1937 results about "Classifier (UML)" patented technology

Disclosed in the present invention are an adaptive deep transfer fault diagnosis method and system, an apparatus and a medium. The method comprises the following steps: S1: collecting vibration acceleration signals of industrial equipment under different working conditions, and dividing same into a source domain data set and a target domain data set; S2: building a self-tuning universal domain adaptive fault diagnosis model, which comprises a shared feature extractor, a known classifier and a plurality of unknown classifiers; S3: separately calculating a classification loss of known faults of the source domain, a discriminative loss of the plurality of unknown classifiers, a target domain soft consistency regularization loss and an information entropy maximization loss; S4: introducing a dynamic weighting strategy based on model uncertainty assessment to optimize the model parameters; and S5: using the model for diagnosis. The present invention can fully mine valid information in data, can establish reliable class decision boundaries, and in addition, uses the self-tuning dynamic update strategy to adjust weightings corresponding to different loss functions, thus allowing for quick generalization of the model to different industrial diagnosis scenarios.

The invention provides a flow analysis and threat detection method and device based on machine learning, and the method comprises the steps: collecting a real-time flow data package of a target network environment, carrying out the protocol analysis and session recombination, and generating a real-time flow feature data set containing multi-dimensional flow features; loading a pre-trained multi-level threat classification model, inputting the real-time traffic feature data set into a feature extraction layer of the model, carrying out normalized coding on traffic features of corresponding dimensions through feature coding channels, generating a real-time feature vector sequence, inputting the real-time feature vector sequence into a primary classifier of the model, and classifying the real-time traffic features according to the real-time feature vector sequence; and performing abnormal probability calculation and cluster division on the real-time feature vector sequence through a mixed detection unit, outputting a primary threat tag and an abnormal confidence coefficient corresponding to each real-time feature vector, inputting the primary threat tag and the abnormal confidence coefficient into an aggregation classifier, performing dynamic weighted aggregation, and generating a comprehensive threat score so as to judge whether a threat response strategy is triggered or not. According to the invention, the accuracy and timeliness of threat detection in a complex network environment can be improved.

A database system generates a prompt for an LLM or other machine learning (ML) model to narrow the search space to highly relevant information about a database. A distinct instance of a classifier, a clustering algorithm, or a topic modeling model can be trained based on information from ML automation within the database system, respectively for each column or table in the database. Model instances can then be used during generative LLM inferencing to identify relevant sources of data to answer the user's question. Thus, the prompt generation combines ML automation and other ML models or an LLM for topic modeling and schema description.

The invention provides an RSVP type unbalanced electroencephalogram signal classification method based on a self-adaptive channel mixed attention mechanism and decoupling learning, and aims to improve the feature extraction and classification performance of RSVP electroencephalogram signals. Firstly, collected RSVP electroencephalogram signals are preprocessed, including common average reference, band-pass filtering and standardization processing, and a training set and a test set are divided; then, multi-scale time and space features are extracted through a parallel convolution layer and a space convolution layer, and the features of different levels are fused through an interpolation method; the training process is decoupled into representation learning and classifier learning, the representation learning uses a triple loss function based on difficult sample sampling, and the classifier learning uses the optimized Focal Loss to perform feature learning and classifier optimization. A self-adaptive channel mixed attention mechanism is introduced, and the flexibility and robustness of the model are enhanced through dynamic weighting and multi-level feature fusion. According to the method, the problem of class imbalance in the RSVP task is solved.

Devices and techniques are generally described for security threat mitigation for generative machine learning models. In some examples, first prompt data including first data associated with a first natural language input and a first span may be determined. An LLM may determine first plan data using the first prompt data. The first plan data may include a call to the first API. A first classifier model may determine a first trust score for the first span. A first attention score may be determined for the first span and the first action plan. Second plan data may be generated based on at least one of the first trust score and the first attention score or the second trust score and the second attention score.

The invention discloses a multi-modal fusion rumor detection method and system based on a dynamic graph convolutional neural network. According to the method, a dynamic feature graph of a language propagation path is constructed, and potential features in the language propagation process are extracted and analyzed by utilizing time sequence changes and key node relations between nodes in a propagation graph. A neural network is adopted to extract and enhance image data, text semantic features are extracted in combination with a text feature modeling network, text feature vectorization expression is achieved based on a BERT model, and rich semantic information is obtained. And a gating mechanism is introduced to dynamically adjust fusion weights of different modal features, and an information fusion strategy is optimized. A collaborative attention mechanism is further adopted for deep fusion, interactive learning of text, image and propagation path features is enhanced, and the relevance of cross-modal and time series data is improved. And finally, inputting the fused feature vectors into a classifier for accurate classification, thereby realizing accurate detection of the social media rumors. According to the method, the multi-modal features are effectively integrated, and the false information identification efficiency is remarkably improved.

Systems and methods for a prompt generation and analysis service for generating and identifying a preferred prompt for performing a function of a large language model (LLM) are provided. The prompt generation and analysis service may generate a set of training prompts for performing a function of an LLM. The prompt generation and analysis service may then query the LLM with the generated set of prompts and characterize the output of the LLM for each prompt. Using the characterization of the output and corresponding prompt, the prompt generation and analysis service can train a classifier model to classify the prompts. The prompt generation and analysis service may generate a set of target prompts for performing a function of an LLM, characterize the target prompts using the training classifier model, and identify a preferred prompt for performing the function based on the classifier model's classification.

The invention belongs to the technical field of federated learning, and particularly relates to a federated learning method and device based on feature comparison optimization and classifier dynamic integration. The method comprises the steps that a global model is initialized through a server and issued to # imgabs0 clients participating in federated learning, the clients conduct local model updating based on the received global model and train the updated local model, then feature extraction is conducted on a local private data set through a trained local feature extractor, and a local private data set is obtained; the method comprises the following steps: constructing a local model to construct a local feature prototype set, uploading the local model, the local feature prototype set and the total sample size of a client to a server, using a contrast learning technology to improve the quality of a global feature prototype when the server globally aggregates, and finally issuing a global prototype and a global feature prototype set which are obtained by aggregation to each client. And executing the next round of learning until the local model converges or reaches the set communication round.

The invention discloses a feature classifier-based thinking chain reasoning mathematical problem solving method and system. The method comprises the following steps of: (1) acquiring a mathematical problem set, and selecting forward and reverse thinking chain examples to splice to form a new set; (2) respectively inputting the new set elements into a model for reasoning to construct a word-level reasoning path spanning tree, and selecting an attention weight matrix after pooling difference processing as node features for storage; (3) traversing all the generated reasoning path spanning trees, and screening nodes meeting requirements to construct a feature classifier training set; (4) training a feature classifier by using a support vector machine algorithm; and (5) through the trained feature classifier, participating in path selection in a pre-training language model reasoning process, and obtaining a relatively accurate reasoning process and answers. By means of the method, adjustment and control of finer granularity of the reasoning path of the pre-training language model can be achieved, accurate reasoning solving of the pre-training language model on mathematical problems is facilitated, and the generalization level of the pre-training language model is improved.

The invention discloses a class incremental learning method based on elastic knowledge storage and compensation, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining images which comprise different tasks and are not intersected in class, constructing a data set of multi-task incremental learning, and dividing the data set into a training set and a test set; constructing a CIL model comprising a feature extractor and a classifier; wherein for the feature extractor, a pre-trained Transform model is given, and the model is made to adapt to a downstream class incremental learning task through a learning task sharing adapter; based on an importance perception parameter regularization framework and a semantic drift compensation framework, performing cross-task training on the CIL model by using the training set; and inputting the test set into the optimal CIL model to realize class incremental learning based on elastic knowledge storage and compensation. According to the method, effective forgetting resistance can be achieved without adding extra parameters, and meanwhile stability is kept and model plasticity is reserved in the training period.

The invention provides a semantic search method for identifying user intention based on AI and a storage medium, and the method comprises the steps: receiving a query text input by a user, and carrying out the preprocessing to extract key information; the method comprises the following steps: pre-training a Transform model, and inputting a query text to obtain an embedded vector; predicting a user intention based on the embedded vector by using a classifier; the key information and the user intention are combined, the query text is expanded through the knowledge graph, and the expanded query text is generated; matching the extended text with entities and relationships in the knowledge graph to obtain a related set; calculating a similarity score of the set and the document; and constructing a user portrait, and generating a document list based on the portrait and the similarity score. According to the method, the query text is deeply understood through the Transform model, the intention is predicted by the classifier, and the user demand is accurately grasped. And furthermore, the query content is enriched through domain dictionary construction, synonym replacement and phrase expansion, and the search accuracy is improved.

The invention discloses a tensor depth semi-supervised learning method for high-dimensional small sample data classification. The tensor depth semi-supervised learning method comprises the steps of preprocessing original high-dimensional small sample data; constructing a deep neural network comprising a feature extraction module and a classifier module; constructing a second-order similarity matrix and a third-order similarity tensor based on the low-dimensional embedding representation obtained by pre-training; combining the second-order similarity matrix and the third-order similarity tensor to construct an objective function containing multi-order smooth constraints; then, taking the low-dimensional embedded representation obtained by pre-training as input, performing iterative optimization on the target function by adopting a gradient descent algorithm through a label propagation network formed by a full connection layer, and generating a pseudo label; and inputting the original high-dimensional small sample data, the low-dimensional embedded features and the pseudo labels into the deep neural network, iteratively updating the network in a semi-supervised mode until convergence, and outputting a final prediction result. According to the method, more accurate label propagation is realized, and the semi-supervised classification precision is improved.

The invention belongs to the technical field of natural language processing, and particularly relates to an adversarial attack text classification method based on granular ball and metric learning, which comprises the following steps: acquiring a to-be-classified text, and inputting the to-be-classified text into a trained coding module to obtain semantic vector representation of the to-be-classified text; mapping the semantic vector representation of the to-be-classified text to the trained grainball space to obtain a grainball center, and inputting the semantic vector representation corresponding to the grainball center into the classifier to obtain a classification result; according to the method, semantic vector representations of similar texts are aggregated into one granular ball by using granular ball clustering to obtain the granular ball space, then semantic vector representations of to-be-classified texts are mapped into the granular ball space to obtain the corresponding granular ball centers, and the influence of adversarial samples is eliminated by using the corresponding granular ball centers for classification, so that compared with the prior art, the method has the advantages that the classification efficiency is improved, and the classification efficiency is improved. And the accuracy of the classifier under the attack condition of the model is improved.

The invention provides a heterogeneous model training method and system based on federated learning. The method comprises the following steps: each client initializes a local heterogeneous model, and divides the local heterogeneous model into a feature extractor and a classifier; the server initializes a global adapter; the server issues the global adapter to each client; each client uses the received global adapter as a to-be-updated local adapter, and uses local data to train the to-be-updated local adapter, the feature extractor and the classifier to generate the current optimal local adapter, the feature extractor and the classifier; wherein in the training process, the local adapter to be updated and the feature extractor share a classifier; each client uploads the current optimal local adapter to the server; and the server aggregates all the received local adapters to obtain an updated global adapter, and iteratively updates the global adapter until the local heterogeneous model on each client meets a preset requirement.

A mine multi-parameter sensing data fusion and analysis method based on machine learning belongs to the field of mine environment monitoring, and comprises the following steps: S1, collecting and manually marking discrete format data and time sequence format data; s2, carrying out feature extraction on the collected mine multi-parameter sensing data in a discrete format, specifically, carrying out feature extraction by adopting a first neural network based on honey pot optimization of chaotic mapping; s3, inputting the collected gas concentration data in the time sequence format into a neural network algorithm optimized based on a star ring principle to perform feature extraction, namely a second neural network; and S4, performing feature cascading on the output of the first neural network and the output of the second neural network, inputting the cascaded features into a classifier for training a classifier model, and specifically, adopting a fractional order neural network algorithm based on dynamic gradient cutting as a classification algorithm for training. According to the invention, data analysis and mine safety assessment are carried out through the mine safety assessment model obtained through training.

Systems and methods for generating summaries from text using a generative model are disclosed. The system is configured to access an article; identify section; provide, to one or more generative models, a prompt including instructions to generate a section summary; generate an article summary based on the section summary; determine, from the article summary, a first concept found in the article summary that is missing from the article; determine, using a classifier, for a first sentence included in the article summary, a confidence score; and provide, for presentation at a client device, a document including the article summary.

The invention discloses an analysis method and system for a dynamic recrystallization structure form of a titanium alloy based on machine learning and a medium, and belongs to the technical field of metal material microstructure quantitative characterization, a Gleeble thermal compression test is processed on a titanium alloy to be tested, multi-modal data is collected, a DRX probability graph is output based on a DRX segmentation model, and then the characteristics of DRX are extracted. And respectively inputting the DRX features into the first-level classifier, carrying out PCA dimension reduction processing, splicing and fusing the prediction probability and the features after dimension reduction, and inputting the spliced and fused features into a second-level classifier to output a two-dimensional DRX segmentation map. Based on the FIB-SEM tomography sequence image, reconstructing a three-dimensional model of the DRX crystal grain so as to carry out consistency verification on the two-dimensional DRX segmentation image; and evaluating a correlation coefficient of the three-dimensional model and the two-dimensional DRX model, and finally carrying out three-dimensional visualization on the space aggregation of the DRX crystal grains. According to the method, the automation degree and efficiency of dynamic recrystallization proportion and type identification are remarkably improved.

The invention provides a self-supervised group behavior recognition method and system based on global and local comparative learning, and the method comprises the steps: extracting individual features through local branches, generating a soft mask by employing a multi-head self-attention mask module, separating significant / non-significant individual features through mask pooling, constructing a comparison sample, and carrying out the recognition of a group behavior through the comparison sample; feature alignment is optimized in combination with cosine similarity and local contrast loss; meanwhile, the spatial interaction relation of the behaviorists is captured by utilizing the spatial global Transform of global branches, short-term action and long-term behavior modes are fused through multi-scale time sequence coding, the consistency is optimized by adopting global comparison loss after spatial and temporal characteristics are aggregated, and finally, the two branch characteristics are integrated through global-local comparison loss for comparison, so that the accuracy of the behavior behavior is improved. And the loss weight is automatically adjusted to simplify parameter adjustment. After training is completed, a group behavior recognition result is output through the classifier according to the extracted spatial-temporal features, and the discrimination ability and robustness of the model to complex group behaviors are effectively improved.

A system and method for determining device attributes using a classifier hierarchy. The method includes: sequentially applying a plurality of sub-models of a hierarchy to a plurality of features extracted from device activity data, wherein the sequential application ends with applying a last sub-model of the plurality of sub-models, wherein each sub-model includes a plurality of classifiers, wherein each sub-model outputs a class when applied to at least a portion of the plurality of features, wherein each class is a classifier output representing a device attribute, wherein applying the plurality of sub-models further comprises iteratively determining a next sub-model to apply based on the class output by a most recently applied sub-model and the hierarchy; and determining a device attribute based on the class output by the last sub-model.

The invention discloses a diffusion model image generation method and device for structural disturbance guidance and consistent distillation fusion, and the method comprises the steps: carrying out the initialization of a first student network through a diffusion model, and initializing a first teacher network; generating an initial state of the submerged space according to the submerged space data set and a noise scheduling function of the diffusion model; introducing the classifier-free guidance after the structural disturbance into a first teacher network to obtain a second teacher network; according to the first teacher network, the second teacher network and the initial state of the submerged space, state prediction is carried out through a solver, and enhanced input of the first teacher network is generated; obtaining consistency loss according to the first student network, the first teacher network and the enhanced input; according to the consistency loss, performing parameter updating on the first student network to obtain a target model; the target model is used for generating a target image. The method can improve the quality of the generated image and increase the generation speed of the diffusion model, and can be widely applied to the technical field of image generation.

The invention discloses an enterprise credit risk assessment method based on big data collection, and relates to the technical field of big data analysis, and the method comprises the steps: constructing a space-time fusion engine, carrying out the fusion through combining a cross-modal attention mechanism, generating a space-time fusion feature, and carrying out the adversarial training through a gradient inversion layer and a domain classifier, eliminating space-time fusion feature distribution differences; based on space-time fusion features, constructing a causal graph skeleton by adopting a conditional independent test algorithm, quantifying causal effect intensity among nodes through a machine learning model, constructing a risk conduction dynamic model, and predicting risk conduction intensity based on the causal effect intensity; and based on the risk conduction intensity, calculating a risk index weight through a dynamic game network, generating an enterprise risk score in combination with the space-time fusion feature, and generating a dynamic risk score through a time sequence neural network. According to the invention, by constructing the space-time fusion engine and combining the cross-modal attention mechanism, efficient fusion of multi-modal data is realized, and the accuracy and robustness of feature expression are improved.

The embodiment of the invention provides an equipment predictive maintenance method and device based on artificial intelligence, relates to the technical field of data processing, and aims to adjust a model focus by identifying statistical characteristics of data and paying attention to characteristics with most significant changes. Moreover, the training sample set of the feature extraction model is adjusted based on the abnormal evaluation result, abnormal data can be suppressed, the model is protected from being interfered by the data, and the feature extraction effect can be optimized. Quantum state modeling can better capture a nonlinear relation in data, information loss of a traditional linear dimension reduction algorithm is avoided, and therefore the expressive power of features after dimension reduction is improved. According to the embodiment of the invention, through data compression and optimization feature extraction based on quantum state amplitude, the prediction precision and reliability of the model are improved, the quality of data input into the classifier model is ensured, and the risk of misjudgment is reduced.

The invention provides a glutenite logging curve identification method and device based on deep learning, and the method comprises the steps: collecting logging curve data, marking the types, and obtaining an original training data set; performing sample generation based on a quantum state mixed generative adversarial network algorithm to realize data expansion; inputting the expanded data into a feature extraction model, and training the feature extraction model; inputting the data after feature extraction into a feature dimension reduction model to train the feature dimension reduction model; inputting the data subjected to dimension reduction into a classifier model to train a classifier; and carrying out glutenite logging curve identification by utilizing the trained model. According to the method, the processing efficiency and the recognition precision of the logging curve data are effectively improved, the diversity and the quality of data generation are enhanced, the optimization problem in a traditional deep learning method is solved, and the generalization ability and the classification accuracy of the model for unknown data are further improved.

The invention relates to the technical field of block chains, in particular to a smart contract vulnerability detection method and device based on multi-modal features, and the method mainly comprises the steps: training a meta-learning model in a dynamic adaptation module, and adjusting the global parameters of a modal feature extraction module, a dynamic gating fusion module and a classifier through the meta-learning model, the dynamic adaptation module comprises a meta-learning model constructed based on an MAML framework, and is used for optimizing global parameters of each module according to vulnerability features learned in pre-training; and inputting the multi-modal fusion feature vector into a classifier, and generating and outputting a vulnerability detection result of the smart contract. According to the method, known vulnerabilities can be accurately detected by fusing multi-modal features, and novel vulnerabilities can be rapidly adapted and detected.

Systems and methods for resource conservation based on query complexity are disclosed. An input query is received (e.g., via a chat interface) and provided to a response classifier, which is a machine-learning classifier that is trained to classify input queries with complexity scores that indicate how difficult it is likely to be for an artificial intelligence (AI) model to generate a response to the input query. If the complexity score exceeds a threshold score, the input query is provided to a first AI model (e.g., a relatively high-complexity AI model having a large number of parameters, relatively long response latencies, and / or other performance characteristics). If the complexity score does not exceed the threshold score, the input query is provided to a second AI model (e.g., a lower-complexity AI model having fewer parameters, shorter response latencies, and / or other differences in performance characteristics relative to the first AI model).

The invention discloses a wind turbine generator equipment fault diagnosis method and system based on a large model. The method comprises the following steps: initializing a feature set according to normal data; selecting a plurality of candidate features according to the mixed score of each candidate feature, and storing the candidate features in a feature set; performing sample division on normal data by using a time sequence segmentation algorithm and constructing multi-dimensional spatial-temporal feature representation; constructing a fault diagnosis model: loading a large language model as an infrastructure, and injecting the multi-dimensional spatio-temporal feature representation into an embedded input layer of the large language model; an adaptive pooling layer is accessed after the output of the large language model, and a double-layer MLP classifier is constructed for realizing the identification and classification of different fault types; the first layer of the double-layer MLP classifier compresses an input feature to half of an original feature dimension and applies GELU activation, and the second layer of the double-layer MLP classifier is mapped to a corresponding fault category space; constructing a knowledge mechanism library, providing prior knowledge of the wind turbine generator for the model, designing a loss function driven by the knowledge of the wind turbine generator, and carrying out model training.

A smart contract fine-grained vulnerability detection method based on expert knowledge comprises the following steps: S1, constructing cue words by using a large language model, analyzing smart contract source codes, and labeling a smart contract data set; the method comprises the following steps: statically analyzing an intelligent contract source code statement, representing a source code element as a vector to obtain a source code fine-grained feature preliminary representation, and merging abstract syntax tree sub-trees into a code graph of a control flow graph node to obtain a graph representation of a source code; s2, acquiring heterogeneous code features and source code fine-grained features of the same smart contract, inputting the heterogeneous code features and the source code fine-grained features into a BGSCVul model for iterative fusion, and constructing a deep learning classifier; s3, by constructing an expert knowledge classifier, making a decision for specific expert knowledge; and S4, fusing the output of each sub-classifier by using a stack pressing method, and outputting final prediction.

The invention relates to the technical field of artificial intelligence, and discloses a student early performance prediction method based on an emotional big language model in an intelligent education scene, and the method comprises the steps: obtaining structured data and unstructured data related to student academic performance, and carrying out the preprocessing; the method comprises the following steps: extracting structured data into a structured feature vector through a multi-layer perceptron, extracting unstructured data through an emotion big language model to obtain an emotion feature vector, performing feature fusion on the structured feature vector and the emotion feature vector in a fusion layer, and introducing an attention mechanism to realize feature interaction. Generating fusion features reflecting behaviors and emotions of the students; and combining a plurality of classifiers into a multi-classifier model through an integrated learning method, and carrying out classification prediction on student academic performance based on fusion features. According to the method, the structural data and the emotional features are fused, the academic performance of the students can be accurately predicted, teachers are helped to recognize the students with poor performance as early as possible, and personalized learning support is provided.

The invention discloses a large reasoning model factuality enhancement method, system and device based on reinforcement learning and a medium. The method comprises the following steps: constructing a large language model reasoning thinking chain factuality detection and enhancement framework (REELANCE), and performing fine-grained fact accuracy evaluation on a reasoning chain output by a large language model; based on the result of the fact check classifier, the reasoning chain factuality is enhanced by adopting a reinforcement learning group strategy optimization technology (GRPO); a multi-dimensional reward mechanism is designed to improve the fact accuracy of the reasoning step; quantitatively analyzing the influence of enhanced training on the nerve activation change in the model through a mechanism interpretability technology; a comprehensive optimization target giving consideration to the fact accuracy and the reasoning quality is established, the fact robustness is improved, the standard reasoning performance is kept, and the balance between the fact and the reasoning ability is achieved; the key problem of inaccurate facts of an inference type large language model in a high-risk scene can be effectively solved; the invention also provides a system, equipment and a medium for realizing the method.

The invention relates to the technical field of unmanned aerial vehicle fault diagnosis, in particular to an unmanned aerial vehicle cross-working-condition fault diagnosis method based on double-alignment federated metric learning. Comprising the steps that a server initializes a global model; the client calculates classification loss based on local data, optimizes measurement learning loss and feature alignment loss by using clustering anchor points and unbiased anchor points, updates a local model, and optimizes a classifier by calculating the alignment loss of a classifier through the unbiased anchor points; the client side calculates a feature anchor point and uploads the anchor point and a local model to the server; the server clusters the feature anchor points by using a parameter-free clustering algorithm to obtain clustering anchor points of each class, averaging the anchor points in each clustering cluster to obtain unbiased anchor points of each class, performing weighted averaging on all local models, and updating a global model; and repeating the above process iteration until convergence. According to the invention, through a double-alignment mechanism and federated metric learning, the problem of feature isomerism and the challenge of poor model generalization ability in unmanned aerial vehicle fault diagnosis are solved.