48 results about "Hurst index" patented technology

The invention discloses a multi-fractal detection method of targets in FRFT (Fractional Fourier Transformation) domain sea clutter and belongs to the radar signal processing field. According to the conventional multi-fractal detection methods of targets in sea clutter, echo sequences in the radar time domain are processed directly, and therefore detection performance of weak moving targets in strong sea clutter background is poor. The multi-fractal detection method of targets in FRFT domain sea clutter is characterized in that fractional Fourier transformation is organically combined with the multi-fractal processing method, and the generalized Hurst index number of the sea-clutter fractional Fourier transformation spectrum is extracted to form detection statistics by comprehensively utilizing the advantage that the fractional Fourier transformation is capable of effectively improving the signal to clutter ratio of the moving target on the sea surface and the feature that the multi-fractal characteristic is capable of breaking the tether of the signal to clutter ratio to a certain extent. The detection method comprehensively utilizes the advantages of phase-coherent accumulation and multi-fractal theory and has excellent separating capability on the weak moving targets in sea clutter; and simultaneously, the method is also suitable for tracking target signals in nonuniform fractal clutter and has popularization and application values.

The invention provides a division and evaluation method for fracture and hole type reservoir flow units. The division and evaluation method for the fracture and hole type reservoir flow units comprises the steps that the standards for dividing flow unit types in an underground reservoir body are determined according to the changing rate of the hurst index after water appears in an oil well and the storage characteristics of the underground reservoir body of a fracture and hole type reservoir; the statistical result of weighted average porosity of the underground reservoir body of all the flow unit types is used as the unified standard for dividing the flow unit types, and the flow unit type division on a whole fracture and hole type reservoir area is carried out by utilizing the unified standard; characteristic parameters reflecting oil well productivity contribution are selected according to flowing laws of fluid of each flow unit type, and the flow units belonging to the corresponding flow unit types are evaluated by utilizing the characteristic parameters. According to the division and evaluation method for the fracture and hole type reservoir flow units, the fact that the units with identical storage characteristics and the flowing laws of the fluid can be divided as the same flow unit is facilitated, the evaluation standard is beneficial for forming efficient developing modes aiming at different flow units for researching the distribution characteristics of remaining oil.

The invention discloses a fault diagnosis method for a rolling bearing based on LCD-MF (Local Characteristic Scale Decomposition)-(Multifractal). The fault diagnosis method comprises the following steps: firstly, obtaining a plurality of intrinsic scale components (ISC) by using LCD, and selecting useful ISCs to respectively calculate Teager energy operators (TEO) to obtain instantaneous amplitude and instantaneous frequency corresponding to each ISC; secondly, analyzing the instantaneous amplitude of each ISC by using multifractal detrended fluctuation analysis (MFDFA), and extracting a generalized Hurst index of each ISC as the multifractal characteristic of each ISC; thirdly, performing dimension reduction by using principal component analysis (PCA) and taking a result obtained by the PCA as a fault feature vector; acquiring a vibration signal of the rolling bearing during work under variable working conditions in real time, processing the acquired vibration signal according to the steps to obtain a corresponding fault feature vector M, and identifying the fault feature vector to realize fault detection and fault positioning of the rolling bearing.

The invention discloses a voice emotion recognition method based on multi-fractal and information fusion. The method comprises the steps that firstly, voice sample data are extracted from a voice library, and a voice sample training set and a voice sample testing set are established; secondly, a nonlinearity characteristic value used for voice emotion recognition is extracted from the voice sample training set according to the nonlinearity characteristic, wherein the nonlinearity characteristic comprises voice signal multi-fractal spectrum and a voice signal broad sense hurst index; thirdly, preprocessing is carried out on the voice sample training set, the nonlinearity characteristic value serves as the input of various weak classifiers, and all the weak classifiers are trained; fourthly, the trained weak classifiers are gathered into a powerful classifier, and the powerful classifier is tested according to voice sample signals in the voice sample testing set; fifthly, new voice signals are classified according to the tested powerful classifier, and the classifications of emotions corresponding to the voice signals are recognized. According to the voice emotion recognition method, and the accuracy of voice signal recognition is greatly improved.

The invention relates to a short-time power load forecasting method based on a long-range dependence FARIMA model. The method includes the following steps that (1) forecasting sample data are obtained according to power load data before a forecasting day; (2) the forecasting sample data are preprocessed, singular points and zero-mean-value are eliminated to obtain a power load sequence {Xt}; (3) an estimated value H of a Hurst index of the power load sequence {Xt} is calculated by means of a rescaled range analysis method; (4) whether the power load sequence meets the requirement of a long-range dependence process is judged according to the obtained estimated value H of the Hurst index, if the answer is positive, a fractional difference parameter d is calculated, and if the answer is negative, the step (1) is repeated; (5) according to the obtained fractional difference parameter d, the FARIMA model of the power load sequence {Xt} is built; (6) according to the FARIMA model, a power load value is forecasted, and an actual forecast value is obtained by carrying out inverse difference on the forecasted power load value to adjust a power scheduling scheme. Compared with the prior art, the method has the advantages of being accurate in result, high in practicality and the like.

The invention discloses a rapid detection method for network flow anomaly, which aims to solve the problem of slow detection speed of the existing detection method for the network flow anomaly. The technical scheme of the invention is as follows: adopting a Hurst index which is used for describing the fractal characteristics of network flow to judge the occurrence of the anomaly; solving the Hurst index through the iteration of the sampled latest flow data; establishing an abnormal judgment threshold through the change of the Hurst index; and carrying out flow anomaly detection directly, and detecting the network flow anomaly in real time, thereby improving the speed of detecting the network flow anomaly.

The invention provides a joint fractal-based method for detecting a small target under a sea clutter background. The joint fractal-based method is higher in detection probability. The detection problem of a non-additive model is transformed into a classification problem, i.e. whether a target exists or not is equivalent to belong to a class in which a pure sea clutter exists, and a characteristic joint detection algorithm is provided. A bilogarithmic graph is established by using a trend fluctuation method through sea clutter data, a slope, namely a Hurst index, is fitted by using a least square method within a scale-free interval, and is used as a characteristic scalar, a nodal increment of a keypoint in the bilogarithmic graph is used as another characteristic scalar, therefore, a double-scalar obtained by each group of sea clutter data corresponds to one point in the bilogarithmic graph, n groups of corresponding points (i=1,...n) of the pure sea clutter data are obtained by using the steps, a space optimal classification line omega is obtained by using a convex hull function, sea clutters of regions in which the target possibly exits are obtained by using the same steps, and finally, by using whether the points exist in the space optimal classification line omega or not as a criterion, when the points exists in the space optimal classification line omega, no target exists, and when the points are outside the space optimal classification line omega, the target exists.

The invention discloses an attack behavior detection method based on flow abnormity and feature analysis, mainly solving the problem of low efficiency of the prior art caused by incapability of preventing unknown vulnerability attacks and the increase of an attack feature database. The technical scheme comprises: 1, obtaining network flow; and 2, calculating the Hurst index of the network flow. If the Hurst index is greater than an alarm threshold value, the network flow is determined not to be attacked; if the Hurst index is smaller than an alarm threshold value, attack flow feature string matching is performed on the flow suspected to be attacked and having the Hurst index is smaller than an alarm threshold value; if matching fails, the network flow is determined not to be attacked; and if matching is successful, the network flow is determined to be attacked, and an alarm is given for a user. The method can effectively intercept attacker attacks specific to unknown vulnerabilities, has high efficiency, and is used for protecting network safety.

The invention discloses a flow analysis method based on an ARMA (Autoregressive Moving Average) model and a chaotic time sequence model. By adopting the flow analysis method, the flow gathering, the feature extraction, the flow analysis, the flow forecasting and the abnormal flow detection can be realized. The flow analysis method comprises the following steps of firstly, monitoring and counting flow by calculating Hurst indexes, ARMA parameters, fractional difference, relevant dimension and time delay and estimating Lyapunov indexes; carrying out certain step-length forecasting on a sample point according to the parameters and the forecasting formula which are provided by the flow analysis; and early warning the abnormal flow in a network according to the variation of the Hurst indexes.

The invention discloses a non-stable signal multi-fractal feature extraction method based on dual-tree complex wavelet transformation. The steps include: performing integration processing on a non-stable signal to be analyzed; performing dual-tree complex wavelet transformation on the integrated signal, and using wavelet decomposition scale coefficients and detail coefficients to obtain fluctuation components of the signal under each scale; using the obtained wavelet coefficient of each scale to estimate the instantaneous frequency of each scale, and obtaining a time scale value of each scale; based on the scale values, performing segmentation on the fluctuation components under each scale; calculating a fluctuation function of each order of the signal, utilizing a double-logarithm relation of the fluctuation functions and the scale values, obtaining a generalized hurst index through least squares fitting, and obtaining scale index of each order; and utilizing legendre transformation to obtain a multi-fractal singular spectrum of the signal. The non-stable signal multi-fractal feature extraction method provided by the invention utilizes dual-tree complex wavelet transformation to perform signal decomposition, overcomes the problem that traditional wavelet transformation lacks translation invariance, ensures accuracy of multi-fractal feature extraction, the arithmetic speed is fast, and thus the method is in favor of online application.

The invention discloses a detection method and detection device for flowing conditions of pulverized coal conveyed in a high pressure dense phase pneumatic mode. The detection method comprises the following steps of collecting static signals on an electrostatic sensor probe and pressure difference signals of a pressure difference sensor, carrying out empirical mode decomposition processing on the collected static signals and the pressure difference signals respectively to obtain a Hurst index H, dividing the static signals and the pressure difference signals into multiple different scales according to fractal characteristics and the value of the Hurst index H, calculating the energy proportion of different scales of the static signals and judging the value of the energy proportion, using the energy proportion and the value relation of different scales as necessary parameters to carry out judgment of the flow condition of the high pressure dense phase pneumatic conveying pulverized coal. The judgment criterions are suitable for various situations of conveying carrier gas, practicability is wide, the static signals and the pressure difference signals are adopted as double judgment standards at the same time, and thus accuracy is high. Signal pickup assembly of the detection device comprises the electrostatic sensor probe, a preposition voltage amplifier circuit, a pressure sensor, a data acquisition card and a computer.

The invention discloses a multi-mode degradation process modeling and residual service life prediction method, and belongs to the technical field of health management. The method comprises the following steps that: firstly, collecting degradation data which is sampled at equal intervals; carrying out change point detection on the degradation data; clustering degradation segments obtained by changepoint segmentation by taking a degradation rate as a characteristic; establishing a degradation model which contains mode switching, and describing the mode switching through one continuous time Markov chain; adopting a method based on quadratic variation to estimate the Hurst index of a degradation process; utilizing a maximum likelihood method to independently estimate the state transition ratematrix of the Markov chain and a drift item coefficient and a diffusion item coefficient under each mode; utilizing a Monte Carlo algorithm to obtain distribution obeyed by the drift item under the influence of state switching in a further period of time; and under a given threshold value, obtaining the distribution of residual service life. By use of the method, the residual service life distribution of systems or equipment which contains various degradation modes can be accurately predicted.

The invention provides a traffic early-warning method and system by means of vehicle conditions and driver physiological parameters. The method includes the steps of obtaining the driver physiological parameters, obtaining real-time vehicle condition data parameters, carrying out time shaft synchronization on the two sets of the parameters, calculating the Hurst index HBIO of the driver physiological parameters and the Hurst index HOBD of the real-time vehicle condition data parameters by means of the R / S analysis method, judging that the driver physiological parameters and the vehicle condition data parameters cause negative influences on traffic conditions and traffic accidents may happen when the HBIO and the HBOD are smaller than a warning threshold at the same time, and carrying out background processing. The driver physiological parameters are extracted to serve as the most important factor causing influences on the traffic conditions, the driver physiological parameters are combined with vehicle condition data so that reliability of monitoring can be improved, the Hurst index of the vehicle condition data parameters and the Hurst index of the driver physiological parameters are calculated by means of the R / S analysis method so that the trend of vehicle conditions and physiological data in the next period can be judged, and the threshold values required by early warning of the vehicle condition data and the physiological parameters are calculated in a quantitative mode.

The invention discloses a content center network interest flooding attack detection method and system. The method comprises the following steps: acquiring flow in a network; dividing the flow into time sequences to obtain a plurality of time sequences; calculating the Hurst index of each time sequence; carrying out small variation accumulation on the Hurst index by adopting a non-parameter accumulation and bilateral detection algorithm to obtain an accumulated value of the Hurst index; calculating the information entropy value of each time sequence; carrying out small variation accumulation onthe information entropy value by adopting a non-parameter accumulation and bilateral detection algorithm to obtain an accumulated value of the information entropy value; and judging whether an interest flooding attack exists in the current network or not according to the accumulated value of the Hurst index and the accumulated value of the information entropy value. By adopting the method or thesystem provided by the invention, the problem of interest flooding attack in the content center network can be solved.

The invention discloses an approximate fractal detection method for targets in a fractional fourier transformer (FRFT) region sea clutter and belongs to the field of radar signal processing. In the existing method for detecting targets in the sea clutter by utilizing the single fractal characteristics, radar time domain echo sequences are directly processed, and the detection performance on the targets moving slightly under strong sea clutter is poor. By means of the approximate fractal detection method, FRFT and the single fractal processing method are ingeniously combined, the advantages of the FRFT for effectively improving the signal to clutter ratio of the targets moving on the sea surface and the characteristics of easy and convenient feature calculation and high accuracy of the signal fractal processing method can be comprehensively utilized, and single Hurst indexes of sea clutter FRFT spectrums can be extracted within the permissible error range for target detection. A detector comprehensively utilizes the advantages of phase-coherent accumulation and the single fractal theory and has good distinguishing capability on the targets moving slightly in the sea clutter, and simultaneously the approximate fractal detection method is suitable for target signal tracking in non-uniform fractal clutter and has wide application value.

The invention provides a network flow characteristic analysis method based on fractional order Fourier transformation. The method includes the steps that firstly, network flow data are extracted; secondly, a network flow characteristic analysis algorithm is adopted, and the Hurst index is obtained based on the network flow data; thirdly, the current network state is analyzed based on network flow feature parameters, and the Hurst index is corrected. The network flow characteristic analysis method based on the fractional order Fourier transformation has the advantages of the time domain estimation method and the frequency domain estimation method, the algorithm time complexity is low, the influences caused by the time scale are small, and robustness is high.

The invention relates to the field of biological information processing, in particular to a DNA sequence similarity detecting method based on Hurst indexes, which can simultaneously detect the similarity of a plurality of DNA sequences, simplifies the computational complexity, improves the operational efficiency, and can increase the difference degree among analysis objects of nearer evolutionary distance. The method comprises the following steps: (1) acquiring DNA coding sequences of different species in the same function area as initial sequences; (2) carrying out digital conversion on the initial sequences acquired in the step (1) to acquire numerical sequences corresponding to the initial sequences; (3) analyzing each numerical sequence acquired in the step (2) by a R / S analysis method to acquire Hurst indexes of each numerical sequence; (4) constructing a distance matrix by utilizing the Hurst indexes acquired in the step (3); and (5) acquiring sequence similarity information from the distance matrix acquired in the step (4).

The invention discloses a hurst index-based slope safety evaluation method, which comprises the following steps of: (1) performing safety classification; (2) preprocessing data; (3) simply prejudging the data; (4) calculating hurst indexes and related parameters; (5) determining a slope safety level; and (6) analyzing other parameters. According to the hurst index-based slope safety evaluation method provided by the invention, the integral safety of a slope is analyzed and evaluated by calculating the hurst index H of each monitoring point in a slope monitoring system and then combining all hurst indexes H; and compared with the conventional other methods, the method has the advantages of capability of eliminating the sudden change of monitoring data caused by field construction and the like and excellently high applicability.

The invention discloses a long-range correlation degradation process remaining life prediction method depending on time and states, and belongs to the technical field of health management. The method comprises the following steps that firstly, sensor data sampled at equal intervals is acquired; a degeneration model is established on the basis of the fractional Brownian motion according to degradation data characteristics; a Hurst index in the model is established by utilizing a quadratic-variation-based method; drift term unknown parameters are estimated by utilizing a likelihood ratio function to the largest extent, wherein the likelihood ratio function is constructed through a Radon-Nikodym derivative; diffusion term unknown parameters are estimated through the maximum likelihood method; then by means of the weak convergence theory, an original degradation process is approximated to a random process which has a time-varying diffusion term coefficient and is based on the Brownian motion; the degradation process is further simplified through a group of transformation; finally, analyzed remaining life distribution is obtained. By means of the method, the remaining life distribution can be accurately predicted.

The invention relates to a short-period forecast method for a power load. The method comprises the following steps of (1) acquiring a historical power load data sequence, and calculating a hurst indexH of the historical power load data sequence; (2) building a fractional Brownian motion model according to the hurst index H; (3) performing global optimization on the hurst index H in the fractionalBrownian motion model to obtain optimal value Hgbest of the hurst index, and further obtaining a fractional Brownian motion optimization model; and (4) forecasting power load data by the fractional Brownian motion optimization model. Compared with the prior art, the method has the advantages that high-accuracy forecast is performed on short-period d instable power load data.

The related fractal elimination method for great much data in ultrasonic intelligent detection comprises: resolving the great much data storage into a duality recognition problem; with finite noise-contained sequence fractal R / S algorithm, extracting Hurst index from signal, presenting a fractal linear level index to distinguish signal. This invention stores only the defective ultrasonic echo signal, has well real-time feature, and high accuracy and stability.

The invention discloses a fractal variable step size least square target detector belonging to the field of radar signal processing. The current statistical method and fractal method are respectively applied to detecting targets in sea clutter, thereby having the defects of complex and inaccurate parameter evaluation or poor real-time. In the invention, the statistical method and the fractal method are combined, parameter evaluation is depended on a fractal Hurst index, a time-invariance characteristic value can be precalculated and stored for the following calculation, simple and convenient calculation, high accuracy and real-time are ensured, and a variable step size least square algorithm has high accuracy for forecasting and high convergence rate. An output value of a self-adaptive linear synthesizer is compared with an original sea clutter sequence to extract a forecast error to form detection statistics. The detector has favorable classifying capacity on sea clutter and targets and the capacity of detecting weak targets in strong sea clutter, is suitable for tracking target signals in fractal signal groups with a power spectrum in a 1 / f Beta form, and has popularization and application values.

The invention discloses a method for applying a fractal and chaos theory combination to short-term prediction of a price series in the financial field. The method comprises the steps of calculating a Hurst index and a statistic quantity, defined in the specification, of a time series by utilizing an R / S analysis method, and determining a cycle of the time series; working out an embedded dimension and delay time of the time series, and performing phase space reconstruction on the time series to generate sample data; performing normalization processing on the sample data; determining a difference value point according to a sequence of sampling time points; determining a vertical scaling factor by utilizing the Hurst index; determining an iterated function system of each group of samples, and determining a final iterated function system according to a weight; extrapolating a time point, setting a corresponding initial value to be zero, setting a step length for enabling a corresponding value to be transformed, and re-performing iteration; and drawing a curve, and calculating an average error of a difference value result and historical data, wherein an extrapolated value corresponding to a minimum error is a predicted value. The method mainly aims at the short-term prediction of the time series with unremarkable self-similarity and periodicity in the financial field; and a predicted result is more accurate than that of a conventional fractal method.

The invention discloses a novel lie detection method based on electrocardio measure. The method comprises acquisition of electrocardio and heart rate, detection of electrocardio fragment wave abnormity, detection of premature beat waves, and calculation of a heart rate local Hurst index value range and calculation of a heart rate mean value. Under non-strenuous exercise state, when a short RR interval mean value and an extremely reduced local Hurst index value range appear at the same time, psychology of fearing negative evaluation by others under high mental pressure is prompted. Under a non-mental pressure state, each fragment wave of electrocardio is normal, and heart rate has normal fluctuation. But under mental pressure state, QRS complex wave intermittent deformation, T-wave intermittent deformation, or premature beat occur, and psychology of fearing negative evaluation by others is prompted. Through detecting four measures of abnormal electrocardio fragment waves, premature beat waves, tachycardia, and extremely reduced local Hurst index value range, a novel lie detection method for detecting anxiety mentality under high mental pressure is obtained.

The invention discloses an SDN abnormal flow detection method based on a re-marking range method, and belongs to the technical field of computer network security. The method comprises the following steps: collecting the number of normal network flow packets of each node (including a controller and each user terminal) of the SDN, and respectively calculating the Hurst index of the normal network flow packets; storing and taking as a network normal index, and setting a threshold value of a normal state; collecting the number of network flow packets with certain known exception of each node, andcalculating the Hurst index of each node as the index of the exception; intercepting a forward sequence by using a window function and calculating a Hurst index of the forward sequence, and if a normal index is finally changed into a certain abnormal index, determining that the abnormality of the mode occurs and determining an abnormality occurrence time point. And if only the index change deviates from the normal value, but the similar abnormal index cannot be found, an exception except the known mode occurs, and an exception time point can be determined. According to the method, the flow state can be detected in real time, whether the flow is abnormal is judged, the abnormality occurrence time can be detected, and the security of the SDN network system is enhanced.

A physical arousal degree comprehensive quantifying method based on heart rate index includes heart rate collecting, calculation of heart rate long-range fluctuation coefficient, calculation of heart rate local Hurst index and calculation of heat rate mean value. High heart rate mean value can purely come from physical arousal caused by body movement. The small local Hurst index value range generally comes from stress physical arousal. The heart rate long-range fluctuation coefficient can stably balance the physical arousal degree in the normal range (smaller than 90 / min). By means of fusion of the above three indexes, the physical arousal comprehensive quantifying index allowing continuous valuing between peace to high arousal is acquired.

The invention intends to provide a method for detecting abnormal traffic, particularly a convenient method for detecting abnormal traffic by calculating the variation of self-similarity indexes. The method can distinguish normal and abnormal traffic and reduce the detection time, which comprises the following steps: Step 1, initiating the sample queue FQueue, initiating the Hurst queue and HQueue for saving Hurst indexes, and initiating the Hurst index variation queue DHQueue; Step 2, calculating the Hurst indexes of the queue FQueue, saving the Hurst indexes to the queue HQueue, adding new nodes to the sample queue FQueue, and delaying for 10 seconds; Step 3, calculating delta H of all the Hurst values in the queue HQueue according to the following formula: delta H = H(m) - H(m-1), and adding the values to the queue DHQueue; and Step 4, reading the last value H(last) in the queue DHQueue on a circulatory basis, under the condition that delta H(last) is more than 0.2, if the difference between H(last) and 2 is more than 0, giving a warning of wittyworm virus, and if H(last) is less than 0.3, giving a warning of DDOS (Distributed Denial of Service) attack.

The invention provides an abnormal flow detection method, device and equipment. The method comprises that a first flow value of flow transmitted in each preset time slot by network equipment in the preset time length is obtained; a first flow sequence including all the first flow values is determined; a first group of wavelet coefficients corresponding to the first flow sequence is calculated; a first Hurst index corresponding to the first group of wavelet coefficients is calculated; if the absolute value of a difference between the first Hurst index and a standard Hurst index is greater thana first threshold, it is determined there is abnormal flow; and otherwise, it is determined that there is no abnormal flow. Thus, existence of the abnormal flow can be detected more accurately, incorrect detection results can be reduced, and false alarm and neglected alarm can be reduced.