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 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.

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 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 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 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.