49 results about "Small worlds" patented technology

A small world infrastructure network of communication units, a plurality of which are stigmergic-capable nodes that are capable of generating and supporting virtual ants. A virtual ant can carry information relating to a node, such as its identification and sequence information, and can be sent by the node to one or more neighboring nodes to establish, recursively, hierarchical forwarding paths. A virtual ant interacts with a node through alteration of the table entries of the destination node receiving the virtual ant.

An efficient person search method utilizing human communications characteristics such as close social network, user-dependent hot list, and high skewness in access popularity. This method makes use of hot lists in the routing process and clusters people of the same community together in the search graph, thereof creating a “small-world” effect to minimize the hop counts routing would have to go through in a Peer-to-Peer environment.

The invention discloses a method for establishing a wireless sensor network with small world and scale-free properties for mainly solving the problems of low energy efficiency and poor survivability of the sensor network. A realizing process comprises the following steps of: deploying the sensor network randomly, and adding super nodes according to a set ratio, wherein all the nodes broadcast andacquire neighbor information; generating small-scale initial topology randomly and selecting to add new nodes; calculating a connection probability between the new nodes and the nodes in the topologyin a distributed way according to the different node types by taking various attributes of the wireless sensor nodes into comprehensive consideration; and connecting the new nodes with the current topology randomly according to the probability, and repeating the process until all the nodes are added into the topology. The method has the advantages of high efficiency, strong practicability and survivability. The wireless sensor network topology generated by the method shows the small world and scale-free properties simultaneously, has the advantages of improving the network communication quality and the network service life, and is suitable for the construction of the sensor network having higher requirement for the energy efficiency and the survivability.

The method of the present invention provides an automatic and optimised selection of the network topology for distributing scheduling of jobs on the computers of the modified network topology. The automatic and optimised selection of the network topology starts from the current topology and a desired number of additional connections. In this way the method of the present invention provides a higher convergence speed for the modified consensus algorithm in comparison, e.g., to a simple ring network. The method exploits the so called small-world networks. Small-world networks are more robust to perturbations than other network architectures. The preferred embodiment provides a workload scheduling system which is highly scalable to accommodate increasing workloads within a heterogeneous distributed computing environment. A modified average consensus algorithm is used to distribute network traffic and jobs amongst a plurality of computers.

The invention relates to a network structure of a seepage Internet of things and a data transmission method, in particular to a stub network structure of the Internet of things without a server or a gateway, and a high-efficiency seepage route selection method based on the Small World Six Degrees of Separation, belonging to the technical field of networking of Internet of things and data transmission. The seepage data transmission method is divided into two-way data transmission and one-way data transmission based on different network links, wherein the two-way data transmission process adopts a probe route selection mode, and the data transmission process adopts a seepage process, i.e., data is encoded and split at a source node, then the data is forwarded through a plurality of routes, and a destination node assembles and decodes the data forwarded by each route to recover source data. The stub network of Internet of things and the seepage data transmission method thereof have low networking and maintenance cost and high reliability of network data transmission, dispense with feedback retransmission, reduce the expenses of network bandwidth, reduce the complexity in realizing a network protocol, ensure the security of network data, enhance the expansibility of the network, and have low requirements for the storage capacity and processing capacity of nodes.

The invention relates to a small world power grid cascading failure restraining method. The method comprises the steps that according to a network state before failure and specific information of a primary failure, a corresponding cascading failure restraining method is formulated, a connecting structure of a failure potential infection area is changed through the facts that a small amount of nodes with low transmission loads are cut off and short-distance accesses between a high cluster failure transmission subnet and power-load node pairs are reduced, and therefore failures are prevented from accumulating.

The invention discloses a distributed-power-source-containing power distribution network reconfiguration method and system. The method includes the steps that a power distribution network reconfiguration model with a distributed power source is built, and a population is initialized; a small-world initial network is built, and an adjacent matrix is obtained; the positions of particles in the initial population are updated with the intelligent optimizing algorithm, a new particle population is obtained, and the fitness of all the particles in the new particle population is calculated; the particles in the particle population are subjected to crossover operation through the adjacent matrix, and excellent particles are diffused to the whole population; 50% of poor particles are eliminated through competition operators; when the maximum preset iteration time number is achieved, the power distribution network reconfiguration result with the distributed power source is output. The whole performance and the convergence capacity of the algorithm are improved, and the defect that an algorithm gets into local optimum in the searching process can be overcome accordingly.

The invention discloses a method and a system for optimally dispatching cascade reservoirs on the basis of quantum-behaved particle swarm algorithms. The method includes acquiring initialized population according to established objective functions for solving problems for optimally dispatching the cascade reservoirs and utilizing the initialized population as parent-generation particles; constructing small-world networks to obtain adjacent matrixes; updating the parent-generation particles according to the adjacent matrixes and generating child-generation particles; computing the fitness of the child-generation particles according to fitness functions; comparing the fitness of the parent-generation particles to the fitness of the child-generation particles by the aid of competition operators, selecting the child-generation particles with the good fitness and utilizing the selected child-generation particles as parent-generation particles for next iteration; judging whether current iteration numbers are larger than the maximum thresholds or not; carrying out computation if the current iteration numbers are larger than the maximum thresholds and outputting cascade reservoir optimal dispatching computation results. The method and the system have the advantages that the quantum-behaved particle swarm algorithms are improved by small-world network models, so that the population diversity can be kept by improved algorithms, the shortcoming of easiness in trapping in local optimization of basic quantum-behaved particle swarm algorithms can be overcome, and effects of optimally dispatching the cascade reservoirs can be improved.

The invention relates to a clustering and multi-hop communication method of network nodes of a wireless sensor, in particular to a clustering and multi-hop communication method of a wireless sensor network based on a small-world model. The algorithm utilizes the small-world model to cluster the sensor network, which improves the clustering coefficient at the network side and enables that the sensor network has favorable performances of small-world network and large clustering coefficient; nodes in each cluster and average hop numbers are restricted so that the load of each cluster can be balanced; and the node with largest residual energy can take turns a cluster head so that the energy of all nodes can be oppositely balanced. The invention can balance the energy consumption of the networknodes in the whole situation, avoids the situations that the network is partitioned into a plurality of complementary connections as a part of nodes are premature failure, prolongs the life cycle ofthe whole network and improves the robustness of the network simultaneously.

The invention discloses a method for measuring class cohesion orientated to an object software system. According to the method, a small world model of a complicated network and the structural entropy theory are combined into the method, namely the larger the colony coefficient is, the smaller the average distance is, the more even the degree distribution is and the larger the cohesion is. Meanwhile, in terms of subgraphs which are not communicated, the product of the proportions of the numbers of nodes of the subgraphs in the overall number of nodes of the subgraphs is used for calculating the cohesion of the whole network. By means of the method, the cohesion of the network between attributes, the cohesion of the network between methods and the cohesions of the network between the methods and the attributes in the class oriented to the object software system are analyzed respectively. Finally, according to the condition of the software system, the weights of the three relations are set so as to calculate the cohesion of the class. Compared with the prior art, the method has the better measuring effect and the practical application value.

The invention discloses an approximate nearest neighbor search method based on a hierarchical navigable small world map. The method comprises the following steps: step 1, aB-HNSW index generation method comprises the steps that a node set P is constructed and traversed, each node is inserted into the corresponding layer of the B-HNSW index, and the B-HNSW index is stored as a binary file; Step 2,B-HNSW index approximate nearest neighbor search method comprises the steps that a target node is input, one nearest neighbor node with the target node is sequentially searched from the maximum layerto the first layer of the B-HNSW index, the nearest neighbor node obtained from the first layer is taken as an entrance node, and k nearest neighbor nodes of the target node are searched and obtainedat the 0-th layer by using a greedy algorithm. According to the method, the mode of inserting new nodes and the layered layout are modified, a more balanced index structure is achieved, and the time for querying the approximate nearest neighbor nodes is faster and more stable.

The invention discloses a geographical position-based large-scale wireless distribution type network self-adaptive cooperative routing method, mainly aiming at solving the problems that the large-scale wireless distribution type network routing method is large in end-to-end time delay, multiple in route hop count, low in route success probability and hard to realize the practical application of small-world long-range connection. The method comprises the realization process: a node builds a neighbor node table, a friend node table and a friend route table according to a geographical position information and small-world theory; when a business is achieved, the node finds out a node which is nearest to a target node from the neighbor node and the friend node to be taken as a next hop node, wherein a data packet is directly transmitted if the found next hop node is the neighbor node, and the data packet is cooperatively transmitted to the friend node if the found next hop node is the friend node, and the process is repeated till that the target node is achieved. The performance of the large-scale wireless distribution type network is improved, and the method is applicable to the rousting process of the large-scale wireless distribution type network.

The embodiment of the invention provides a predication method and a device based on an echo state network (ESN), which relates to the communication field. Through generating a plurality of small-world and non-scale dynamic pool groups, the type and the topology structure of an original ESN dynamic pool structure are changed, and better predication effects can be generated for a nonlinear chaotic time system. The method comprises steps: a second dynamic pool is built, wherein the second dynamic pool is composed of N small-world dynamic pools and Y non-scale dynamic pools according to a complex network theory, and N and Y are larger than 0; a first dynamic pool replaces the second dynamic pool, and a lateral boundary suppression mechanism is used for associating the N small-world dynamic pools in the second dynamic pool with the N non-scale dynamic pools; a training set with a specified length is inputted to update the second dynamic pool, and a connection matrix Wout after updating is further obtained; and according to the Wout after updating, the following formula is used for predication: y(n+1)=f<out>(Wout(u(n+1), x(n+1), y(n))).

The invention discloses a small world property-based engineering information organization method, which comprises the following steps of: first performing characteristic data description on an engineering information file to form a characteristic vector; then establishing a network topology structure with small world properties; and finally storing, retrieving and maintaining the newly added engineering information file based on the network topology structure. By the method, effective support is provided for the long-term storage, information extension and post maintenance of information, fine grit comparison is performed in the calculation of similarity among different engineering information nodes, and the information query precision and accuracy are improved.

The invention discloses a virtual reality using interface system. The interface system presents the appearance of a three-dimensional small world, wherein the three-dimensional small world comprises a user virtual image, a plurality of triggerable objects and a plurality of non-triggerable objects; each triggerable object is associated with executable three-dimensional software and enters the associated three-dimensional software after being triggered; and the non-triggerable objects are pure appearance scenery objects. Compared with the prior art, the virtual reality using interface system disclosed in the invention is capable of enabling all the commands required by the virtual world experience to be completed in a virtual world, and even the persons that do not understand the computers can operate, so that the virtual world immersion degree is deepened and the tedious steps of the virtual reality using are simplified; and through specific appearance user self-designed functions, the small world can become a unique product of the users, so that the likeability of the users is strengthened.

A small world infrastructure (SWI) of a general packet communications network and a method of determining, establishing and maintaining a hierarchical forwarding path (HFP) interconnecting communications units (CUs) of the small world infrastructure. The SWI includes a domain that has a given communication unit CU as a message packet source, a plurality of associated communications units each in direct contact with the given CU, and a plurality of HFPs each providing the direct contact between the given CU and one of the associated CUs, respectively. The method includes providing these communications units in which there are HFPs between first and second CUs and between the second and the third CUs, and a third HFP is constructed between the first and the third CUs.

The invention discloses a myoelectricity classification method based on an improved small-world echo state network. Firstly, a small-world network is used for improving a reserve pool structure of theESN, then an edge adding probability is used for improving the small-world network, and the network is called as an improved small-world echo state network, so that the adaptability of a reserve poolis improved, and the generalization ability and stability of the ESN are improved. Then, the output weight of the network can be obtained by training the network, and the output weight is used as a corresponding feature. Electromyographic signals of six actions of falling down, walking, sitting, squatting, going upstairs and going downstairs are collected, corresponding features are extracted through ISWLESN, and then feature dimensions are reduced through PCV. And finally, the performance of the network features is represented by using the scatter diagram, the class separability index and the DBI. Results show that ISWLESN has good clustering performance, and has high precision when used for support vector machine classification.

The invention discloses a method for determining a threshold value of a dynamic brain function network, and belongs to the field of signal processing. The invention combines the characteristics of thesliding time window technology, the correlation coefficient matrix constructed in each time window is sparsely binarized with different thresholds, the rationality of the threshold can be judged by the small-world and integrity of the brain function network, so the blindness in constructing the dynamic brain function network can be reduced through the determined threshold by the correlation analysis method, and then a reasonable brain function network can be constructed. At the same time, it can not only avoid the randomness in the process of threshold selection, but also facilitate the studyof the subsequent dynamic brain function network in a reasonable situation.

The invention discloses a simulation network generation method applicable to estimation of a network node classification method. The method comprises the following steps: firstly, generating a primary simulation network structure similar with a real network according to appointed topological structure parameters including the quantity of nodes, the quantity of edges, the maximum degree, distribution of the average degree, a small world, a community structure and the like; and secondly, generating a label of each node in the network according to appointed label characteristics, so as to obtain a simulation network with labels, which is applicable to estimation of a network node classifier. According to the simulation network generation method disclosed by the invention, a topological structure similar with the real network can be generated, and label information of the nodes is considered, so that the generated simulation network is relatively good for comprehensively estimating the node classification method; and in a network generation process, the topological structure and a label generation process are separated, so that the independence of parameter influences can be effectively guaranteed, and the dependence degrees to different attributes of the classification method can be more accurately estimated.

The invention relates to a simulation method for social information dissemination and prediction based on a DWM. The method comprises the following steps: (1) enabling a user to set model parameters of simulation and specify the number of nodes of the simulation, the association degree of the nodes and other parameters; (2) creating a topological structure and a confidence matrix for a model according to the parameters set by the user and the DWM; (3) setting and simulating the driving force of social media; (4) simulating the dissemination of information in the society frame by frame according to a confidence rule; and (5) calculating results and change rules of the simulation. The simulation method disclosed by the invention has the beneficial effects as follows: a small world model can be developed, a method for modelling an information dissemination mechanism by establishing a two-world model is firstly proposed by using the influence of geographical factors on social information dissemination, the model also considers to modify general unweighted graphs of the social structure and the geographical factors as weighted graphs, and the confidence rule is designed based on personality characteristics of human beings such as group psychology and viewpoint inertia.

Automatic publishing and retrieval in a networked community of computers based on a Small World Adaptive Network (SWAN) type of network. Each computer installs a program which automatically scans data items in its computer's environment to find unique IDs in accordance with UID templates. For each data item found containing a UID, a data record is created comprising the UID associated with the network address of the data item. If permitted by a ruleset, that data record is published to the network by creating a node having the (hashed) UID as its identity or by adding the network address of that data item to common ID lists of existing nodes having that same UID identity. A search request in respect of a UID will find a node and obtain all the network addresses in its list, enabling the requester to send retrieval requests for each corresponding data item.

The invention discloses an image retrieval method, a device, a computing device and a medium based on a small world network. The method includes creating an image base; extracting the local features of each image in the image library; learning the local features of each image in the image library to obtain a learning dictionary; according to the learning dictionary, the eigenvector of each image in the image library is generated, and the small world network of the eigenvector is constructed; extracting local features of the target image; matching the local features of the target image with thefeatures of the image in the small world network; according to the matching result, the images that satisfy the similarity threshold condition in the image library are selected and/or the images in the image library are sorted from high to low according to the similarity measure. A learn dictionary is obtained by machine learn, and a small-world network with image characteristics is constructed according to that learn dictionary, and the small-world network is used as an image retrieval model, thereby greatly improving the processing efficiency and accuracy of image retrieval and increasing the service throughput.