38 results about "Multi scale entropy" patented technology

The invention provides a bearing fault diagnosis method and system device based on improved empirical wavelet transform. The method comprises: step one, collecting different fault bearing signals as analysis signals and converting a time domain waveform into a frequency domain waveform; step two, drawing an upper envelope of a frequency spectrum and transforming a frequency peak with a tight support into a flat top; step three, screening flat tops in the frequency domain based on criteria, removing meaningless flat tops, and keeping a main frequency; step four, using a minimum value between adjacent flat tops as the boundary of spectrum segmentation; step five, establishing wavelet filters respectively for segmented frequency spectrums and decomposing the signals into N mode components; step six, calculating similarity values between mode components and the original signals by using a cross-correlation coefficient and selecting a component with the highest similarity value; and step seven, taking a fault sample, calculating an IMF component with the largest correlation coefficient of the sample, calculating a multi-scale entropy of the IMF component, and drawing the multi-scale entropy curve of the sample to realize fault classification.

The invention relates to a pipeline multi-point leakage positioning method based on improved VMD. The method comprises the following steps of collecting an original leakage signal of a pipeline; performing overall local area mean value decomposition on the original leakage signal to obtain a plurality of PF components; calculating a correlation coefficient of each PF component, screening out the required PF component according to the correlation coefficient, performing signal reconstruction according to the screened PF component, and determining K value of variational mode decomposition; performing variational mode decomposition on the reconstructed signal to obtain a plurality of IMF components, calculating a multi-scale entropy value of each IMF component, and screening the IMF components according to the multi-scale entropy value of each IMF component; and performing signal reconstruction on the screened IMF component, and performing cross-correlation positioning calculation on eachleakage signal subjected to blind source separation to complete pipeline leakage positioning. According to the method, the leakage signal of the pipeline can be effectively extracted, the influence of low relevant components and noise in the original leakage signal is eliminated, and the final positioning result is more accurate.

The invention relates to a method for diagnosing the parameter inconsistency of power battery packs, and belongs to the technical field of battery management. The method comprises the following steps:S1: selecting power batteries having different initial performance and similar initial performance, forming two types of battery packs in a series parallel manner, and collecting technical parametersthereof; S2: simulating real vehicle working conditions under different roads, controlling the temperature of each monomer in the battery packs, performing a charge-discharge experiment on the powerbattery packs, collecting the voltage, current and temperature data of each battery cell, and establishing a real vehicle working condition test database; S3: performing data processing and feature extraction on time domain data of the collected voltage, current and temperature by using a feature extraction method; and S4, evaluating the consistency of the battery packs for the extracted featuresby using a weight method, and achieving parameter inconsistency diagnosis by combining a multi-scale entropy with an artificial neural network. By adoption of the method provided by the invention, faulty batteries can be diagnosed in real time, the diagnosis accuracy of the parameter inconsistency of the battery packs is improved, and timely maintenance is facilitated.

The invention provides a pipeline leakage signal identification method based on improved ELMD multi-scale entropy. The pipeline leakage signal identification method includes the steps that firstly, acquired experimental data are subjected to noise pre-processing to eliminate low-correlation components in signals; then pre-processed signals are subjected to ELMD processing to obtain PF components;an endpoint effect problem reserved by ELMD decomposition is weakened through a peak value waveform matching method; the multi-scale entropy of the PF components is calculated correspondingly, and multi-scale entropy values of leakage signals are arrayed and compared to eliminate background noise; a feature vector is constructed by selecting a principal PF component according to the multi-scale entropy values; the feature vector is used as an input vector of a BP neural network to train the network; and a to-be-tested sample is input into a trained BP neural network to obtain a pipeline leakage identification result. The pipeline leakage signal identification method based on the improved ELMD multi-scale entropy can adapt to various conditions of pipelines and has good testing accuracy.

The invention discloses a cyberspace security situation real-time detection method. The method comprises the following steps: original characteristic extraction that original network data packet characteristics are obtained from a network, multi-scale entropy calculations that sample entropy of an original data packet characteristic sequence is calculated at different time scales, detector training that a mature immunization detector is trained and generated by utilizing a sample entropy characteristic vector and a negative-selection algorithm at the different time scales, network threat security detection that a network sample is detected by utilizing the trained mature immunization detector at the different time scales, cyberspace security situation calculations that cyberspace security situations at the different time scales and different network layers, and situation visualization that the cyberspace security situations are expressed by different colors of curve charts at different time and the different network layers. The time scales considered in the method is relatively comprehensive, the fusion level is high, a situation assessment result is relatively accurate, a complex characteristics of a network behavior can be described, and the whole process of a network threat behavior can be carved in a fine-grained manner.

The invention provides a method for diagnosing faults of bearings on the basis of multi-scale symbolic dynamics entropy. The method has the advantages that concepts of multiple scales, symbolic dynamics and entropy are combined with one another, the time sequence complexity can be quantitatively measured by symbolic dynamics entropy, the symbolic dynamics entropy is an important nonlinear featureof a time sequence, and entropy values can change along with change of system states; concepts of multi-scale entropy are utilized, the symbolic dynamics entropy on different scales is computed, and accordingly the complexity of signals can be measured from different time scales; the multi-scale symbolic dynamics entropy is applied to diagnosing the faults of the rolling bearings, fault information of the rolling bearings can be extracted by the aid of the multi-scale symbolic dynamics entropy, the method is combined with classifiers, and accordingly three different fault types of the rollingbearings can be accurately identified.

The invention discloses a sound signal noise reduction method and system of foreign matter in an electric energy meter on the basis of short-time energy, multi-scale entropy and EMD. The method includes the steps that noise data generated by shaking of the foreign matter in the electric energy meter is collected; foreign matter sound signal data is extracted through short-time energy positioning;the extracted foreign matter sound signal data is subjected to empirical mode decomposition (EMD), and the multi-scale entropy of each stage of intrinsic mode function (IMF) component obtained throughEMD is calculated; the multi-scale entropy of the IMF components is subjected to noise reduction and filtering to obtain sound signals, obtained after noise reduction and filtering, of the foreign matter in the electric energy meter. The method has the advantages that short-time energy positioning can be used for data extraction to increase the processing speed; the sound signals of the foreign matter in the electric energy meter are processed through multi-scale entropy and empirical mode decomposition, which is a self-adaptive process, and the defect that traditional spectrum subtraction noise reduction estimation is not accurate is overcome; meanwhile, introduction of new noise is avoided to the maximum degree, and the foreign matter in the electric energy meter can be accurately detected.

The invention provides a human body balance estimation method. The human body balance estimation method comprises the steps of acquiring pressure center-of-gravity data of a body to be measured; adopting a multi-scale entropy algorithm to process the pressure center-of-gravity data to obtain an area value under a multi-scale entropy curve, wherein the area value is used as the complexity of the pressure center-of-gravity data and is used for measuring the balance of the body to be measured; determining the center-of-gravity track of the pressure center-of-gravity data and calculating the areaof a graph formed by the center-of-gravity track to obtain a pressure center-of-gravity track area, wherein the pressure center-of-gravity track area is used for measuring the balance of the body to be measured; measuring the balance of the body to be measured through the ratio of the track area to the complexity. Through the ratio of the track area to the complexity, the balance of the human bodyor the measured body is determined, specifically, according to information included in the change of the center of gravity of the human body, the balance is calculated, so that the method overcomes the defects of a linear system algorithm and a nonlinear system algorithm, and the balance of the human body can be accurately estimated.

The invention provides a method for detecting heart diseases based on multi-scale entropy. Firstly, original electrocardiosignals are put into a band-pass filter to filter away partial noise, then amplification is carried out by using signal differentiation and square methods to obtain magnified signals of R wave features, an R wave position is marked by using a dynamic threshold adjusting method,and RR interphase sequences of the electrocardiosignals are obtained; according to the RR interphase sequences of the electrocardiosignals, empirical mode decomposition is carried out, the signals are extended, then the electrocardiosignals are decomposed by constructing upper and lower envelope lines of the signals to obtain IMF components, and eigenfunction signals of the electrocardiosignals of healthy persons and patients with the heart diseases are obtained; the multi-scale entropy of the eigenfunction signals is calculated through the IMF components, the eigenfunction signals of the electrocardiosignals of the healthy persons and the patients with the heart diseases are classified by using a classification function of a support vector machine, and the electrocardiosignals of the healthy persons and the patients with the heart diseases are distinguished. The method for detecting the heart diseases based on the multi-scale entropy can timely detect the healthy condition of the heart and is conductive to knowing disease principles.

A health monitoring method comprises the steps of acquiring human body electrocardio data with a conductive fabric serving as an electrocardio electrode; acquiring human body pulse data through a blood oxygen probe; transmitting the electrocardio data and the pulse data wirelessly to a monitoring device which performs calculation according to the received electrocardio data and pulse data through a multi-scale entropy algorithm respectively to obtain two groups of result data; drawing a graph with the result data serving as a vertical coordinate and obtained scale factors of the result data serving as a horizontal ordinate, and performing evaluation analysis according to values and trends of curves of the drawn graph; transmitting evaluation analysis information to a client and displaying the evaluation analysis information; and emitting alarm when the evaluation analysis information meets preset conditions. The invention further provides a corresponding health monitoring device. According to the method and the device, the overall level of human body health can be analyzed after processing through the multi-scale entropy algorithm.

The invention discloses a multi-scale entropy characterization method of inner defect distribution of an anchoring system. The multi-scale entropy characterization method comprises the following steps: detecting an anchor rod needing to be detected in a working site by utilizing an anchor rod anchoring quality non-destructive testing instrument based on a stress wave method and identifying an anchor rod anchoring length; carrying out empirical mode decomposition on a non-destructive testing signal and decomposing the non-destructive testing signal into a series of intrinsic mode functions which are arrayed according to levels of the frequency of the signal; calculating the frequency of each layer of intrinsic mode function through utilizing Fourier transformation; abandoning the intrinsic mode function with the frequency more than 1kHz and the final layer of intrinsic mode function; overlapping all layers of the intrinsic mode functions with the frequency smaller than 1kHz; and reconstructing a new signal for multi-scale entropy analysis. The change degree of a reflection signal is described through measuring the complexity of a reflected signal, so that the positions of anchoring defects are identified; and the method can be used for effectively judging the positions of the anchoring defects and accurately evaluating the anchoring quality.

The invention provides a fault characteristic extraction method for switch equipment based on a big data platform. According to the fault characteristic extraction method, the problem that the characteristic extraction cannot be performed efficiently and accurately on various types of faults when massive data of switch equipment faults in the prior art are faced is mainly solved. An implementation scheme of the method comprises the steps of establishing a hadoop sub-platform to carry out data collection, storage and pre-processing; establishing a SparkR platform to perform distributed calculation of MMSE (Multivariate Multi-Scale Entropy), and storing a calculation result into an HDFS (Hadoop Distributed File System); downloading the calculation result from the HDFS, and drawing a multivariate sample entropy curve of each fault of the switch equipment by use of R software; and selecting a multivariate sample entropy value in a corresponding scale factor range as a characteristic parameter of each fault according to the multivariate sample entropy curve of each fault. According to the fault characteristic extraction method for the switch equipment based on the big data platform, the whole scheme is designed precisely and completely, the capacities of massive data storage and distributed calculation are achieved, the efficiency and accuracy of the fault characteristic extraction are high, and a basis can be provided for diagnosis and prejudgment of the faults of the switch equipment in time.

The invention relates to a rolling bearing fault diagnosis method based on vibration signal analysis, and belongs to the field of mechanical fault diagnosis and signal processing. According to the rolling bearing fault diagnosis method based on the vibration signal analysis, firstly, empirical mode decomposition (DEMD) is carried out on vibration signals of a bearing, and a plurality of intrinsicmode function (IMF) components with physical significance are obtained through the decomposition; then the correlation coefficient between component signals and the original vibration signals is calculated, the components containing fault characteristic information are selected through the correlation coefficient, and the multi-scale entropy of the selected components is calculated to form an eigenvalue vector; and at last, the eigenvalue vector is input into a support vector machine (SVM) to complete the recognition of the working state of a rolling bearing. According to the rolling bearing fault diagnosis method based on the vibration signal analysis, low-energy high-frequency signals are decomposed through DEMD, the multi-scale entropy is calculated to be an characteristic, the SVM is utilized to classify, the accuracy of bearing fault recognition is improved, and the practicability is comparatively high.

The invention discloses a method for determining the optimum analysis length of vibration measurement data. A sensor device is arranged at the key position of the measured structure to obtain the vibration measurement data of the structure. The data of different lengths N are extracted, and the appropriate scale factor S is selected, and the vibration data are coarsely grained by the improved coarse graining method. The pseudo-near imminent method and mutual information method are used to determine the phase space reconstruction parameters m, tau of the coarse-grained data, respectively, and the phase space reconstruction is carried out. The permutation entropy and entropy values at different scales are calculated, and the homogenization of the permutation entropy and entropy values is taken as the multi-scale permutation entropy value MPE. The multi-scale entropy values of N vibration measurement data with different lengths are calculated, and the MPE (Li) which satisfies the precision requirement is selected. Then the shortest data length corresponding to MPE (Li) is defined as the best analysis length of vibration measurement data.

The invention discloses a multi-scale incremental entropy algorithm for evaluation of time sequence complexity. For an original electrophysiological time sequence, the original sequence is firstly coarse-grained to obtain coarse-grained sequences in corresponding scale; then the incremental entropy of each coarse-grained sequence is calculated to obtain the incremental entropy value of the sequence at the scale, so the fluctuation of the signal complexity with the scale is calculated, and the characteristic of the signal is extracted. With the increase of the time scale, the evaluation error of the entropy value is small, the consistency of the algorithm is good, and the calculation time is short. The shortcomings of increasing of the original multi-scale entropy with time scale, decreasing of the data length and the undefined entropy value are overcome. The method has better recognition performance on two signals than multi-scale sample entropy.

The invention discloses a self-adaptive exposure adjustment multi-scale entropy fusion underwater image enhancement method, which comprises the steps of applying generalized bounded logarithm additionor subtraction operation to an original underwater image to derive derivative images with different exposures; calculating multi-scale Gaussian pyramid images with different exposure quantities and multi-scale Laplace pyramid images with different exposure quantities; calculating multi-scale Gaussian pyramid weight images with different exposures; obtaining a normalized multi-scale Gaussian pyramid weight iteration image with different exposure quantities; obtaining a multi-scale pyramid fusion image; obtaining a pyramid fusion image; and obtaining an enhanced image based on rich gradient information of the pyramid fusion image. According to the method, the underwater image is enhanced, so that the contrast and saturation of the enhanced image can be well considered, image details are richer and clearer, and the contrast, information entropy, color information and the like of the whole image can be further improved.

Over 350 million people worldwide suffer from depression, a third of whom are medication resistant. Seizure therapy remains the most effective treatment in depression, even when many treatments fail. The utility of seizure therapy is limited due to its cognitive side effects and stigma. The biological targets of seizure therapy remain unknown, hindering design of new treatments with comparable efficacy. Seizures impact the brains temporal dynamicity observed through electroencephalography. This dynamicity reflects richness of information processing across distributed brain networks subserving affective and cognitive processes. We investigated the hypothesis that seizure therapy impacts mood (depressive symptoms) and cognition by modulating brain temporal dynamicity. We obtained resting-state EEG from thirty-four patients (age=46.0±14.0, 21 females) receiving two types of seizure treatments—electroconvulsive therapy or magnetic seizure therapy. We employed multi-scale entropy to quantify the complexity of brain's temporal dynamics before and after seizure therapy. We discovered that reduction of complexity in fine time scales underlined successful therapeutic response to both seizure treatments. Greater reduction in complexity of fine time scales in parieto-occipital and central brain regions was significantly linked with greater improvement in depressive symptoms. Greater increase in complexity of coarse time scales was associated with greater decline in cognition including the autobiographical memory. These findings were region- and time-scale specific. That is, change in complexity in occipital regions (e.g., O2 electrode or right occipital pole) at fine time-scales was only associated with change in depressive symptoms, and not change in cognition, and change in complexity in parieto-central regions (e.g., Pz electrode or intra and transparietal sulcus) at coarser time-scale was only associated with change in cognition, and not depressive symptoms. Finally, region and time-scale specific changes in complexity classified both antidepressant and cognitive response to seizure therapy with good (80%) and excellent (95%) accuracy, respectively. In this study, we discovered a novel biological target of seizure therapy; complexity of the brain resting-state dynamics. Region and time-scale dependent changes in complexity of the brain resting-state dynamics is a novel mechanistic marker of response to seizure therapy that explains both the antidepressant response and cognitive changes associated with this treatment. This marker has tremendous potential to guide design of the new generation of antidepressant treatments.

The invention discloses an adaptive gain image enhancement method based on fractional order multi-scale entropy fusion. The adaptive gain image enhancement method comprises the steps: dividing an original underwater image into non-overlapping rectangular image blocks; obtaining enhanced output images which are consistent with the scale number and based on the fractional order; calculating the information entropy and contrast of the fused image; determining an output image with enhanced contrast in the image block; obtaining a global contrast enhanced image; converting the original underwater image from the RGB image into a grayscale image; calculating a gradient image corresponding to the grayscale image, and solving an adaptive gain function of the gradient image; calculating a final adaptive gain underwater enhanced image based on fractional order multilayer entropy fusion; and outputting an adaptive gain underwater enhanced image based on fractional order multilayer entropy fusion.According to the adaptive gain image enhancement method, enhancement processing is carried out on the underwater image, so that details of the enhanced image are richer and clearer, and the contrast ratio, information entropy, color information and the like of the whole image can be further improved.

The invention relates to a method for evaluating the static balance capability of a multi-modal signal. The method for evaluating the static balance capability of the multi-modal signal comprises the steps that firstly, a two-channel lower limb surface electromyographic signal, a two-channel pressure center signal, a two-channel angular velocity and a two-channel angular signal of a human body are collected to form a multi-modal signal, then feature extraction is carried out on the multi-modal signal through a multivariate multi-scale entropy feature extraction method based on multivariate empirical modal decomposition, and the obtained feature vector is input into a support vector machine for static balance capability evaluation. According to the method for evaluating the static balance capability of the multi-modal signal, the complexity of the signal can be quantitatively analyzed, and the influence of the multivariate signal on the static balance ability of the human body can be comprehensively considered. The experimental result shows that the method obtains a high evaluation and recognition rate of the human static balance ability, and the recognition result is better than other methods.

The invention provides a blood pressure estimating method and device, and relates to the technical field of medical care. The blood pressure estimating method comprises the following steps of obtaining input parameters, wherein the input parameters comprise an encode signal, characteristic parameters and multi-scale entropy, the encode signal is a signal obtained after first signal data is encodedby a self encoding machine, the first signal data comprises a first electrocardiosignal and a first pulse wave signal, the first electrocardiosignal is an electrocardiosignal of a user, the first pulse wave signal is a pulse wave signal of the user, the characteristic parameters are physiologic signal indexes of the first signal data, and the multi-scale entropy represents the complexity of the first signal data; and inputting the input parameters to a blood pressure estimation model to obtain an estimated blood pressure, wherein the estimated blood pressure has corresponding relation with the encode signal, the characteristic parameters and the multi-scale entropy. According to the blood pressure estimating method disclosed by the invention, extraction of various types of data is performed on the first signal data of the user, and various types of data is used for estimating the blood pressure of the user, so that the precision rate of blood pressure estimation can be improved.

The invention discloses a technical method for measuring a mineral particle size based on multi-element multi-scale entropy. The method comprises the following steps: firstly, respectively utilizing a sobel operator to enhance boundary, for three color variables R, G and B of coarse crushing, intermediate crushing and fine crushing mineral images; averaging each row of gradient data of ternary color variable of each level image; continuously intercepting an average row sequence of each color variable in fixed length according to the sequence; respectively averaging the data in each particle size data set of the ternary color variable; constructing a (n-m) set ternary compound delaying vector at the scale E and calculating a multivariate sample entropy, thereby acquiring multiple multi-scale entropy at 20 scales; lastly, establishing an associated model of the entropy and the mineral particle size utilizing the calculated multivariate sample entropy value of each-level mineral image gradient data, thereby detecting the mineral particle size under each-level crusher.

The invention relates to a brain signal complexity based individual age prediction method which comprises the following steps of acquiring transmission light intensity data of a brain tissue of a testee through a near-infrared imaging system; converting the transmission light intensity data to brain physiological information data, and calculating a multi-scale entropy based on the brain physiological information data; calculating a multi-scale entropy value corresponding to each detection point in a brain region of each testee, classifying the test points according to various brain function networks, and averaging the multi-scale entropy values for representating all the test points in one brain function network to obtain a brain function network based multi-scale entropy index; and takingthe multi-scale entropy index of each brain function network as the input feature of a support vector regression method, and using a linear kernel function to obtain a correlation equation of the brain signal complexity and an age. The method can accurately predict the age of the testee according to the complexity of a resting state brain signal as well as is simple in testing process and convenient to operate.

A method and apparatus is described for segmenting an image, for adaptively scaling an image, and for automatically scaling and cropping an image based on codestream headers data. In one embodiment, a file that can provide a header that contains multi-scale entropy distribution information on blocks of an image is received. For each block, the block is assigned to a scale from a set of scales that maximizes a cost function. The cost function is a product of a total likelihood and a prior. The total likelihood is a product of likelihoods of the blocks. The image is segmented by grouping together blocks that have been assigned equivalent scales. In one embodiment, the file represents an image in JPEG 2000 format.

The invention relates to a water quality data cleaning method based on empirical wavelet transform and multi-scale entropy. According to the method, after the frequency spectrum of original data is subjected to adaptive segmentation through fast Fourier transform, the original data with noise is decomposed into different intrinsic mode functions IMF through empirical wavelet transform. According to different characteristics of the whole IMF, adaptive adjustable parameters based on multi-scale entropy are introduced into a threshold function, so that the noise removal performance is improved. Finally, the high-frequency noise of the points over the entire IMF is filtered, which includes more effective data amplitudes and less noise retention. The method is more suitable for time series datacleaning, and achieves a better effect on the noise removal precision of synthetic analog data and field water quality data.

The invention relates to a sea surface small target detection method based on the polarization signal multi-scale entropy characteristics, the radar echo signal single feature is adopted, the algorithm structure is simple, and the calculated amount and complexity are small; a parallel architecture and an algorithm can be adopted, so that the requirement of real-time processing of radar echo signals is further met; and after the polarized multi-scale entropy index is adopted, the unit containing the small target distance is detected due to the minimum multi-scale entropy index value. Moreover, under the polarized multi-scale entropy index, the multi-scale entropy index of the sea surface clutter shows stable or unchanged characteristics in all test data, and part of sudden fluctuations can be eliminated by adopting a fitting method, so that the characteristics of the sea surface clutter can be well identified, and the target detection performance is further improved.