30 results about "Entropy estimation" patented technology

A seed value making method and device for a PRNG (Pseudo Random Number Generator) are provided. The seed value making method includes the steps of: accumulating in memory units of a First Data Pool data from various external sources, analyzing the data to determine a source type, computing entropy estimations for each of the external sources basing on the source type as determined, and generating a seed value by using the entropy estimations and the data accumulated in the memory units of the First Data Pool. Accordingly, in generating a seed value, dynamic estimation of random sources rate, and classification of sources on slow and fast ones, and reliable and unreliable ones, can be provided, and also, seed values can be made with taking in account rate and reliability of the sources.

A method for assessing intelligibility of speech represented by a speech signal includes providing a speech signal and performing a feature extraction on at least one frame of the speech signal so as to obtain a feature vector for each of the at least one frame of the speech signal. The feature vector is input to a statistical machine learning model so as to obtain an estimated posterior probability of phonemes in the at least one frame as an output including a vector of phoneme posterior probabilities of different phonemes for each of the at least one frame of the speech signal. An entropy estimation is performed on the vector of phoneme posterior probabilities of the at least one frame of the speech signal so as to evaluate intelligibility of the at least one frame of the speech signal. An intelligibility measure is output for the at least one frame of the speech signal.

The invention discloses a converter transformer tap switch condition monitoring system and a non-stationary signal spectrum estimation method. The system comprises a vibration signal sensor which is disposed on a housing of a transformer and is used for monitoring the operation process of a converter transformer tap switch; a digital filter for filtering noise waves; and a vibration data collection function module which is used for eliminating various types of signal noises. The method employs a maximum entropy-spectrum method, carries out solving of data outside a known limited data autocorrelation array through employing an extrapolation method under the condition of the maximum information entropy, and estimates the power spectrum density of a to-be-detected signal. The method achieves the estimation of the tap switch under the non-intruding detection, achieves the system inspection of the converter transformer tap switch, can reduce the incidence rate of faults in two preset correction periods, and employs the modern spectrum estimation method of maximum entropy estimation for the spectrum estimation of a non-stationary signal of an isolation switch, so as to further improve the analysis precision and accuracy.

The position and direction (0-27, FIG. 2) of each elevator car (A-D) in a group of cars is recorded along with time and traffic rate of the elevator group to provide a data stream. The canonic representation of the position and direction data is reduced, to eliminate symmetry (FIGS. 1, 3 and 4) resulting from the relative positions and directions of the cars being the same except for the identification of which car is at which position and direction. An entropy estimation algorithm is used to provide a plot of entropy as a function of time, which is then translated from the other data in the stream to entropy as a function of traffic rate (FIG. 5). A maximum traffic rate is chosen, and thereafter, during normal operation, if the current rate is higher than the maximum, an elevator group parameter is altered to increase the traffic-handling capability of the group, but if the current traffic rate is lower than the maximum, an elevator parameter is altered in a manner to decrease the traffic-handling capability of the group.

The invention discloses a constant-depth-tooth bevel gear fault diagnosis method based on modified cepstrum and belongs to the technical field of fault diagnosis. The method includes: firstly, measuring a constant-depth-tooth bevel gear box by an acceleration sensor to acquire gear acceleration vibration signals to be analyzed; secondly, introducing the acquired signals to MATLAB (matrix laboratory) to obtain original signals, and subjecting the original signals to Fourier transform and logarithmic transformation to obtain a log power spectrum; thirdly, calculating maximum entropy of the log power spectrum by maximum entropy estimation algorithm; fourthly, drawing a maximum entropy spectrogram (namely a modified cepstrum diagram) of the log power spectrum by MATLAB, and accurately extracting gear fault feature information according to amplitude distribution in the modified cepstrum diagram. The method is an effective fault feature information extraction method, is applicable to fault diagnosis for constant-depth-tooth bevel gears, and is available for quickly and accurately extracting fault feature frequency to finally allow for fault diagnosis on the gears. The method is novel for the fault diagnosis and feature extraction for constant-depth-tooth bevel gears, and is effectively referable for composite fault diagnosis techniques for gears and other rotating machines.

The embodiment of the invention provides an atrial fibrillation detection device and a storage medium, which comprises an extraction module for extracting P-wave and QRS-wave waveform information in an ECG signal; a first defined module is connected with the extraction module to determine P-wave dispersion according to P-wave waveform information, and P-wave dispersion is used to express the distribution dispersion of P-wave waveform. A second defined module is connected with the extraction module to determine the characteristics of RR interval change according to the information of QRS-wave waveform information. A calculation module is connected with the second defined module to calculate the sample entropy of RR interval change characteristics by entropy estimation method. A third defined module is connected with the first defined module and the calculation module to determine whether the ECG signal is atrial fibrillation according to P-wave dispersion, sample entropy and preset classification model. The atrial fibrillation detection device and storage medium can improve the robustness of the atrial fibrillation detection.

The invention discloses a network anomaly detection method based on traffic data sample statistics and balanced information entropy estimation. The invention belongs to the technical field of networksecurity, comprises the steps of flow data collection, data format unification, data feature analysis and network anomaly determination, and is a detection method for estimating the overall situationby using a balance method of the sample information entropy based on network traffic small sample data features to identify the DoS and Port Scan attack rejection in the network.

A random number generating system operates to generate an output number bit sequence based on an entropy estimation of a true random number bit sequence, the randomness of the output number bit being an improvement of the randomness of the true random number bit sequence. A physical random number generator (20) communicates the true random number bit sequence to an entropy estimator (40), which generates an estimation signal indicative of the randomness of the true random number bit sequence. The estimation signal is communicated to a decimator (50) whereby, in accordance with estimation signal, the decimator (50) generates the output number bit as a representation of a decimation of a mixing of the true random number bit sequence and the pseudo random number bit sequence, or as a representation of a decimation of the psuedo random number bit sequence when the pseudo random number bit sequence is generated as a function of the true random number bit sequence.

A method for assessing intelligibility of speech represented by a speech signal includes providing a speech signal and performing a feature extraction on at least one frame of the speech signal so as to obtain a feature vector for each of the at least one frame of the speech signal. The feature vector is input to a statistical machine learning model so as to obtain an estimated posterior probability of phonemes in the at least one frame as an output including a vector of phoneme posterior probabilities of different phonemes for each of the at least one frame of the speech signal. An entropy estimation is performed on the vector of phoneme posterior probabilities of the at least one frame of the speech signal so as to evaluate intelligibility of the at least one frame of the speech signal. An intelligibility measure is output for the at least one frame of the speech signal.

The invention relates to a system and a method for controlling CCSDS (consultative committee for space data system) image compressing code in spatial TDICCD (time delayed integration charge coupled device) camera application. The system comprises image wavelet transformation, bit plane coding, entropy coding and code stream organization. A frame construction unit, image block information estimation and once code distribution are disposed in front of the image wavelet transform. Code section entropy estimation, secondary code distribution and a bit plane depth controller are disposed in the bit plane coding. The image is a CCD image using lines as unit. An M*N image is built by the frame construction unit to be used as a frame unit for compression. By the system and the method and secondary code distribution, self-adaptation code distribution can be performed to different image contents and code sections.

The invention provides an atrial fibrillation detection device and a storage medium, which comprises an extraction module for extracting P-wave and QRS-wave waveform information in an ECG signal; a first defined module is connected with the extraction module to determine the PR interval change characteristics according to the P-wave waveform information. A second defined module is connected with the extraction module to determine the characteristics of RR interval change according to the waveform information of QRS wave. A calculation module is connected with the second defined module to calculate the sample entropy of RR interval change characteristics by entropy estimation method. A third defined module is connected with the first defined module and the calculation module to determine whether the ECG signal is atrial fibrillation according to the characteristics of PR interval change, sample entropy and preset classification model. The atrial fibrillation detection device and storagemedium can improve the robustness of atrial fibrillation detection.

This invention relates to methods and devices for channel identification. The invention is particularly concerned with techniques for non-line of sight channel identification. In embodiments of the invention the methods and devices are used for channel identification in wireless geolocation systems. Embodiments of the invention make use of an entropy estimation of the channel to distinguish channel conditions and in particular to identify line-of-sight and non-line-of-sight channels and which can be used to solve the NLOS problem of determining relative distances between transmitter and receiver. In particular embodiments an entropy estimation of the channel impulse response (CIR) is used to construct a robust entropy-based channel identification technique. As a result, more accurate localization in indoor and other multipath environments may be possible.

A seed value making method and device for a PRNG (Pseudo Random Number Generator) are provided. The seed value making method includes the steps of: accumulating in memory units of a First Data Pool data from various external sources, analyzing the data to determine a source type, computing entropy estimations for each of the external sources basing on the source type as determined, and generating a seed value by using the entropy estimations and the data accumulated in the memory units of the First Data Pool. Accordingly, in generating a seed value, dynamic estimation of random sources rate, and classification of sources on slow and fast ones, and reliable and unreliable ones, can be provided, and also, seed values can be made with taking in account rate and reliability of the sources.

The invention relates to artificial intelligence and image compression technologies, and discloses an image processing method used for image compression and decompression. The method provided by the embodiment of the invention comprises the following steps: performing Huffman decoding on first JPEG image data to obtain a quantization coefficient; performing entropy estimation on the quantization coefficient to obtain an entropy estimation result, wherein the entropy estimation result is used for carrying out probability estimation on the quantization coefficient; and performing arithmetic coding on the quantization coefficient and the entropy estimation result to obtain first compressed data, wherein the storage space occupied by the first compressed data is smaller than the storage space occupied by the first JPEG image data. In the embodiment of the invention, after the first JPEG image data is subjected to Huffman decoding to obtain the quantization coefficient, the first compressed data is obtained, and in the process, the first JPEG image data does not need to be converted into an RGB image for entropy estimation, so that irrelevant information is reduced, and the JPEG image compression performance is improved.

The invention discloses a human body meridian point positioning method based on electrocardiosignal intelligent information processing. The method comprises the following steps of: selecting any one meridian, which has an acupuncture point needing to be determined, in human body meridians, selecting an area with a diameter being 3cm on skin where the acupuncture point needs to be positioned, dividing the area by adopting a network structure, taking meshes in the network structure as test points, and adopting a physiological signal acquisition instrument to acquire an electrocardiosignal of each test point obtained according to a bipolar limb lead method standard I; then, carrying out three-level decomposition on each electrocardiosignal of each test point by adopting a dbl wavelet packet, reconstructing each decomposed signal at single scale, carrying out entropy estimation on each reconstructed single signal, and adopting an entropy estimation value as an energy characteristic value of each test point of the acupuncture point; and carrying out division on acupuncture point data and non-acupuncture point data through adopting a hierarchical-K means hybrid clustering method so as to obtain an accurate position of the needed acupuncture point.

The invention discloses an integrated hydrological station network evaluation model based on the combination of Copula function and information entropy theory, comprises the following steps: selectingpotential Archimedean family Copula by using semi-parametric evaluation method, calculating mutual information of two variables and total correlation (TC) of multiple variables; Based on the organiccombination of Copula function theory and information entropy principle, Copula function theory is use to determine that mutual information estimation value under the multi-dimensional variable, MACEsite optimization criterion (minimum value of copula entry, MACE) is proposed, which makes up for the shortcoming of single entropy estimation of mutual information. The invention also adopts three posterior evaluation indexes to have a final evaluation on the model effect: (1) Nash-Sutcliffe efficiency coefficient; (2) negative Copula entropy after averaging; (3) joint information entropy.

The invention relates to a power distribution network single-phase ground fault identification method based on multi-source information and belongs to the technical field of power system relay protection. Firstly suddenly changed energy of a zero sequence voltage is taken as functional starting of single-phase ground fault identification; then whether a single-phase ground fault occurs is judged by combining the suddenly changed energy of the zero sequence voltage and on-off state information changing situation; and finally maximum entropy estimation power spectrum is adopted for further distinguishing an on-off action and the single-phase ground fault. The method provided by the invention adds maximum entropy estimation zero sequence current power spectrum identification on the basis of identifying the fault by adopting the suddenly changed energy of the zero sequence voltage as well as on-off state inforamtion, thus the power distribution network single-phase ground fault identification method based on the multi-source inforamtion is formed, and miss-selection of the single-phase ground fault can be effectively avoided.

The invention puts forward a sufficient entropy digital physical noise device, which comprises a first oscillator, a second oscillator, a first single edge counter, a second single edge counter, a sampling control unit, a counting result conservator, an entropy estimation circuit, a judgment module and a random digit storage module. Through the entropy estimation circuit, an internal entropy valuechange is monitored during the operation period of a noise source; the oscillators of two same structures are adopted to eliminate an influence of deterministic interference on a counting result andsolve the output certainty problem of the noise source; and in a sampling process, a single edge counting method is used, and the problem of poor output balance of the noise source due to an uneven duty ratio in the period of a sampled signal is solved. By use of the device, the random digit output by the noise source can be guaranteed to own a sufficient entropy property.

The invention provides a high-speed sufficient-entropy digital physical noise source device comprising a first oscillator, a second oscillator, a linear wave converter, an analog-digital converter, anentropy estimation circuit, a bit width converter and a random number storage. According to the device provided by the invention, the enough random quantity is collected within an extremely short sampling interval via the analog-digital converter, so that the throughput rate of the noise source is significantly improved; meanwhile, the entropy estimation circuit and the bit width converter are used for continuously monitoring an entropy output by a noise source during the running period of the noise source, so that the situation that the output has the characteristic of sufficient entropy isensured, and thus the random number service with high quality is provided. Furthermore, the bit width converter can adjust the size of the bit width meeting the sufficient entropy condition in real time according to the transmitted measurement result, and thus the robustness of the noise source when in use is enhanced.

The invention discloses a human body meridian point positioning method based on a genetic cluster. The method comprises the following steps of: selecting any one meridian, which has an acupuncture point needing to be determined, in human body meridians, wherein a signal generator is respectively connected with a stimulation point and a ground point on the meridian through silver electrodes, and sending a stimulation signal; selecting an area with a diameter being 3cm on skin where the acupuncture point needs to be positioned, dividing the area by adopting a network structure, taking meshes in the network structure as test points, and adopting a signal collector to monitor response signals of the test points; carrying out three-level decomposition on each response signal of each test point by adopting a dbl wavelet packet, reconstructing each decomposed signal at single scale, carrying out entropy estimation on each reconstructed single signal, and adopting an entropy estimation value as an energy characteristic value of each test point of the acupuncture point; and taking an obtained entropy energy characteristic vector of each response signal of each test point as a sample, carrying out clustering division on energy through the genetic cluster, and accurately positioning the acupuncture point of the meridian.

Embodiments of the present invention include a compression system including one or more compressibility inputs; a compression predictor configured to predict the compressibility of data based on the one or more compressibility inputs; a compressor configured to compress the data; and one or more compression inputs. At least one of the compression predictor or the compressor is configured to determine how to compress the data based on the one or more compression inputs. The one or more compressibility inputs comprise at least one of an object extension dictionary, an object signature dictionary, and an entropy estimation algorithm library. The one or more compression inputs comprise at least one of a quality of service profile, a device performance profile and a compression algorithm performance chart.

The present invention provides an atrial fibrillation detection device and a storage medium. The atrial fibrillation detection device includes: an extraction module for extracting P wave waveform information and QRS wave waveform information in ECG signals; a first determination module connected to the extraction module , for determining the change characteristics of the PR interval according to the P wave waveform information; the second determination module is connected with the extraction module, and is used for determining the change characteristics of the RR interval according to the QRS wave waveform information; the calculation module is connected with the second determination module, using The entropy estimation method is used to calculate the sample entropy of the change characteristics of the RR interval; the third determination module is connected with the first determination module and the calculation module, and is used to determine the ECG Whether the signal is atrial fibrillation. The invention can improve the robustness of atrial fibrillation detection.

A random number generating system operates to generate an output number bit sequence based on an entropy estimation of a true random number bit sequence, the randomness of the output number bit being an improvement of the randomness of the true random number bit sequence. A physical random number generator (20) communicates the true random number bit sequence to an entropy estimator (40), which generates an estimation signal indicative of the randomness of the true random number bit sequence. The estimation signal is communicated to a decimator (50) whereby, in accordance with estimation signal, the decimator (50) generates the output number bit as a representation of a decimation of a mixing of the true random number bit sequence and the pseudo random number bit sequence, or as a representation of a decimation of the psuedo random number bit sequence when the pseudo random number bit sequence is generated as a function of the true random number bit sequence.

Embodiments of the present invention include a compression system including one or more compressibility inputs; a compression predictor configured to predict the compressibility of data based on the one or more compressibility inputs; a compressor configured to compress the data; and one or more compression inputs. At least one of the compression predictor or the compressor is configured to determine how to compress the data based on the one or more compression inputs. The one or more compressibility inputs comprise at least one of an object extension dictionary, an object signature dictionary, and an entropy estimation algorithm library. The one or more compression inputs comprise at least one of a quality of service profile, a device performance profile and a compression algorithm performance chart.

The invention discloses a human body meridian point positioning method based on a genetic cluster. The method comprises the following steps of: selecting any one meridian, which has an acupuncture point needing to be determined, in human body meridians, wherein a signal generator is respectively connected with a stimulation point and a ground point on the meridian through silver electrodes, and sending a stimulation signal; selecting an area with a diameter being 3cm on skin where the acupuncture point needs to be positioned, dividing the area by adopting a network structure, taking meshes in the network structure as test points, and adopting a signal collector to monitor response signals of the test points; carrying out three-level decomposition on each response signal of each test point by adopting a dbl wavelet packet, reconstructing each decomposed signal at single scale, carrying out entropy estimation on each reconstructed single signal, and adopting an entropy estimation value as an energy characteristic value of each test point of the acupuncture point; and taking an obtained entropy energy characteristic vector of each response signal of each test point as a sample, carrying out clustering division on energy through the genetic cluster, and accurately positioning the acupuncture point of the meridian.

The invention discloses an entropy theory based assembly contact stress distribution assessment method which includes the steps of firstly, building a measurement coordinate system and a three-dimensional solid model to a to-be-tested part, subjecting the three-dimensional solid model to finite element cell meshing, and calculating strain energy density of each cell; secondly, calculating the overall entropy estimate value, the overall maximum entropy, the overall normalized entropy value Hs according to the strain energy density of each cell; thirdly, judging whether Hs is greater than or equal to the set threshold value, if so, then taking Hs as an assessment index to assess assembly stress distribution uniformity of surfaces of the to-be-tested part, and if not, then going to the next step; fourthly, building multiple sections perpendicular to the Z-axis of the measurement coordinate system; fifthly, searching a section with convex hulls of which quantity and area accounting for the maximum ratio to the surface area of the to-be-tested part, and calculating the convex hull height entropy estimate value, the convex hull height maximum entropy, and the convex hull normalized entropy value Hcs; sixthly, building the assessment index Ec that is equal to AHS + BHCS; and finally assessing the assembly stress distribution uniformity of the surfaces of the to-be-tested part according to the assessment index Ec.

The invention provides a median-based network flow entropy evaluation method and apparatus. The method includes the following steps: obtaining a data packet of network data flow and sending the data packet to slave nodes in a Storm cluster; receiving estimations value of an intermediate item S returned by the slave nodes, wherein the estimation values of the S are obtained through a preset entropy estimation algorithm by the slave nodes in the Storm cluster according to the received data packet; ordering the received estimation values of the S according to the magnitude; obtaining a median of the ordered estimation values of the S, taking the median as a final estimation value of the S; and obtaining an estimation value of an entropy of the network data flow according to the final estimation value of the S. In the method, the Storm cluster includes at least one salve node. The method increases the accuracy of entropy estimation while the storage space and the calculating complexity are not increased.

The present invention provides an atrial fibrillation detection device and a storage medium. The atrial fibrillation detection device includes: an extraction module for extracting P wave waveform information and QRS wave waveform information in ECG signals; a first determination module connected to the extraction module , used to determine the P wave change characteristics according to the P wave waveform information; the second determination module is connected to the extraction module and used to determine the PR interval change characteristics according to the QRS wave waveform information; the third determination module is connected to the extraction module for Determine the RR interval change characteristics according to the QRS wave waveform information; the calculation module is connected with the third determination module, and is used to calculate the information entropy of the RR interval change characteristics by using the entropy estimation method; the fourth determination module is connected with the first determination module and the second determination module. 2. The determination module is connected with the calculation module, and is used to determine whether the electrocardiographic signal is atrial fibrillation according to the change characteristics of the P wave, the change characteristics of the PR interval, information entropy and a preset classification model. The invention can improve the robustness of atrial fibrillation detection.

The invention provides a median-based network flow entropy evaluation method and apparatus. The method includes the following steps: obtaining a data packet of network data flow and sending the data packet to slave nodes in a Storm cluster; receiving estimations value of an intermediate item S returned by the slave nodes, wherein the estimation values of the S are obtained through a preset entropy estimation algorithm by the slave nodes in the Storm cluster according to the received data packet; ordering the received estimation values of the S according to the magnitude; obtaining a median of the ordered estimation values of the S, taking the median as a final estimation value of the S; and obtaining an estimation value of an entropy of the network data flow according to the final estimation value of the S. In the method, the Storm cluster includes at least one salve node. The method increases the accuracy of entropy estimation while the storage space and the calculating complexity are not increased.