113 results about "Cyclic network" patented technology

Systems and methods are described for configuring customer premises equipment in a wireless network in response to reconnection of subscriber station to a base station. A set of rules is provided that governs configuration of a network interface based in part on whether the interface connects to a different base station after reconnection. Reconfiguration may include cycling power of the network interface and the decision to cycle power may be based on network configuration determined after the loss of communication. This decision can be made after the loss of communication and after the network interface is reconnected to the wireless network through the same or a different base station. The rules may be processed by a combination of subscriber equipment, network interface and base station. Power may be cycled responsive to a message transmitted by the one base station to the network interface.

The invention discloses a path-based knowledge graph embedding method, which comprises the following steps of for a given single knowledge graph or a joint knowledge graph consisting of two knowledgegraphs, firstly sampling the given single knowledge graph by using a partial random walk method; next, modeling the obtained sampling path by using a new jumping cyclic network, so that the network can overcome the defect that the traditional cyclic neural network cannot identify the knowledge graph structure while effectively learning the dependency among elements in the path. For the output of the jump recurrent neural network, a type-based noise comparison estimation method is adopted to efficiently evaluate the loss of the network, and iteration is carried out in a feedback updating mode to carry out training. Compared with the prior art, the knowledge graph embedding trained by the method has the advantages of high expressibility, strong generalization ability and the like, and the accuracy and the efficiency of knowledge graph completion and entity alignment can be remarkably improved by applying the method.

The invention discloses a traditional Chinese medicine health consultation text named entity recognition method based on transfer learning, which comprises the following steps: S1, selecting a text named entity recognition annotation data set in other fields, constructing a neural network, and performing pre-training; S2, respectively constructing a one-way recurrent neural network, and performinglanguage model training by utilizing the traditional Chinese medicine health consultation text unlabeled data set to obtain a forward language model and a reverse language model. And S3, fusing the loop network layer output characteristics of the unidirectional language model, and combining the full connection network layer and the conditional random field layer to obtain a final named entity recognition model. And S4, naming the entity by using the traditional Chinese medicine health consultation text to identify the labeled data set, and carrying out fine tuning training. According to the method, named entities in other fields can be migrated to identify labeled text knowledge and unlabeled text knowledge in the field, so that the identification accuracy and recall rate of the named entities of the traditional Chinese medicine health consultation text are effectively improved, and the convergence speed of the model is increased.

A method for query expansion uses a representation of an input query object, such as an image, to retrieve representations of similar objects retrieved using the query object representation as a query. Given the set of image representations, a weight is predicted for each using a prediction model which assigns different weights to the image representations. An expanded query is generated as a weighted aggregation (e.g., sum) of the query object representation and at least a subset of the set of similar object representations in which each object representation is weighted with its predicted weight. A higher weight can thus be given to one of the similar object representations, in the expanded query, than to another.

VLSI layouts of generalized multi-stage and pyramid networks for broadcast, unicast and multicast connections are presented using only horizontal and vertical links with spacial locality exploitation. The VLSI layouts employ shuffle exchange links where outlet links of cross links from switches in a stage in one sub-integrated circuit block are connected to inlet links of switches in the succeeding stage in another sub-integrated circuit block so that said cross links are either vertical links or horizontal and vice versa. Furthermore the shuffle exchange links are employed between different sub-integrated circuit blocks so that spacially nearer sub-integrated circuit blocks are connected with shorter links compared to the shuffle exchange links between spacially farther sub-integrated circuit blocks. In one embodiment the sub-integrated circuit blocks are arranged in a hypercube arrangement in a two-dimensional plane. The VLSI layouts exploit the benefits of significantly lower cross points, lower signal latency, lower power and full connectivity with significantly fast compilation.The VLSI layouts with spacial locality exploitation presented are applicable to generalized multi-stage and pyramid networks, generalized folded multi-stage and pyramid networks, generalized butterfly fat tree and pyramid networks, generalized multi-link multi-stage and pyramid networks, generalized folded multi-link multi-stage and pyramid networks, generalized multi-link butterfly fat tree and pyramid networks, generalized hypercube networks, and generalized cube connected cycles networks for speedup of s≧1. The embodiments of VLSI layouts are useful in wide target applications such as FPGAs, CPLDs, pSoCs, ASIC placement and route tools, networking applications, parallel & distributed computing, and reconfigurable computing.

VLSI layouts of generalized multi-stage and pyramid networks for broadcast, unicast and multicast connections are presented using only horizontal and vertical links with spacial locality exploitation. The VLSI layouts employ shuffle exchange links where outlet links of cross links from switches in a stage in one sub-integrated circuit block are connected to inlet links of switches in the succeeding stage in another sub-integrated circuit block so that said cross links are either vertical links or horizontal and vice versa. Furthermore the shuffle exchange links are employed between different sub-integrated circuit blocks so that spacially nearer sub-integrated circuit blocks are connected with shorter links compared to the shuffle exchange links between spacially farther sub-integrated circuit blocks. In one embodiment the sub-integrated circuit blocks are arranged in a hypercube arrangement in a two-dimensional plane. The VLSI layouts exploit the benefits of significantly lower cross points, lower signal latency, lower power and full connectivity with significantly fast compilation. The VLSI layouts with spacial locality exploitation presented are applicable to generalized multi-stage and pyramid networks, generalized folded multi-stage and pyramid networks, generalized butterfly fat tree and pyramid networks, generalized multi-link multi-stage and pyramid networks, generalized folded multi-link multi-stage and pyramid networks, generalized multi-link butterfly fat tree and pyramid networks, generalized hypercube networks, and generalized cube connected cycles networks for speedup of s≧1. The embodiments of VLSI layouts are useful in wide target applications such as FPGAs, CPLDs, pSoCs, ASIC placement and route tools, networking applications, parallel & distributed computing, and reconfigurable computing.

Described herein are systems and methods for determining how to automatically answer questions like ''Where did Harry Potter go to school '' Carefully built knowledge graphs provide rich sources of facts. However, it still remains a challenge to answer factual questions in natural language due to the tremendous variety of ways a question can be raised. Presented herein are embodiments of systems and methods for human inspired simple question answering (HISQA), a deep-neural-network-based methodology for automatic question answering using a knowledge graph. Inspired by human's natural actions in this task, embodiments first find the correct entity via entity linking, and then seek a proper relation to answer the question--both achieved by deep gated recurrent networks and neural embedding mechanism.

The invention discloses an image super-resolution reconstruction method. The method comprises the following steps: making a learning sample set; Learning and training: a single-frame model of the convolutional neural network is adopted as a recurrent network module, a super-resolution mapping model of a recurrent neural network mode comprising N + 1 recurrent network modules is constructed, and inthe training process, a single lowest-resolution image is input into each recurrent network module in the super-resolution mapping model for single-time training; Replacing a low-resolution picture and repeatedly carrying out the single training; Carrying out single training for multiple times; And reconstruction: inputting the low-resolution image to be reconstructed into the trained super-resolution mapping model to obtain a high-resolution image. According to the method, the model is guided to learn low-frequency and high-frequency information of the image at the same time by adding the output error items related to the blurred image in the performance indexes, so that the image super-resolution reconstruction result can give consideration to reliability and details; And meanwhile, thestorage space can be greatly saved through the super-resolution mapping model in a cyclic network form.

The invention provides a skeleton action recognition method based on a multi-stream spatial attention graph convolution SRU network. The method comprises the following steps: firstly, embedding a graph convolution operator into a simple cycle unit to construct a graph convolution model to capture time-space domain information of skeleton data; meanwhile, in order to enhance the discrimination between the joint points, a spatial attention network and a multi-stream data fusion mode are designed, and a graph convolution simple cycle network model is further expanded into a multi-stream spatial attention graph convolution SRU. According to the method, the high classification precision is maintained, the complexity of the method is remarkably reduced, the reasoning speed of the model is increased, the balance between the calculation efficiency and the classification precision is achieved, and the method has a very wide application prospect.

A method, a network apparatus, and a tangible machine-medium thereof for detecting a looping network packet are provided. A receiving module receives a network packet. A memory stores a routing table comprising an address related variable, a sequence number related variable and a radius related variable. A determining module determines whether the network packet is a looping network packet by comparing the parameters of the routing table with those of the network packet. With these arrangements, the routing loop caused by transmission of the network packets can be detected using current network structures.

The invention discloses a mechanical equipment health state identification method based on an embedded cycle network. The mechanical equipment health state identification method based on the embeddedcycle network comprises the following steps: firstly, obtaining a vibration signal sample of mechanical equipment and dividing the vibration signal sample into a plurality of sections of adjacent local vibration sub-signals; then extracting a time-frequency image from the sub-signals, and taking the time-frequency image as input after standardized processing; then constructing a depth residual network and training the depth residual network so as to learn various pieces of local information in the signals; and finally, embedding the trained residual network into an input layer of the cycle network by utilizing thought of migration learning, fixing a convolution part so that the convolution part does not participate in training, and re-training the embedded network by utilizing the originalsample input again so as to learn information between adjacent periods; and diagnosing the health state of the mechanical equipment by the trained embedded cycle network. According to the method, thelocal vibration characteristic and the relation between adjacent parts are modeled, the influence of noise and local interference in the signals on a classification effect is weakened, and the applicability of the network in practical engineering is improved.

The invention discloses a software defect auxiliary dispatching method based on the activeness of a text and a developer. By considering text information of a defect report and the activeness of the developer, on one hand, a two-way circulating network and pooling method is utilized to extract text characteristics of the defect report, on the other hand, a one-way circulating network is utilized to extract developer activeness characteristics at a specific moment, the two-way circulating network and pooling method and the one-way circulating network are blended, a new defect dispatching model based on the activeness of the text of the defect report and the activeness of the developer is given, and therefore the problem that the auxiliary defect dispatching accuracy is low is well solved. An experiment result on four different open-source item data sets of Eclipse and the like shows that, compared with work of the same kind, the defect dispatching prediction accuracy is obviously improved.

The invention provides an electronic medical record part-of-speech tagging method, a model training method and related device in the technical field of natural language processing. The method comprises the following steps of after acquiring a output sequence of a circulation network by using semantic information of a preliminary learning training word segmentation sequence of the circulation network, using a semantic connection network to process the output sequence of the circulation network to learn deep semantic information of a learning training segmentation sequence in the way of learningthe training segmentation sequence multiple times so that a prediction part-of-speech labeling result corresponding to a training part-of-speech sequence. Model parameters of a part-of-speech labeling model are updated based on the prediction part-of-speech labeling result and a training part-of-speech labeling result corresponding to the training segmentation sequence. Compared with the prior art, the part-of-speech labeling model can fully learn deep semantic information and long-distance feature information of the sample sequence, and the accuracy of part-of-speech labeling can be improved.

The invention discloses an unsupervised remote sensing image super-resolution reconstruction method based on a recurrent neural network. The whole network is composed of two circulation networks, thefirst circulation network takes a low-resolution training image xt as input, a high-resolution image y is generated through a first generation network, and then a low-resolution image G2 (y) is generated through a second generation network. The second circulation network takes the high-resolution training image yt as input, a low-resolution image x is generated through a second generation network,and the low-resolution image x generates a high-resolution image G1 (x) through the first generation network. And finally, reconstructing a low-resolution remote sensing image through the trained first generation network to obtain a high-resolution image. According to the reconstruction method, the non-paired high-resolution and low-resolution remote sensing image can be used for network training, and the problem that a traditional method cannot use a non-matched image pair for training is solved.

The invention discloses a transformer complex working condition identification method based on deep learning. The method comprises the following steps that 1, original sample data is acquired; 2, theoriginal sample data is built into a data set with type identification, a data set without type identification and a data set of test data; 3, according to a certain time window, window obtaining andgrouping are conducted on the built data sets; 4, voltage and current sequence signals in the window are processed to obtain frequency spectrum data; 5, the frequency spectrum data is subjected to recurrent neural network training; 6, a trained recurrent neural network is tested and optimized; 7, field data is input to the optimized recurrent neural network for conducting identification and precise positioning of transformer complex working conditions. According to the method, by adopting the recurrent network, complex mixed faults can be accurately judged and precisely positioned, and the robustness and a practical level of transformer complex working condition identification are improved.