140 results about "Fuzzy entropy" patented technology

A technique for finding an optimal threshold for binarization of a gray scale image employs fuzzy reasoning. A triangular membership function is employed which is dependent on the degree to which the pixels in the image belong to either the foreground class or the background class. Use of a simplified linear fuzzy entropy factor function facilitates short execution times and use of membership values between 0.0 and 1.0 for improved accuracy. To improve accuracy further, the membership function employs lower and upper bound gray level limits that can vary from image to image and are selected to be equal to the minimum and the maximum gray levels, respectively, that are present in the image to be converted. To identify the optimal binarization threshold, an iterative process is employed in which different possible thresholds are tested and the one providing the minimum fuzzy entropy measure is selected.

The invention discloses a remote sensing image change detection method based on neighbourhood similarity and threshold segmentation and aims to overcome the defect that the traditional method has poor noise immunity and low detection accuracy in terms of the change detection of the target with high noise. The realization process comprises the following steps: (1) using the strength normalization formula to carry out gray level matching on two remote sensing images; (2) using neighbourhood similarity distance measure to construct a similar matrix of the two remote sensing images; (3) combing the similar matrix to construct a difference image of the two remote sensing images; (4) constructing a two-dimension gray level column diagram for the difference image, using the 2D-OTSU method to determine the segmentation threshold value and separating the target area from the background area; and (5) using the fuzzy entropy method to continue classifying the unprocessed edges and noise points. The invention has the advantages of good noise immunity and high detection accuracy for the changing target and can be used for detecting targets with changes of multitemporal remote sensing images.

The invention relates to a rolling bearing fault diagnosis method based on an improved multi-scale fuzzy entropy. A vibration signal of a rolling bearing is collected; an improved multi-scale fuzzy entropy of the vibration signal is calculated; the improved fuzzy entropies of the first eight scales are used as bearing fault feature vectors; the fault feature vectors are classified into a training set and a testing set; with the training set, a support vector machine is trained and the testing set is predicted by using the trained model; and according to a prediction result, a working state and a fault type of the rolling bearing are identified. On the basis of improvement of the fuzzy entropy algorithm, a total mean value is used for replacing a local mean value in the traditional fuzzy entropy calculation and improved fuzzy entropies under different scales are calculated. Because of the improved multi-scale fuzzy entropy, the signal features can be reflected comprehensively and thus the operating state of the bearing can be evaluated accurately. Therefore, the diversified bearing state information can be extracted; and the recognition rate during the fault mode recognition process is improved.

The invention provides a large missile equipment retirement safety control method based on an improved fuzzy entropy weight method. The safety control method comprises the steps that a multi-layer hierarchical large missile equipment retirement safety risk assessment index system is constructed, a fuzzy statistical method is used for determining a fuzzy assessment matrix, a comprehensive weight iscalculated through a comprehensive entropy weight method and an analytic hierarchy method, and a multiplication-bounded operator is used for comprehensive assessment. Compared with the prior art, safety risk analysis is conducted on large missile equipment retirement disposal in processes, stages and types, the constructed multi-layer hierarchical assessment index system can ensure the integrityof input indexes of the assessment model, information repetition caused by high correlation between the assessment indexes is effectively reduced, and the workload for safety risk assessment and safety control is reduced; the comprehensive weight is calculated through the entropy weight method and the analytic hierarchy method, so that deviation caused by subjective factors is avoided, the scientificity and objectivity of the weight are improved, and comprehensive assessment of all levels of indexes corresponding to the multi-layer assessment index system is achieved.

The invention relates to an AC and DC power distribution network power supply mode and an evaluation method. According to features of the AC and DC power distribution network, a typical AC and DC power distribution network topology structure is built, evaluation indexes are built respectively from five aspects of technical benefits, economic benefits, social benefits, environmental benefits and the practicality, an entropy weight fuzzy comprehensive evaluation method is then adopted, and a relative superiority and inferiority rank for the typical AC and DC power distribution network power supply mode is obtained according to the evaluation indexes. The method comprises the following steps: (1) a network topology in the typical power distribution network power supply mode is built; (2) an AC and DC hybrid power distribution network evaluation index system is built; and (3) a fuzzy entropy weight evaluation method is adopted to evaluate the typical power supply mode. Compared with the prior art, the method of the invention has the following advantages that an AC and DC power distribution network design and evaluation method for the system is provided, and effective guidance is provided for transformation of the existing distribution network and planning and design of a future AC and DC power distribution network.

The invention provides an evaluation method based on a gridding power distribution network. The evaluation method comprises the following steps of: determining division principle of a regional grid power distribution network ; setting three types of power distribution network evaluation indicators of a first-grade grid, a second-grade grid and a third-grade grid; refining the three types of power distribution network evaluation indicators and calculating each indicator; dividing the indicators according to the coordination, safety and diversity of the power distribution network, and calculating an indicator weight by using a fuzzy entropy method; and repeating the steps to obtain a comprehensive evaluation value of the gridding power distribution networks of different regions. The evaluation method based on the grid power distribution network has the beneficial effects that with a construction thought of "refining the power distribution network from bottom to top", the requirements on the power supply reliability are met grade by grade in terms of the requirements of users, so that the self-management and self-healing capability of the power distribution network is improved; and the evaluation method is suitable for analyzing a power supply environment of the gridding power distribution network and an evaluation result can provide decision-making basis to an intelligent power distribution network.

The invention discloses an infrared image dividing method and system for power system equipment based on wavelet analysis. Firstly, wavelet conversion, fuzzy entropy, a genetic algorithm and a mathematical morphology are used for carrying out image processing on an infrared image of the power system equipment. The method comprises the following steps: firstly, carrying out thermal failure detection on the power system equipment by an infrared thermal imager to obtain a thermal image; eliminating mixed noise of the thermal image by using the wavelet conversion to inhibit background interferences and enhance a target; carrying out combined optimization operation by applying the fuzzy entropy and the genetic algorithm to determine an optimal threshold value; extracting a target; solving a discontinuous boundary problem by using a waterline region dividing method of the mathematical morphology and dividing the image so as to find a largest communication region; and separating a target region. Finally, a position of a failure point of the power equipment and an element with a failure can be judged clearly according to the separated target region; an accident is prevented form occurring and overhauling under poweroff is not blindly carried out; the operation reliability of a power system is improved.

The invention relates to an electromyographic signal tumble detection method based on a WKFDA. First, surface electromyographic signals are collected from the relevant muscle tissue of the lower limbs of a human body, the action signal segment of the surface electromyographic signals is determined through an energy threshold value, and fuzzy entropy is extracted from the surface electromyographic signals in the action signal segment to serve as characteristics to be classified; then, characteristic sample points are projected to a characteristic space, linear judgment is performed in the characteristic space, and therefore nonlinear discrimination of an original input space can be achieved in an implicit mode. Due to the contribution that corresponding balance weight is adopted for adjusting sample nuclear matrixes, influences of unbalanced data on the classification performance can be overcome. Due to the adoption of the nonlinear mapping, the data processing capacity of a Fisher linear discrimination algorithm based on nucleuses is greatly improved. The experiment result shows that a high tumble mode average recognition rate is achieved through the method, and the recognition result is superior to that of other classification methods.

The invention discloses a fuzzy entropy-based noisy signal processing method and an iterative singular spectrum (SSA) soft threshold denoising method. The method is suitable for noisy signals. Assuming that the noisy signal of length N xin = {x1, x2, ..., xN} and assuming that the additive white noise therein is uncorrelated with the signal, a d-dimensional vector is constructed and the similarityand fuzzy probability are defined by utilizing an original signal xin; a (d + 1)-dimensional vector is constructed and the corresponding similarity and fuzzy probability are defined by the same method; and the fuzzy entropy is defined in the drawing of the description. For components obtained by utilizing a known signal decomposition method, the singular spectrum distribution of all the components is defined as a fuzzy entropy spectrum. The fuzzy entropy for quantifying the complexity of the system in a chaos theory is utilized to characterize a noise plane and provide a more effective path for the processing of the noisy signal; the fuzzy entropy spectrum-based iterative singular spectrum (SSA-IST) soft threshold denoising method has the denoising performance better than that of the traditional truncated singular spectrum method, and wavelet transform and empirical mode decomposition denoising method.

The invention discloses a fall detection method based on human body acceleration multi-feature fusion and KNN. The fall detection method collects human motion information through two acceleration sensors, extracts characteristic parameters of human acceleration, and reduces the feature set from 162 dimensions to 12 dimensions through the data compression method of principal component analysis. Through improved KNN machine learning algorithm, that is, through clustering method to search sample points in the fall and non-fall two categories, training sample clusters are formed, and according tothe fuzzy entropy calculation weighted Euclidean distance to find the nearest neighbor K points, the action to be classified into the K nearest neighbors belong to the majority of the category. The fall detection method aims at the action with the highest frequency of daily life as an experiment, and provides a fall detection algorithm based on human acceleration multi-feature fusion and KNN, which has the sensitivity of 100%, can detect the fall quickly and effectively, and has high specificity, and does not mistakenly judge the daily action as the fall.

The invention discloses a characteristic vector extraction method for rolling bearing fault mode identification and state monitoring. The time wavelet energy spectrum fuzzy entropy of rolling bearing vibration signals is used as a characteristic vector so that rolling bearing fault mode identification can be realized, the operation state of a rolling bearing can also be monitored in real time and the early fault in the operation process of rolling bearing can be timely diagnosed. According to the time wavelet energy spectrum fuzzy entropy characteristic vector extraction method, the method can be simultaneously used for mode identification and operation state monitoring of different fault types of the rolling bearing so that the defect of the conventional method of respectively processing the two problems can be overcome, and the range of the similar research method for fault diagnosis of the rolling bearing can be greatly extended. Besides, the time wavelet energy spectrum fuzzy entropy acts as a single characteristic vector so that the method has higher fault mode identification efficiency in comparison with the multi-characteristic vector analysis method. Compared with the conventional rolling bearing operation state monitoring indicators, the method is more timely and accurate in monitoring the operation state of the rolling bearing.

The invention belongs to the field of multi-attribute decision making, and discloses a method and system for interval intuitionistic fuzzy multiple attribute decision-making base on improved entropy and score function. Aiming at the popular problem of supplier selection, the fuzzy entropy of uncertainty and hesitation is used to determine the weight of each index under the condition that the weight of supplier attribute is completely unknown, and the objective weighting method is used to correct the deviation caused by the agent preference. The new score function is used to sort the scheme set, and the final supplier selection scheme is obtained. The invention utilizes an improved method to calculate the attribute weight according to the contribution degree of the attribute to the decisionscheme. In view of the limitation of the existing score function, a new score function is proposed. The weight of the attribute is correctly and reasonably calculated, and the score function makes upthe problem the other sorting functions fail to sort certain interval numbers to a certain extent. When the invention selects suppliers for enterprises, the invention provides a more objective and reasonable method.

The invention discloses a vibration signal analysis method for a main bearing of a wind power transmission system based on improved HHT and fuzzy entropy. The method comprises the steps of collectinga vibration signal, performing EMD decomposition on the vibration signal, screening a real IMF component by a relative entropy theory, and performing Hilbert spectrum analysis to obtain marginal spectrum through synthesis. According to the method, the fault characteristics of the main bearing of the wind power transmission system can be stably and effectively extracted, and the interference of thenoise to a characteristic frequency can be effectively filtered. A false eigenmode function component generated by empirical mode decomposition is eliminated with proposed improved Hilbert-Huang transform, the marginal spectrum synthesized by the real component is made, and the true frequency distribution of the signal is reflected. Combined with the fuzzy entropy theory, an eigenmode function which best reflects fault bearing characteristics is screened out.

The invention provides a high-frequency oscillation rhythm detection method based on an optimal fuzzy clustering Gaussian mixture model. A method based on clustering analysis is adopted to detect thehigh-frequency oscillation rhythm, fuzzy entropy, short-time energy, a power ratio and a frequency spectrum center of mass are selected as the characteristics of an epilepsy electroencephalogram, thecharacteristics are formed into a characteristic vector as the input of a clustering algorithm, an expectation-maximization Gaussian mixture model clustering algorithm is adopted to classify the characteristic vector, and a fuzzy c-mean clustering algorithm is adopted to obtain the initialization parameter of the expectation-maximization Gaussian mixture model clustering algorithm; and a median and a quartile range are selected to analyze each category of statistics characteristics, and the high-frequency oscillation rhythm is detected. The method has the beneficial effects that the detectionspeed of the high-frequency oscillation rhythm of the epilepsy electroencephalogram is improved, and doctors are assisted in carrying out epilepsy diagnosis and epileptogenic focus excision.

The invention provides a classifier training method and a computer readable storage medium, and the classifier training method comprises the steps: carrying out the decomposition of a time sequence ofan electroencephalogram signal containing crymodynia information, so as to obtain decomposed sub-band components; determining a ratio of the energy value of any sub-band component to the sum of the energy values of all the sub-band components, a fine composite multi-scale dispersion entropy of any sub-band component, a fine composite multi-scale fuzzy entropy and an autoregression model coefficient; determining a feature set according to the ratio of the energy value of any sub-band component to the sum of the energy values of all the sub-band components, the fine composite multi-scale dispersion entropy, the fine composite multi-scale fuzzy entropy and the autoregression model coefficient; and training the classifier according to the feature set to obtain the target classifier. The classification effect of the classifier obtained through training according to the feature variables on the crymodynia information is better, and the reliability of the diagnosis result of the neurologicaldiseases is improved.

The invention discloses a rotating machine fault diagnosis method based on wavelet packet decomposition. The method comprises the following steps: 1) collecting vibration signals of a rotating machinein a normal state and a fault state; 2) selecting a wavelet basis function for fault feature extraction; 3) according to the selected wavelet basis function, obtaining sub-signals of different frequency bands of the vibration signal through wavelet packet decomposition; 4) calculating a fuzzy entropy value of the sub-signal to obtain a fault feature vector; 5) performing feature importance sorting according to the correlation, and selecting a set number of fault feature vectors with top sorting results according to the sorting results; 6) using a classifier to construct a fault diagnosis model, dividing the selected fault feature vector and the category label into a training set and a test set, and using the training set as the input of the model to train the model; and 7) inputting the test set into the fault diagnosis model to obtain a fault diagnosis result. According to the method, high-quality fault features can be effectively extracted, and the accuracy of fault diagnosis is improved.

The invention belongs to the technical field of network data processing and discloses a method for detecting the credibility of user behavior based on a fuzzy entropy weight method and a cloud model,wherein, a behavior attribute cloud is established, a hierarchical cloud is established, membership degrees of m behavior clouds to n hierarchical clouds are calculated by an association formula, anda membership degree matrix is obtained accordingly. According to the fuzzy entropy weight method, the weight of each attribute of the membership matrix element is obtained. The evaluation system provided by the invention takes into account that the calculation is only around three digital characteristics of the cloud, does not involve more complex parameters, and the calculation process is simple,so that the final evaluation result is obtained by multiplying the membership degree matrix and the weight vector. The invention determines the evaluation matrix according to the relationship betweenthe attribute cloud and the hierarchical cloud, and the cloud model itself is a reflection of the uncertain nature of things, and corresponds to the randomness and uncertainty of behaviors, and the evaluation process does not involve subjective factors, and the evaluation result is more reasonable and credible.

The invention discloses a fuzzy entropy space clustering analysis method based on orderly information entropy, comprising the following steps: S1, inputting a standardized matrix, and letting an integer i=1; S2, assigning an ith value corresponding to a relationship set D to a fuzzy entropy space parameter, and initializing intermediate sets A and C; S3, letting an intermediate set B be empty; S4, if R(xj, xk)>=lambda, deciding that B=B union {xj} and A=A\{xj}; S5, if R(xj, xk)>=lambda, deciding that B=B union {xs} and A=A\{xs}, or going to S6; S6, letting C=C union {B}, and letting an integer i+1 assign a value to i; repeating the steps S2 to S6 until the integer i is equal to the size m of the relationship set D; S7, if A is empty, deciding that X(lambda)=C, and calculating the orderly information entropy at the moment; S8, if HP(min)=min{X(lambda)}, deciding that min=lambda and going to S9, or going to S2; and S9, outputting an optimal granularity level.

The invention provides an AUV underwater terrain matching adaptation area division method based on fuzzy entropy. The method comprises the following steps: 1, dividing a priori map into a plurality of grids; 2, solving terrain entropy, terrain difference entropy and terrain standard deviation of a sub-map in each grid; 3, determining a weight relation between the terrain entropy and terrain difference entropy common to a global map and a subordinating degree function and a fuzzy rule between the terrain standard deviation and terrain entropy weight; 4, solving the adaptive ability of the sub-map in the grid according to the weight relation among the terrain standard deviation, the terrain entropy and terrain difference entropy and the subordinating degree function and the fuzzy rule of the terrain entropy weight in the sub-map of each grid; and 5, dividing the adaptive areas according to the sub-map adaptive ability obtained in the previous step. According to the method disclosed by the invention, the influences of the terrain entropy and terrain difference entropy under different terrains on the terrain adaptive ability are comprehensively considered, and the adaptive ability degree in a certain area can be well reflected.

The invention discloses an NSGAII-based multi-objective optimization decision-making method for a stratospheric aerostat. The method comprises the following steps of (1) building a stratospheric aerostat model, wherein the stratospheric aerostat model is a bielliptical geometric model composed of two semiellipsoids; (2) selecting skin volume, a resistance coefficient and a maximum loop stress of the aerostat as optimization objectives, and selecting skin thickness and a geometric slenderness ratio of the aerostat as optimization variables according to the built aerostat model; (3) performing optimization calculation on the aerostat model by using a multi-objective evolution algorithm, and performing continuous iteration to obtain a non-inferior solution of Pareto front; (4) performing decision-making analysis on an optimization result by adopting a fuzzy entropy weight-based TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method; and (5) determining an optimal scheme by adopting the TOPSIS method. According to the method, a relatively optimal scheme closest to a positive ideal solution and far away from a negative ideal solution is obtained through the fuzzy entropy weight-based TOPSIS multi-attribute decision-making method.

The invention discloses a mechanical arm movement speed proportional control method based on myoelectricity. The method comprises the steps that firstly, surface electromyogram signals of the extensorcarpi ulnaris and the flexor carpi radialis are acquired through an electromyogram acquisition instrument, movement initial positions and movement end positions are determined as the movement electromyogram signals by means of an energy threshold value method, and the smooth window average power of the signals are extracted; and the original electromyogram signals are subjected to multiscale decomposition by means of a wavelet analysis method, multiscale fuzzy entropy features of the signals are extracted, the multiscale fuzzy entropy features and the average power form a feature vector to beinput into an extensional K nearest neighbour model classifier, hand movements are identified, and meanwhile, through movement speeds of an orthogonal polynomial fitting operator and a mechanical arm, the mechanical arm is finally controlled to complete corresponding movements at corresponding speeds. By adopting the mechanical arm movement speed proportional control method based on the myoelectricity, the naturalness and initiative of human-computer interaction are improved, accordingly, the operation accuracy and convenience are improved, the danger level of operation of the mechanical armis lowered, and the mechanical arm can complete relatively complex and dangerous tasks.