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47 results about "Disjoint-set" patented technology

In computer science, a disjoint-set data structure (also called a union–find data structure or merge–find set) is a data structure that tracks a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph.

Traffic playback method and system for virtual network

The invention relates to a traffic playback method and system for a virtual network. The traffic playback method includes a first step of capturing and processing real traffic, extracting a real IP address set R_IP, a second step of conducting depth-first search on a bipartite graph which is generated by real traffic communication relationships, dividing the real IP address set R_IP into two disjoint sets, namely, a set R_IPA and a set R_IPB, a third step of dividing all virtual nodes which are in communication through any virtual network routing interface v_interfacei into two disjoint sets, namely a set V_IPAi and a set V_IPBi, a fourth step of calculating similarities of all the virtual network routing interfaces and a real traffic collecting point, a fifth step of selecting a virtual network interface which is most similar to the real traffic collecting point to be used as a mapping node of the traffic collecting point, conducting IP address mapping based on the mapping mode, and a sixth step of traversing the real traffic again to achieve real traffic playback in the virtual network. When the traffic is played back in the virtual network through the traffic playback method and system for the virtual network, the real traffic communication environment is restored as good as possible, and the virtual network traffic system is improved.
Owner:INST OF INFORMATION ENG CHINESE ACAD OF SCI

Traffic routing tree construction method based on geographic location

InactiveCN107911293AReduce complexityAvoid the problem of many conflicting packagesData switching networksNODALMassive gravity
The invention provides a traffic sensing routing tree construction method based on geographic location routing. The method comprises the steps that a disjoint set data structure is used to track the membership of each node in a cluster; a priority queue data structure is used to store virtual bandwidths and distances to all adjacent clusters; the virtual bandwidth of two adjacent clusters is the sum of all available bandwidths between two cluster nodes, and a pair of adjacent clusters with the maximum attraction are merged first; the cluster head of a cluster with many nodes appears as a new cluster head; the cluster center is the average of the locations of all nodes in the cluster; the virtual bandwidth of the new cluster to the adjacent cluster is calculated from the bandwidth of the original cluster to the adjacent cluster; the highest available bandwidth link from the new cluster to the adjacent cluster is the highest bandwidth link from the original cluster to the adjacent cluster; and until only one cluster left or the rest of the clusters have no attraction, the method ends, and a large cluster with all nodes of a routing tree is acquired, wherein the cluster head of the large cluster is the node of the routing tree root. According to the invention, location and traffic information are considered at the same time; the number of collision packets and the average path hops are reduced; and the average path throughput is increased.
Owner:TIANJIN UNIV

Two-stage behavior recognition fine classification method based on graph convolutional network

The invention discloses a two-stage behavior recognition fine classification method based on a graph convolutional network. According to the method, the accuracy of behavior recognition is improved mainly by reclassifying a difficult category set, and the method is divided into three stages: in the first stage, training a coarse classification model; in the second stage, acquiring difficult category sets and training difficult category set models, acquiring the difficult category sets by a confusion matrix of the rough classification model on the test set and a union-check set algorithm, and training the difficult category set models for the different difficult category sets; and in a third stage: during on-line inference, according to an inference result of the rough classification model, inputting the samples which need to be finely classified into the difficult category set model for re-classification. According to the method provided by the invention, aiming at the problem that a model is difficult to classify similar actions, the problem that the similar actions are difficult to classify is relieved to a certain extent by a rough classification-fine classification two-stage architecture, the accuracy of behavior recognition is improved, and a better result is obtained on a public data set.
Owner:FUDAN UNIV +1
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