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12776 results about "Network structure" patented technology

Network structure. Network structure is a term used to describe the method of how data on a network is organized and viewed.

Method and apparatus for information mining and filtering

The present invention combines a data processing structure with a graphical user interface (GUI) to create an information analysis tool wherein multiple functions are combined in a network to extract information from multiple data sources. The functional network is created, and graphically represented to the user, by linking individual operations together. The combination of individual operations is not limited by the input or output characteristic of any single operation. The form of the input to or output from a by individual operation, whether from a database or from another operation, is the same. That is, both the input to and the output from an analysis function is a list of document identifiers and corresponding document characteristics. Because the form of the input and output from each operation is the same, arbitrary combinations into of operations may be created. Moreover, functional networks of individual operations can then be used for database retrieval as well as to filter data streams. Furthermore, the user is able to create a visual representation of the structure forming a functional network which may be dynamically updated as new data is added or functions switched in or out. Because, inter alia, the network structure dynamically responds to information as it is presented to the network, the visual representation of the network conveniently provides the user with information concerning the characteristics of the database or stream of data that are substantially unavailable through conventional search, filtering, or clustering techniques alone.

Compressed low-resolution image restoration method based on combined deep network

The present invention provides a compressed low-resolution image restoration method based on a combined deep network, belonging to the digital image / video signal processing field. The compressed low-resolution image restoration method based on the combined deep network starts from the aspect of the coprocessing of the compression artifact and downsampling factors to complete the restoration of a degraded image with the random combination of the compression artifact and the low resolution; the network provided by the invention comprises 28 convolution layers to establish a leptosomatic network structure, according to the idea of transfer learning, a model trained in advance employs a fine tuning mode to complete the training convergence of a greatly deep network so as to solve the problems of vanishing gradients and gradient explosion; the compressed low-resolution image restoration method completes the setting of the network model parameters through feature visualization, and the relation of the end-to-end learning degeneration feature and the ideal features omits the preprocessing and postprocessing; and finally, three important fusions are completed, namely the fusion of the feature figures with the same size, the fusion of residual images and the fusion of the high-frequency information and the high-frequency initial estimation figure, and the compressed low-resolution image restoration method can solve the super-resolution restoration problem of the low-resolution image with the compression artifact.

Block chain bottom consensus mechanism and block chain system based on block chain bottom consensus mechanism

InactiveCN107341660AReduce the concentration of computing powerNo centralization riskPayment protocolsPayment circuitsData synchronizationEqual probability
The invention discloses a block chain bottom consensus mechanism and a block chain system based on the consensus mechanism. The block chain bottom consensus mechanism comprises a consensus network structure, participants, a first protocol, a second protocol, and a third protocol; the consensus network structure is used for providing a carrier for transmitting and synchronizing data; the participants are used for participating in data storage and evaluation in the consensus network structure; the first protocol is used for voting for specific participants based on the rotate account sub-protocol and generating a new block when equal probability rotating; the second protocol is used for providing group voting or estimate for all participants holding tokens or digital shares based on the group estimate consensus sub-protocol and generating an extra new block; and the third protocol is used for connecting with each consensus mechanism network based on the gear consensus routing sub-protocol. According to the invention, the characteristics of the block chain data structure and the point to point network communication are combined to realize the secure, efficient, decentralized and application scenario flexible data synchronization consensus.

Energy efficient wireless sensor network routing method

The invention discloses a routing method for the wireless sensor network with efficient energy, which is suitable for the layered sensor network structure. The routing method is composed of initialization, cluster building, adjacent clusters routing and routing maintenance, wherein, an initialization process of the protocol makes a Sink node obtain a topology and network average energy of the sensor network, and each node obtains hop counts from the node to the Sink node; in the stage of the cluster building, a repeated division method is used to divide sensor network clusters, the divided clusters are even, and a leader cluster node is undertaken by nodes with higher residual energy; the adjacent clusters routing uses an ant colony algorithm to determine the probability of using a link to send information according to the link pheromone concentration, and the link pheromone concentration is increased with the information transmission on the link and is reduced with the time going; and the routing maintenance stage is responsible for updating link pheromone concentration, and makes the nodes inside the cluster with higher residual energy undertake the leader cluster in turn. The routing method can reduce the consumption of the network total energy, can balance the consumption of the node energy and can prolong the network life cycle.

Method for preparing graphene-carbon nano tube hybrid composite

The invention provides a method for preparing a graphene-carbon nano tube hybrid composite and relates to a method for preparing a functional high molecular material and a device thereof. The method comprises the following steps of: carrying out the stirring and sonic oscillation treatment on graphene and carbon nano tubes to preform an entangled network structure, thoroughly mixing the entangled network structure and polymer particles, and thus obtaining an uniformly-mixed system in which the graphene-carbon nano tube network is coated on the surfaces of the polymer particles after removing the solvent; and putting the uniformly-mixed system in a mould, hot-compacting, and obtaining the graphene-carbon nano tube hybrid composite after cooling and demoulding. By mingling the graphene and the carbon nano tubes in advance to form the communicated network structure, the method realizes the advantage complementation of the graphene and carbon nano tube structures and enables the hybrid composite to have favorable electric conduction and heat conduction properties. The method can be widely used in the fields such as aviation, transportation and communication, electronic industry, civil facilities, construction, chemical industry and the like, can be produced in the industrial scale, and has the advantages of low cost and environmental friendliness.

Method and apparatus for quantizing and compressing neural network with adjustable quantization bit width

The invention relates to the technical field of neural networks, and specifically provides a method and apparatus for quantifying and compressing a convolutional neural network. The invention aims to solve the existing problem of large loss of network performance caused by an existing method for quantifying and compressing a neural network. The method of the invention comprises the steps of obtaining a weight tensor and an input eigen tensor of an original convolutional neural network; performing fixed-point quantization on the weight tensor and the input eigen tensor based on a preset quantization bit width; and replacing the original weight tensor and the input eigen tensor with the obtained weight fixed-point representation tensor and the input feature fixed-point representation tensor to obtain a new convolutional neural network after quantization and compression of the original convolutional neural network. The method of the invention can flexibly adjust the bit width according to different task requirements and can realize quantization and compression of the convolutional neural network without adjusting the algorithm structure and the network structure so as to reduce the occupation of memory and storage resources. The invention further provides a storage apparatus and a processing apparatus, which have the above beneficial effects.
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