduction of computational burden.The detected fault signals are directly passed through the M-MFDFA classifier for fault type classification.To enhance the performance of the proposed classifier,statistical data is obtained from the M-MFDFA feature vectors,and the obtained data is plotted in 2-D and 3-D scatter plots for better visualization.Accurate fault distance estimation is carried out for all types of faults in the DC ring bus microgrid with the assistance of recursive least squares with a forgetting factor(FF-RLS).To verify the performance and superiority of the proposed classifier,it is compared with existing classifiers in terms of features,classification accuracy(CA),and relative computational time(RCT).
基金Projects(61271321,61573253,61401303)supported by the National Natural Science Foundation of ChinaProject(14ZCZDSF00025)supported by Tianjin Key Technology Research and Development Program,China+1 种基金Project(13JCYBJC17500)supported by Tianjin Natural Science Foundation,ChinaProject(20120032110068)supported by Doctoral Fund of Ministry of Education of China
文摘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.
文摘Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (EEG) channel for the purpose of building an automated sleep staging system based on the hybrid prediction engine model. The testing results of the model were promising as the classification accuracies were 98.85%, 92.26%, 94.4%, 95.16% and 93.68% for the wake, non-rapid eye movement S1, non-rapid eye movement S2, non-rapid eye movement S3 and rapid eye movement sleep stages, respectively. The overall classification accuracy was 85.18%. We concluded that it might be possible to employ this approach to build an industrial sleep assessment system that reduced the number of channels that affected the sleep quality and the effort excreted by sleep specialists through the process of the sleep scoring.
基金Project supported by the Natural Science Foundation for the Returned Overseas Chinese Scholars of the Ministry of Human Resources of China (Grant No. 2008102SB90203)Nanjing Normal University,China (Grant No. 2008102XLH0044)
文摘Detrended fluctuation analysis （DFA） is a method foro estimating the long-range power-law correlation exponent in noisy signals. It has been used successfully in many different fields, especially in the research of physiological signals. As an inherent part of these studies, quantization of continuous signals is inevitable. In addition, coarse-graining, to transfer original signals into symbol series in symbolic dynamic analysis, can also be considered as a quantization-like operation. Therefore, it is worth considering whether the quantization of signal has any effect on the result of DFA and if so, how large the effect will be. In this paper we study how the quantized degrees for three types of noise series （anti-correlated, uncorrelated and long-range power-law correlated signals） affect the results of DFA and find that their effects are completely different. The conclusion has an essential value in choosing the resolution of data acquisition instrument and in the processing of coarse-graining of signals.
