32 results about "Hierarchical neural network" patented technology

The invention discloses a wireless communication network intelligent optimization method and system based on neuron collaboration, and relates to the technical field of wireless communication. The method comprises the following steps: mapping a communication node into a bottom layer sensing neuron and constructing a neural state variable set; constructing a node neural situation function based on the variables; when the local threshold value is exceeded, excitation pulses are generated and uploaded to middle-layer convergence neurons; the middle layer carries out pulse space aggregation and extreme value search, and outputs a selection strategy and an adjustment strategy; and reporting to a top layer to execute whole network neural synchronization index analysis, and optimizing the wireless communication network. The technical problems of low spectrum resource utilization rate and unstable network performance caused by the fact that a traditional wireless communication network cannot realize high-efficiency spectrum allocation and dynamic topology reconstruction under user mobility change are solved, and the purposes of realizing local quick response and global collaborative optimization by constructing a layered neural network and improving the network performance are achieved. And the spectrum resource utilization rate is improved, and the network dynamic adaptive capability is enhanced, so that the user service quality is guaranteed.

The invention discloses a time-varying and time-lag multi-CSTR system adaptive control method and system based on a neural network, and the method comprises the steps: collecting key operation parameters of a multi-CSTR system in real time, constructing a time-varying and time-lag dynamic model through a recurrent neural network, and updating a time-lag parameter estimation value. And designing a hierarchical neural network structure comprising an upper global coupling model and a lower local compensation model, designing an adaptive algorithm based on a stability theory to adjust the weight of the neural network, and generating a control instruction to drive an execution mechanism. The system correspondingly comprises a data acquisition layer, a time-varying time-delay estimation module and the like. According to the scheme, the problems of time varying, time lag and coupling of the multi-CSTR system are solved, and stable operation of the system is guaranteed.

The invention relates to the technical field of data analysis, and discloses an AI-based cloud data intelligent analysis and service management system and method. The system comprises a cloud data priority division module, a resource demand prediction module, a cloud data scheduling scheme generation module and a service management dynamic adjustment module. The method comprises the steps of firstly collecting multi-source cloud data, performing data preprocessing, and then performing data priority division; secondly, introducing a hierarchical asynchronous processing architecture to construct a hierarchical neural network prediction model, and outputting a predicted value according to a data priority; establishing a resource scheduling objective function according to the predicted value, optimizing cloud data scheduling parameters in a resource scheduling process by using a multi-strategy fusion grey wolf algorithm, and generating a cloud data scheduling scheme; and finally, calculating a service management health degree, and establishing a health degree response mechanism closed-loop dynamic adjustment cloud data scheduling scheme. By analyzing and processing the cloud data, the purposes of intelligent analysis and service management are achieved, and the method is accurate and objective.

A system and method for robotic teleoperation enable a teleoperated robotic element to perform a sequence of actions based on intention estimation, eliminating the need for continuous human control. The system includes a robotic teleoperation input that receives motion inputs and gaze data from a human operator performing a robotic teleoperation task. A robotic teleoperation feature extractor analyzes and processes the motion inputs and the gaze data into sequential input data. A multi-window model assigns hierarchical prediction windows to the input data, generating windowed sequential data. A hierarchical neural network processes the windowed sequential data to determine low-level action intentions and high-level task intentions, and generates an intention estimation based on the low-level action intentions and the high-level task intentions. A hierarchical dependency model incorporates hierarchical dependent loss to refine the intention estimation.

The invention discloses a heterogeneous wind power plant power control method and system based on a hierarchical neural network. The method comprises the following steps: S1, acquiring heterogeneous wind turbine generator parameters; s2, establishing a heterogeneous wind power plant state space model according to the heterogeneous wind turbine generator parameters; s3, establishing a heterogeneous wind power plant prediction model based on a time domain convolutional network-self-attention mechanism hierarchical neural network, training the heterogeneous wind power plant prediction model through a training set constructed by heterogeneous wind turbine generator parameters, and outputting a model compensation amount to correct a heterogeneous wind power plant state space model; and S4, by taking minimization of voltage and power fluctuation of the heterogeneous wind power plant as a dual control target, on the basis of the corrected heterogeneous wind power plant state space model, through a time sequence optimization heterogeneous wind power plant power instruction, realizing dual suppression of voltage and power fluctuation. The method has the advantages of improving power prediction precision, suppressing voltage fluctuation and power oscillation and the like.

To provide a medical image processing apparatus, a hierarchical neural network, a medical image processing method, and a program configured to implement highly accurate and real-time detection and classification of a region of interest.SOLUTION: A medical image processing apparatus is configured to: acquire a medical image; process the medical image using a feature extraction network of a hierarchical neural network to extract, from the medical image, first features and second features which are relatively higher in resolution than the first features; process the first features with a first sub network of the hierarchical neural network to detect a region of interest included in the medical image; and process the second features with a second sub network of the hierarchical neural network to classify the region of interest.SELECTED DRAWING: Figure 3

The invention provides a diversified multi-hop question generation method and system based on knowledge combination sampling, and relates to the technical field of multi-hop questions.The generation method comprises the steps that context knowledge and target answers are obtained, and the target answers are obtained based on a hierarchical neural network architecture; obtaining conditional probability distribution data and an initial knowledge combination according to the context knowledge and the target answer; obtaining a plurality of diversified knowledge combinations according to the initial knowledge combination and the conditional probability distribution data through a preset sampling strategy; and generating a corresponding multi-hop question according to all the diversified knowledge combinations and the target answer through a preset multi-hop question generation model. The whole process does not need complex intermediate links and can be realized by relying on a mature neural network architecture, the manpower and hardware cost of technology landing is reduced, for example, when a multi-hop question bank is constructed in the education field, a teacher only needs to input a textbook text and knowledge points, multi-hop questions of different examination angles can be automatically generated, and the efficiency is improved. And the problem of data sparsity of the multi-hop question-answer data set is effectively relieved.

The application discloses a power tower point cloud segmentation method and system based on a layered neural network, and the method comprises the following steps: acquiring point cloud data of a power tower to be processed and a surrounding environment; inputting the point cloud data into a layered neural network; sampling the point cloud data by using a multi-stage set sampling layer; performing up-sampling on a local feature vector of each centroid obtained by sampling by using a multi-stage feature propagation layer, thereby completing feature learning of the point cloud data, obtaining a global feature vector of the entire point cloud, mapping the global feature vector of the entire point cloud into a category score vector by using a full connection layer, and converting the category score vector into a probability distribution by using a Softmax activation layer, so as to obtain the probability of each point in the point cloud data belonging to different categories. The application aims to better adapt to the point cloud data features of the power tower, and realizes fast, accurate and automatic segmentation of the power tower point cloud data by using the layered neural network.

The invention discloses a fast table tennis ball rotation estimation method based on a hierarchical neural network, which comprises the following steps: constructing a table tennis ball flight path data set which comprises seven rotation types of no rotation, upward rotation, downward rotation, left upward rotation, right upward rotation, left downward rotation and right downward rotation, and each rotation type comprises multiple gears of rotation speeds; constructing a hierarchical rotation estimation network, wherein the network comprises a plurality of hybrid convolution attention modules connected in series; performing network training based on the constructed hierarchical rotation estimation network to obtain a trained hierarchical rotation estimation network; and inputting a plurality of front sampling points of the track to be measured into the trained layered rotation estimation network, and synchronously outputting the rotation type, the coarse granularity rotation speed and the fine granularity rotation speed to obtain a rotation estimation result. According to the method, convolution and a multi-head attention mechanism are fused through the hierarchical neural network, and the self-adaptive gating module and the hierarchical constraint loss function are combined, so that synchronous estimation of the rotation type and the rotation speed is realized, the recognition precision is remarkably improved, and the problem of misjudgment is solved.

The invention relates to the technical field of middleware service load prediction and resource optimization, in particular to a middleware service multi-granularity load prediction and intelligent resource pooling performance optimization method and system. The system comprises a local load feature extraction module, a global load distribution modeling unit, an adaptive resource scheduling engine and a resource utilization rate monitoring feedback loop. The load data processing efficiency is improved through a sliding window mechanism and a distributed architecture, the load prediction accuracy is enhanced by utilizing a hierarchical neural network and dynamic weight adjustment, and efficient resource allocation is realized in combination with task priority analysis and heterogeneous resource adaptation. The resource utilization rate and the task processing efficiency in the complex distributed system can be remarkably improved, and the method has wide application prospects and practical value.

The invention discloses a power-economic mapping matching method and system based on an improved hierarchical neural network model, and the method comprises the steps: inputting power data and economic data into a feature coding layer for feature extraction, and obtaining context semantic features of the power-economic data; inputting the context semantic features into an event relation modeling layer, constructing a multi-type graph structure by taking time nodes as event nodes in the graph structure, and modeling the multi-type graph structure to obtain a power-economic state sequence; performing feature matching on the electric power-economic state sequence through a preset electric power-economic feature matching mechanism of a feature matching layer to obtain a semantic similarity between the current electric power economic state and a target economic structure; and the output prediction layer constructs a task output structure according to different task requirements on the basis of semantic similarity, and obtains a final conclusion of power-economy mapping in combination with structural features and a matching result. According to the invention, the expression ability and decision-making assistance ability of the model to a complex coupling system are significantly improved.

The invention discloses a multi-scale point cloud attention defect detection method for key parts of aerospace equipment, and belongs to the technical field of intelligent detection, and the method comprises the steps: a point cloud optimization module carries out the preprocessing of an input sparse point cloud through a geometry-density bimodal optimization mechanism, and highlights the spatial distribution characteristics of a potential defect region; the point cloud up-sampling module constructs an encoder-generator-discriminator cascade architecture by means of a multi-network fusion mechanism, and realizes global point cloud construction and adversarial network generation of dense point clouds through a self-attention mechanism; the defect detection module realizes recognition and positioning of typical defect areas such as cracks, pits and deformation in dense point clouds through local feature extraction and point cloud density area analysis by means of a defect area clustering detection mechanism based on semantic guidance. According to the method, the accuracy and robustness of defect detection are remarkably improved, and the method is suitable for high-sensitivity automatic detection and three-dimensional positioning of surface defects of key parts of aerospace equipment.

The invention designs a method for predicting the health state of an energy storage battery, and the method comprises the steps: S1, carrying out the collection and monitoring of the charging and discharging process of an operation battery, and collecting and storing the charging and discharging capacity data of each cycle of the battery; s2, preprocessing the collected discharge capacity data of the batteries, calculating battery health state data, constructing a time sequence sample by using a sliding window mechanism, and dividing the battery health state data of each battery into an input sequence and an output sequence; s3, constructing a hierarchical neural network architecture and training the hierarchical neural network architecture; s4, performing Bayesian optimization on the hierarchical neural network architecture based on an Optuna framework; and S5, predicting the energy storage battery by using the optimized neural network architecture to obtain the health state of the battery. Through the above design, the prediction precision of the battery health state can be improved, and the stability and robustness of overall prediction are enhanced.

The invention discloses a heterogeneous processing unit dynamic scheduling method and system of a hierarchical neural network, and belongs to the technical field of heterogeneous computing resource scheduling. The invention aims to solve the problems of non-uniform task allocation, low resource utilization rate and poor real-time performance of a deep neural network in a heterogeneous computing environment. A static-dynamic scheduling strategy in which neural network task hierarchical division, an execution time prediction model based on machine learning, scheduling optimization objective function construction and a genetic algorithm and simulated annealing algorithm are combined is mainly adopted. According to the method, efficient dynamic scheduling of the neural network reasoning tasks among heterogeneous processing units such as a CPU, a GPU, an NPU and an FPGA can be achieved, and the overall calculation efficiency and the resource utilization rate are improved.

A method of training a hierarchical neural network in hardware comprising a plurality of neurons having tree structure connections between respective ones of the plurality of neurons to compute plausible inferences based on input data. The method includes initiating a WTA neuron ensemble to connect to data input neurons and produce a winner-take-all, attaching data records match with top neurons and branching out at WTA junctions to form a hierarchy network tree, converting a stream of the input data into neuron bipolar signals, updating neuron weights with Hebbian and anti-Hebbian rules, and repeating the attaching, converting, and updating until the input data is exhausted.

The application relates to the field of geomagnetic vector measurement, and discloses a magnetic target intelligent positioning method and system based on a layered neural network architecture. The method comprises the following steps: acquiring multi-point magnetic field data measured by a magnetic sensor array; calculating magnetic gradient tensor data according to the magnetic field data and the spatial position of the sensor; inputting the magnetic gradient tensor data into a pre-constructed layered neural network model; extracting local space-time features from the magnetic gradient tensor time series data of each measurement point through a sub-network layer; performing time series fusion and global context modeling on the feature sequences output by all sub-network branches through a global fusion network layer; and outputting the three-dimensional spatial position coordinates of the magnetic target according to the fused global features through an output layer. The implementation of the application is a deep integration of physical analysis methods and data-driven methods, can guide the mapping relationship between the magnetic gradient tensor and the position of the magnetic target of the deep learning model, and retains the strong nonlinear fitting capability, thereby enhancing the positioning accuracy of the magnetic target.

The invention relates to a self-adaptive agile control method for a high-speed maneuvering flight-oriented intelligent unmanned aerial vehicle with a body, and the method comprises the steps: building a four-rotor unmanned aerial vehicle kinetic model, and outputting the coupling characteristics of aerodynamic force and torque and the prediction information of a future motion state; constructing a reinforcement learning strategy of the Lyapunov stability constraint, and obtaining a compensation control strategy according to output information and tracking errors of the kinetic model; constructing a hierarchical neural network architecture, and generating a motion control instruction of the quad-rotor unmanned aerial vehicle based on a compensation control strategy and latest state information fed back by a sensor; non-linear interference in flight is estimated through a non-linear interference observer, the non-linear interference is fed back to an adaptive control law in the hierarchical neural network, and control input of the hierarchical neural network is optimized in a prediction window through a rolling horizon optimization algorithm; and generating a motion control instruction for intercepting the target unmanned aerial vehicle by using the compensated and optimized adaptive control law and the control input, and sending the motion control instruction to an execution mechanism of the four-rotor unmanned aerial vehicle.

A hierarchical neural network for predicting out of stock products comprises an input layer that receives data from data sources that store disparate datasets having different levels of attribute detail pertaining to products for sale in stores of a retailer. A first level of neural networks processes the data from the data sources into respective learned intermediate vector representations. A second level comprises a concatenate layer that concatenates the learned intermediate vector representations from the second level into a combined vector representation. A third level comprises a feed forward network that receives the combined vector representation and outputs to the retailer an out of stock probability indicating which store and product combinations are likely to have out of stock products over a predetermined timeframe.

This paper discloses a method for constructing a lattice thermal conductivity model based on composite elemental and structural descriptors. The method comprises: obtaining elemental and structural information of a material; analyzing the material's elemental and structural information to extract composite descriptors; screening the composite descriptors using a genetic algorithm to determine important descriptors and other descriptors; generating an ensemble descriptor set based on the important descriptors and other descriptors; and constructing a lattice thermal conductivity model by hierarchically learning the ensemble descriptor set using a hierarchical neural network. This method constructs a highly accurate statistical ensemble hierarchical neural network that can accurately and efficiently predict lattice thermal conductivity based on elemental composition and structural information, thereby accelerating the discovery and development of new materials.

This invention discloses an intelligent analysis and control system and method for the entire lifecycle of gas wells based on artificial intelligence, relating to the field of oil and gas field development technology. It constructs a differentiable physical enhancement model, in which the gas well physical equations are embedded as differentiable hierarchical neural networks. Using historical data for training, a digital twin model is constructed that conforms to physical laws and possesses data fitting capabilities, and the digital twin model is end-to-end differentiable. Based on the differentiable physical enhancement model, a safety rating function is designed, mapping each state-action pair to a continuous safety score. This invention, by constructing a differentiable physical enhancement model, integrates gas well physical laws with data-driven capabilities, ensuring that state prediction and strategy output conform to physical mechanisms, avoiding unauthorized operations, and improving control reliability. Through the safety rating function and safety rating strategy gradient algorithm, safety constraints are implemented in the strategy optimization process, mitigating the impact of unsafe actions and ensuring gas well production safety.

The application discloses a kind of efficient image restoration methods based on global dependence modeling, comprising: S1, obtaining damaged image;S2, input damaged image into multi-scale hierarchical neural network model, shallow feature extraction, deep feature extraction and feature fusion are carried out in turn, and fusion feature is obtained;S3, the residual map obtained by processing fusion feature is added to damaged image, and restoration image is obtained.The dependence relationship between global pixels can be captured by superpixel dependence calculation and transfer, and image restoration is carried out by the dependence relationship;The image restoration method provided by the application greatly reduces the model calculation cost, while ensuring the effectiveness of the method, the floating point operation number (FLOPs) is lower compared with the existing method.

This invention discloses a hierarchical method for parsing and annotating single-cell chromatin accessibility data. The method includes: acquiring single-cell chromatin accessibility data; screening genomic region features based on cell type specificity to determine multiple feature sets; converting the genomic region features of each cell into a structured sequence; processing the structured sequence using a hierarchical neural network model, extracting local features within sub-units to obtain sub-unit representations, and integrating global features between sub-units to obtain cell-level representation vectors; and predicting the cell type of each cell using a classification model based on the cell-level representation vectors to generate cell type annotation results. This application effectively overcomes the high-dimensional sparsity and cell abundance imbalance problems of single-cell chromatin accessibility data, achieving stable identification and annotation of low-abundance cell types and improving the interpretability of data analysis.

The present application relates to the technical field of aquaculture, and in particular to an aquaculture gradient temperature control system and method based on heat pump data deep feature extraction, which comprises a data acquisition module, a feature extraction module, a deep learning prediction module, an energy efficiency optimization module, a fuzzy control module and an execution control module. The system acquires data on the inlet and outlet temperatures of the heat source side, the inlet and outlet temperatures of the user side, the system flow and the system power, pre-processes the data and extracts a multi-dimensional feature vector, including temperature gradient features, temperature difference features and frequency domain features. A hierarchical neural network structure is used to predict the system energy efficiency ratio, a temperature difference-energy efficiency mapping relationship is constructed and the optimal temperature difference set value is determined. A three-dimensional fuzzy reasoning engine is used to generate control parameters, which adjust the water pump speed and the heat pump operating state, thereby achieving gradient temperature control of the aquaculture environment and meeting the breeding needs of high-value aquatic products that are sensitive to temperature.

The invention discloses a sea condition estimation method based on fusion of hierarchical dynamic features and a class perception prototype, and the method comprises the steps: obtaining a ship motion time series data set for sea condition estimation, and carrying out the segmentation according to a set time step, and obtaining a plurality of local feature embedding; constructing a hierarchical selective kernel network based on a hierarchical neural network, and performing down-sampling on local feature embedding layer by layer to obtain an advanced feature vector; wherein each layer performs multi-scale feature extraction on local feature embedding through a gating attention mechanism module; and a prototype classifier is constructed for known different sea condition categories, prototype vectors corresponding to the different sea condition categories are included, and a predicted sea condition category is output by calculating the similarity between the advanced feature vectors and the prototype vectors. According to the method, more stable and recognizable feature representation can be learned from complex and noisy ship motion data, and the generalization ability and prediction accuracy of the model in a class imbalance scene are improved.

The present invention relates to a device and method for estimating a virtual reality pose through a haptic device, wherein the device comprises: an input unit that receives input point data for estimating the pose of the haptic device through a plurality of input devices; a global feature extraction unit that inputs the input point data into a Multilayer Perceptron (MLP) model to extract point-specific features and extracts global features based on the point-specific features; and a pose estimation unit that inputs the global features into a hierarchical neural network to estimate the pose of the haptic device.

The invention relates to an air conditioner power prediction and control method based on a comprehensive intelligent zero-carbon power plant cloud platform. The method comprises the steps that S1, historical operation data and real-time operation data of an air conditioner and a temperature and humidity sensor are collected; s2, on the basis of the actual layout of the air conditioner and the structure of the room, a multi-level neural network model is constructed and used for predicting the power of the air conditioner; s3, training the neural network model by adopting historical operation data; s4, establishing an optimization algorithm objective function of the optimal air conditioner control strategy; s5, determining an optimal air conditioner control strategy through an optimization algorithm; s6, deploying the neural network model and an air conditioner control strategy optimization algorithm on a comprehensive intelligent zero-carbon power plant cloud platform; and S7, after the comprehensive intelligent zero-carbon power plant cloud platform receives a demand response instruction issued by the power grid, an optimal control instruction is obtained through an embedded algorithm, and the instruction is sent to each air conditioner device. Compared with the prior art, the method has the advantages of low calculation cost, more accurate prediction effect and the like.

The application discloses a kind of based on hierarchical neural network's heterogenous wind farm power control method and system, method includes steps: S1, obtains heterogenous wind turbine parameter;S2, according to heterogenous wind turbine parameter, establishes heterogenous wind farm state space model;S3, establishes the heterogenous wind farm prediction model based on time domain convolution network-self attention mechanism hierarchical neural network, heterogenous wind farm prediction model is trained by training set constructed by heterogenous wind turbine parameter, and the model compensation amount is output to correct heterogenous wind farm state space model;S4, with the minimum heterogenous wind farm voltage and power fluctuation as dual control target, based on the corrected heterogenous wind farm state space model, by time sequence optimization heterogenous wind farm power instruction, realize voltage and power fluctuation dual inhibition.The application has the advantages of improving power prediction accuracy, inhibiting voltage fluctuation and power oscillation.

The invention provides a network security insurance underwriting evaluation method, device and equipment fusing multi-source heterogeneous data and a medium, and relates to the field of network security insurance, and the method comprises the steps: obtaining multi-source data of an insured person, the multi-source data comprises risk factor data and insurance factor data, the risk factor data comprises external rating data and internal control data of the insured person information system; analyzing and processing the risk factor data, constructing a risk factor characteristic factor set, analyzing and processing the insurance factor data, and constructing an insurance factor characteristic factor set; based on the risk element feature factor set and the insurance element feature factor set, constructing a network security insurance underwriting evaluation model of a multi-modal feature hierarchical neural network architecture by simulating risk decision tree logic; and inputting the risk factor data and the insurance factor data into the network security insurance underwriting evaluation model, and outputting an underwriting conclusion. According to the invention, the comprehensiveness and accuracy of the underwriting evaluation conclusion are improved by analyzing the multi-source data generated in the evaluation process.

The present invention relates to a virtual reality pose estimation apparatus and method using a haptic device. The apparatus comprises: an input unit for receiving, through a plurality of input devices, input point data for the estimation of a pose of a haptic device; a global feature extraction unit which inputs the input point data into a multilayer perceptron (MLP) model so as to extract point-wise features, and which extracts a global feature on the basis of the point-wise features; and a pose estimation unit for estimating the pose of the haptic device by inputting the global feature into a hierarchical neural network.

The invention discloses a shaftless rim type pipeline generator intelligent body, which comprises a shaftless rim power generation module and an intelligent control module, the shaftless rim power generation module is used for executing a power generation instruction, the intelligent control module is used for sending a power generation control instruction, and the shaftless rim power generation module and the intelligent control module are fixedly connected and realize data interaction and instruction transmission; the sensing sub-module is used for collecting multi-dimensional data; the analysis sub-module processes the collected data and outputs an analysis result; the hierarchical neural network learning sub-module is internally provided with a neural network, receives an analysis result and outputs an optimal result; the decision sub-module outputs and analyzes a result based on a neural network; and the security assurance sub-module is used for providing multi-dimensional protection. According to the shaftless structure, the power generation efficiency and reliability are improved, the shaftless rim type rotor design is adopted, friction and water flow blocking of a traditional center shaft are eliminated, water flow resistance is reduced, and the power generation efficiency is improved; the self-lubricating bearing prolongs the maintenance period, the operation stability is obviously improved, the power generation efficiency is improved, and the annual average maintenance cost is reduced.