45 results about "Hybrid learning" patented technology

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention relates to the technical field of automatic driving and intelligent control, in particular to a multi-transportation-equipment cooperative motion control method based on multi-stage mixed learning and a storage medium, and the method comprises the steps: generating expert demonstration data through employing a single-vehicle motion control algorithm based on an expert rule, performing initialization training on the strategy network through online iterative supervised learning to obtain a pre-training strategy model; the multi-vehicle cooperative motion control method comprises the following steps: selecting a multi-vehicle cooperative motion model, loading parameters of the model into an Actor strategy network of multi-agent reinforcement learning, performing interactive training on a plurality of agents in a simulation environment by adopting a centralized training and decentralized execution normal form, and performing online iterative optimization on the strategy based on a composite reward function and generalized advantage estimation to obtain a multi-vehicle cooperative motion control strategy. According to the method, the complementary advantages of imitation learning and reinforcement learning are exerted, the training efficiency, the strategy performance and the collaborative operation capability and robustness of the system in a complex scene are improved, and the method can be directly applied to collaborative scheduling and control of transportation equipment groups in scenes such as surface mines and ports.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention relates to a key flow-based SRv6 explicit path intelligent scheduling method and system, and belongs to the technical field of communication information. The method comprises the following steps: carrying out criticality scoring through a semi-supervised-migration mixed learning model, and outputting a key stream; constructing a full-dimensional network twinborn model, pre-judging a congestion risk trend, and verifying a link path survival rate; generating a low-delay source node list through a key flow path enhancement algorithm based on the key flow and link path survival rate; analyzing the low-delay source node list to create a micro-isolation queue; when the digital twin detects that the actual state deviates from the predicted value, calling a pre-verified backup path identification chain to complete path switching in real time; and performing closed-loop optimization based on the key operation state data by using a double-channel learning model. The technical problems that in the prior art, key flow recognition precision is low, SRv6 path scheduling and network state adaptability is poor, fault switching lags behind, and core model optimization is insufficient are solved.

The invention relates to a non-intrusive load decomposition method and device based on a parallel mixed learning model, and the method comprises the steps: obtaining to-be-predicted load active power data, carrying out the preprocessing, inputting the data into a pre-trained parallel mixed deep learning model, and obtaining a load decomposition result; the parallel hybrid deep learning model comprises a CNN channel and a GRU channel which are arranged in parallel, and a feature splicing layer which is connected with the CNN channel and the GRU channel, the CNN channel is used for carrying out feature space dimensionality reduction on input features so as to obtain electric appliance space features, the GRU channel is used for extracting load time sequence dynamic features according to the input features, and the load time sequence dynamic features are matched with the feature splicing layer; and the feature splicing layer is used for fusing the electric appliance space features and the load time sequence dynamic features, and obtaining a load decomposition result through a meta-learner. Compared with the prior art, the method improves the efficiency of non-intrusive load decomposition, and has excellent performance and generalization ability in a complex power consumption scene.

The invention discloses a data center global temperature control optimization method based on online-offline hybrid reinforcement learning, and the method comprises the steps: constructing an interpretable global state space fusing physical information, and guaranteeing that a state variable has a clear physical meaning; a combined action space is defined, and cooperative control of the cold source side and the tail end side is achieved; designing a multi-objective reward function based on physical model driving, and comprehensively considering energy consumption, temperature stability, carbon emission and physical optimization indexes; obtaining a basic security policy through offline pre-training, and extracting reliable behaviors from historical data by using a conservative Q learning algorithm; strategy optimization is achieved through online safety fine adjustment, and gradual adjustment is conducted under multiple constraints to adapt to real-time changes; and finally, deploying an optimization strategy to realize closed-loop control of the system, and establishing a continuous learning mechanism to cope with long-term drift. According to the method, a complete offline-online mixed learning system is established, and the energy efficiency of the data center temperature control system is improved.

The invention belongs to the field of intelligent prostheses, and particularly relates to an intelligent prosthetic control algorithm based on EMG electromyographic signals, which comprises the following steps: S1, electromyographic signal acquisition: acquiring the electromyographic signals of the forearm of a user through a multi-channel surface electromyographic sensor, the electromyographic signals being sEMG signals; s2, signal preprocessing; according to the time-frequency feature enhancement and mixed learning architecture, the system can realize more accurate and stable small sample gesture recognition, the processing capability and classification performance of the non-stationary electromyographic signals are remarkably improved, and in addition, due to the fact that deep features and discriminative manual features are fused and a lightweight network and a multi-modal decision mechanism are combined, the recognition efficiency of the non-stationary electromyographic signals is improved. A user can realize reliable and smooth gesture interaction in a low-delay and low-data-dependence embedded environment, and does not need to depend on a large amount of labeled data or complex computing resources, so that the whole method has higher adaptability and practicability.

The embodiment of the invention discloses a transmission task scheduling optimization method and device based on a hybrid learning strategy, and relates to the field of the Internet of Things and artificial intelligence. The method comprises the following steps: constructing a task scheduling optimization model taking minimization of the total bandwidth cost of a system in a preset time period as a target function, initializing parameters of a hybrid learning strategy particle swarm optimization algorithm, including particle swarm scale, particle position and speed, iteratively optimizing a bandwidth allocation scheme according to the hybrid learning strategy particle swarm optimization algorithm, and optimizing the bandwidth allocation scheme according to the particle swarm optimization algorithm. And updating the individual historical optimal position of each particle and the global historical optimal position of the population in each iteration process, and when iteration reaches the maximum number of iterations, outputting a bandwidth allocation scheme corresponding to the global optimal position as an optimal transmission task scheduling scheme. In this way, fast and efficient allocation of network bandwidth resources is achieved.

The invention discloses a hybrid learning-based flexible job shop energy-saving batch scheduling method, which relates to the technical field of shop scheduling and comprises the following steps of: establishing a flexible job shop energy-saving scheduling model by taking minimization of maximum completion time and total energy consumption of a machine as optimization objectives; the method comprises the following steps of: constructing a mapping relationship between an operation process and machine selection by adopting a three-layer coding mode, initializing a population through multiple initialization strategies, decoding a coding part, and generating an initial solution; constructing a neighborhood structure, performing population evolution through an adaptive crossover and mutation operator, and adjusting the crossover and mutation probability according to the population evolution degree; searching an optimal solution through a plurality of search strategies; traversing the population to perform non-dominated sorting, and adjusting the crossover mutation rate according to a non-dominated sorting result; and judging whether an iteration termination condition is met or not, if not, continuing iteration, and if so, outputting the optimal Pareto frontier solution. According to the invention, the energy consumption is reduced while the processing time is minimized.

The invention discloses a novel power system false data injection attack detection method based on CPO-CNN-SE. The method comprises the following steps: preprocessing a measurement test of a novel power system; performing hyper-parameter optimization by using a crown porcupine optimization algorithm (CPO) to improve the precision of the CNN detection model; meanwhile, in consideration of synchronous increase of the FDIA detection speed, a compression excitation (SE) attention mechanism is introduced, a CNN-SE mixed learning model is formed, and the characterization efficiency of the CNN on FDIA spatial features is enhanced; and finally, carrying out binary classification on the FDIA feature data through a full connection layer, a sigmoid classifier and a binary cross entropy loss function. According to the invention, the FDIA detection precision is improved, and the synchronous improvement of the detection speed is realized.

The application discloses an unbalanced target classification method, product, medium and equipment, relates to the field of class unbalanced learning of image classification, and solves the problem of poor classification effect caused by class unbalanced learning in real domain data through mixed learning of real domain data and virtual domain data, that is, through virtual-real data mixed training, improves the classification accuracy of tail classes in the real domain, thereby improving the target classification performance in the real domain data. Meanwhile, the application solves the domain offset problem through hidden layer feature enhancement in the network, improves the target classification precision in the real domain, and finally achieves the effect of greatly improving the offshore target classification accuracy in the real domain.

The application discloses a kind of outbound line decision-making method and system based on incoming call display perception, the method includes: determining outbound task and obtaining the number attribute of outbound line, the number attribute and outbound task are modeled to obtain number feature vector and outbound service feature vector;Based on the willingness prediction network of pre-set, according to number feature vector and outbound service feature vector, the willingness score sequence corresponding to outbound line is obtained by prediction;For any outbound line, according to the constraint condition of willingness score and line cost, the target outbound line executed by outbound task is determined by calculating through the pre-set resource allocation algorithm;The feedback sample of target outbound line corresponding outbound task is obtained, and the willingness prediction network and resource allocation algorithm are continuously updated by hybrid learning mechanism.It can be seen that the application can realize the intelligent selection and dynamic optimization of outbound line, so as to effectively improve the reach rate of intelligent outbound system.

A computer system and method for training a machine learning system to perform a classification task by dividing input data into one of a plurality of classes. The system is configured to receive training data for each class from which a representation of each class can be derived, wherein each class is described by a plurality of representations; process the training data to form, for at least one class, a first proxy for a relatively global portion of training data items and a plurality of proxies for different relatively local portions of training data items, each proxy corresponding to a representation of data belonging to the class. For each training data item, the system is configured to: evaluate a match between the training data item and the proxies; estimate the class of the training data item from the match rating; adjust the proxies by updating a weighting matrix to reduce the distance between the training data item and the proxies of the estimated class. Defining a plurality of proxies in this way can result in richer and more stable representations of object classes.

The application provides a hybrid learning type image compressive sensing method and system based on semantic guidance, belongs to the technical field of image processing and compressive sensing, and comprises the following steps: acquiring an input image, dividing the input image into a plurality of non-overlapping image blocks, inputting the image blocks into a semantic coding network, and using the semantic coding network to extract high-dimensional semantic embedding features of the image blocks and generate corresponding semantic mask vectors; constructing a semantic guidance adaptive sampling matrix based on the semantic mask vectors, so that the sampling matrix dynamically changes with the semantic features of the image blocks; after obtaining the adaptive sampling matrix, performing a compressive measurement operation on the image blocks to obtain basic sampling measurement values and semantic adaptive sampling measurement values, respectively, splicing the two measurement results in the measurement dimension to obtain a final compressive measurement vector; and sequentially performing semantic perception pre-reconstruction, hybrid Transformer-CNN block level reconstruction and pixel level fine reconstruction based on the compressive measurement vector, and outputting a reconstruction result.

A hybrid learning method for intracranial electroencephalogram (EEG) signal processing based on contrastive learning and mask reconstruction is proposed. After acquiring EEG signals offline and constructing a training set, an IntraBraM network is built, comprising a convolutional network block encoder, a Transformer encoder, a decoder, and a linear prediction layer. Following contrastive and mask reconstruction training, the trained IntraBraM is used for real-time EEG signal classification online. This invention addresses both the pre-training paradigm and model structure design, creating a pre-training paradigm and model structure adapted to the intracranial EEG modality. This enhances the model's performance on downstream tasks within the intracranial EEG modality, provides robustness to the spatial coordinates of adversarial electrodes, and improves generalization ability across subject settings.

The invention belongs to a cell recognition technology, and particularly provides a cell focusing and detection method based on mixed learning, which comprises the following steps: A1, performing cell recognition in an initially focused image by using a depth target detection algorithm, and deleting bounding boxes of which the area is greater than a threshold ST to obtain bounding boxes of suspected cells; a2, performing automatic focusing on the cell surface by using a DCS focusing method; a3, performing information enhancement on the image; and A4, inputting the enhanced cell picture into a YOLO-DeCDIT (Yolo-DeCDIT) algorithm to carry out cell recognition and positioning, wherein the C2PSA module in the yolk is replaced by DeCDIT by the algorithm. The method has the advantages of high cell recognition accuracy and the like.

The embodiment of the application provides a wind turbine full life cycle prediction method and device based on multi-physical field coupling, which comprises the following steps: dividing the whole structure of the wind turbine into structural components and related spaces, collecting multi-physical field sensor data from the whole structure, discretizing the structural components of the wind turbine into a plurality of finite elements, defining the related spaces as nodes connected to the finite elements, constructing a corresponding wind turbine network topology, establishing corresponding physical field control equations for the finite elements based on the network topology, determining a corresponding global coupling equation set, and solving the global coupling equation set by using a numerical method to determine the corresponding physical response data. Real-time multi-physical field sensor data and real-time physical response data are input into a set hybrid learning model for unsupervised and supervised hybrid learning to determine the life cycle prediction result of the wind turbine. The application can improve the efficiency and accuracy of wind turbine fault prediction.

An object of the system according to the embodiment is to increase the time for questions and answers in an online or hybrid study meeting or lecture meeting.SOLUTION: A system includes a chat insertion part, a reaction analysis part, and a question generation part. The chat insertion unit automatically inserts a chat. The reaction analysis unit analyzes a reaction of the participant to the chat inserted by the chat insertion unit. The question generation unit automatically generates a question based on the reaction analyzed by the reaction analysis unit.SELECTED DRAWING: Figure 1

The invention provides a method for establishing a value-oriented content selection model in an ideological and political education plan, and belongs to the technical field of neural network models. A memory matrix is realized in a plurality of hidden layers to store historical semantic vectors, core features are selectively reserved through a forgetting gating unit, a symbol knowledge base is established to store value logic rules and convert the value logic rules into differentiable soft logic constraints, and a sparse attention mechanism and a gradient check point technology are adopted to reduce calculation complexity. And setting an adaptive gradient cutting threshold value and a dynamic learning rate zoom factor to perform model training, thereby solving the technical problem that effective modeling of the long text cross-paragraph value logic consistency in the ideological and political education plan value oriented evaluation process is difficult.

Devices, systems, methods, and media are disclosed for domain adaptation of a trained machine learning model using hybrid learning. A hybrid approach to domain adaptation is disclosed that combines aspects of discrepancy-based, adversarial, and reconstruction-based approaches to achieve an end-to-end trained model for performing a prediction task (such as semantic segmentation) on a sparsely labeled dataset in a target domain, by leveraging a richly-labeled dataset in the source domain. Some embodiments may also provide a trained domain translation model for generating synthetic data samples in a first domain based on input data samples from a second domain.

The invention provides a mixed teaching-oriented student behavior data acquisition and analysis method. The acquisition method comprises an offline student behavior data acquisition method and an online student behavior data acquisition method. The offline student behavior data collection method is carried out through offline student behavior data collection software, when a teacher prepares to start a course, the software is opened, and preset course information and a two-dimensional code for students to sign in are projected to a large screen; after the student signs in, the behavior of the student is recorded through a sensor of the mobile phone; the online student behavior data acquisition method is executed through an online education platform. The online education platform provides a background management system for teachers, and online learning behaviors of students can be tracked in real time; according to the invention, an educator and an educational institution can more comprehensively understand learning behaviors of students in a mixed learning environment, and a scientific basis is provided for further improving teaching quality.

Disclosed herein is an apparatus and method for learning mixed data for approximate queries. The apparatus receives mixed data including relational data about information for identifying an object and spatiotemporal data about the trajectory of the object moving in a target space, discretizes the relational data and the spatiotemporal data based on a level of detail that is preset for each designated area of the target space corresponding to the trajectory of the object, and generates a mixed learning model that learns the relational data and the spatiotemporal data for each level of detail using multiple relational models and spatiotemporal models.

The invention discloses a neural network asymptotic tracking control method suitable for a robot system, and the method comprises the steps: introducing synaptic plasticity and internal plasticity into a conventional echo state network through simulating the adaptive characteristics of a biological nervous system, enabling the synaptic plasticity to dynamically adjust the connection weight of a neuron, enabling the internal plasticity to adaptively adjust the dynamic characteristics of the neuron, and enabling the neural network to continuously adjust the connection weight of the neuron; synchronous optimization learning of the neural network weight and the neuron state is realized, and an echo state network with a mixed learning mechanism is formed. The novel neural network is combined with a robust error symbol integral (RISE) controller, by means of the powerful nonlinear approximation capability of the neural network and the integral characteristic and the robust mechanism of the RISE controller, an unknown nonlinear part widely existing in a robot system is effectively processed, and asymptotic tracking control of the robot system on an expected trajectory is achieved. A simulation result verifies the effectiveness and stability of the method.

The invention provides a hybrid learning type image compressed sensing method and system based on semantic guidance, and belongs to the technical field of image processing and compressed sensing, and the method comprises the steps: obtaining an input image, dividing the input image into a plurality of image blocks which are not overlapped with each other, inputting the image blocks into a semantic coding network, the semantic coding network is used for extracting high-dimensional semantic embedding features of the image blocks and generating corresponding semantic mask vectors; constructing a semantic-guided adaptive sampling matrix based on the semantic mask vector, and realizing dynamic change of the sampling matrix along with the semantic features of the image blocks; after the self-adaptive sampling matrix is obtained, compression measurement operation is executed on the image blocks, a basic sampling measurement value and a semantic self-adaptive sampling measurement value are obtained respectively, and measurement results of the two parts are spliced on the measurement dimension to obtain a final compression measurement vector; and performing semantic perception pre-reconstruction, hybrid Transform-CNN block-level reconstruction and pixel-level fine reconstruction in sequence based on the compressed measurement vector, and outputting a reconstruction result.

The invention discloses a storage battery remote capacity checking system and method, and the method comprises the steps: controlling a storage battery to discharge when capacity checking equipment in the capacity checking system receives a capacity checking instruction, collecting multi-modal time sequence data, and transmitting the multi-modal time sequence data to a cloud platform, and enabling the cloud platform to upload the multi-modal time sequence data to a block chain; and inputting the multi-modal time series data into a hybrid learning model comprising a feature extraction network and a gradient boosting decision tree sub-model to obtain estimated capacity and confidence, and uploading the estimated capacity and confidence to a block chain, on one hand, the multi-modal time series data and a capacity checking result are uploaded to the block chain, so that the capacity checking data and the result can be prevented from being tampered; on the one hand, an effective anti-counterfeiting and tracing mechanism is provided, auditing and compliance requirements can be met, on the other hand, a mixed learning model of a feature extraction network and a gradient boosting decision tree sub-model is adopted, accurate features can be extracted for various complex working conditions, the stability and precision of capacity prediction can be improved through the gradient boosting decision tree sub-model, and the accuracy of capacity prediction is improved. And the generalization ability is better.

The embodiment of the application provides a kind of based on machine learning's wind turbine deep self-diagnosis method and device, method includes: by dividing wind turbine overall structure into structural components and relevant space, and from overall structure, multiple physical field sensor data is collected, the structural components of wind turbine are discretized into multiple finite elements, and relevant space is defined as the node connected finite element, corresponding wind turbine network topology structure is constructed, corresponding physical field control equation is established for finite element based on network topology structure, corresponding global coupling equation group is determined, and numerical method is used to solve global coupling equation group, corresponding physical response data is determined, real-time multiple physical field sensor data and real-time physical response data are input into the set hybrid learning model to carry out unsupervised and supervised hybrid learning, to determine the life cycle prediction result of wind turbine, the accuracy and efficiency of the application can improve wind turbine fault diagnosis and prediction.

The invention discloses a crowd sensing network missing data recovery method, relates to the technical field of Internet of Things associated data speculation, and aims to solve the problem of data missing caused by non-uniform node distribution in a crowd sensing network and realize high-quality sensing data speculation. According to the technical scheme, the method specifically comprises the following steps: firstly, aiming at the dynamic characteristics of the crowd sensing network, constructing a node influence range model based on the real-time spatial position information of each sensing node in each time period, so as to precisely define the sensing association boundary of different nodes in the spatial dimension; and secondly, taking the node influence range model as core guidance, and constructing a mixed learning model fusing two mechanisms of adversarial learning and self-expression learning, thereby effectively utilizing internal association of sensing data, remarkably improving accuracy and reliability of missing data recovery, and providing technical support for stable operation and data application of the crowd sensing network.

The application relates to a key flow-based SRv6 explicit path intelligent scheduling method and system, and belongs to the field of communication information technologies.The application comprises the following steps: key degree scoring is performed through a semi-supervised-transfer hybrid learning model to output a key flow; a full-dimension network twin model is constructed to predict a congestion risk trend and verify a link path survival rate; based on the key flow and the link path survival rate, a low-latency source node list is generated through a key flow path enhancement algorithm; a micro-isolation queue is created by analyzing the low-latency source node list; when a digital twin detects that an actual state deviates from a predicted value, a pre-verified backup path identifier chain is called to complete path switching in real time; and a double-channel learning model is used to perform closed-loop optimization based on key operation state data.The application solves the technical problems of low key flow recognition accuracy, poor adaptability of SRv6 path scheduling to network states, lagging fault switching and insufficient core model optimization in the prior art.

A hidden semi-Markov model includes plural second hidden Markov models each containing plural first hidden Markov models using types of movement of a person as states. The plural second hidden Markov models each use partial actions that are parts of actions determined by combining plural movements as states. In the hidden semi-Markov model observation probabilities are leant for each type of the movements of the plural first hidden Markov models using unsupervised learning. The learnt observation probabilities are fixed, and input first supervised data is augmented to give second supervised data, and transition probabilities of the movements of the first hidden Markov models are learned by supervised learning in which the second supervised data is employed. The learnt observation probabilities and transition probabilities are employed to build the hidden semi-Markov model that is a model for estimating segments of the partial actions.

The invention relates to the technical field of ship power equipment, in particular to a propeller propulsion load dynamic torque prediction system based on hybrid learning. Comprising the steps of constructing a mathematical relationship between propeller rotating speed and torque; predicting the rotating speed of the propeller based on an ANFIS algorithm; propulsive load dynamic torque prediction. The method can accurately predict the dynamic torque of the propulsion load, helps the ship to realize the optimal performance, and reduces the fuel consumption to the greatest extent, thereby reducing the operation cost and improving the economic benefit.