40 results about "Learning set" patented technology

The invention discloses an AI-based multimodal transport resource collaborative dynamic configuration method, which comprises the following steps of: constructing a real-time data layer for acquiring multi-dimensional data; preprocessing the data of the real-time data layer; dynamically constructing a digital twin platform based on the preprocessed data information; training a plurality of agents; carrying out cooperative training on a plurality of agents under a federated learning cluster framework; jointly training a global AI model; the AI decision center generates an optimal or nearly optimal dynamic resource configuration decision; the dynamic configuration engine dynamically schedules resources and generates an instruction; issuing the generated detailed instruction to a physical system of an execution layer; and the IoT equipment continuously monitors the execution state and the physical environment change, and feeds back new data to the real-time data layer. According to the invention, the bottleneck of data islands and response delay is broken through, and a cost-aging-carbon emission multi-target balanced intelligent decision-making system is constructed; and performing multi-agent collaborative training under a federated learning framework to realize cross-domain collaborative optimization.

The invention discloses an unmanned aerial vehicle cluster environment adaptive optimization method based on machine learning, which relates to the technical field of unmanned aerial vehicle cluster cooperative control and comprises the steps of basic framework construction, training parameter optimization, dynamic strategy adjustment, anti-interference communication enhancement and fault tolerance self-reconstruction. Coupling the optimized federated learning model with a multi-modal data fusion module, performing self-attention mechanism fusion on sensor data after time synchronization, constructing a high-dimensional state vector containing an agent state and an environment feature, inputting the high-dimensional state vector into a reinforcement learning framework to generate a joint action decision, and realizing fault detection through an LSTM network. And performing fault compensation by using redundant sensor data and the multi-modal fusion model. Through a dynamic graph attention mechanism and multi-target federated learning, the cluster can dynamically adjust a strategy to cope with complex environments such as electromagnetic interference and obstacle change, and a multi-agent collaborative decision and residual error compensation fault self-healing mechanism ensures that the cluster can still complete a task when a node fails or communication is interrupted.

The embodiment of the invention provides a federated learning contribution evaluation method and device, and the method comprises the steps: carrying out the grouping of a plurality of edge computing devices, and obtaining a plurality of sub-federated learning sets; and for the target federated learning sub-set, aggregating model update information corresponding to each edge computing device in the target federated learning sub-set, and determining a collaborative contribution value of the target federated learning sub-set based on the performance index of the updated global model on the common test set. Through a first linear programming solver, according to the collaborative contribution values of the multiple federated learning sub-sets, obtaining the maximum loss value corresponding to all the federated learning sub-sets and optimizing the maximum loss value to obtain the minimized maximum loss value, and through a second linear programming solver, obtaining the maximum loss value corresponding to all the federated learning sub-sets; and according to the maximum loss value after all the sub federated learning sets are minimized and the reference contribution values corresponding to the plurality of edge computing devices, target contribution vectors corresponding to the plurality of edge computing devices are determined, and the contribution degree of each edge computing device in the training process is accurately quantified.

The invention discloses a ground vegetation leaf area index remote sensing inversion method based on a remote sensing image. The method comprises the following steps: data acquisition and preprocessing; performing multi-scale space-time non-local filtering fusion; dynamic feature extraction; carrying out transfer learning fine tuning; time sequence deep learning integration; and model output and post-processing. According to the invention, through multi-source space-time fusion and dynamic feature distribution, vegetation LAI inversion with high resolution and high continuity is realized; the transfer learning and the time sequence deep network enhance the adaptability of the model to a new region and time sequence change; the stability and reliability of large-scale application are guaranteed through full-process automation and uncertainty evaluation, and the model is obviously superior to an existing single-source or static model.

The invention discloses a hypergraph-based unknown network attack detection method for an electric power Internet of Things terminal, relates to the technical field of network attack detection, and solves the problems of insufficient model expression ability, overfitting and learning set deviation caused by the fact that a model method in the prior art ensures that known classes are fully separated and unknown classes are far away from the centers of the known classes. According to the method, dual modeling capabilities of a graph structure and a time sequence structure are combined, so that the method can effectively adapt to complex distribution characteristics in a dynamic electric power Internet of Things environment, and is particularly suitable for the problems of non-uniformity, burstiness, unknown traffic characteristic change and the like in an electric power Internet of Things terminal data stream; and the adaptability of the model to the diversified data structure of the edge device is improved. A dimension compression mechanism is introduced in the structural design of the model, the parameter quantity of the model is effectively reduced, lightweight deployment and end-side reasoning on an edge node or an industrial terminal are ensured, and therefore the real-time attack detection capacity of the power system is improved.

The invention discloses a knowledge exchange method based on learning before forgetting and application thereof, and relates to the technical field of machine learning and knowledge management, a pre-trained deep learning model is selected as a basic model, and a retention set, a forgetting set and a learning set are constructed; the basic model carries out new knowledge learning on the learning set, and the training target of the model in the stage is that the accuracy of the learning set is close to 1, and meanwhile the accuracy of the reserved set is kept unchanged; after the basic model completes new knowledge learning, knowledge irrelevant to a new task is forgotten through a selective forgetting mechanism, and the training target of the model at the stage is that the accuracy rate of a forgetting set is close to 0, and meanwhile the accuracy rates of a reserved set and a learning set are kept unchanged. According to the method, continuous learning and machine forgetting are integrated together, so that the problem of contradiction between useless knowledge forgetting and new knowledge learning when a deep learning model processes knowledge updating of a pre-training model is solved. According to the knowledge exchange method based on first learning and then forgetting, the specified knowledge can be selectively forgotten while efficient learning of new knowledge is ensured, so that more refined knowledge regulation and control are realized, and the adaptability and the stability of the model are improved.

The present invention relates to a semi-supervised fatigue test state monitoring method based on adaptive confidence active learning, which belongs to the field of equipment state monitoring technology and solves the problems of high labeling cost and poor adaptability to working conditions in traditional monitoring methods. First, a data set consisting of fatigue test state monitoring data is constructed and divided into an initial training set, an active learning set and a validation set. Then, an LSTM is used to construct a state recognition model, and the initial training set is used for pre-training. After pre-training, an active learning model is constructed by adding a Softmax function, and the confidence of the active learning set samples is calculated. Samples below the threshold are screened out, and a retraining set is formed after manual labeling. The retraining set is used to train the state recognition model and adjust hyperparameters, and finally the model is evaluated on the validation set. By integrating active learning with adaptive confidence and a semi-supervised learning mechanism, the present invention significantly reduces the dependence on labeled data, has lower labeling costs, and has strong adaptability to complex working conditions.

This set provides an efficient scale sight-reading learning tool for piano beginners, allowing them to easily learn how to read musical scales on the staff and the correct key placement, even without a piano. [Solution] The scale reading learning set 1, which has a main body with four octaves of musical staff lines, is equipped with rotatably supported sheet winding knobs 4 and 5, and a transparent sheet for writing musical notes 6 that is stretched over the sheet winding knobs 4 and 5 via a transparent sheet for writing musical notes retraction hole 7. By rotating the sheet winding knobs 4 and 5, the musical note marks written on the transparent sheet for writing musical notes 6 move along the musical staff lines. This makes the scale reading learning set 1 compact, limits the range of the musical staff lines to four octaves, and improves the efficiency of learning to read musical notes by allowing the transparent sheet for writing musical notes 6 to move smoothly from side to side and allowing the written musical notes to be read immediately.

The application discloses a method for expanding random telegraph noise (RTN) based on a storage neural network, and the signal expansion process is realized based on an artificial neural network of a novel storage unit. According to partial RTN measured signals as a learning set, the expansion process of signal prediction reasoning can realize expansion of the signals with an arbitrary time length, and accelerates extraction of the time parameters of the RTN. The method has important significance for development of a physical unclonable function (PUF) technology based on the RTN and information data security.

The invention relates to the technical field of intelligent operation and maintenance of a power system, in particular to an extra-high voltage transformer substation handover test data fusion and abnormity early warning method, which comprises the following steps: collecting and preprocessing multi-source heterogeneous original data to generate a standardized test data set; based on a pre-trained multi-dimensional association rule model, hidden association rules among the test parameters are mined, weighted fusion is executed, and fusion data representing the overall health state of the equipment are generated; comparing the fusion data with a dynamic threshold interval generated by a historical normal sample, calculating a deviation degree and mapping the deviation degree into an abnormal confidence degree; and triggering visual early warning signals of different levels according to the abnormal confidence coefficient, and feeding back a new sample to the learning set for iteratively optimizing the multi-dimensional association rule model and the dynamic threshold interval. According to the method, deep fusion analysis of test data, dynamic quantitative evaluation of the health state and continuous optimization of the model can be realized, and the accuracy of anomaly recognition and the adaptability of the system are improved.

This application provides a federated learning contribution evaluation method and device. Multiple edge computing devices are grouped to obtain multiple sub-federated learning sets. For a target sub-federated learning set, model update information corresponding to each edge computing device in the target sub-federated learning set is aggregated. Based on the performance metrics of the updated global model on a public test set, the collaborative contribution value of the target sub-federated learning set is determined. Using a first linear programming solver, the maximum loss value corresponding to all sub-federated learning sets is obtained based on the collaborative contribution value of the multiple sub-federated learning sets, and the maximum loss value is optimized to obtain the minimized maximum loss value. Using a second linear programming solver, based on the minimized maximum loss value of all sub-federated learning sets and the reference contribution value corresponding to the multiple edge computing devices, the target contribution vector corresponding to each of the multiple edge computing devices is determined, accurately quantifying the contribution of each edge computing device in the training process.

A method for setting up an apparatus (1) for biological processes (3), in which process parameters are specified for a plurality of biological processes (3) with computer assistance, that for each biological process (3) a process state is automatically captured, that the particular process state is evaluated using a specified objective with computer assistance, and that from the evaluations the apparatus (1) is set up, with computer assistance, through specification of learned set-up parameters. In addition, an apparatus (1) for biological processes (3) is provided with which the proposed method can be carried out in a particularly advantageous manner.

The invention discloses a curative effect prediction method based on multi-organ metastasis genome data, and belongs to the technical field of medical models, and the method specifically comprises the following steps: collecting clinical pathological characteristics, multi-organ metastasis genome data and a treatment scheme of a breast cancer patient, and recording a metastasis part and a load state; dimensionality reduction is conducted on high-dimensional genome data through a regularization algorithm, feature importance is evaluated in combination with a nonlinear model, and clinical, treatment and genome features related to treatment response are screened out; inputting the screened features into a machine learning and deep learning framework, randomly dividing a training set and a test set in a layered manner, optimizing hyper-parameters through cross validation, and constructing a classic machine learning set model and a deep learning model based on an attention mechanism; disturbing test queue treatment scheme data, evaluating the consistency of model recommendation and an actual scheme, and verifying the prediction capability and clinical practicability of the model; according to the method, multi-dimensional data are integrated, and the curative effect prediction accuracy of the metastatic breast cancer is improved.

This invention discloses a method for dynamic compensation of polarization signals in low-altitude complex terrain, an unmanned aerial vehicle (UAV) communication device, and a system, belonging to the field of wireless communication. The method includes: utilizing an edge computing platform to collect and analyze channel polarization state data in real time; employing an adaptive dynamic compensation algorithm; setting a set of candidate modulation schemes based on the channel's signal-to-noise ratio; and constructing a constraint function based on the set of candidate modulation schemes to achieve high-fidelity transmission of polarization signals. The system also integrates interference prediction, federated learning cluster collaboration, and secure communication modules, thereby achieving high security and robust communication while ensuring low latency and low power consumption. This invention effectively solves the problems of polarization signal mismatch, weak anti-interference capability, and poor environmental adaptability in existing technologies under complex terrain, improving the reliability and security of UAV communication systems, and has significant application value and market prospects.

The description relates to deep learning cluster scheduler modular toolkits. One example can include generating a deep learning cluster scheduler modular toolkit that includes multiple DL scheduler abstraction modules and interactions between the multiple DL scheduler abstraction modules and allows user composition of the multiple DL scheduler abstraction modules to realize a deep learning scheduler.

The furnace temperature field reconstruction method based on the radiation transfer equation and the multi-layer feedforward neural network fusion comprises the following steps: calibrating the radiation detector by using a blackbody furnace to establish the relationship between the image intensity and the radiation intensity; establishing a detailed radiation transfer equation RTE considering the medium absorption, scattering and wall effect by using a DOM algorithm; generating a data set as a learning set of the neural network by smoothing the temperature field and the fluent simulation; constructing a multi-layer feedforward neural network; predicting the temperature of each grid by the learned model to obtain the furnace cross-section temperature field. The method couples the physical constraint of the radiation transfer equation with the feedforward neural network, avoids the shortcomings of the large amount of calculation of the pure physical constraint and the lack of physical theory support of the pure data driving, has small workload for replacing the fluent model simulation learning set for different sites, and can quickly obtain the temperature measurement result by calling the pre-learned model, and occupies less computing resources.

Various embodiments of the present disclosure provide machine learning architectures and optimization techniques for improving predictive functionality of a computer. The techniques comprise generating a cohort-level optimization dataset with a plurality of entity-level predictive features and a plurality of feature-level predictive features for a plurality of entity data objects using a machine learning ensemble model. The techniques comprise identifying a plurality of iterative candidate outputs through a series of optimization iterations. During each optimization iteration, an iterative candidate output may be generated by applying optimization model and a constraint set combination to the cohort-level optimization dataset. The techniques comprise selecting a target output from the plurality of iterative candidate outputs based on selection criteria.

Provided are a learning device that efficiently generates image data while suppressing an operation amount when an image is generated using a machine learning model using a variational autoencoder (VAE), and an inference device that executes a predetermined inference process on target image data using the machine learning model.SOLUTION: An image processing device that exhibits a function as at least one of a learning device and an inference device includes a first machine learning model to which input image data is input from an image input unit, a second machine learning model to which a latent variable generated by the first machine learning model is input and from which output image data is output, and a learning process execution unit that executes a process of learning a setting value with the two machine learning models. The execution unit converts input image data into a latent variable using a first machine learning model, generates output image data from the latent variable using a second machine learning model to learn a setting value, maintains a brightness component of at least one of the input image data and the output image data, and downsamples only a tint component.SELECTED DRAWING: Figure 2

A self-learning cluster control system for supply chain computing infrastructures, comprising a supply chain data acquisition unit, a node profiling unit, a cluster formation unit, a condition assessment unit, a self-learning control unit, and a resource allocation unit, wherein the system is configured to dynamically group supply chain nodes into clusters based on continuously acquired condition data and to enable adjustment of cluster allocation based on demand trends, inventory levels, transportation capacities, delivery times, and disruption risks.

This invention discloses a joint optimization method for edge computing-based vehicular network (V2N) resource management based on the DDPG algorithm. The method involves: establishing a network architecture integrating edge services and federated learning in the V2N, initializing network parameters; training a model using a dataset and calculating optimal model parameters; modeling the joint optimization problem as a Markov decision problem and training it using the DDPG algorithm, updating network parameters, recording the average reward, obtaining a decision network, and performing dynamic network offloading scheduling, resource allocation, and service model caching to achieve joint optimization of edge computing-based V2N resource management. This invention improves the real-time performance of V2N services based on edge computing and federated learning, exhibiting good convergence performance and joint optimization effects; it also enhances the security of privacy data on edge servers and the service quality for V2N users, and can be widely applied to practical mobile terminal applications such as path planning and navigation, and remote vehicle diagnostics.

The application provides a communication configuration method, a federated learning method, a device, an electronic device and a medium, wherein the communication configuration method comprises: determining a task identifier of a federated learning task and a positioning domain name of each party participating in the federated learning task; determining a communication domain name of the each party based on the task identifier and the positioning domain name of the each party; determining a communication certificate between the each party based on the positioning domain name of the each party; and configuring the communication domain name of the each party and the communication certificate between the each party to the each party, so that the each party establishes a communication connection and performs the federated learning task based on the communication connection. The communication configuration method, the federated learning method, the device, the electronic device and the medium provided by the application can realize automatic configuration of the communication connection of the each party, greatly simplify the configuration process of the federated learning cluster under the premise of ensuring the communication security and reliability.

The present invention discloses a digital engineer project training teaching method based on robot learning, which relates to the field of teaching technology. The present invention adjusts the task difficulty and content in real time according to the student's personal performance and ability status through dynamic task allocation and personalized learning path optimization. The adaptive teaching mode enables each student to learn at a pace and difficulty that suits him or her. It integrates project management tools, version control systems and real-time feedback mechanisms, so that students can obtain instant guidance and optimization suggestions in actual operations, provide real-time operation suggestions when submitting codes and merging versions, and automatically generate version snapshots and associate them with task progress, so that students can continuously optimize their operations in practice. It automatically generates learning reports, evaluates students' code quality, task completion efficiency and version control rationality, provides students with comprehensive learning feedback, and provides data support for teachers to improve teaching strategies.

Provided is an X-ray CT apparatus including a learned model generated by acquiring one or more learning data sets from one imaging without increasing exposure of a subject, and performing machine learning using the acquired learning set. The learned model is a model after learning in which a low-quality image is input data and a high-quality image is training data. The low-quality image and the high-quality image are obtained based on the same learning measurement data or learning projection data obtained by logarithmically converting the learning measurement data. The low-quality image is a CT image reconstructed from partial data obtained by dividing the learning measurement data or the learning projection data, and the high-quality image is a CT image obtained by reconstructing the learning projection data.

The application provides a phenotype prediction method and system based on feature reduction and generalized inverse technology, which adaptively fuses linear and nonlinear effects, constructs a new model of adaptive fusion of linear and nonlinear effects of prediction factors for crop phenotype prediction, and evaluates the importance of different effects on the phenotype through the method of adaptive weight adjustment, so as to realize the function of comprehensively considering the influence of linear main effect and nonlinear relationship in crop phenotype prediction. When solving the model, the feature reduction is performed on the training data set, and the model is quickly and effectively solved by using the generalized inverse, so that the determination efficiency of the phenotype value of biological materials is improved. The learning set and the test set obtained by randomly dividing the experimental data set for multiple times are used for model learning, so that the system error of the model is reduced, and the stability of the phenotype prediction result is improved. The effectiveness of the application for phenotype prediction is verified based on DNA molecular marker genotype data and metabolite-based intermediate omics data.

The invention discloses a transformer micro-water content prediction method, system and device based on PSO-EMD-RF and a medium, and relates to the technical field of electrical equipment.The method comprises the steps that micro-water content time sequence data is divided based on a time sequence cross validation mode, and an initial training set and an initial test set are obtained; on the basis of a PSO algorithm, EMD decomposition is carried out on data in the initial training set and the initial test set, and a decomposition training set and a decomposition test set are obtained; performing feature and label construction on the decomposition training set and the decomposition test set to obtain a first supervised learning set and a second supervised learning set; constructing a micro-water IMF component prediction model according to the first supervised learning set and the second supervised learning set based on an RF model; and obtaining target micro-water content data, and predicting the micro-water IMF component according to the target micro-water content data based on the micro-water IMF component prediction model to obtain a micro-water IMF prediction component. The method has the effect of improving the micro-water content prediction accuracy.

The invention discloses a digital employee automatic construction method based on business system self-learning, and the method comprises the steps: receiving a natural language demand description of a business system through a self-learning integrated agent, calling a large language model to carry out intention understanding, and generating a structured tool development plan; the self-learning integrated agent adopts a three-stage self-learning strategy of exploration-learning-optimization to generate a parameterized script; the self-learning integrated agent encapsulates the optimized parameterized script into a standardized tool conforming to the MCP protocol specification, and encapsulates the structured business data model into a data structure definition document of the tool; the self-learning integrated agent registers the standardized tool to an MCP tool registration center; a digital employee agent loads a tool and business data structure definition from an MCP tool registration center, intelligent tool calling is carried out by adopting an'intention-planning-execution 'three-layer decision-making architecture, business task execution is completed, and the construction threshold and cost of digital employees are greatly reduced.

To provide a teaching material for facilitating composition in a language to be learned.SOLUTION: To provide a language teaching material set which enables a user to easily learn grammar by using composition cards on which composition terms of a learning object language and headings of speaker language concepts as superordinate concepts of the composition terms are described and arranging the composition cards corresponding to the headings of the speaker language concepts arranged in the word order of the learning object language on a composition sheet.SELECTED DRAWING: Figure 9

The application discloses a multimodal transport resource collaborative dynamic configuration method based on AI, which comprises the following steps: constructing a real-time data layer for collecting multidimensional data; preprocessing the data of the real-time data layer; dynamically constructing a digital twin platform based on the preprocessed data information; training multiple intelligent agents; and collaboratively training the multiple intelligent agents under a federal learning cluster framework; jointly training a global AI model; an AI decision hub generating an optimal or near-optimal dynamic resource configuration decision; a dynamic configuration engine dynamically scheduling resources and generating instructions; issuing the generated detailed instructions to the physical system of the execution layer; and an IoT device continuously monitoring the execution state and physical environment changes and feeding new data back to the real-time data layer. The application breaks through the data island and response delay bottleneck, constructs an intelligent decision system balancing the cost-time-carbon emission multi-objective, and realizes cross-domain collaborative optimization through the collaborative training of multiple intelligent agents under the federal learning framework.

This invention discloses a machine learning-based adaptive optimization method for UAV swarm environments, relating to the field of UAV swarm cooperative control technology. The method includes basic framework construction, training parameter optimization, dynamic policy adjustment, anti-interference communication enhancement, and fault-tolerant self-reconstruction. It couples an optimized federated learning model with a multimodal data fusion module, fuses time-synchronized sensor data using a self-attention mechanism, constructs a high-dimensional state vector containing agent states and environmental features, inputs it into a reinforcement learning framework to generate joint action decisions, implements fault detection through an LSTM network, and utilizes redundant sensor data and the multimodal fusion model for fault compensation. Through dynamic graph attention and multi-objective federated learning, the swarm can dynamically adjust its strategy to cope with complex environments such as electromagnetic interference and obstacle changes. Multi-agent cooperative decision-making and residual compensation fault self-healing mechanisms ensure that the swarm can still complete its tasks when nodes fail or communication is interrupted.