130 results about "Fuzzy classification" patented technology

A system provides for fuzzy classification in comparisons of scanner responses. A web application test suite performs tests against a web application by sending client requests from a testing computer to the server running the web application and checking how the web application responds. A thorough web application security scan might involve thousands of checks and responses. As a result, some sort of programmatic analysis is needed. One such evaluation involves comparing one response against another. Response matching that compares two HTTP responses might use fuzzy classification processes.

The invention discloses a wireless communication network intelligent-antenna-covered scene automatic classification and recognition method. The method includes the steps of extracting the length characteristic, the width characteristic, the density characteristic, the collineation characteristic and the shadow characteristic of a community covered by intelligent antennas according to three-dimensional geographic information, conducting excavation analysis on the scene through a clustering tool, selecting certain characteristic parameter values to define and classify the scene so as to obtain the classification of the certain scene covered by the intelligent antennas, and conducting classification recognition on the basis of the classification. According to the method, for the randomness of the existing scene classification standard and the problems of the fuzzy classification boundary and the like, through the combination with the geographic position and the building information of the GIS, the subjective index parameters are defined, the community scene is classified and recognized, and therefore the objectivity and the accuracy which are hard to achieve through manual classification are achieved. Parameters of the intelligent antennas are set according to the scene with the determined classification, and therefore performance of the intelligent antennas can be better given into play, and the requirements of the area for coverage, interference and throughput are met.

An image editing application (or a blur classification module thereof) may automatically estimate a coherent defocus blur map from a single input image. The application may represent the blur spectrum as a differentiable function of radius r, and the optimal radius may be estimated by optimizing the likelihood function through a gradient descent algorithm. The application may generate the spectrum function over r through polynomial-based fitting. After fitting, the application may generate look-up tables to store values for the spectrum and for its first and second order derivatives, respectively. The use of these tables in the likelihood optimization process may significantly reduce the computational costs of a given blur estimation exercise. The application may minimize an energy function that includes a data term, a smoothness term, and a smoothness parameter that is adaptive to local image content. The output blur map may be used for image object depth estimation.

The invention discloses a sensor network trust evaluation method based on node behaviors and a D-S evidence theory, comprising the following five steps: 1) designing various trust factor strategies for nodes in a wireless sensor network; 2) setting trust factor weights according to network application scenes and simultaneously calculating the node behavior coefficient mu to obtain the direct trust value and multiple indirect trust value of an evaluated object; 3) calculating a fuzzy subset membership function for each trust value by utilizing the concepts of membership and linguistic variables of a fuzzy set theory, performing fuzzy classification on the various the trust values to form the basic confidence function of the D-S evidence theory; 4) calculating the evidence difference of thedirect trust value and the indirect trust values of the evaluated node and altering the weights of the indirect trust values; and 5) adopting the Dempster synthesis rule to obtain the comprehensive trust value of the evaluated node and a final basic confidence designated value according to the altered trust weights. The invention solves the problem of difficult identification of malicious nodes in a network and ensures the safety of network data transmission.

The invention relates to an intelligent irrigation fertilization decision-making control system, comprising an input unit, an output unit, a data acquisition unit and an irrigation control unit, wherein, a decision-making analysis unit arranged in a decision control database receives the data from the input unit and exchanges data with a privilege unit after processing the received data, and then exchanges data with a fuzzy reasoning unit which also receives output data of a data management unit; after performing logical judgment and processing according to the received data, the fuzzy reasoning unit outputs the data to a control management unit; a sensor of the data acquisition unit acquires and transfers the soil humidity data to the data management unit; a controller of the irrigation control unit receives the data from the control management unit, and transfers the data to a switching quantity output module after processing, and the switching quantity output module transfers the data to a solenoid valve. The intelligent irrigation fertilization decision-making control system has the advantages that: crop physiological water demand indicators are introduced into the computer automatic control field, the irrigation fertilization decision-making is carried out through an irrigation indicator database and the fuzzy classification technology.

The utility model discloses a load soft measurement method for a coal grinding machine in the thermal power plant, which aims to solve the load detecting difficulty existing long time ago of the coal grinding machine in the thermal power plant. The utility model acquires the information attributing the grinder load by the self-adaptive feature extraction from the grinder noise, the vibration and the differential pressure signal, creates a plurality of rules to identify the condition and the fitting polynomial under the rules after the fuzzy classification of the coal grindability factor, water content of the coal, steel balls loading and the load and finally integrates the output of all the rules to get the grinder load. The utility model has the advantages of overcoming the failure to adapt to the condition changes of the prior method, acquiring good accuracy and sensitivity under various conditions and providing guarantee for effective monitor of the grinder.

The invention belongs to the technical field of road traffic detection and particularly discloses an expressway road traffic state estimation method based on a dynamic Bayesian network; the method comprises the following steps: (1) extracting relevant parameters of the road traffic state as nodes; (2) determining an interrelationship among the nodes and establishing the dynamic Bayesian network; (3) carrying out a fuzzy classification on data of the observable nodes, analyzing the historical data to obtain a clustering center of each classification and determining a membership degree of the data of the observable data, belonging to each classification; 4) for a target node selected in the dynamic Bayesian network, acquiring a corresponding conditional probability and a transition probability and establishing each moment characteristic table of the selected target node; 5) inputting road traffic flow parameters of the current moment to the dynamic Bayesian network and triggering to reason a target of each moment to obtain a traffic state estimation result. According to the expressway road traffic state estimation method disclosed by the invention, the uncertainty in a single parameter estimation state is solved and simultaneously the relevance in the traffic state is considered, so that better effect and reliability when the road traffic state is estimated are achieved.

The invention relates to a method for identifying industrial thermal anomaly region based on multi-source satellite remote sensing data, which comprises the following steps: acquiring multi-source satellite remote sensing data of pollution monitoring region; obtaining visible light data, near infrared data and thermal infrared sensing data from multi-source satellite remote sensing data; the vegetation index, surface type information and specific surface emissivity of the pollution monitoring area being calculated; calculating the brightness temperature of thermal infrared radiation in the pollution monitoring area; calculating the surface temperature data of the monitoring area; extracting ground image data of monitoring area from multi-source satellite remote sensing data, and dividing the ground image data into a plurality of sub-area image data; extracting region feature information of each sub-region image data; fuzzy classification of sub-region image data to obtain potential industrial region image data; industrial thermal anomaly regions being identified based on surface temperature data and potential industrial region image data.

The present invention discloses a dynamic state peak-valley time-of-use tariff method for improving new energy absorption capability. The method provided by the invention is characterized by dynamically guiding user rational electricity consumption, building a demand response assessment model taking a dynamic state peak-valley price into account and simulating that a user predicate the changing of force according to the new energy to dynamically respond so as to prompt the new energy absorption capability. The method comprises: (1) performing cluster analysis of the current payload of a system and dynamically dividing a peak balka period to obtain the peak-valley time-of-use tariff; (2) determining the classification of each data sample according to the maximum membership principle through adoption of a FCM cluster algorithm to perform effective peak balka fuzzy classification of the system payload of each period; and (3) building a demand response assessment model taking the dynamic state peak-valley price into account to take the set operation cost and the minimum wind abandoning as an optimal object, and introducing corresponding constraints to obtain corresponding system wind abandoning electric quantity. The dynamic state peak-valley time-of-use tariff method for improving the new energy absorption capability has a wide range of application.

The invention belongs to the remote sensing monitoring technical field, and particularly relates to a winter wheat remote sensing recognition method capable of comprehensively using key seasonal aspect characters and a fuzzy classification technology. The winter wheat remote sensing recognition method capable of comprehensively using the key seasonal aspect characters and the fuzzy classification technology includes steps: preprocessing data, preparing an abundance map under coarse resolution of a research area, obtaining membership degrees of pixel elements for winter wheat under a middle and high resolution scale; performing comprehensive judgment and the like. The winter wheat remote sensing recognition method capable of synthesizing the key seasonal aspect characters and the fuzzy classification technology synthesizes a method which is based on a seasonal aspect rhythm and uses time advantages of low resolution remote sensing with the fuzzy classification technology which uses spectrum information of middle and high resolution remote sensing, and thereby obtains a middle and high resolution identification result with definite space distribution, remedies respective defects of the method based on the seasonal aspect rhythm and the fuzzy classification technology, not only solves uncertain problems in the fuzzy classification technology when membership probabilities of the pixel elements for various types are comparative, but also solves the problem that an abundance map obtained by using the seasonal aspect characters can not show the definite space distribution of crops, and provides new monitoring and estimating means to remote sensing monitoring of the winter wheat.

Embodiments of the invention provide a fuzzy classification technology-based slope reliability parameter obtaining method and apparatus, and belong to the field of data processing. The method comprises the steps of generating k training sample vectors through an orthogonal design method according to mean values and standard deviations corresponding to m uncertainty parameters respectively; according to the k training sample vectors and one or more deterministic parameter values, obtaining slope stability coefficients corresponding to the k training sample vectors through a slope stability analysis method; by taking the k training sample vectors as independent variables and taking the slope stability coefficients corresponding to the k training sample vectors as dependent variables, forming a mapping relationship, and obtaining a mapping relationship expression through a support vector machine algorithm; and according to randomly generated N to-be-tested sample vectors obeying joint probability distribution, the mapping relationship expression and a preset instability state fuzzy judgment function, obtaining slope reliability parameters. The slope stability is quantized through the instability state fuzzy judgment function, so that the accuracy of the slope reliability parameters is improved.

The invention discloses an efficient indoor positioning method based on a location fingerprint. The method comprises the steps of firstly sampling fingerprint data at a fixed position in a positioning area, reconstructing a whole fingerprint library via a SRSVD algorithm, then using a FCM algorithm to fuzzily classify the reconstructed fingerprint library, fitting each kind of RSS values with a corresponding position coordinate by using a BP neural network, at last, at a positioning stage, matching a RSS vector of an unknown target with each kind of central vectors, then classifying the RSS vector as a certain kind with the highest similarity, and then computing an output position coordinated by using the fit BP neural network. The method provided by the invention can work in a complex indoor environment, and is low in workload of collecting the fingerprint library at an offline stage, low in computation complexity, and high in positioning accuracy.

The invention discloses an improved support vector machine-LIBS (laser-induced breakdown spectroscopy) combined sorting method for steel materials. The method comprises the steps of detecting a series of rolled steel samples with known marks with an LIBS system to obtain data matrixes of the rolled steel with different marks, and utilizing a support vector machine to establish a sorting model on the known category data, wherein in the modeling process, an improved modeling method, namely combined modeling is used; after data of the sample to be measured is input to the model, performing fuzzy classification with a one-to-many method, screening candidate categories, and performing sophisticated classification by a one-to-one method, and finally determining the category of the data to be measured. According to the method, the one-to-many and one-to-one modeling methods are combined for use, and advantages of the methods are fully utilized, so the data to be measured can be subjected to two layers of analyzing systems of the fuzzy classification and the sophisticated classification, the influence of useless category information on a prediction process is reduced, the prediction accurate rate can be obviously improved and the calculating cost is reduced.

The invention discloses a hyperspectral image blur classification method and device based on a related vector machine. The method includes the following steps that a training sample set is determined, a sparse Bayesian classification model is used for selecting a kernel function, and a related vector machine classification forecasting model is built; aiming at the training sample set, a one-to-one method is adopted for constructing multiple classes of classifiers of the related vector machine, and classifier parameters are optimized through cross validation; the classifiers of the related vector machine are used for performing fuzzy classification on hyperspectral images. According to the hyperspectral image blur classification method based on the related vector machine (RVM), a sequence sparse Bayesian learning algorithm is adopted for improving training speed of the RVM, and aiming at the RVM classifiers, constructed through the one-to-one method, of the RVM, pairwise-coupled probability output is converted into the membership degree relative to ground feature classifications. Compared with a SVM, the RVM is simple in parameter selection and high in classification speed, mixed pixels can be identified by the utilization of the blur membership degree, and reliability of image classification is effectively improved.

A method for efficiently processing large-scale image and video data under the network environment comprises the following steps that data characteristics of collected massive image or video sequences are analyzed, and an effective large-scale data reduction method is proposed; a characteristic image clustering method and a fuzzy classification method are proposed, and the massive images are classified; the images are coded, represented and classified through image or data characteristics, the images are compressed, and data modeling and transmitting are implemented; multi-dimension effective characteristic parameters of targets under the network environment are extracted. The method improves the instantaneity and the accuracy of information processing, valuable references are provided for information processing under uncertain conditions, and important theoretical reference and practical significance are achieved. The method can save resource, reduce expenditures and reduce unnecessary breakage or damage or the like, and important reference and consulting effects can be performed on the development of target identifying and tracking in military, civil and public security systems, rod traffic and the like based on video systems.

The invention discloses an object-oriented multiple scale mountainous city land coverage information obtaining method, comprising the steps of: S101, establishing a classification system in advance; S102, performing multiple scale image segmentation on a shot remote sensing image; S103, comparing images after each time of segmentation to determine a best segmentation scale image, wherein the image after each time of segmentation is composed of a plurality of pattern spots of different shapes; S104, establishing the fuzzy classification standard of each land type, and correspondingly classifying the pattern spots in the best segmentation scale image meeting the fuzzy classification standard to corresponding land types; S105, establishing a nearest classification standard, and performing nearest classification on the pattern spots not classified in the best segmentation scale image so as to classify the pattern spots not classified to a land type with nearest characteristics, thereby realizing image object complete classification; and S106, generating classified images, merging classifications and generating classified images.

The invention provides a method for identifying carbonate rock fluid based on a fuzzy C mean cluster in oil exploration. According to the method, chaotic quantum particle swarm optimization (CQPSO) and a fuzzy C mean (FCM) algorithm are organically bonded, chaotic particle swarm optimization is utilized to initialize a membership matrix, the problem that a traditional fuzzy C mean algorithm is sensitive to initialization can be effectively solved, high capability for searching global optimal solution is possessed, and fuzzy classification capability is remarkably improved. The method is introduced into carbonate rock fluid identification, the problem that rock physical analysis results and seismic inversion results are not matched due to frequency dispersion of seismic data can be effectively solved, and identification accuracy of the carbonate rock fluid is improved. Besides, by means of the method, probability of properties of various fluids can be calculated, evaluation on indeterminacy of fluid identification can be conducted so that exploration risks can be effectively reduced, and a new research thought for fully utilizing various prestack elastic information to achieve carbonate rock reservoir fluid identification is provided.

The invention discloses a method for quantitatively analyzing intermittent energy synergistic effect based on a time domain, and belongs to the technical field of new energy power-generation grid-connection. The method comprises the following steps: analyzing intermittent energy long-term synergistic effect from two time scales of years-months and days based on related coefficients; determining an intermittent energy daily output fluctuation characteristic classification method based on fuzzy classification; computing two intermittent energy synergistic effect indexes: the intermittent energy synergistic effect related coefficient and an intermittent energy synergistic effect fluctuation index; analyzing intermittent energy years-months and days scale synergistic effect according to the intermittent energy synergistic effect related coefficient; and analyzing intermittent energy short-term synergistic effect based on the intermittent energy synergistic effect fluctuation index. The synergistic effects of the intermittent energies are analyzed from the time scales in different levels to determine the relevance and complementary between the intermittent energies, and the theoretical basis and guidance are provided for the power network planning containing a high permeability intermittent energy regional power grid.

Techniques for data classification include receiving, at a local computing system, a query from a remote computing system, the query comprising data associated with a commodity, the data comprising one or more attributes of the commodity; matching the one or more attributes of the commodity with one or more terms of a plurality of terms in a word matrix that includes a plurality of nodes that each include a term of the plurality of terms and a plurality of links that each connect two or more nodes and define a similarity between the two or more nodes; generating, based on the matching, a numerical vector for the business enterprise commodity; identifying one or more classification regions that each define a classification of the commodity; and preparing the classifications for display at the remote computing system.

The invention is mainly used in the field of artificial intelligence and relates to an imbalance sample weighting method suitable for training of a support vector machine. The imbalance sample weighting method comprises the steps of utilizing clustering and Fisher discriminant rate criterion to perform redundant data reduction, then calculating the distances between reduced data samples and a fuzzy classification face, endowing corresponding weights according to the distances and then using the weighted data samples to perform training of the support vector machine. For solving the problem that a traditional support vector machine is still needed to be modified and improved on the aspect of large dataset processing or imbalance data sample processing, a novel algorithm is provided, corresponding weighting is conducted on the reduced large sample data again so as to perform training learning of the support vector machine, the training speed of the support vector machine is improved, the classification accuracy of the support vector machine is also improved, and great benefit is brought to classification of datasets of large samples.

The invention discloses an infrared image segmentation method based on improved FCM (fuzzy C-means) and mean drift, and mainly solves the problems over-segmentation of a segmentation result due to the fact that local convergence is easily caused in a segmentation process in a conventional mean drift segmentation method. The infrared image segmentation method comprises steps as follows: (1), an original infrared image is input; (2), the original infrared image is subjected to primary segmentation with a mean drift algorithm; (3), a clustering center and a clustering number which are required by secondary image segmentation are determined with a minimum/maximum method; (4), the result image after primary segmentation is converted into an initial value of secondary segmentation; (5), pixel points of the initial value of secondary segmentation are subjected to fuzzy classification; and (6), boundaries of different areas are sketched, and an image segmentation result is output. According to the method, the segmentation accuracy is improved while the segmentation efficiency is guaranteed, and the method has the advantages of smooth edges and clear contour of the segmentation result and can be effectively applied to military or civil aspects of infrared precision guide, target recognition and tracking and the like.