13 results about "Neural control" patented technology

Systems, devices, and methods for treating drug-refractory epilepsy are disclosed. [Solution] In one embodiment, a method for treating epilepsy is disclosed, comprising detecting electrophysiological signals of a subject using a first electrode array coupled to a first intravascular carrier. The method further comprises analyzing the electrophysiological signals using a neuromodulator electrically coupled to the first electrode array, and stimulating a target site in the subject's body using a second electrode array coupled to a second intravascular carrier implanted in a portion of a body blood vessel above the base of the subject's skull.

This invention provides an advanced AGI (Awareness, Intelligence, and Knowledge) mind guidance system and method that solves problems such as complex consciousness content analysis. It includes: S1: Neural control decoding; S2: Developmental gap analysis; S3: Strategy generation and ethical review; S4: Multi-channel dynamic guidance; S5: Performance retrospection and evolution. This invention has advantages such as good consciousness content analysis effect and high level of intelligence.

The application discloses a kind of based on digital twinning linear control chassis energy consumption simulation method and system, including following steps: S1, constructs digital twinning model, generates state sequence by collecting and processing operation data;S2, using neural control differential equation extraction fusion state;S3, based on fourier neural operator simulation calculation power sequence;S4, through long-term memory network prediction trend, total energy consumption is generated using Simpson integral;S5, disturbance control parameter is generated disturbance energy consumption sequence using SASP algorithm;S6, compare two kinds of energy consumption, and control parameter is optimized using PPO algorithm;S7, according to the total energy consumption of optimization result regeneration, and with actual energy consumption error analysis, update digital twinning model.The application fuses neural control differential equation, fourier neural operator etc., with the advantages of high modeling precision, energy consumption prediction accuracy and high simulation efficiency.

The application discloses an AI pest situation identification and early warning method and system based on multispectral imaging. The method uses a multispectral imaging unit to obtain multi-band images of pests, and synchronously obtains time, point, weather, and crop growth period information. Through correction, registration, and band reliability evaluation, standardized multispectral images are obtained. Based on a background spectrum dictionary and sparse reconstruction residual, the pest area is extracted and the adhered pests are separated. Combined with the prior key parts, the spectral shape part joint feature is constructed. The collaborative network with a neural controlled differential equation spectral line coding branch and a part hypergraph attention branch is used to complete pest identification. Then, the unknown pest is identified in combination with the prototype memory library, and a pest situation spatio-temporal hypergraph is constructed to realize risk early warning. The method can improve the pest identification accuracy, unknown pest identification ability, and pest situation early warning accuracy in complex scenes.

The application discloses a public safety crowd flow early warning method and system fusing space-time residual learning, relates to collecting video sensing data, passing count data, wireless residence migration data and scene context data of a target public area, and performing time synchronization and space mapping; a region-channel directed topological graph is constructed according to a building plan, a BIM model, a CAD drawing or an electronic map, node features and edge features are extracted, and a node-edge joint space-time state tensor is generated; baseline flow prediction results are obtained based on trend period decomposition and neural controlled differential equations, and residuals are modeled based on fractional order gated time series convolution and hypergraph attention propagation, crowd state prediction values in a future prediction window are generated, risk indexes are calculated, and early warning grades, risk areas and disposal suggestions are output. The application can improve the accuracy and adaptability of crowd flow early warning in complex public scenarios.

The embodiment of the application provides a kind of based on neural control hierarchical hybrid planning articulated vehicle motion planning method.Application in vehicle engineering technical field, the method is by obtaining the starting pose, target pose and environmental obstacle information of vehicle;The starting pose, target pose and environmental obstacle information of vehicle are input into trained neural guide planner and are analyzed and handled, obtain first path planning result, neural guide planner includes: environment encoder and planning network;According to the feasibility verification condition of pre-established, the segmented verification result of first path planning result is verified, and the segmented verification result is obtained;According to segmented verification result, the kinematic planning controller constructed is used to optimize and adjust first path planning result, and the final path planning result is obtained, and the final path planning result includes vehicle pose information and corresponding control instruction.The method improves the planning efficiency and path feasibility, enhances the robustness and practicality under complex working conditions.

This invention relates to the field of intelligent medical device technology, specifically to an intelligent health patch system and its control method based on gastro-brain axis communication. The system includes an intelligent patch integrating a multimodal physiological sensing module for collecting gastric electrical signals and heart rate variability signals, and a biomimetic control execution module for performing transcutaneous electrical stimulation and tactile feedback; a mobile computing terminal configured with a gastro-brain axis state assessment model for evaluating the user's hunger drive level based on the physiological signals, and a personalized control decision engine for generating control instructions. The mobile computing terminal can, based on the output of the assessment model, achieve non-invasive closed-loop control of the gastro-brain axis: for the first time, closed-loop management of "ascending signal sensing" and "descending neural control" of the gastro-brain axis is realized on a consumer-grade wearable patch, making the intervention more physiologically targeted and scientific.

This invention discloses a data synchronization acquisition method and system within a ring main unit, comprising the following steps: S1, acquiring operational data, recording sampling time identifiers, and constructing an operational data sequence; S2, performing time evolution processing on the sampling time identifiers, extracting the synchronization time center, and constructing a time deviation sequence; S3, performing continuous time compensation on the sampling time identifiers, and performing time remarking processing on the operational data sequence to obtain a calibration data sequence; S4, calculating the clock drift based on the calibration data sequence, performing correlation attention calculation and collaborative decision-making to form a sampling control sequence; S5, performing synchronization sampling triggering and frame structure reassembly processing to generate a data frame sequence as the synchronization result; S6, updating the algorithm parameters when a preset trigger condition is met. This invention employs neural control differential equations, etc., and possesses advantages such as high synchronization accuracy, good dynamic compensation effect, and high consistency of acquisition results.

This invention relates to the fields of medical data processing and multimodal emotion computing, and discloses an emotion state recognition system for vocal micro-expressions in patients with advanced cancer. The system constructs a temporal state vector reflecting the patient's neural control ability by calculating the neural conduction lag rate and the physiological vocal energy index. A state-adaptive gating module generates a confidence mask to weight the multimodal features. Subsequently, an emotion decoupling and reconstruction module uses manifold orthogonal projection to separate the weighted features into physiological harm perception components and psychological emotional response components, and combines sparse dictionary reconstruction to remove noise. Finally, a fusion decision and grading module combines a temporal convolutional network with an adaptive threshold calibration mechanism based on the energy index to output pain grading results and decision signals. This invention can effectively distinguish between a patient's subjective masking and physiological exhaustion state, decouple the physical and psychological attributes of pain, and avoid missed detections due to weak reactions.

This invention discloses a method and system for predicting the state of an electric arc furnace based on graph convolutional networks, comprising the following steps: collecting multi-source operating parameters during the electric arc furnace smelting process to generate irregularly sampled time-series data of multi-source operating parameters; constructing a continuous time axis based on timestamps to generate a continuous time-domain multivariate state observation sequence; constructing an electric arc furnace operating parameter graph based on the dynamic correlation between operating parameters and generating time-varying graph structure state features; using Bernstein polynomials to propagate spectral domain features of the time-varying graph structure state features to generate graph-spectral coupled state features; inputting the graph-spectral coupled state features into a neural controlled differential equation embedded with Bernstein polynomials to generate a continuous evolution trajectory of the hidden state of the electric arc furnace; generating a predicted furnace state value at the target prediction time based on the continuous evolution trajectory of the hidden state, and outputting the electric arc furnace state prediction result. This invention can achieve continuous dynamic prediction of the furnace state of an electric arc furnace.

This invention discloses a remote collaborative operation system for multiple electric excavators based on the Internet of Things (IoT), comprising: a status processing module for acquiring operational status data and preprocessing it to form a status data stream; a path construction module for constructing control path data; a controlled differential prediction module for constructing an improved neural controlled differential equation and outputting prediction results; a work map update module for creating a work map and updating its status; a task charging orchestration module for determining the set of excavators requiring charging and the target charging node; a collaborative planning module for obtaining the path sequence and operation sequence of each excavator based on the Dijkstra algorithm; and a command feedback module for issuing commands to the corresponding excavators, executing the command packets, and feeding back the execution status. This invention achieves remote collaborative operation of multiple electric excavators by combining the improved neural controlled differential equation and the Dijkstra algorithm.

The application discloses a kind of based on knowledge graph's electronic medical record accurate search method and system, comprising the following steps: obtaining data and preprocessing, form clinical observation time series data sequence;Based on clinical observation time series data sequence constructs medical knowledge graph and generates medical knowledge control path;Continuous time clinical observation control path is constructed, and double control path input structure is formed;Improved neural controlled differential equation is input, and initial clinical state continuous trajectory is obtained;Medical reachable domain determination and reachable domain projection operation are executed, and target clinical state continuous trajectory is obtained;According to search request, reverse continuous time integral calculation is executed, and candidate historical clinical state continuous trajectory set is obtained;The stability evaluation value of trajectory is calculated;According to trajectory stability evaluation value, sorting is carried out and electronic medical record search result is output.The application combines knowledge graph and neural controlled differential equation, realizes the time continuity and stability of electronic medical record accurate search.

The application belongs to the technical field of medical information processing and computer aided decision, and relates to a walking aid equipment matching recommendation system for brain disease complicated with lower limb movement disorder, which aligns sensing data to generate a synchronous disturbance response data stream through a multi-modal response data processing module; a neural control gain calculation module extracts reflection characteristics and performs weighted calculation to generate a gain index; a dynamic mechanical impedance logic analysis module performs condition judgment to generate an impedance index; a pathological feature space mapping module maps the indexes into two-dimensional pathological feature coordinate points; an equipment matching rule association module generates a digital equipment matching zoning map based on a knowledge base; and a personalized recommendation generation module generates a recommendation report containing equipment types and parameter configurations based on a position matching degree and a displacement interpolation algorithm. The application solves the problem that traditional experience recommendation is difficult to quantitatively match the neural and double physical pathological mechanisms of patients by constructing an auxiliary decision model based on data analysis.