50 results about "Fuzzy correlation" patented technology

The system and method for determining fuzzy cause and effect relationships in an intelligent workload management system described herein may combine potential causes and effects captured from various different sources associated with an information technology infrastructure with substantially instantaneous feedback mechanisms and other knowledge sources. As such, fuzzy correlation logic may then be applied to the combined information to determine potential cause and effect relationships and thereby diagnose problems and otherwise manage interactions that occur in the infrastructure. For example, information describing potential causes and potential effects associated with an operational state of the infrastructure may be captured and combined, and any patterns among the information that describes the multiple potential causes and effects may then be identified. As such, fuzzy logic may the be applied to any such patterns to determine possible relationships among the potential causes and the potential effects associated with the infrastructure operational state.

The system and method for determining fuzzy cause and effect relationships in an intelligent workload management system described herein may combine potential causes and effects captured from various different sources associated with an information technology infrastructure with substantially instantaneous feedback mechanisms and other knowledge sources. As such, fuzzy correlation logic may then be applied to the combined information to determine potential cause and effect relationships and thereby diagnose problems and otherwise manage interactions that occur in the infrastructure. For example, information describing potential causes and potential effects associated with an operational state of the infrastructure may be captured and combined, and any patterns among the information that describes the multiple potential causes and effects may then be identified. As such, fuzzy logic may the be applied to any such patterns to determine possible relationships among the potential causes and the potential effects associated with the infrastructure operational state.

The invention discloses a fuzzy set based intelligent evidence theory inspection information fusion method. The method includes the steps of 1), collecting sensor information acquired from a robot in intelligent inspection; 2), calculating mutual support degrees of multiple sensors by the aid of fuzzy correlation function; 3), calculating fuzzy membership degrees of the sensors according to measurement data of the sensors and representing credibility of information provided by the sensors with the membership degrees; 4), converting support degrees and credibility of the sensors into basic probability assignments namely mass functions; 5), performing fusion on the information of the sensors by the aid of a D-S theoretical synthesis rule. With the method, technical problems that data sources in fusion of the information of the sensors of the intelligent inspection system in the prior are subjective, credibility of the sensors and mutual relation among the sensors are not objectively reflected resulting in large error in information fusion, low accuracy and the like are solved.

The invention discloses a fuzzy logic and evidence reasoning-based transformer insulation stress calculation and evaluation method. The method comprises the following steps of firstly normalizing field data, namely, converting input data for describing a normal value, a limit value and the like of a transformer into dimensionless variables of a [0,1] region; secondly fuzzifying the input data, namely, mapping the input data to a fuzzy membership function, wherein a trapezoidal membership function, a generalized bell-like membership function or a Gaussian membership function can be used; thirdly building a transformer stress evaluation tree model, namely, establishing nodes from the input data of the transformer to an insulation stress of the transformer, and a connection mode of the nodes, and correlating the nodes of upper and lower layers by using a fuzzy correlation matrix; fourthly determining a weight value between the nodes, namely, obtaining a synthesized weight value by using a subjective weight value of an analytic hierarchy process and an objective weight value of eigenvector analysis; and finally obtaining the insulation stress of the transformer, namely, determining a total stress level of the transformer by using a reasoning mechanism and an evidence synthesis rule. The method has the advantages in accuracy, flexibility and capability of processing measurement uncertainty.

The invention discloses a low-voltage power distribution network fault positioning system and method based on topology dynamic identification. The system comprises a data acquisition and transmission layer, a fault positioning layer and an application layer. Wherein the user meter box data acquisition module and the branch box data acquisition module respectively acquire data and upload related fault information; the fault positioning module is used for sending related instruction notifications to the branch box data acquisition module and the user meter box data acquisition module, obtaining data information, operation state data of all lines of the low-voltage power distribution network and user electric energy meter data, evaluating node stability in a topological structure of the low-voltage power distribution network, selecting a shortest path, judging a fault reason, and sending the fault information to the branch box data acquisition module and the user meter box data acquisition module. Positioning the position of a fault node by using a fuzzy correlation threshold value between the nodes; and finally, the fault positioning information is uploaded to a cloud platform, and the fault information is transmitted to a user and working operation and maintenance personnel, so that the working personnel can quickly and accurately find out the faulted equipment or line, and repair the faulted equipment in time.

The invention relates to an image retrieval method. The method comprises the following steps of extracting color characteristics of an image by using a color histogram, and taking two characteristics such as a color vector of the color histogram and the height of a color column as the retrieval basis; calculating similarity by using the fuzzy membership function in a fuzzy set theory, judging the similarity through alpha fuzzy relation; introducing non-subsampled contourlet transform (NSCT) to extract the texture characteristic of the image at the same time; resolving the image by NSCT; extracting the mean value and standard deviation of sub-band coefficient in multiple directions of different layers and taking the mean value and standard deviation as feature vectors and index of the image in an image library, calculating similarity of the images by using the fuzzy membership function in a fuzzy set theory, wherein because the multiscale, multidirectionality and translation invariance property, great direction information can be kept after resolving, the method can completely describe the textural features of the image; finally, the two algorithms are combined, retrieving the image by using comprehensive features.

The invention relates to a marine target motion estimation method based on multi-source SAR satellites. The method comprises the steps of: detecting a marine target by using a multi-source SAR satellite image obtain a target position and an image slice, and then performing parameter extraction by using the image slice to obtain a target length; performing fuzzy correlation by using the target position and the target length to solve the attribution problem of the same target in different satellite images; eliminating heading fuzziness by the time and the positions of the target in different satellite images to obtain the accurate heading of the marine target, and then obtaining the accurate speed of the marine target by speed iterative estimation. The method accurately solves the heading and the speed of the marine target through the target correlation between the SAR satellites, and can realize the effective fusion of the multi-source SAR satellite information.

The invention relates to a method for positioning a temporary voltage drop source on line by adopting fuzzy similarity match. The method is characterized by comprising the following steps: establishing a node positive sequence voltage match index calculation model by taking the positive sequence voltage of existing nodes of the whole network as a characteristic quantity, and positioning the temporary voltage drop source by utilizing the fuzzy similarity of voltage match indexes and the match index similarity of monitored voltage vectors. The method disclosed by the invention can be used for accurately positioning the temporary voltage drop source by utilizing existing limited monitoring point information. The method disclosed by the invention can be used for calculating the voltage match index of the positive sequence voltage of a node during temporary voltage drop and matching and identifying by utilizing fuzzy correlation degree and a line fault characteristic set established off line, thereby being insensitive to data accuracy. Besides, the method disclosed by the invention can be used for integrally judging and identifying the temporary voltage drop source by simultaneously adopting the information of all monitoring nodes and the information of a network topology, is rich in data source and has certain fault tolerance, and thus high accuracy and universality are achieved in positioning the temporary voltage drop source by utilizing the method.

The invention relates to the color image matching analyzing method based on the color contents and its distribution. The invention proceeds the matching analyzing to the input image and the referent image stored in the data base, which is a set of images with a certain special feature, based on the combination of the color histogram fuzzy correlation and the shape matrix analysis, to determine whether there is an image matching the input one in the referent image data base. It is composed of the following steps. Extract the color histogram of the input image and the referent image to determine whether the two images are matched according to the matching degree of the two histograms. Then extract the given area morphological feature of the input image to get its shape matrix, and determine the matching degree according to the comparative result between the shape matrix of the input image and the referent one. The invention is more effective than the existing relevant technique, with lower erroneous judgement rate and higher accuracy when applied to identify and analyze the imperfect image.

The invention provides a method for predicting the quality of medium and heavy plates based on an evolution fuzzy correlation rule. A fitting function can be used for replacing a consequent of the fuzzy correlation rule, thereby enabling the fuzzy correlation rule to achieve the regression prediction function on the basis that a classification function is achieved. The method comprises the steps:obtaining the fuzzy correlation rule corresponding to steel rolling data, wherein the steel rolling data is the steel rolling data of the medium and heavy plates; carrying out the learning of the consequent of the fuzzy correlation rule, and constructing a fitting function on the basis of the consequent of the fuzzy correlation rule; obtaining the testing data, judging whether the testing data ismatched with the fuzzy correlation rule or not, carrying out the prediction through a fitting function generated according to the matching rule if the testing data is matched with the fuzzy correlation rule, or else carrying out the weighing through the fitting functions of all fuzzy correlation rules at a current moment, and obtaining a prediction result. The invention relates to the technical field of data mining.

The invention discloses a rolling bearing intelligent fault diagnosis method based on a multi-classification fuzzy correlation vector machine. The acceleration sensor is used for collecting vibrationsignals of the rolling bearing; wavelet packet energy feature vectors of all operation states are obtained under different noise intensities; a new class center-based membership degree calculation method is introduced; based on this, a multi-classification fuzzy correlation vector machine is constructed to realize intelligent fault diagnosis of the rolling bearing; the fault feature vector sampleset is trained and tested by using the multi-classification fuzzy correlation vector machine, and the test result is compared with the actual fault type, so that the effectiveness of the diagnosis method is verified, and the intelligent fault diagnosis of the rolling bearing is realized. The diagnosis method overcomes the defect that a traditional intelligent fault diagnosis method is not high indiagnosis accuracy in a strong noise environment, is high in fault diagnosis efficiency and good in anti-noise performance, is suitable for rolling bearing fault diagnosis in a complex noise environment, and has good engineering value and application prospect.

The invention provides a wireless data transmission equipment testing data mining system and method based on LabVIEW and Matlab hybrid programming, and relates to testing data mining systems and methods. The wireless data transmission equipment testing data mining system and method based on LabVIEW and Matlab hybrid programming solve the problems that according to a manual identification method, accurate identification and extraction of the relevance between testing data are difficult, the workload is high, LabVIEW is difficult to use, and operation convenience and graphical attractiveness of a graphical user interface of Matlab are poorer than those of the LabVIEW. The system comprises a data preprocessing module, a parameter sequence extraction module, a waveform display module, a grey correlation analysis module and a fuzzy correlation rule mining module. The system is used in the following steps that firstly, simplified arrays are obtained; secondly, graphical display is conducted on double-precision numeric data; thirdly, the improved grey correlation degree r* is calculated; fourthly, strong correlation rule array expression rules are systemized; fifthly, the correlation relationship between parameter sequences is worked out. The wireless data transmission equipment testing data mining system and method based on LabVIEW and Matlab hybrid programming are applied to the field of testing data mining.

The invention discloses an SAR image non-reference quality evaluation method based on multi-view amplitude statistics characteristics. The problems of high implementation difficulty and low stability in the prior art are solved. The method comprises the steps that 1 a training sample and a test sample are extracted from an SAR image database; 2 the global equivalent visual acuity, the local maximum equivalent visual acuity and the ratio of the global equivalent visual acuity to the local maximum equivalent visual acuity of SAR images are calculated; 3 the fuzzy correlation coefficient of the SAR images is calculated; 4 fitting square root gamma distribution in the homogeneity region in the SAR images is selected; 5 the eigenvectors of all samples are extracted; 6 an eigenvector threshold is set to classify noise images; 7 a quality evaluation prediction model is trained for SAR images with different noise pollution types; 8 the quality value of the test sample is calculated; and 9 the quality of the test sample is determined according to the test sample quality value. According to the invention, the practicability and accuracy of SAR image quality evaluation are greatly improved, and the method can be used for screening SAR images.

The invention provides a cold rolling mill roller micro displacement real-time monitoring and dynamic compensation control early warning method, which relates to the technical field of monitoring control, and comprises the following steps: monitoring roller temperature field distribution through infrared thermal imaging, establishing a mapping relation between displacement and temperature gradient, calculating thermal deformation and compensating displacement drift; constructing a rolling mill equipment domain knowledge graph and a diagnosis rule base, and performing fault diagnosis by utilizing graph embedding learning; and generating a compensation optimization strategy and establishing fuzzy correlation mapping to calculate an optimal parameter. According to the method, accurate monitoring and compensation of the micro displacement of the roller are achieved, and the equipment fault early warning capacity and the rolling precision are improved.

The invention discloses an electric vehicle charging process multistage equipment fault diagnosis method and system based on a neural network, and the method comprises the following steps: obtaining statistical historical fault data; analyzing historical fault data, and constructing a multistage equipment fault tree in the charging process of the electric vehicle by using a fault tree analysis method; obtaining a fault symptom set and a fault cause set according to the fault tree; analyzing the fuzzy correlation between the fault symptom set and the fault cause set by using a fuzzy mathematics diagnosis method; constructing a neural network fault diagnosis model in the electric vehicle charging process; and inputting the actual fault data into the neural network fault diagnosis model to obtain a fault diagnosis result. The invention can improve the fault diagnosis precision in the charging process of the electric vehicle, and guarantees the charging safety of the electric vehicle.

The invention discloses a design intention-oriented product correlation information modeling method. Based on a fuzzy set theory, establishment and quantitative expression of multiple constraint criterions are performed according to a correlation relationship among product parts; each constraint correlation is assigned with a triangular fuzzy number; a part identification frame with the multiple constraint criterions is established; data is extracted from a design knowledge database and is subjected to analysis processing in a multiple constraint criterion sorting fusion mode; a final evidence body of multiple constraint criterion fusion is obtained; fuzzy correlation intensity is defuzzified; comprehensive correlation intensity of products is obtained; and a quantitative product design structure matrix is formed. The method has the advantages of high design accuracy and accordance with a design intention for the problem that the design intention cannot be accurately expressed due to fuzzy and incomplete correlation data in a conventional complicated product correlation information modeling process.

The invention discloses a fuzzy correlation opportunity planning-based energy storage-containing comprehensive energy system scheduling method, which belongs to the technical field of power system scheduling, and specifically comprises the steps of performing mathematical modeling on energy storage equipment in a comprehensive energy system, and linearizing the loss cost of single energy storage charging and discharging; building fuzzy representation of the wind power and load prediction uncertainty, and relaxing the power balance equation containing the uncertainty; converting the power balance equation into a maximum opportunity function, taking the maximum opportunity function as one of target functions, and establishing a multi-target day-ahead optimization scheduling model taking maximum fuzzy event possibility and minimum operation cost as targets; constraining the output of other equipment in the comprehensive energy system, specifically including combined cooling heating and power equipment constraints, gas boiler constraints, electric refrigerator constraints and power grid exchange power constraints; and solving the established multi-target day-ahead scheduling model to obtain an optimal day-ahead scheduling result. The related opportunity planning method in the fuzzy environment is applied to comprehensive energy system scheduling, and economic operation of the system is achieved while safe and reliable energy supply is guaranteed.

The invention belongs to the technical field of gas pipelines, and particularly relates to a gas engineering construction quality evaluation system based on big data, which comprises the following steps of: firstly, constructing a defect feature tensor, introducing a space-time grid analysis technology to analyze defect disposal aging interval data and location defect reproduction frequency data, and constructing a defect feature tensor on the basis of a preset quality-efficiency reliability quantification rule; defect features are converted into quantifiable quality-effect scores, then deep fusion of multi-source heterogeneous data is achieved by establishing a space-time incidence matrix of welding defects, welding process parameters and environment monitoring parameters, and on this basis, a fuzzy correlation analysis technology and a preset process compliance quantification rule are adopted to achieve quality-effect evaluation of the welding defects. Complex process parameters and environmental conditions are converted into quantifiable process scores, the process scores and quality-effect scores are integrated to generate a gas pipeline welding quality comprehensive evaluation value of a target contractor, and a quantifiable intelligent decision support system is provided for gas pipeline construction quality management and control of the contractor.

A human eye sight tracking and positioning method adopting six-point method block fuzzy weighting is characterized by comprising the following steps: step S10, screen area segmentation; s20, performing image preprocessing and pupil cornea vector extraction; s30, calculating a six-point pupil cornea vector of a single area; step S40, preliminary calibration of a sight tracking position and solvingof a calibration factor vector of a first region; s50, solving calibration factor vectors of all regions; s60, solving a pupil cornea error vector; s70, establishing a fuzzy system, and solving fuzzyrelevancy; s80, fuzzy weighted calibration is carried out; and S90, carrying out real-time positioning calculation and sight tracking. According to the human eye sight tracking and positioning methodadopting six-point method block fuzzy weighting, the problems of large calculation amount and poor result global adaptability caused by limitation and defects of related technologies are further solved to a certain extent.

The invention particularly relates to a radar and AIS track association method based on an adaptive dynamic threshold. The method comprises the following steps: acquiring radar and AIS data and analyzing target information; based on the radar target information, least square fitting of a sliding window is adopted for a radar track, and the radar course is solved based on the radar track; calculating an adaptive dynamic threshold based on the distance of the radar target, the size of the AIS target and the measurement error of the sensor; determining an initial correlation quality factor based on the time initial correlation threshold value and the adaptive dynamic threshold value, and when the initial correlation quality factor meets the initial correlation quality threshold value, performing track initial correlation on the radar target and the AIS target; and establishing a fuzzy factor set based on the Euclidean distance, the navigational speed, the course and the orientation between the radar target and the AIS target, determining a fuzzy correlation quality factor based on the fuzzy factor set, and performing track correlation on the radar target and the AIS target when the fuzzy correlation quality factor meets a fuzzy correlation quality threshold. According to the method, the problems of missed association and wrong association caused by a fixed distance threshold are solved.

The invention provides a deep learning-based automatic toning system and method for a record. The method comprises the following steps of: determining a toning constraint condition of the record in visual constraints according to style similarity of shallow visual features between adjacent recorded image frames in the record and brightness difference between the adjacent recorded image frames; performing fuzzy correlation on the scene semantic elements of the narrative segments in the record through the cross correlation among the scene semantic features to obtain the element membership of the scene semantics in the narrative segments, and further determining the scene attention of different narrative segments in the record according to all the element membership; and according to the scene attention and the hue constraint conditions of the different narrative segments, determining the chroma reconstruction cost of the record in the toning process, and carrying out automatic toning processing on the record based on the chroma reconstruction cost. According to the scheme, the visual features and the semantic elements can be fused in the automatic toning process of the record, and toning coordination with narrative logic as the guide is achieved.

The invention provides a hyperspectral data optimization method and device, equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring to-be-processed hyperspectral data, and performing multi-scale feature extraction on the to-be-processed hyperspectral data to obtain a multi-scale feature vector; calculating the fuzzy correlation degree between different wavebands of the to-be-processed hyperspectral data, performing waveband screening according to the fuzzy correlation degree to obtain a reserved waveband, and performing dimensionality reduction on the to-be-processed hyperspectral data according to the reserved waveband to obtain dimensionality-reduced hyperspectral data; dividing the dimension-reduced hyperspectral data into a plurality of local areas, and performing adaptive denoising on each local area according to local data features of each local area to obtain denoised hyperspectral data; and performing data reconstruction according to the multi-scale feature vector and the denoised hyperspectral data to obtain optimized hyperspectral data. According to the method, the hyperspectral data can be optimized more efficiently and accurately, and the quality and the application value of the hyperspectral data are improved.

The invention discloses an optical observation correlation method based on mixed multi-hour ephemeris, and belongs to the technical field of space target monitoring. Aiming at the problem that the correlation performance is reduced when the ephemeris quality is unstable due to dependence on a single ephemeris in the prior art, the method comprises the following steps of: firstly, constructing a mixed set containing multi-source ephemeris; secondly, intelligently selecting a leading ephemeris and a lagging ephemeris which are closest in time for each observation arc section and each target; then, the two ephemeris are used for association respectively, and union sets of results are obtained, so that an initial association set is expanded; and for the generated fuzzy correlation, performing primary disambiguation based on the comprehensive confidence, and performing secondary accurate disambiguation on the residual fuzzy condition by comparing the weighted angular distance residual error root-mean-square value of the whole arc section. According to the method, the dependence on single-term and single-source ephemeris precision is effectively reduced, available information is fully utilized, and the correlation robustness and accuracy of optical observation data under the complex ephemeris condition are remarkably improved.

A risk prediction method based on Granger causality test and interpretable reinforcement learning is proposed. This method first studies the reinforcement process of fitness and reward functions based on autonomous driving semantics. It then investigates fuzzy correlation analysis methods based on Granger causality. Based on the reinforcement learning results, the Granger causality analysis is transformed. Finally, a Granger causality-based reinforcement learning model for autonomous driving risk prediction is proposed, which also enables risk factor tracing based on time series data. This method enables autonomous vehicles to perform self-detection of road conditions and simple risk avoidance with minimal human intervention.

The invention discloses a design intention-oriented product correlation information modeling method. Based on a fuzzy set theory, establishment and quantitative expression of multiple constraint criterions are performed according to a correlation relationship among product parts; each constraint correlation is assigned with a triangular fuzzy number; a part identification frame with the multiple constraint criterions is established; data is extracted from a design knowledge database and is subjected to analysis processing in a multiple constraint criterion sorting fusion mode; a final evidence body of multiple constraint criterion fusion is obtained; fuzzy correlation intensity is defuzzified; comprehensive correlation intensity of products is obtained; and a quantitative product design structure matrix is formed. The method has the advantages of high design accuracy and accordance with a design intention for the problem that the design intention cannot be accurately expressed due to fuzzy and incomplete correlation data in a conventional complicated product correlation information modeling process.

The invention relates to the color image matching analyzing method based on the color contents and its distribution. The invention proceeds the matching analyzing to the input image and the referent image stored in the data base, which is a set of images with a certain special feature, based on the combination of the color histogram fuzzy correlation and the shape matrix analysis, to determine whether there is an image matching the input one in the referent image data base. It is composed of the following steps. Extract the color histogram of the input image and the referent image to determine whether the two images are matched according to the matching degree of the two histograms. Then extract the given area morphological feature of the input image to get its shape matrix, and determine the matching degree according to the comparative result between the shape matrix of the input image and the referent one. The invention is more effective than the existing relevant technique, with lower erroneous judgement rate and higher accuracy when applied to identify and analyze the imperfect image.

The present invention discloses a method for continuously tracking a vehicle motion trajectory, which mainly includes four stages: perception of target motion information, construction of short trajectory segments, association of short trajectory segments, and reconstruction of interrupted target trajectories. First, a millimeter-wave radar installed above the detection road is used to perceive the position and speed information of traffic targets within the detection range. Reliable short trajectory segments are formed by connecting adjacent-frame targets according to the radar detection results. Then, a fuzzy correlation function is constructed based on spatio-temporal characteristics and motion characteristics to describe the matching relationship between the reference trajectory and the new trajectory, and the short trajectories are connected into long trajectories. Finally, the gaps in the new and old trajectories that satisfy the association relationship are reconstructed based on the cubic Hermite interpolation method to achieve continuous tracking of the vehicle motion trajectory. The present invention can not only improve the accuracy of target number estimation, but also solve problems such as trajectory breakage and multiple trajectories for a single target caused by incorrect association in traditional algorithms.

The invention relates to a method for positioning a temporary voltage drop source on line by adopting fuzzy similarity match. The method is characterized by comprising the following steps: establishing a node positive sequence voltage match index calculation model by taking the positive sequence voltage of existing nodes of the whole network as a characteristic quantity, and positioning the temporary voltage drop source by utilizing the fuzzy similarity of voltage match indexes and the match index similarity of monitored voltage vectors. The method disclosed by the invention can be used for accurately positioning the temporary voltage drop source by utilizing existing limited monitoring point information. The method disclosed by the invention can be used for calculating the voltage match index of the positive sequence voltage of a node during temporary voltage drop and matching and identifying by utilizing fuzzy correlation degree and a line fault characteristic set established off line, thereby being insensitive to data accuracy. Besides, the method disclosed by the invention can be used for integrally judging and identifying the temporary voltage drop source by simultaneously adopting the information of all monitoring nodes and the information of a network topology, is rich in data source and has certain fault tolerance, and thus high accuracy and universality are achieved in positioning the temporary voltage drop source by utilizing the method.