699 results about "Fuzzy clustering" patented technology

The invention discloses a semi-supervised classification method of unbalance data, which is mainly used for solving the problem of low classification precision of a minority of data which have fewer marked samples and high degree of unbalance in the prior art. The method is implemented by the following steps: (1) initializing a marked sample set and an unmarked sample set; (2) initializing a cluster center; (3) implementing fuzzy clustering; (4) updating the marked sample set and unmarked sample set according to the result of the clustering; (5) performing the self-training based on a support vector machine (SVM) classifier; (6) updating the marked sample set and unmarked sample set according to the result of the self-training; (7) performing the classification of support vector machines Biased-SVM based on penalty parameters; and (8) estimating a classification result and outputting the result. For unbalance data which have fewer marked samples, the method improves the classification precision of a minority of data. And the method can be used for classifying and identifying unbalance data having few training samples.

The invention relates to the technical field of digital image processing and pattern recognition, discloses a fuzzy clustering steel plate surface defect detection method based on pre classification and aims to overcome defects of judgment missing and mistaken judgment by the existing steel plate surface detection method and improve the accuracy of steel plate surface defect online real-time detection effectively during steel plate surface defect detection. The method includes the steps of 1, acquiring steel plate surface defect images; 2 performing pre classification on the images acquired through step 1, and determining the threshold intervals of image classification; 3, classifying images of the threshold intervals of the step 2, and generating white highlighted defect targets; 4, extracting geometry, gray level, projection and texture characteristics of defect images, determining input vectors supporting a vector machine classifier through characteristic dimensionality reduction, calculating the clustering centers of various samples by the fuzzy clustering algorithm, and adopting the distances of two cluster centers as scales supporting the vector machine classifier to classify; 5, determining classification, and acquiring the defect detection results.

The invention provides an electric vehicle charge-discharge optimized dispatching method based on virtual electricity price. The method comprises the following steps: an electric energy public service platform predicts and samples the basic daily load information of a target area within an optimization time interval; when a new EV is connected to a charging pile within the target area, the network connection information of the new EV is read; a user input the charging information of the vehicle; an EV charge-discharge power model is constructed; virtual electricity price is calculated to indirectly reflect the load level of the target area; a dispatching model with the charge-discharge power as an optimization variable is constructed; dynamic time-of-use electricity price for user cost calculation is determined by combining wavelet analysis preprocessing and fuzzy clustering methods; the user makes an automatic response decision; a charge-discharge operation is performed on the EV according to the decision of the user and a plan is uploaded. The electric vehicle charge-discharge optimized dispatching method based on virtual electricity price is capable of realizing peak clipping and valley filling of EV cluster load and reducing the charge-discharge cost of the user on the basis of meeting the charging requirement of the user and the capacity limitation of a power distribution transformer. In case of a great EV cluster scale, the electric vehicle charge-discharge optimized dispatching method based on virtual electricity price is still capable of meeting grid side expectations.

The invention relates to a method for analyzing facial expressions on the basis of motion tracking, in particular to a technique for face multi-feature tracking and expression recognition. The method comprises the following steps: pre-processing an inputted video image, and carrying out the face detection and face principle point location to determine and normalize the position of the face; modeling the face and expressions by using a three-dimensional parametric face mesh model, extracting the robust features and tracking the positions, gestures and expressions of the face in the inputted video image by combining the online learning method, so as to achieve the rapid and effective face multi-feature tracking; and taking the tracked expression parameters as the features for expression analysis; and carrying out the expression analysis by using an improved fuzzy clustering algorithm based on Gaussian distance measurement, so as to provide the fuzzy description of the expression.

An apparatus and method for segmenting three-dimensional (3D) medical images containing a region of interest is provided that identifies a first set of seed points within the region of interest and a second set of seed points outside the region of interest. A first sphere is constructed within the region of interest. Voxels contained within the medical image are classified using a spatial constrained fuzzy clustering algorithm. A plurality of second spheres is generated. Ones of the plurality of second spheres are accepted that satisfy the homogeneity function threshold as defined by the spatial constricted fuzzy clustering algorithm. A three-dimensional area is grown that defines the region of interest. The region of interest defined by the three-dimensional area is displayed.

The invention relates to an expressway charge data track matching based traffic state recognition method, belonging to the field of intelligent transportation. Real road network charge data provided by an expressway management system is fully utilized to fully excavate the connotation of the charge data, then the charge data is processed, matched, analyzed and calculated to obtain the inter-site travel time and traffic flow of the whole road network, then a fuzzy clustering method is utilized to recognize the traffic state between any sites on the road network, and the method comprises the following four concrete steps: handling data exception; calculating the average travel time; calculating link traffic flow; and recognizing the traffic state based on fuzzy clustering. By utilizing the method, accurate traffic condition information can be provided for a manager, thus the manager can timely and accurately know the traffic state, and a reliable data basis is provided for developing anexpressway information service.

The invention provides a K nearest fuzzy clustering WLAN indoor locating method based on REE-P, relating to the indoor locating method in the field of identification. The method comprises the following steps of: 1. measuring and recording a RSS signal received by an user terminal at a point to be located; 2. ensuring K reference points which are most similar to the signal characteristic of the point to be located with a K nearest method; 3. classifying the RSS value of the selected reference points with a fuzzy clustering algorithm, computing the square of the difference between component in each clustering center vector and the RSS value from corresponding AP, accumulating the values in the clustering, and selecting one with the lowest sum; 4. reusing the fuzzy clustering algorithm to classify the positions of all the reference points and select the reference points which have the most same reference points as that selected from step 3; and 5. taking the sum of the reference points from step 3 and step 4, and taking the average coordinate of the reference points to be taken as the position of the point to be located. The method solves the problem of error location caused by the reference points of the K nearest method, and is used for identifying the position.

The invention discloses an obstacle detection and road surface segmentation algorithm based on a three-dimensional laser radar, and the algorithm comprises the steps: (1), scanning the surrounding environment through the three-dimensional laser radar to obtain the point cloud information of the surrounding environment, and transforming the point cloud information to a local right-angle coordinatesystem from the coordinate system of the laser radar; (2), extracting an interest data point of the three-dimensional laser radar; (3), extracting a laser radar scanning single line through a radar detection angle clustering method; (4), segmenting the laser radar scanning single line through neighborhood fuzzy clustering based on AIC criterion; (5), accurately locating a road edge and a road surface line end point through corner detection. Compared with the prior art, the method can achieve the real-time and effective extraction of a passable region of a road surface, is high in precision andreliability, is small in judgment error in a recognition process, and can be widely used for an actual occasion of the extraction of the passable region of a structured road based on the three-dimensional laser radar.

The invention discloses a traffic jam judging method based on a video detection technology. By adopting a digital image processing technology, the background model of a traffic video image is established, foreground extraction and foreground de-noising are carried out on the background model, road occupancy is calculated, and a traffic jam judging model is established, thus finishing the judgment of the traffic jam state by the four steps. The traffic jam judging model comprises a jam fuzzy clustering judger and an auxiliary judger, the video image processing technology is utilized to obtain one parameter, i.e. the road occupancy, thus calculating the occupancy variance and the absolute value of occupancy variation, and being capable of finishing the judgment of the traffic jam state by using the three finite parameters.

The invention discloses an SAR (synthetic aperture radar) image change detection method combining multi-threshold segmentation with fuzzy clustering. The method mainly aims at overcoming the defect of existing fuzzy clustering algorithms and is used for SAR image change detection by combining multi-threshold segmentation with fuzzy clustering. The implementation steps of the method include: (1), subjecting two SAR images to median filtering; (2), calculating to obtain a logarithmic ratio differential image after normalization; (3), adopting the Otsu method based on standard particle swarm optimization to perform multi-threshold segmentation to the logarithmic ratio differential image after normalization; (4), initializing membership matrixes U0 and U1; (5), adopting the FLICM (fuzzy local information C-means) algorithm to perform fuzzy clustering to pixels which cannot be determined whether changes occur or not after multi-threshold segmentation; (6), deblurring; and (7), outputting change detection results. The multi-threshold segmentation and fuzzy clustering are combined for SAR image change detection, so that change detection time is reduced, and change detection accuracy is improved.

A collision and injury mitigation system (10) for an automotive vehicle (12) is provided. The system (10) includes two or more object detection sensors (15) that detect an object and generate one or more object detection signals. A controller (16) is electrically coupled to the two or more object detection sensors and performs a fuzzy logic technique to classify the object as a real object or a false object in response to the one or more object detection signals. A method for performing the same is also provided.

The invention discloses a morphological analytical apparatus and method for erythrocytes. According to the invention, a sample is placed under an automatic microscope and amplified, morphological images of all the cells in the sample are collected by a CCD (charge-coupled device) and digitalized by an image digitizer, and then segmentation and positioning of the images and extraction of target characteristic parameters are carried out; morphological characteristic parameters of every erythrocyte are separated with a classifier established on the basis of a neural network; normalization treatment is carried out on morphological characteristic parameter data of every variety of erythrocytes with a characteristic fusion cage established on the basis of fuzzy clustering; statistical analysis is carried out on each variety of obtained normalization parameters; comprehensive statistical analysis is carried out on a plurality of the normalization parameters and is expressed with figures or numerical tables so as to determine whether morphology of the erythrocytes is normal; and sources and properties of the erythrocytes can be identified through detection of erythrocytes with abnormal morphology.

This invention relates to one two-dimension fuzzy poly spot noise filter and brightness compensation B type of hypersonic cutting method, which comprises the following steps: extending the image brightness information to the update two-dimension poly fuzzy type near the pixel zone based on image brightness information; leading each heter diffusion spot noise filter to the fuzzy poly aim function to provide near information and to strengthening spot robust property; leading two-dimension fuzzy poly aim function with brightness compensation factor based on the noisy on uneven hypothesis and using image aim and background even to strengthen uneven noise robust.

The invention discloses a method for partitioning a genetic fuzzy clustering image and provides a method for partitioning a fuzzy clustering image on the basis of a genetic algorithm, which aims to solve the problem that a fuzzy C mean value algorithm is sensitive to noise and is easy to generate an overclosed clustering center due to noise influence. The partitioning method comprises the following steps of: firstly, carrying out noise resistant pretreatment on an original image by a gray level and neighborhood information; then obtaining an initially optimal clustering center by utilizing a genetic fuzzy clustering algorithm; and finally calculating the membership degree of each pixel in an image according to the obtained initially optimal clustering center by a histogram amendment clustering center of the image after noise resistance to obtain a partition result. The method adopts an improved gray level similarity function in the noise resistant pretreatment and ensures the noise resistant effect in noise with larger strength; and a clustering center distance punitive measure is added into the genetic fuzzy clustering algorithm, thereby the image with serious noise interference and a smaller target to be partitioned can be effectively partitioned, and the correct clustering center and an accurate partition result can be obtained.

The invention discloses a wireless sensor network multi-target tracking method for fuzzy clustering particle filtering. Firstly, the method performs coarse relevance based on tracking threshold algorithm to sensor node measuring data for eliminating parts of clutter; fine relevance data is subject to linear optimum blend by establishing respective FCM algorithm for fine relevance in the observation space of each sensor node; and finally particle filtering is used for predicting the state of each target. The invention can effectively avoid multiple-target track loss and error relevance and realize precise tracking of multiple targets.

A method for identifying the orientation of an interesting object in a digital medical image comprises steps of creating a rectangular interesting image mask that covers the interesting object, based on the original digital medical image; generating a rough image based on the interesting image mask, the rough image coarsely describing the interesting object; and identifying the orientation of the interesting object based on the rough image. A method for segmenting interesting objects in digital medical images may also comprise steps of creating a rectangular interesting image mask that covers said interesting object, based on an original digital medical image; generating a rough image based on the interesting image mask, the rough image coarsely describing the interesting object; and performing a post-process on the rough image.

The invention relates to an RSS (received signal strength) fingerprint database based secondary fuzzy clustering indoor-positioning method which includes the following steps of firstly, setting a position of an indoor signal coordinate node and forming an indoor positioning system; secondly, selecting a position of a reference point, measuring an RSS vector accepted by the reference point, and setting up an RSS fingerprint database; thirdly, clustering primarily to obtain a point same with a point to be positioned; fourthly, obtaining a reference point half similar to the point to be positioned, and obtaining an overall nearest point; fifthly, clustering secondarily to obtain a point same with the overall nearest point; and sixthly, weighting all points according to similarity, and computing coordinates of the point to be positioned. The RSS fingerprint database based secondary fuzzy clustering indoor-positioning method has the advantages that impact on positioning accuracy from multiple paths and non-line-of-sight and the like can be avoided effectively, no extra facility support is required and positioning algorithm complexity and cost of a positioning system are reduced.

Disclosed is a real-time music recommendation method based on context pre-filtering. For each online active user, operations include firstly extracting historical data (such as the time, occasion and weather when the users listen to the music) of all users, constructing a 'user-music-context' ternary data model, and establishing an individual recording set formed by music and context for each user; secondly, constructing a current context similar record set of user K neighbors, and transferring the ternary data model into a binary model formed by the users and the music; finally, adopting a collaborative filtering based on fuzzy clustering algorithm to predict the degree of preference by the users to different music. The method has the advantages that context information is considered fully, and the music which is more consistent with users' preferences, current moods and surroundings can be recommended.

The invention discloses a cluster fusion method for warning information in a heterogeneous network environment. The method comprises the steps of firstly, realizing the unification of frame formats of warnings in a heterogeneous network and the establishment of a warning similarity matrix through a warning preprocessing method; secondly, realizing cluster analysis of similar warnings by utilizing a fuzzy cluster algorithm and dividing the warnings in the heterogeneous network into a plurality of fuzzy warning clusters according to the distances from the warnings to a fault source; and thirdly, fusing warning information in one fuzzy warning cluster by utilizing a Dempster-Shafer theory, and by considering the fusion of warning confidence degrees, finally forming comprehensive warning information, so that the volume of warning data in the heterogeneous network is reduced. The introduced comprehensive warning concept can greatly improve the efficiency of warning processing in an information communication network so as to shorten the warning processing duration of a warning database.

The invention relates to a sector traffic state multilevel fuzzy evaluation method based on objective indicators. The method comprises the following four steps: firstly, analyzing data indicators reflecting a sector traffic state to construct a multilevel evaluation indicator system; secondly, performing correlation analysis on indicator sets of different evaluative dimensions in the evaluation indicator system by adopting fuzzy clustering, and designing a reasonable sector traffic state indicator evaluation scheme according to a clustering result; thirdly, introducing a subjective-objective combined entropy weight method, eliminating subjective identification blindness and determining the weights of different level indicators in the indicator evaluation scheme; lastly, setting fuzzy evaluation grades, designing a corresponding membership function based on the data frequency of each indicator sample in the indicator evaluation scheme, calculating the comprehensive evaluation vector of the sector traffic state by using a proper fuzzy composition operator, and judging the sector traffic state level.

The invention discloses a fault diagnosis method and a fault diagnosis device of a power transformer. The method comprises the following steps: establishing a state characteristic data table based on an in-oil dissolved gas sample with a definite fault type; carrying out normalized treatment on the state characteristic data table and establishing a normalized fault table; calculating based on the normalized fault table to obtain various fault type clustering centers; based on the clustering centers, establishing a state standard spectrum matrix; calculating through an improved main component analysis method to obtain a characteristic value, a characteristic vector and a main component contribution rate; setting a threshold value and correspondingly selecting a main component; and calculating an Euler distance between a sample to be detected and the main component of a state characteristic sample main component and taking a state characteristic sample corresponding to a minimum distance value as a diagnosis result. The fault diagnosis method and device of the power transformer have the following advantages that a state standard spectrum is calculated by utilizing fuzzy clustering, and subject data removal and sample quantity restriction are avoided; meanwhile, the dimension of the data can be reduced and main characteristics for representing fault types are refined; and the accuracy of latent fault diagnosis in the power transformer is effectively improved.