79 results about "Functional networks" patented technology

There is disclosed a network device in a network system connected to a directory server and a plurality of network devices, in which a combined function is generated utilizing two or more functions out of a plurality of functions able to be provided by a first network device constituting the plurality of network devices, and the generated combined function is registered in the directory server.

The invention discloses a method, a network element and a system for processing QoS (Quality of Service) parameters and a storage medium. The method comprises the following steps that: enabling a control plane network element to acquires a first QoS parameter between terminal equipment and a user plane functional network element; and acquiring capability information of the first network domain, determining a second QoS parameter of the first network domain according to the capability information of the first network domain and the first QoS parameter, and sending the first information of the second QoS parameter to the first network domain. Wherein the first network domain comprises at least one of a wireless access network, a backhaul network and a user plane function network element. Through adoption of the scheme, the data packet scheduling accuracy and the user experience can be improved.

Disclosed is a routing selection method, which is applied to a converged network of a WLAN access network and a 3GPP core network. A functional network element is arranged between the WLAN access network and the 3GPP core network. The method comprises: when UE accesses through a WLAN access network, a functional network element forwarding to an AAA an authentication request message sent by the UE, and forwarding to an access network element of the WLAN access network the authentication response message sent by the AAA; and/or the functional network element receiving an IP address request message sent by the access network element of the WLAN access network. Also disclosed at the same time is a functional network element. The present invention can conduct routing selection for 3GPP UE according to the type of the accessing UE and the service type, thereby reducing the access pressure of a 3GPP network.

The invention relates to a method for a multiple input multiple output (MIMO) functional network to achieve blind equalization of wireless laser communication electric domain signals, in particular to an electric domain signal blind equalization method under the circumstance that a communication channel between a transmitter and a receiver of wireless laser communications has fading characteristics. The method for the MIMO functional network to achieve the blind equalization of the wireless laser communication electric domain signals is applicable to laser communication networks, popularizes an artificial neural network to a functional network by using generalization of the artificial neural network, designs a blind equalization processing method based on an MIMO functional frame, designs an MIMO functional network structure and a network state update principle thereof, uses functional network output drive and a principle of neural network nonlinear dynamics fully, decomposes singular value of a receipt signal matrix to obtain approximate subspace value of a signal to be detected, and performs mapping by using the approximate subspace value as an input vector, so that rapid convergence is achieved by depending on small data volume only, and the receipt signal can reappear in a true signal subspace.

The application provides a discovery method and device based on a servitization architecture. In the method, a control network element sends a discovery response to a first functional network element,and the discovery response comprises a determined safety parameter and an access address or an identifier of a second functional network element; the first functional network element receives the discovery response sent by the control network element and sends an access request to the second functional network element according to the address or the identifier of the second functional network element, which is included in the discovery response, and the access request comprises the received safety parameter; and the second functional network element receives the access request sent by the first functional network element, verifies correctness of the safety parameter and determines whether the access request is authorized by the first functional network element on the basis of correctnessof the safety parameter. By the discovery method and device provided by the application, communication times can be reduced to a certain degree, and communication complexity is reduced.

The invention relates to the field of wireless communication technology and provides a data forwarding method. The data forwarding method comprises the following steps: receiving information of a first bearer in a first network by a session management functional network element from an access and mobility management functional network element, and sending flow information and forwarding information of a first flow in a second network to the access and mobility management functional network element, wherein the flow information is used for indicating a data forwarding flow, and the forwarding information is used for forwarding the first flow to a tunnel corresponding to the first bearer. By virtue of the scheme provided by the embodiment of the invention, access network equipment in the second network can send only a flow requiring data forwarding to a user plane functional network element according to the flow information and the forwarding information, and all the flows do not need tobe forwarded to the user plane functional network element, so that transmission resources are saved.

The present invention relates to an electroencephalogram signal analysis method. The method includes the following steps: S1, establishing a brain functional network based on electroencephalogram signals; S2, labelling different brain functional networks according to the brain functional network to complete graph convolution network model training; and S3, performing type recognition on the novelbrain functional network according to the trained graph convolutional network model to complete electroencephalogram classification. Compared with the prior art, the electroencephalogram signal analysis method provided by the invention can accurately describe the detailed characteristics and state of a brain, the method can provide a novel method for intelligent judgment, early warning and treatment of the diseases when applied to the brain functional network of a patient with cerebral nervous system related diseases.

The invention discloses a data processing method. The method comprises the steps that a first data analysis network element receives a first request sent by a first functional network element, whereinthe first request is used for requesting a first analysis result and a first data analysis network element of the terminal so as to determine that the terminal is a roaming terminal, the first analysis result of the terminal is generated according to the first request, and the first analysis result is fed back to the first functional network element through a first response. According to the embodiment of the invention, the related data of the roaming terminal can be analyzed, and the analysis result is generated, so that the integrity and accuracy of the analysis result related to the roaming terminal are ensured.

The invention belongs to the technical field of functional composite materials, and particularly relates to a low-reflection and high-absorption electromagnetic shielding polymer composite material with a gradient electric conduction-uniform heat conduction dual-function network structure and a preparation method of the low-reflection and high-absorption electromagnetic shielding polymer composite material. The composite material comprises a gradient conductive carbon nanotube network with a vertical orientation foam structure, and a uniformly heat-conducting hexagonal boron nitride/carbon nanotube functional network jointly constructed by a uniformly dispersed hexagonal boron nitride filler network constructed by blending and pouring processes in carbon nanotube foam and a gradient carbon nanotube network. Conductive carbon nanotube foam is successfully compounded with high-thermal-conductivity but insulating boron nitride, the low-reflection and high-absorption characteristics of the electromagnetic shielding composite material are realized through gradient prefabricated network structure control, and hexagonal boron nitride is directionally arranged under the effect of volume limitation by constructing an oriented foam structure prefabricated network, so that the optimal optimization of the heat-conducting property of the composite material is realized, and the goal of simultaneously optimizing the low-reflection and high-absorption properties and the heat-conducting property of the electromagnetic shielding composite material is finally realized.

The present invention discloses a task-state electroencephalogram signal analysis method based on algebraic topology and belongs to specific applications of a complex network analysis technology in the field of neural signal processing. The method comprises the following steps: using electroencephalogram signals as a data source, constructing distance relationship of electrodes at different spatial positions by calculating coherence between the electrodes, using the algebraic topology method to dynamically construct a simplex-based brain functional network, characterizing task-state electroencephalogram signal neural characteristics, further analyzing nature of the brain functional network by calculating Betti number and Euler's characteristic number, and realizing a quantitative study ofa brain function model of subjects under a task state. The task-state electroencephalogram signal analysis method is verified to perform well in the task-state electroencephalogram signal analysis, provides the new method for measuring neural responses in the task state, explores new rules and evidence for brain-like computing, thus can inspire artificial intelligence frameworks and specific algorithms design, etc., and promotes development of a new generation of artificial intelligence.

The invention relates to a brain function connection network analysis method based on a resting state functional magnetic resonance image, and the method is characterized in that the method comprises the following steps: S1, obtaining an rs-fMRI image, and carrying out the preprocessing of the rs-fMRI image; s2, segmenting the rs-fMRI image by adopting three brain templates with different scales, and constructing three graphs G1, G2 and G3; s3, according to the obtained three graphs G1, G2 and G3, hidden feature representation of the graphs is learned by adopting a GNN module, and preliminary prediction labels L1, L2 and L3 are obtained; obtaining a final prediction label after voting; s4, performing significance analysis on a pooling result of the GNN module in the step S3, and obtaining a brain region with significant difference in the functional network; and S5, mapping the prominent brain region of the functional network to the Yeo 7 brain functional network map, and obtaining the mapped functional network connection, namely the individual difference functional sub-network. According to the method, the brain network is constructed by using the three different scales of brain templates, the integrated learning strategy is adopted, the multi-scale information of the multiple brain templates is fully fused, and the model performance is improved.

The invention discloses a disaster recovery method and system of a service chain and a guarantee center. The guarantee center classifies functional network elements according to popular degrees by utilizing counter information of a flow table item of an open virtual switch Open Vswitch; the guarantee center carries out fault detection of the functional network elements; when a fault of a popular network element is detected by the guarantee center, a main flow table item of the popular network element is deleted, and flow switching is carried out on the popular network element with the fault according to a pre-configured backup flow table item; and when a fault of a common network element is detected by the guarantee center, a main flow table item of the common network element is deleted, and a switching flow table item is added to carry out flow switching on the common network element. According to the utilization rates of the functional network elements, popular and common network elements are determined, when faults of the popular and common network elements are detected, different flow switching is carried out on the popular and common network elements, network resources are utilized reasonably, and the user experience during flow switching is improved.

A method for identifying brain-state dependent functional areas of unitary pooled activity (FAUPAs) using a statistical model that does not require a priori knowledge of the activity-induced ideal response signal time course is provided. A system for identifying a functional network in a brain of a living object includes a FAUPA identifier configured to identify FAUPAs by analyzing a plurality of images of the brain over a predetermined period. The plurality of images include a plurality of voxels, and the FAUPA identifier analyzes each voxel of the plurality of voxels in relation to one or more surrounding voxels of each voxel until each voxel of the plurality of voxels is evaluated. A brain network identification module configured to construct the functional network based on the identified FAUPAs that are functionally connected. A display module configured to display images of the brain depicting the FAUPAs included in the functional network.

A method for determining an intracortical working state of a functional network of the brain includes acquiring a number of first blood oxygen saturation level time point vectors of a number of gray matter voxels of a cortex of a brain functional network template, each of the blood oxygen saturation level time point vectors respectively having blood oxygen saturation level signals of each of the gray matter voxels at a number of continuous time points in a particular time period. The method further includes clustering the blood oxygen saturation level time point vectors as a number of gray matter voxel cooperative time point categories by taking the blood oxygen saturation level signals as objects, the gray matter voxel cooperative time point categories being sets of a number of discrete time points of the gray matter voxels respectively in the particular time period. The method includes determining the number of gray matter voxel cooperative time point categories as the intracortical working state of the first cortex.

The invention discloses a method for extracting features of neurosis based on a graph theory and machine learning. The method comprises the steps: firstly, constructing a cerebral functional network of a body dimension and an emotion dimension in case of the neurosis based on EEG data, and separately selecting a phase lag index and a weighted phase lag index as measures of a network middle edge by taking 62 channels as network nodes; and extracting network topological attributes--global efficiency corresponding to the two indexes, carrying out fusing so as to obtain eigenvectors for identifying the body dimension and the emotion dimension, and carrying out classification by using a machine-learning classification model. Network abnormal link positions of two dimensions can be positioned by mapping the cerebral functional network corresponding to the features to a 3D brain model. According to the method, a marker can be further provided for identification on the neurosis, and targets for further researching diseases are provided.

The invention provides a network data processing method, a network element and a system. The network data processing method comprises the following steps that: a data analysis network element sends a request message to at least one functional network element, wherein the request message comprises a data sampling mode, the request message is used for indicating the at least one functional network element to report network data by adopting the data sampling mode, and the data sampling mode accords with a random selection principle; and the data analysis network element receives the first network data from the at least one functional network element and performs association processing on the first network data received from the at least one functional network element. Since the data sampling mode accords with the random selection principle, the at least one functional network element randomly selects the network data according to the data sampling mode, and only reports a small amount of network data to the data analysis network element, thereby reducing the data acquisition load of the data analysis network element.

Intraoperative functional mapping using an intraoperative thermal imaging system is described. The system enables higher resolution images, faster acquisition speeds, and is non-invasive. The high resolution functional maps can provide physiologic information, prognostic information, and functional network structures to a neurosurgeon in a time efficient manner.

The embodiment of the invention provides a data information acquisition method and device, related equipment and a medium. The method comprises the following steps: acquiring a capability calling subscription request sent by a second functional network element based on subscription request parameter information, wherein the signing request parameter information is determined by authentication response parameter information, the authentication response parameter information is obtained by performing authentication processing on the capability calling request by the network open function equipment, and the capability calling request is sent by the second function network element based on the first subscription rule; acquiring access parameter information from the signing request parameter information, and performing signing verification on the capability calling signing request based on the access parameter information; generating capability calling contract signing response informationfor the capability calling contract signing request when contract signing verification succeeds, and returning the capability calling contract signing response information to the second function network element, wherein the capability calling subscription response information is used for indicating the second functional network element to access the first functional network element. By adopting the method and the device, the data information acquisition efficiency can be improved.

The invention belongs to the technical field of urban traffic, and particularly relates to a link prediction method and device in an urban road network. The method comprises the steps of collecting anurban road network data set, and calculating an attribute value of each urban place; dividing each attribute value into class clusters by utilizing a cloud model to obtain a dichotomy result about whether link connection is established between the attribute and the city node or not, and calculating the determinacy of each attribute for the dichotomy label; calculating a frequent binomial set of the two associated attributes according to a binary classification result to construct a two-layer functional network model structure; training the two-layer functional network model, and minimizing atarget loss function by adopting a least square method; inputting urban road network data to be tested into the trained two-layer functional network model, and outputting a binary classification labelfor judging whether a road is generated between two urban places in the urban road network; according to the invention, the high dependence of a complex network structure is overcome, and the link prediction effect is optimized, so that the prediction accuracy is improved.

The present invention discloses a method for extracting brain function states. The method comprises the following steps: 1) extracting time series from functional magnetic resonance imaging; 2) dividing the time series into overlapping sub-segments of fixed length and calculating correlation coefficients among the sub-segments to construct a time-dependent dynamic function network; 3) vectorizingthe dynamic function network and matching the vectorized dynamic function network into a functional connection aggregation matrix; 4) improving and training a deep autoencoder model and inputting thefunctional connection aggregation matrix into the deep autoencoder for dimensionality reduction to obtain several feature vectors; 5) determining the optimal number of clusters and clustering the matrix composed of the feature vectors to obtain public functional networks of different functional states; and 6) analyzing residence time, conversion times and stability of the various functional statesand determining the brain function states. The method has reference value for study of brain cognitive functions and is helpful for discovering the differences among different functional states and the changing laws among the functional states.

The invention discloses a human body balance function quantitative evaluation system based on a feedback control process, the system is composed of an acceleration sensing module, a body motion posture detection module, a display interaction module and a balance ability evaluation module, a user executes a virtual aiming task to maintain a part of the body of a human body at a specific angle and orientation; meanwhile, the spatial position of a part of the body is detected through an acceleration sensor worn by a human body, and sensor data are processed by adopting a linear dynamics method and a functional network analysis method to obtain a real-time quantitative evaluation result of dynamic body feeling and a dynamic kinematic chain body weakness index. The system has excellent portability, can quantitatively evaluate the balance function of the upper and lower limbs and the core muscle group of the human body including the dynamic proprioceptive sensation and the dynamic kinematic chain body weakness index of the human body, and helps patients with body injury and postoperative body to estimate the self rehabilitation degree.