224 results about "Degree matrix" patented technology

The invention discloses a cold-chain logistic stowage intelligent recommendation method based on spectral clustering. Scores of users for a stowage line are conveyed through a cold chain for cold-chain logistic stowage intelligent recommending, a score matrix is built, the Euclidean distance is used for calculating the user similarity, a degree matrix is used for calculating a Laplacian matrix, feature vectors are obtained by calculating feature values of the orderly Laplacian matrix, a K-means algorithm is used for clustering the feature values to obtain a user group with the similar interesting stowage line, and a stowage line is recommended inside the user group with the similar interesting stowage line, so that cold-chain logistic stowage intelligent recommending is achieved, the cold-chain logistic vehicle non-load ratio is lowered, and the profit rate of cold-chain logistic transport vehicles is increased.

The invention discloses a multisource information fusion method in an evidence high-conflict environment, which comprises the following steps of: determining the mutual support degree of evidences according to evidence distances, further calculating the relative discount factor of each evidence by taking a characteristic vector corresponding to the maximum characteristic value of a evidence support degree matrix pattern as a weighting vector of the evidence, wherein the evidence with the maximum weighing coefficient is a key evidence; and discounting each evidence information, and fusing the information of multisource sensor data by using the D-S (Dempster/Shafer) rule so as to increase the accuracy and precision of a monitoring proposition judgment under an evidence high-conflict status.The method can effectively eliminate the uncertainties of multisource information, increase the accuracy of information evaluation and overcome the limitation that correct evaluation results can not be acquired by using the traditional D-S evidence theory under the evidence high-conflict status, and has the advantages of simple algorithm, high convergence rate and the like.

The embodiment of the invention discloses a video classification method, and is used for overcoming the defect in the prior art that videos cannot be accurately classified, and improving the degree of accuracy of video classification. The video classification method includes obtaining information in a video, the information in the video including image information, light stream information and acoustic information; utilizing a deep neural network to generate first reference information corresponding to the image information, second reference information corresponding to the light stream information and third reference information corresponding to the acoustic information; processing the video according to the first reference information, the second reference information and the third reference information to obtain a confidence degree matrix of the video and a category relation matrix of the video; and substituting the confidence degree matrix of the video and the category relation matrix of the video into an objective function to obtain an objective fusion parameter of the video, wherein the objective fusion parameter is used for classifying the video.

Provided is an image feature extraction method based on global and local structure fusion, characterized by comprising: 1) constructing a weight adjacent map; 2) determining laplacian matrix of similar matrix, degree matrix and images, 3) determining scatter matrix inside the kind and between the kind; 4) determining projection matrix, 5) identifying. The invention provides a feature extraction method of fusing the global structure information and the local structure information, wherein complex features fused of the global feature and the local feature are extracted, thereby the method has strong resolving power. The method not only has the characteristics of holding the reflection method locally, namely holding the characteristics of manifold structure of data; moreover has the characteristics of linear discrimination analysis method, namely assembling the date of the kind more compact to enlarge the distance between the kinds. The invention is applied in image recognition, thereby increasing identifying performance.

The present invention provides a broadcast television subscriber grouping system and method based on spectral clustering integration. The system comprises: an input unit, for inputting audience preference parameters; a program database, for storing program playing information; an audience rating database, for collecting program watching information from subscribers; an audience preference space construction unit, for calling a data source from the program database and the audience rating database according to an attributive character index input by the input unit, and obtaining attributive character index data of the subscribers for the types of programs, thereby forming a preference matrix; a first grouping unit, for grouping the subscribers multiple times based on the audience preference space; a matching unit, for performing a consensus match on clusters in a grouping set by using a consensus function, so as to construct a cluster relationship diagram; a second grouping unit, for converting the cluster relationship diagram into a cluster relationship degree matrix, which is used as a similarity matrix, and for grouping the clusters by using a spectral clustering method; and an integration unit, for setting a group as a group in which a data point is located, wherein the number of occurrences of the data point in a cluster in the group is greatest.

The invention discloses a multi-agent genetic clustering algorithm-based image segmentation method, which mainly solves the problems that the prior art is sensitive to an initial clustering center, has low convergence rate and is easily trapped in a local extremum. In the method, image clustering segmentation is converted into a global optimization problem. The method comprises the following steps of: firstly, extracting two-dimensional gray scale information of a neighborhood median and a neighborhood mean of pixel points of an image to be segmented to construct a new two-dimensional histogram; secondly, combining a multi-agent genetic algorithm (MAGA) with a fuzzy C-mean (FCM) clustering algorithm and obtaining an optimal clustering center and a membership degree matrix by using the global optimization capability of the MAGA; and finally, outputting clustering tags according to the maximum membership degree principle so as to realize image segmentation. The method has high anti-noise capability and high convergence rate, can improve the image segmentation quality and the stability of a segmentation result and can be used for extracting and identifying image targets.

The invention disclose a method for performing image segmentation by using manifold spectral clustering, which is used for solving the problems of large storage capacity and low computing efficiency and segmentation accuracy in the existing method. The method for performing the image segmentation by using the manifold spectral clustering comprises the following steps: (1) inputting an image, extracting colors and textural features of the image, and obtaining a manifold set of the input image by using a watershed algorithm; (2) computing the manifold feature set, constructing a distance matrix, and acquiring a manifold distance matrix by using the Floyd algorithm; (3) computing a similarity matrix so as to construct a degree matrix and a normalization laplacian matrix; (4) carrying out eigen-decomposition on the normalization laplacian matrix so as to construct a spectral matrix; and (5) normalizing the spectral matrix to obtain a normalization spectral matrix, acquiring the label vector of the manifold set by a K-means algorithm, and outputting a segmentation result. The method for performing the image segmentation by using the manifold spectral clustering has the advantages of small storage capacity and high computing efficiency and segmentation accuracy, and can be used for detecting focal areas of medical images, detecting defects on precision component surfaces, and processing geographic and geomorphic pictures shot by satellites.

The invention relates to a point cloud simplification processing method based on a resampling method and an affine clustering algorithm, which comprises the following steps: 1, setting a threshold value of the simplified target point number; 2, uniformly sampling an initial point cloud D to acquire a point subset SD of the initial point cloud D and searching a k nearest neighboring point of each point in the subset SD; 3, calculating the curvature CV of each point in the SD by using the k nearest neighboring point acquired in the step 2; 4, calculating the similarity between the points in theSD to acquire a similarity matrix S; 5, inputting S and CV as an AP algorithm by applying an AP clustering algorithm and calculating a representative degree matrix and an adaptive selecting degree matrix between the points; selecting representing points according to an iteration result; if the number D of the representing points is smaller than the threshold value, namely D=D-SD, turning to the step 2; and adding a representing point label selected each time into the same matrix till a target value is achieved to acquire a final point set FD. The invention simplifies the calculation, reducesthe occupied memory capacity and has favorable effectiveness.

The invention belongs to the field of data source pattern matching, and relates to a global body-based multi-data-source-pattern matching method. The method includes: converting multiple to-be-matchedpatterns into unified data models, namely pattern bodies; respectively carrying out pattern matching on each pattern body after conversion and a global body according to various related algorithms ofpattern matching, combining calculation results of the multiple matching algorithms to respectively obtain similarity relations between elements in each pattern body and the global body, and using asimilarity degree matrix for representation; and finally, using an aggregation strategy of the similarity relations to aggregate the above-mentioned obtained similarity relations according to transitivity of the similarity relations to obtain a matching result between every two of the multiple data source patterns. According to the method, the problem that one-to-one pattern matching needs to be carried out among multiple data source patterns in or among enterprises can be solved, quality and efficiency of multi-data-source-pattern matching can be significantly improved, and better scalabilityis realized.

A vehicle driving risk assessment method belongs to the technical of vehicles. The vehicle driving risk assessment method is technically characterized by comprising, S1, utilizing Internet of vehicle equipment to collect data of braking, steering, acceleration and travelled distance of vehicles to be tested; S2, establishing an assessment factor set; S3, structuring the membership functions of assessment factors; S4, providing object comments; S4, calculating the membership degree vector of every single factor to form a membership degree matrix; S6, establishing an assessment factor weight matrix; S7, performing operation of B=RoA on the membership degree matrix R and the assessment factor weight matrix A to obtain an assessment result. The vehicle driving risk assessment method can perform comprehensive assessment on driving risks as well as independent risk factors, thereby being beneficial to analyzing the influence of every dangerous driving factor on the driving risks.

The invention discloses a risk assessment system based on a fuzzy matrix analytic hierarchy process. The system comprises an assessment index system generation module, an assessment level system generation module, an index quantification module, a index weight calculating module, a membership degree matrix construction module, a fuzzy comprehensive result calculating module and a risk assessment module, wherein the risk assessment module comprises a risk situation assessment sub-module, a risk analysis sub-module and a risk control sub-module. The risk assessment system based on the fuzzy matrix analytic hierarchy process of the invention can analyze various uncertain factors and indexes appearing in the risk assessment process by using the fuzzy matrix and the analytic hierarchy process (AHP) in the risk assessment.

The invention discloses a personalized recommendation method and apparatus used for sparse big data. Behavior records generated between users and commodities can be obtained through a user historical behavior database, so that related data can be efficiently and comprehensively found, and a behavior matrix between the users and the commodities is generated; when the behavior records generated between the users and the commodities are relatively sparse, all the commodities in the behavior matrix are included in corresponding commodity clusters of a commodity cluster set through the similarity between the commodities, and the membership degrees of the users to the commodity clusters are calculated, so that the membership degrees can be used for describing the users; the membership degrees of the users to the commodity clusters can enable the characteristics of the users to be more remarkable; the similarity of the users calculated based on the membership degrees is more accurate; and the accuracy of recommendation based on similar users in collaborative filtering is improved. The commodity cluster dimension of a membership degree matrix is far smaller than the dimension of the commodities in the behavior matrix, so that the time and space resources of user similarity calculation are greatly saved and the recommendation efficiency is improved.

The invention is applicable to the technical field of privacy protection, and provides a spectral clustering method based on differential privacy protection. The method includes the steps of pre-processing sample data; calculating a similarity matrix; based on a k-near-value, simplifying the similarity matrix; adding the random noise satisfying Laplace distribution to the similarity matrix; constructing an adjacent matrix and a degree matrix based on the similarity matrix after random noise perturbation; obtaining the Laplace matrix based on adjacency matrix and degree matrix; obtaining the first m large eigenvalues and corresponding eigenvectors of Laplace matrices; normalizing the eigenvector to form eigenmatrix; using k-means clustering method to cluster the feature matrix to get the label of clustering. A spectral clustering algorithm is used to calculate the sample similarity between the sample data as the weight value between the data points, and then differential privacy algorithm is used to add random noise of Laplace distribution to the weight value to interfere with the weight value to achieve the purpose of privacy preservation. The interfered data can not only achieve privacy preservation but also ensure the effectiveness of clustering.

A spectral clustering method for automatically determining the number of clusters based on a neighboring point method comprises the steps of 1) normalizing all dimensions of a data set; 2) calculating an interval sparse distance matrix by a neighboring point method and defining the matrix as local scale parameters of distance mean values of the neighboring points to obtain a whole sparse similarity matrix; 3) determining the local density of each data point and the minimum distance to other points with a higher local density by calling a CCFD method, and obtaining the number of singular points generated by the fitting outside a confidence interval; 4) calculating a degree matrix D and a Laplacian matrix L according to a formula and extracting an eigenvector group by eigen decomposition of L; 5) outputting clustering results; and 6) selecting and outputting the clustering result with the optimal number of neighboring points corresponding to the maximum Fitness function value. According to the invention, the local scale parameter of each data point can be estimated according to data distribution, the number of clustering centers is automatically determined, and the parameter adaptation of the number of neighboring points is realized.

The invention belongs to the field of computer vision and three-dimensional reconstruction, particularly relates to a depth map and IMU-based high-dynamic scene three-dimensional reconstruction methodand system, which aims to solve the problem that the mobile equipment cannot realize the high-dynamic scene three-dimensional reconstruction. The method comprises the following steps of converting anacquired current frame depth map; carrying out image background segmentation by combining the rotation degree matrix after IMU data integration; performing the current camera attitude tracking basedon the camera attitude of the previous frame and the background segmentation result of the current frame; performing the volume data fusion according to the current camera posture and the image; and finally, performing three-dimensional rendering according to the volume data information to obtain a high-dynamic scene three-dimensional model. According to the method, the dynamic/static segmentationcan be efficiently carried out by means of the color information, the depth information and the IMU information, the dynamic voxels in the model are eliminated, and the rapid robust three-dimensionalreconstruction on a mobile device of a scene containing a dynamic object can be achieved.

The invention discloses a recognition method for vulnerable lines of a power system. The recognition method comprises the steps that 1, running state parameters of a current power system are collected, and the real power flow P<m> of one line in the power system is determined; 2, an N-1 safety verification result is determined according to the real power flow P<m> of the line, so that an influence degree matrix S of a correlation network of the current power system is solved; 3, an extensive matrix S<extend> is obtained according to the influence degree matrix S; 4, a PageRank convergency value of the corresponding line is calculated according to the extensive matrix S<extend> to determine the vulnerable degree of the line. According to the recognition method for the vulnerable lines of the power system, by collecting the running state parameters of the current power system and constructing the correlation network and the extensive matrix, the PageRank convergency values of all the lines are accurately calculated to determine the vulnerable degrees of all the lines, therefore, large-sample cascading failure simulation is prevented from being performed, and the recognition efficiency is improved on the premise that the recognition accuracy is guaranteed.

The invention discloses a power grid planning risk evaluation system and method based on a grey correlation degree TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). The power grid program risk evaluation system comprises a power grid planning data acquisition module, a power grid planning scheme input module and a controller, wherein the power grid planning data acquisition module and the power grid planning scheme input module are independently connected with the controller; and the controller comprises a power grid risk evaluation index matrix generation module, a power grid risk evaluation index matrix solving module, a grey correlation degree matrix construction module and a power grid planning scheme risk sorting module. The invention has the beneficial effects that the power grid planning risk evaluation system introduces the controller which can process power grid planning data and sort power planning schemes to carry out optimal sorting on power grid enterprise risk indexes, a power grid planning scheme can be evaluated on the whole, potential risks in a power grid planning process can be effectively prevented and solved, power grid planning operation cost is saved, and power grid reliability is improved.

The invention provides a fusion method and a device for fault diagnosis results, wherein, the method comprises the following steps of: acquiring a plurality of fault diagnosis results obtained by a plurality of diagnosis methods; wherein each diagnosis method corresponds to a fault diagnosis result; the corresponding fault diagnosis results of each diagnosis method are transformed into individualmatching degree according to the individual matching degree matrix. Individual matching degree represents the degree of trust in the fault diagnosis results given by each diagnostic method. Accordingto the transformed individual matching degree, the confidence level of multi-fusion results of multi-fault diagnosis results is determined by pre-established confidence rule database. The confidence rule base is established according to several fault fusion samples in advance, and the confidence level represents the trust degree of each fusion result. According to the confidence of the fusion results, the final fault diagnosis results are determined. The technical scheme improves the accuracy of the fault diagnosis result and ensures the efficient and safe operation of the equipment.

The invention discloses an action enhancement-based human face cheat-proof identification method and belongs to the technical field of digital image processing. Through an action enhancement technology, action information input in a CNN+LSTM network video is enhanced; in addition, for overcoming the defect of position information loss in an existing CNN+LSTM framework, an LSTM structure is added to the back of the last pooling layer, and a full connection layer is removed, so that the purpose of retaining position information is achieved, and an extracted sequence feature has a better distinguishing capability; and meanwhile, an attention mechanism is added to an improved framework, and by arranging a position confidence degree matrix, the confidence degree of a region with a remarkable position change is increased, so that LSTM more focuses on an action information concentration region.

The invention relates to a traffic state prediction method based on clustering analysis and Markov models. The method comprises the steps of selecting the traffic volume and the traffic occupancy rateas evaluation indexes of the traffic state of urban crossroads; performing clustering analysis on the traffic volume and the traffic occupancy rate by a K means clustering method according to a largenumber of traffic flow data collected by a traffic control center; determining a weight set of the traffic volume and the traffic occupancy rate by a method combining an entropy evaluation method andan analytic hierarchy method; determining a membership degree function of the traffic volume and the traffic occupancy rate by a linear analysis method, and obtaining a membership degree matrix; calculating a fuzzy comprehensive evaluation matrix; evaluating the existing traffic state according to the maximum membership degree principle; and predicating the future traffic state on the basis of aMarkov model according to the existing traffic state. The traffic state prediction method is applicable to practical cases; and the prediction precision is improved.

The invention discloses an image segmentation method based on a validity index of fuzzy clustering, which comprises the steps of 1, performing classification by using fuzzy C-mean clustering algorithm; 2, building an objective function, and judging whether a termination condition is met or not or whether the maximum number of iterations is reached or not; 3, performing initialization and updating a membership degree matrix and a clustering center; 4, calculating the compactness and the separability, and acquiring an index value; and 4, acquiring the optimal clustering number at a maximum value of the index value. The image segmentation method can perform accurate division on a pixel set and is applicable to complex and overlapped pixel sets with noise pixels, thereby being capable of performing good segmentation on an image.

A method, system, and software for predicting a brownout or blackout in a smart power grid network. A network vulnerability characterization is selected among line susceptance, modified line susceptance, power traffic, and power loss. The selected characterization is analyzed based on a calculation matrix such as a pseudo-degree matrix, pseudo-Laplacian matrix, or a pseudo-adjacency matrix. A centrality score, such as degree centrality or eigenvector centrality, is determined for at least one bus in the network based on the selected network vulnerability characterization and the corresponding calculation matrix. A series of network simulations are performed based on removal of at least one bus in the network. The network simulations are specific to the selected vulnerability characterization and corresponding calculation matrix.

The invention provides a tag-co-occurred tag clustering method. A tag matrix, a common tag matrix, a tag importance degree matrix and a similarity matrix are defined in order to improve clustering effectiveness; feature vectors of tags are determined by extracting tag co-occurrence information; the method that the distance between one object and another object is calculated by using geometrical distance in the traditional clustering algorithm is changed into the method by using Pearson correlation coefficient in the way that the similarity is calculated by extracting the feature vectors; the tag-co-occurred tag clustering method which is used for clustering the tags by being combined with the K-means clustering algorithm is provided. The clustering method provided by the invention is better than the other clustering methods in effect and has good effectiveness and feasibility.

A predication control based multi-time-lag intelligent power grid economic scheduling method relates to intelligent power grid scheduling. The method includes 1, constructing a network structure typological graph and obtaining an adjacent matrix, a degree matrix and a Laplacian matrix; 2, constructing a mathematic model for intelligent power grid economic scheduling and acquiring system parameters; 3, designing a power generation device output state predicator; 4, giving initial values of the power generation devices; 5, setting a communication mode so as to enable each power generation device to communicate with an adjacent device only, operating a distributive control algorithm and adjusting the state of each node; 6, judging whether the incremental cost state of all the nodes achieves consistence or not, if the consistence is achieved, turning to step 7, and if the consistence is not achieved, returning to step 5; 7, obtaining the optimal solution of the intelligent power grid economic scheduling problem.