309 results about "Dynamic clustering" patented technology

A distributed system provides for separate management of dynamic cluster membership and distributed data. Nodes of the distributed system may include a state manager and a topology manager. A state manager handles data access from the cluster. A topology manager handles changes to the dynamic cluster topology. The topology manager enables operation of the state manager by handling topology changes, such as new nodes to join the cluster and node members to exit the cluster. A topology manager may follow a static topology description when handling cluster topology changes. Data replication and recovery functions may be implemented, for example to provide high availability.

Methods, systems, and media are disclosed for clustering. One embodiment includes receiving, by a configuration manager server, a request to create a dynamic cluster member for a dynamic cluster, wherein the request is for work load balancing and the dynamic cluster member is a process. Further, the embodiment includes parameterizing, by the configuration manager server, to create parameters for a dynamic cluster server template based on configuration files of nodes within a nodegroup. The configuration files and the dynamic cluster server template are stored in a central location associated with the configuration manager server. The embodiment also includes populating, by the configuration manager server, the parameters of the dynamic cluster server template with values associated with the nodes. Finally, the embodiment includes instantiating, by the configuration manager server, the process, associated with at least one of the nodes, from the dynamic cluster server template, thereby, creating the dynamic cluster member.

The invention discloses a geographic and geomorphic characteristic construction method based on laser radar and image data fusion and belongs to the automatic control field. The method specifically comprises 1) obtaining 3D laser point clouds and panoramic pictures of the surrounding environment of a ground unmanned mobile platform at present; 2) matching the 3D laser point clouds and the panoramic pictures and obtaining matched images; 3) dividing the 3D laser point clouds based on different distribution characteristics corresponding to each laser point and carrying out clustering based on a dynamic clustering algorithm of each distribution characteristic to obtain a plurality of region classes; 4) finding passable region classes in the plurality of region classes based on travel ability of the ground unmanned mobile platform; 5)obtaining landform identification vectors of the passable region classes by utilizing a denseness SIFT algorithm; and 6) carrying out landform classification on the passable region classes based on the landform identification vectors and by utilizing a classifier. The method is suitable for passable geographic and geomorphic characteristic construction of the ground unmanned mobile platform.

A cluster application deployment model provides an efficient computer implemented technique for the deployment of applications on a shared file system, and includes an automated mechanism for mapping the shared application image into the local file system of a new node of a cluster.

The invention discloses a wind power plant parameter identification and dynamic equivalence method based on operation data, comprising the following steps of a) performing recognition on wind generation set control model parameters based on testing data, b) combining with operation data and choosing characteristic variables reflecting the wind generation set and the wind power plant under influence and utilizing the improved fuzzy K average value dynamic clustering algorithm to perform cluster division, c) performing network simplification and parameter optimizing to obtain a wind power plant dynamic equivalence model based on global optimal position mutation particle swarm algorithm, and d) under disturbance input, comparing the wind power dynamic equivalence model with the detailed model dynamic respond to verify the validity of the equivalence model. The wind field dynamic equivalence model constructed by the invention can accurately reflect the dynamic characteristics of grid-connection points of the wind plant and have important construction application values, and can be used in the analysis of the stability of the double-fed wind power plant access power system and provide theory support for the programming and operation scheduling of the wind power plant power system.

A distributed system provides for separate management of dynamic cluster membership and distributed data. Nodes of the distributed system may include a state manager and a topology manager. A state manager handles data access from the cluster. A topology manager handles changes to the dynamic cluster topology. The topology manager enables operation of the state manager by handling topology changes, such as new nodes to join the cluster and node members to exit the cluster. A topology manager may follow a static topology description when handling cluster topology changes. Data replication and recovery functions may be implemented, for example to provide high availability.

The invention discloses a dynamic clustering mechanism-based target tracking method for a wireless sensor network. In the method, an initial cluster head is formed by a method for broadcasting the END values of nodes in a region; the residual energy of the nodes and distances from the nodes to a target are considered at the same time; in the process of selecting the nodes which participate in target tracking, the condition of the residual energy of the nodes is considered, and only the nodes that the received signal strength indicator (RSSI) signal strength of a received target is more than a certain threshold value are selected to participate in the tracking; the concept of a temporary cluster head is introduced into dynamic clustering adjustment, so that the target is not lost in the process of establishing a next cluster; and the target is tracked by a least square method so as to perform curve fitting on the track of the target. Based on a cluster head selecting mechanism with RSSI strength, factors such as a clustering algorithm, the residual energy of the nodes, the RSSI signal strength of the target and the like are considered, so that tracking precision is ensured, energy consumption is reduced, and the service life of the network is prolonged.

The invention relates to a collaboration communication method for multiple cell base station dynamically clustering, which belongs to the field of wireless information transmission, such as wireless local area network, wideband radio access, mobile communication or the like. The method divides all base stations into base station clusters including few base stations, and executing collaboration communication between base stations in the same cluster. Meanwhile, customer clusters capable of collaboration communication with the base station in the base station cluster according to strength of the pilot signal to ensure accurate communication between the collaboration base station and the customer. Collaboration is effectively realized and cell system capacity is enhanced by adopting the provided method.

The invention discloses a short-term traffic flow weighted combination prediction method, which comprises the following steps of: (1) organizing historical traffic flow data by utilizing a dynamic clustering algorithm; (2) performing short-term traffic flow prediction by using an improved nearest neighbor nonparametric regression method; (3) performing the short-term traffic flow prediction by taking a cluster which is the most similar to a current point in a historical database as a training sample of a fuzzy neural network and using a fuzzy neural network model; and (4) determining the weight of a combined prediction method according to a prediction error of the improved nearest neighbor nonparametric regression method and the fuzzy neural network model in the last time bucket, and outputting a final prediction result in a weighted combination way. A traffic flow in the last time bucket and a traffic flow of related turning at an upstream road junction are taken into account, the training sample of the fuzzy neural network is optimized, and the final prediction result is output in the weighted combination way, so that short-term traffic flow prediction accuracy and real-time performance are improved.

The invention discloses a method for measuring the influence of users on the topic hierarchy for MicroBlog. The method comprises the steps of 1, calculating the similarity of MicroBlog texts; 2, obtaining a topic set according to the dynamic clustering algorithm correlation; 3, measuring the influence of the users on the topic hierarchy according to standards: calculating the information propagation influence of the users, calculating the MicroBlog value influence of the users and calculating the activeness influence of the users; 4, establishing a user influence measurement model. According to the method, the effects of the three factors of information propagation, user activeness and user MicroBlog value on the influence of the users are comprehensively considered, the user influence measurement model is established, the influence of the users on the topic hierarchy can be measured more accurately through the model, then the users playing a key role in the topic propagation can be found, and public opinion monitoring and tracing in the MicroBlog network have the guaranteeing and monitoring effects.

A new vehicle target identification feature extraction based on a new profile comprises an overall shape information, local statistical information and direction information compound identification feature. It puts forward a composite dual valve value identification method with quick algorithm. It uses C-Mean dynamic clustering and ternary tree principle to form an optimized matching sequence to greatly improve the identification efficiency and speed. It can be widely used in remote image identification, remote monitoring, intelligent traffic management, military scout, and so on.

A cluster application deployment model provides an efficient computer implemented technique for the deployment of applications on a shared file system, and includes an automated mechanism for mapping the shared application image into the local file system of a new node of a cluster.

The invention discloses a search engine with automatic classification function, it includes: the automatic collection and the pretreatment module, which are used to automatically collect the documents from the network, and carry out the pretreatment, and transfer the documents for the computer to read and real-time renews documents; the index module, which is used on the participle after the automatic collection and the pretreatment module processing formatted documents, after and separately gives the participle the word and the documents entrusts with word marking and documents marking, according to word marking and the documents marking, establish index access; the automatic classified module, which can be used in to train and classify automatically the formatted documents that had to deal with; the searches display module, which searches suitable document based keyword appearance in the server, and setups in the client. The invention can classify and search effectually and intellectualized document by Chinese and English.

The invention discloses a two-stage isomerism clustering underwater wireless sensor network and a routing method of the underwater wireless sensor network. The two-stage isomerism clustering underwater wireless sensor network comprises upper-layer sensor nodes, lower-layer sensor nodes, an offshore sink node and a base station on land. All the sensor nodes in a monitoring region of the underwater wireless sensor network form a two-stage isomerism clustering structure. The lower-layer sensor nodes form first-stage clusters and first-stage cluster head nodes. The upper-layer sensor nodes form second-stage clusters and a dynamic cluster head chain. Second-stage cluster head nodes transmit data to the offshore sink node through the dynamic cluster head chain. The offshore sink node is in direct wireless communication with the base station on land, and then the base station transmits the data to a remote control center. Through the method, average energy consumption of the nodes can be effectively reduced, the survival time of the network can be prolonged, and the underwater wireless sensor network is suitable for monitoring an underwater application environment with numerous nodes, high in expandability, more balanced in node energy consumption and suitable for large-scale application.

The invention discloses a dynamic clustering wireless sensor network cipher key management method. The dynamic clustering wireless sensor network cipher key management method comprises steps that allnodes of a network are divided into three layers such as a base station, a cluster head node, and a common node according to node functions, and then a system network structure model is established; the parameters of the system network structure model are initialized to generate a cipher key, and at last, communication between the base station and the cluster head node, and the communication between the common node and the cluster head node are realized, and when the nodes are captured or energy is exhausted during the operation process of the network, the cipher key in the network is updated.The method provided by the invention is advantageous in that high safety performance is provided, and the common attacks of the wireless sensor network such as node forgery attacks, message replay attacks, and denial of service attacks are resisted, and at the same time, on aspects of network connectivity, storage overhead, and network power consumption, compared to conventional schemes, a largeadvantage is provided, and the method can be used in a large-scale layer cluster type wireless sensor network.

The present inventionprovides a multi-objective programming radio sensing network routing arithmetic based on the dynamic cluster, essentially comprising the following steps: 1) inputting the basic information of the sensor network, calling the first energy-consumption pattern and accounting the energy consumed by the whole network for once data transmission, the best cluster-head data kopt; 2) calling the dynamic cluster pattern, setting the variables and class number of the dynamic cluster pattern, the variable of the cluster is the coordinate of the node, and the class number is the best cluster-head data kopt, defining the indicator function of the comprehensive evaluation: C(P)=w1f(P)-w2W(P)+w3D(P), according to the indicator function, accounting the costs of the routes from the source node to the destination node, after that, selecting the link with the smallest cost as the route from the source node S to the destination node D. The present invention provides a multi-objective programming radio sensing network routing arithmetic based on the dynamic cluster which has quick accounting speed and comparatively small energy consumption and reduces the complexity, effectively satisfying the requirements to the network OoS service.

A cluster application deployment model provides an efficient computer implemented technique for the deployment of applications on a shared file system, and includes an automated mechanism for mapping the shared application image into the local file system of a new node of a cluster.

The invention discloses a large-scale concurrent access oriented distributed service cluster scheme. Through dynamic cluster establishment and addition, service can be dynamically established, extended or deleted, the extendibility and availability of the service are improved, the service can be deployed and used in different regions and environments according to demands, and the localized service capability is improved. According to the scheme, two layers of service cluster models are designed, each service cluster is independently managed, when load balance is obtained in a server node layer, a service layer is managed in an extension mode; and the load balance in the service layer is realized. According to a system provided by the invention, each piece of service is identified by a service access address URL, can be uniformly accessed by all service using clients, is independent of the service establishers corresponding to the clients and is beneficial for uniform management and distribution of a server.

The embodiment of the invention discloses a compressive sensing based dynamic clustering wireless sensor network data collecting method. The compressive sensing based dynamic clustering wireless sensor network data collecting method comprises the steps of obtaining one or more event source positions in a wireless sensor network, determining a sensor node nearest to each event source position as a cluster head and clustering the sensor nodes in the wireless sensor network with each cluster head as center; collecting data in each cluster, wherein the data sensed by the sensor nodes in each corresponding cluster are collected by means of a compressive sensing method. The embodiment of the invention discloses further provides a compressive sensing based dynamic clustering wireless sensor network data collecting device. The compressive sensing based dynamic clustering wireless sensor network data collecting device and method can use an event source as the center to perform clustering, utilize the compressive sensing technology to collect the data in the clusters, perform dynamic clustering on the wireless sensor network according to position change of the event source and accordingly enable the data collected in the nodes in the clusters to be high in dependency, transmission times can be fewer at the same accuracy, and accordingly the service life of the network is prolonged.

The invention discloses a real-time message exchange platform and a distributed cluster establishment method. The platform includes an application server cluster and a data server cluster. The application server cluster and the data server cluster are in communication connection through a gigabit local-area network. The platform has the advantages that any quantity of loads (common / gateway) and load balances are supported so that expandability of a system is greatly strengthened; on-line-user supporting capacity of the system is greatly strengthened through service resources provided by a large quantity of nodes; system operation and maintenance cost is reduced by not limiting start sequence of the nodes; through dynamic cluster entrance and exit of the nodes and automatic reconstruction of a system cluster network, a system dynamic expansion capability is increased, system stop time is reduced and system operation and maintenance cost is reduced; and through distributed storage of data, supporting capability for large-capacity data and system-fault resistance capability of the system are improved.

The invention relates to a dynamic clustering reduced hidden feedback overhead method in the technical field of wireless communication, which comprises the steps of: 1, dividing a service cell in which a user is located; 2, selecting a multipoint cooperation cell; 3, after detecting pilot frequency information by a user, expressing channel state information by using a signal-to-noise ratio; 4, rearranging physical resource blocks by the user according to the channel condition; 5, judging the clustering of the rearranged physical resource blocks; 6, with a cluster as a feedback unit, feeding back the channel information to a base station; and 7, scheduling the resources by a service base station according to the fed back channel information. The user feedback overhead can be reduced through double-codebook pre-coding and dynamic clustering under the premise of not influencing the system capacity, thus the invention is widely suitable for a complicated network system and occasions with large user service traffic.

The invention discloses a method of target tracking and energy consumption optimization of a dynamic cluster mechanism of a wireless sensor network. Creation of initial clusters is simple and effective, whether relevant nodes are added into a cluster head is decided by relevant nodes according to residual energy and target signal intensity, dynamic adjustment is conducted on a cluster structure according to movement of a target, and the relevant nodes are awakened in real time to continuously track the target. While tracking precision is ensured, energy consumption is reduced and the service life of the network is prolonged. In addition, dynamic cluster member nodes carry out tracking calculation on the position of the target through a method of maximum likelihood, precision is ensured, and detection radiuses of the nodes of a sensor can reduce the energy consumption of the whole network in a classification mode. The algorithm can effectively track the target, due to the fact that algorithms in which the dynamic cluster mechanism and the detection radiuses of the nodes of the sensor are classified are adopted, nodes which participate in the track every single moment are the nodes with the residual energy and the position optimized, and therefore, tracking precision of the target is ensured, energy consumption can be reduced greatly, and the service life of the network can be prolonged remarkably.

A method for servicing requests in a dynamic cluster that includes receiving, by a first replica of the dynamic cluster, a first request that includes a first membership label from a client, wherein the client stores the first membership label and a first cluster list, determining whether the first membership label is obsolete, and sending a second membership label and a second cluster list to the client when the first membership label is obsolete, wherein the client updates the first cluster list with the second cluster list.

The invention discloses an allocation method for wireless resources of an ultra dense network based on dynamic clustering. The allocation method comprises a base station dynamic clustering process and a resource block allocating process, and is characterized in that in the base station dynamic clustering process, dynamic clustering is performed on base stations randomly distributed in the network, a lot of base stations in the network are clustered according to an improved K-mean clustering method, and an effective allocation space is provided for inter-cluster resource block allocation of different modes of users; and in the resource block allocating process, joint processing is performed on single base station resource allocation of center users and inter-cluster CoMP resource allocation of edge users according to a clustering result in the step one, resource blocks with an excellent channel state of the base stations are allocated preferentially in clusters where the users are located according a provided proportional fairness based resource block allocation method, the received interference is reduced at the same time, the proportional fairness among the different modes of users is ensured, and an optimal resource block allocation result is acquired. The method disclosed by the invention can effectively improve the sum rate of the system users and achieves an ultimate objective of overall network resource optimization.

An apparatus, program product, and method update the cluster infrastructure version used by a group resident in a clustered computer system without requiring a shut down of the group during the update. The cluster infrastructure software in individual nodes in the clustered computer system is updated while the group is maintained in an active state. After the cluster infrastructure software is updated, the group is then notified of the update. In response to the notification, the cluster infrastructure version used by the group is dynamically updated to that of the updated cluster infrastructure software, thus making additional functions supported by the new version of the cluster infrastructure software available for use by all group members.

A method and apparatus is provided for hand-off of a mobile User Equipment (UE) across a femto cellular network. The method includes dynamically clustering a plurality of neighboring femto cells in a vicinity of a first femto cell to which a mobile UE is currently attached. The dynamic cluster of neighboring femto cells is moved across the femto cellular network in accordance with movement of the mobile UE across the femto cellular network.

The invention relates to a wireless communication networking method of an electric car charging pile cluster. According to the method, the core is that a geographical-location-information-based self-networking route control method of a wireless sensor network is provided. Node / charging pile position information is obtained by a non-ranging positioning algorithm. On the basis of a self-adaption dynamic cluster idea, a hierarchical network is constructed. In terms of the network environment of the charging occasion, a novel threshold defining method is provided by considering a partial node density and a distance factor between the node and the sink base station, thereby improving the network load balancing degree. Moreover, the hotspot problem is also relieved to a certain extent. In order to ensure connection of a larger-scale network, an auxiliary cluster head node is introduced; and the cluster nod and the sink node carry out route path selection according to a trust value. Furthermore, after limited circulation, a partial optimized route with the section upper limit performance index constraint is determined based on the route optimization model. Therefore, the provided method has the great economic benefit foreground relative to the wired framework; and the networking capability and the data processing capability of the management system can be effectively improved.

This document discloses a software method, data structure, apparatus, and article of manufacture that dynamically and symmetrically clusters multidimensional data onto linear storage mediums. The disclosed embodiments provide methods to store, maintain, and retrieve multidimensional space based on dimensions and possibly dimension groups of two or more dimensions. These methods dynamically access, add, update, or remove dimension key values from any dimension or any group of dimensions while simultaneously enforcing the symmetry from all combinations of all dimensions and dimension groups. Further, this document discloses data structures herein that map the multidimensional data to one-dimensional storage mediums.

The invention discloses a method for clustering low-voltage distribution network transformer districts based on fuzzy clustering. The method comprises the steps that characteristic indexes of the low-voltage distribution network transformer districts are established; characteristic index data to be analyzed are input, and then an original data matrix is established; standard processing is conducted on the original data matrix, so that a fuzzy matrix is obtained, and a fuzzy similar matrix of the fuzzy matrix is established according to the Euclidean distance algorithm; a fuzzy equivalent matrix is established, the fuzzy equivalent matrix is converted into a Lambda-cut matrix equivalent to the fuzzy equivalent matrix, a dynamic clustering diagram is formed, clustering analysis of the low-voltage distribution network transformer districts to be analyzed is achieved, and after the number of categories is determined, a clustering result of the low-voltage distribution network transformer districts is output according to analysis demand; according to the clustering result of the low-voltage distribution network transformer districts, data characteristics of the transformer districts of each category are analyzed, whether the transformer districts of each category are in urgent need for treatment is judged, the transformer districts in urgent need for treatment are screened out, and a follow-up treatment scheme is provided preliminarily. The method for clustering the low-voltage distribution network transformer districts based on fuzzy clustering has the advantages that the recognition speed is high, the classification accuracy is high, and classification effectiveness is high.