585 results about "Proactive learning" patented technology

The invention relates to a multi-modal image threshold segmentation preprocessing method based on a convolutional neural network, and the method comprises the steps: unifying an image into a standard space, carrying out the pixel value mapping, carrying out the resampling, generating high and low frequency sub-bands, carrying out the soft threshold denoising of the high frequency sub-bands, enhancing the contrast of the low frequency sub-bands, and carrying out the fusion; an optimized VGGnet framework is constructed; a noise adversarial network is generated to carry out active learning loop training on a convolutional neural network model; the image is input into the model for prediction; a local entropy and a gradient magnitude are calculated based on a prediction result; optimal segmentation is realized by setting a double-layer matrix of a feature tag-segmentation method; grey matter Dice calculation is carried out on the segmented images, and preprocessing parameters of unqualified images are optimized through a dynamic parameter adjusting module based on a Gaussian process regression model. The segmentation precision and the processing efficiency of the multi-modal image are effectively improved, and the adaptability of the model to a complex image is enhanced.

The present disclosure describes methods, systems, apparatus, and media for object identification and classification, utilizing multi-feature and multi-modal data. This includes shape, material, brand, price, odor, taste, tactility, and sound. The system integrates a server space for data processing, a querying device for iterative searches, and a data interface module for refining results. It features AI-driven image optimization, feature extraction, and pattern recognition, employing novel techniques for fusing multi-feature and multi-modal embeddings utilizing multi-head attention. Additionally, a linker module powered by two active learning with feedback loops AI models consolidates scattered data into a unified object information database. The system also employs novel AI algorithms for isolating the object of interest through a saliency map and semantic analysis, as well as for enhancing raw images with a GAN-autoencoder.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention relates to a security management knowledge graph construction and dynamic updating method based on association modeling, and the method comprises the steps: collecting multi-source heterogeneous data in the field of security management, carrying out the recognition of the data type, carrying out the data conversion, setting an active learning mechanism in a named entity recognition model, carrying out the training, and recognizing an entity from the data. The entity is extracted; constructing an attention-enhanced graph neural network to analyze image data, calculating an attention weight for each node and edge in a message transmission process, and updating node information to realize relation extraction; based on the entity and relational data, constructing a preliminary security management knowledge graph; reasoning and supplementing missing information and relations based on an external standard knowledge base; the data change increment is detected in real time to update the security management knowledge graph, nodes and relationships are newly added or updated in the security management knowledge graph, the security management knowledge graph is efficiently constructed and dynamically updated, and support is provided for cross-modal intelligent question answering and real-time security evaluation.

The invention discloses an industrial surface defect image classification method based on hybrid query strategy active learning, and belongs to the technical field of computer image processing and machine learning. The invention aims to solve the technical problems of high cost, long period and low rare defect recognition rate caused by category imbalance due to dependence on large-scale manual labeling. The core of the method is to execute a hybrid query strategy in an iterative loop: firstly, screening out a candidate sample set of model cognitive ambiguity through uncertainty measurement of prediction entropy; secondly, in the candidate set, a core set and hierarchical thought diversity sampling method is adopted to select a final to-be-labeled sample with both characteristic representativeness and category balance. And the query batch is manually annotated and then is used for carrying out iterative updating and optimization on the model. According to the method, the recognition precision and generalization ability of the classification model on various defects can be remarkably improved with extremely low manual labeling cost, and the model development period is greatly shortened.

A knowledge graph construction method based on active learning and incremental learning comprises the following steps: S1, preprocessing data from a plurality of heterogeneous data sources, and extracting entities, relationships and attributes to form an initial knowledge network; s2, vectorizing elements in the initial knowledge network by using a knowledge graph embedding model, and performing entity alignment based on vector similarity to obtain an initial knowledge graph; s3, screening out candidate knowledge triples with high uncertainty and / or high representativeness from the initial knowledge graph by adopting an active learning strategy, and obtaining user labeling information corresponding to the candidate knowledge triples; s4, performing iterative optimization on a knowledge extraction model and / or a knowledge graph embedding model according to the user labeling information; s5, new data are fused into the optimized knowledge graph in an incremental learning mode, and knowledge conflict detection and resolution are carried out in the fusion process; and S6, circularly executing the steps S3 to S5 until the knowledge graph meets a preset quality condition.

The invention relates to the technical field of deep learning, provides an optimization method of a defect detection model and defect detection equipment, and can be used for industrial quality inspection. According to the method, at least one to-be-detected product data set in production is used for carrying out multi-round iterative optimization on a defect detection model after pre-training, the defect that traditional model training is isolated from a production line environment is overcome, and closed-loop iterative detection and optimization of the model are achieved. In each optimization process, confidence coefficient learning and active learning are combined, the uncertainty of a model is quantified through confidence coefficient learning, a global confidence coefficient threshold value is determined according to the confidence coefficient corresponding to the defect type of each piece of to-be-detected product data, and the threshold value is dynamically adjusted by using the recall rate and the number of optimization times. And during active learning, sample data screening is carried out by using the adjusted global confidence threshold, so that blind labeling or over-fitting labeling is avoided, the quality of the screened samples is ensured, and the accuracy and generalization of a retraining defect detection model are improved.

A system and method of real-time optimization of drilling performance metrics during a well drilling operation, for oil and gas as well as geothermal wells, or wells drilled for any other purpose. In a preferred form, the system receives information about allowable drilling metrics and real-time information of performance indicators. The drilling performance metrics and performance indicators are used to build a model to predict drilling parameters likely to optimize one or more drilling performance metrics.

The invention discloses an intelligent catalyst design method and system based on multi-objective optimization and deep learning, and aims to solve the problems of limitation of single-objective optimization, high computing resource consumption and the like in traditional catalyst design. According to the method, graph data are constructed by fusing atomic-scale and macroscopic features, multi-target prediction is carried out by utilizing a multi-task graph neural network (GNN), and collaborative optimization of adsorption energy and stability loss is realized by combining an NSGA-II multi-target optimization algorithm and a dynamic weight adjustment strategy of Bayesian optimization. A closed loop is verified through active learning and virtual experiments, candidate materials are dynamically selected, the model is updated, and the efficiency and precision of material design are remarkably improved. The method is suitable for rapid discovery and optimization of the high-performance catalyst, and has a wide application prospect.

The invention discloses an active learning and physical loss-based fatigue crack growth rate prediction method, which comprises the following steps of: splicing and fusing a preprocessed stress intensity factor, a stress ratio, pre-strain and stress amplitude, and taking the fused characteristics as the input of a model; embedding the Jones model into a loss function of the neural network, constructing a physical information time sequence model based on the Jones model, setting a parameter set, selecting a most valuable training sample from a training set by using active learning, retraining the model by using the selected most valuable training sample, selecting an optimal model, and evaluating the optimal model by using a test set; fusing the actually measured stress intensity factor, stress ratio, pre-strain and stress amplitude of the to-be-predicted material, and inputting into the obtained prediction model to obtain a predicted value of the fatigue crack growth rate. By adopting the technical scheme of the invention, the fatigue crack growth rate of the material in the whole life period can be efficiently and accurately predicted at low cost.

The invention discloses an Internet of Vehicles CAN bus intrusion detection method based on noise perception active learning, and belongs to the technical field of Internet of Vehicles safety and machine learning. The invention aims to solve the technical problems of false label noise interference, high manual labeling cost, high attack missing report rate caused by class imbalance and the like. The core of the method is to execute a noise sensing mixed query strategy in an iterative loop: firstly, generating a pseudo tag through clustering and correcting by using an integrated noise detector; secondly, calculating uncertainty scores and noise probabilities of the samples, fusing the uncertainty scores and the noise probabilities to obtain a comprehensive score, and preferentially selecting the samples with high uncertainty and low noise probabilities; and then adaptively selecting a sampling strategy according to the model performance and applying category balance constraint. The query batch is used to iteratively update the model while dynamically adjusting the classification threshold to reduce the missing report rate. According to the method, the influence of pseudo label noise can be effectively suppressed, and the attack detection precision and generalization capability are remarkably improved with extremely low labeling cost.

The invention provides a collaborative classification method and system fusing advantages of large and small models, and the method comprises the steps: inputting to-be-classified data into a trained zero-sample classification small model, and outputting candidate label domain screening information and an initial classification result; inputting the to-be-classified data, the candidate label domain screening information and the initial classification result into the trained large model, and outputting a final classification result; the training process of the small model comprises the following steps: inputting training data into the zero sample classification small model to obtain a preliminary prediction result; screening and obtaining pseudo label data based on an active learning strategy; checking and re-marking the pseudo-label data by using the large model to obtain a modified pseudo-label data set; and carrying out iterative training on the zero sample classification small model by utilizing the modified pseudo label data set. The method has the advantages that a small model has classification performance close to that of a large model while keeping lightweight calculation characteristics; the calculation burden of a large model is reduced, and the accuracy of classification decision is improved.

The invention relates to the technical field of internet big data and information security, and designs a gearbox fault diagnosis method based on multi-source agent federal field generalization by combining the interaction capability of wide-area data and a local diagnosis agent. According to the method, a dual regularization mechanism is introduced in the training process of each local diagnosis agent, and the problem of negative migration is solved by actively learning domain-invariant fault features, so that the cross-working-condition and cross-equipment generalization diagnosis performance of a final model is remarkably enhanced on the premise of protecting data privacy; meanwhile, a central server is replaced by a decentralized coordination network realized by a block chain technology, and a committee consensus protocol is executed to ensure that the generation process of the global model is open, transparent and non-tampering, so that the single-point fault risk is fundamentally eliminated, the security and robustness guarantee is provided for the whole coordination process, and the method has the advantages of being high in practicability and the like. And the long-term applicability and the fault diagnosis accuracy and reliability of the method in a dynamically changing industrial environment are ensured.

The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts to classify documents; (iii) evaluate the classification performance of prompts; (iv) generate updates to prompts; and / or (v) train classifiers. In some embodiments, one or more processors: generate a prompt for input to the generative AI model; generate classifications for a set of documents from the corpus of documents by inputting the set of documents and the prompt to the generative AI model; based on the classifications, provide the set of documents to a review platform for manual review by a reviewer; obtain review data associated with a subset of documents from the set of documents; and train, by executing a training algorithm, a classifier using the review data as ground truth data, wherein the training algorithm is configured to analyze extracted relevant document portions of the subset of documents to train the classifier.

The invention discloses a main shock and aftershock full-life vulnerability analysis method based on active learning, and belongs to the field of structural anti-seismic safety evaluation. The method solves the problems that an existing full-life analysis method needs high calculation cost and does not consider the influence of environmental factors and main shock and aftershock coupling effects on the initial damage of the structure. The agent model is constructed based on high-precision and low-precision data fusion, and a meta-learning mechanism is introduced to describe internal association among different degradation states, so that the sample utilization efficiency is improved; a generalized learning function is combined with a two-stage active learning strategy to dynamically select a training sample point with the most information amount, efficient refinement of a prediction model in each degradation scene is gradually realized, the calculation cost is further reduced, and the agent model prediction precision is improved. And finally, through a time-varying two-dimensional limit state equation, explicitly considering the correlation between the initial damage evolved along with time and the structure residual capacity, and effectively fusing the coupling effect of environmental degradation and the seismic sequence. The method can be applied to structure vulnerability analysis.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence data annotation and cue word automatic construction engine system, which comprises a data annotation module for firstly carrying out preliminary annotation on data based on a pre-training model, then automatically annotating a data sample through an active learning algorithm, and meanwhile, monitoring the quality of annotated data in real time; and the cue word automatic construction module generates cue words based on task analysis, optimizes the cue words by using a reinforcement learning technology, and performs classified storage and management on the generated cue words. By adopting the semi-automatic labeling and active learning labeling functions, not only can the workload be greatly reduced, but also the unnecessary labeling work can be reduced, so that the labeling efficiency is remarkably improved, the labeling time is shortened, the large-scale data labeling requirement is met, and the problem of efficiency bottleneck caused by slow manual labeling is solved.

The invention relates to the technical field of new energy power system instability risk assessment, in particular to a new energy power system frequency instability risk assessment method based on a heterogeneous graph attention network. The method comprises the following steps: constructing a wind power-photovoltaic heterogeneous graph model according to a power grid topology; performing combined sampling on different operation modes and anticipated disturbances, and determining corresponding input feature vectors; calculating a frequency stability index value label of the sample set; expanding the training set through an active learning iteration process, and carrying out model training; and inputting the collected operation data into the trained heterogeneous graph attention model, outputting a frequency stability index value, and evaluating the system frequency instability risk in combination with the risk matrix. By adopting the frequency instability risk assessment method for the new energy power system based on the heterogeneous graph attention network, the problem of low efficiency of risk assessment in a high-dimensional uncertain scene is solved, and the heterogeneous graph attention network can reflect the influence of different types of devices at different positions and disturbance types on the dynamic frequency of the system; and the accuracy of frequency instability risk assessment is improved.

The invention belongs to the field of artificial intelligence active learning, and particularly relates to a medical intelligent labeling method based on adaptive density clustering and active learning, which comprises the following steps: acquiring an unlabeled sample set, and converting all unlabeled samples into embedded vectors by adopting a pre-trained medical contrast encoder; randomly selecting part of unlabeled samples from the unlabeled data set for labeling to form a labeled data set training classification model; performing local density calibration according to the embedded vector to obtain a high-density core set and a boundary candidate set; extracting unlabeled samples from the high-density core set and the boundary candidate set according to an adaptive weight evaluation method, labeling the unlabeled samples, and adding the labeled samples into a labeled data set; training a classification model by adopting the annotation data set, if the classification model converges, ending the training, and otherwise, carrying out the next round of iteration; the trained classification model is adopted to realize classification of the medical images; according to the method, the medical active learning performance is remarkably improved through boundary sensitive screening of density clustering and a dynamic balance mechanism of adaptive weight.

The invention discloses a concrete strength detection method based on image recognition. The method comprises the following steps: generating a corrected concrete image data set; outputting a shared feature map set; obtaining a semantic mask set by adopting a rasterized pyramid decoder and a channel attention mechanism; performing microstructure feature statistics based on the semantic mask set to obtain a microstructure index set; outputting a concrete strength predicted value set through a transfer learning and fine tuning regression mechanism; performing incremental updating on the strength prediction network by using a pseudo label-active learning loop mechanism to obtain an updated strength prediction network; and reasoning the corrected concrete image data set by using the updated strength prediction network to generate a concrete strength visualization result map set. According to the method, the interference of a non-intensity correlation noise region on a segmentation result is effectively suppressed, and the accuracy of a microstructure index is remarkably improved.

The invention provides a multi-objective optimization method fusing a classifier and an active learning strategy, and belongs to the technical field of artificial intelligence and optimization design, and the method specifically comprises the steps: obtaining a sample data set containing a plurality of target performance function values; performing non-dominated sorting on the sample data set to extract a Pareto frontier point set, and extracting geometric features from the point set as a geometric feature vector sequence; using the geometric features and the corresponding convex marks to train a classifier model of a Transform architecture, wherein the classifier model supports a multi-head attention mechanism and a position coding structure; judging the convex confidence coefficient of the current Pareto front by using a classifier model, and obtaining a candidate scheme; and inputting the candidate scheme into a double-attention mechanism prediction model based on context reasoning, and predicting the multi-target performance of the candidate scheme. According to the method, the experiment frequency can be reduced, the target performance can be accelerated to be achieved, and the intelligence and efficiency of the multi-target optimization process are improved.

The invention discloses a dynamic active learning and knowledge graph agent-based airworthiness document review method, which comprises the following steps of: collecting and processing a multi-modal heterogeneous data source to obtain a standardized knowledge base; based on the standardized knowledge base, constructing an aviation domain knowledge system by using dynamic active learning and a man-machine cooperation mechanism; constructing a knowledge graph and performing knowledge enhancement; and dynamically constructing a review context based on a retrieval enhancement generation method in combination with the knowledge graph, and injecting an intelligent agent to perform intelligent data review and report generation. According to the method, automatic review of airworthiness documents is realized through a dynamic active learning-agent review method, and the manual review efficiency is remarkably improved; domain knowledge is injected into an intelligent agent by utilizing an RAG technology, so that the accuracy of a general large language model in an airworthiness review task is improved; through knowledge semantic driving and human-in-the-loop knowledge enhancement, the problem of scarcity of high-quality data in the field is relieved; models of different parameter scales are cooperatively utilized, and computing power resources are saved.

The invention discloses a semi-automatic labeling method and system for rail transit engineering construction video images, and the method comprises the steps: removing redundant frames from a video stream, extracting key frames, and forming a block index set; and driving the multi-modal large model to pre-annotate the image by using the constructed cue word, and carrying out binarization processing on an annotation result. Based on active learning, migrating unmarked samples to a marked set for multiple times and training a key sample classifier CD until the scale of the marked set reaches the standard; meanwhile, a confidence classifier CC is trained by comparing manual and pre-labeling results. And for residual samples in the unmarked set, after the residual samples reach the standard through a CC precision test, marking tasks are divided according to a threshold value theta: high-confidence samples are pre-marked, and low-confidence samples are manually marked. And finally, updating the set of the manually labeled samples, and determining whether to retrain the model or not according to category distribution. On the premise of ensuring the labeling quality, the blindness of manual labeling is effectively reduced, and the efficiency of the labeling process is improved.

The invention provides a river flow online measurement and calculation method based on multi-source data fusion, and belongs to the technical field of river flow measurement. A Bayesian neural network and active learning collaborative hydrological memory reconstruction model is adopted to carry out high-confidence data interpolation on a sensor failure period and quantify uncertainty, fractional calculus is introduced to model a river memory effect, and flow evolution is analyzed through a fractional order water balance equation. And selecting a steady-state or non-constant flow calculation mode according to the flow change rate, inverting an optimal flow field for the non-constant flow by adopting a four-dimensional variational data assimilation method in combination with regularization constraint and time smoothing constraint, and outputting a flow measurement result and an uncertainty quantitative index. The technical problems that flow measurement and calculation data are missing and accurate interpolation is difficult due to sensor failure under the extreme hydrological condition are solved.

The invention discloses a power system multi-scene adaptive transient stability evaluation method based on active transfer learning, and the method comprises the following steps: constructing a transient stability evaluation model based on a Transform encoder, building a mapping relation between data and a stable state, and obtaining the source domain data of a power system; constructing a pre-training model based on the source domain data, and when it is detected that the pre-training model enters a new electrical system scene, performing short-term simulation to obtain an unlabeled target domain sample; calculating the information amount of each target domain sample through an active learning strategy; and carrying out fine adjustment on parameters of the model by utilizing transfer learning, selecting a transfer scheme by considering a difference degree between data distribution, enabling the transient stability evaluation model to quickly finish final updating training, evaluating the transient stability of the power system by using the updated model, and outputting a transient stability evaluation result. According to the method, the transient stable state of the power system can be accurately evaluated, and the model updating efficiency is higher.

The invention relates to the technical field of visual identification, and discloses an annotation, evaluation and learning integrated visual identification platform, which comprises a data acquisition module, an annotation enhancement module, a detection model training module, a dynamic evaluation module, an active learning module and an optimization closed loop module. Through integration of data acquisition, annotation enhancement, detection model training, dynamic evaluation, active learning, closed loop optimization and other modules, full-process collaboration is realized based on an improved YOLOv8 algorithm, and the annotation efficiency and quality are improved through auxiliary annotation, quality verification and cross-modal annotation association functions of the annotation enhancement module. The ICR algorithm and the sample screening strategy of the active learning module solve the cold start problem and reduce the labeling workload, and the dynamic evaluation module realizes model interpretability quantification through a class activation thermodynamic diagram, a MobileSAM segmentation mask, N-IoU, N-Recall and other indexes, so that an efficient and accurate one-stop solution is provided for a visual identification task.

Provided in the present application are an active learning and augmentation method for a complex skill in a heterogeneous scenario and a sim-to-real transfer method. The active learning and augmentation method comprises: first, collecting state data by means of the interaction of multiple executors and an environment; storing same in a shared experience replay buffer; after sampling, alternately training the executors and discriminators; and finally selecting the optimal executor to be deployed, so as to implement active learning and augmentation of a complex skill. The sim-to-real transfer method comprises: in a 3C assembly digital twin environment, collecting multi-modal data to construct a skill knowledge base; generating a policy sequence; using technologies such as residual reinforcement learning to perform optimization; and, by means of a coding and decoding model, transferring a skill policy from simulation to reality. Without a teacher model or teaching data, the solution provided in the present application improves skill learning efficiency by means of combining reinforcement learning and knowledge distillation techniques, and implements the sim-to-real transfer of operating skills from a simulation environment to a real assembly environment.

The invention discloses a graph-guided data labeling treatment method for curved-surface shell defect detection, and belongs to the technical field of data labeling treatment, and the method comprises the following steps: S1, collecting curved-surface shell defect data, and labeling the curved-surface shell defect data; s2, based on the labeled curved surface shell defect data, performing data enhancement on a defect area through mask guidance, and generating a data labeling set; s3, inputting the data labeling set into the dual-network nested framework to generate a prediction result of the soft label; and S4, based on a prediction result of the soft label, performing iterative optimization on the data annotation set and the dual-network nested framework by adopting a graph-guided active learning strategy to complete data annotation treatment. Through a mask-guided data enhancement strategy and in combination with a dual-network nested frame, the system improves the data annotation quality of a high-resolution curved surface shell image with complex backgrounds such as curved surface distortion, high reflective interference and long tail distribution.

The invention belongs to the technical field of steel defect detection in a real industrial scene, and particularly relates to a steel defect detection method combined with an active learning strategy, which comprises the following steps: constructing a steel surface defect data set; performing initial training based on the lightweight YOLO model; screening out a high-value sample from an unlabeled steel image through an uncertainty evaluation strategy; preferentially performing expert labeling on the selected samples to form an updated training set; performing model training on the updated training set and dynamically optimizing the network weight; a new sample is introduced into each round of iteration, an active learning closed-loop system is continuously improved, and the detection precision under the small sample condition is improved. According to the method, the manual marking cost is effectively reduced, the cross-scene adaptive capacity is improved, and a feasible path and theoretical support are provided for construction of a large-scale defect detection system in the iron and steel industry.

The invention provides a double-criterion active learning carbonate rock lithology classification method and system. The method comprises the steps of obtaining conventional logging information of multiple wells; in the data preprocessing step, a logging curve is intercepted into sample segments, and data are made to conform to the input shape of the model; building a hybrid model Bi-LSTM-DANN combining a bidirectional long-short term memory neural network and a domain adversarial neural network, and inputting the intercepted samples into the network; domain adversarial pre-training: defining a loss function to determine hyper-parameters and training the network to obtain a pre-training model; the active learning fine tuning adopts a dual-criterion active learning method combining KMeans and a minimum confidence method, and the network is further fine-tuned to obtain a final model; and testing and applying the model. According to the method, two sample query strategies are fused, so that the prediction performance of the model is greatly improved; the performance of the model is further improved through domain confrontation, accurate identification of the lithology of the carbonate rock is achieved, and the system has good mobility and robustness.

The invention provides a multi-objective optimization method for material performance, and belongs to the technical field of material science and artificial intelligence, and the process of the method provided by the invention takes a small sample learning model as a core, combines an active learning optimization strategy, and realizes quantitative optimization of multi-objective performance indexes in material research and development in an extreme service environment. According to the method, limited test data are fully utilized for learning, and the number of physical tests is remarkably reduced; through intelligent optimization search, the efficiency and the success rate of searching for the material design scheme meeting the multi-target performance requirement are improved, and the method has important significance in accelerating development of new materials in a severe environment.