186 results about "Connectome" patented technology

The invention relates to the technical field of multi-modal nerve images and machine learning, in particular to an execution function prediction method, device and equipment based on a Riemannian manifold and a multi-modal connection group. According to the method, the structure connection group and the function connection group are mapped to the agent space of the Riemannian manifold, the multi-modal brain connection group is constructed, prediction modeling is carried out in combination with the random forest algorithm, and therefore the model capable of effectively predicting the performance of the individual execution function is constructed; projecting the extracted individual connection group to a tangent space by synthesizing data of the structure connection group and the function connection group, and constructing a Tangent connection matrix reflecting individual differences; and connecting the matrix by using a random forest algorithm and combining with a Tangent. The method not only overcomes the limitation of dependence on a single data source, but also optimizes the information extraction process, improves the capability of identifying the related mode from the complex neural network data, and improves the accuracy and robustness of the prediction result.

The invention discloses a micro-grid intelligent operation and maintenance controller and method, and relates to the technical field of micro-grid operation and maintenance. The intelligent operation and maintenance subsystem is formed by interactively connecting a plurality of cluster servers, each cluster server adopts a small convolutional neural network to establish a fault information set corresponding to each front-end device, and the cluster servers are utilized to train and learn a convolutional neural network model so as to obtain a small convolutional neural network and a large convolutional neural network; therefore, the state data set is identified so as to judge whether fault information exists or not. The micro-grid intelligent operation and maintenance controller and the micro-grid intelligent operation and maintenance method have good openness and compatibility, can be suitable for various micro-grids composed of different primary devices, can automatically judge micro-grid faults and carry out corresponding fault processing under the unattended condition, improve the micro-grid fault processing efficiency and reduce the fault processing cost. The dependence of micro-grid operation on operation and maintenance personnel is reduced, and the operation stability of the micro-grid is improved.

The invention relates to a multistable mechanical metamaterial structure with a tension characteristic, which belongs to the field of mechanical metamaterials and mainly comprises a multistable mechanical metamaterial cell structure, and the multistable mechanical metamaterial cell structure comprises a tension integral structure component, a degree-of-freedom constraint component and a connecting component. Under the action of a tensioning integral structure, when the structure is not subjected to external load, the structure is subjected to interaction of internal stress, and the balance is achieved in a self-stabilization state; according to the multistable mechanical metamaterial cell element structure with the tension characteristic, the structure of the position where each side of the square outline is located has two steady-state configurations, and the cell element structures are connected through a rectangular array to form the multistable mechanical metamaterial structure with the tension characteristic. The tensegrity structure and the mechanical metamaterial structure are combined, the types of existing mechanical metamaterial cell element structures are enriched, and a brand new direction is opened up for design, analysis and application of the tensegrity mechanical metamaterial structure.

The invention discloses a brain connection coupling analysis method based on grey matter morphological similarity-white matter fiber bundles, relates to the technical field of neuropsychiatric imaging, and has the technical key points that the method can be used for analyzing grey matter-white matter brain connection group coupling modes of the whole brain level, the level in brain network communities and the level between the brain network communities; the integration characteristics of structural tissues of the brain on different scales can be understood; the reliability of statistical analysis is improved by adopting a spatial autocorrelation correction method; based on a specific machine learning fusion model, the clinical application value of the model is verified.

The embodiment of the invention provides a connecting assembly for a flexible neural electrode and a flexible neural electrode kit. The flexible neural electrode comprises a near-end contact part, the near-end contact part comprises at least one near-end contact, a through hole is formed in the position of the near-end contact, and the through hole is used for exposing a conductive layer at the position of the near-end contact; the connecting assembly comprises a connecting terminal, the connecting terminal comprises a first connecting component, the first connecting component comprises at least one conductive needle, and the conductive needle can be inserted into the through hole and is electrically connected with the near-end contact through the conductive layer exposed in the through hole. According to the connecting assembly, reliable electric connection between the flexible neural electrode and the external equipment can be achieved, meanwhile, the multi-channel and high-density connection requirements are met, and the stability and adaptability of the structure are improved.

The invention discloses a high-precision multi-physics field reconstruction method based on a nested neural network architecture. The method comprises the following steps: S1, acquiring open-source multi-physics field data; s2, a multi-scale Fourier operator-driven U-Net physical field reconstruction model is constructed, the physical field reconstruction model comprises an embedding module, a coding module and a decoding module, the coding module comprises at least one coding layer, each coding layer is formed by sequentially connecting a multi-scale Fourier neural operator layer and a down-sampling layer, and the multi-scale Fourier neural operator layer and the down-sampling layer are embedded into the embedding module; the decoding module comprises at least one decoding layer, and each decoding layer is formed by sequentially connecting a multi-scale Fourier neural operator layer and an up-sampling layer; s3, training the physical field reconstruction model based on a training set; and S4, performing quantitative analysis and qualitative analysis on the reconstructed physical field by using the test set.

The utility model relates to the technical field of skull repairing, in particular to a splicable skull repairing system which comprises a first skull repairing piece, a second skull repairing piece and a plurality of connecting assemblies used for connecting the first skull repairing piece and the second skull repairing piece to the skull of the human body, and the first skull repairing piece and the second skull repairing piece are arranged in an up-down spaced mode. The first skull repairing piece comprises a plurality of first splicing strips which are arranged side by side in an abutting mode, the second skull repairing piece comprises a plurality of second splicing strips which are arranged side by side in an abutting mode, and the arrangement direction of the second splicing strips and the arrangement direction of the first splicing strips are staggered; a plurality of convex tongues are arranged on the second skull repairing sheet in an array mode, and a plurality of clamping grooves are formed in the first skull repairing sheet in an array mode; and the second skull repairing sheet is connected with the first skull repairing sheet through the clamping matching of the convex tongue and the clamping groove. The skull repair block has the advantages that the risk that the skull repair block collapses when bearing impact is reduced, the strength of the spliced skull repair block is improved, a patient is protected, the operation time is shortened, and the cost and the postoperative infection rate are reduced.

The invention discloses a tunnel supporting system based on a multi-cavity composite duct piece structure and intelligent monitoring and evaluation, and the tunnel supporting system comprises an assembly type multi-cavity duct piece structure, a multi-directional self-locking type buckle connection assembly, a multi-mode intelligent evaluation system and an active early warning subsystem, the assembly type multi-cavity segment structure is formed by splicing a plurality of multi-cavity segments, and the interior of the assembly type multi-cavity segment structure is divided into a plurality of cavities through longitudinal and transverse partition plates. The multi-modal intelligent evaluation system integrates a CNN model, an LSTM model, an XGBoost model and a model fusion scoring module to score the stability of the tunnel support system; the active early warning subsystem comprises multiple types of intelligent sensors, an edge computing node and a cloud reasoning module. The system is matched with a flexible self-locking connection technology, an intelligent supporting stability analysis method fusing multi-model dynamic evaluation is adopted, active early warning linkage is achieved through real-time edge data collection and a multi-stage judgment mechanism, and the stability, intelligence and reliability of a tunnel supporting structure are remarkably improved.

Systems, methods, and computer program products for machine unlearning on identity graph neural networks may obtain an identity graph including a plurality of graphs, each graph including a plurality of edges and a plurality of nodes for the plurality of edges, and, in each graph, each edge and each node is associated with a same identity; apply at least one edge augmentation algorithm to the identity graph to make the identity graph a biconnected identity graph; split the biconnected identity graph into a plurality of biconnected components, such that there are no articulation points in each biconnected component; for each biconnected component, train a graph neural network that corresponds to that biconnected component to generate a graph embedding and a local minima; train an ensemble neural network on the graph embedding and the local minima of each biconnected component; and provide the trained ensemble neural network.

The application provides a process industry soft measurement method based on feature extraction and incremental learning. The method introduces an end-to-end neural network soft measurement model composed of multiple parallel time series feature extraction self-encoder networks and a regressor network connected in front and back, extracts time series features from multi-process variable time series data, and uses the time series features for performance index soft measurement modeling. In addition, based on the time series features extracted by the model, the method proposes an incremental learning method based on time series features. The soft measurement method provided by the application can effectively and accurately perform soft measurement on some performance indexes that are difficult to directly measure or have high measurement cost in the process industry, and through the incremental learning method, the soft measurement model can long-term maintain good soft measurement performance in the industrial data stream with strong real-time performance, thereby providing reference and guidance for monitoring, measurement, optimization and control of various parameters in the process industry production process.

Example implementations relating to mounting assemblies are disclosed herein, including in one example a skull mount having a substantially longitudinal bracing member and an engaging member comprising a curved portion configured to engage a portion of an animal skull such as a base of the brain cavity. One or more adjusting members adjustably position and secure the bracing member and the engaging member to securely engage and retain a skull of an animal therebetween by engaging the curved portion of the engaging member with at least a portion of the skull such as the brain cavity. A base assembly attached to a mounting surface is coupled to a connecting assembly removably coupling the bracing member and engaging member assembly to the base assembly, the connecting assembly comprising a pivot rod and a sleeve configured to receive and removably retain the pivot rod.

An apparatus includes a plurality of beads and a linking assembly joining the beads together. The beads include a first bead having a first external contact surface, and a second bead having a second external contact surface. A magnet is contained within the housing. The beads and the linking assembly are arranged in an annular arrangement and sized to form a loop around an anatomical structure in a patient. The loop may move between a contracted and expanded configuration. The loop is magnetically biased toward the contracted configuration by a magnetic bias of the beads. The first external contact surface and the second external contact surface engage each other as the loop transitions into the contracted configuration to drive the first bead and the second bead into a predetermined orientation relative to each other about the linking assembly in the contracted configuration.

The invention relates to the technical field of brain science image processing, and discloses a brain tissue neuronal morphology three-dimensional reconstruction and connection group analysis method and system, and the method comprises the steps: carrying out the feature point extraction and matching and optical flow registration of continuous ultrathin section electron microscope images, and obtaining three-dimensional image volume data; carrying out semantic segmentation on cell bodies, dendrites, axons and dendritic spines through a three-dimensional deep convolutional neural network, and driving secondary registration through confidence feedback; detecting synapses and classifying the synapses as excitatory or inhibitory types; executing topology perception skeletonized reconstruction and performing segmentation correction through topology anomaly feedback constraint; and constructing a synaptic connection matrix and executing graph theory analysis.

A system processes images of documents, for example, identification documents. The system transforms an image of a document to generate an image that represent the document in a canonical form. For example, if the input image has a document that is tilted at an angle with respect to the sides of the image, the system modifies the orientation of the document to show the document having sides aligned with the sides of the image. The system stores user accounts that include user information including images. The system generates a graph of nodes that represent user accounts with edges determined based on similarity scores between user accounts. The system determines connected components of user accounts, such that each connected component represents user accounts that have a high likelihood of being duplicates.

The invention discloses a physiological signal monitoring method and wearable equipment, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of heart state judgment for an electrocardiogram in the prior art. Comprising the steps that basic information of a user and physiological signals collected in real time are obtained, the physiological signals comprise electrocardiosignals, electromyographic signals and acceleration signals of the heart, the physiological signals are collected based on a multi-mode sensor on the wearable device, and the wearable device is fixed to the body of the user through a connecting assembly of a strip type structure; the physiological signals are subjected to multi-modal fusion based on a multi-modal fusion algorithm, physiological parameter data are obtained, a monitoring task is determined, and the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; and calling a monitoring prediction model which is matched with the monitoring task and has completed model training to perform monitoring processing on the physiological parameters and the user basic information to obtain a monitoring result, and performing feature comparison based on the monitoring result.

According to the measuring scale based on the spinal surgery, when the depth of an excision position needs to be measured, the measuring scale based on the spinal surgery is adopted for measurement, the measuring assembly is pushed to move, so that the measuring assembly moves relative to the measuring end and drives the connecting assembly to move, and the connecting assembly moves to drive the reading assembly to move; therefore, the stroke length of the measuring assembly relative to the measuring end is measured. The scale surface is perpendicular to the measuring end, so that the reading can be accurately and quickly carried out by directly lowering the head to enable the sight line to be perpendicular to the scale surface without looking at the head horizontally in the reading process, and the problem of measuring errors caused by sight line errors is avoided. Secondly, since the scale surface is perpendicular to the measuring end, the doctor can complete measurement without squatting, so that the measuring speed is increased; meanwhile, in the measuring process, the surgeon and the first operation assistant can synchronously and clearly read numbers, and the accuracy of the reading result is improved through mutual correction of the two persons.

The invention provides an intelligent process arrangement method, system and device based on vue-flow and a medium, and belongs to the technical field of front-end process arrangement. A node data structure and an interaction rule are unified by formulating a node standardization interface and an attribute panel interface; developing a public connection component and providing a style self-definition capability; packaging special nodes such as a large model based on a standardized interface; constructing a node registration class to manage node uniqueness and connection logic, and providing a component registration interface and a development auxiliary method; the registration node is adapted to a vue-flow framework, and normal rendering is ensured; and finally realizing the rendering logic and state style unification of the node and the attribute panel. The standardization degree and the development efficiency of a process arrangement system are improved, the node compatibility is guaranteed through a standardized interface, and the framework adaptation difficulty is reduced. Connection logic management and state styles uniformly optimize user experience; according to the overall scheme, the stability and maintainability of the system are enhanced, and a solid technical support is provided for intelligent process arrangement.

The invention belongs to the technical field of data processing, and relates to a SeaTunnel-based data offline synchronization method and device, computer equipment and a storage medium, and the method comprises the following steps: formulating a visual parameter configuration component page according to a connection component and a conversion component in SeaTunnel; binding a component generation strategy according to page component configuration; according to the component generation strategy, a SeaTunnel script is generated; and the generated SeaTunnel script is synchronized to a DolphinScheduler task, and the generated SeaTunnel script is synchronized to the DolphinScheduler task. SeaTunnel script configuration is carried out in a visual mode, the development difficulty is reduced, the specification of the SeaTunnel script is defined, and the script abnormal rate is reduced, so that the development efficiency is improved, and the problems that development is difficult, errors are prone to occurring, and the efficiency is low can be solved.

The application discloses a physiological signal monitoring method and a wearable device, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of existing electrocardiogram for heart state judgment. The method comprises the following steps: acquiring user basic information and a physiological signal collected in real time, wherein the physiological signal comprises a heart electrocardiogram signal, a muscle electrocardiogram signal and an acceleration signal, the physiological signal is collected based on a multi-modal sensor on a wearable device, and the wearable device is fixed on the user body through a belt type connection assembly; performing multi-modal fusion on the physiological signal based on a multi-modal fusion algorithm to obtain physiological parameter data, and determining a monitoring task, wherein the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; calling a monitoring prediction model matched with the monitoring task and having completed model training to perform monitoring processing on the physiological parameter and the user basic information, obtaining a monitoring result, and performing feature comparison based on the monitoring result.

The invention discloses a multi-modal medical image segmentation system based on modal complementation and state space modeling, and the system is constructed based on a 3D U-Net architecture, and comprises a first residual module, a second residual module, a first connection module and a segmentation head which are connected in sequence, N encoders which are formed by connecting convolution modules and down-sampling modules in sequence, each decoder is formed by sequentially connecting a residual error module, a connection module and a transposition module. One path of 3D input data is input to the depth channel convolution module, and the other path of 3D input data is input to the first residual module. And a transpose module output signal of the Nth decoder and a second residual module output signal are input into the first connection module together, are connected by the first connection module and then are output into the segmentation head, and the segmentation head outputs a segmentation signal of the multi-modal data. According to the method, the complementary characteristic between different modes in the multi-mode medical image is fully utilized, and the global context modeling and local structure sensing capabilities are considered at the same time.

The utility model discloses a perception examination mechanism for neurology, which relates to the technical field of examination equipment, and comprises a shell, a sliding chute is arranged on one side of the shell, an adjusting structure is arranged on the upper part in the shell, a perception examination structure is arranged on the lower part of the shell, and the sliding chute is arranged in the sliding chute. A connecting assembly is installed between the adjusting structure and the perception checking structure. The percussion hammer and the examination needle can be used for examining a patient by replacing the perception examination assembly, when the percussion hammer is used, after one end of the hammer body is attached to the skin of the patient, the rotary table is rotated, the hammer body is lifted to different heights through the cooperation of the sliding plate and the rectangular plate, after the hook plate is pulled, the percussion hammer hammers the patient, and the higher the sliding plate is lifted, the higher the percussion hammer is. The larger the knocking force of the percussion hammer is, the higher the percussion force of the percussion hammer is, the rising height of the percussion hammer each time can be recorded through the scale marks, so that the perceptual condition of a patient is recorded, and when the tactile needle is replaced, the insertion depth of the tactile needle is recorded according to the scale marks, and meanwhile the perceptual reaction of the patient when the tactile needle is inserted into different depths is recorded.

The application discloses a robot positioning method and system based on deep learning, relates to the technical field of artificial intelligence, and sends a global environment map and a local environment map under the global environment map into a trained deep neural network model to obtain an accurate candidate pose of a robot; the candidate pose is finely corrected through robot movement, and finally the best robot pose is obtained; the deep neural network model comprises a convolution layer conv0, a maximum value pooling layer maxpool, a convolution layer conv, a cross attention layer cross_attn and a candidate pose acquisition layer Maxop; the maximum value pooling layer maxpool and the convolution layer conv are sequentially connected to form a layer group, a plurality of layer groups are sequentially connected, the layer group located at the uppermost layer is connected to the output of the convolution layer conv0 as the shallowest scale, and the output of the convolution layer conv0 and all layer groups are sequentially connected to the cross attention layer cross_attn and the candidate pose acquisition layer Maxop to output the best pose of the robot; the robot positioning method and system improve the accuracy and robustness of robot positioning.

The invention relates to the technical field of wafer detection, and discloses a wafer defect detection carrying table and a use method thereof, the wafer defect detection carrying table comprises a carrying table, two lifting assemblies and two connecting assemblies; the two lifting assemblies are located on the two sides of the objective table, and each lifting assembly is provided with a lifting end. The first end of one connecting assembly is hinged to the lifting end of one lifting assembly, and the second end of the connecting assembly is hinged to one end, in the first direction, of the objective table; in the other connecting assembly, the first end of the connecting assembly is hinged to the lifting end of the other lifting assembly, and the second end of the connecting assembly is hinged to the other end, in the first direction, of the objective table; wherein at least one of the connecting assemblies can elastically stretch out and draw back in the second direction; the first end and the second end of at least one connecting assembly can be circumferentially limited in the second direction, and the second direction is that the first end of the connecting assembly faces the second end of the connecting assembly; the imaging definition of an imaging device on a part with a warping degree of a wafer can be improved, so that defect information of the wafer can be acquired.

The invention discloses a robot positioning method and system based on deep learning. Relates to the technical field of artificial intelligence. The method comprises the following steps: sending an obtained global environment map and a local environment map under the global environment map into a trained deep neural network model to obtain an accurate candidate pose of a robot; fine correction is conducted on the candidate poses through robot motion, and finally the optimal robot pose is obtained; the deep neural network model comprises a convolutional layer conv0, a maximum value pooling layer maxpool, a convolutional layer conv, a cross attention layer crossattn and a candidate pose acquisition layer Maxop; the maximum pooling layer maxpool and the convolution layer conv are connected in sequence to form a layer group, the multiple layer groups are connected in sequence, the layer group located on the uppermost layer serves as the shallowest scale to be connected to the output of the convolution layer conv0, and the output of the convolution layer conv0 and the output of all the layer groups are connected to the cross attention layer crossattn and the candidate pose obtaining layer Maxop in sequence so as to output the optimal pose of the robot; according to the robot positioning method and system, the accuracy and robustness of robot positioning are improved.

The application discloses a magnetocardiogram gradient instrument and an electrocardio collection circuit packaging structure and a signal processing system, relates to the technical field of medical equipment, and realizes high-density integration of hardware and reliable protection through a rigid protection frame formed by a circuit chip, a layer plate structure and a connecting assembly, shortens a signal transmission path, reduces the influence of environmental vibration, dust, moisture and electromagnetic disturbance on a synchronous sampling link, and provides a stable data basis for multi-modal fusion imaging. The signal processing system comprises multi-modal synchronous collection, noise suppression, data fusion imaging and a diagnosis auxiliary module, generates a high-precision space-time dynamic imaging atlas by constructing a cardiac electromagnetic field joint forward model and applying time and space double constraints, and outputs a cardiovascular risk assessment, a cardiac arrhythmia positioning and a myocardial ischemia quantification result in combination with machine learning. The application solves the problems of low integration, poor anti-interference and insufficient single modal detection precision of existing equipment.

The embodiment of the invention discloses a testing device. The testing device comprises a cable, a connecting assembly and a stimulating assembly which are connected in sequence. Wherein the connecting assembly is provided with a limiting groove for limiting and fixing the implantable electrode, a plurality of conductive elastic sheets in conductive connection with the cable are arranged in the limiting groove, and the conductive elastic sheets are used for being detachably and electrically connected with a wire of the implantable electrode, so that the implanted implantable electrode is tested. The stimulation assembly is provided with a conductive probe electrically connected with the cable, and can apply a test electric signal to the nervous tissue to carry out a stimulation reaction test, thereby assisting in accurately confirming the position of the target spot. According to the testing device, the electrode testing function and the nerve stimulation testing function are integrated into a single device, the structure is compact, operation is visual, and the accuracy, smoothness and convenience of an operation are remarkably improved.

The invention discloses a modulation identification method, device and equipment based on a pulse convolutional neural network, and relates to the technical field of wireless communication, and the method comprises the steps: connecting and combining a plurality of pulse residual blocks fusing an attention mechanism, and constructing a convolutional layer and a full connection layer of the pulse residual blocks through LIF neurons; based on the pulse residual blocks of the connection combination, creating a deep network model which takes a pulse residual neural network as a backbone network framework and is fused with an attention mechanism; and obtaining an IQ signal data set to train the created deep network model, thereby realizing modulation identification classification of the IQ signal data to be identified based on the trained deep network model. The wireless signals are modulated and identified through the improved deep network model, the effects of improving the identification speed and reducing the operation power consumption are achieved, and improvement of the performance of a wireless communication system is facilitated.

The present invention relates to the field of cloud computing technology, and particularly to a method and apparatus for visual orchestration of cloud computing resources. It mainly includes: creating connection components for connecting resource components and defining the constraint conditions of the connection components; integrating the resource components and the connection components into composite components and defining the constraint conditions of the composite components, and taking the composite components as the smallest unit of orchestration; using the connection components in the composite components to orchestrate each connection of the composite components, checking and validating according to the constraint conditions of the connection components, and screening out the connection components that can be connected to it when used in subsequent orchestration starting from a connection component. The present invention can improve the reusability of the orchestration components; and obtain the connection components for subsequent orchestration use through the screening of available connection components, thereby enhancing the usability.

The utility model discloses an acquisition / stimulation device and a dual-mode electromyography, and relates to the electromyography technical field, the acquisition / stimulation device comprises a housing, a PCB, a stimulation assembly, an acquisition assembly, a control assembly and a connection assembly, the housing is provided with an accommodation cavity; the PCB is arranged in the accommodating cavity; the stimulation assembly is arranged on the PCB; the acquisition assembly is arranged on the PCB; the control assembly is arranged on the PCB and electrically connected with the stimulation assembly and the acquisition assembly. The connecting assembly is arranged on the PCB, one side of the connecting assembly is electrically connected with the control assembly, and the other side of the connecting assembly is partially exposed out of the shell and is used for being electrically connected with the electrode plate; the control assembly controls the connection assembly to be electrically connected with the stimulation assembly or the acquisition assembly, so that the acquisition / stimulation device has an acquisition state and a stimulation state. The acquisition / stimulation device provided by the utility model aims to realize two functions of acquiring electromyographic signals and improving or recovering stimulated muscles or muscle groups through one set of equipment.

This application provides a disease intelligent diagnostic device based on brain structural connectivity identifiers and its application. The device includes (1) a data acquisition module; (2) a data processing module: reconstructing the spin distribution function of the data and distributing the spin distribution function in a standard space; (3) a data projection module: projecting the spin distribution functions of patients and healthy individuals into the standard space to obtain the Z-value of the maximum direction within the voxel; and (4) a Connectome Identifier extraction module: reducing the dimensionality of the Z-value to form a 1-dimensional feature value. The device of this application can be used for the diagnosis of various brain diseases and the determination of lesion location and severity.