28 results about "Hide node" patented technology

The invention discloses a transformer area topological structure estimation method, system, equipment and medium based on multi-dimensional power utilization characteristics, and belongs to the technical field of power distribution transformer area topologies, and the method comprises the steps: collecting multi-source data, carrying out dynamic weight distribution and abnormal value correction, carrying out the characteristic extraction according to the collected multi-source data, and generating a high-dimensional characteristic vector; clustering user nodes, dividing cluster labels, performing topological modeling according to a cluster division result, generating a topological graph, and performing anomaly verification through multi-dimensional anomaly scoring and abnormal power utilization detection; and performing incremental model parameter correction and multi-objective optimization according to anomaly verification feedback, and generating dynamic topological graph rendering and multi-dimensional decision suggestions by integrating topological modeling, anomaly verification and optimization results. According to the method, accurate estimation of a topological structure is realized through dynamic clustering and hidden node recognition, real-time diagnosis of abnormal nodes and self-correction of a model are realized by means of a multi-dimensional abnormal scoring and self-adaptive optimization mechanism, and the accuracy and operation and maintenance efficiency of transformer area management are improved.

The invention relates to the technical field of underwater communication, in particular to a collision avoidance method suitable for digital underwater acoustic communication, which is characterized in that a neighbor propagation delay mapping database is maintained at each communication node, and inter-node propagation delay information is acquired through passive monitoring and active detection. When a node wants to send data, combining delay mapping to predict a channel occupation time slot, and judging whether the sending is conflicted with an RTS / CTS handshake stage, a data transmission stage and an ACK (Acknowledgement Character) confirmation stage which are carried out by a neighbor; if a conflict risk exists, the node delays to send the RTS and then initiates handshake until the security window is finished, so that collision caused by hidden nodes and exposed nodes is avoided, meanwhile, parallel transmission under the conflict-free condition is allowed, the channel space-time multiplexing efficiency is improved, the network throughput can be remarkably improved, the data packet collision rate is reduced, and the network performance is improved. And the node access fairness is improved.

The present application belongs to the technical field of data processing and multi-source information fusion, and is used to solve the problem that the prior art cannot detect and effectively respond to the collective time drift caused by hidden node faults in the synchronization tree in real time. Specifically, it is a multi-source data correlation integration method based on artificial intelligence, which comprises: obtaining multiple data sources and their synchronization topology information; in response to a request for time sequence correlation of a first data source and a second data source, determining the data source with the smaller value between the first trust risk value and the second trust risk value as the reference source, and the other data source as the source to be calibrated; calculating the absolute value of the difference between the nominal time offset and the witness time offset as the consistency residual; and performing correlation determination based on the consistency residual. By introducing a physical reference benchmark independent of the synchronization tree and a periodic traceability prisoner test, the present application can accurately identify the collective time drift caused by hidden node faults in the synchronization tree, thereby effectively blocking the step-by-step transmission of errors along the synchronization tree.

The application provides a method for determining information and an electronic device. The method comprises enabling a first user equipment to provide auxiliary information, and determining the auxiliary information. The method for determining information provided by the application can be used to determine an auxiliary user equipment, and provide auxiliary information to other user equipments, so as to avoid problems such as hidden node or half-duplex problem.

The application provides a vehicle networking fairness transmission method based on convex optimization and reinforcement learning, and belongs to the technical field of vehicle networking and data transmission. The technical scheme is as follows: S1: an improved confirmation ACK frame and a data DATA frame are designed to realize hidden node detection; S2: mathematical modeling of the transmission fairness problem; S3: fairness transmission based on convex optimization and reinforcement learning. The application effectively discovers hidden nodes, alleviates transmission conflicts caused by hidden nodes by improving the transmission power and the 'continuation' field of the data frame DATA, and finally improves the throughput; the reinforcement learning and convex optimization method are used to jointly optimize the transmission strategy, solve the minimization problem of the difference between the transmission data amounts of fast and slow vehicles, and improve the data fairness of vehicle networking transmission.

The invention relates to a human body action recognition method and device based on heterogeneous space-time diagram convolution, and belongs to the technical field of computers, and the method comprises the steps: obtaining motion video data, and extracting a frame sequence from the motion video data based on a fixed time interval; extracting a joint target point and a target object of each video frame in the frame sequence, and constructing a first heterogeneous space-time diagram based on the joint target point and the target object; generating feature vectors for replacing shielded nodes according to the trained diffusion model and the shielded nodes in the first heterogeneous space-time diagram, and obtaining a second heterogeneous space-time diagram; and inputting the second heterogeneous space-time diagram into the graph neural network model to obtain a target semantic feature, and determining an action category based on the target semantic feature. According to the method, the accuracy and robustness of human body action recognition in a complex real scene (especially in the presence of shielding) are remarkably improved.

A method includes receiving a training data set, which includes one or more operating parameters of a plasma processing system and ion energy distribution function (IEDF) features corresponding to the one or more operating parameters. The one or more operating parameters include at least one process setpoint from a process recipe, at least one measured operating parameter, or at least one simulated operating parameter. The IEDF features include at least one measured IEDF feature or at least one simulated IEDF feature. The method further includes training an artificial neural network (ANN) using the one or more operating parameters as input, and the IEDF features as output. The ANN includes an input layer including one or more input nodes corresponding to the one or more operating parameters, a single hidden layer including one or more hidden nodes, and an output layer including output nodes corresponding to the IEDF features.

The invention discloses an aeromagnetic interference compensation method based on a radial basis function network. The method comprises the following steps: taking a 16-term coefficient matrix as input data of a deep neural network; a hidden space is constructed for data in hidden nodes in a hidden layer by using a radial basis function, the hidden layer performs certain conversion on input data, and low-dimensional mode input data is converted into a high-dimensional space, so that a linear inseparable problem in the low-dimensional space becomes linearly separable in the high-dimensional space; training a sample set of aeromagnetic data to obtain a network center and other weight parameters meeting supervision requirements; fitting the aeromagnetic interference by using the obtained weight parameter information to obtain predicted magnetic interference; and calculating the compensated standard deviation according to the predicted magnetic interference. The anti-interference capability of the model can be effectively improved.

The invention discloses a social network inference method and a security monitoring method, and relates to the technical field of network identification. A social network inference method comprises the following steps: collecting income time sequence characteristic data of all nodes in a game dynamics system; calculating a reference degree sequence formed by all node reference degrees; inhibiting the behavior of the node i, and obtaining new income time sequence characteristic data of all nodes after the node i is inhibited; obtaining a new reference degree sequence based on new income time sequence feature data of all nodes obtained after the suppression of the node i; and comparing a new reference degree sequence obtained after the node i is inhibited with the reference degree sequence of the nodes, finding out the nodes with changed reference degrees so as to obtain network nodes connected with the node i, judging the number of hidden nodes connected with the node i, and finally inferring the network structure of the whole social network. A complex network structure in a nonlinear dynamic system is captured, connection information between nodes is deeply mined, and accuracy and comprehensiveness are improved.

The application relates to the technical field of control cabinets, in particular to a control cabinet remote intelligent diagnosis method and system based on multi-source data fusion, which comprises the following steps: acquiring a visible light image, infrared thermal image data and loop current of a target control cabinet; based on semantic analysis of the visible light image, a hidden node that is shielded is identified, a heat conduction agent area is locked and a central skeleton path is extracted; the heat conduction agent area is analyzed to determine a temperature rise gain strategy, the infrared thermal image data is compensated to generate corrected temperature rise data; a temperature gradient vector of the infrared thermal image data along the central skeleton path is calculated, and a heat source position feature is output based on the directivity of the temperature gradient vector relative to the hidden node; a theoretical heat production potential is generated based on the loop current, and a logic interlocking criterion is constructed. The application can realize accurate reverse attribution of hidden contact faults under non-line-of-sight conditions through multi-modal logic decoupling.

Disclosed are a transmission or reception method and apparatus for reducing interference of a hidden node in an OBSS in a wireless LAN system. According to one embodiment of the present disclosure, a method performed by a first station (STA) in a wireless LAN system comprises the steps of: monitoring, by a first STA associated with a first access point (AP) of a first basic service set (BSS), an inter-BSS physical layer protocol data unit (PPDU) transmitted from a second STA belonging to a second BSS or a second STA; transmitting, to the first AP, report information including information for indicating that the inter-BSS PPDU is received or not received; and receiving, from the first AP, scheduling information of the first STA based on the reporting information.

The invention discloses a method for predicting the material demand of a deep learning model based on swarm intelligence, and aims to solve the problems that a conventional hyper-parameter optimization method is low in efficiency and is liable to fall into local optimum. The method comprises the following steps: firstly, constructing a deep learning model containing the number of layers, the number of hidden nodes, a learning rate and regularization parameters, and defining an optimization interval of each hyper-parameter; secondly, designing an improved sparrow search algorithm (ISSA), enhancing global search capability through Cauchy variation disturbance, balancing exploration and development of a refraction reverse learning strategy, and adjusting a self-adaptive early warning value to improve convergence stability so as to optimize a hyper-parameter combination; further taking a verification set RMSE as a fitness target, combining with parallelization calculation to accelerate model performance evaluation, and screening optimal hyper-parameter configuration; and finally, predicting material demand data by using the optimized model, performing transverse comparison with LSTM, ARIMA, GRU, GCN and other models, and verifying the effectiveness of the model through multiple indexes such as RMSE, MAPE and R. Experiments show that the ISSA optimization method provided by the invention significantly reduces prediction errors, the optimized model has higher accuracy and robustness in a complex material demand scene, and meanwhile, the reliability of performance improvement is confirmed through statistical significance test.

The application relates to a data transmission protection method based on combination of an HMAC-MD5 algorithm and key pre-distribution, which combines a key pre-distribution scheme and an HMAC-MD5 algorithm based on symmetric cryptography and integrates a key pre-distribution management scheme based on a hash mechanism; that is, before data information participates in communication encryption transmission, the key held by a node is preloaded, and the one-way characteristic of a hash function is utilized to hide part of the key information of the node; the application utilizes the one-way characteristic of the hash function and the key pre-distribution to hide part of the key information of the node to a certain extent, that is, to reduce the number of key leaks of the node, and then to reduce the possibility of destruction of other links of the network by an attacker, so that the security of node communication is improved.

Embodiments of the present disclosure relate to communication. According to embodiments of the present disclosure, a first terminal device receives first control information indicating a first set of resources reserved by a second terminal device and second control information indicating a second set of resources reserved by a third terminal device, respectively. If the first set of resources and the second set of resources overlap with each other, the first terminal device whether a condition associate with coordination of terminal devices is satisfied. If the condition is satisfied, the first terminal device transmits an indication to at least one of the second and third terminal devices. In this way, collisions in resources can be avoided. Further, it also can solve the hidden node problem.

The invention relates to the technical field of financial credit data processing and distributed storage, and discloses a distributed storage method and system for dynamic index query of a debtor relation graph. The method comprises the following steps: processing existing multi-source associated data by using a scene attribute completion and cross-source conflict resolution method, and generating a debtor associated node panoramic attribute matrix; constructing a debtor multi-level associated value directed graph based on the matrix; generating a hidden key node multi-dimensional evaluation table through an on-graph hidden node value migration evaluation algorithm; and generating a debtor associated node communication coping scheme through a communication coping suggestion matching algorithm in combination with distributed fragmentation storage and dynamic index service, and archiving the debtor associated node communication coping scheme. According to the method, the problem of priority misjudgment of the implicit associated nodes is solved, accurate integration of multi-source data is achieved, and distributed efficient storage and query are achieved.

The invention discloses a communication scheduling method and device, and relates to the field of communication. In the method, an access point (AP) sends first indication information to a station (STA), the first indication information is used for indicating idle state duration, and the idle state duration is used for determining a channel monitoring window, so that the monitoring window covers a process from request to send frame (RTS) to detection of channel clear frame (CTS) clear channel assessment (CCA) detection; and after a confirmation response based on the first indication information sent by the STA is received, performing communication scheduling with the STA based on the monitoring window. By adopting the above design, collision in a scene with many hidden nodes can be effectively reduced, the network retransmission overhead is greatly reduced, and the network performance is improved.

Systems and methods of preparing a mixed quantum state using a thermodynamic - quantum hybrid scheme are provided. A system comprises a thermodynamic processor, a quantum processor, and a controller operatively coupled to the thermodynamic processor and the quantum processor. A plurality of samples from a data state is received. A plurality of configuration parameters of a thermodynamic processor is initialized to initial values, the thermodynamic processor having a plurality of visible nodes and a plurality of hidden nodes. A plurality of configuration parameters of a quantum processor is initialized to initial values. The hidden nodes of the thermodynamic processor are sampled. Based on samples of the visible nodes and sample of the hidden nodes and on the data state, the configuration parameters of the thermodynamic processor are updated. Based on samples of the visible nodes and samples of the hidden nodes, the configuration parameters of the quantum processor are updated.

This application provides a method, method, device, and apparatus for indicating resource allocation. The method includes: a target UE broadcasting transmission channel resource information, wherein the number of hidden nodes within a preset range of the target UE meets preset conditions, and the transmission channel resource information is used to indicate the transmission channel resources for the target UE to receive data. This method solves the technical problem of resource conflicts arising from different UEs selecting the same transmission resources due to hidden nodes.

Systems and methods of preparing a mixed quantum state using a thermodynamic - quantum hybrid scheme are provided. A system comprises a thermodynamic processor, a quantum processor, and a controller operatively coupled to the thermodynamic processor and the quantum processor. A plurality of samples from a data state is received. A plurality of configuration parameters of a thermodynamic processor is initialized to initial values, the thermodynamic processor having a plurality of visible nodes and a plurality of hidden nodes. A plurality of configuration parameters of a quantum processor is initialized to initial values. The hidden nodes of the thermodynamic processor are sampled. Based on samples of the visible nodes and sample of the hidden nodes and on the data state, the configuration parameters of the thermodynamic processor are updated. Based on samples of the visible nodes and samples of the hidden nodes, the configuration parameters of the quantum processor are updated.

Systems and methods resolve the hidden node problem in Wi-Fi communications and significantly reduce intra-extended service set (ESS) interference management and inter-ESS interference, thereby, improving network performance and enhancing user experience. In various embodiments, this is achieved by using connection discovery techniques that optimize user device associations with radio frequency (RF) units and streamline network management. Certain techniques leverage power detectors and timing references for efficient device identification and interference management. Further, some embodiments optimize multi-user multiple-input multiple-output (MU-MIMO) applications by improving user device selection and increasing areal capacity using two-antenna RF units.

Systems and methods resolve the hidden node problem in Wi-Fi communications and significantly reduce intra-extended service set (ESS) interference management and inter-ESS interference, thereby, improving network performance and enhancing user experience. In various embodiments, this is achieved by using connection discovery techniques that optimize user device associations with radio frequency (RF) units and streamline network management. Certain techniques leverage power detectors and timing references for efficient device identification and interference management. Further, some embodiments optimize multi-user multiple-input multiple-output (MU-MIMO) applications by improving user device selection and increasing areal capacity using two-antenna RF units.

This invention discloses a method and system for discovering hidden nodes in WebTunnel bridge nodes based on active detection, belonging to the field of communication security detection technology. The method includes: deploying at least one non-egress routing node in the Tor network as a controlled honeypot relay node; establishing a two-hop circuit originating from a controlled client, where the first hop of the two-hop circuit is the WebTunnel bridge node and the second hop is the controlled honeypot relay node; controlling the controlled client to send a RELAY_EARLY information element to the controlled honeypot relay node via the two-hop circuit, causing the controlled honeypot relay node to generate log records based on the signaling and data forwarding behavior from the WebTunnel bridge node to the controlled honeypot relay node, and analyzing these log records to obtain the hidden node discovery result of the WebTunnel bridge node; combining the hidden node discovery result, generating a multi-dimensional feature profile of the hidden node and bridging behavior in the log records. This invention can improve the accuracy and traceability of bridge node identification and forensic analysis.

The application discloses a social network inference method and a safety monitoring method, and relates to the technical field of network identification.A social network inference method comprises the following steps: collecting the income time sequence characteristic data of all nodes in a game dynamics system; calculating a reference degree sequence composed of the reference degrees of all nodes; suppressing the behavior of a node i, obtaining new income time sequence characteristic data of all nodes after the node i is suppressed; obtaining a new reference degree sequence based on the new income time sequence characteristic data of all nodes obtained after the node i is suppressed; comparing the new reference degree sequence obtained after the node i is suppressed with the reference degree sequence of the node, finding out the nodes whose reference degrees change, and obtaining the network nodes connected with the node i, simultaneously judging the number of hidden nodes connected with the node i, finally inferring the network structure of the entire social network, capturing the complex network structure in the nonlinear dynamics system, deeply mining the connection information between the nodes, and improving the accuracy and comprehensiveness.

The invention discloses a neural network intelligent optimization method and system based on data node analysis, and relates to the technical field of deep learning, and the method comprises the steps: obtaining basic data information, building a demand based on a neural network, dividing basic data, obtaining a training data set and a test data set, and obtaining a test data set according to the training data set; and obtaining an input category number and an output category number. According to the method, data categories are subjected to clustering analysis, the stability and reliability of data in the neural network construction process are ensured, errors caused by manual label adding are avoided, the number of nodes in each layer in the neural network is accurately controlled through the hidden node coefficient, the accuracy of the neural network is improved, and the method is suitable for large-scale popularization and application. The neural network hidden node condition is accurately evaluated through weight pruning in the training process, the neural network optimization is realized through pruning the hidden nodes, the neural network optimization efficiency is improved through the mean value coefficient, and the stability and reliability of the neural network are ensured.

The application provides a vehicle networking fairness transmission method based on convex optimization and reinforcement learning, and belongs to the technical field of vehicle networking and data transmission. The technical scheme is as follows: S1: an improved confirmation ACK frame and a data DATA frame are designed to realize hidden node detection; S2: mathematical modeling of the transmission fairness problem; S3: fairness transmission based on convex optimization and reinforcement learning. The application effectively discovers hidden nodes, alleviates transmission conflicts caused by hidden nodes by improving the transmission power and the 'continuation' field of the data frame DATA, and finally improves the throughput; the reinforcement learning and convex optimization method are used to jointly optimize the transmission strategy, solve the minimization problem of the difference between the transmission data amounts of fast and slow vehicles, and improve the data fairness of vehicle networking transmission.