79 results about "Entropy principle" patented technology

The invention provides a method for early warning of sensitive client electric energy experience quality under the voltage dip disturbance. The method comprises the steps that based on the S conversion rapid algorithm and an increment SVM classifier, voltage dip disturbances of sensitive clients are automatically identified; based on identification results of the voltage dip disturbances, voltage tolerance curves of devices corresponding to multiple types of sensitive clients at different load levels are determined; historical monitoring data of the voltage dip disturbances serve as samples, the samples are converted into sample values of a voltage dip amplitude ponderance index MSI and a lasting time ponderance index DSI, a probability density function of the MSI and the DSI is determined on the basis of the maximum entropy principle, the sensitive device fault probability is evaluated, and the probabilities of the sensitive devices corresponding to the sensitive clients at the voltage dip level are obtained. By the adoption of the method for early warning of sensitive client electric energy experience quality under the voltage dip disturbance, the electric energy quality disturbance condition can be accurately monitored, whether a client load is influenced by the disturbance or not is determined according to the load sensitivity degree of each client, and potential risks of load operation are found.

The invention discloses a heart rate measuring method based on a low-end imaging device. The method is mainly applied to measuring a non-contact heart rate under daily ambient light illumination conditions. The method comprises the following steps that the video including the human face area is read according to RGB channels, the grey average of the human face area in each frame is calculated, and three curves are drawn with the number of frames as variables. A threshold value is set based on the information entropy principle, and the cross-color situation of the imaging device is judged according to the entropy of the three curves. When the cross-color situation is serious, the waveform of a representative pulse wave is extracted by utilizing the independent component analysis method. When the cross-color situation is not serious, a simple division algorithm is carried out on a red channel curve and a green channel curve, the waveform of the representative pulse is obtained, the Fourier transform is carried out on the waveform, and the heart rate is obtained by extracting the frequency corresponding to the maximum frequency value within the range between 0.5Hz and 3Hz.

Provided is an automatic identification method of a foliar disease image of a greenhouse vegetable. The automatic identification method of the foliar disease image of the greenhouse vegetable comprises the steps of carrying out image collection on a foliar disease of the greenhouse vegetable, automatically generating a threshold, carrying out estimation by using a two-dimensional maximum entropy principle and combining the average grey degree grade and the intra-neighborhood grey degree grade of an image, optimizing the automatically-generated threshold by using a differential evolution algorithm, using an average value of results obtained through more than 30 times of differential evolution algorithm optimization which is independently carried out to serve as a threshold for image segmentation, carrying out segmentation on the known foliar disease image of the greenhouse vegetable by using the threshold, obtaining an image of the area of a disease speck, analyzing features of the disease speck, obtaining feature parameters such as the color, the texture and the shape of the disease speck of the foliar disease image of the greenhouse vegetable, carrying out fusion on the features of the disease speck, and carrying out disease type feature identification. The automatic identification method of the foliar disease image of the greenhouse vegetable can achieve rapid and effective diagnosis of the foliar diseases in a greenhouse without damage to sick leaves of the greenhouse vegetable, and can be well applied to disease monitoring of the greenhouse vegetable.

The invention belongs to the technical field of digital signal processing, such as seismic data processing, and particularly relates to a mall-scale threshold denoising method based on wavelet transform; and the theoretical basis is provided for selecting frequency dividing parameters and a proper wavelet threshold during wavelet transform denoising. The method includes firstly scanning seismic data in a small-scale manner, and acquiring a relevant coefficient in a time window; secondly setting a threshold, judging whether the data in the small-sized time window are mainly seismic signals or noise signals, and then adopting proper frequency dividing parameters and a wavelet threshold to denoise; when the seismic channels are mainly of the seismic signals, adopting conventional wavelet decomposition and a conventional hard threshold or soft threshold; when the seismic channels are mainly of the noise signals, performing wavelet packet decomposition on the seismic signals, and setting a following floating threshold method by adopting the best entropy principle; finally performing wavelet reconstruction on denoised wavelet scales, and acquiring a seismic channel set with higher noise ratio after denoising.

The invention discloses an artificial circuit fault diagnosis pattern sorting algorithm based on signal characteristic space modeling. According to the method, signals collected by test nodes are utilized, optimal fractional Fourier transform (FrFT) and R type cluster analysis are performed on the signals on the basis of the maximum entropy principle (MEP) to describe the characteristics of fault samples, and different spatial distribution modeling of faults are conducted; according to the sort separability criterion of the characteristic evaluation of minimum-in-cluster-distance and maximum-between-cluster-distance, objective optimization functions of nuclear parameters are constructed, and on the basis of a self-adaption genetic algorithm, the objective functions are optimally solved, and the nuclear parameters are adjusted; in combination with Q type cluster analysis, a hierarchical support vector machine classifier (SVC) is constructed to find and separate the faults; and through the algorithm, sensitivity reflecting fault characteristics can be extracted from measurement signals, and higher fault diagnosis speed and higher fault diagnosis accuracy are achieved. The fault diagnosis examples of a Continuous-Time State-Variable Filter circuit and an ML-8 radar prove the speediness and effectiveness of the algorithm.

This invention relates to automatic identification to communication signals and analysis technology aiming at testing and analyzing multi-bit frequency shift key controlled signals characterizing in comparing the mean value of the absolute value of the variance rate sequence of the normalized instant frequencies with the set threshold value to judge MFSK signal, when analyzing, Smooth filtration is used in reducing shake of the MFSK signal transient frequency then the entropy principle is used in analyzing the histogram for the distribution of transient frequencies to estimate the state number of code elements and frequency sphere with an equal probability distribution principle then to use the equal frequency distance to get more accurate code element state number, frequency sphere and central frequency.

A novel method is employed for collecting optimizer statistics for optimizing database queries by gathering feedback from the query execution engine about the observed cardinality of predicates and constructing and maintaining multidimensional histograms. This makes use of the correlation between data columns without employing an inefficient data scan. The maximum entropy principle is used to approximate the true data distribution by a histogram distribution that is as “simple” as possible while being consistent with the observed predicate cardinalities. Changes in the underlying data are readily adapted to, automatically detecting and eliminating inconsistent feedback information in an efficient manner. The size of the histogram is controlled by retaining only the most “important” feedback.

The invention discloses a social network oriented user relationship analysis method, which comprises the following steps: (1) utilizing a web crawler or each social network to open an API (Application Program Interface) platform to obtain original data; (2) extracting the personal attribute of a user, the friend relationship information of the user and the association information of the user, and establishing a personal interest impact factor function, a friend relationship impact factor function and an association-driven impact factor function according to the extracted information; (3) on the basis of a maximum entropy principle, constructing a user relationship analysis module, and carrying out parameter training by the user relationship analysis model to obtain an optimal parameter set; and (4) according to the optimal parameter set and the user relationship analysis module, predicting whether users have a relationship or not. By use of the method, the driving strength of the impact factor can be quantized, and the method also can be used for predicting the development tendency of user relationships and is favorable for finding unknown links and future links in the social network.

The invention discloses a water resource bearing capacity evaluation method. The method comprises the following six steps of: firstly, determining an evaluation index weight by utilizing an information entropy principle; secondly, constructing a set pair model of an evaluation region and water resource bearing capacity evolution, introducing a concept of contact numbers, and contacting the identical discrepancy contrary of the set pair model; thirdly, based on an evaluation index system, calculating the contact numbers of each layer respectively from the bottom to the top, obtaining the contact number of the last layer in turn by combining a weight vector, and analogizing one by one by obtain the contact numbers of the overall system; fourthly, determining the values of the contact numbers of the systems at each level and a principal value of an n-component contact number by utilizing an equipartition principle; fifthly, equally dividing an interval [-1,1] into n levels by utilizing the equipartition principle according to the concept of the contact numbers to obtain n level intervals; and finally, comparing the values of the contact numbers of each level in the fourth step and the n level intervals divided equally in the fifth step, and analyzing the average mu is positioned in which interval, wherein the level corresponding to the interval in which the average mu is positioned is the evaluation level of the water resource bearing capacity.

The invention discloses a maximum information entropy-based chemical data stream real-time abnormity detection method. According to the method, a real-time clustering algorithm DSC-Stream (The Dimension Space Cluster-Stream) is used; a data dimension space is used as a starting point to divide the data dimension space into a plurality of dimension clusters according to the maximum entropy principle; the data in the same dimension cluster forms a micro cluster so as to realize the real-time clustering of the data streams; the algorithm can be used for effectively decreasing the quantity of the invalid grids generated in the traditional density grid algorithm so as to greatly improve the operational efficiency; meanwhile, through a method for setting dimension cluster similarity, the algorithm can be used for effectively solving the problem that the traditional density algorithm is sensitive to the scanning radius and the density threshold to cause poor robustness and can be better adapted to the stream data.

The invention provides a portable ultrasonic Doppler fetal heart rate monitor which is safe, is accurate in detection and has multiple functions and a controlling method thereof, belonging to the technical field of medical devices. The fetal heart rate monitor comprises an ultrasonic transmitting and receiving unit, a fetal heart rate signal extraction unit and a fetal heart rate signal processing unit, wherein an output end of the ultrasonic transmitting and receiving unit is connected with an input end of the fetal heart rate signal extraction unit; and an output end of the fetal heart ratesignal extraction unit is connected with an input end of the fetal heart rate signal processing unit. The controlling method of the fetal heart rate monitor comprises the following steps: 1, performing digital filter on a fetal heart rate signal extracted by the fetal heart rate extraction unit by adopting a wavelet packet mixed nose threshold denoising method based on the maximum entropy principle; and 2, adopting a generalized kernel correlated algorithm to performing heart rate detection on the fetal heart rate signal processed in the step 1.

The invention discloses an information entropy-based digital storage oscilloscope vertical resolution improving method. The information entropy-based digital storage oscilloscope vertical resolution improving method includes the following steps that: as for over-sampled single acquisition signals, the probability and maximum entropy of each sample are estimated through adopting the maximum entropy principle; the uncertainties of the samples are calculated according to the probabilities of the samples, so that a confidence interval can be obtained; coarse quantization errors in the samples are eliminated according to the confidence interval; the fusion weight coefficient of data is calculated according to information quantity rate; data fusion is performed on effective samples according to the fusion weight coefficient; samples with coarse quantization errors are replaced by data fusion results, so that sample data can be reconstructed; and output samples can be obtained through adopting an average-based extraction algorithm. The information entropy-based digital storage oscilloscope vertical resolution improving method of the invention is used for improving the vertical resolution of a digital storage oscilloscope. Through the combination of the information entropy-based data fusion and the average-based extraction filtering algorithm, the information entropy-based digital storage oscilloscope vertical resolution improving method can improve the vertical resolution of the digital oscilloscope and the signal noise ratio of output signals and will not affect analog bandwidth under the actual sampling rate of the oscilloscope.

The invention discloses an SAR image man-made target detection method based on visual contrast and information entropy. Local contrast information of an image is extracted by simulating simple cells in a primary visual cortex according to biological visual features; a computing pool model is learnt and S units are integrated into a complex unit C, and Gaussian filtering processing is performed so that a global salient graph is generated; threshold segmentation is performed on the salient graph according to the maximum information entropy principle; isolated pixel sets are eliminated by morphological filtering processing, and connectivity of the pixel sets is enhanced; and target edge information is extracted and displayed on an original SAR image so that a target detection result is obtained. The achieved beneficial effects are that effective detection of the man-made target in the SAR image can be realized, computation burden is low, processing efficiency is high and project implementation is facilitated, and thus the method can be used for rapid detection of the small man-made target.

The invention discloses an electric power supply system based on the maximum entropy principle. A method for calculating probabilistic energy flow of a gas integrated energy system comprise the stepsof: solving electric power; obtaining steady state energy flow of natural gas integrated energy system, node voltage, branch power, node pressure of natural gas system and pipeline flow correspondingto the reference operating point, and calculating the reference sensitivity matrix of electric power system and natural gas system. The central moments of each order are transformed into semi-invariants of each order, and the electric power is considered simultaneously. Coupling relationship between natural gas; According to the product of the semi-invariant and the sensitivity matrix, it is transformed into the semi-invariant of the node voltage, the branch power, the node pressure of the natural gas system and the disturbance part of the pipeline flow rate, and then transformed into the final central moments of each order. Based on the final order center moments and the maximum entropy model, the probabilistic energy flow results for electricity-natural gas integrated energy systems. Theinvention can effectively solve the problem of electric power. Probabilistic Energy Flow of Natural Gas Integrated Energy System.

The invention relates to an X-ray image enhancement method based on dual-energy spectrum and wavelet fusion. The method includes the steps of obtaining material specific attenuation changes by changing energy spectrum distribution of an X-ray source in accordance with the principle that attenuation coefficient of a matter is related to energy; selecting the maximum of high frequency coefficient gradients and multiplying the maximum by a weighting factor (the weighting factor is greater than 1) according to the low-contrast feature of X-ray CT (computerized tomography) imaging as a fusion method for increasing high frequency coefficients and highlighting details; calculating the fractal dimension of low frequency coefficients to obtain the complexity, performing image fusion enhancement on dual-energy spectrum two images through objective and subjective weight fusion factors in the maximum entropy principle for overcoming the deficiency that weight selection has no theoretical basis and is too subjective, and making up for deficiencies of a single CT image. The X-ray image enhancement method based on dual-energy spectrum and wavelet fusion is significant.

The invention provides a network health degree testing method and a network health degree testing system. The method comprises the following steps of: acquiring multiple characteristic values of a network; setting preset health degrees for characteristic combinations of the characteristic values respectively; calculating the maximum entropy value of each characteristic combination based on the maximum entropy principle; and taking the preset health degree of the characteristic combination corresponding to the maximum entropy value as the health degree of the network. The sorting operation state of the current network is obtained aiming at the operation characteristic of the current network by acquiring the conventional network states and using the maximum entropy principle and finally revealed in a form of health degree, so that evaluation of the network health degree is realized, subjectivity and inaccuracy of human evaluation are overcome, and the problem of difficult human evaluation under the complex network condition is solved.

The invention discloses an information entropy principle-based method for fault diagnosis of a switch power supply; therefore, defects that a current diagnostic method needs more test points, a programming algorithm is complex, there are a few diagnosable fault types, and accurate positioning can not be realized can be overcome. When a fault diagnosis is carried out, a magnetic leakage signal of a switch power supply board magnetic element is obtained; a spectral entropy characteristic Hf, a time domain entropy characteristic Ht, a peak-to-peak value characteristic Vpp, a mean value characteristic a, a root mean square characteristic r, and a variance characteristic sigma of the magnetic leakage signal are extracted; and all the extracted characteristic values are compared with characteristic values in a characteristic value table that is established before the diagnosis, so that it is determined whether there is a fault on the switch power supply and what a type of the fault is. According to the invention, there is a few test points that are needed according to the method; and the fault diagnosis of the power supply can be realized only needing the magnetic leakage signal of the power supply board magnetic element. And the method is especially suitable for an occasion on which a contact type fault diagnosis can not be carried out as well as can be applied to system tests and fault diagnoses of various switch power supplies.

The invention discloses a hydrological situation evaluation method considering hydrological index distribution rules. The method comprises steps that an IHA is selected as an index system for evaluating river hydrological situation change; the maximum entropy principle and the minimum information entropy are utilized to select a probability density function consistent with hydrological rules; an index change degree is represented through difference of shannon entropy of each IHA component index in two periods; multi-attribute decision-making weighing is utilized to calculate weight of diversity change consistent with hydrological indexes; after each index weight is acquired through calculation, a change degree of each index multiplies corresponding index weight, summation is carried out to acquire an integral change degree of all the indexes, namely, a hydrological situation change degree considering hydrological distribution is acquired. The hydrological change degree is evaluated with pertinence, and more reasonable support is provided for local water resource management departments.

The invention discloses a load balancing scheduling method based on a maximum entropy principle. The concept of thermodynamic entropy is introduced into load balancing. The internal relation between entropy and load balance is studied. The resource balance distribution is taken as a target. A balanced scheduling problem of the load is equivalent to a maximum entropy problem for solving the system.Meanwhile, a theoretical model and basic properties of the system entropy are given, the entropy is used as a measurement index of the system load balance degree, scheduling and migration are carriedout on the system load according to the entropy change, the system can reach the load balance target faster and better, the response time is shortened, the throughput rate is increased, and thereforethe performance of the cluster system is improved.

The invention discloses a typhoon influence considered method for calculating a combined return period of an ocean extreme value, comprising the specific steps of: (1), determining four constraint conditions of a two-dimensional maximum entropy distribution function; (2), deducing a two-dimensional maximum entropy combined distribution mode; (3), determining an integer explicit expression of a mixed distribution moment of an extreme value wave height and an extreme value water level; (4), deducing an undetermined parameter equation set relevant to the mixed distribution moment; (5) constructing a nested composite maximum entropy distribution new mode for calculating the combined return period of the extreme value wave height and the extreme value water level in a typhoon influenced sea area; and (6), verifying the effectiveness of the new method. In the invention, the extreme value wave height and the extreme value water level are comprehensively considered, the influence on the extreme value wave height and the extreme value water level from the typhoon is considered and the whole mode is based on the maximum entropy principle so that the apriority and the artificial assumption of the mode are avoided. The integer explicit expression of the new mode is convenient for the engineering application; and through two groups of ten undetermined parameters, the new mode can be used for fitting the observation data of different sea areas more precisely and flexibly, thereby having wider range of application.

The invention provides a discontinuous context modeling and maximum entropy principle based gene compression method. The method includes that in a first stage, two actual situations with reference sequences and without reference sequences are considered simultaneously, and repetitive sequences in or among gene sequences are represented with a dictionary method, so that compression efficiency is improved; in a second stage, a statistical encoder is composed of a predictor and an arithmetic encoder, the predictor introduces a modeling scheme in a discontinuous bit context combination type on the basis of traditional continuous context models for non-repetitive sequences, and prediction probabilities independently generated by all models are merged with a Logistic regression formula generated on the basis of the maximum entropy principle, so that a more accurate final prediction probability value is obtained and put into the arithmetic encoder. By the method, compression efficiency can be improved remarkably, and efficient storage is realized.

The invention relates to an evaluation method of uncertainty in the entropy measurement for multisensor neural network of poor information, and belongs to measuring and testing field. The method is characterized in following steps:(1)using RBF(Radial Basis Function) to establish a data fusion model of multisensor data conditions under the condition of poor information, acquiring fusion sequence reflecting the measuring process;(2)using the maximum entropy principle to process obtained fused sequence, to obtain the probability density function of the fusion sequence;(3)using obtained probability density function to process true value and regional evaluation, to realize uncertain measurement with poor information. The method allows small sample of measure data, the unknown probability distribution and can make full use of the existing information of measured data to realize effective evaluation of uncertain measurement.

The invention discloses a city CA model establishment method based on a maximum entropy principle. The method comprises the steps that two-stage land utilization grid data is acquired and reclassified; the classified data is superimposed to obtain a city land utilization increase range, and random sampling and coordinate calculation are conducted in the range to obtain sample point data; a space variable influencing city land utilization extension is processed, and a result is used as a classification model constraint condition; a sample point and the constraint condition are used for model training to obtain a classification model with the maximum conditional entropy; a land utilization matrix and a cell corresponding to a matrix element are established, a space coordinate of the cell isinput into the model, the conditional probability of each cell classified as urban land under influence of the constraint condition is obtained and used as the CA model cell transition probability, and a city extension CA model is established on the basis of the CA model cell transition probability and in combination with neighborhood constraint. According to the method, the CA model with the maximum entropy is established and applied to urban land extension simulation, the randomness problem is considered, and the method helps to more precisely simulate city development.

The invention discloses a short-term load prediction method based on incremental learning, and the method comprises the steps: building an initial data set and an incremental data set according to thedivision of seasons, carrying out the correlation analysis, extracting input load data, and carrying out the maximum and minimum normalization processing; using an improved Adaboost. RT integration algorithm for training the initial data set and the incremental data set, establishing an initial integration model and an intermediate integration model, and obtaining an evolution integration model on the basis; and finally, inputting to-be-predicted load data into the initial integration model, the intermediate integration model and the evolutionary integration model at the same time, and performing load prediction by applying a maximum entropy principle based on a particle swarm optimization algorithm in combination with a plurality of models.

The invention belongs to the field of medium-term and long-term load forecasting in power distribution system planning and short-term load forecasting in power distribution system planning, relating to a cooperative load forecasting method based on maximum informational entropy. The method comprises the following steps of: calculating the statistical characteristics of an original forecasting scheme; analyzing the confidence level of the original forecasting scheme; obtaining a cooperative probability distribution function; simultaneously taking the statistical characteristics of an upper level and a lower level as constraint information, and obtaining the cooperative probability distribution function based on the maximum informational entropy principle; and obtaining a cooperative forecasting principle: based on the cooperative probability distribution function, calculating mathematical expectation and maximum probability, and finally determining the high scheme, medium scheme and low scheme of cooperative load forecasting. The method applies the maximum informational entropy principle in the theoretical research of load forecasting under power grid cooperative planning mode, and can realize the information comprehension of multi-section, multi-path and multi-scheme, thereby effectively solving the problem of data collision of an upper level network and a lower level network, realizing the cooperative forecasting of the upper network and the lower network and providing the reference basis for the planning and operation of the power distribution system planning.

The invention provides a turbine blade probability damage tolerance analysis method. According to the method, the problems of strong nonlinearity of a blade crack propagation life model, large uncertainty and correlation among parameters and the like are solved, and the fatigue crack propagation life distribution of the turbine blade can be predicted only according to the parameter distribution. The method comprises that firstly, a crack propagation life model is logarithmic; Nataf change and orthogonal change are utilized to convert the related random variables into independent normal randomvariables, then two-dimensional dimension reduction integration is utilized to solve the first four-order moment of the crack propagation life, and finally the probability density function (PDF) and the cumulative distribution function (CDF) of the crack propagation life are solved based on the maximum entropy principle (MEP). The method can predict the fatigue crack propagation life distributionof the turbine blade and guide the evaluation of the detection period of the turbine blade.

The invention relates to an attribute interval identification method for intelligent power distribution network efficiency evaluation. A three-layer attribute measuring matrix between intelligent power distribution network basic data and efficiency is built, intelligent power distribution network efficiency attribute intervals are identified automatically on the basis of index data standardization, and attribute measuring interval and comprehensive attribute measurement value of maximum entropy principle calculating performance index, standard index and comprehensive index. The method includes the steps of firstly, reading information required by the evaluation; secondly, building an attribute measuring matrix of comprehensive index, standard index and performance index; thirdly, standardizing index data; fourthly calculating attribute measuring intervals of comprehensive index, standard index and performance index; fifthly, calculating comprehensive attribute measurement of efficiency index, and identifying attribute intervals of intelligent power distribution network efficiency so as to realize intelligent power distribution network efficiency evaluation.

The invention discloses a maximum entropy principle-based shallow sea water flow field acoustic tomography method. According to the method, transmitting-receiving nodes are arranged in a sea area to be tested, so that measured acoustic signals can be obtained; the transmission delay difference of the same path in an opposite direction is obtained; prior flow field information is utilized to construct the relationship of the measured delay difference and flow field distribution, and an inversion mathematical model is constructed; the inversion mathematical model is solved by using the maximum entropy principle; and a convergence solution is obtained by using an iterative method. As indicated by simulation instances, the maximum entropy principle-based shallow sea water flow field acoustic tomography method can be utilized to construct two-dimensional flow field information of the target sea area by using the transmission delay difference and achieve higher least-square accuracy.

The invention discloses a hydrological station network optimization model based on Copula entropy. The model comprises the steps of optimization of a Copula function, calculation of an information transfer value and recombination and optimization of a station network; based on organic combination of a Copula function theory and an information entropy principle, with replacing traditional mutual information amount with Copula entropy being main improvement, and combining a distance amount among station pairs, a basic evaluation index of an information transfer strength amount among stations is provided, and a determination method of an information transfer amount average index (AI) and a threshold value range is creatively provided to conduct station network combination on a potential station network again. According to the hydrological station network optimization model based on the Copula entropy, the Copula entropy is well exerted in evaluation of the hydrological station network, the limitation of joint probability density function estimation between the station pairs is well solved, the information transfer amount among the station pairs is quantitatively described, the station network is objectively evaluated and optimized, and the model has reasonability and effectiveness.