44 results about "Functional network" patented technology

The invention relates to a depression risk detection method based on self-adaptive multi-scale neighborhood perception fusion space-time diagram convolution, and aims to solve the problems that traditional depression diagnosis is subjective and existing electroencephalogram detection space-time feature modeling is insufficient. The method comprises the following steps: acquiring electroencephalogram signals of a testee, and extracting frequency spectrum features through a frequency domain feature extraction module; in combination with an adjacent matrix (independent of an electrode physical distance) which is adaptively learned and normalized in training, embedded features are generated through a graph convolutional neural network; and then capturing long-time-history dependence through a dynamic feature extraction module, realizing feature multi-level fusion by means of a multi-cascade multi-scale convolution module, finally integrating local and global features through an adaptive feature fusion module, and inputting a prediction layer to output a detection result. According to the method, electroencephalogram signal time-space domain joint modeling is achieved, brain region function connection dynamic and multi-scale neural features are effectively captured, information redundancy and key feature loss are avoided, classification accuracy and generalization ability are remarkably improved, depression-related key brain region and function network features can be revealed, and the method has high interpretability and potential clinical application value.

The invention provides an automatic test system and method for a third-party high-power repeater, and relates to the technical field of automatic test equipment, and the system comprises an integrated reconfigurable passive link box, a control host, a vector signal generator and a vector signal analyzer. The integrated reconfigurable passive link box comprises a box body, a radio frequency switch matrix network, an embedded function network, a control port, an input port, an output port, an uplink interface and a downlink interface. The radio frequency switch matrix network and the embedded function network are arranged in the box body. The input port, the output port, the uplink interface and the downlink interface are arranged on the surface of the box body. The vector signal generator is connected with the input port, the vector signal analyzer is connected with the output port, and a third-party high-power repeater to be tested is arranged between the uplink interface and the downlink interface. According to the invention, the radio frequency switch matrix network and the embedded function network are packaged in the integrated reconfigurable passive link box, and the control host is utilized to carry out automatic testing on the third-party high-power repeater according to the preset process, so that frequent operation processes are avoided, and damage to instruments is reduced.

The invention relates to the technical field of neurotrauma prognosis image analysis, and discloses a craniocerebral trauma prognosis prediction analysis system based on a three-dimensional model. According to the system, multi-scale segmentation and topology construction are carried out on a craniocerebral three-dimensional image of a patient, the morphological evolution rate of a trauma area is tracked, and key signal events in the trauma evolution process are accurately recognized in combination with an edema signal change curve. The system further quantitatively analyzes dynamic deviations associated with the integrity of normal brain tissue fiber bundles when a signal event occurs, thereby generating a lesion propagation path and mapping it to functional network nodes of a standard brain map, ultimately identifying a prognostic key brain network. According to the technical scheme, key event capture and path foresight prediction in the dynamic propagation process of the secondary injury after the craniocerebral trauma are realized, and the accuracy of prognosis evaluation is improved.

Disclosed in the embodiments of the present application are a quantization-based data processing method and apparatus, and a device and a medium. The method comprises: using a plurality of bit quantization modes to respectively perform quantization processing on an original model parameter corresponding to a model function network, so as to obtain a quantized model parameter corresponding to each bit quantization mode; then, acquiring a first output result of the model function network under the original model parameter, and acquiring a second output result of the model function network under each quantized model parameter; and next, on the basis of the first output result and a plurality of second output results, selecting a target bit quantization mode from among the plurality of bit quantization modes, wherein the target bit quantization mode is used for performing quantization processing on the original model parameter during an inference phase of the model function network. The technical solution of the present application improves the accuracy of quantization processing, and achieves good model performance.

Various systems and methods for providing decentralized reputation management in a named-function network are described herein. A compute node is configured to access an information centric network (ICN) interest packet from a user device, the ICN interest packet including a function name and a data name; construct a named-function network (NFN) interest packet using the function name; transmit the NFN interest packet to a function provider; receive an NFN data packet with a version of a function corresponding to the function name; construct a named-data network (NDN) interest packet using the data name; receive an NDN data packet with a data value corresponding to the data name; determine that the version of the function is not on a denylist; and initiate execution of the version of the function with the data value in response to determining that the version of the function is not on the denylist.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

The application discloses a critical patient sedation state evaluation method based on a multi-modal network model, relates to the technical field of medical monitoring and signal processing, and comprises the following steps: analyzing a plurality of channel physiological signals of a patient, constructing a dynamic time-varying function network, and performing multi-scale community detection to identify stable function modules in the network and dynamic connection modes thereof. The network topology mode is fused with instantaneous phase information of the signals to form a fusion feature space-time atlas. After the atlas is subjected to deep feature extraction and structured coding, a sequence modeling network is used to capture time sequence dependence, and finally a quantitative sedation evaluation signal is output by a decoder. Through dynamic modular structure analysis of the brain function network and cross-level feature fusion, the method realizes more continuous and accurate objective evaluation of the sedation state.

The invention relates to the technical field of medical image analysis and brain network research, in particular to a glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises the steps of obtaining and preprocessing fMRI and structural MRI data of a patient, eliminating a tumor area through a focus shielding registration strategy, and reducing mass effect interference; dividing a tumor core region, a peritumoral abnormal region and a normal brain region; a Yeo-17 network template is registered to an individual brain region to realize mapping; defining a tumor core area as an independent network unit, and forming a new set with 17 normal networks; calculating the connection strength of whole brain voxels and network functions, and judging functional connection voxels according to a threshold value; and quantifying the intratumoral function ratio RIFR and the peritumoral ligation ratio RPTR, and generating a visual map. According to the method, an individual brain function network is accurately mapped, tumor heterogeneity interference is overcome, repeatable quantitative indexes are provided, and a reliable imaging analysis tool is provided for brain glioma function protection and clinical research.

The application relates to a method and device for predicting the risk of depression of a post-stroke patient, a medium and an electronic device. The method comprises: acquiring a brain image of the post-stroke patient and a standard graph of a depression functional network, the standard graph of the depression functional network being used to represent a brain functional network related to post-stroke depression; calculating a network damage score of the post-stroke patient according to the brain image and the standard graph of the depression functional network; acquiring a standard graph of depression structural disconnection, the standard graph of the depression structural disconnection being used to represent a brain disconnection distribution related to post-stroke depression; calculating a structural disconnection score of the post-stroke patient according to the brain image and the standard graph of the depression structural disconnection; inputting the network damage score, the structural disconnection score and clinical information data of the post-stroke patient into a prediction model to determine the risk of depression of the post-stroke patient after several months of onset through the prediction model. The application can predict the risk of depression according to the direct impact of a stroke lesion on a brain network responsible for emotion regulation in the brain.

[0009] An embodiment of the present application provides a communication method and a communication device. The method includes a step in which a second network function network element receives a service request message from a first network function network element and determines whether to provide a service to the first network function network element based on a first token. The service request message is used to request the second network function network element to provide a service to the first network function network element, and the service request message includes a first token and second service domain information, where the second service domain information indicates a service area of ​​the service requested by the first network function network element, and the first token includes the first service domain information, where the first service domain information indicates a service area range in which the first network function network element can obtain the service from the second network function network element. In the solution of the present application, the first service domain information is added to the first token to reduce or prevent the first network function network element from accessing the service without authorization, thereby ensuring network security.

The invention belongs to a network construction evaluation method, and aims to solve the technical problems that an existing brain function network construction method is lack of personalized partitions, insufficient in nonlinear neural association description, weak in cross-scene generalization ability and difficult to support precision and large-scale clinical application of brain-computer interface neural regulation. According to the brain-computer interface regulation-oriented personalized brain function network construction and evaluation method provided by the invention, a personalized brain function network is constructed based on neural activity mode characterization, and personalized brain function network construction of heterogeneous fMRI data is realized by fusing neurodynamics knowledge constraint and data-driven modeling; the method can be used as a universal modeling tool for heterogeneous fMRI data, and provides reliable technical support for brain-computer interface nerve regulation and control.

This invention discloses a hypergraph representation method for brain functional networks. The steps include: preprocessing resting-state functional magnetic resonance imaging (fMRI) to obtain time series data for all brain regions; dividing the entire time series into multiple overlapping sub-sequence segments using a sliding window; constructing a dynamic brain functional network and transforming it into an optimization model; constructing a hypergraph of the dynamic brain functional network using the nearest neighbor algorithm; dynamically modifying the hypergraph structure through convolution operations and extracting features to obtain a new dynamic hypergraph; extracting the Laplacian matrix of the dynamic hypergraph; constructing the manifold regularization term of the Laplacian matrix and simultaneously introducing the manifold regularization term and the L1 norm regularization term into the optimization model to obtain the hypergraph representation of the brain functional network. This invention is used to represent functional interactions and higher-order relationships between multiple brain regions, determine discriminative brain functional network classification features, and effectively improve the classification performance of brain disease features.

Method for analyzing a mechatronic system, wherein the mechatronic system has one or more functions, the functions comprising one or more hardware and / or software functions, and in the method - a first network (20) in the form of a tree graph with several hierarchy levels and nodes (201, 202, 203, 204, 205, 206) arranged in the hierarchy levels is provided, wherein the nodes (201-206) are each representative of one of the functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the functions represented by the nodes (201-206), wherein the top hierarchy level has a single node (201) as the initial node, - a node of the tree graph of the first network (20) is specified as the first trigger node, - Software network data is provided that is representative of the software functions of the mechatronic system and their dependencies, - depending on the first trigger node and the software network data, a second network (30) in the form of a tree graph with several hierarchy levels and nodes (301, 302, 303, 304, 305) arranged in the hierarchy levels is determined, wherein the nodes (301-305) are each representative of one of the software functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the software functions represented by the nodes (301-305), wherein the top hierarchy level has a single node (301) as the initial node, and - depending on the first network of action (20) and the second network of action (30), the mechatronic system is analyzed, wherein an analysis of the mechatronic system includes that - the first network (20) is extended by the second network (30) such that the first trigger node of the first network (20) corresponds to the initial node of the second network (30), and - the mechatronic system is analyzed depending on the extended first network of action (40), wherein the mechatronic system has one or more diagnostic functions that are representative of one or more software functions for diagnosing the mechatronic system, in which - a node of the tree graph of the extended first network (40) is specified as the second trigger node, - one of the diagnostic functions is specified and assigned to the second trigger node, - Diagnostic network data is provided that is representative of the diagnostic functions of the mechatronic system and their dependencies, - depending on the second trigger node and the diagnostic network data, a third network (50) is determined in the form of a tree graph with several hierarchy levels and nodes (501, 502, 503, 504, 505) arranged in the hierarchy levels, wherein the nodes (501-505) are each representative of one of the diagnostic functions of the mechatronic system and are linked to each other via one or more logical operators, such that the tree graph represents dependencies between the diagnostic functions represented by the nodes (501-505), wherein the top hierarchy level has a single node (501) as the initial node, such that the second trigger node of the extended first network (40) corresponds to the initial node of the third network (50), and - depending on the extended first network (40) and the third network (50) the mechatronic system is analyzed.

The invention discloses a functional network structure design method and application of a high-thermal-conductivity wave-absorbing composite material. The method comprises the following steps: S1, adding wave-absorbing functional particles into a polymer matrix by a melt blending or solution mixing method to obtain a wave-absorbing composite material; and crushing or crushing the wave-absorbing composite material to obtain the wave-absorbing functional particles. S2, mixing the wave-absorbing functional particles with the heat-conducting functional particles, so that the wave-absorbing functional particles are coated with the heat-conducting functional particles to form composite particles; and then loading the composite particles into a mold, pre-pressing to discharge gas among the particles, then carrying out hot pressing, and cooling to obtain the composite material with the three-dimensional communicated heat-conducting and wave-absorbing dual-function network structure. According to the invention, the heat-conducting functional particles are selectively distributed at a polymer particle micro-area interface through a hot-pressing process to construct an isolation structure, a continuous heat-conducting network is formed, and the composite material with excellent heat-conducting property and wave-absorbing property is obtained and can be used as an electronic chip packaging material and used in the field of electromagnetic compatibility.

The invention provides a cerebral apoplexy postoperative care risk early warning method and system fused with a brain image, and the method comprises the steps: improving the prediction precision through multi-modal data fusion, and integrating clinical physiological features, focus function influence points and brain connection group disconnection features to construct an initial data set; learning the importance of each feature by using the primary model, adjusting the weight of the brain function map, and optimizing the focus influence integral to enhance the representation ability; for a sample imbalance problem, generating a synthetic sample based on an importance coefficient according to a local density and a weighted distance, and realizing minority class expansion; and training a risk early warning model based on the balanced data set, inputting a new patient by adopting the same feature extraction process, outputting a personalized postoperative care risk level, and realizing an intelligent assessment closed loop from image anatomy to a functional network.

Functional networks are mapped for individuals and group populations based on magnetic resonance imaging, and the resulting functional mapping data (e.g., probabilistic maps of functional networks and / or integration zones where multiple functional networks overlap and / or interact) are used to guide or otherwise monitor the delivery of neuromodulation therapies. Individual-specific functional network maps can be generated based on an overlapping template matching that is capable of assigning multiple networks to a given grayordinate.

A functional network map of the somatomotor cognitive action network (SCAN) is generated from magnetic resonance data based on a template matching of time-course signals with one or more functional network templates. A first network map is generated using correlations of the time-course signals with the functional network template(s) at a first threshold value. The first network map is then updated by reassigning grayordinates in the first network map based on correlations generated using a second threshold value that is higher than the first threshold value. A network confidence map can be generated to indicate the confidence in network assignments. The network confidence map is generated using a permutation-based analysis in which dense time series data are partitioned, the partitioned shuffled based on a number of permutations. Network assignments are generated for each permutation and compared across shuffles to assess network confidence.

System and methods that improve the use of network functions on edge data networks are disclosed. A mechanism that supports edge-assisted UE context and trigger collection is described herein, where a UE can actively send its context information and / or any trigger (e.g. a request for a network function / service to be deployed in edge data networks, etc.) to an edge enabler server, which processes the received context and triggers from its UEs. The edge enabler server may also forward UE context and triggers to 5G core network (5GC) upon receiving a solicitation request from the 5GC, or if the 5GC has already made a subscription for getting notification on UE context and triggers. A network repository function is provided as a new network function collect, store, and manage edge data network information.

The application discloses a multi-center depression recognition method and system based on decoupling of cross-subject relationship networks, relates to the technical field of medical image processing and artificial intelligence diagnosis, and comprises the following steps: acquiring subject data of a plurality of collection centers, wherein the subject data comprises brain image data; constructing an initial individual brain function network based on the brain image data; performing individual brain network representation learning on the initial individual brain function network to obtain a subject-level brain network representation; and performing decoupling learning and joint optimization based on disease-related and collection center-related cross-subject relationship networks, and then guiding iterative updating of the individual brain network through a group-level disease discrimination representation, so that the common characteristics related to depression can be better mined, the influence of the differences between the collection centers on feature learning is weakened, the representation and discrimination ability of the model for disease characteristics is improved, the model has more stable performance in a multi-center scene, and the generalization performance of cross-center data is improved.

The present application discloses a sensing delay management method and device. The method comprises: a first functional network element receiving a sensing service request, the sensing service request comprising delay requirements of a sensing service; sending first information to a second functional network element, the first information being used for indicating delay requirements for the second functional network element to execute a sensing-related operation that comprises at least one of the following: acquiring original sensing data, calculating sensing data, transmitting sensing data, and storing sensing data; and sending second information to a third functional network element, the second information being used for indicating delay requirements for the third functional network element to execute a sensing-related operation that comprises at least one of the following: calculating sensing data, transmitting sensing data, and storing sensing data. In the method of embodiments of the present application, delay slicing and management are performed for each operational process of a sensing service according to delay requirements of the sensing service, thereby ensuring reliability of an execution process of the sensing service.

This invention relates to the field of medical image processing technology, specifically a method for constructing a brain functional network based on a large language model. First, the functional magnetic resonance imaging (fMRI) data of the subject is preprocessed, brain regions are divided based on a pre-defined atlas, and average time series data are extracted. Then, a large language model is used, referencing a neuroscience knowledge base, to generate prior knowledge text descriptions for each brain region. Simultaneously, instance-level text descriptions are generated based on the activation state and correlation of the time series data. The prior knowledge text and instance-level text are fused and converted into node feature vectors using a professional text encoder. Furthermore, this method constructs a brain map containing node features and edge connections, which is then input into a graph neural network for feature learning and classification training. By deeply integrating prior textual knowledge from the medical field with image data, a brain functional network rich in semantic information is constructed, significantly improving the accuracy of brain disease identification and the model's generalization ability.

The invention belongs to the field of brain image data processing, and particularly relates to a brain function gradient analysis method and device, a medium and a program product. The brain function gradient analysis method comprises: obtaining a gradient space of a first function network and a gradient space of a second function network, the first function network and the second function network being the same function network of different brains or different function networks of the same brain, the function network is a set comprising at least two brain regions which are mutually correlated in function; and calculating a bulldozer distance between the gradient space of the first function network and the gradient space of the second function network to obtain a gradient spacing. According to the method, spatial characteristics such as shapes, ranges and dispersion are captured by adopting the distance of the bulldozer, and full mining of image data is realized; and the post-traumatic stress disorder is predicted based on the brain function gradient analysis method after bulldozer distance improvement, so that the post-traumatic stress disorder can be identified more effectively.

Embodiments of the present application relate to automatic deactivation and activation of configuration functions of a network device that are incompatible with execution of an online software upgrade process. A network device can be configured to identify a first configuration data structure included in the network device and can be configured to obtain a data packet associated with an ISSU process, the data packet including a second configuration data structure. The network device can be configured to identify, based on the first configuration data structure and the second configuration data structure, one or more configuration functions of the network device that will be inactive during execution of the ISSU process. The network device can be configured to cause the one or more configuration functions of the network device to be deactivated and thereafter cause the ISSU process to be executed. The network device can be configured to, after causing the ISSU process to be executed, cause the one or more configuration functions of the network device to be activated.

The present disclosure discloses a model construction method and device, a storage medium and an electronic device, which are used for constructing a brain-like model of a pulse neural network based on biological brain topology constraints, and relate to the technical field of computational neuroscience. The present disclosure solves the problem of lack of biological rationality of the brain-like model of the pulse neural network. The model construction method comprises: dividing brain regions of to-be-processed functional magnetic resonance imaging data to obtain M brain region image data; generating M model nodes based on the M brain region image data; generating N model edges based on a correlation coefficient matrix between the M model nodes; screening the N model edges based on a preset network topology threshold to obtain S model edges meeting a preset condition; generating a topology constraint of a brain-like model based on a biological brain function network based on the M model nodes and the S model edges; and constructing the brain-like model based on the topology constraint. The present disclosure can improve the biological rationality of the brain-like model constructed based on the pulse neural network.

The invention discloses a method and system for predicting infant brain function connection based on morphological characteristics and a diffusion model, a storage medium and electronic equipment. The method in the formula comprises the following steps: extracting cortex morphological characteristics in an infant structure magnetic resonance image (sMRI), constructing a morphological similarity network (MSN), and realizing infant brain function full-connection layer prediction by utilizing a diffusion model in combination with classifier irrelevant guidance and a cross-modal attention mechanism. According to the method, individual development features are stably captured by using a longitudinal information extraction module, accurate mapping from morphological features to function connection is realized in combination with a classifier irrelevant guidance diffusion model and a morphological guidance attention mechanism, the prediction precision and stability are remarkably improved, and the prediction efficiency is improved. The problem that infant functional magnetic resonance imaging (fMRI) data is scarce or low in quality can be effectively solved, functional networks in different development stages are accurately reconstructed, and the accuracy of infant early brain development monitoring is improved.

The application relates to the technical field of medical big data processing and artificial intelligence auxiliary diagnosis, in particular to an early Alzheimer's disease diagnosis method, which comprises the following steps: preprocessing and registering structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) image data of a to-be-diagnosed object, constructing a mixed feature pyramid to extract multi-scale anatomical features, adopting a space-time manifold embedding module to extract dynamic functional features, strengthening pathological correlation features through a cross-dimension double attention mechanism, and finally realizing diagnosis classification through multi-modal feature adaptive fusion. The application can accurately capture the deep correlation between brain structure microlesions and functional network abnormalities, and significantly improve the diagnosis accuracy of Alzheimer's disease and early mild cognitive impairment.

The invention relates to the technical field of communication, in particular to a service verification method and device, and aims to guarantee the efficiency and accuracy of service verification and improve the range of service verification through a virtual-real combination mode of a digital twin network and a physical twin network. The method comprises the following steps: acquiring a service verification request, wherein the service verification request comprises a service demand of a network service; according to the service verification request, a verification environment corresponding to the network service is configured based on a digital twin network and a physical twin network corresponding to the physical network; obtaining a verification result of the network service, wherein the verification result is determined by verifying the service capability of the network service in the verification environment; wherein the verification environment comprises a first verification environment constructed based on a digital twin network and a second verification environment constructed based on a physical twin network; alternatively, the verification environment is constructed based on at least one digital twin function network element in the digital twin network and at least one physical twin function network element in the physical twin network.

The invention discloses a preparation method of composite molding conductive silica gel, and belongs to the technical field of functional polymer composites.The preparation method of the composite molding conductive silica gel comprises a silica gel matrix; the functional network is integrated in the silica gel substrate, the functional network comprises a sensing unit, the sensing unit is configured to generate a detectable resistance signal in response to mechanical deformation, and the resistance signal presents a negative pressure resistance effect in an initial stretching stage. According to the invention, stable and adjustable'negative pressure resistance-positive pressure resistance 'asymmetric response is realized in conductive silica gel, the limitation that a traditional sensor can only provide monotonic signals is changed, a solid hardware foundation is provided for capturing high-dimensional information sensing of strain size, rate and mode, and through an exquisite microstructure design, the sensor can be used for sensing high-dimensional information of strain size, rate and mode. The material not only realizes high sensitivity and high signal-to-noise ratio, but also has unique strain rate sensitivity, so that the material can accurately capture dynamic information which cannot be identified by a traditional sensor.

The application belongs to a network construction evaluation method, aiming at the technical problems that the existing brain function network construction method lacks personalized partition, the nonlinear neural correlation is not well described, the cross-scene generalization ability is weak, and it is difficult to support the precision and large-scale clinical application of brain-computer interface neural regulation, a personalized brain function network construction and evaluation method for brain-computer interface regulation is provided, the personalized brain function network is constructed based on the neural activity mode representation, the personalized brain function network construction for heterogeneous fMRI data is realized by fusing the neural dynamics knowledge constraint and the data-driven modeling, and the personalized brain function network construction for heterogeneous fMRI data can be used as a general modeling tool, and reliable technical support is provided for brain-computer interface neural regulation.