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93 results about "Sorting network" patented technology

In computer science, comparator networks are abstract devices built up of a fixed number of "wires", carrying values, and comparator modules that connect pairs of wires, swapping the values on the wires if they are not in a desired order. Such networks are typically designed to perform sorting on fixed numbers of values, in which case they are called sorting networks.

Browse type service perception analysis method

The invention relates to a browse type service perception analysis method and belongs to the mobile communication field. With the browse type service perception analysis method adopted, massive user browse type service perception data acquired from a terminal side can be effectively utilized to analyze, position and delimit causes in the whole-network level for poor browse type service perception of users, and therefore, related parameters, algorithms, protocols and the like of a network can be optimized, so that the ability of the mobile network to carry browse type services can be improved, and references can be provided for the improvement of the browse type service perception of the users. According to the browse type service perception analysis method, causes for poor browse type service perception are analyzed according to terminal data acquired in actual mobile networks, namely, a plurality of 3G networks of regional and municipal telecommunications companies, and serious problems such as excessively long DNS resolution time and long time delay of the first packet of the Sina website exist in a certain network; and the problems are confirmed by the optimization departments of the telecommunications companies, and the optimization departments of the telecommunications companies carry out corresponding network adjustment, for example, optimize DNS servers and increase local Sina CDN servers, and subsequent tests also verify that the problems are solved, and indexes are improved.
Owner:BEIJING UNION UNIVERSITY

Method for optimizing multi-grade light-splitting passive optical network of distribution communication network

The invention relates to a method for optimizing a multi-grade light-splitting passive optical network of a distribution communication network. The method is that the network at the current grade bears the gene optimization result of the network at the previous grade through the cascading genetic algorithm. The method specifically comprises the steps of designing a gene code; creating constraint conditions for the gene code, and selecting adaptability functions; performing crossover and variation operation for the gene code on the premise that the constraint conditions for the gene code are met; performing multi-generation iterative crossover and variation operation to obtain the optimal gene code of the PON network at the current level; returning to step (1) to bear the gene code of previous level when the number of the network layers of the multi-grade light-slitting passive optical network is less than n, and updating the adaptability function; finishing the optimization when the number of the network layers of the multi-grade light-slitting passive optical network is more than 1. According to the method, the optimal light splitter and relatively high network star topology in the network are selected from the optimized multi-grade light-splitting network, and therefore, the network communication construction cost is saved; the network planning is constructed into a mathematical model, thus the expandability is improved, and the calculation complexity is reduced.
Owner:STATE GRID CORP OF CHINA +2

Three-dimensional point cloud head posture estimation system and method based on ordered regression and soft labels

The invention discloses a three-dimensional point cloud head posture estimation system and method based on ordered regression and soft labels. The system comprises a feature learning network module which is used for the layered feature extraction of point cloud data; the prediction network module is used for mapping the features obtained by the feature learning network module to a head attitude angle to obtain an angle prediction value, and substituting the angle prediction value and the head attitude angle serving as a label into a first loss function; the sorting network module is used for carrying out dimension division on the head attitude angle to form a plurality of subtasks, generating a soft label according to the relationship between the head attitude angle serving as a label andthe subtasks, carrying out value prediction on the features obtained by the feature learning network module, and substituting the value prediction of the point cloud data and the soft label into a second loss function; network updating module. The loss of the sorting network module and the loss of the prediction network module are combined to introduce the sorting network so as to guide the learning of the prediction network, so that the feature extraction is more accurate, and the precision of the prediction network is improved.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Pushing and grabbing collaborative sorting network based on double viewing angles and sorting method and system thereof

The invention discloses a pushing and grabbing collaborative sorting network based on double viewing angles and a sorting method and system thereof. The trained pushing and grabbing collaborative sorting network comprises a pushing full convolutional network and a grabbing full convolutional network, and the network is applied to robot pushing and grabbing collaborative sorting. The sorting methodcomprises the following steps of correspondingly acquiring point cloud graphs of an object scene to be sorted from two viewing angles, rotating a top view of the point cloud graphs, correspondingly inputting a plurality of rotating images into the pushing full convolutional network and the grabbing full convolutional network to obtain two thermodynamic graphs with Q values output by the networks,and selecting the thermodynamic diagram with the larger Q value as a final thermodynamic diagram; and according to the pixel point corresponding to the maximum Q value in the thermodynamic diagram and the rotation angle of the rotation image corresponding to the thermodynamic diagram, controlling the robot to execute the sorting action of the network corresponding to the thermodynamic diagram, and then completing sorting. According to the sorting method, double viewing angles are combined with deep Q learning, so that the grabbing success rate is high and the generalization ability is high inthe face of a disordered stacking scene.
Owner:HUAZHONG UNIV OF SCI & TECH
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