227 results about "Division algorithm" patented technology

The present invention discloses an intelligent social network recommender system. The system comprises: a data obtaining module and a data processing module, wherein the data obtaining module is used for carrying on webpage data crawling and storing the data into a database, and the data processing module is used for carrying on website data crawling, establishing a social network with a user as a node and an interaction of users as an edge relationship, wherein one user edge relationship is occurred by generation of one interaction of the users in the network, otherwise no user edge relationship, and the users with the edge relationship present a neighbor relation and a establishment of a communication network of the social network is finished; and dividing communities by a community division algorithm, selecting an influential index according to the emphasis in different social networks, to analyze influential ranking on the user with similar characteristics in the single community, carrying on a classification of a user value with analysis of a user interest sticker, and plotting out a larger community to carry on analysis of influetial value on the users, thereby realizing an advantage of improving the identification accuracy of important users in the social network.

The invention relates to the field of unmanned aerial vehicle algorithm calculating, in particular to a multi-unmanned aerial vehicle traversal search algorithm based on sub-region division. According to the designed multi-unmanned aerial vehicle traversal search algorithm based on sub-region division, a specific region division mode and a multi-unmanned aerial vehicle route planning and task allocating mode are given, and a practical and feasible scheme is provided for unmanned aerial vehicle traversal searching. It is verified that the algorithm is valid through a specific algorithm generating living example, no special demand is needed for the shape of a searching region, and the algorithm is suitable for multiple irregular regions and has the universality. Compared with other division algorithms, the region division algorithm has the advantages that achievement is easy, repeated searching of unmanned aerial vehicles can be effectively avoided, the consumed time of the unmanned aerial vehicles during sub-region connection is shortened, and a larger region can be searched for under the condition of the same property and number of the unmanned aerial vehicles.

The invention belongs to the field of air traffic management, and discloses a method for dynamically dividing a sector based on airspace traffic characteristics, which aims to confirm a sector division matched with airspace traffic changes according to airspace traffic change control, so as to realize optimal utilization of airspace resources. The method comprises three aspects, namely airspace information extraction, airspace modeling and sector optimized division algorithm. First, databases such as an airspace topological structure are constructed on basis of airspace static structural information and airspace dynamic information; then, a complicated network model is used for describing an airspace structure through regarding key points as nodes and air routes as sides; third, similarity of associated nodes in the complicated network model is calculated according to airspace dynamic information, a complicated weighing network is established, and an airspace convex cell model is established by utilizing the K-Means algorithm based on the complicated weighing network; and finally, the airspace convex cell model is combined with the hybrid coding genetic algorithm to obtain the final sector division result.

The invention provides a full-coverage path planning method of a mobile robot. The full-coverage path planning method is a novel coverage algorithm by solving the problems of optimizing of coverage division and path planning in the process; the pre-acknowledging of map space environment is not required; the areas are divided in the coverage process, and the process is an incremental type coverageprocess; the coverage is dynamically divided, and the obstacles which dynamically appear can be selected and planned in real time, so as to complete double tasks of coverage and obstacle avoidance. The full-coverage path planning method is not only suitable for map environments using topology environment rules, but also suitable for the environment spaces in the complicated areas; compared with the cow tillage division algorithm, more advantages are realized.

The invention discloses a point cloud data classification method based on deep learning. The method provides a multi-scale point cloud classification network, and comprises the steps of firstly, providing a multi-scale local area division algorithm on the basis of completeness, adaptivity, overlap and multi-scale characteristic requirements of the local area division, and obtaining a multi-scale local area by taking the point cloud and the characteristics of different levels as input; and then constructing the multi-scale point cloud classification network comprising a single-scale feature extraction module, a low-level feature aggregation module, a multi-scale feature fusion module and the like. The network fully simulates the action principle of the convolutional neural network, and hasthe basic characteristics that the local receptive field becomes larger and larger and the feature abstraction degree becomes higher and higher along with the increase of the network scale and depth.The method of the invention respectively obtains the 94.71% and 91.73% classification accuracies at the standard public data set ModelNet 10 and ModelNet 40, is in a leading or equivalent level in thesimilar work, and the feasibility and effectiveness of the method are verified.

The invention provides a discharge ultraviolet imaging quantization parameter extraction method of high-voltage electric equipment. Firstly, by means of digital image grey level transformation, a threshold division algorithm, a morphological filter algorithm and a small region image elimination algorithm, a discharge region image in an ultraviolet image of the high-voltage electric equipment is segmented, then a boundary point of each region is extracted by means of a multi-region boundary tracking algorithm, and on the basis of the preceding operation, one or more geometrical parameters of a light spot are calculated and used for carrying out quantitative analysis on discharge strength of the high-voltage electric equipment. According to the method, adverse influences of gain of an ultraviolet imager and observation distances on analysis results can be eliminated, quantitative analysis can be carried out on discharge strength of a plurality of discharge points, and accuracy of analysis results is greatly improved. Besides, due to the fact that the parameter comprises position information of the discharge points, and conditions are created for accurate positioning of discharge defects.

The invention discloses a screen display method and a terminal device. The method comprises: obtaining relation information of elements on the screen, wherein the elements are objects displayed on the screen or carriers for human-computer interaction; obtaining at least one of the sight information or gesture information of a user; determining an interested region of the user by use of a region division algorithm and in combination with the relation information of elements on the basis of at least one of the sight information or the gesture information, wherein the determined interested region of the user comprises object elements and extension elements which are corresponding to at least one of the sight information or the gesture information, and the relation between the extension elements and the object elements is represented by the relation information of elements; performing optimizing operation to the determined interested region of the user and displaying the optimized interested region of the user on a screen. The screen display method and the terminal device provided by the embodiment of the invention can increase the efficiency of human-computer interaction by displaying automatically optimized interested region of a user.

The invention discloses an improved two-dimensional maximum entropy division night vision image fusion target detection algorithm which comprises the steps that a two-dimensional histogram is improved, that is the two-dimensional histogram is established according to the maximum gray scale of a gray scale-weighted area, a weight is selected, the maximum entropy is calculated by using the histogram, and infrared and low light images are divided. Compared with the traditional maximum entropy division algorithm, an effect in the aspect of target detection is obvious, and background suppression and target extraction functions are provided; and then a phase of the multi-dimensional characteristic and the validation of operation are verified, and the divided infrared and low light images are subjected to characteristic level fusion target detection. The detection algorithm has a better effect and better applicability in the aspects of target detection under a complicated background and multi-target detection.

The invention discloses a risk gang identification method and device. The method comprises the steps that a risk knowledge graph is constructed based on user historical behaviors and a lost user list,the risk knowledge graph comprises non-user nodes, lost user nodes hitting the lost user list and common user nodes not hitting the lost user list, and the non-user nodes represent attribute characteristics of a user; the risk template is acquired, Wherein the risk template comprises a plurality of template nodes and connecting edges between the template nodes, the template nodes comprise one ormore of a non-user node, a lost credit user node and a common user node, and the connecting edges are used for representing that the connected template nodes have an association relationship; nodes matched with the risk template and connecting edges between the nodes are extracted from the risk knowledge map to form a risk map; And the nodes in the risk map are divided into a plurality of groups by adopting a community division algorithm, and the nodes in each group are identified as the same risk gang.

A method for correcting the deformation of cerebral tissue in the navigation system of neurosurgical operation includes such steps as obtaining target tissue (cerebral tissue) by 3D automatic division algorithm based on MRI, generating the lattice of cerebral tissue, assigning the relative biomechanical attribute to each lattice unit, creating physical mode, tracking the movement of exposed cerebral cortex layer, finit element calculating to obtain the deformation for cerebral tissue, and updating the 3D data field before operation by an algorithm to direct the operation.

The invention discloses a quick single-disk failure recovery method for triple-erasure-correcting codes. The method includes: dividing each stripe unit of each stripe into p-1 sub-blocks during recovery of single stripes; acquiring a total number R<idea1> of the sub-blocks, used for recovering a failed disk, in each stripe; in an array of p+2 disks, initializing a feasible solution value for failure of the failed disk I into 3<p-1>; initializing a counter j=0; converting j into a recovered sequence which is ternary and which shares p-1 elements by means of the division algorithm; calculating a sum of bits with 0, 1 and 2 values in the recovered sequence of p-1 bits; comparing the three numbers pairwise; judging whether or not absolutes of differences are all smaller than or equal to 1; if yes, starting an effectiveness test function and judging whether or not test blocks, represented with the recovered sequence, are able to recover all data blocks in the failed disk i. The letter p is a prime number, with p+2 being equal to the number of disks in an RAID (redundant array of independent disks), and R<idea1>=R<low>+w%. The quick single-disk failure recovery method for triple-erasure-correcting codes has the advantages that disk recovery process can be quickened and system reliability is improved.

The invention discloses an urban hierarchical regional division method based on a vector map data model. The method comprises the steps that a corresponding road network diagram structure and polygon boundary data are established for a region needing to be divided; a corresponding diagram structure and boundary polygon are established according to the road network diagram structure and the polygon boundary data, a path is searched to divide an original polygon into two parts, and paths are continuously searched in the newly generated polygons to conduct diagram division till all the polygons cannot be divided; a city is divided into primary regions according to primary road data, all the primary regions are traversed, secondary road data under the primary regions are extracted, and secondary regions under the regions are divided out by applying a regional division algorithm, so that a hierarchical structure of urban regions is formed. According to the urban hierarchical regional division method based on the vector map data model, a diagram structure is used for storing vector map data, operations are all based on the vector data, original data of a map can be completely reserved, and the data can be processed in an undistorted mode.

The invention provides a vision-based metal surface defect detection method, which comprises: S1, collecting images by adopting a line-scan-camera-based method; S2, carrying out defect division; and S3, recognizing the target defect by using a pattern recognition algorithm. According to the present invention, a set of the complete solving scheme containing the line-scan-camera-based image collecting method, the defect division algorithm and the detect classification algorithm is designed, such that the accurate division of the bright defect and the dark defect is successfully achieved, and thegood defect classification effect is achieved.

The invention discloses a foundation automatic cloud detection method based on superpixel division. The foundation automatic cloud detection method based on the superpixel division comprises the steps of dividing an input red-green-blue (GRB) cloud picture into a plurality of superpixel blocks with a superpixel division algorithm, obtaining a characteristic picture according to a picture with various color channels, calculating a local threshold value of each superpixel block, obtaining a threshold value matrix by using bilinear interpolation algorithm, obtaining an judging result of each pixel point by comparing the threshold value matrix and the characteristic picture, and obtaining an cloud detection result of the input GRB cloud picture by using the judging result of each pixel point and correspondence of locations of the pixel points. According to the foundation automatic cloud detection method based on the superpixel division, consistency of the superpixel blocks is maintained to the maximum extend, a good cloud detection result is obtained, and the robustness and the accuracy are high.

The invention provides a data transmission method of an opportunistic network, and relates to the technical field of computer network communication. The method comprises following steps of firstly, obtaining key social features of nodes; establishing a weighted adjacency matrix of the network; secondly, selecting a suitable community division algorithm; dividing the network into communities of suitable scales; then designing optimum relay selection strategies according to community features and graph theories; and finally designing a routing algorithm; and establishing the data transmission system of the mobile opportunistic network. Compared with the traditional method for designing the data transmission strategies of the mobile opportunistic network based on history meeting information, the method has the advantages that the social features of the nodes are selected; the network is unnecessarily operated by an extra period of time; the topological structure established by the method is stable without changing with time; the communities established by the method can reflect the relationships among the nodes more truly; and the data can be transmitted more effectively by the designed routing strategies.

The invention provides an inspection robot positioning method and an automatic charging method. Through twice linear division, a charging room model is rebuilt; then, a key point of the rebuilt charging model is used for realizing the precise positioning of a robot. The continuous laser data set is firstly divided through a fast and efficient secondary point set division algorithm; three edge features with high robustness are obtained; then, the charging room model is rebuilt; key points corresponding to the rebuilt charging room model are used for positioning the robot. The positioning accuracy is ensured; meanwhile, the relying on the charging room wall corner turning points is avoided; the relying degree on a charging room building process is low.

The invention provides a checker cocooning frame silkworm cocoon screening device and method based on image processing. The device comprises a rack, a working table, a checker cocooning frame and a CMOS camera. The CMOS camera is fixed to the working table through a support, and the checker cocooning frame is located below the CMOS camera. Accordingly, automatic checker cocooning frame silkworm cocoon counting and screening are conducted by combining an image processing technology, the CMOS camera is used for extracting the collected image by means of a binaryzation and corrosion division algorithm and region characteristics, checker cocooning frame silkworm cocoon pictures are processed, and silkworm cocoons are separated from the checker cocooning frame effectively; by marking target connected domains and counting the number of the target connected domains, the number of the silkworm cocoons is obtained, and characteristic extraction is conducted by means of area descriptors of all target connected domains; by means of judgement on the connected domain area, bad cocoons such as double cocoons and extra small cocoons are distinguished, and the counting effect is accurate and rapid.

The invention belongs to the field of medical image treatment and application, and relates to a soft tissue surface deformation tracking method. The method comprises a soft tissue division algorithm and comprises the following steps: carrying out grid processing on an extracted target tissue to obtain an initial soft tissue surface point set; acquiring a deformed soft tissue surface point set by a three-dimensional laser scanning instrument or intraoperative three-dimensional imaging equipment; carrying out crude registration on a deformed soft tissue surface and an initial soft tissue surface by a rigid registration method; and finally acquiring a point-to-point mapping relation of the two point sets on the basis of a sub-block type energy function minimum non-rigid registration algorithm. The method is easy to implement and reliable in precision, and can be integrated in an existing navigation system, intraoperative soft tissue deformation correction is implemented, so that the precision of the navigation system is improved greatly, and clinical application is facilitated.

The embodiment of the invention discloses an image similarity comparison method. Through two elements of the fusion division image mass center Euclidean distance and the division image area, the automatic comparison on pictures of ultrasound image sequence groups can be realized; the pictures with poorer quality can be selected and removed. The embodiment of the invention also discloses an image similarity comparison device and a computer readable storage medium. By using the invention, the direct embedment into the ultrasound image division algorithm can be realized; the division image robustness is improved; in addition, the labor resources can be greatly saved.

The present invention relates specifically to a modified digital signature algorithm together with a polynomial-based hash function, in which the last step of the calculation of the final hash value, the exponentiation, is omitted. Such a modification eliminates some of the potential attacks to which a basic hash function algorithm is susceptible. It further introduces several flexibilities to a digital signature scheme. For example, hashing and MAC-ing procedures omit an exponentiations step, whereby the security of data is increased as the possibility of successful attack is diminished. Furthermore, the present invention may be implemented either by way of hardware or software. It may also be capable of generating a digital signature for any set of parameters extracted from a message. Generation of a digital signature may occur without the step of a hashing or MAC-ing procedure.