2056results about "Biological neural network models" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a task scheduling execution method, device, equipment and medium based on intention recognition, and the method comprises the steps: fusing an interaction request and a dialogue history library to generate a session context, and carrying out the analysis to obtain an intention recognition result; task planning is carried out based on the intention recognition result and a knowledge base, and a task subitem set and an execution sequence are generated; matching the task subitem set with a resource directory to generate an execution resource list; scheduling the task subitem set by using the execution sequence to generate an ordered task sequence; generating an execution plan in combination with the ordered task sequence and the execution resource list; and triggering execution based on the execution plan, and aggregating output to generate an execution result and a process tracking record. According to the method, automatic connection of session analysis, task planning, resource matching, sequential scheduling and execution triggering is achieved, manual operation splitting among multiple systems is eliminated, and task processing efficiency and execution accuracy are improved.

Embodiments of the present disclosure relate to multi-view LIDAR perception with motion cues for autonomous and semi-autonomous machines and applications. A DNN may be used to detect objects, a navigable space, weather or surface conditions, artifacts, and / or other parts or features of an environment based on multiple views of LIDAR data from multiple time slices. The DNN may include multiple input channels for processing multiple views of sensor data from multiple time slices to provide motion cues, and the extracted features from the different time slices may be geometrically projected from a first 2D view to a second 2D view, combined with features that were extracted from the second 2D view, and applied to a subsequent stage of the DNN. The data generated by the DNN may be provided to the drive stack of an autonomous vehicle or other ego-machine to enable safe planning and control of the vehicle.

A publishing system with components, including:a system configured to receive at least one document including text that defines a base alphabet in one or more formats;a system configured to provide additional data for a reader to better understand the document which includes:a method of encoding or marking up non-phonetic words in the document to enable the reader to decode sounds of each non-phonetic word; anda system configured to output an encoded document with the text and the additional data in one or more formats,wherein the method of automatically encoding the non-phonetic words to make the encoded words phonetic:for at least one character (“spelling character”) in the non-phonetic word, using a compound character that includes the spelling character and a sound character, wherein the sound characters:are human-readable characters in the base alphabet and / or in one or more secondary alphabets,are added to the spelling characters to indicate that each spelling character makes the usual sound of the sound character,are added so that spelling characters can be visually discriminated from sound characters,are added such that a reader can recognize the non-phonetic word by sight because the spelling of the word is unchanged, andare added to the spelling characters such that the spelling characters and the sound characters remain human-readable such that the spelling character and the sound character of each compound character are within one visual field; andautomatically outputting the encoded words in a human-readable form / format such that the compound characters in the encoded word visually indicate which of the spelling characters have a sound other than their usual sound and what sound each character makes in the non-phonetic word when it does not make its usual sound.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and / or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

The invention discloses a full-closed-loop automatic spraying method, system and device based on geometrical characteristic driving and multi-physical quantity feedback and a storage medium. The method comprises the following steps: performing three-dimensional scanning on a workpiece and extracting geometric features; generating a spraying track and a spraying posture for keeping a topological structure based on the geometrical characteristics; motion, process and coating quality parameters are collected in real time through a sensor, and are processed and fused to form a state vector; inputting the state vector into an intelligent control module, performing optimization by combining model predictive control, fuzzy logic and a neural network, and outputting trajectory and process parameters; a control strategy is updated according to the quality deviation of online detection, and closed-loop correction is carried out; spraying is automatically executed in the closed spraying cabin, and unmanned operation of the whole process is achieved. According to the method, complex curved surface spraying can be completed without manual programming, and the method has the advantages of high flexibility, multi-parameter closed loop, self-adaptive optimization and the like.

The invention provides an image privacy protection-oriented reversible adversarial sample generation system and method, and relates to the technical field of artificial intelligence and image privacy protection. The system comprises an input module, a semantic guidance mask generation module, a potential confrontation generation module and a reversible steganography embedding module. The input module acquires an original image, generates a binary mask in the semantic guidance mask generation module, and generates adversarial text description by adopting a visual language model; a final image potential representation is generated in a potential adversarial generation module through forward diffusion and reverse sampling of a diffusion model, and adversarial disturbance is introduced by adopting a gradient optimization algorithm to generate an adversarial sample image; and finally, discrete wavelet transform is adopted in the reversible steganography embedding module to separate out a high-frequency sub-band, and a reversible neural network is combined to obtain a secret-containing hidden image. According to the method, the three requirements of visual authenticity, visual anonymity and machine identifiability can be met at the same time, and an innovative solution is provided for privacy protection in an advanced face recognition scene.

The invention discloses a data processing method and system for a precise automatic multi-axis numerical control electric spark forming machine, and the method comprises the steps: mapping preprocessed data into a digital twin model for fusion analysis, and extracting fusion features including instantaneous energy density, an effective machining angle, a thermal error compensation amount and a chip removal efficiency evaluation value; based on the fused feature data, performing real-time state evaluation and future discharge trend prediction by using a LightGBM neural network model, and outputting a processing prediction result; and a multi-target optimization function is established, an improved NSGA-II algorithm is utilized to carry out rapid optimization search by taking the current processing parameters as an initial population, a multi-axis cooperative motion instruction is generated, and the multi-axis cooperative motion instruction is transmitted to a multi-axis motion control module and a pulse power supply module to be executed. And the processing stability and reliability are improved.

The present invention relates to a high temperature and drought composite disaster monitoring and early-warning method and system, belonging to the technical fields of disaster risk assessment and early warning. Internal correlation features and abnormality information of high temperature and drought events are input into a model, multi-time-space scale features of the high temperature and drought events can be identified accurately, high event identification accuracy and space resolution are achieved, the progress can be predicted progressively, the drought and high temperature threshold change can be monitored closely, and fine forecasting and early warning can be performed in different periods, regions and intensities, thereby ensuring that indicators are in the same time scale, avoiding the complication of the high temperature and drought process caused by frequent time and space discontinuities of the indicators in a single point or small region, and ensuring the suitability for any periods of the process.

A method for processing a piece of textual information. The piece of textual information is parsed into a set of plaintext input tokens. Each of the plaintext input tokens is individually transformed using a first binary data transformation, to achieve a set of binary input tokens. Each of the set of binary input tokens is transformed individually or collectively, using an embedding data transformation, into one or several vectorized input tokens. The one or several vectorized input tokens is / are fed to a first neural network. A response is received from the first neural network in the form of one or several vectorized output tokens.

A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and / or outer walls of the coronary arteries of the patient.

The invention provides a virtual sample generation method and system based on a non-stationary neural network Gaussian process, and the method comprises the steps: obtaining original data, judging the non-stationarity, and judging whether a statistical characteristic changes with an input position or not; if not, a hidden variable model fusing the neural network and the Gaussian process is constructed, and a hidden variable space representing non-stationary distribution is obtained through training; sampling based on hidden variable space probability distribution; using the model to generate = (, Z) (Z, Z)-1X through non-stationary high-dimensional mapping, which is a non-stationary kernel function, Z is a low-dimensional hidden variable, and X is an input variable; checking the consistency of the high-dimensional virtual samples and the original data, and screening a virtual sample set meeting statistical consistency; according to the method, high-quality and high-diversity virtual samples can be generated in small sample and non-stationary scenes.

The invention relates to a high-speed rail station building ventilation control method and system based on a physical information neural network, and the method specifically comprises the following steps: collecting related parameters of a high-speed rail station building, and carrying out the preprocessing; based on an electric field intensity theory, constructing a passenger space-time distribution function according to the preprocessed data to calculate dynamic heat source intensity; constructing a PINN model of 10 layers of MLP, and constructing a loss function according to physical constraints to train the PINN model; according to output of the PINN model, a control target is deduced in combination with ventilation parameters, and then ventilation of the high-speed rail station building is controlled; and comparing a derivation result with an actual condition, if the deviation is too large, performing fine adjustment on the PINN model, and correcting the output derivation result until the deviation is within a specified range. According to the method, related data are collected and converted into dynamic heat source items which can be identified by the PINN model, and the model is trained through physical constraints, so that the accuracy of a derivation result can be improved, and the ventilation control precision and the energy-saving effect of the high-speed rail station building are improved.

The invention provides a CNN-PINN-based gas turbine combustion chamber performance prediction method, belongs to the technical field of combustion chamber combustion, and aims to solve the problems of high resource consumption and high grid dependence of the conventional CFD calculation at present, and the method comprises the steps: S1, collecting historical operation data of a combustion chamber, generating simulation data through numerical simulation, and carrying out the calculation of the simulation data; performing comparative analysis on the simulation data and the collected experimental operation data to prepare a data set; s2, preprocessing the data set; s3, establishing a CNN-PINN neural network prediction model according to the data characteristics and the target; s4, establishing a loss function containing a data error term and a physical information error term; s5, training and evaluating the established model by using the preprocessed data; and S6, performing performance prediction on target data by using the trained model, and performing reverse normalization on a prediction result to obtain prediction data corresponding to multiple targets.

The application relates to the technical field of motor life prediction, and provides a stepping motor life prediction method and system based on multi-physical field coupling, electrical parameters of a stepping motor, multi-physical field data in a running state and a multi-dimensional feature vector corresponding to the multi-physical field data are acquired, a physical damage index corresponding to the stepping motor is determined according to the electrical parameters, the multi-physical field data and a preset physical damage index calculation formula, the multi-dimensional feature vector is input into a trained data-driven model, a data-driven damage index corresponding to the stepping motor is acquired, and a life prediction result of the stepping motor is acquired according to the physical damage index, the data-driven damage index and a preset life prediction formula. In this way, the influence of the multi-physical field data on the life of the stepping motor can be comprehensively considered, the limitation of a single mode in predicting the life of the stepping motor is overcome by fusing the physical damage index and the data-driven damage index, and the accuracy of the life prediction result of the stepping motor is improved.

The invention relates to the technical field of industrial compressed air system intelligent control, in particular to a digital air compression station intelligent control method and system based on AI self-learning, and the system comprises a data collection layer, an AI prediction layer, an optimization control layer and an execution layer. The data acquisition layer realizes accurate acquisition and preprocessing of multi-dimensional data through a three-stage high-precision sensor network and an edge computing node; the AI prediction layer adopts an XGBoost and LSTM mixed model to be combined with an online learning mechanism to realize accurate prediction of the gas consumption demand and the energy efficiency ratio within 1-24 hours; the optimization control layer completes air compressor operation combination optimization and pressure dynamic control based on an improved NSGA-II algorithm and self-adaptive PID adjustment; the execution layer realizes control instruction landing through frequency converter and cluster cooperative scheduling. Meanwhile, an equipment health degree monitoring module, a multi-source data fusion correction module and a waste heat-gas consumption collaborative optimization module are innovatively introduced, and the problems that a traditional air compression station is low in energy efficiency, poor in self-adaption, insufficient in prediction precision and lack of equipment management are solved.

The present invention relates to a device (6) for associate physiological signals from a user (51), said physiological signals being multimodal physiological signals, with commands of a brain-computer interface - BCI - (71) using a trained user-specific machine learning system, and a device for training said a trained user-specific machine learning system. Specifically, the invention features a hardware-agnostic, multimodal BCI powered by generative artificial intelligence, cognitive AI agents, and Riemannian geometry, with reinforcement learning techniques aimed at making the BCI adaptive to each user's cognitive and affective states, and physicality, by translating said physiological and neurophysiological signals into passive and active (mental) commands of connected devices and digital environments.

The invention discloses an oil and gas long-distance pipeline automatic control system fault identification method based on deep learning, and the method comprises the following steps: collecting and preprocessing a time sequence, and generating a standardized input sequence; constructing a physical constraint feature set, and generating a physical observable representation; encoding the valve opening instruction time sequence and the pump station frequency instruction time sequence to form a combined input sequence; obtaining Koopman state representation, and generating a linear evolution state sequence; a residual nonlinear compensation structure is introduced to generate residual features, and a comprehensive state sequence is formed; time scale splitting is carried out, and fast, medium-speed and slow time scale feature sequences are decomposed; identifying anomalies and generating a time scale anomaly feature set; leakage energy loss indexes are calculated and fused to form fault mode drift representation; judging the pipeline state, and outputting a fault recognition result. According to the invention, a deep Koopman operator structure is adopted, accurate identification of pipeline faults is realized, and the method has the advantage of high diagnosis reliability.

The present invention relates to a privacy-preserving EV charging authorization and billing system, and a method for the same. The proposed system is configured to integrate permissioned blockchain with fully homomorphic encryption. The present invention aims to eliminate plaintext exposure mitigates single point of failure, by performing all authorization and billing computations on encrypted data and recoding transactions immutably, wherein an EV user securely generates encrypted authorization and billing requests using FHE-based public keys. The charging station routes these encrypted requests to the blockchain network, which records immutable encrypted transactions and verifies them via consensus. The FHE computation layer performs secure operations on the encrypted data for authorization and billing, while smart contracts execute automated verification and billing computations, ensuring transparency and auditability.

The invention relates to the technical field of artificial intelligence, and provides an intelligent insurance matching method and system combining a user portrait and a clause knowledge base. The method comprises the following steps: constructing a first portrait of a user based on a user data set and optimizing to generate a second portrait; extracting features from the insurance clause knowledge base in a multi-dimensional manner to construct a product portrait; performing demand coverage risk evaluation on the product portrait based on the second portrait of the user to generate a demand coverage risk matrix; performing guarantee conflict risk evaluation to generate a guarantee conflict risk matrix; introducing an underwriting risk analysis channel, and generating an underwriting risk matrix in combination with the second portrait of the user; and fusing the three types of risk matrixes to perform multi-level optimization matching, and generating an insurance optimization matching graph, so as to solve the technical problem of significant deviation between the recommendation result and the actual risk preference and guarantee demand of the user, realize dynamic and accurate matching of the insurance product and the user demand, reduce the guarantee conflict risk, improve the correlation of the recommendation result, and improve the user experience. And the technical effect of personalized insurance configuration requirements is met.

The invention relates to the technical field of electromagnetic metasurfaces and bionic computing, and discloses a bionic information processing method and system based on an electromagnetic metasurface. The method comprises the following steps: S1, mapping DNA sequence information into a multi-dimensional coding state of the bionic metasurface information processing unit in an electromagnetic adjustable parameter space; s2, realizing co-evolution of the multi-dimensional coding state in space, frequency, phase and amplitude dimensions by dynamically regulating and controlling electromagnetic response parameters of the bionic metasurface information processing unit; and S3, using propagation and interference of electromagnetic waves in the diffraction neural network and a near-field coupling effect between the bionic metasurface information processing units to complete copying, logical operation and information read-write operation of the DNA sequence information. The functions of DNA coding, copying, logic calculation, information reading and writing and the like are uniformly mapped into the programmable electromagnetic metasurface platform, and cooperative processing of information in the aspects of space, electromagnetism and algorithm is achieved.

The application provides a data security method, related device and system, the method relates to the field of virtualization, the method is applied to a heterogeneous device, part or all of hardware resources in the heterogeneous device are divided into a trusted execution environment (TEE) side, the TEE side of the heterogeneous device is preset with confidential data, and the method comprises the following steps: a processor of the heterogeneous device verifies whether a virtual machine running on the TEE side of a host can obtain the confidential data in the heterogeneous device on the TEE side; in the case that the virtual machine running on the TEE side of the host cannot obtain the confidential data, the TEE side of the heterogeneous device receives an access request of the virtual machine running on the TEE side of the host to the TEE side of the heterogeneous device. By implementing the embodiment of the application, the confidential data can be protected from being leaked and stolen.

The invention belongs to the technical field of communication, and particularly relates to a multipath channel DOA estimation method based on a heterogeneous attention double-branch neural network, and the method comprises the steps: constructing a training set, a verification set and a test set through stratified sampling; introducing a multi-head attention mechanism to construct a heterogeneous attention double-branch neural network for realizing intelligent DOA estimation; designing a frequency weighted loss function as a loss function of neural network model training; a dynamic attention mechanism and an early stop mechanism are designed in a model training process to prevent an overfitting phenomenon. According to the method, the problem of serious DOA estimation model overfitting in a small sample and data non-uniform real acquisition data scene is effectively relieved, and the DOA estimation accuracy is improved.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and / or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

The invention discloses a river and lake water bloom prediction method and system based on an integrated diffusion learning model, and the method comprises the steps: carrying out the preprocessing of an original water quality time sequence according to the water quality monitoring data of a target river and lake region, and constructing a standardized water quality time sequence input data set; disturbing the data set based on a conditional diffusion generation model, and constructing a plurality of initial condition diversified input disturbance sets; a plurality of input disturbances of the disturbance set are sent into a deep neural network prediction model for parallel prediction, so that a plurality of algal bloom prediction orbits are formed to jointly form an integrated prediction result under disturbance driving; and statistical analysis and fusion processing are carried out to form prediction result distribution with uncertainty quantification capability, and visual display is carried out. According to the method, the uncertainty of the prediction result can be quantitatively described while the prediction precision is kept, and a more reliable decision basis can still be provided for water environment scheduling, ecological early warning and emergency response especially under extreme hydrological conditions such as flood and drought.

The invention relates to the technical field of data processing, in particular to a multi-source data fusion method based on a railway station building and a city, which comprises the following steps: acquiring an IFC format data stream and a CityGML data stream; performing space-time correlation integration on the two types of data streams to generate a data set with engineering constraints; based on the geometric topological relation and the construction stage characteristic parameters, an improved ant colony optimization algorithm is adopted for coordinate conversion; constructing a railway component and city element mapping rule base and carrying out semantic mapping; performing multi-working-condition collision detection by adopting a parallel universe simulation algorithm based on the construction event identifier; carrying out collaborative decision analysis by adopting a hierarchical response mechanism based on conflict types and severity; a verification environment variable set is injected into a preset digital twin platform, and verification parameters are dynamically adjusted by adopting a particle swarm optimization algorithm. According to the method, the problem of multi-source data collaborative fusion of the railway station building and the city is solved, and the accuracy of data processing and the robustness of the system are improved.

A computer-implemented system for improving radiation dose reconstruction in adaptive real-time radiotherapy, comprising a data acquisition module for capturing treatment data in real time during radiotherapy, a preprocessing module for preprocessing the captured data, a deep-learning dose reconstruction engine for reconstructing the delivered radiation dose distribution using a trained deep-learning model, a dose evaluation and comparison module for comparing the reconstructed dose distribution with a planned dose distribution, and an adaptive decision support module to assist in adjusting radiotherapy treatment parameters based on the comparison results.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and / or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Described is a system for feeding pet avatars by accessing a particular geographic location for a first user; accessing map data for the particular geographic location; identifying that a pet avatar for a second user is associated with the particular geographic location; causing display, on a first computing device of the first user, of: the particular geographic location using the map data; the pet avatar; and an interface element configured to feed the pet avatar; receiving a user selection of the interface element from the first user; causing display, on the first computing device for the first user, of an indication of the pet avatar being fed corresponding to the interface element; and transmitting a notification to a second computing device of the second user including an indication that the pet avatar is being fed corresponding to the interface element.