30 results about "Fuzzy group" patented technology

The invention relates to a privacy protection method based on trusted third party attribute fuzzy grouping under the cloud environment. The method comprises an attribute fuzzy grouping access control method and an interaction protocol based on trusted third party protection, and is mainly used for three stages of cloud user registration, cloud end data creation and cloud end data access, and the stages support direct interaction and indirect interaction. Through the access control method based on attribute fuzzy grouping, conversion of an access entity, an access control policy and user requests is achieved on the three stages by the trusted third party, leakage of privacy information of users can be effectively avoided, and authenticity of anonymous access users is ensured. Through the interaction protocol based on trusted third party protection, interaction behaviors of the user, the trusted third party and the cloud service party are stipulated, data encryption, timestamp and other technological means are added to the protocol, and safety of user data, user identities and attribute privacy is guaranteed in the interaction process of the user, the trusted third party and the cloud service party.

The invention discloses a measuring point selection method in analog circuit fault diagnosis. The method includes the steps that firstly, in a preset tolerance range, fault analog simulation is conducted on a circuit, so that measured data of measuring points of faults are obtained; then, the measuring points are selected through a measuring point selection algorithm based on heuristic mode graph searching, root nodes are initialized firstly and serve as target nodes, all the measuring points in a measuring point set are adopted as successor nodes to expand the target node, simulation data of one measuring point corresponding to each successor node are screened according to data identification, fuzzy groups are divided according to screened address data, the probability of occurrence of the fuzzy groups is obtained, information entropy of each successor node is calculated, the node with the smallest information entropy is selected as the optimal node and deleted from the measuring point set, and the optimal node serves as a target node and is expanded continuously until the information entropy is zero or the measuring point set is empty. According to the method, the measuring points can be preferably selected, so that an optimal measuring point selection scheme is obtained, and fault diagnosis is achieved through fewer measuring points.

The invention discloses an analog circuit fault positioning and parameter recognition method based on a genetic algorithm. Acquiring a transmission function of the measuring point; analyzing fuzzy group information of the analog circuit; the method comprises the following steps: determining a representative fault element of each fuzzy group, when an analog circuit has a fault, firstly obtaining acurrent output, then determining a fault element parameter value vector corresponding to the current output by utilizing a genetic algorithm so as to locate the representative fault element with the fault, and then determining a parameter value range of the representative fault element based on the genetic algorithm. According to the invention, fault diagnosis and fault element parameter recognition of the analog circuit can be effectively realized.

The invention discloses a method for optimizing analog circuit measuring points based on a sawtooth wave and a genetic algorithm. The method for optimizing the analog circuit measuring points based onthe sawtooth wave and the genetic algorithm comprises the following steps that fault simulation is carried out on each fuzzy group representative element of each element of the analog circuit for a plurality of times, and the sawtooth wave is used as an input of the analog circuit during analog to obtain fault voltages corresponding to the measuring points under different frequencies, so that analog circuit data are obtained; individuals in the genetic algorithm are initialized, the individuals are processed, then the feature vector of the corresponding measuring points from the analog circuit data is screened according to a measuring point selection scheme corresponding to the individuals, cross-verification is carried out on a preset classifier, the obtained classification accuracy rateis used as a fitness value of the individuals, then a next generation population is generated to continue processing until an iteration end condition is reached, and the individual with the greatestfitness in the current population is used as the optimal individual, and the measuring point selection scheme corresponding to the individual is a measuring point optimizing scheme. The method for optimizing the analog circuit measuring points based on the sawtooth wave and the genetic algorithm can effectively realize the measuring point optimizing and improve the accuracy of fault diagnosis whenanalog circuit fault diagnosis is carried out based on multiple frequencies.

The present invention discloses a post-test simulation fault diagnosis method based on a genetic algorithm. The method is characterized in that: an output voltage of an analog circuit at the measurement point under different frequency excitation signals is obtained after measurement; a transmission function and fuzzy groups of the analog circuit are obtained after analysis; each fuzzy group selects a representative fault component; when the population is initialized, each representative fault component corresponds to one sub-population; parameters of the representative fault components take values within the fault range of the corresponding sub-population individuals, and other fault components take values within the tolerance range; for each iteration, each sub-population is first crossedand mutated to generate child populations, and after the child populations are combined with the parent population, the next-generation population is preferably obtained according to the objective function value; and the representative fault component in the optimal individual of the last-generation population with the parameter value within the fault range is a fault diagnosis result. Accordingto the method provided by the present invention, the accuracy of fault diagnosis can be effectively improved.

The invention relates to a cooperative control method of bionic fish for large-range water monitoring. According to the method, water quality parameter information is acquired by means of group control of multiple mobile nodes. A pilot bionic node and follower bionic nodes are shaped in tunas. The pilot bionic node floats and inspects in a surface area, informs the follower bionic nodes in water about queue information by means of hydroacoustic broadcasting, collects the water quality parameter information acquired by the follower bionic nodes, and communicates with a shore base station. The follower bionic nodes receive the queue information, move with the pilot bionic node in a certain geometric form, and collect and send the water quality parameter information. The pilot and follower bionic nodes are communicated in a hydroacoustic manner; the pilot bionic node is communicated with the shore base station by the GPRS (general packet radio service) technology; a group system is controlled in a distributed fuzzy group control manner, remote control is available, the test range is wide, precision is high, water quality parameters of different water areas are monitored online, and the applicable prospect is promising.

The invention discloses an analog circuit fault feature range determination method based on a genetic algorithm. The method comprises the steps of firstly performing fuzzy group analysis on an analogcircuit, thus acquiring the information of each fuzzy group; respectively performing the genetic algorithm on a representative element of each fuzzy group once, and thus acquiring a boundary vector ofthe convex region of each fuzzy group circle model parameter feature vector range; and when the genetic algorithm is operated each time, using the element parameter vector as the individual of a fuzzy group population, acquiring a circle model parameter corresponding to each individual, and optimizing the individual according to the circle model parameter. According to the method, the analog circuit fault feature vector range under the influence of tolerance is acquired through combining the circle model parameter with the genetic algorithm, and an accurate fault dictionary is provided for fault diagnosis.

The invention discloses an analog circuit fuzzy group identification method. The general characteristic of the fault voltage of an analog circuit element is derived through a theory, wherein the characteristic is that a real part and an imaginary part meet an equation of a circle. According to the characteristic, the fuzzy group identification method is provided. The method includes the steps that fault-free simulation is conducted on each fault source firstly, so that a fault-free voltage value of each measuring point is obtained; simulation is conducted under two fault conditions, so that two fault voltage values are obtained; according to the three voltage values, an equation set of the circle is solved, so that circle characteristic parameters are obtained; the circle characteristic parameters corresponding to the fault sources are compared and the fault sources with the three same parameters are classified into a fuzzy group. According to the method, a transmission function is not needed, an achieving method is simple, a fuzzy group identification result is not related to a testing method, and the accuracy is the same as that of a transmission function and symbol analysis method.

The invention discloses an analog circuit fault diagnosis method based on a circular model and a neural network. Firstly, fuzzy group analysis is carried out on an analog circuit, and each piece of fuzzy group information is obtained; then circular model parameters of different measuring points of all the elements in the analog circuit under different test frequencies are obtained through circularmodel simulation, and a feature vector is obtained through construction to be used as a training sample for training a constructed neural network; and a circular model parameter of each measuring point at each test frequency is obtained according to degradation data during fault diagnosis, so as to form a test feature vector to be input into a trained neural network for fault diagnosis. Accordingto the method, the fault diagnosis of the analog circuit is realized by combining the circular model parameters with the neural network, so that the fault diagnosis rate of the analog circuit can beeffectively improved.

The invention discloses a linear analog circuit fault diagnosis method based on circular model parameters. The method comprises steps of firstly, carrying out fuzzy group analysis on a linear analog circuit; obtaining a representative fault element corresponding to each fuzzy group; obtaining the label of each element corresponding to each representative fault element through circular model simulation; obtaining element parameters based on the label array, thereby obtaining an upper limit vector and a lower limit vector of the circular model parameter of each representative fault element, training the upper limit vector and the lower limit vector as boundary data to obtain an SVM-based multi-classification model, obtaining circular model parameters according to degradation data during fault diagnosis, and inputting the circular model parameters into the multi-classification model for diagnosis. By obtaining the boundary data, the training speed of the SVM-based multi-classification model can be effectively improved, and the fault diagnosis precision of the linear analog circuit is improved.

The invention discloses a multi-level fuzzy evaluation method for a barrier lake. The method comprises the following steps: determining an evaluation index set U influencing subsequent development ofthe barrier lake; establishing a developable comment set V = {v1,v2,v3,v4} of the barrier lake; constructing a membership function equation,and establishing a fuzzy relation matrix R which is equal to[rij] n * m; determining the weight A of each grading index,and determining the weight of each grading index by adopting an improved analytic hierarchy process; adopting the maximum membership principle and finding out the Bmax with the maximum component in the B vector,enabling the evaluation corresponding to the Bmax to serve as the result of fuzzy comprehensive evaluation,and achieving comprehensive evaluation of the exploitability of the barrier lake. According to the method,a fuzzy group decision-making method and a multi-level decision-making theory are introduced,a multi-level multi-factor decision-making weir dam exploitability argumentation mathematical model is established from three criterion layers of engineering safety risks,resource feasibility and ecological environment benefits,the exploitability of the weir dam is evaluated,and reference and guidance are provided for permanent regulation of the weir lake.

The invention claims a multi-objective reactive power optimization method for a power system based on a DSICA algorithm. The method comprises the following steps of: establishing a multi-objective reactive power optimization mathematical model of a power system, and setting system parameters and algorithm parameters; initializing the individual country in the algorithm, and getting the target function value by load flow calculation; replicating individual countries to external storage spaces; performing algorithm iteration to update the individual position of the countries by using the proposed individual position updating method and distance strategy; performing non-inferior ranking and crowding distance calculation on the updated individual countries together with the previous generationof individual countries; judging whether the iterative number is satisfied, if so, entering the next step, if not, returning to the iterative number repeatedly; outputting the Pareto optimal solutionset formed by the external storage space, using a fuzzy group decision method to find the optimal compromise solution and outputting the solution. The invention has the advantages of wide searching range, strong searching ability and high solving quality in processing the multi-objective reactive power optimization problem of the electric power system, and proves the effectiveness and superiorityof the invention.

The embodiment of the invention provides a communication data acquisition method in visible light communication, device and equipment. Through extracting a baseband symbol sequence in visible light communication signals, each baseband symbol in the baseband sequence is taken as a clustering sample; based on a plurality of fuzzy groups of a fuzzy C-means clustering algorithm, according to each clustering sample, the clustering center of each fuzzy group is updated, and the membership degree of each clustering sample in each fuzzy group is updated; according to the clustering center of each fuzzy group and the membership degree of each clustering sample in each fuzzy group, a fuzzy matrix is updated to acquire the optimal fuzzy matrix; and according to each clustering sample and the optimalfuzzy matrix, communication data are acquired. Thus, during a communication signal demodulation process, inter-symbol interference, delay distortion and interference generated by noise generated to the system to communication data extraction can be effectively suppressed, the bit error rate is reduced, and the quality of visible light communication signal receiving at a receiving end can be improved.

The invention discloses an analog circuit fault positioning and fault element parameter identification method. The method comprises the steps of acquiring a transmission function of a measuring point;analyzing fuzzy group information of the analog circuit; determining a representative fault element of each fuzzy group; obtaining a feature matrix of each representative fault element based on the transfer function; constructing an overdetermined equation set of polynomial fitting, calculating to obtain a coefficient vector corresponding to each representative fault element; when the analog circuit has a fault, carrying out state monitoring on the measurement point, carrying out fault positioning based on monitoring data, and then carrying out fault element parameter identification by utilizing a genetic algorithm for the positioned fault. By the adoption of the method, fault diagnosis and fault element parameter identification of the analog circuit can be effectively achieved. The method has the two advantages of rapid positioning of simulation before measurement and accurate parameter identification of simulation after measurement at the same time, and all parameter drift faults donot need to be stored in advance.

The invention discloses an analog circuit fault diagnosis method based on polynomial fitting and state monitoring. The method comprises steps: a transfer function of a monitoring point is acquired, fuzzy group information of the analog circuit is analyzed, a representative fault component of each fuzzy group is determined, a characteristic matrix of each representative fault component is obtainedbased on the transfer function, over determined systems by polynomial fitting are constructed, a coefficient vector corresponding to each representative fault component is calculated and obtained, andwhen the analog circuit fails, the monitoring point is subjected to state monitoring, a test matrix and a constant term matrix are acquired, a characteristic vector which enables a least squares distance to exist between the test matrix and the equation determined by the characteristic vector of each representative fault component is solved, and the corresponding representative fault component isthe fault diagnosis result. Thus, fault diagnosis on the analog circuit can be effectively realized.

The invention discloses a generalized orthogonal fuzzy group decision-making-based hypertension risk early warning scheme determination method, which comprises a group decision-making method integrating an interval value generalized orthogonal fuzzy distance, a Yager operator, a CRITIC-WASPAS decision-making method, a decision maker weight and a score function, and a researched group decision-making algorithm is used for hypertension risk management. A case implementation result of the method is consistent with an opinion given by a decision maker, and the group decision method provided by the invention is verified to be effective.

The invention discloses an AC motor asymmetric branch winding arrangement optimization design method and an asymmetric branch winding arrangement scheme. The method of the invention combines the traditional asymmetric winding research method, a branch winding arrangement scheme serves as the minimal unit, fuzzy grouping processing is carried out on the branch winding arrangement scheme, search match is carried out on each branch winding arrangement scheme in a group, and finally, a winding arrangement scheme with small branch amplitude asymmetry degree and maximal phase difference is obtained. Minimization of the asymmetry degree of the asymmetric branch winding arrangement scheme can be realized, and thus, loop current between branches can be reduced, winding loss is reduced, and the motor operation efficiency and the stability are improved; and during the optimization process, a large amount of useless schemes can be filtered automatically, the winding design precision and the efficiency are greatly improved, and the practicality is strong.

The invention claims to protect a multi-objective reactive power optimization method of a power system based on a DSICA algorithm. It includes the following steps: establish a multi-objective reactive power optimization mathematical model of the power system, set system parameters and algorithm parameters; initialize the country individual in the algorithm, and obtain the objective function value through power flow calculation; copy the country individual to the external storage space; algorithm iteration, using the proposed The individual position update method and distance strategy of the colonial country update the individual position of the country; perform non-inferiority sorting and congestion distance calculation on the updated country individual and the previous generation country individual; judge whether the number of iterations is satisfied, and if so, go to the next step, if If it is not satisfied, return to repeated iterations; output the Pareto optimal solution set formed by the external storage space, and use the fuzzy group decision method to find the optimal compromise solution and output it. The invention has wide search range, strong optimization ability and high solution quality in dealing with the multi-objective reactive power optimization problem of electric power system, which proves the effectiveness and superiority of the invention.

The invention discloses a combined neural network circuit fault diagnosis method considering fuzzy group pre-discrimination, and belongs to the technical field of fault diagnosis. The method first determines the fault isolation fuzzy group of a test point to perform separability determination of the fault fuzzy group, establishes a combined neural network diagnosis sub-model for the separable fuzzy group state-test matrix, and uses the combined neural network to complete the fault diagnosis. The method provided by the invention makes the setting of classification labels in the neural network model more reasonable, improves the accuracy of the fault diagnosis based on the neural network, and has high operation efficiency by running at most one neural network model for each diagnosis, whichexpands the application range of the neural network model, and improves the diagnostic effect.

The invention discloses a combined neural network circuit fault diagnosis method considering fuzzy group pre-discrimination, and belongs to the technical field of fault diagnosis. The method first determines the fault isolation fuzzy group of a test point to perform separability determination of the fault fuzzy group, establishes a combined neural network diagnosis sub-model for the separable fuzzy group state-test matrix, and uses the combined neural network to complete the fault diagnosis. The method provided by the invention makes the setting of classification labels in the neural network model more reasonable, improves the accuracy of the fault diagnosis based on the neural network, and has high operation efficiency by running at most one neural network model for each diagnosis, whichexpands the application range of the neural network model, and improves the diagnostic effect.

The invention discloses a method for diagnosing faults of analog circuits based on fault characteristic regions. Firstly, the fuzzy group analysis is performed on the analog circuit to obtain the information of each fuzzy group, and the representative components of each fuzzy group are respectively within the tolerance range of component parameter values. Set several component parameter vectors, obtain the boundary vector of the convex domain of the circle model parameter eigenvector range of each fuzzy group, use contour fitting to obtain the contour of the fault feature area of ​​each fuzzy group, and obtain the circle of the current analog circuit according to the degradation data during fault diagnosis Model parameters, to judge whether the circle model parameters are located in the fault feature area, so as to realize fault diagnosis. The invention can obtain the fault characteristic area of ​​the analog circuit under the influence of the tolerance, so as to realize the fault diagnosis.

The invention belongs to the technical field of aviation, and relates to a troubleshooting analysis method of an aircraft system. The method comprises the following steps: step 1, from the perspective of maintenance, based on an engineering fault mode influence analysis result, forming a fault mode influence analysis integration table based on supportability analysis; 2, determining a troubleshooting demand according to a fault mode influence analysis integration table based on supportability analysis, and forming a fault fuzzy group list; 3, determining a troubleshooting sequence of each replaceable unit in the fault fuzzy group list; and 4, formulating a troubleshooting program according to the determined troubleshooting sequence.